Dr. Benjamin R. Lintner

I.Y. Fung, C.D. Koven (UC Berkeley); W. Buermann (UCLA)

A.B. Gilliland (Air Resources Laboratory)

C.J. Tucker, J.E. Pinzon (Goddard Space Flight Center)

A. Angert (Weizmann Institute of Science); K.P. Bowman (Texas A&M)

Department of Atmospheric & Oceanic Sciences and Institute of Geophysics & Planetary Physics

University of California Los Angeles

Blowin’ in the wind:Blowin’ in the wind: Atmospheric circulation, tracer transport,

and CO2 variability

Conservation of CO2

€

∂χ∂t

+ Γ(χ ) = S(χ )

€

χ =[CO2] Concentration of CO2

€

Γ(χ ) =v

∇ ⋅(v v χ ) Transport of CO2

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S(χ ) = Sδ(p − ps) + P(χ ) Source of CO2

( emission + production)

0

The big picture…

€

∂χ∂t

+ Γ(χ ) = S(χ )

Q: How can we obtain this term?Bottom-up: Field measurements (direct but costly)Top-down: Inversions (indirect but uncertain)

General inversion methodology: For a set of observations χobsi,construct and

minimize a cost function J assuming a set of unit source/sink basis regions ûk and a forward transport model G.

€

J = σ χ i

2 (χ iobs − ˆ χ i)

2

i

∑

€

ˆ χ ik = Gi( ˆ u k )⇒ ˆ χ i = mk

ˆ χ ik

k

∑

€

S est = m kk

∑ ˆ u k

€

∂J

∂mk mk = m k

€

=0

Q: What kind of information can be extracted?

Q: How much confidence do we have in G?

Lat-lon distribution of fossil fuel (FF) CO2*

Strong interhemispheric (meridional) gradient roughly co-located with the ITCZ; (generally) weak zonal gradients, reflecting rapid east-west mixing, although locally large near source regions.

Lat-height distribution of FF CO2

Meridional gradients are relaxed aloft compared to the surface; a “reversed” vertical gradient is characteristic of low latitudes in the nonsource (Southern) hemisphere.

Lat-Height characteristics in the Tropics are broadly understood in terms of the mean meridional overturning (Hadley) circulation.

2-box model

€

SS (t)

€

SN (t)€

χS (t)

€

χN (t)

€

y0

€

yS

€

yN

€

∂χN (t)

∂t= SN (t) + vχ{ }

y0

€

{A} = AdppT

pS∫

€

∂χS (t)

∂t= SS (t) − vχ{ }

y0

€

AN = A{ }dyy0

yN∫

€

AS = A{ }dyyS

y0∫

€

vχ{ }y0

≈ −κdχ

dyy0

≈ −χ N (t) − χ S (t)

τ IHT (t)

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τ IHT (t) = 2χ N (t) − χ S (t)

SN (t) − SS (t) −∂tχ N (t) + ∂tχ S (t)Interhemispheric exchange time (τIHT):

Time required for χS to “catch-up” to χN.

Definitions:

Anthropogenic CO2 in a 2-box model

Observational Constraints:

χsfc= 2.5 ppmv (χcolumn 2/3χsfc = 1.7 ppmv)

χG/t = (1/2)(χN +χS)/t = 1.5 ppmv/yr

Consider Fossil Fuel (FF): SNFF = 6 PgC/yr*; SS

FF ~ 0 pgC/yr

For τIHT ~ 1 yr χ(est) = 3.0 ppmv (4.5 ppmv at surface)

STotal = 3.4 PgC/yr SNSink - SS

Sink = 2.6 pgC/yr

(SNTotal + SS

Total) = 3.0 PgC/yr

SNSink = 2.8 PgC/yr; SS

Sink = 0.2 PgC/yr

The implied “sink” partitioning is:

*Note: 1PgC = 0.5 ppmv (mixed over whole troposphere)

Q: How do we obtain this value?

Estimates of annual-mean τIHT

Air

mas

s

CC

l 3F

CH

3CC

l 3

CH

4

14C

O2

85K

r

SF

6

All

Ob

s.

