Estimating regional fluxes of CO₂ and CH₄ using space-borne observations of XCH₄ : XCO2

We use the GEOS-Chem global 3-D atmospheric chemistry transport model to interpret XCH4:XCO2 column ratios retrieved using a proxy method from the Japanese Greenhouse gases Observing SATellite (GOSAT). The advantage of these data over CO2 and CH4 columns retrieved independently using a full physics optimal estimation algorithm is that they suffer less from scattering-related regional bias. We show the model is able to reproduce observed global and regional spatial (mean bias =0.7%) and temporal variations (global r2=0.92) of this ratio with model bias <2.5%. We also show these variations are driven by emissions of CO2 and CH4 that are typically six months out of phase which may reduce the sensitivity of the ratio to changes in either gas. To simultaneously estimate fluxes of CO2 and CH4 we use a formal Bayesian inverse model infrastructure. We use two approaches to independently resolve flux estimates of these two gases using GOSAT observations of XCH4:XCO2: (1) the a priori error covariance between CO2 and CH4 describing common source from biomass burning; and (2) also fitting independent surface atmospheric measurements of CH4 and CO2 mole fraction that provide additional constraints, improving the effectiveness of the observed GOSAT ratio to constrain fluxes. We demonstrate the impact of these two approaches using Observing System Simulation Experiments. A posteriori flux estimates inferred using only the GOSAT ratios and taking advantage of the error covariance due to biomass burning are not consistent with the true fluxes in our experiments, as the inversion system cannot judge which species' fluxes to adjust. This can result in a posteriori fluxes that are further from the truth than the a priori fluxes. We find that adding the surface data to the inversion dramatically improves the ability of the GOSAT ratios to infer both CH4 and CO2 fluxes. We show that using real GOSAT XCH4:XCO2 ratios together with the surface data during 2010 outcompetes inversions using the individual XCH4 or the full-physics XCO2 data products. Regional fluxes that show the greatest improvements have model minus observation differences with a large seasonal cycle such as Tropical South America for which we report a small but significant annual source of CO2 compared to a small annual sink inferred from the XCO2 data. Based on our analysis we argue that using the ratios we may be reaching the limitations on the precision of these data.