Integration of surface-based and space-based atmospheric CO2 measurements for improving carbon flux estimates using a new developed 3-GAS inversion model

The ongoing necessity and developmental trajectory in atmospheric CO2 assimilation system revolves around enhancing the optimization efficacy of assimilation inversions through the synergistic integration of distinct observational datasets. In this study, we developed an new inversion system-Global Greenhouse-Gases Assimilation System (3-GAS), by coupling the global atmospheric transport model GEOS-Chem with the four-dimensional local ensemble transform Kalman filter (4D-LETKF) algorithm. We compiled an integrated dataset utilizing the widely recognized carbon observation data from ObsPack (Observation Package), GOSAT (The Greenhouse Gases Observing Satellite), and OCO-2 (Orbiting Carbon Observatory-2) to estimate global surface fluxes. The integration of multiple observational data significantly amplifies spatial coverage, enhances the variability in flux inversion, and fosters improvements in localized assimilation efficacy. The spatial distribution of the carbon flux of the combined assimilation emerges as a comprehensive outcome, born from the assimilation with individual observational data. This distribution is primarily influenced by the coverage attributes of the original observations and the meticulous selection of observations during the assimilation process. The validation with surface and aircraft observations reveals that the results of combined assimilation possess composite advantages, which are manifested in alleviating certain intrinsic errors inherent to individual assimilation. The combined assimilation improves the accuracy based on root-mean-square error reduced by 10%, 16% and 21% in comparison to surface-only, OCO-2-only and GOSAT-only assimilation. Eventually, predicated upon preliminary inversion results and regional characteristics, we endeavor to propose a more refined filtration scheme and error determination method for the integrated dataset, as the initiative for the efficient assimilation of multi-source data.