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Photo: NASA Linking Regional Aerosol Emission Changes with Multiple Impact Measures through Direct and Cloud-Related Forcing Estimates Tami C. Bond & Yanju Chen University of Illinois at Urbana-Champaign Xin-Zhong Liang & Hao He University of Maryland David G. Streets, Ekbordin Winijkul, and Fang Yan Argonne National Laboratory Praveen Amar, Danielle Meitiv Clean Air Task Force

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Page 1: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Photo NASA

Linking Regional Aerosol Emission Changeswith Multiple Impact Measures through

Direct and Cloud-Related Forcing Estimates

Tami C Bond amp Yanju Chen University of Illinois at Urbana-Champaign

Xin-Zhong Liang amp Hao He University of Maryland

David G Streets Ekbordin Winijkul and Fang Yan Argonne National Laboratory

Praveen Amar Danielle Meitiv Clean Air Task Force

2

Organization

Project overview ndash Tami Bond

Size-resolved emission inventory ndash Dave Streets reported by Tami Bond

US regional cloud modeling ndash Hao He

Emission-to-forcing measures ndash Yanju Chen

Policy-relevant metricsndash Praveen Amar reported by Tami

3

Project Overview

Or Why we Did What We Did

(Tami Bond)

4

Review from May 2012The simple view

A dose-response curve for the atmosphere Forcing

Emission

5

Review from May 2012

Bounding-BC lesson

The big uncertainty in BC-rich sources

BC direct forcing ~ bounded BC cloud forcing

~ large uncertainties ndash especially in icemixed

OC + SO4 direct forcing ~ small for BC-rich sources

OC + SO4 cloud forcing ~ large and probably negative

Itrsquos the indirect effects of co-emitted species that cause big questions about immediate forcing

6

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

Bounding-BC Fig 38

7

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

Bounding-BC Fig 38

8

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Bounding-BC Fig 38

9

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Remainder of aerosol forcing is in low-BC categories (total -095)

Bounding-BC Fig 38

10

Review from May 2012

So you got [some scientific thing]

right Who cares Tell me if I should

turn this off

Can you wait 6 months I have

to run my modelhellip

11

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

12

12

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

Complex I ⎛ J K ⎞

Bounding-BCCF = FCsumEFi ⎜sumrforci j +sum frespik ⎟⎜ ⎟ equation 111amp1 ⎠i=1 ⎝ j=1 k=1

responseclimate emission perforcing factor

emissionforcing perfuel emissionconsumption

part f jrforci j = partei PD

13

Definition

Emission-Normalized Forcing (ENF)

including ENDRF Direct Radiative

EN IRF Indirect Radiative

Forcingmodeled approximates this Emissionmodeled part f jrforci j =

partei PD

14

Detour Climate ldquometricsrdquo

Normal people think A metric is something you can measure and report

The climate policy community says A metric is a well-defined calculation that can be used to equate a mass emission of some species to a mass emission of the big bear CO2

15

s

Some climate metrics

Absolute global warming potential Global warming potential

Global temperature potential

For short-lived species (τlt4 mo)ENF emission-normalized forcing is the only model output required to calculate any of these metrics Other considerations affect the values of emission metrics but they all come from models of the carbon cycle or Earthrsquo heat capacity NOT from models of aerosols

16

Complaints against ENF You canrsquot do

that for CLOUDSForcing is

not linear

Anyway none of

the model runs were designed

for that

Itrsquos different in every

REGION

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 2: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

2

Organization

Project overview ndash Tami Bond

Size-resolved emission inventory ndash Dave Streets reported by Tami Bond

US regional cloud modeling ndash Hao He

Emission-to-forcing measures ndash Yanju Chen

Policy-relevant metricsndash Praveen Amar reported by Tami

3

Project Overview

Or Why we Did What We Did

(Tami Bond)

4

Review from May 2012The simple view

A dose-response curve for the atmosphere Forcing

Emission

5

Review from May 2012

Bounding-BC lesson

The big uncertainty in BC-rich sources

BC direct forcing ~ bounded BC cloud forcing

~ large uncertainties ndash especially in icemixed

OC + SO4 direct forcing ~ small for BC-rich sources

OC + SO4 cloud forcing ~ large and probably negative

Itrsquos the indirect effects of co-emitted species that cause big questions about immediate forcing

6

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

Bounding-BC Fig 38

7

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

Bounding-BC Fig 38

8

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Bounding-BC Fig 38

9

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Remainder of aerosol forcing is in low-BC categories (total -095)

Bounding-BC Fig 38

10

Review from May 2012

So you got [some scientific thing]

right Who cares Tell me if I should

turn this off

Can you wait 6 months I have

to run my modelhellip

11

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

12

12

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

Complex I ⎛ J K ⎞

Bounding-BCCF = FCsumEFi ⎜sumrforci j +sum frespik ⎟⎜ ⎟ equation 111amp1 ⎠i=1 ⎝ j=1 k=1

responseclimate emission perforcing factor

emissionforcing perfuel emissionconsumption

part f jrforci j = partei PD

13

Definition

Emission-Normalized Forcing (ENF)

including ENDRF Direct Radiative

EN IRF Indirect Radiative

Forcingmodeled approximates this Emissionmodeled part f jrforci j =

partei PD

14

Detour Climate ldquometricsrdquo

Normal people think A metric is something you can measure and report

The climate policy community says A metric is a well-defined calculation that can be used to equate a mass emission of some species to a mass emission of the big bear CO2

