GLOBAL CO INVERSE ANALYSIS

GLOBAL CO INVERSE ANALYSISGLOBAL CO INVERSE ANALYSIS

Avelino F. Arellano, Jr. and Prasad S. Kasibhatla

Nicholas School of the Environment and Earth Sciences,Duke University, NC

GFED Collaborators: Louis Giglio (SSAI), Jim Randerson (UCIrvine), Jim Collatz (NASA GSFC), Guido van der Werf (formerly FAS), Ruth deFries (UMd)

Overarching theme:

Develop spatially and temporally robust estimates of CO sources using the integration of global chemical transport modeling, in-situ and remote-sensed CO

measurements

EDGARv2

FFBF source

GFEDv1

BIOM source

GEOS-CHEM

BIOG source

PRIORS

RESPONSE FUNCTIONS

GEOS-CHEM

EDGARv2

FFBF source

GFEDv1

BIOM source

GEOS-CHEM

BIOG source

PRIORS

RESPONSE FUNCTIONS

GEOS-CHEM

BIOMASS BURNING SEASONALITY

‘ensemble’ of MOPITT-inferred top-down estimates

Error Variance Scenarios (Sa, Se)

Error Covariance Structure

(geostatistical approach)

Comparison with GEOS-CHEM std emissions

Comparison with other estimates

EDGARv2

FFBF source

GFEDv1

BIOM source

GEOS-CHEM

BIOG source

PRIORS

RESPONSE FUNCTIONS

GEOS-CHEM






NEW PRIORS



GFEDv2

EDGARv2

FFBF source

GFEDv1

BIOM source

GEOS-CHEM

BIOG source

PRIORS

RESPONSE FUNCTIONS

GEOS-CHEM






NEW PRIORS



GFEDv2

MCMC SIMULATION USING NOAA CMDL CO Error Covariance

Structure (global scaling)Sensitivity of PDF

assumptions

Fossil Fuel / Biofuel

Biomass Burning

Biogenic

g CO m-2 yr-1

Final basis: Solve for 12 regional FFBF (annual), 1 global biogenic and

7 aggregated regional BIOM (monthly).

Total source categories : 163

Extension of Arellano et al. (2004) Time-independent CO Inversion

Old basis functions

New basis functions

Same FFBF source from EDVARv2 with GEIA NMHC using Altshuler et al. (1991) yields.

Same BIOM source from GFEDv1 for 1999 to 2001.

Change Biogenic CO from GEIA to GEOS-CHEM std. biogenic emissions (isoprene w/ NOx-based yield, monoterpene, methanol & acetone)

Pre-subtracted CO from methane oxidation

Prior Sources (xa)

Net PrimaryProduction

Allocation=f (treecover)

AbovegroundBiomass C

BelowgroundBiomass C

Combustion

BelowgroundLitter C

AbovegroundLitter C

f(A,E,M) f(A,E)

Respiration

Respiration(Fire Induced

Mortality)

AbovegroundBurned Litter C

BelowgroundFire-Mortality C

f(A,1-E,M)

f(A,M)

Fuelwoodcollection

Herbivoreconsumption

f(A,E)

GFED1 Fire Emissions ProductSSAI, NASA/GSFC, UCI, UMd, Duke

BURNT AREA• 38N-38S: Calibration of VIRS fire counts using a limited MODIS burnt area dataset from 2001 construct 1998-2001 burnt areas; Extend to 1996 using grid-box specific ratios of ATSR/VIRS fire counts from 1998-2001 • Extratropics: Calibration of ATSR fire counts (1 scalar) using country-level fire statistics for Canada + AVHRR-derived burnt area for Russian Far EastFUEL LOAD• Calculated using CASA modified to include fire EMISSIONS• Emission factors from Andreae and Merlet (2001)

GFED2 coming soon!

