globalchange  > 气候变化事实与影响
DOI: 10.1016/j.atmosenv.2017.03.056
Scopus记录号: 2-s2.0-85018978826
论文题名:
CMAQ simulation of atmospheric CO2 concentration in East Asia: Comparison with GOSAT observations and ground measurements
作者: Li R; , Zhang M; , Chen L; , Kou X; , Skorokhod A
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2017
卷: 160
起始页码: 176
结束页码: 185
语种: 英语
英文关键词: Atmospheric CO2 ; GOSAT ; RAMS-CMAQ ; WDCGG ; XCO2
Scopus关键词: Atmospheric chemistry ; Greenhouse gases ; Satellites ; Atmospheric CO ; Correlation coefficient ; GOSAT ; Ground-based observations ; Regional chemical transport model ; Satellite observations ; WDCGG ; XCO<sub>2</sub> ; Carbon dioxide ; carbon dioxide ; atmospheric modeling ; carbon dioxide ; concentration (composition) ; correlation ; GOSAT ; ground-based measurement ; measurement method ; model validation ; observational method ; simulation ; air conditioning ; air quality ; Article ; atmosphere ; correlation coefficient ; density ; gas ; gases observing satellite ; greenhouse gas ; priority journal ; seasonal variation ; simulation ; space and space related phenomena ; spatiotemporal analysis ; surface property ; uncertainty ; Far East
Scopus学科分类: Environmental Science: Water Science and Technology ; Earth and Planetary Sciences: Earth-Surface Processes ; Environmental Science: Environmental Chemistry
英文摘要: Satellite observations are widely used in global CO2 assimilations, but their quality for use in regional assimilation systems has not yet been thoroughly determined. Validation of satellite observations and model simulations of CO2 is crucial for carbon flux inversions. In this study, we focus on evaluating the uncertainties of model simulations and satellite observations. The atmospheric CO2 distribution in East Asia during 2012 was simulated using a regional chemical transport model (RAMS-CMAQ) and compared with both CO2 column density (XCO2) from the Gases Observing SATellite (GOSAT) and CO2 concentrations from the World Data Centre for Greenhouse Gases (WDCGG). The results indicate that simulated XCO2 is generally lower than GOSAT XCO2 by 1.19 ppm on average, and their monthly differences vary from 0.05 to 2.84 ppm, with the corresponding correlation coefficients ranging between 0.1 and 0.67. CMAQ simulations are good to capture the CO2 variation as ground-based observations, and their correlation coefficients are from 0.62 to 0.93, but the average value of CMAQ simulation is 2.4 ppm higher than ground-based observation. Thus, we inferred that the GOSAT retrievals may overestimate XCO2, which is consistent with the validation of GOSAT XCO2 using Total Carbon Column Observing Network measurements. The near-surface CO2 concentration was obviously overestimated in GOSAT XCO2. Compared with the relatively small difference between CMAQ and GOSAT XCO2, the large difference in CO2 near surface or their vertical profiles indicates more improvements are needed to reduce the uncertainties in both satellite observations and model simulations. © 2017 Elsevier Ltd
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82584
Appears in Collections:气候变化事实与影响

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作者单位: State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China; Institute of Urban Meteorology, China Meteorological Administration, Beijing, China; Center for Excellence in Urban Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, China; University of Chinese Academy of Sciences, Beijing, China; Laboratory of Atmospheric Gas Species, A.M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Moscow, Russian Federation

Recommended Citation:
Li R,, Zhang M,, Chen L,et al. CMAQ simulation of atmospheric CO2 concentration in East Asia: Comparison with GOSAT observations and ground measurements[J]. Atmospheric Environment,2017-01-01,160
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