15

s

Some climate metrics

Absolute global warming potential Global warming potential

Global temperature potential

For short-lived species (τlt4 mo)ENF emission-normalized forcing is the only model output required to calculate any of these metrics Other considerations affect the values of emission metrics but they all come from models of the carbon cycle or Earthrsquo heat capacity NOT from models of aerosols

16

Complaints against ENF You canrsquot do

that for CLOUDSForcing is

not linear

Anyway none of

the model runs were designed

for that

Itrsquos different in every

REGION

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 3: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

3

Project Overview

Or Why we Did What We Did

(Tami Bond)

4

Review from May 2012The simple view

A dose-response curve for the atmosphere Forcing

Emission

5

Review from May 2012

Bounding-BC lesson

The big uncertainty in BC-rich sources

BC direct forcing ~ bounded BC cloud forcing

~ large uncertainties ndash especially in icemixed

OC + SO4 direct forcing ~ small for BC-rich sources

OC + SO4 cloud forcing ~ large and probably negative

Itrsquos the indirect effects of co-emitted species that cause big questions about immediate forcing

6

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

Bounding-BC Fig 38

7

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

Bounding-BC Fig 38

8

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Bounding-BC Fig 38

9

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Remainder of aerosol forcing is in low-BC categories (total -095)

Bounding-BC Fig 38

10

Review from May 2012

So you got [some scientific thing]

right Who cares Tell me if I should

turn this off

Can you wait 6 months I have

to run my modelhellip

11

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

12

12

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

Complex I ⎛ J K ⎞

Bounding-BCCF = FCsumEFi ⎜sumrforci j +sum frespik ⎟⎜ ⎟ equation 111amp1 ⎠i=1 ⎝ j=1 k=1

responseclimate emission perforcing factor

emissionforcing perfuel emissionconsumption

part f jrforci j = partei PD

13

Definition

Emission-Normalized Forcing (ENF)

including ENDRF Direct Radiative

EN IRF Indirect Radiative

Forcingmodeled approximates this Emissionmodeled part f jrforci j =

partei PD

14

Detour Climate ldquometricsrdquo

Normal people think A metric is something you can measure and report

The climate policy community says A metric is a well-defined calculation that can be used to equate a mass emission of some species to a mass emission of the big bear CO2

15

s

Some climate metrics

Absolute global warming potential Global warming potential

Global temperature potential

For short-lived species (τlt4 mo)ENF emission-normalized forcing is the only model output required to calculate any of these metrics Other considerations affect the values of emission metrics but they all come from models of the carbon cycle or Earthrsquo heat capacity NOT from models of aerosols

16

Complaints against ENF You canrsquot do

that for CLOUDSForcing is

not linear

Anyway none of

the model runs were designed

for that

Itrsquos different in every

REGION

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 4: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

4

Review from May 2012The simple view

A dose-response curve for the atmosphere Forcing

Emission

5

Review from May 2012

Bounding-BC lesson

The big uncertainty in BC-rich sources

BC direct forcing ~ bounded BC cloud forcing

~ large uncertainties ndash especially in icemixed

OC + SO4 direct forcing ~ small for BC-rich sources

OC + SO4 cloud forcing ~ large and probably negative

Itrsquos the indirect effects of co-emitted species that cause big questions about immediate forcing

6

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

Bounding-BC Fig 38

7

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

Bounding-BC Fig 38

8

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Bounding-BC Fig 38

9

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Remainder of aerosol forcing is in low-BC categories (total -095)

Bounding-BC Fig 38

10

Review from May 2012

So you got [some scientific thing]

right Who cares Tell me if I should

turn this off

Can you wait 6 months I have

to run my modelhellip

11

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

12

12

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

Complex I ⎛ J K ⎞

Bounding-BCCF = FCsumEFi ⎜sumrforci j +sum frespik ⎟⎜ ⎟ equation 111amp1 ⎠i=1 ⎝ j=1 k=1

responseclimate emission perforcing factor

emissionforcing perfuel emissionconsumption

part f jrforci j = partei PD

13

Definition

Emission-Normalized Forcing (ENF)

including ENDRF Direct Radiative

EN IRF Indirect Radiative

Forcingmodeled approximates this Emissionmodeled part f jrforci j =

partei PD

14

Detour Climate ldquometricsrdquo

Normal people think A metric is something you can measure and report

The climate policy community says A metric is a well-defined calculation that can be used to equate a mass emission of some species to a mass emission of the big bear CO2

15

s

Some climate metrics

Absolute global warming potential Global warming potential

Global temperature potential

For short-lived species (τlt4 mo)ENF emission-normalized forcing is the only model output required to calculate any of these metrics Other considerations affect the values of emission metrics but they all come from models of the carbon cycle or Earthrsquo heat capacity NOT from models of aerosols

16

Complaints against ENF You canrsquot do

that for CLOUDSForcing is

not linear

Anyway none of

the model runs were designed

for that

Itrsquos different in every

REGION

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 5: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

