Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model
Abstract ∼ ∼ 42 km) simulations. The default treatment incurs significant errors near the surface, particularly over sharp emission gradient zones. Much larger errors are observed in high-resolution simulations. It substantially underestimates the aerosol burden, surface concentration, and aerosol sources over highly polluted regions, while it overestimates these quantities over less-polluted adjacent areas. Large errors can persist at higher elevation for daily mean estimates, which can affect aerosol extinction profiles and aerosol optical depth (AOD). We find that the revised treatment significantly improves the accuracy of the aerosol emissions from surface and elevated sources near sharp spatial gradient regions, with significant improvement in the spatial heterogeneity and variability of simulated surface concentration in high-resolution simulations. In the next-generation E3SM running at convection-permitting scales where the resolved spatial heterogeneity is significantly increased, the revised emission treatment is expected to better represent the aerosol emissions as well as their lifecycle and impacts on climate.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model ; volume:17 ; number:8 ; year:2024 ; pages:3507-3532 ; extent:26
Geoscientific model development ; 17, Heft 8 (2024), 3507-3532 (gesamt 26)
- Creator
- DOI
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10.5194/gmd-17-3507-2024
- URN
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urn:nbn:de:101:1-2405090437427.153992940342
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:44 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Hassan, Taufiq
- Zhang, Kai
- Li, Jianfeng
- Singh, Balwinder
- Zhang, Shixuan
- Wang, Hailong
- Ma, Po-Lun
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