Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 12
Articles | Volume 18, issue 12
https://doi.org/10.5194/gmd-18-3681-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-3681-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 12
Development and technical paper
 | 
20 Jun 2025
Development and technical paper |  | 20 Jun 2025

Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching

Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring

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Short summary
Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
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