5

Review from May 2012

Bounding-BC lesson

The big uncertainty in BC-rich sources

BC direct forcing ~ bounded BC cloud forcing

~ large uncertainties ndash especially in icemixed

OC + SO4 direct forcing ~ small for BC-rich sources

OC + SO4 cloud forcing ~ large and probably negative

Itrsquos the indirect effects of co-emitted species that cause big questions about immediate forcing

6

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

Bounding-BC Fig 38

7

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

Bounding-BC Fig 38

8

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Bounding-BC Fig 38

9

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Remainder of aerosol forcing is in low-BC categories (total -095)

Bounding-BC Fig 38

10

Review from May 2012

So you got [some scientific thing]

right Who cares Tell me if I should

turn this off

Can you wait 6 months I have

to run my modelhellip

11

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

12

12

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

Complex I ⎛ J K ⎞

Bounding-BCCF = FCsumEFi ⎜sumrforci j +sum frespik ⎟⎜ ⎟ equation 111amp1 ⎠i=1 ⎝ j=1 k=1

responseclimate emission perforcing factor

emissionforcing perfuel emissionconsumption

part f jrforci j = partei PD

13

Definition

Emission-Normalized Forcing (ENF)

including ENDRF Direct Radiative

EN IRF Indirect Radiative

Forcingmodeled approximates this Emissionmodeled part f jrforci j =

partei PD

14

Detour Climate ldquometricsrdquo

Normal people think A metric is something you can measure and report

The climate policy community says A metric is a well-defined calculation that can be used to equate a mass emission of some species to a mass emission of the big bear CO2

15

s

Some climate metrics

Absolute global warming potential Global warming potential

Global temperature potential

For short-lived species (τlt4 mo)ENF emission-normalized forcing is the only model output required to calculate any of these metrics Other considerations affect the values of emission metrics but they all come from models of the carbon cycle or Earthrsquo heat capacity NOT from models of aerosols

16

Complaints against ENF You canrsquot do

that for CLOUDSForcing is

not linear

Anyway none of

the model runs were designed

for that

Itrsquos different in every

REGION

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 6: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

6

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

Bounding-BC Fig 38

7

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

Bounding-BC Fig 38

8

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Bounding-BC Fig 38

9

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Remainder of aerosol forcing is in low-BC categories (total -095)

Bounding-BC Fig 38

10

Review from May 2012

So you got [some scientific thing]

right Who cares Tell me if I should

turn this off

Can you wait 6 months I have

to run my modelhellip

11

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

12

12

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

Complex I ⎛ J K ⎞

Bounding-BCCF = FCsumEFi ⎜sumrforci j +sum frespik ⎟⎜ ⎟ equation 111amp1 ⎠i=1 ⎝ j=1 k=1

responseclimate emission perforcing factor

emissionforcing perfuel emissionconsumption

part f jrforci j = partei PD

13

Definition

Emission-Normalized Forcing (ENF)

including ENDRF Direct Radiative

EN IRF Indirect Radiative

Forcingmodeled approximates this Emissionmodeled part f jrforci j =

partei PD

14

Detour Climate ldquometricsrdquo

Normal people think A metric is something you can measure and report

The climate policy community says A metric is a well-defined calculation that can be used to equate a mass emission of some species to a mass emission of the big bear CO2

15

s

Some climate metrics

Absolute global warming potential Global warming potential

Global temperature potential

For short-lived species (τlt4 mo)ENF emission-normalized forcing is the only model output required to calculate any of these metrics Other considerations affect the values of emission metrics but they all come from models of the carbon cycle or Earthrsquo heat capacity NOT from models of aerosols

16

Complaints against ENF You canrsquot do

that for CLOUDSForcing is

not linear

Anyway none of

the model runs were designed

for that

Itrsquos different in every

REGION

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 7: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

7

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

Bounding-BC Fig 38

8

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Bounding-BC Fig 38

9

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Remainder of aerosol forcing is in low-BC categories (total -095)

Bounding-BC Fig 38

10

Review from May 2012

So you got [some scientific thing]

right Who cares Tell me if I should

turn this off

Can you wait 6 months I have

to run my modelhellip

11

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

12

12

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

Complex I ⎛ J K ⎞

Bounding-BCCF = FCsumEFi ⎜sumrforci j +sum frespik ⎟⎜ ⎟ equation 111amp1 ⎠i=1 ⎝ j=1 k=1

responseclimate emission perforcing factor

emissionforcing perfuel emissionconsumption

part f jrforci j = partei PD

13

Definition

Emission-Normalized Forcing (ENF)

including ENDRF Direct Radiative

EN IRF Indirect Radiative

Forcingmodeled approximates this Emissionmodeled part f jrforci j =

partei PD

14

Detour Climate ldquometricsrdquo

Normal people think A metric is something you can measure and report

The climate policy community says A metric is a well-defined calculation that can be used to equate a mass emission of some species to a mass emission of the big bear CO2

15

s

Some climate metrics

Absolute global warming potential Global warming potential

Global temperature potential

For short-lived species (τlt4 mo)ENF emission-normalized forcing is the only model output required to calculate any of these metrics Other considerations affect the values of emission metrics but they all come from models of the carbon cycle or Earthrsquo heat capacity NOT from models of aerosols

16

Complaints against ENF You canrsquot do

that for CLOUDSForcing is

not linear

Anyway none of

the model runs were designed

for that

Itrsquos different in every

REGION

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 8: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

