Estimating regional C fluxes by exploiting observed

correlations between CO and CO2

Paul Palmer

Division of Engineering and Applied Sciences Harvard University

http://www.people.fas.harvard.edu/~ppalmer

IPCC

61 60

5.51.6

+ ballpark flux estimates for fast exchange processes (109

tonnes C)

IPCC

Chinese Government Statistics Shown Downward Trend in Chinese CO2 Emissions

(Streets et al., Science, 294, 1835-1837, 2001)

China Energy Databook v6, 2004

Ch

ina G

DP (

Bill

ion

19

95

yu

an

co

nst

an

t)

Year

Large uncertainty

E = A F

Bottom-up Emission Inventories are Very Uncertain

Emissions (Tg C yr-

1)

Activity Rate (Tg fuel yr-

1) (amount of fuel burned)

Emission Factor (TgC / Tg fuel)

Coal-burning cook stoves in Xian, China

RH + OH … CO CO2

1000s km

Direct & indirect emissions

CMDL site

Many 100s km10s km

Increasing model transport error

Remote data have limitations in estimating regional C budgets

Aircraft data can improve level of disaggregation of continental emissions

110 E 120 E 130 E 140 E 150 E 160 E

Longitude

0 N

10 N

20 N

30 N

40 N

50 N

Lat

itu

de

DC-8 FlightsP-3B Flights

cold front

cold air

warm air

Main transport processes:

DEEP CONVECTION

OROGRAPHIC LIFTING

FRONTAL LIFTING

100 E 130 E 160 E 190 E 220 E 250 E 280 E

Longitude

0 N

10 N

20 N

30 N

40 N

50 N

60 N

La

titu

de

DC-8 FlightsP-3B Flights

Feb – April 2001

NASA TRACE-P

Sources of CO from Asia

Main sink is the hydroxyl radical (OH) Lifetime ~1-3 months

Product of incomplete combustion

BB BF

FF

+Oxidation of hydrocarbons

BF BB

FF

Offshore China

Over Japan

Slope (> 840 mb) = 51

R2 = 0.76

Slope (> 840 mb) = 22

R2 = 0.45

Suntharalingam et al, 2004

ATMOSPHERIC CO2:CO CORRELATIONS PROVIDE UNIQUE INFORMATION ON SOURCE REGION AND

TYPE

- CO2:CO emission ratios vary with combustion efficiency

- Range in regional emission ratios reflect mix of sources and variation in fossil fuel combustion ratio

A priori bottom-up

Top-down

CO CO

CO

2

CO

2

Observation vector y

State vector (Emissions x)

Modeling Overview

Inverse model

x = Fluxes of CO and CO2 from Asia (Tg C/yr)

y = TRACE-P CO and CO2 concentration data

Forward model(GEOS-CHEM)

x = xa + (KTSy-1K + Sa

-1)-1 KTSy-1(y – Kxa)^

y = Kxa +

Jacobian describes CTM

http://www-as.harvard.edu/chemistry/trop/geos/index.html

GEOS-CHEM global 3D chemical transport

model

•Driven by NASA GMAO met data (3/6 hr)

•2x2.5o resolution/30 vertical levels

•O3-NOx-VOC-aerosol coupled chemistry

•Evaluated using ground-based, aircraft, and satellite observations

Consistent CO and CO2 Emissions Inventories Biomass

burning: Variability from observed daily firecount data (AVHRR)

Heald et al, 2003

Anthropogenic emissions for 2001: domestic ff, biofuel, transport, industrial ff Streets et al, 2003

Seasonal Cycle of Chinese CO and CO2 Emissions during TRACE-P

TERRESTIAL BIOSPHERE: CASA (Randerson, et al, 1997) OCEAN BIOSPHERE: Takahashi et al, 1999

Gt

C y

r-1

Fra

cti

on

of

an

nu

al

em

issio

ns

CO

Annual Mean

Streets et al, 2003

TOTAL

FOSSIL

BIOSPHERE

BIOBURN

BIOFUEL

TOTAL

TRACE-P

[OH] from full-chemistry model (CH3CCl3 = 6.3 years)

State vector x = emissions from individual countries and individual processes

Estimating the Jacobian [CO]/COemission

China (CH)

Japan (JP)

Southeast Asia (SEA)

Rest of World (ROW)

Global 3D CTM 2x2.5 deg resolution

Korea (KR)

Boreal Asia (BA)

