Articles | Volume 23, issue 1
https://doi.org/10.5194/acp-23-523-2023
https://doi.org/10.5194/acp-23-523-2023
Research article
 | 
13 Jan 2023
Research article |  | 13 Jan 2023

Machine learning of cloud types in satellite observations and climate models

Peter Kuma, Frida A.-M. Bender, Alex Schuddeboom, Adrian J. McDonald, and Øyvind Seland

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Cited articles

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Short summary
We present a machine learning method for determining cloud types in climate model output and satellite observations based on ground observations of cloud genera. We analyse cloud type biases and changes with temperature in climate models and show that the bias is anticorrelated with climate sensitivity. Models simulating decreasing stratiform and increasing cumuliform clouds with increased CO2 concentration tend to have higher climate sensitivity than models simulating the opposite tendencies.
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