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

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 CO2 surface flux estimates?

Heald et al. [2004] Kopacz et al. [2007]

CO and CO2

Common sources (not all) biomass burning, fossil fuel and biofuel combustion

Lifetime CO and CO2 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-CO2 error correlations, we can perform a joint inversion to improve estimates of CO2 surface fluxes

Satellite data available CO: 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-)

Key: quantify CO-CO2 correlations

CO - CO2 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-CO2 correlations allow identifying different types of sources and their underestimates or overestimates.

CO - CO2 correlations during TRACE-P (source error corr.) joint inversion

Palmer et al. [2006] analytical inversion: T 1 1 1 T 1

a aˆ + ( + ) ( )

ˆa

x x K S K S K S y Kx

x

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

14-member vector of a posteriori CO (6) and CO2 (8) flux regions

CO - CO2 correlations during TRACE-P (aircraft obs. corr.) joint inversion

Conclusion 2: Significant improvements in a posteriori CO2 found at correlation coefficients >0.7 in the observed concentrations

Palmer et al. [2006] analytical inversion:

CO2 sink

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 - CO2 correlations (concentrations)

Use AIRS data to compute correlations

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

Kopacz et al. [2007]

optimized/a priori CO emissions

END

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]

Current CO-CO2 inversion project

First step: use CO-CO2 correlations derived by Dylan Jones and Ryan Field to check inversion system

Goal: how will CO2 surface flux inversion benefit from OCO data

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…

Palmer et al. [2006]

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 σ uncertainty

‡ 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

Computing CO - CO2 correlations (emissions)

‡1 species spatial/temporal correlations only