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Published March 2020 | public
Journal Article

Specific patterns of XCO₂ observed by GOSAT during 2009–2016 and assessed with model simulations over China

Abstract

Spatiotemporal patterns of column-averaged dry air mole fraction of CO₂ (XCO₂) have not been well characterized on a regional scale due to limitations in data availability and precision. This paper addresses these issues by examining such patterns in China using the long-term mapping XCO₂ dataset (2009–2016) derived from the Greenhouse gases Observing SATellite (GOSAT). XCO₂ simulations are also constructed using the high-resolution nested-grid GEOS-Chem model. The following results are found: Firstly, the correlation coefficient between the anthropogenic emissions and XCO₂ spatial distribution is nearly zero in summer but up to 0.32 in autumn. Secondly, on average, XCO₂ increases by 2.08 ppm every year from 2010 to 2015, with a sharp increase of 2.6 ppm in 2013. Lastly, in the analysis of three typical regions, the GOSAT XCO₂ time series is in better agreement with the GEOS-Chem simulation of XCO₂ in the Taklimakan Desert region (the least difference with bias 0.65±0.78 ppm), compared with the northern urban agglomeration region (−1.3±1.2 ppm) and the northeastern forest region (−1.4±1.4 ppm). The results are likely attributable to uncertainty in both the satellite-retrieved XCO2 data and the model simulation data.

Additional Information

© 2020 Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature. Received 29 January 2018; Revised 25 May 2019; Accepted 11 June 2019; Published 13 January 2020. ACOS v7.3 were produced by the ACOS/OCO-2 project at the Jet Propulsion Laboratory, California Institute of Technology, and obtained from the JPL website co2.jpl.nasa.gov. We are grateful for NASA, ACOS/OCO-2 project, NIES GOSAT Project and geos-chem team. This research was supported by the National Key Research and Development Program of China (Grant No. 2016YFA0600303) and the Key Deployment Projects of the Chinese Academy of Sciences (Grant No. ZDRW-ZS-2019-1-3).

Additional details

Created:
August 22, 2023
Modified:
October 18, 2023