8

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Bounding-BC Fig 38

9

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Remainder of aerosol forcing is in low-BC categories (total -095)

Bounding-BC Fig 38

10

Review from May 2012

So you got [some scientific thing]

right Who cares Tell me if I should

turn this off

Can you wait 6 months I have

to run my modelhellip

11

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

12

12

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

Complex I ⎛ J K ⎞

Bounding-BCCF = FCsumEFi ⎜sumrforci j +sum frespik ⎟⎜ ⎟ equation 111amp1 ⎠i=1 ⎝ j=1 k=1

responseclimate emission perforcing factor

emissionforcing perfuel emissionconsumption

part f jrforci j = partei PD

13

Definition

Emission-Normalized Forcing (ENF)

including ENDRF Direct Radiative

EN IRF Indirect Radiative

Forcingmodeled approximates this Emissionmodeled part f jrforci j =

partei PD

14

Detour Climate ldquometricsrdquo

Normal people think A metric is something you can measure and report

The climate policy community says A metric is a well-defined calculation that can be used to equate a mass emission of some species to a mass emission of the big bear CO2

15

s

Some climate metrics

Absolute global warming potential Global warming potential

Global temperature potential

For short-lived species (τlt4 mo)ENF emission-normalized forcing is the only model output required to calculate any of these metrics Other considerations affect the values of emission metrics but they all come from models of the carbon cycle or Earthrsquo heat capacity NOT from models of aerosols

16

Complaints against ENF You canrsquot do

that for CLOUDSForcing is

not linear

Anyway none of

the model runs were designed

for that

Itrsquos different in every

REGION

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 9: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

9

Cumulative forcing (add successive categories)

BC forcing positive (+033) Total forcing positive (+015)

BC forcing positive (+072) Total forcing still positive (+021) but becoming less certainly so because of cloud uncertainties

BC forcing positive (+101) Total forcing nearly neutral (-006) because of large OC amp its cloud forcing (note simple sum differs from BC median produced by Monte Carlo analysis)

Remainder of aerosol forcing is in low-BC categories (total -095)

Bounding-BC Fig 38

10

Review from May 2012

So you got [some scientific thing]

right Who cares Tell me if I should

turn this off

Can you wait 6 months I have

to run my modelhellip

11

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

12

12

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

Complex I ⎛ J K ⎞

Bounding-BCCF = FCsumEFi ⎜sumrforci j +sum frespik ⎟⎜ ⎟ equation 111amp1 ⎠i=1 ⎝ j=1 k=1

responseclimate emission perforcing factor

emissionforcing perfuel emissionconsumption

part f jrforci j = partei PD

13

Definition

Emission-Normalized Forcing (ENF)

including ENDRF Direct Radiative

EN IRF Indirect Radiative

Forcingmodeled approximates this Emissionmodeled part f jrforci j =

partei PD

14

Detour Climate ldquometricsrdquo

Normal people think A metric is something you can measure and report

The climate policy community says A metric is a well-defined calculation that can be used to equate a mass emission of some species to a mass emission of the big bear CO2

15

s

Some climate metrics

Absolute global warming potential Global warming potential

Global temperature potential

For short-lived species (τlt4 mo)ENF emission-normalized forcing is the only model output required to calculate any of these metrics Other considerations affect the values of emission metrics but they all come from models of the carbon cycle or Earthrsquo heat capacity NOT from models of aerosols

16

Complaints against ENF You canrsquot do

that for CLOUDSForcing is

not linear

Anyway none of

the model runs were designed

for that

Itrsquos different in every

REGION

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 10: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

10

Review from May 2012

So you got [some scientific thing]

right Who cares Tell me if I should

turn this off

Can you wait 6 months I have

to run my modelhellip

11

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

12

12

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

Complex I ⎛ J K ⎞

Bounding-BCCF = FCsumEFi ⎜sumrforci j +sum frespik ⎟⎜ ⎟ equation 111amp1 ⎠i=1 ⎝ j=1 k=1

responseclimate emission perforcing factor

emissionforcing perfuel emissionconsumption

part f jrforci j = partei PD

13

Definition

Emission-Normalized Forcing (ENF)

including ENDRF Direct Radiative

EN IRF Indirect Radiative

Forcingmodeled approximates this Emissionmodeled part f jrforci j =

partei PD

14

Detour Climate ldquometricsrdquo

Normal people think A metric is something you can measure and report

The climate policy community says A metric is a well-defined calculation that can be used to equate a mass emission of some species to a mass emission of the big bear CO2

15

s

Some climate metrics

Absolute global warming potential Global warming potential

Global temperature potential

For short-lived species (τlt4 mo)ENF emission-normalized forcing is the only model output required to calculate any of these metrics Other considerations affect the values of emission metrics but they all come from models of the carbon cycle or Earthrsquo heat capacity NOT from models of aerosols

16

Complaints against ENF You canrsquot do

that for CLOUDSForcing is

not linear

Anyway none of

the model runs were designed

for that

Itrsquos different in every

REGION

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 11: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

