ICON in Climate Limited-area Mode (ICON Release Version 2.6.1): a new regional climate model [post]

Trang Van Pham, Christian Steger, Burkhardt Rockel, Klaus Keuler, Ingo Kirchner, Mariano Mertens, Daniel Rieger, Guenther Zaengl, Barbara Frueh
2020 unpublished
Abstract. For the first time the limited-area mode of the new weather and climate model ICON has been used for a continuous long-term regional climate simulation over Europe. Building upon ICON-LAM, ICON-CLM v.0 (ICON in Climate Limited-area Mode, hereafter ICON-CLM) is an adaptation for climate applications. A first version of ICON-CLM is now available and has already been integrated into a starter package (ICON-CLM_SP). The starter package provides users with a technical infrastructure that
more » ... frastructure that facilitates long-term simulations as well as model evaluation and test routines. ICON-CLM and ICON-CLM_SP were successfully installed and tested on two different computing systems. Test with different domain decompositions showed bit-identical results, and no systematic outstanding differences were found in the results with different model time steps. Comparison was done between ICON-CLM and COSMO-CLM (the recommended model configuration by the CLM-Community) performance. For that, an evaluation run with ERA-Interim boundary conditions was carried out with the setups similar to the COSMO-CLM recommended optimal setups. ICON-CLM results showed biases in the same range as those of COSMO-CLM for all evaluated surface variables. This is remarkable because the COSMO-CLM simulation was carried out with the latest model version which has been developed for two decades and was carefully tuned for climate simulations on the European domain. Furthermore, ICON-CLM already showed a better performance for air temperature, its daily extremes, and total cloud cover. Results for precipitation and mean sea level pressure did not show clear advantage from any model. However, as ICON-CLM is still in the early stage of development, there is still much room for improvement.
doi:10.5194/gmd-2020-20 fatcat:n5ggywexfvg3zczrfj2zcyobaq