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Estimation of regional surface CO2 fluxes with GOSAT observations using two inverse modeling approaches

Inverse estimation of surface C02 fluxes is performed with atmospheric transport model using ground-based and GOSAT observations. The NIES-retrieved C02 column mixing (Xc02) and column averaging kernel are provided by GOSAT Level 2 product v. 2.0 and PPDF-DOAS method. Monthly mean C02 fluxes for 64 regions are estimated together with a global mean offset between GOSAT data and ground-based data. We used the fixed-lag Kalman filter to infer monthly fluxes for 42 sub-continental terrestrial regions and 22 oceanic basins. We estimate fluxes and compare results obtained by two inverse modeling approaches. In basic approach adopted in GOSAT Level4 product v. 2.01, we use aggregation of the GOSAT observations into monthly mean over 5x5 degree grids, fluxes are estimated independently for each region, and NIES atmospheric transport model is used for forward simulation. In the alternative method, the model-observation misfit is estimated for each observation separately and fluxes are spatially correlated using EOF analysis of the simulated flux variability similar to geostatistical approach, while transport simulation is enhanced by coupling with a Lagrangian transport model Flexpart. Both methods use using the same set of prior fluxes and region maps. Daily net ecosystem exchange (NEE) is predicted by the Vegetation Integrative Simulator for Trace gases (VISIT) optimized to match seasonal cycle of the atmospheric C02 . Monthly ocean-atmosphere C02 fluxes are produced with an ocean pC02 data assimilation system. Biomass burning fluxes were provided by the Global Fire Emissions Database (GFED); and monthly fossil fuel C02 emissions are estimated with ODIAC inventory. The results of analyzing one year of the GOSAT data suggest that when both GOSAT and ground-based data are used together, fluxes in tropical and other remote regions with lower associated uncertainties are obtained than in the analysis using only ground-based data. With version 2.0 of L2 Xc02 the fluxes appear reasonable for many regions and seasons, however there is a need for improving the L2 bias correction, data filtering and the inverse modeling method to reduce estimated flux anomalies visible in some areas. We also observe that application of spatial flux correlations with EOF based approach reduces flux anomalies.