Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6)
Abstract Representing mixing state of black carbon (BC) is challenging for global climate models (GCMs). The Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4) represents aerosols as fully internal mixtures with uniform composition within each a...
Published in: | Journal of Advances in Modeling Earth Systems |
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American Geophysical Union (AGU)
2024
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ftdoajarticles:oai:doaj.org/article:6a9fe34937db4288a6c9269306f4a498 2024-09-09T19:27:26+00:00 Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6) Wenxiang Shen Minghuai Wang Nicole Riemer Zhonghua Zheng Yawen Liu Xinyi Dong 2024-01-01T00:00:00Z https://doi.org/10.1029/2023MS003889 https://doaj.org/article/6a9fe34937db4288a6c9269306f4a498 EN eng American Geophysical Union (AGU) https://doi.org/10.1029/2023MS003889 https://doaj.org/toc/1942-2466 1942-2466 doi:10.1029/2023MS003889 https://doaj.org/article/6a9fe34937db4288a6c9269306f4a498 Journal of Advances in Modeling Earth Systems, Vol 16, Iss 1, Pp n/a-n/a (2024) mixing state representation machine learning black carbon aerosol aerosol CCN activation climate model improvement Physical geography GB3-5030 Oceanography GC1-1581 article 2024 ftdoajarticles https://doi.org/10.1029/2023MS003889 2024-08-05T17:49:59Z Abstract Representing mixing state of black carbon (BC) is challenging for global climate models (GCMs). The Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4) represents aerosols as fully internal mixtures with uniform composition within each aerosol mode, resulting in high degree of internal mixing of BC with non‐BC species and large mass ratio of coating to BC (RBC, the mass ratio of non‐BC species to BC in BC‐containing particles). To improve BC mixing state representation, we coupled a machine learning (ML) model of BC mixing state index trained on particle‐resolved simulations to the CAM6 with MAM4 (MAM4‐ML). In MAM4‐ML, we use RBC to partition accumulation mode particles into two new modes, BC‐free particles and BC‐containing particles. We adjust RBC to make the modeled BC mixing state index (χmode) match the one predicted by the ML model (χML). On a global average, the mass fraction of BC‐containing particles in accumulation mode decreases from 100% (MAM4‐default) to 48% (MAM4‐ML). The globally averaged χmode decreases from 78% (MAM4‐default) to 63% (MAM4‐ML, 19% reduction) and agrees well with χML (66%). The RBC decreases by 52% for accumulation mode and better agrees with observations. The hygroscopicity drops by 9% for BC‐containing particles in accumulation mode, leading to a 20% reduction in the BC activation fraction. The surface BC concentration increases most (6.9%) in the Arctic, and the BC burden increases by 4%, globally. Our study highlights the application of the ML model for improving key aerosol processes in GCMs. Article in Journal/Newspaper Arctic black carbon Directory of Open Access Journals: DOAJ Articles Arctic Journal of Advances in Modeling Earth Systems 16 1 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
mixing state representation machine learning black carbon aerosol aerosol CCN activation climate model improvement Physical geography GB3-5030 Oceanography GC1-1581 |
spellingShingle |
mixing state representation machine learning black carbon aerosol aerosol CCN activation climate model improvement Physical geography GB3-5030 Oceanography GC1-1581 Wenxiang Shen Minghuai Wang Nicole Riemer Zhonghua Zheng Yawen Liu Xinyi Dong Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6) |
topic_facet |
mixing state representation machine learning black carbon aerosol aerosol CCN activation climate model improvement Physical geography GB3-5030 Oceanography GC1-1581 |
description |
Abstract Representing mixing state of black carbon (BC) is challenging for global climate models (GCMs). The Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4) represents aerosols as fully internal mixtures with uniform composition within each aerosol mode, resulting in high degree of internal mixing of BC with non‐BC species and large mass ratio of coating to BC (RBC, the mass ratio of non‐BC species to BC in BC‐containing particles). To improve BC mixing state representation, we coupled a machine learning (ML) model of BC mixing state index trained on particle‐resolved simulations to the CAM6 with MAM4 (MAM4‐ML). In MAM4‐ML, we use RBC to partition accumulation mode particles into two new modes, BC‐free particles and BC‐containing particles. We adjust RBC to make the modeled BC mixing state index (χmode) match the one predicted by the ML model (χML). On a global average, the mass fraction of BC‐containing particles in accumulation mode decreases from 100% (MAM4‐default) to 48% (MAM4‐ML). The globally averaged χmode decreases from 78% (MAM4‐default) to 63% (MAM4‐ML, 19% reduction) and agrees well with χML (66%). The RBC decreases by 52% for accumulation mode and better agrees with observations. The hygroscopicity drops by 9% for BC‐containing particles in accumulation mode, leading to a 20% reduction in the BC activation fraction. The surface BC concentration increases most (6.9%) in the Arctic, and the BC burden increases by 4%, globally. Our study highlights the application of the ML model for improving key aerosol processes in GCMs. |
format |
Article in Journal/Newspaper |
author |
Wenxiang Shen Minghuai Wang Nicole Riemer Zhonghua Zheng Yawen Liu Xinyi Dong |
author_facet |
Wenxiang Shen Minghuai Wang Nicole Riemer Zhonghua Zheng Yawen Liu Xinyi Dong |
author_sort |
Wenxiang Shen |
title |
Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6) |
title_short |
Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6) |
title_full |
Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6) |
title_fullStr |
Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6) |
title_full_unstemmed |
Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6) |
title_sort |
improving bc mixing state and ccn activity representation with machine learning in the community atmosphere model version 6 (cam6) |
publisher |
American Geophysical Union (AGU) |
publishDate |
2024 |
url |
https://doi.org/10.1029/2023MS003889 https://doaj.org/article/6a9fe34937db4288a6c9269306f4a498 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic black carbon |
genre_facet |
Arctic black carbon |
op_source |
Journal of Advances in Modeling Earth Systems, Vol 16, Iss 1, Pp n/a-n/a (2024) |
op_relation |
https://doi.org/10.1029/2023MS003889 https://doaj.org/toc/1942-2466 1942-2466 doi:10.1029/2023MS003889 https://doaj.org/article/6a9fe34937db4288a6c9269306f4a498 |
op_doi |
https://doi.org/10.1029/2023MS003889 |
container_title |
Journal of Advances in Modeling Earth Systems |
container_volume |
16 |
container_issue |
1 |
_version_ |
1809896854048997376 |