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Global Terrestrial Ecosystem Carbon Flux Inferred from TanSat XCO2 Retrievals
- Source :
- Journal of Remote Sensing, Vol 2022 (2022)
- Publication Year :
- 2022
- Publisher :
- American Association for the Advancement of Science (AAAS), 2022.
-
Abstract
- TanSat is China’s first greenhouse gases observing satellite. In recent years, substantial progresses have been achieved on retrieving column-averaged CO2 dry air mole fraction (XCO2). However, relatively few attempts have been made to estimate terrestrial net ecosystem exchange (NEE) using TanSat XCO2 retrievals. In this study, based on the GEOS-Chem 4D-Var data assimilation system, we infer the global NEE from April 2017 to March 2018 using TanSat XCO2. The inversion estimates global NEE at −3.46 PgC yr-1, evidently higher than prior estimate and giving rise to an improved estimate of global atmospheric CO2 growth rate. Regionally, our inversion greatly increases the carbon uptakes in northern mid-to-high latitudes and significantly enhances the carbon releases in tropical and southern lands, especially in Africa and India peninsula. The increase of posterior sinks in northern lands is mainly attributed to the decreased carbon release during the nongrowing season, and the decrease of carbon uptakes in tropical and southern lands basically occurs throughout the year. Evaluations against independent CO2 observations and comparison with previous estimates indicate that although the land sinks in the northern middle latitudes and southern temperate regions are improved to a certain extent, they are obviously overestimated in northern high latitudes and underestimated in tropical lands (mainly northern Africa), respectively. These results suggest that TanSat XCO2 retrievals may have systematic negative biases in northern high latitudes and large positive biases over northern Africa, and further efforts are required to remove bias in these regions for better estimates of global and regional NEE.
- Subjects :
- Environmental sciences
GE1-350
Physical geography
GB3-5030
Subjects
Details
- Language :
- English
- ISSN :
- 26941589
- Volume :
- 2022
- Database :
- Directory of Open Access Journals
- Journal :
- Journal of Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.8991cd9f9b1941ce8772e5bd8003a444
- Document Type :
- article
- Full Text :
- https://doi.org/10.34133/2022/9816536