Understanding processes that control dust spatial distributions with global climate models and satellite observations
Dust aerosol is important in modulating the climate system at local and global scales, yet its spatiotemporal distributions simulated by global climate models (GCMs) are highly uncertain. In this study, we evaluate the spatiotemporal variations of dust extinction profiles and dust optical depth (DOD...
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00054654 2023-05-15T18:25:41+02:00 Understanding processes that control dust spatial distributions with global climate models and satellite observations Wu, Mingxuan Liu, Xiaohong Yu, Hongbin Wang, Hailong Shi, Yang Yang, Kang Darmenov, Anton Wu, Chenglai Wang, Zhien Luo, Tao Feng, Yan Ke, Ziming 2020-11 electronic https://doi.org/10.5194/acp-20-13835-2020 https://noa.gwlb.de/receive/cop_mods_00054654 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00054305/acp-20-13835-2020.pdf https://acp.copernicus.org/articles/20/13835/2020/acp-20-13835-2020.pdf eng eng Copernicus Publications Atmospheric Chemistry and Physics -- http://www.atmos-chem-phys.net/volumes_and_issues.html -- http://www.bibliothek.uni-regensburg.de/ezeit/?2069847 -- 1680-7324 https://doi.org/10.5194/acp-20-13835-2020 https://noa.gwlb.de/receive/cop_mods_00054654 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00054305/acp-20-13835-2020.pdf https://acp.copernicus.org/articles/20/13835/2020/acp-20-13835-2020.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess CC-BY article Verlagsveröffentlichung article Text doc-type:article 2020 ftnonlinearchiv https://doi.org/10.5194/acp-20-13835-2020 2022-02-08T22:34:54Z Dust aerosol is important in modulating the climate system at local and global scales, yet its spatiotemporal distributions simulated by global climate models (GCMs) are highly uncertain. In this study, we evaluate the spatiotemporal variations of dust extinction profiles and dust optical depth (DOD) simulated by the Community Earth System Model version 1 (CESM1) and version 2 (CESM2), the Energy Exascale Earth System Model version 1 (E3SMv1), and the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) against satellite retrievals from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer (MODIS), and Multi-angle Imaging SpectroRadiometer (MISR). We find that CESM1, CESM2, and E3SMv1 underestimate dust transport to remote regions. E3SMv1 performs better than CESM1 and CESM2 in simulating dust transport and the northern hemispheric DOD due to its higher mass fraction of fine dust. CESM2 performs the worst in the Northern Hemisphere due to its lower dust emission than in the other two models but has a better dust simulation over the Southern Ocean due to the overestimation of dust emission in the Southern Hemisphere. DOD from MERRA-2 agrees well with CALIOP DOD in remote regions due to its higher mass fraction of fine dust and the assimilation of aerosol optical depth. The large disagreements in the dust extinction profiles and DOD among CALIOP, MODIS, and MISR retrievals make the model evaluation of dust spatial distributions challenging. Our study indicates the importance of representing dust emission, dry/wet deposition, and size distribution in GCMs in correctly simulating dust spatiotemporal distributions. Article in Journal/Newspaper Southern Ocean Niedersächsisches Online-Archiv NOA Merra ENVELOPE(12.615,12.615,65.816,65.816) Southern Ocean Atmospheric Chemistry and Physics 20 22 13835 13855 |
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Niedersächsisches Online-Archiv NOA |
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article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Wu, Mingxuan Liu, Xiaohong Yu, Hongbin Wang, Hailong Shi, Yang Yang, Kang Darmenov, Anton Wu, Chenglai Wang, Zhien Luo, Tao Feng, Yan Ke, Ziming Understanding processes that control dust spatial distributions with global climate models and satellite observations |
topic_facet |
article Verlagsveröffentlichung |
description |
Dust aerosol is important in modulating the climate system at local and global scales, yet its spatiotemporal distributions simulated by global climate models (GCMs) are highly uncertain. In this study, we evaluate the spatiotemporal variations of dust extinction profiles and dust optical depth (DOD) simulated by the Community Earth System Model version 1 (CESM1) and version 2 (CESM2), the Energy Exascale Earth System Model version 1 (E3SMv1), and the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) against satellite retrievals from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer (MODIS), and Multi-angle Imaging SpectroRadiometer (MISR). We find that CESM1, CESM2, and E3SMv1 underestimate dust transport to remote regions. E3SMv1 performs better than CESM1 and CESM2 in simulating dust transport and the northern hemispheric DOD due to its higher mass fraction of fine dust. CESM2 performs the worst in the Northern Hemisphere due to its lower dust emission than in the other two models but has a better dust simulation over the Southern Ocean due to the overestimation of dust emission in the Southern Hemisphere. DOD from MERRA-2 agrees well with CALIOP DOD in remote regions due to its higher mass fraction of fine dust and the assimilation of aerosol optical depth. The large disagreements in the dust extinction profiles and DOD among CALIOP, MODIS, and MISR retrievals make the model evaluation of dust spatial distributions challenging. Our study indicates the importance of representing dust emission, dry/wet deposition, and size distribution in GCMs in correctly simulating dust spatiotemporal distributions. |
format |
Article in Journal/Newspaper |
author |
Wu, Mingxuan Liu, Xiaohong Yu, Hongbin Wang, Hailong Shi, Yang Yang, Kang Darmenov, Anton Wu, Chenglai Wang, Zhien Luo, Tao Feng, Yan Ke, Ziming |
author_facet |
Wu, Mingxuan Liu, Xiaohong Yu, Hongbin Wang, Hailong Shi, Yang Yang, Kang Darmenov, Anton Wu, Chenglai Wang, Zhien Luo, Tao Feng, Yan Ke, Ziming |
author_sort |
Wu, Mingxuan |
title |
Understanding processes that control dust spatial distributions with global climate models and satellite observations |
title_short |
Understanding processes that control dust spatial distributions with global climate models and satellite observations |
title_full |
Understanding processes that control dust spatial distributions with global climate models and satellite observations |
title_fullStr |
Understanding processes that control dust spatial distributions with global climate models and satellite observations |
title_full_unstemmed |
Understanding processes that control dust spatial distributions with global climate models and satellite observations |
title_sort |
understanding processes that control dust spatial distributions with global climate models and satellite observations |
publisher |
Copernicus Publications |
publishDate |
2020 |
url |
https://doi.org/10.5194/acp-20-13835-2020 https://noa.gwlb.de/receive/cop_mods_00054654 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00054305/acp-20-13835-2020.pdf https://acp.copernicus.org/articles/20/13835/2020/acp-20-13835-2020.pdf |
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ENVELOPE(12.615,12.615,65.816,65.816) |
geographic |
Merra Southern Ocean |
geographic_facet |
Merra Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_relation |
Atmospheric Chemistry and Physics -- http://www.atmos-chem-phys.net/volumes_and_issues.html -- http://www.bibliothek.uni-regensburg.de/ezeit/?2069847 -- 1680-7324 https://doi.org/10.5194/acp-20-13835-2020 https://noa.gwlb.de/receive/cop_mods_00054654 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00054305/acp-20-13835-2020.pdf https://acp.copernicus.org/articles/20/13835/2020/acp-20-13835-2020.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5194/acp-20-13835-2020 |
container_title |
Atmospheric Chemistry and Physics |
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20 |
container_issue |
22 |
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13835 |
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13855 |
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1766207290429407232 |