Articles | Volume 18, issue 12
https://doi.org/10.5194/gmd-18-3681-2025
https://doi.org/10.5194/gmd-18-3681-2025
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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Cited articles

Baldwin, M. P., Ayarzagüena, B., Birner, T., Butchart, N., Butler, A. H., Charlton‐Perez, A. J., Domeisen, D. I. V., Garfinkel, C. I., Garny, H., Gerber, E. P., Hegglin, M. I., Langematz, U., and Pedatella, N. M.: Sudden Stratospheric Warmings, Rev. Geophys., 59, e2020RG000708, https://doi.org/10.1029/2020rg000708, 2021. a
Bonnet, P.: Paulinebonnet111/bonnet24_gmd_automatic_tuning_ atm_paper: Automatic tuning code after 1st review (Version Dec2024), Zenodo [code], https://doi.org/10.5281/zenodo.14267203, 2024. (code is also available at: https://github.com/EyringMLClimateGroup/bonnet24gmd_automatic_tuning_atm, last access: 17 June 2025) a, b
Cleary, E., Garbuno-Inigo, A., Lan, S., Schneider, T., and Stuart, A. M.: Calibrate, emulate, sample, J. Comput. Phys., 424, 109716, https://doi.org/10.1016/j.jcp.2020.109716, 2021. a, b
Couvreux, F., Hourdin, F., Williamson, D., Roehrig, R., Volodina, V., Villefranque, N., Rio, C., Audouin, O., Salter, J., Bazile, E., Brient, F., Favot, F., Honnert, R., Lefebvre, M.-P., Madeleine, J.-B., Rodier, Q., and Xu, W.: Process-Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement, Journal of Advances in Modeling Earth Systems, 13, https://doi.org/10.1029/2020ms002217, 2021. a, b
Crueger, T., Giorgetta, M. A., Brokopf, R., Esch, M., Fiedler, S., Hohenegger, C., Kornblueh, L., Mauritsen, T., Nam, C., Naumann, A. K., Peters, K., Rast, S., Roeckner, E., Sakradzija, M., Schmidt, H., Vial, J., Vogel, R., and Stevens, B.: ICON-A, The Atmosphere Component of the ICON Earth System Model: II. Model Evaluation, J. Adv. Model. Earth Syst., 10, 1638–1662, https://doi.org/10.1029/2017ms001233, 2018. a, b, c
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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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