Optimisation of ICON-CLM for the EURO-CORDEX domain: developments, sensitivities, tuning

Geyer, Beate; Campanale, Angelo; Churiulin, Evgenii; Feldmann, Hendrik; Goergen, Klaus; Hagemann, Stefan; Ho-Hagemann, Ha Thi Minh; Karadan, Muhammed Muhshif; Keuler, Klaus; Khain, Pavel; Lawand, Divyaja; Ludwig, Patrick; Maurer, Vera; Petrov, Sergei; Poll, Stefan; Purr, Christopher; Russo, Emmanuele; Schubert-Frisius, Martina; Schulz, Jan-Peter; Singh, Shweta; Steger, Christian; Truhetz, Heimo; Will, Andreas

doi:10.5194/gmd-19-5439-2026

Articles | Volume 19, issue 12

https://doi.org/10.5194/gmd-19-5439-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-19-5439-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 12

Methods for assessment of models

|

24 Jun 2026

Methods for assessment of models |

| 24 Jun 2026

Optimisation of ICON-CLM for the EURO-CORDEX domain: developments, sensitivities, tuning

Beate Geyer, Angelo Campanale, Evgenii Churiulin, Hendrik Feldmann, Klaus Goergen, Stefan Hagemann, Ha Thi Minh Ho-Hagemann, Muhammed Muhshif Karadan, Klaus Keuler, Pavel Khain, Divyaja Lawand, Patrick Ludwig, Vera Maurer, Sergei Petrov, Stefan Poll, Christopher Purr, Emmanuele Russo, Martina Schubert-Frisius, Jan-Peter Schulz, Shweta Singh, Christian Steger, Heimo Truhetz, and Andreas Will

Supplement

https://doi.org/10.5194/gmd-19-5439-2026-supplement

Data sets

ICON release 2024.07 ICON partnership (DWD, MPI-M, DKRZ, KIT, C2SM) https://doi.org/10.35089/WDCC/ICONRELEASE2024.07

Model code and software

LiMMo (Linear Meta-Model optimization for regional climate model) S. Petrov and A. Will https://doi.org/10.5281/ZENODO.14662291

SPICE (Starter Package for ICON-CLM Experiments) B. Geyer https://doi.org/10.5281/zenodo.10047046

EvaSuite (Evaluation Suite for climate data) R. Petrik et al. https://doi.org/10.5281/zenodo.19223685

COPAT2-ICON scripts and routines B. Geyer et al. https://doi.org/10.5281/zenodo.18078427

Short summary

Complex models in environmental science typically have a lot of tuning parameters, which has to be set by the users depending on the application. This study presents a new method of objective tuning of a huge number of parameters, by combining expert judgement with automated tuning called Linear Meta-Model optimisation (LiMMo). The method is successfully applied to the regional climate model named ICON (Icosahedral Non-hydrostatic)-CLM (Climate Limited-area Modelling) over Europe.