Global monthly gridded atmospheric carbon dioxide concentrations under the historical and future scenarios
文献类型:期刊论文
作者 | Cheng, Wei1,2; Dan, Li3; Deng, Xiangzheng1,2,4; Feng, Jinming3; Wang, Yongli3; Peng, Jing3; Tian, Jing1; Qi, Wei5; Liu, Zhu6; Zheng, Xinqi7,8 |
刊名 | SCIENTIFIC DATA
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出版日期 | 2022-03-11 |
卷号 | 9期号:1页码:13 |
DOI | 10.1038/s41597-022-01196-7 |
通讯作者 | Dan, Li(danli@tea.ac.cn) ; Deng, Xiangzheng(dengxz@igsnrr.ac.cn) ; Wang, Yongli(wangyl@tea.ac.cn) ; Tian, Jing(tianj.04b@igsnrr.ac.cn) |
英文摘要 | Increases in atmospheric carbon dioxide (CO2) concentrations is the main driver of global warming due to fossil fuel combustion. Satellite observations provide continuous global CO2 retrieval products, that reveal the nonuniform distributions of atmospheric CO2 concentrations. However, climate simulation studies are almost based on a globally uniform mean or latitudinally resolved CO2 concentrations assumption. In this study, we reconstructed the historical global monthly distributions of atmospheric CO2 concentrations with 1 degrees resolution from 1850 to 2013 which are based on the historical monthly and latitudinally resolved CO2 concentrations accounting longitudinal features retrieved from fossil-fuel CO2 emissions from Carbon Dioxide Information Analysis Center. And the spatial distributions of nonuniform CO2 under Shared Socio-economic Pathways and Representative Concentration Pathways scenarios were generated based on the spatial, seasonal and interannual scales of the current CO2 concentrations from 2015 to 2150. Including the heterogenous CO2 distributions could enhance the realism of global climate modeling, to better anticipate the potential socio-economic implications, adaptation practices, and mitigation of climate change. |
WOS关键词 | GREENHOUSE-GAS CONCENTRATIONS ; CO2 ; RETRIEVALS ; EXTENSIONS ; EMISSIONS ; SATELLITE |
资助项目 | National Key Research and Development Program of China[2016YFA0602500] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA23070400] ; state key program of National Natural Science Foundation of China[91425303] ; state key program of National Natural Science Foundation of China[71533004] |
WOS研究方向 | Science & Technology - Other Topics |
语种 | 英语 |
WOS记录号 | WOS:000767813100011 |
出版者 | NATURE PORTFOLIO |
资助机构 | National Key Research and Development Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; state key program of National Natural Science Foundation of China |
源URL | [http://ir.igsnrr.ac.cn/handle/311030/172220] ![]() |
专题 | 中国科学院地理科学与资源研究所 |
通讯作者 | Dan, Li; Deng, Xiangzheng; Wang, Yongli; Tian, Jing |
作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Inst Atmospher Phys, Key Lab Reg Climate Environm Temp East Asia, Beijing 100029, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Chinese Acad Meteorol Sci, Inst Tibetan Plateau & Polar Meteorol, Beijing 100081, Peoples R China 6.Tsinghua Univ, Dept Earth Syst Sci, Beijing 100084, Peoples R China 7.China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China 8.MNR China, Technol Innovat Ctr Terr Spatial Big Data, Beijing 100036, Peoples R China 9.Capital Normal Univ, Coll Resource Environm & Tourism, Beijing 100048, Peoples R China 10.Capital Normal Univ, Key Lab 3D Informat Acquisit & Applicat Minist, Beijing 100048, Peoples R China |
推荐引用方式 GB/T 7714 | Cheng, Wei,Dan, Li,Deng, Xiangzheng,et al. Global monthly gridded atmospheric carbon dioxide concentrations under the historical and future scenarios[J]. SCIENTIFIC DATA,2022,9(1):13. |
APA | Cheng, Wei.,Dan, Li.,Deng, Xiangzheng.,Feng, Jinming.,Wang, Yongli.,...&Wang, Xiaoyu.(2022).Global monthly gridded atmospheric carbon dioxide concentrations under the historical and future scenarios.SCIENTIFIC DATA,9(1),13. |
MLA | Cheng, Wei,et al."Global monthly gridded atmospheric carbon dioxide concentrations under the historical and future scenarios".SCIENTIFIC DATA 9.1(2022):13. |
入库方式: OAI收割
来源:地理科学与资源研究所
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