11

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

12

12

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

Complex I ⎛ J K ⎞

Bounding-BCCF = FCsumEFi ⎜sumrforci j +sum frespik ⎟⎜ ⎟ equation 111amp1 ⎠i=1 ⎝ j=1 k=1

responseclimate emission perforcing factor

emissionforcing perfuel emissionconsumption

part f jrforci j = partei PD

13

Definition

Emission-Normalized Forcing (ENF)

including ENDRF Direct Radiative

EN IRF Indirect Radiative

Forcingmodeled approximates this Emissionmodeled part f jrforci j =

partei PD

14

Detour Climate ldquometricsrdquo

Normal people think A metric is something you can measure and report

The climate policy community says A metric is a well-defined calculation that can be used to equate a mass emission of some species to a mass emission of the big bear CO2

15

s

Some climate metrics

Absolute global warming potential Global warming potential

Global temperature potential

For short-lived species (τlt4 mo)ENF emission-normalized forcing is the only model output required to calculate any of these metrics Other considerations affect the values of emission metrics but they all come from models of the carbon cycle or Earthrsquo heat capacity NOT from models of aerosols

16

Complaints against ENF You canrsquot do

that for CLOUDSForcing is

not linear

Anyway none of

the model runs were designed

for that

Itrsquos different in every

REGION

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 12: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

12

12

Need a forcing-to-emission ratio

Simple ForcingmodeledForcing = x Emissionsourcesource Emissionmodeled

Complex I ⎛ J K ⎞

Bounding-BCCF = FCsumEFi ⎜sumrforci j +sum frespik ⎟⎜ ⎟ equation 111amp1 ⎠i=1 ⎝ j=1 k=1

responseclimate emission perforcing factor

emissionforcing perfuel emissionconsumption

part f jrforci j = partei PD

13

Definition

Emission-Normalized Forcing (ENF)

including ENDRF Direct Radiative

EN IRF Indirect Radiative

Forcingmodeled approximates this Emissionmodeled part f jrforci j =

partei PD

14

Detour Climate ldquometricsrdquo

Normal people think A metric is something you can measure and report

The climate policy community says A metric is a well-defined calculation that can be used to equate a mass emission of some species to a mass emission of the big bear CO2

15

s

Some climate metrics

Absolute global warming potential Global warming potential

Global temperature potential

For short-lived species (τlt4 mo)ENF emission-normalized forcing is the only model output required to calculate any of these metrics Other considerations affect the values of emission metrics but they all come from models of the carbon cycle or Earthrsquo heat capacity NOT from models of aerosols

16

Complaints against ENF You canrsquot do

that for CLOUDSForcing is

not linear

Anyway none of

the model runs were designed

for that

Itrsquos different in every

REGION

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 13: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

13

Definition

Emission-Normalized Forcing (ENF)

including ENDRF Direct Radiative

EN IRF Indirect Radiative

Forcingmodeled approximates this Emissionmodeled part f jrforci j =

partei PD

14

Detour Climate ldquometricsrdquo

Normal people think A metric is something you can measure and report

The climate policy community says A metric is a well-defined calculation that can be used to equate a mass emission of some species to a mass emission of the big bear CO2

15

s

Some climate metrics

Absolute global warming potential Global warming potential

Global temperature potential

For short-lived species (τlt4 mo)ENF emission-normalized forcing is the only model output required to calculate any of these metrics Other considerations affect the values of emission metrics but they all come from models of the carbon cycle or Earthrsquo heat capacity NOT from models of aerosols

16

Complaints against ENF You canrsquot do

that for CLOUDSForcing is

not linear

Anyway none of

the model runs were designed

for that

Itrsquos different in every

REGION

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 14: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

14

Detour Climate ldquometricsrdquo

Normal people think A metric is something you can measure and report

The climate policy community says A metric is a well-defined calculation that can be used to equate a mass emission of some species to a mass emission of the big bear CO2

15

s

Some climate metrics

Absolute global warming potential Global warming potential

Global temperature potential

For short-lived species (τlt4 mo)ENF emission-normalized forcing is the only model output required to calculate any of these metrics Other considerations affect the values of emission metrics but they all come from models of the carbon cycle or Earthrsquo heat capacity NOT from models of aerosols

16

Complaints against ENF You canrsquot do

that for CLOUDSForcing is

not linear

Anyway none of

the model runs were designed

for that

Itrsquos different in every

REGION

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 15: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

15

s

Some climate metrics

Absolute global warming potential Global warming potential

Global temperature potential

For short-lived species (τlt4 mo)ENF emission-normalized forcing is the only model output required to calculate any of these metrics Other considerations affect the values of emission metrics but they all come from models of the carbon cycle or Earthrsquo heat capacity NOT from models of aerosols

16

Complaints against ENF You canrsquot do

that for CLOUDSForcing is

not linear

Anyway none of

the model runs were designed

for that

Itrsquos different in every

REGION

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 16: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

16

Complaints against ENF You canrsquot do

that for CLOUDSForcing is

not linear

Anyway none of

the model runs were designed

for that

Itrsquos different in every

REGION

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 17: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

17

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 18: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

18

Need Emission-normalized forcingfor both direct forcing and cloudmechanisms

Objective 3 Determine functional relationships that express changes in direct and cloud radiative forcing as a function of emission changes in particular locations

But waithellip

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 19: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

19

enrsquot accurate(older version of

eModel)

