An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: development and offline assessment for use in an aerosol–climate model
We describe an emulator of a detailed cloud parcel model which has been trained to assess droplet nucleation from a complex, multimodal aerosol size distribution simulated by a global aerosol–climate model. The emulator is constructed using a sensitivity analysis approach (polynomial chaos expansion...
Published in: | Geoscientific Model Development |
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Online Access: | http://www.osti.gov/servlets/purl/1368370 https://www.osti.gov/biblio/1368370 https://doi.org/10.5194/gmd-10-1817-2017 |
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ftosti:oai:osti.gov:1368370 2023-07-30T04:07:04+02:00 An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: development and offline assessment for use in an aerosol–climate model Rothenberg, Daniel Wang, Chien 2023-06-26 application/pdf http://www.osti.gov/servlets/purl/1368370 https://www.osti.gov/biblio/1368370 https://doi.org/10.5194/gmd-10-1817-2017 unknown http://www.osti.gov/servlets/purl/1368370 https://www.osti.gov/biblio/1368370 https://doi.org/10.5194/gmd-10-1817-2017 doi:10.5194/gmd-10-1817-2017 54 ENVIRONMENTAL SCIENCES 2023 ftosti https://doi.org/10.5194/gmd-10-1817-2017 2023-07-11T09:19:38Z We describe an emulator of a detailed cloud parcel model which has been trained to assess droplet nucleation from a complex, multimodal aerosol size distribution simulated by a global aerosol–climate model. The emulator is constructed using a sensitivity analysis approach (polynomial chaos expansion) which reproduces the behavior of the targeted parcel model across the full range of aerosol properties and meteorology simulated by the parent climate model. An iterative technique using aerosol fields sampled from a global model is used to identify the critical aerosol size distribution parameters necessary for accurately predicting activation. Across the large parameter space used to train them, the emulators estimate cloud droplet number concentration (CDNC) with a mean relative error of 9.2% for aerosol populations without giant cloud condensation nuclei (CCN) and 6.9% when including them. Versus a parcel model driven by those same aerosol fields, the best-performing emulator has a mean relative error of 4.6%, which is comparable with two commonly used activation schemes also evaluated here (which have mean relative errors of 2.9 and 6.7%, respectively). We identify the potential for regional biases in modeled CDNC, particularly in oceanic regimes, where our best-performing emulator tends to overpredict by 7%, whereas the reference activation schemes range in mean relative error from -3 to 7%. The emulators which include the effects of giant CCN are more accurate in continental regimes (mean relative error of 0.3%) but strongly overestimate CDNC in oceanic regimes by up to 22%, particularly in the Southern Ocean. Finally, the biases in CDNC resulting from the subjective choice of activation scheme could potentially influence the magnitude of the indirect effect diagnosed from the model incorporating it. Other/Unknown Material Southern Ocean SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Southern Ocean Geoscientific Model Development 10 4 1817 1833 |
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Open Polar |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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ftosti |
language |
unknown |
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54 ENVIRONMENTAL SCIENCES |
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54 ENVIRONMENTAL SCIENCES Rothenberg, Daniel Wang, Chien An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: development and offline assessment for use in an aerosol–climate model |
topic_facet |
54 ENVIRONMENTAL SCIENCES |
description |
We describe an emulator of a detailed cloud parcel model which has been trained to assess droplet nucleation from a complex, multimodal aerosol size distribution simulated by a global aerosol–climate model. The emulator is constructed using a sensitivity analysis approach (polynomial chaos expansion) which reproduces the behavior of the targeted parcel model across the full range of aerosol properties and meteorology simulated by the parent climate model. An iterative technique using aerosol fields sampled from a global model is used to identify the critical aerosol size distribution parameters necessary for accurately predicting activation. Across the large parameter space used to train them, the emulators estimate cloud droplet number concentration (CDNC) with a mean relative error of 9.2% for aerosol populations without giant cloud condensation nuclei (CCN) and 6.9% when including them. Versus a parcel model driven by those same aerosol fields, the best-performing emulator has a mean relative error of 4.6%, which is comparable with two commonly used activation schemes also evaluated here (which have mean relative errors of 2.9 and 6.7%, respectively). We identify the potential for regional biases in modeled CDNC, particularly in oceanic regimes, where our best-performing emulator tends to overpredict by 7%, whereas the reference activation schemes range in mean relative error from -3 to 7%. The emulators which include the effects of giant CCN are more accurate in continental regimes (mean relative error of 0.3%) but strongly overestimate CDNC in oceanic regimes by up to 22%, particularly in the Southern Ocean. Finally, the biases in CDNC resulting from the subjective choice of activation scheme could potentially influence the magnitude of the indirect effect diagnosed from the model incorporating it. |
author |
Rothenberg, Daniel Wang, Chien |
author_facet |
Rothenberg, Daniel Wang, Chien |
author_sort |
Rothenberg, Daniel |
title |
An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: development and offline assessment for use in an aerosol–climate model |
title_short |
An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: development and offline assessment for use in an aerosol–climate model |
title_full |
An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: development and offline assessment for use in an aerosol–climate model |
title_fullStr |
An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: development and offline assessment for use in an aerosol–climate model |
title_full_unstemmed |
An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: development and offline assessment for use in an aerosol–climate model |
title_sort |
aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: development and offline assessment for use in an aerosol–climate model |
publishDate |
2023 |
url |
http://www.osti.gov/servlets/purl/1368370 https://www.osti.gov/biblio/1368370 https://doi.org/10.5194/gmd-10-1817-2017 |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_relation |
http://www.osti.gov/servlets/purl/1368370 https://www.osti.gov/biblio/1368370 https://doi.org/10.5194/gmd-10-1817-2017 doi:10.5194/gmd-10-1817-2017 |
op_doi |
https://doi.org/10.5194/gmd-10-1817-2017 |
container_title |
Geoscientific Model Development |
container_volume |
10 |
container_issue |
4 |
container_start_page |
1817 |
op_container_end_page |
1833 |
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1772820156921151488 |