Linear calculation is straightforward:

JCHBB= [CO]CHBBCOCHBB/emissions

0-2 km

Latitude [deg]

CO

[p

pb

]C

O2 [p

pm

]4-6 km

2-4 km

GEOS-CHEMTRACE-P Observations

Remove CO2 bias using 10th

percentile of [CO2]: 4-4.5 ppm

Linear Inverse Model

x = xa + (KTSy-1K + Sa

-1)-1 KTSy-1(y –

Kxa)

S = (KTSy-1K + Sa

-1)-1

Xs = retrieved state vector (the CO sources)Xa = a priori estimate of the CO sourcesSa = error covariance of the a priori K = forward model operatorSy = error covariance of observations = instrument error + model error + representativeness error

Gain matrix

^

^

Sy Measurement accuracy Representation

Model error (most important)

GEOS-CHEM

Error specification for CO and CO2

Sa Anthropogenic (c/o Streets): China (78%), Japan (17%), Southeast Asia (100%), Korea (42%) – uniform 25% Biomass burning: 50% 30% Chemistry (~CH4): 25% Biosphere: 75%

GEOS-CHEM

2x2.5 cell

TRACE-P

All latitudes

(measured-model) /measured

Alt

itu

de [

km

]

Mean bias

RRE

CO

(y*RRE)2 ~38ppb (CO)

~1.87ppm (CO2)

RRE = total observation error

NUMBER OF EIGENVALUES OF PREWHITENED JACOBIAN 1 =

DOF

K = S KS~

-1/2 1/2

aCO: CH ANTH*, KRJP&, SEA, CH BB, BA BB@, ROW

CO2: CH ANTH*, KRJP&, CH BB$, BA BB@, BS, ROW (inc SEA$)*Collocated sources; &coarse resolution forces merging; $observed gradients too weak to resolve source; @not well resolved

Rodgers, 2000

Independent Inversion of CO and CO2 emissionsA priori

A posteriori

CO

2 e

mis

sio

ns

[Tg

Marc

h 2

001]

CO

em

issio

ns

[

Tg

yr-

1]

Biospheric CO2

Anthropogenic CO2

1~ K

Results consistent with [CO2]:[CO] analysis

•Estimated Chinese anthropogenic CO(CO2) sources are currently too low (high).

•Chinese biospheric CO2 fluxes are estimated too high.

CO2 state vector

A posteriori correlation matrix illustrates the ambiguity between anthropogenic and biospheric CO2

emissions

Chinese anthropogenic CO2

Chinese biospheric

CO2

^C

Monte Carlo approach to modeling correlations between

CO and CO2ECO = (A + AA) (FCO + COFCO)

ECO2 = (A + AA) (FCO2 + CO2FCO2)

Carbon Conservation (CO+CO2 ~ 0.9-1.0)

Perturbed F

N

N

Unperturbed F 10.9

r > 0 CO

Emissions

CO

2

Em

issi

on

s

F

A

CO Emissions

CO

2

Em

issi

on

s

F

A

A >> F A << F

r < 1

Interpretation of correlations

VALUES OF UNCERTAINTY FROM STREETS’ INCONSISTENT WITH DATA ANALYSIS AND LEAD

TO SMALL CO2:CO CORRELATIONS

E = A FA: CO 5-25%; CO2 5-20%

F: CO 50 - 200%; CO2 5-10%Correlations: China ~0 Korea/Japan -0.2 Southeast Asia ~0

Correlations within sectors > lumped sectors

Alternative Correlations Tested…

CO

2:C

O C

orr

ela

tion

Chinese anthropogenic

Korea + Japan

Southeast Asia

Streets’Min(A 25%)Min(A 50%)

Also r = 0.5,…,1.0

A correlation of > 0.7 is needed to start decoupling biospheric and anthropogenic

CO2A

poste

riori

Un

cert

ain

ty

[un

it]

Anthropogenic CO2

Biospheric CO2

Anthropogenic CO

Lowest correlations correspond to those calculated using Monte Carlo method

Future satellite missions

The “A Train”

MODIS/ CERES IR Properties of Clouds

AIRS Temperature and H2O Sounding

Aqua

1:30 PM

CloudsatPARASOL

CALPSO- Aerosol and cloud heightsCloudsat - cloud dropletsPARASOL - aerosol and cloud polarizationOCO - CO2

