Machine learning uncovers aerosol size information from chemistry and meteorology to quantify potential cloud‐forming particles

Cloud condensation nuclei (CCN) are mediators of aerosol-cloud interactions, which contribute to the largest uncertainty in climate change prediction. Here, we present a machine learning (ML)/artificial intelligence (AI) model that quantifies CCN from model-simulated aerosol composition, atmospheric trace gas, and meteorological variables. Comprehensive multi-campaign airborne measurements, covering varied physicochemical regimes in the troposphere, confirm the validity of and help probe the inner workings of this ML model: revealing for the first time that different ranges of atmospheric aerosol composition and mass correspond to distinct aerosol number size distributions. ML extracts this information, important for accurate quantification of CCN, additionally from both chemistry and meteorology. This can provide a physicochemically explainable, computationally efficient, robust ML pathway in global climate models that only resolve aerosol composition; potentially mitigating the uncertainty of effective radiative forcing due to aerosol-cloud interactions (ERFaci) and improving confidence in assessment of anthropogenic contributions and climate change projections.

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Author Nair, Arshad Arjunan
Yu, Fangqun
Campuzano‐Jost, Pedro
DeMott, Paul J.
Levin, Ezra J. T.
Jimenez, Jose L.
Peischl, Jeff
Pollack, Ilana B.
Fredrickson, Carley D.
Beyersdorf, Andreas J.
Nault, Benjamin A.
Park, Minsu
Yum, Seong Soo
Palm, Brett B.
Xu, Lu
Bourgeois, Ilann
Anderson, Bruce E.
Nenes, Athanasios
Ziemba, Luke D.
Moore, Richard H.
Lee, Taehyoung
Park, Taehyun
Thompson, Chelsea R.
Flocke, Frank
Huey, Lewis Gregory
Kim, Michelle J.
Peng, Qiaoyun
Publisher UCAR/NCAR - Library
Publication Date 2021-11-16T00:00:00
Digital Object Identifier (DOI) Not Assigned
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Resource Version N/A
Topic Category geoscientificInformation
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Metadata Date 2023-08-18T18:17:20.579152
Metadata Record Identifier edu.ucar.opensky::articles:24922
Metadata Language eng; USA
Suggested Citation Nair, Arshad Arjunan, Yu, Fangqun, Campuzano‐Jost, Pedro, DeMott, Paul J., Levin, Ezra J. T., Jimenez, Jose L., Peischl, Jeff, Pollack, Ilana B., Fredrickson, Carley D., Beyersdorf, Andreas J., Nault, Benjamin A., Park, Minsu, Yum, Seong Soo, Palm, Brett B., Xu, Lu, Bourgeois, Ilann, Anderson, Bruce E., Nenes, Athanasios, Ziemba, Luke D., Moore, Richard H., Lee, Taehyoung, Park, Taehyun, Thompson, Chelsea R., Flocke, Frank, Huey, Lewis Gregory, Kim, Michelle J., Peng, Qiaoyun. (2021). Machine learning uncovers aerosol size information from chemistry and meteorology to quantify potential cloud‐forming particles. UCAR/NCAR - Library. http://n2t.net/ark:/85065/d79c71wf. Accessed 19 June 2025.

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