Articles | Volume 20, issue 22
Atmos. Chem. Phys., 20, 13835–13855, 2020
https://doi.org/10.5194/acp-20-13835-2020
Atmos. Chem. Phys., 20, 13835–13855, 2020
https://doi.org/10.5194/acp-20-13835-2020

Research article 17 Nov 2020

Research article | 17 Nov 2020

Understanding processes that control dust spatial distributions with global climate models and satellite observations

Mingxuan Wu et al.

Data sets

MERRA-2 tavgM_2d_aer_Nx: 2d, Monthly mean, Time-averaged, Single-Level, Assimilation, Aerosol Diagnostics V5.12.4 Global Modeling and Assimilation Office https://doi.org/10.5067/FH9A0MLJPC7N

MERRA-2 tavgM_2d_adg_Nx: 2d, Monthly mean, Time-averaged, Single-Level, Assimilation, Aerosol Diagnostics (extended) V5.12.4 Global Modeling and Assimilation Office https://doi.org/10.5067/RZIK2TV7PP38

MERRA-2 inst3_3d_asm_Nv: 3d, 3-Hourly, Instantaneous, Model-Level, Assimilation, Assimilated Meteorological Fields V5.12.4 Global Modeling and Assimilation Office https://doi.org/10.5067/WWQSXQ8IVFW8

MERRA-2 inst3_3d_aer_Nv: 3d, 3-Hourly, Instantaneous, Model-Level, Assimilation, Aerosol Mixing Ratio V5.12.4 Global Modeling and Assimilation Office https://doi.org/10.5067/LTVB4GPCOTK2

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Short summary
The spatiotemporal distributions of dust aerosol simulated by global climate models (GCMs) are highly uncertain. In this study, we evaluate dust extinction profiles, optical depth, and surface concentrations simulated in three GCMs and one reanalysis against multiple satellite retrievals and surface observations to gain process-level understanding. Our results highlight the importance of correctly representing dust emission, dry/wet deposition, and size distribution in GCMs.
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