ABSTRACT   Monitoring atmospheric carbon dioxide (CO2) from space-borne state-of-the-art hyperspectral instruments can provide a high precision global dataset to improve carbon flux estimation and reduce the uncertainty of climate projection. Here, we introduce a carbon flux inversion system for estimating carbon flux with satellite measurements under the support of “The Strategic Priority Research Program of the Chinese Academy of Sciences---Climate Change: Carbon Budget and Relevant Issues”. The carbon flux inversion system is composed of two separate parts: the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing (IAPCAS), and CarbonTracker-China (CT-China), developed at the Chinese Academy of Sciences. The Greenhouse gases Observing SATellite (GOSAT) measurements are used in the carbon flux inversion experiment. To improve the quality of the IAPCAS-GOSAT retrieval, we have developed a post-screening and bias correction method, resulting in 25%–30% of the data remaining after quality control. Based on these data, the seasonal variation of XCO2 (column-averaged CO2 dry-air mole fraction) is studied, and a strong relation with vegetation cover and population is identified. Then, the IAPCAS-GOSAT XCO2 product is used in carbon flux estimation by CT-China. The net ecosystem CO2 exchange is −0.34 Pg C yr−1 (±0.08 Pg C yr−1), with a large error reduction of 84%, which is a significant improvement on the error reduction when compared with in situ–only inversion.

Key words: retrieval algorithm, satellite remote sensing, CO2, carbon flux, GOSAT