1yr

Observations (see Lintner 2003)

Mean : 1.280.33 years

AN

U

GF

DL

-GC

TM

GIS

S

MU

TM

CC

C

CS

U

-SK

YH

I

GIS

S-U

VIC

/UC

B

NIR

E

TM

2

All

Mod

.

1yr

TransCom Models (Denning et al., 1999)

Mean : 0.880.22 years

IHT versus dynamics

0.5

0.7

0.9

1.1

1.3

18 20 22 24 5 6 7 8 9

SF6 CH4 CFC11

τ IHT (

year

s)

Peak Hadley Strength (x 1010 kg/s) JJA Land Precip. at 18N (mm/day)

From GISS simulations of Rind et al., 2007

Changes to model vertical (+ horizontal) resolution alter dynamics, which in turn alters τIHT. Generally, stronger Hadley circulation (land region convection) favors faster IHT.

τIHT seasonal cycle

From Lintner et al., 2004

0.86 years

Seasonal cycle: ±20-30% of annual mean

“fast” IHT/small τIHT in winter/summer and “slow” IHT/large τIHT in spring/autumn

0.2

0.6

1.0

1.4

1.8

NCEP/MATCH

GISS-UCB 1.27 years

J MF A JM J SA O DN

year

s

Q: What is the source of this seasonality?

CFC-11 (1979-1988)

0.73 years

Transport partitioning

€

vv χ

Total{

=v v χ

Mean1 2 3

+v v * χ *

Stationary Eddy1 2 3

+ ′ v v ′ χ

Transient Eddy1 2 3

€

A =1

x f − x i

Adxxi

x f∫

€

A* = A − A

€

A =1

t f − ti

Adtt i

t f∫

€

′ A = A − A

Defining:

Meridional transport in the GISS-UCB model

Month

x 10

7 kg/

mon

th

€

vχ{ }

€

v χ{ }

€

v* χ *{ }

€

′ v ′ χ { }

1.50

1.25

1.00

years

€

τ IHT

DJF Streamfunction

MAM StreamfunctionMean meridional, stationary-eddy, and transient eddy: ~1/3 of annual-mean total transport (Denning et al., 1999)

From seasonal cycleRind et al., 2007 sensitivity

τ IHT (

year

s)

8 12 16 20

1.0

1.4

1.8

Max. Hadley Strength (x 1010 kg/s)

Seasonality dominated by mean meridional (Hadley) circulation, with fast (slow) IHT occurring when Hadley circulation is strongly asymmetric (symmetric) w.r.t. equator.

Regional transport characteristics

6

0

-16€

vχJanuary Julyx 107 kg/month

-30

-10 10

30

€

′ v ′ χ x 105 kg/monthJanuary July

Outflow associated with deep convection

MOPITT CO for January 20-27, 2001 at 500 mb

From Edwards et al., 2003

Small scale vertical transport: Biomass burning plumes are uplifted near the ITCZ and diverged aloft.

τIHT interannual variability (IAV)

El Niño

La Niña

GISS-UCB

NCEP/MATCH

Interannual variations: ±5-10% of annual mean

“fast” IHT/small τIHT during La Niña events and “slow” IHT/large τIHT El Niño events(?)

Sources of GISS-UCB IAV

From Lintner et al., 2004

10E-40E

60E-100E 110E-150E

140W-110W

40W-10W

160E-160W

75W-45W

Often, more intense convection favors faster IHT (e.g., Indian Ocean/South Asia during JJA; South America during ASO), but not always (e.g., Eastern Pacific).

Conditional- averaged STRENGTH of convection for fast IHT and slow IHT.

Sources of GISS-UCB IAV

From Lintner et al., 2004

10E-40E

60E-100E 110E-150E

140W-110W

40W-10W

160E-160W

75W-45W

Conditional- averaged LOCATION of convection (ITCZ) for fast IHT and slow IHT.

