1. Improving estimates of CO 2 fluxes through a CO-CO 2 adjoint inversion Monika Kopacz, Daniel J. Jacob, Parvadha Suntharalingam April 12, 2007 3 rd GEOS-Chem users meeting

2. So far: successful CO source inversion using MOPITT data (Optimized/a priori) Asian CO source during TRACE-P (Spring 2001) analytical inversion adjoint inversion Greatly increased resolution of surface sources Goals achieved: (1) developed high resolution adjoint inversion capabilities, (2) improved CO source estimates How can we use this experience to improve CO 2 surface flux estimates? Heald et al. [2004]Kopacz et al. [2007]

3. CO and CO 2 Common sources (not all) biomass burning, fossil fuel and biofuel combustion Lifetime CO and CO 2 are both relatively long lived, especially if we consider observations few days downwind sources AND concentrations are correlated PROJECT IDEA: If we know the CO-CO 2 error correlations, we can perform a joint inversion to improve estimates of CO 2 surface fluxes Satellite data available CO: MOPITT (1999-present), AIRS (late 2002-present), TES (late 2004-present), SCIAMACHY (2002-present); CO 2 : AIRS (late 2002- present), SCIAMACHY (2002- present), OCO (late 2009-) Key: quantify CO-CO 2 correlations

4. CO - CO 2 correlations during TRACE-P, March-April 2001 (in aircraft data) Suntharalingam et al. [2004] Population 1: mixed boundary layer outflow from China, Korea and Japan Population 2: boundary layer outflow from northeastern China Population 3: midtropospheric background air concentrations a priori emission inventory (CO/CO2 emission ratio) Conclusion: CO-CO 2 correlations allow identifying different types of sources and their underestimates or overestimates.

5. CO - CO 2 correlations during TRACE-P (source error corr.)  joint inversion Palmer et al. [2006] analytical inversion: Conclusion 1: Since most of CO source uncertainty is in emission factors (>> in activity rate), little benefit of source CO 2 - CO error correlation in a joint CO 2 -CO inversion 14-member vector of a posteriori CO (6) and CO 2 (8) flux regions

6. CO - CO 2 correlations during TRACE-P (aircraft obs. corr.)  joint inversion Conclusion 2: Significant improvements in a posteriori CO 2 found at correlation coefficients >0.7 in the observed concentrations Palmer et al. [2006] analytical inversion: CO 2 sink

7. Data-derived correlations: Palmer et al. [2006], Suntharalingam et al. [2004]: TRACE-P data Model-derived correlations: Dylan Jones and Ryan Field (U. Toronto) using GEOS-Chem columns (GEOS3-GEOS4 differences) Computing CO - CO 2 correlations (concentrations) Use AIRS data to compute correlations

8. Adjoint inversion (GC) model requirements Previous work: (Kopacz et al. 2007) v6.02.05 GEOS3 (off-line) CO adjoint code MOPITT averaging kernels (+adjoint) Current project: v6.02.05 (v7?) GEOS4 (off-line) CO-CO 2 adjoint code satellite averaging kernels from AIRS, SCIAMACHY and OCO CO-CO 2 error correlations computed using AIRS data Kopacz et al. [2007] optimized/a priori CO emissions

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10. Current CO-CO 2 inversion project Modeling system: CO-CO 2 adjoint inversion code ready for ingesting data (and correlations) Potential applications: GEOS3 (2000-November 2002) Available satellite CO and CO 2 data: late 2002 - present AIRS global CO retrieval at 500mb (09/25/02) McMillan et al. [2004] SCIAMACHY- AIRS CO 2 comparison Barkley et al. [2006]

11. Current CO-CO 2 inversion project First step: use CO-CO 2 correlations derived by Dylan Jones and Ryan Field to check inversion system Goal: how will CO 2 surface flux inversion benefit from OCO data Second step: Use AIRS data to compute error correlation and perform a joint CO-CO 2 inversion Third step: Include pseudo-OCO data with its representative error in a joint inversion Ongoing: possibly using other data sets: TES, MOPITT, SCIAMACHY…

12. Palmer et al. [2006]

13. Monte Carlo methods: As applied in Palmer et al. [2006] in CO-CO 2 inversion Idea: perturb activity rates and emission factors by their estimated 1 σ uncertainty ‡ Ad hoc approach: As applied in Stavrakou and Muller [2006] in an adjoint inversion of CO-NO x sources Idea: assign (spatial) correlations in ad hoc manner, e.g. correlation within the same country: 0.5, correlation of the same type of emission 0.25 etc. ‡ Other: As applied in Baker et al. [2006] (CO 2 OSSEs for OCO) and many others Idea: Apply exponentially decaying error on fluxes which is then correlated in a straight-forward covariance calculation Computing CO - CO 2 correlations (emissions) ‡1 species spatial/temporal correlations only