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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Related Dataset #1 : ATom: L2 In Situ Measurements of Aerosol Microphysical Properties (AMP)

Related Dataset #2 : ATom: L2 Measurements from CU High-Resolution Aerosol Mass Spectrometer (HR-AMS)

Related Dataset #3 : ATom: L2 In Situ Measurements from NOAA Nitrogen Oxides and Ozone (NOyO3) Instrument

Related Dataset #4 : DISCOVER-AQ Campaign
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Author Nair, A. A.
Yu, F.
Campuzano-Jost, P.
DeMott, P. J.
Levin, E. J. T.
Jimenez, J. L.
Peischl, J.
Pollack, I. B.
Fredrickson, C. D.
Beyersdorf, A. J.
Nault, B. A.
Park, M.
Yum, S. S.
Palm, B. B.
Xu, L.
Bourgeois, I.
Anderson, B. E.
Nenes, A.
Ziemba, L. D.
Moore, R. H.
Lee, T.
Park, T.
Thompson, C. R.
Flocke, Frank M.
Huey, L. G.
Kim, M. J.
Peng, Q.
Publisher UCAR/NCAR - Library
Publication Date 2021-11-16T00:00:00
Digital Object Identifier (DOI) Not Assigned
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Topic Category geoscientificInformation
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Metadata Date 2025-07-11T16:09:53.254081
Metadata Record Identifier edu.ucar.opensky::articles:24922
Metadata Language eng; USA
Suggested Citation Nair, A. A., Yu, F., Campuzano-Jost, P., DeMott, P. J., Levin, E. J. T., Jimenez, J. L., Peischl, J., Pollack, I. B., Fredrickson, C. D., Beyersdorf, A. J., Nault, B. A., Park, M., Yum, S. S., Palm, B. B., Xu, L., Bourgeois, I., Anderson, B. E., Nenes, A., Ziemba, L. D., Moore, R. H., Lee, T., Park, T., Thompson, C. R., Flocke, Frank M., Huey, L. G., Kim, M. J., Peng, Q.. (2021). Machine learning uncovers aerosol size information from chemistry and meteorology to quantify potential cloud-forming particles. UCAR/NCAR - Library. https://n2t.org/ark:/85065/d79c71wf. Accessed 30 July 2025.

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