期刊
ADVANCES IN ATMOSPHERIC SCIENCES
卷 34, 期 8, 页码 965-976出版社
SCIENCE PRESS
DOI: 10.1007/s00376-017-6221-4
关键词
retrieval algorithm; satellite remote sensing; CO2; carbon flux; GOSAT
资金
- Strategic Priority Research Program-Climate Change: Carbon Budget and Relevant Issues [XDA05040200]
- National Key Research and Development Program of China [2016YFA0600203]
- National Natural Science Foundation of China [41375035, 31500402]
- Chinese Academy of Sciences Strategic Priority Program on Space Science [XDA04077300]
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.
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