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

文献类型：期刊论文

作者Liu, Shu'an6; Pan, Xiaofeng5; Xiong, Xiangyun5; Sun, Tianle5; Xue, Lin5; Zhang, Huifang4; Fang, Junjun3,4; Fang, Jingchun3,4; Zhang, Guchun1; Xu, Hui1
刊名ATMOSPHERIC RESEARCH
出版日期2024-09-01
卷号307页码:15
关键词CO2 inversion system Global carbon surface fluxes Surface-based and space-based atmospheric CO2 Measurements GEOS-Chem 4D-LETKF 3-GAS
ISSN号0169-8095
DOI10.1016/j.atmosres.2024.107477
英文摘要The ongoing necessity and developmental trajectory in atmospheric CO 2 assimilation system revolve 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 fourdimensional 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 from 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 9%, 22% and 33% in comparison to surface-only, GOSAT-only and OCO-2-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.
WOS关键词SEASONAL-VARIATION ; GREENHOUSE GASES ; VERSION 9 ; GOSAT ; ENSEMBLE ; RETRIEVALS ; SATELLITE ; EXCHANGE ; DIOXIDE ; CYCLE
资助项目Jiangsu Province's Special Fund for Carbon Peak and Carbon Neutrality Technological Innovation[BE2023855] ; Innovation Project of LREIS[KPI005] ; National Key R&D Program of China[2022YFB3903705] ; National Natural Science Foundation of China[41771114] ; National Natural Science Foundation of China[41977404]
WOS研究方向Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:001264126400001
出版者ELSEVIER SCIENCE INC
资助机构Jiangsu Province's Special Fund for Carbon Peak and Carbon Neutrality Technological Innovation ; Innovation Project of LREIS ; National Key R&D Program of China ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/207537]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Chen, Baozhang
作者单位1.China Natl Adm Coal Geol, Jiangsu Mineral Resources & Geol Design & Res Inst, Xuzhou 221006, Peoples R China
2.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China
3.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resource & Environm Informat Syst, Beijing 100101, Peoples R China
5.Shenzhen Environm Monitoring Ctr Guangdong Prov, Shenzhen 518049, Peoples R China
6.Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Shu'an,Pan, Xiaofeng,Xiong, Xiangyun,et al. Integration of surface-based and space-based atmospheric CO2 measurements for improving carbon flux estimates using a new developed 3-GAS inversion model[J]. ATMOSPHERIC RESEARCH,2024,307:15.
APA Liu, Shu'an.,Pan, Xiaofeng.,Xiong, Xiangyun.,Sun, Tianle.,Xue, Lin.,...&Chen, Baozhang.(2024).Integration of surface-based and space-based atmospheric CO2 measurements for improving carbon flux estimates using a new developed 3-GAS inversion model.ATMOSPHERIC RESEARCH,307,15.
MLA Liu, Shu'an,et al."Integration of surface-based and space-based atmospheric CO2 measurements for improving carbon flux estimates using a new developed 3-GAS inversion model".ATMOSPHERIC RESEARCH 307(2024):15.

入库方式： OAI收割

来源：地理科学与资源研究所

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