1. GLOBAL 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

2. EDGARv2 FFBF source GFEDv1 BIOM source GEOS-CHEM BIOG source PRIORS RESPONSE FUNCTIONS GEOS-CHEM

3. 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 (S a, S e ) Error Covariance Structure (geostatistical approach) Comparison with GEOS- CHEM std emissions Comparison with other estimates

4. 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 (S a, S e ) Error Covariance Structure (geostatistical approach) NEW PRIORS Comparison with GEOS- CHEM std emissions Comparison with other estimates GFEDv2

5. 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 (S a, S e ) 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

6. 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/ NO x -based yield, monoterpene, methanol & acetone) Pre-subtracted CO from methane oxidation Prior Sources (x a )

7. GFED1 Fire Emissions Product SSAI, 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 East FUEL 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

8. 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 (4 o x5 o 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) Inverse Solution

9. Sample of 4 o x5 o monthly averaged MOPITT CO, model CO columns and error covariance used in the analyses April 2000 July 2000 October 2000 January 2001 Unscaled S e 1/2 Scaled S e 1/2 April 2000 July 2000 October 2000 January 2001 molecule/cm 2 April 2000 July 2000 October 2000 January 2001 April 2000 July 2000 October 2000 January 2001 MOPITT, y Prior Model, Kx a molecule/cm 2 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) S e = S m + S r. Diagonal elements of unscaled S m represented as variance of residual (obs-model) about the monthly mean Set a minimum  of 0.15 molecule cm -2

10. 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

11. 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.

12. Inversions incorporating various error covariance scenarios 1) Assume S e and S a are diagonal matrices a) Unscaled S e and base S a (50% of x a ) b) Scaled S e (  2 tuned) and base S a c) Unscaled S e and ‘loose’ S a (minimum of 5 Tg) d) Scaled S e and ‘loose S a 2) Model the structure of S m and solve the structural parameters  in the inversion Geostatistical approach similar to Michalak et al. (2004) and covariance modeling in data assimilation (Dee and daSilva,1999) Isotropic: Anisotropic (Riishjogaard,1998): Non-stationary:

13. 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

14. 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 ?

15. 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 CO 2 modeling produced better fit with TransCOM

16. 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

17. 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.

18. 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(x a, S a ), e~N(0,  S e ) ). 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: 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

19. Burning Issues???