CALIPSOAura

OMI - Cloud heights

OMI & HIRLDS – Aerosols

MLS& TES - H2O & temp profiles

MLS & HIRDLS – Cirrus clouds

1:38 PM

OCO

1:15 PM

OCO - CO2 column

C/o M. Schoeberl

• Launch date in 2007. • Will provide column CO2

measurements• 3 spectrometers that measure CO2 at

1.61 m and 2.05 m and O2 at 0.76

m• Field of view of spectrometers is 1x1.5

km2 • Sun-synchronous orbit with 16-day

repeat cycle and 1:15 pm equator crossing time

Orbiting Carbon Observatory (OCO)

New Concept: Testing science objectives of satellite instruments before launch

Tropospheric Emission Spectrometer (TES)

• Launched in July 2004• An IR, high resolution Fourier

spectrometer • Measures spectral range 3.3 - 15.4 m• Limb and nadir view (footprint is 8x5

km2)• Sun-synchronous orbit with 16-day

repeat cycle Will measurements of CO and CO2 from TES and OCO provide accurate constraints on carbon fluxes from different regions in Asia?

Jones et al, 2004

Simulation: Constraining Asian Carbon Fluxes from Space

Generate pseudo-data from the satellites for March 1-31, 2001

Inverse model with realistic instrument and model errors, and which accounts for data loss due to cloud cover and the vertical sensitivity of the instruments

CO2 column along OCO orbit (1 day)CO (825 mb) along TES orbit (1 day)

ppmppb

Jones et al, 2004

Significant reduction in uncertainty in estimates of the dominant Asian biospheric fluxes (China and Boreal Asia)

ChinaFuel

JP/KRFuel

SE AsiaFuel

IndiaFuel

ChinaBB

SE AsiaBB

IndiaBB

BorealAsia BB

China JapanKorea

SEAsia

India BorealAsia

Rest ofworld

A priori

A posteriori

ChinaFuel

JP/KRFuel

SE AsiaFuel

IndiaFuel

ChinaBB

SE AsiaBB

IndiaBB

BorealAsia BB

China JapanKorea

SEAsia

India BorealAsia

Rest ofworld

A priori

A posteriori

ChinaFuel

JP/KRFuel

SE AsiaFuel

IndiaFuel

ChinaBB

SE AsiaBB

IndiaBB

BorealAsia BB

China JapanKorea

SEAsia

India BorealAsia

Rest ofworld

A priori

A posteriori

ChinaFuel

JP/KRFuel

SE AsiaFuel

IndiaFuel

ChinaBB

SE AsiaBB

IndiaBB

BorealAsia BB

China JapanKorea

SEAsia

India BorealAsia

Rest ofworld

A priori

A posteriori

ChinaFuel

JP/KRFuel

SE AsiaFuel

IndiaFuel

ChinaBB

SE AsiaBB

IndiaBB

BorealAsia BB

China JapanKorea

SEAsia

India BorealAsia

Rest ofworld

A priori

A posteriori

ChinaFuel

JP/KRFuel

SE AsiaFuel

IndiaFuel

ChinaBB

SE AsiaBB

IndiaBB

BorealAsia BB

China JapanKorea

SEAsia

India BorealAsia

Rest ofworld

A priori

A posteriori

ChinaFuel

JP/KRFuel

SE AsiaFuel

IndiaFuel

ChinaBB

SE AsiaBB

IndiaBB

BorealAsia BB

China JapanKorea

SEAsia

India BorealAsia

Rest ofworld

A priori

A posteriori

CO Sources

CO2 Sources

Biospheric CO2

A P

oste

riori

Err

or

Esti

mate

s

[%]

ChinaFuel

JP/KRFuel

SE AsiaFuel

IndiaFuel

ChinaBB

SE AsiaBB

IndiaBB

BorealAsia BB

ChinaFuel

JP/KRFuel

SE AsiaFuel

IndiaFuel

ChinaBB

SE AsiaBB

IndiaBB

BorealAsia BB

Chinese biospheric fluxes weakly coupled to anthropogenic emissions

Jones et al, 2004

Closing Remarks

•Estimated Chinese anthropogenic CO(CO2) sources are currently too low (high).

•Chinese biospheric CO2 fluxes are estimated too high but they are coupled to anthropogenic CO2. Correlations between CO2 and CO can decouple these signals.

• Emission correlations summed over sectors are too weak – need r > 0.7, impossible with current inverse model configuration.

•Work in progress – much still to explore.