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GLOBAL CO INVERSE ANALYSIS. Avelino F. Arellano, Jr. and Prasad S. Kasibhatla Nicholas School of the Environment and Earth Sciences, Duke University, NC. Overarching theme : - PowerPoint PPT Presentation
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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
BIOMASS BURNING SEASONALITY
‘ensemble’ of MOPITT-inferred top-down estimates
Error Variance Scenarios (Sa, Se)
Error Covariance Structure
(geostatistical approach)
NEW PRIORS
Comparison with GEOS-CHEM std emissions
Comparison with other estimates
GFEDv2
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)
NEW PRIORS
Comparison with GEOS-CHEM std emissions
Comparison with other estimates
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
A M J J A S O N D J F M 0
10
20
30
A M J J A S O N D J F M 0
10
20
30
A M J J A S O N D J F M
0
10
20
30
A M J J A S O N D J F M
0
10
20
30
40
50
BIOM-SLA
A M J J A S O N D J F M 0
10
20
30
A M J J A S O N D J F M 0
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)
BIOMASS BURNING SEASONALITY
BIOMASS BURNING SEASONALITY
April 2000 to March 2001
Tg
CO
A M J J A S O N D J F M 0
5
10
15
20
A M J J A S O N D J F M 0
10
20
30
A M J J A S O N D J F M 0
10
20
30
A M J J A S O N D J F M
0
10
20
30
A M J J A S O N D J F M
0
10
20
30
40
50
BIOM-SLA
A M J J A S O N D J F M 0
10
20
30
A M J J A S O N D J F M 0
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???