ALBERT

Beschreibung: We present the application of an integrated global carbon cycle modeling system to the estimation of monthly regional CO2 fluxes from the column-averaged dry air mole fractions of CO2 (XCO2) retrieved from the spectral observations made by the Greenhouse gases Observing SATellite (GOSAT). The regional flux estimates are to be publicly disseminated as the GOSAT Level 4 data product. The forward modeling components of the system include an atmospheric tracer transport model, an anthropogenic emissions inventory, a terrestrial biosphere exchange model, and an oceanic flux model. The atmospheric tracer transport was simulated using isentropic coordinates in the stratosphere and was tuned to reproduce the age of air. We used a fossil fuel emission inventory based on large point source data and observations of nighttime lights. The terrestrial biospheric model was optimized by fitting model parameters to match observed atmospheric CO2 seasonal cycle, net primary production data, and a biomass distribution map. The oceanic surface pCO2 distribution was estimated with a 4-D variational data assimilation system based on reanalyzed ocean currents. Monthly CO2 fluxes of 64 sub-continental regions, between June 2009 and May 2010, were estimated from the GOSAT FTS SWIR Level 2 XCO2 retrievals (ver. 02.00) gridded to 5° × 5° cells and averaged on a monthly basis and monthly-mean GLOBALVIEW-CO2 surface-based observations. Our result indicated that adding the GOSAT XCO2 retrievals to the GLOBALVIEW observations in the flux estimation would bring changes to fluxes of tropics and other remote regions where the surface-based observations are sparse. The uncertainty of these remote fluxes was reduced by as much as 60% through such addition. For many of these regions, optimized fluxes are brought closer to the prior fluxes by the addition of GOSAT data. For the most of the regions and seasons considered here, the estimated fluxes fell within the range of natural flux variability estimated with the component models.