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Global monthly gridded atmospheric carbon dioxide concentrations under the historical and future scenarios

Increases in atmospheric carbon dioxide (CO(2)) concentrations is the main driver of global warming due to fossil fuel combustion. Satellite observations provide continuous global CO(2) retrieval products, that reveal the nonuniform distributions of atmospheric CO(2) concentrations. However, climate...

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Detalles Bibliográficos
Autores principales: Cheng, Wei, Dan, Li, Deng, Xiangzheng, Feng, Jinming, Wang, Yongli, Peng, Jing, Tian, Jing, Qi, Wei, Liu, Zhu, Zheng, Xinqi, Zhou, Demin, Jiang, Sijian, Zhao, Haipeng, Wang, Xiaoyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8917170/
https://www.ncbi.nlm.nih.gov/pubmed/35277521
http://dx.doi.org/10.1038/s41597-022-01196-7
Descripción
Sumario:Increases in atmospheric carbon dioxide (CO(2)) concentrations is the main driver of global warming due to fossil fuel combustion. Satellite observations provide continuous global CO(2) retrieval products, that reveal the nonuniform distributions of atmospheric CO(2) concentrations. However, climate simulation studies are almost based on a globally uniform mean or latitudinally resolved CO(2) concentrations assumption. In this study, we reconstructed the historical global monthly distributions of atmospheric CO(2) concentrations with 1° resolution from 1850 to 2013 which are based on the historical monthly and latitudinally resolved CO(2) concentrations accounting longitudinal features retrieved from fossil-fuel CO(2) emissions from Carbon Dioxide Information Analysis Center. And the spatial distributions of nonuniform CO(2) under Shared Socio-economic Pathways and Representative Concentration Pathways scenarios were generated based on the spatial, seasonal and interannual scales of the current CO(2) concentrations from 2015 to 2150. Including the heterogenous CO(2) distributions could enhance the realism of global climate modeling, to better anticipate the potential socio-economic implications, adaptation practices, and mitigation of climate change.