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Improving estimates of CO 2 fluxes through a CO-CO 2 adjoint inversion

Leveraging CO and CO2 data correlations to refine surface flux estimates by performing a joint inversion using error correlations. Explore CO-CO2 sources from biomass burning, fossil fuel combustion, and more. Utilize satellite data from MOPITT, AIRS, TES, and SCIAMACHY for improved modeling. Project aims to better understand and quantify CO-CO2 relationships for accurate estimates. Pilot study shows potential for significant enhancements in inversion accuracy.

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Improving estimates of CO 2 fluxes through a CO-CO 2 adjoint inversion

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  1. Improving estimates of CO2 fluxes through a CO-CO2 adjoint inversion Monika Kopacz, Daniel J. Jacob, Parvadha Suntharalingam April 12, 2007 3rd GEOS-Chem users meeting

  2. So far: successful CO source inversion using MOPITT data adjoint inversion analytical inversion Greatly increased resolution of surface sources Heald et al. [2004] Kopacz et al. [2007] (Optimized/a priori) Asian CO source during TRACE-P (Spring 2001) Goals achieved:(1) developed high resolution adjoint inversion capabilities, (2) improved CO source estimates How can we use this experience to improve CO2 surface flux estimates?

  3. CO and CO2 Common sources (not all)biomass burning, fossil fuel and biofuel combustion LifetimeCO and CO2 are both relatively long lived, especially if we consider observations few days downwind sources AND concentrations are correlated Satellite data availableCO: MOPITT (1999-present), AIRS (late 2002-present), TES (late 2004-present), SCIAMACHY (2002-present); CO2: AIRS (late 2002-present), SCIAMACHY (2002- present), OCO (late 2009-) PROJECT IDEA: If we know the CO-CO2error correlations, we can perform a joint inversion to improve estimates of CO2 surface fluxes Key: quantify CO-CO2 correlations

  4. CO - CO2 correlations during TRACE-P, March-April 2001 (in aircraft data) 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-CO2 correlations allow identifying different types of sources and their underestimates or overestimates. Suntharalingam et al. [2004]

  5. CO - CO2 correlations during TRACE-P (source error corr.)  joint inversion Palmer et al. [2006] analytical inversion: 14-member vector of a posteriori CO (6) and CO2 (8) flux regions Conclusion 1: Since most of CO source uncertainty is in emission factors (>> in activity rate), little benefit of source CO2-CO error correlation in a joint CO2-CO inversion

  6. CO - CO2 correlations during TRACE-P (aircraft obs. corr.)  joint inversion Palmer et al. [2006] analytical inversion: Conclusion 2: Significant improvements in a posteriori CO2 found at correlation coefficients >0.7 in the observed concentrations CO2 sink

  7. Computing CO - CO2 correlations (concentrations) Model-derived correlations:Dylan Jones and Ryan Field (U. Toronto) using GEOS-Chem columns (GEOS3-GEOS4 differences) Data-derived correlations:Palmer et al. [2006], Suntharalingam et al. [2004]: TRACE-P data 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-CO2 adjoint code • satellite averaging kernels from AIRS, SCIAMACHY and OCO • CO-CO2 error correlations computed using AIRS data optimized/a priori CO emissions Kopacz et al. [2007]

  9. END

  10. Current CO-CO2 inversion project • Modeling system: CO-CO2 adjoint inversion code ready for ingesting data (and correlations) • Potential applications: GEOS3 (2000-November 2002) • Available satellite CO and CO2 data: late 2002 - present AIRS global CO retrieval at 500mb (09/25/02) McMillan et al. [2004] SCIAMACHY-AIRS CO2 comparison Barkley et al. [2006]

  11. Current CO-CO2 inversion project First step: use CO-CO2 correlations derived by Dylan Jones and Ryan Field to check inversion system Second step: Use AIRS data to compute error correlation and perform a joint CO-CO2 inversion Third step: Include pseudo-OCO data with its representative error in a joint inversion Ongoing: possibly using other data sets: TES, MOPITT, SCIAMACHY… Goal: how will CO2 surface flux inversion benefit from OCO data

  12. Palmer et al. [2006]

  13. Computing CO - CO2 correlations (emissions) Monte Carlo methods:As applied in Palmer et al. [2006] in CO-CO2 inversion Idea: perturb activity rates and emission factors by their estimated 1 σuncertainty • Ad hoc approach:As applied in Stavrakou and Muller [2006] in an adjoint inversion of CO-NOx 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] (CO2 OSSEs for OCO) and many others Idea: Apply exponentially decaying error on fluxes which is then correlated in a straight-forward covariance calculation • 1 species spatial/temporal correlations only

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