Review from May 2012Relative location of BC and clouds

affects direct forcingIt surely also affects indirect forcing

In this earlier study we found that the modeled clouds

wer 2550

Community AtmospherWg

250 Wg1050

Wg

Zarzycki amp Bond GRL 2010

Note Also affects semi-direct forcing see Ban-Weiss et al Clim Dyn 2011

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 20: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

20

Strategy Compare modeled fields with ISCCP observations ISCCP = International Satellite Cloud Climatology Project

image nasagov

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 21: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

21

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 22: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

22

Need Confidence in modeled clouds before inferring cloud forcing from a model

Objective 2 Employ an ensemble of parameterizations in regional-scale models to identify best estimates and uncertainties for fields of direct and cloud-related forcing

But waithellip

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 23: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

23

Aerosol effects are size-dependent

(direct effect)

(ind

irect

ef

fect

)

decreasing size

Bauer et al ACP 2010 for carbonaceous aerosols

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 24: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

24

Need Knowledge of emission sizedistributions

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 25: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

25

Need Knowledge of particle size beginningwith emission

Objective 1 Develop size-resolved speciated emission inventories of aerosols and aerosol precursors

But waithellip

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 26: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

26

YES Irsquom waitinghellip

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 27: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

27

YES Irsquom waitinghellip

ALL DONE

image gogobambinicom

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 28: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

28

Need Policy-distilled measures or metrics

Objective 4 Iterate emission-to-forcing measures as communication tools between decision makers and climate scientists

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 29: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

29

gonne

Size-Resolved Emission Inventory

Or Why we Did What We Did

(David Streets Ekbordin Winijkul Fang Yan - Ar presented by Tami)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 30: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

30

Procedure

PM size distribution from literature [by sector fuel and technology]

Parameterize size distribution [lognormal or mixture distribution]

(see next slide)

Emission factors (EF g PM kg fuel)

Emission factors by size (EFDp g PM kg fuel )

[Continuous distribution or discrete bins]

Fuel consumptionSize-resolved PM emission inventory

We already had this

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 31: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

31

101

Parameterizing size distribution Fit with lognormal distributionhellip

1 ⎡ (ln D minus ln D )2 ⎤ ⎢ ⎥f (ln D ) = exp minus

p pg

p 2 ln π σ g ⎢ 2ln σ g

2 ⎥ ⎣ ⎦

hellipor bimodal distribution f x = w f x w f x le le1( ) ( )+ ( ) 0 w1 1 2 2

PM mass fraction distribution (power plant) 10

-2 -1 00

02

04

06

08

1

dMas

sFra

ctio

ndl

ogD

p

Measurement Comp1 (lognormal) Comp2 (lognormal) Mixture (1+2)

w1 = 0132

D pg

= 0176μm σ

g = 153

w2 = 0935

D pg

= 278μm σ

g = 24

Data source Zhao et al( 2010) AE[Fig1b Plant 12]

10 10 10D (μm)

p

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 32: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Global size-resolved emission inventory

32

Size-resolved global emission inventory of primary particulate matter (PM) from energy-related combustion sources E Winijkul F Yan Z Lu D G Streets T C Bond Y Zhao Submitted to Atmos Env 28 August 2014

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 33: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Work includes uncertainty and illustrative reduction scenarios

33

Residential Switching from solid fuel to LPG

Winijkul et al submitted 2014

Industrial Baghouses on cement kilns

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 34: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Regional Cloud Modeling

34

(Hao He Xin-Zhong Liang ndash Univ of Maryland)

Or Get the Clouds Right

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 35: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Modeling Approach   We used the mesoscale ClimatendashWeather Research and

Forecasting model (CWRF) model   Total aerosol field (not just BC) is produced by global models   CWRF has alternative parameterizations for cloud properties

aerosol properties and radiation transfer Purpose Investigate range of climate forcing in models that agree with observations

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 36: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

types(13)

bgd (1)

sul (2)

ssa (2)

dst (2)

cab (2)

tot (3)

bgd (1)

sul (2)

ssa (2)

optics (1152)

Aerosol(106)

vertical profiles

(8)

(72)

(16) dst (2)indirect effects(18) ccn (6)

cab (2)

cloud radius (3)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta

infrared (9)

gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw

solar (10)

Radiation(102)

rrtmlw swrad

topographic effect

cst ( 3)

ccs ( 4)

ccb ( 5)

cci ( 2)

bin ( 3)

rel ( 6)

rei ( 7)

rer ( 1)

cover(360)

liquid ( 7)

ice (16)

rain ( 3)

graup ( 1)

liquid ( 4)

ice (17)

rain ( 2)

graup (1)

snow ( 1)

optics (137088)

Cloud(1010)water (3)

radius(42)

(336)

(408)

emis ( 3)geometry

(9)binding ( 3)

overlap ( 3)

mosaic(9)

snow ( 1)

(408)

(3333333333336)

Cloud

Radiation

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooosssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooolllllllllllllllllllllllllllllllllll(((((((((((((((((((((((11000000000006666666666666666666666666666666666666666666666666666666666666))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))Aerosol

(1010) (106)

(102)

Cloud-Aerosol-Radiation (CAR) Ensemble Model

1018 configurations

earth orbit

surface characteristicsradiative gases

obse

rvat

ion

in s

itu amp

sat

ellit

e

C

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 37: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Frequency distribution of TOA radiative flux and CRF averaged over [60ordmS 60ordmN] in January 2004 from the CAR ensemble of 960 members