Monthly emissions at 1x1 resolution for 1997-2001 available at www.nicholas.duke.edu/people/faculty/prasad/research/biomassburning/biomassburning.html

Calculation of Jacobian Matrix, K

• Used Tagged-CO of GEOS-CHEM

• Calculated an 8-month response function for each BIOM source category

• Calculated time-independent response functions for FFBF and BIOG CO source categories

• Used GEOS-3 meteorological fields for 2000 and 2001 (4ox5o resolution)

• Used ‘Updated’ OH fields (new: Fiore et al., 2003, old: Bey et al., 2001)

• Adopted ‘newer’ version of GEOS-CHEM (v5-5-03 from v4-26)

)()(ˆ a1

eT11

a1

eT

a KxySKSKSKxx

11a

1e

T SKSKS )(ˆ

Inverse Solution

Sample of 4ox5o monthly averaged MOPITT CO, model CO columns and error covariance used in the analyses

50S

EQ

50N

50S

EQ

50N

50S

EQ

50N

150W 100W 50W 0 50E 100E 150E

50S

EQ

50N

150W 100W 50W 0 50E 100E 150E

0.05 0.1 0.15 0.2 0.25

April 2000

July 2000

October 2000

January 2001

Unscaled Se1/2 Scaled Se

1/2

April 2000

July 2000

October 2000

January 2001

molecule/cm2

50S

EQ

50N

50S

EQ

50N

50S

EQ

50N

150W 100W 50W 0 50E 100E 150E

50S

EQ

50N

150W 100W 50W 0 50E 100E 150E

0.5 1 1.5 2 2.5 3

April 2000

July 2000

October 2000

January 2001

April 2000

July 2000

October 2000

January 2001

MOPITT, y Prior Model, Kxa

molecule/cm2

• Same data selection procedure and required transformations described in Arellano et al. (2004)

• Only used Phase 1 L2 V3 MOPITT CO columns (April 2000 to April 2001)

• Se = Sm+ Sr. Diagonal elements of unscaled Sm represented as variance of residual (obs-model) about the monthly mean

• Set a minimum of 0.15 molecule cm-2

50S

EQ

50N

50S

EQ

50N

50S

EQ

50N

150W 100W 50W 0 50E 100E 150E

50S

EQ

50N

-0.25-0.2-0.15-0.1-0.05 0 0.05 0.1 0.15 0.2 0.25

0 1 2 3 40

0.2

0.4

0.6

0.8

1

0 1 2 3 40

0.2

0.4

0.6

0.8

1

0 1 2 3 40

0.2

0.4

0.6

0.8

1

0 1 2 3 40

0.2

0.4

0.6

0.8

1

distance (1000 km)

June 2000

September 2000

December 2000

March 2001

Empirical Spatial Analysis of Residuals (from Enting, 2002)

• Based on the linear model:

y = Kx + e

• Residual expressed as

• Isotropic component best modeled as SOAR function

• Model similar to Heald et al. (2004)

• Analysis suggests the presence of correlation in the error

M

qtqqrqrtrt VλUyν

L

lexp

L

l1exp)l(sρ

150W 100W 50W 0 50E 100E 150E

50S

EQ

50N

MLO

EIC

BMW

TAP

ASC

SEY

-0.5 0 0.5

Empirical Spatial Analysis of Residuals (from Enting, 2002)

Correlation of residual CO columns at selected NOAA CMDL stations indicative of non-stationarity. Shown are correlation coefficients of the neighboring grid boxes around a station point located at the center of each correlation matrix plot.

