Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2
<p>Aeolian dust has significant impacts on climate, public health, infrastructure and ecosystems. Assessing dust concentrations and the impacts is challenging because the emissions depend on many environmental factors and can vary greatly with meteorological conditions. We present a data-drive...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-05-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/16/3013/2023/gmd-16-3013-2023.pdf |
| _version_ | 1827936882929958912 |
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| author | K. Klingmüller J. Lelieveld J. Lelieveld |
| author_facet | K. Klingmüller J. Lelieveld J. Lelieveld |
| author_sort | K. Klingmüller |
| collection | DOAJ |
| description | <p>Aeolian dust has significant impacts on climate, public health, infrastructure and ecosystems. Assessing dust concentrations and the impacts is
challenging because the emissions depend on many environmental factors and can vary greatly with meteorological conditions. We present a data-driven
aeolian dust scheme that combines machine learning components and physical equations to predict atmospheric dust concentrations and quantify the
sources. The numerical scheme was trained to reproduce dust aerosol optical depth retrievals by the Infrared Atmospheric Sounding Interferometer on
board the MetOp-A satellite. The input parameters included meteorological variables from the fifth-generation atmospheric reanalysis of the European
Centre for Medium-Range Weather Forecasts. The trained dust scheme can be applied as an emission submodel to be used in climate and Earth system
models, which is reproducibly derived from observational data so that a priori assumptions and manual parameter tuning can be largely avoided. We
compared the trained emission submodel to a state-of-the-art emission parameterisation, showing that it substantially improves the representation of
aeolian dust in the global atmospheric chemistry–climate model EMAC.</p> |
| first_indexed | 2024-03-13T08:17:31Z |
| format | Article |
| id | doaj.art-3d252aa032204b35b43c980497d0c41e |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-03-13T08:17:31Z |
| publishDate | 2023-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-3d252aa032204b35b43c980497d0c41e2023-05-31T13:00:23ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032023-05-01163013302810.5194/gmd-16-3013-2023Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2K. Klingmüller0J. Lelieveld1J. Lelieveld2Department of Atmospheric Chemistry, Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, 55128 Mainz, GermanyDepartment of Atmospheric Chemistry, Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, 55128 Mainz, GermanyClimate and Atmosphere Research Center, The Cyprus Institute, P.O. Box 27456, 1645 Nicosia, Cyprus<p>Aeolian dust has significant impacts on climate, public health, infrastructure and ecosystems. Assessing dust concentrations and the impacts is challenging because the emissions depend on many environmental factors and can vary greatly with meteorological conditions. We present a data-driven aeolian dust scheme that combines machine learning components and physical equations to predict atmospheric dust concentrations and quantify the sources. The numerical scheme was trained to reproduce dust aerosol optical depth retrievals by the Infrared Atmospheric Sounding Interferometer on board the MetOp-A satellite. The input parameters included meteorological variables from the fifth-generation atmospheric reanalysis of the European Centre for Medium-Range Weather Forecasts. The trained dust scheme can be applied as an emission submodel to be used in climate and Earth system models, which is reproducibly derived from observational data so that a priori assumptions and manual parameter tuning can be largely avoided. We compared the trained emission submodel to a state-of-the-art emission parameterisation, showing that it substantially improves the representation of aeolian dust in the global atmospheric chemistry–climate model EMAC.</p>https://gmd.copernicus.org/articles/16/3013/2023/gmd-16-3013-2023.pdf |
| spellingShingle | K. Klingmüller J. Lelieveld J. Lelieveld Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2 Geoscientific Model Development |
| title | Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2 |
| title_full | Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2 |
| title_fullStr | Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2 |
| title_full_unstemmed | Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2 |
| title_short | Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2 |
| title_sort | data driven aeolian dust emission scheme for climate modelling evaluated with emac 2 55 2 |
| url | https://gmd.copernicus.org/articles/16/3013/2023/gmd-16-3013-2023.pdf |
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