Generally, more extreme ITCZ displacements favor faster IHT (broadly consistent with chaotic advective mixing noted by Bowman and Cohen, 1997)

But there is significant region-to-region variation…..

Summary of IHT

• 2-box model mean exchange times (τIHT) of 0.8-1.2 years, with pronounced seasonality (20-30% of mean) and IAV (5-10%)

From the perspective of carbon cycle, these variations impact

estimates of the meridional distribution of CO2 sources/sinks.

• τIHT reflects contributions from both zonal-mean and regional circulations

Results are model-dependentObtaining a detailed picture of 3D interhemispheric transport

pathways, i.e., how (and where) transport across the “interhemispheric transport barrier” occurs, is desirable.

What are the temporal characteristics (subseasonal, seasonal, interannual) of these pathways?

Mauna Loa Observatory (MLO), Hawai´i

Hurricane Flossie

155ºW, 19ºN 3397 m a.s.l.

CO2 seasonality at MLO

2002 2003 2004 2005 2006 2007

380

384

376

372

Respiration

Photosynthesis

CO2 to atmos.

CO2 from atmos.

Detrended Seasonal Cycle

Amplitude

ppmv

Changing MLO CO2 amplitude

2000

Temp (K)

-0.8

-0.4

0.0

0.4

0.8

0.9

1.0

1.1

1.2

Relative Amp

Land Surface Temp (30N-80N)

1960 1970 1980 1990

Q: What accounts for this change of behavior?

Mauna Loa Amplitude

Keeling (1996): increasing MLO amplitude from 1960s-early 1990s consistent with high latitude land surface warming and northward “greening”

Since the early 90s, MLO amplitude has decreased but NH land surface temperatures have continued to warm.

MLO and atmospheric circulation

From Lintner et al., 2006

JFM AMJ

JAS OND

NCEP Reanalysis 700 mb wind (vectors) and streamflow (contours)

6-10 day Lagrangian back trajectories

Eurasia

N. America NCEP/MATCH FF SF6 HCFC22

Role of atmospheric transport

1960 1970 1980 1990 2000

0.9

1.0

1.1

1.2

-2

-1

0

1

2

Comparison of observed MLO Amp (black) to MATCH-simulated (blue) with no source/sink variability: Decrease in MATCH over the 1990s suggests nonnegligible transport signature.

Observed MLO Amp

MATCH simulated Amp

Q: What is the source of the simulated trend?

Simulated FF (AMJ)222Rn (AMJ)

From Buermann et al., 2007

Data from simulation of Higuchi et al., 2003: Some indication of a trend over the 1990s?

Backtrajectory cluster analysis*

“Long range” cluster“Short range” cluster

1988 1992 1996 20001988 1992 1996 2000

-2

-1

0

1

2

-2

-1

0

1

2Yearly membership in

short range cluster AMJ FF Yearly membership in

long range cluster AMJ FF

From Lintner et al., 2006*Results shown are for April-May-June (AMJ)

Changing temperature and moisture influence

Warm-season temperatures: persistent +ve correlations with MLO Amp from 1960s-mid 1970s but little thereafter….

Warm-season hydrology: recent development of strong correlations for North Americaeffect of North American drought during 1998-2003 reduced carbon uptake, resulting in decreased amplitude

From Buermann et al., 2007

Schematic of MLO amplitude controls

Eurasia

North America

Cold season

Warm season

Cold season

Warm season

Temperature: 1yr-lag

Temperature Drought/rain cycles

Temperature: 1yr-lag 1 and 2 yr-lag 2 yr-lag

PhotosynthesisPhotosynthesis

Respiration

Respiration

Photosynthesis

Transport

Summary of MLO

• Because of the relationship of the observing site relative to large-scale circulation, MLO is most sensitive to Eurasian influence during boreal cold season and North America influence during boreal warm season.

Provides some basis for the geographic distribution of correlationsNonstationarity/changing influence through time

• Some of the 1990s downward trend may be directly attributable to changes in transport as seen at the observing site.

Less Eurasian-originating transport in boreal spring reduces amplitude (support from backtrajectories/222Rn)