Inversions incorporating various error covariance scenarios

1) Assume Se and Sa are diagonal matrices

a) Unscaled Se and base Sa (50% of xa)

b) Scaled Se (2 tuned) and base Sa

c) Unscaled Se and ‘loose’ Sa (minimum of 5 Tg)

d) Scaled Se and ‘loose Sa

2) Model the structure of Sm and solve the structural parameters in the inversion

Geostatistical approach similar to Michalak et al. (2004) and covariancemodeling in data assimilation (Dee and daSilva,1999)

Isotropic:

Anisotropic (Riishjogaard,1998):

Non-stationary:

i,jjiji,m ρσσ)( S

Lα,, L

dexp

L

d1exp ji,ji,

SOARj,i

θρρ

θand,

)cov(

rt,mrt,ort

10

5qtqqtxqrt

m

yyVλUψ

ψS

T,LθandρρwhereT

exp

)(ρσσ)(

SOARi,j

2

jij,i

jii,jjiji,m

yy

yyS

April 2000 to March 2001

Tg

CO

A M J J A S O N D J F M 0

5

10

15

20


10

20

30


10

20

30

A M J J A S O N D J F M

0

10

20

30


0

10

20

30

40

50

BIOM-SLA


10

20

30


10

20

30

40

50

0 10 200

1020

Prior

Posterior

• Apparent shift in seasonality for SAF(similar to Petron et al. 2004, Bremer et al., 2004 but inconsistent with estimates derived from area burned (Tansey et al., 2005, Hoelzemann et al., 2004 )• Large overestimation for NAF, SLA Jun/Jul • Significant underestimation in BIOM TBO May, OCN&IND Sep/Oct, NLA Apr

BIOM NAF BIOM SAF

BIOM NLA BIOM SLA

BIOM OCN&IND BIOM SAS&SEA&MDE

BIOM NAM&EUR&RUS&EAS (TBO)



April 2000 to March 2001

Tg

CO


5

10

15

20


10

20

30


10

20

30


0

10

20

30


0

10

20

30

40

50

BIOM-SLA


10

20

30


10

20

30

40

50

0 10 200

1020

Prior

PosteriorGEOS-CHEM interannual

BIOM NAF BIOM SAF

BIOM NLA BIOM SLA

BIOM OCN&IND BIOM SAS&SEA&MDE

BIOM NAM&EUR&RUS&EAS (TBO)

• Consistent with GEOS-CHEM estimates for NAF, SAF (early burning period), SLA Jun/Jul, TBO May• Discrepancies in NLA Apr, SAF (late season)• How about OCN&IND and SAS&SEA in Nov ?

150W 50W 50E 150E

50S

EQ

50N

DUKE BIOM APR00 -- 22 Tg

5

10

15

20

150W 50W 50E 150E

50S

EQ

50N

GEOS BIOM APR00 -- 42 Tg

5

10

15

20

150W 50W 50E 150E

50S

EQ

50N

DUKE-GEOS BIOM APR00

-20

0

20

150W 50W 50E 150E

50S

EQ

50N

GFEDv1 BIOM MAY00 -- 23 Tg

2

4

6

8

10

150W 50W 50E 150E

50S

EQ

50N

GEOS BIOM MAY00 -- 31 Tg

2

4

6

8

10

150W 50W 50E 150E

50S

EQ

50N

GFEDv1-GEOS BIOM MAY00

-10

0

10

150W 50W 50E 150E

50S

EQ

50N

DUKE BIOM JUN00 -- 48 Tg

5

10

15

150W 50W 50E 150E

50S

EQ

50N

GEOS BIOM JUN00 -- 23 Tg

5

10

15

150W 50W 50E 150E

50S

EQ

50N

DUKE-GEOS BIOM JUN00

-10

0

10

150W 50W 50E 150E

50S

EQ

50N

GFEDv1 BIOM JUL00 -- 51 Tg

2

4

6

8

10

150W 50W 50E 150E

50S

EQ

50N

GEOS BIOM JUL00 -- 33 Tg

2

4

6

8

10

150W 50W 50E 150E

50S

EQ

50N

GFEDv1-GEOS BIOM JUL00

-10

0

10

SPATIAL DISTRIBUTION OF GLOBAL BIOMASS BURNING CO EMISSIONS

g CO cm-2 month-1

Magnitude difference consistent with MOPITT

Overestimation appears to be due to large area burned estimates from GFEDv1 ( new area burnt estimates for GFEDv2 show smaller area burnt )

