Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 24, issue 22
Articles | Volume 24, issue 22
https://doi.org/10.5194/acp-24-13025-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-13025-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 24, issue 22
Research article
 | 
26 Nov 2024
Research article |  | 26 Nov 2024

Analysis of the cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning

Yichen Jia, Hendrik Andersen, and Jan Cermak

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Short summary
We present a near-global observation-based explainable machine learning framework to quantify the response of cloud fraction (CLF) of marine low clouds to cloud droplet number concentration (Nd), accounting for the covariations with meteorological factors. This approach provides a novel data-driven method to analyse the CLF adjustment by assessing the CLF sensitivity to Nd and numerous meteorological factors as well as the dependence of the Nd–CLF sensitivity on the meteorological conditions.
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