Articles | Volume 22, issue 7
https://doi.org/10.5194/acp-22-4737-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-4737-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing the potential for simplification in global climate model cloud microphysics
Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
Sylvaine Ferrachat
Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
David Neubauer
Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
Martin Staab
Max-Planck-Institut für Gravitationsphysik (Albert-Einstein-Institut), Hanover, Germany
Ulrike Lohmann
Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
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- Ice Aggregation in Low‐Level Mixed‐Phase Clouds at a High Arctic Site: Enhanced by Dendritic Growth and Absent Close to the Melting Level G. Chellini et al. 10.1029/2022JD036860
- Machine learning and the quest for objectivity in climate model parameterization J. Jebeile et al. 10.1007/s10584-023-03532-1
Latest update: 09 Jul 2025
Short summary
Cloud microphysical processes shape cloud properties and are therefore important to represent in climate models. Their parameterization has grown more complex, making the model results more difficult to interpret. Using sensitivity analysis we test how the global aerosol–climate model ECHAM-HAM reacts to changes to these parameterizations. The model is sensitive to the parameterization of ice crystal autoconversion but not to, e.g., self-collection, suggesting that it may be simplified.
Cloud microphysical processes shape cloud properties and are therefore important to represent in...
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