Shift in seasonality applied to CO2 modeling produced better fit with TransCOM

150W 50W 50E 150E

50S

EQ

50N

DUKE BIOM AUG00 -- 48 Tg

10

20

30

40

150W 50W 50E 150E

50S

EQ

50N

GEOS BIOM AUG00 -- 50 Tg

10

20

30

40

150W 50W 50E 150E

50S

EQ

50N

DUKE-GEOS BIOM AUG00

-10

0

10

150W 50W 50E 150E

50S

EQ

50N

GFEDv1 BIOM SEP00 -- 67 Tg

5

10

15

150W 50W 50E 150E

50S

EQ

50N

GEOS BIOM SEP00 -- 44 Tg

5

10

15

150W 50W 50E 150E

50S

EQ

50N

GFEDv1-GEOS BIOM SEP00

-10

0

10

150W 50W 50E 150E

50S

EQ

50N

GFEDv1 BIOM OCT00 -- 38 Tg

5

10

15

150W 50W 50E 150E

50S

EQ

50N

GEOS BIOM OCT00 -- 43 Tg

5

10

15

150W 50W 50E 150E

50S

EQ

50N

GFEDv1-GEOS BIOM OCT00

-10

0

10

150W 50W 50E 150E

50S

EQ

50N

GFEDv1 BIOM NOV00 -- 29 Tg

5

10

15

150W 50W 50E 150E

50S

EQ

50N

GEOS BIOM NOV00 -- 33 Tg

5

10

15

150W 50W 50E 150E

50S

EQ

50N

GFEDv1-GEOS BIOM NOV00

-10

0

10

g CO cm-2 month-1

SPATIAL DISTRIBUTION OF GLOBAL BIOMASS BURNING CO EMISSIONS

GFEDv1 missed

small-scale fires ?

Despite GFEDv1’s overestimation,

it still needs to increase emissions to be consistent with MOPITT

Feedback on consistency of GFEDv1 emissions with MOPITT will be incorporated in GFEDv2

NAM ERU EAS SAS SEA IND0

60

120

180

240

NAF SAF NLA SLA MDE OCN

0

30

60

90

NAF SAF NLA SLA OCN SAS TBO

30

70

110

150

GLOB250

300

350

400

450

FFBF

FFBF

BIOM

Tg

CO

Regions BIOG

SUMMARY OF ANNUAL CO SOURCE ESTIMATES

This represents our improved estimates of the sources. This also provides a larger information on the uncertainty.

We can use these estimates as our new prior and specify the uncertainty to be range of the estimates rather than the posterior uncertainties calculated from the inverse solution.

-2 0 2 40

0.5

1

1.5

2

2.5

pd

f

0 1 20

5

10

15GLOB-BIOG

x20

pd

f

-1 0 1 20

1

2

3

4FFBF-NLA

x5

pd

f

-1 0 1 20

2

4

6

8FFBF-EAS

x3

pd

f

Using the ensemble posterior estimates from MOPITT as our new prior and the range of estimates as representative of the uncertainties (i.e. improved prior information)

We conduct time-independent inverse analysis using NOAA CMDL CO measurements by Markov Chain Monte Carlo (MCMC) technique to solve for p(x |y, , ) where and are hyperparameters ( i.e. x~N(xa, Sa), e~N(0, Se) ). We also test the sensitivity of the estimates to x PDF assumption (lognormal versus normal). Assumed ~N(1,S), is uniform from 0 to 5.

Inverse Modeling of CO Sources by MCMC Slice Gibbs sampling

Results:

1 1.5 2 2.5 3

Prior Normal

Prior LogNormal

Post NormalPost LogNormal

Post LogNormal with Sa scaling

Post LogNormal with Sa and S

e scaling

• Some sources are sensitive to PDF assumptions (FFBF-NLA) while well-resolved sources are not.

• Higher posterior suggests a weaker prior constraint

• NOAA CMDL information evident in reduction of global biogenic source

Burning Issues???

GLOBAL CO INVERSE ANALYSIS

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