Understanding processes that control dust spatial distributions with global climate models and satellite observations
Abstract
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 themore »
- Authors:
-
[1];
[4];
[2];
[6];
[6];
[7];
- Univ. of Wyoming, Laramie, WY (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Univ. of Wyoming, Laramie, WY (United States); Texas A & M Univ., College Station, TX (United States)
- NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Univ. of Colorado, Boulder, CO (United States)
- Univ. of Wyoming, Laramie, WY (United States)
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Publication Date:
- Research Org.:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); National Aeronautics and Space Administration (NASA)
- OSTI Identifier:
- 1725865
- Report Number(s):
- PNNL-SA-151334
Journal ID: ISSN 1680-7324
- Grant/Contract Number:
- AC05-76RL01830; AC02-05CH11231; NNX16AO94G; 80NSSC20K0952
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Atmospheric Chemistry and Physics (Online)
- Additional Journal Information:
- Journal Name: Atmospheric Chemistry and Physics (Online); Journal Volume: 20; Journal Issue: 22; Journal ID: ISSN 1680-7324
- Publisher:
- European Geosciences Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES
Citation Formats
Wu, Mingxuan, Liu, Xiaohong, Yu, Hongbin, Wang, Hailong, Shi, Yang, Yang, Kang, Darmenov, Anton, Wu, Chenglai, Wang, Zhien, Luo, Tao, Feng, Yan, and Ke, Ziming. Understanding processes that control dust spatial distributions with global climate models and satellite observations. United States: N. p., 2020.
Web. https://doi.org/10.5194/acp-20-13835-2020.
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. United States. https://doi.org/10.5194/acp-20-13835-2020
Wu, Mingxuan, Liu, Xiaohong, Yu, Hongbin, Wang, Hailong, Shi, Yang, Yang, Kang, Darmenov, Anton, Wu, Chenglai, Wang, Zhien, Luo, Tao, Feng, Yan, and Ke, Ziming. Tue .
"Understanding processes that control dust spatial distributions with global climate models and satellite observations". United States. https://doi.org/10.5194/acp-20-13835-2020. https://www.osti.gov/servlets/purl/1725865.
@article{osti_1725865,
title = {Understanding processes that control dust spatial distributions with global climate models and satellite observations},
author = {Wu, Mingxuan and Liu, Xiaohong and Yu, Hongbin and Wang, Hailong and Shi, Yang and Yang, Kang and Darmenov, Anton and Wu, Chenglai and Wang, Zhien and Luo, Tao and Feng, Yan and Ke, Ziming},
abstractNote = {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.},
doi = {10.5194/acp-20-13835-2020},
journal = {Atmospheric Chemistry and Physics (Online)},
number = 22,
volume = 20,
place = {United States},
year = {2020},
month = {11}
}
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