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...
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| Format: | Article |
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Copernicus GmbH
2018
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| Online Access: | http://hdl.handle.net/1721.1/113898 https://orcid.org/0000-0002-8270-4831 https://orcid.org/0000-0002-3979-4747 |
| _version_ | 1826195282186993664 |
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| author | Rothenberg, Daniel Alexander Wang, Chien |
| author2 | Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences Rothenberg, Daniel Alexander Wang, Chien |
| author_sort | Rothenberg, Daniel Alexander |
| collection | MIT |
| 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. 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. |
| first_indexed | 2024-09-23T10:10:15Z |
| format | Article |
| id | mit-1721.1/113898 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T10:10:15Z |
| publishDate | 2018 |
| publisher | Copernicus GmbH |
| record_format | dspace |
| spelling | mit-1721.1/1138982022-09-26T16:12:30Z 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 Alexander Wang, Chien Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences Rothenberg, Daniel Alexander Wang, Chien 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. 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. National Science Foundation (U.S.) (Grant 1122374) National Science Foundation (U.S.) (Grant AGS-1339264) United States. Department of Energy (Grant DE-FG02-94ER61937) 2018-02-27T18:18:49Z 2018-02-27T18:18:49Z 2017-04 2017-02 2018-02-23T14:01:47Z Article http://purl.org/eprint/type/JournalArticle 1991-9603 1991-959X http://hdl.handle.net/1721.1/113898 Rothenberg, Daniel, and Chien Wang. “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.” Geoscientific Model Development 10, 4 (April 2017): 1817–1833 © 2017 The Author(s) https://orcid.org/0000-0002-8270-4831 https://orcid.org/0000-0002-3979-4747 http://dx.doi.org/10.5194/gmd-10-1817-2017 Geoscientific Model Development Attribution 3.0 Unported (CC BY 3.0) https://creativecommons.org/licenses/by/3.0/ application/pdf Copernicus GmbH Copernicus Publications |
| spellingShingle | Rothenberg, Daniel Alexander 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 |
| 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_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_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_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 |
| url | http://hdl.handle.net/1721.1/113898 https://orcid.org/0000-0002-8270-4831 https://orcid.org/0000-0002-3979-4747 |
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