Uncertainty in Cloud-Aerosol-Radiation Modeling

Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

ISCCP Other obs Black

CAR median Blue CAR ensemble

best one chosen for these comparisons

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 38: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Modeling Approach   Meteorology ECWMF ERA interim reanalysis   Canadian Centre for Climate Modeling and Analysis

(CCCMA) radiation scheme

  Model run from 2001 to 2006 with the first year (2001) as spin-up Average from 2002 to 2006 is presented

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 39: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

One base case Five aerosol fields

Case No Case Name Temporal Resolution Aerosol input

1 Noaerosol NA Aerosol radiation Off 2 Default Monthly MISR Climatology 3 NCAR Monthly NCAR CAM2 model 4 GOCART$ Monthly GOCART model 5 CAM5 Monthly UIUC CAM5 model 6 CAM5rsquo Monthly UIUC CAM5 model

$ Chin et al 2014 Assuming all BC and OC are hydrophilic Assuming only 85 of BC and OC are hydrophilic

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 40: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

No aerosol

40

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 41: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

No aerosol

41

Clear-sky flux Differences from ISCCP

NCAR CAM5 GOCART

Default (MISR)

Note the scale Errors in flux are 10s of W m-2 Forcing is lt 5 W m-2

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 42: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

42

Cloudy-sky flux Differences from ISCCP

No aerosol Default (MISR)

NCAR CAM5 GOCART

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 43: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Comparison between modeled and observed fluxes (average over Continental US) Error bars are std dev of all grid boxes

43

(MISR)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 44: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Model bias Difference between CWRF results and ISCCP

44

(MISR)

None

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 45: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Aerosol radiative effects Difference between modeled results with amp without aerosols

45

(MISR)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 46: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

46

BC and OC partition have substantial impacts on the radiation simulations

1)  Impacts on clear sky flux are uniform

2) Cloud radiative effects are large (plusmn 5 Wm2) and regionally dependent

for instance opposite effects are suggested in the southeast US and

in the northwest US

Total Flux at TOA Clear Sky Flux at TOA

Cloud Radiative Effect at TOA

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 47: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Emission-to-forcing measures

47

(Yanju Chenndash Univ of Illinois)

Or Model Interpretation for Policy Relevance

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 48: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Step 1 Test linearity and regionality

  Basis to obtain forcing-per-emission relationship assumed by emission metrics

  Direct forcing ndash probably linear   Cloud forcing ndashmay be nonlinear with respect to

aerosol concentration (Quaas et al 2009)   May vary by region

48

Emission

Forc

ing

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 49: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Experimental Design

  Reduce BC from N America (AM BC)   Reduce BC from Asia (AS BC)

  Reduce OC from N America (AM OC)   Reduce OC from Asia (AS OC)

49

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 50: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Model Description and Configuration

  Modified Community Atmosphere Model (CAM51)

ndash  Three-modal aerosol module (MAM3) (Liu et al 2012)

ndash  Improved BC spatial and temporal distribution with modified convective transport and

wet removal

ndash  Tagged BCOC emission for direct calculation of burden and forcing

  Anthropogenic emissions from IPCC emission datasets for year 2000 (Lamarque et al 2010)

  Model is configured to run in off-line mode (Ma et al 2013)

ndash  Model reads in prescribed meteorological fields

ndash  Model driven by ERA-interim data

ndash  Semi-direct effect cannot be simulated

  Each simulation is run for 5 years with 2 months for model spin-up

50

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 51: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Need for off-line meteorology

51

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 52: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Linearity diagnostic for a single species

52

R =F100 minusF50F100 minusF0

100 present-day

emission

50 present-day emission

0 emission

R 05 Forcing is linear in emission Rlt 05 Small emission change from present-day

produces less forcing change than one would expect

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 53: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Linearity of Global Mean Forcing

Linear for

Direct forcing by BC Indirect forcing by BC

Direct forcing by OC

Sublinear for Indirect forcing by OC

53

2 regions AM=North America AS=Asia

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 54: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Emission-normalized forcing

54 54

Indirect forcing ENIRF

3-4 times higher in N America (not saturated)

Reducing same amount of BCOC in these two regions will result in greatly different cloud change

Direct radiative forcing

ENDRF

similar for N America and Asia

Bounding-BC values

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 55: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Is cloud forcing visible

55 Koch et al ACP 11 1051 2011

Fossil fuel Diesel Biofuel

Multi-model study of effects on liquid clouds

Each row is from a

different model

No forcing pattern visible

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 56: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Regional Location of Indirect Forcing

Significant region was statistically determined using paired t-test between IRF0 IRF50 and 0 at significance level a = 01

Example OC from Asia

56

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 57: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Optimum grid box size for testing significance

  Box too small Each box noisy few boxes significant   Box too large Includes regions with little impact too few boxes are

significant

  30deg x 30deg is optimum   Significant grid boxes equal global mean forcing the rest are noise

Global mean (AMBC)

57

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 58: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Radiative Forcing in Significant Regions

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

RDRF

RIRF

(a) AMBC reduction

Longitude

Latit

ude

180W 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180E-90

-60

-30

0

30

60

90

noise significant

(b) AMOC reduction bull  Only 20-30 of

the grid boxes are significant

bull  Near and downwind of source region

58

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 59: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Linearity in Significant Regions

AMBC ASBC AMOC ASOC03

035

04

045

05

055

06

065

07

R o

f DR

F

AMBC ASBC AMOC ASOC0

01

02

03

04

05

06

07

08

09

1

R o

f IR

F

bull  Direct radiative forcing (DRF) is linear in all regions

bull  Indirect radiative forcing (IRF) is nonlinear in some significant regions especially for OC

59

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 60: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Cause of Nonlinearity

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of NUM (accumulation)

R o

f CC

N (s

s =

01

)

AMBCASBCAMOCASOC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CCN (ss = 01)

R o

f CD

NU

MC

0 01 02 03 04 05 06 07 08 09 10

01

02

03

04

05

06

07

08

09

1

R of CDNUMC

R o

f IR

F

Emission CN CCN droplet (CDNUMC) IRF

linear nonlinear linear

bull  Nonlinearity occurs when cloud droplets are formed from CCN bull  Formation of droplets is limited and does not increase as the

number of CCN increases bull  Of course this depends on model parameterizationhellip

60

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 61: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Summary ndash Indirect forcing

  Apparent effect on cloudsndash ENIRF N Am OC gt N Am BC gt Asian OC gt Asian BC   In high-aerosol regions reducing present-day aerosol

has a less-than-linear effect   Global average forcing can be attributed to a subset

(lt40) of significant regions However comparison with observations calls modeling of aerosol-cloud effect in North America into question Nextndash Compare global amp regional aerosol effect in North America

61

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 62: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Policy-relevant metrics

62

(Praveen Amar Danielle Meitivndash Clean Air Task Force presented by Tami)

Or Get the Story Right

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 63: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Original goal Communicate with policy makers to see what metrics they want

63

ldquoCommunicating the science and policy implications of black carbonrdquo ndash CATF report

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 64: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Main messages had nothing to do with metrics

64

  Scientists need understandable ways to communicate black carbonrsquos effects to non-specialists bull Even terms like ldquoradiative forcingrdquo and ldquofeedbackrdquo are

not as straightforward as you think   People want to hear about certainty not uncertainty

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 65: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Main messages had nothing to do with metrics

65

  Equating BC and CO2 Some are wary in other situations (eg California) itrsquos required bull People do not want to think about time horizons

Thatrsquos our job bull People do not want to think about metrics Ditto

  There is not yet a good way to communicate immediacy of forcing changes bull Watch this space

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 66: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Summary of outcomes ndash easy ones

66

1 Size-resolved inventory complete 4 Metrics are up to us Make it easy

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 67: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Summary of outcomes ndash hard ones

67

2 The constraint problem Looking to confirm small changes (forcing) in a large signal (clouds) 3a Forcing is nearly linear in emission if regions are treated individually

- Average over statistically significant (30x30) boxes - High-aerosol regions have lower indirect forcing per emission More promising to reduce there

3b Cloud models donrsquot match observations - Reason to doubt emission-to-forcing is not the modelrsquos nonlinear nature but its inability to match reality

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 68: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Done Questions

68

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 69: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Modeling approach

69

Supplemental slides

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 70: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Radiative flux in CWRF

70

We calculated total radiative flux TOA (TOAFlux) clear sky flux TOA (CTOAFlux) and cloud radiative

effect (CldRE) as TOAFlux = SWdownTOA + LWdownTOA ndash SWupTOA ndash LWupTOA

CTOAFlux = SWdownTOAclear + LWdownTOAclear ndash SWupTOAclear ndash LWupTOAclear

CldRF = TOAFlux ndash CTOAFlux

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71

Page 71: Linking Regional Aerosol Emission Changes with Multiple ... · Global size-resolved emission inventory 32 Size-resolved global emission inventory of primary particulate matter (PM)

Selected References   Chin M and Coauthors 2014 Multi-decadal aerosol variations from 1980 to

2009 a perspective from observations and a global model Atmos Chem Phys 14 3657-3690

  Dee D P and Coauthors 2011 The ERA-Interim reanalysis configuration and performance of the data assimilation system Quarterly Journal of the Royal Meteorological Society 137 553-597

  Li J K von Salzen Y Peng H Zhang and X Z Liang 2013 Evaluation of black carbon semi‒direct radiative effect in a climate model Journal of Geophysical Research Atmospheres 118 4715-4728

  Liang X Z M Xu X Yuan T J Ling H Choi F Zhang L G Chen S Y Liu S J Su F X Qiao Y X He X L Wang K Kunkel W Gao E Joseph V Morris T Yu J Dudhia and J Michalakes 2012 Regional Climate-Weather Research and Forecasting Model Bull Amer Meteorol Soc 93 1363-1387

  Liang X Z and F Zhang 2013 The cloud-Aerosol-Radiation (CAR) ensemble modeling system Atmos Chem Phys 13 8335-8364

  Zhang F X-Z Liang J Li and Q Zeng 2013 Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects Journal of Geophysical Research Atmospheres 118 7733-7749

  Zhang F X-Z Liang Q Zeng Y Gu and S Su 2013 Cloud-Aerosol-Radiation (CAR) ensemble modeling system Overall accuracy and efficiency Advances in Atmospheric Sciences 30 955-973 71