the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
COSMO-CLM regional climate simulations in the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework: a review
Roman Brogli
Praveen Kumar Pothapakula
Emmanuele Russo
Jonas Van de Walle
Bodo Ahrens
Ivonne Anders
Edoardo Bucchignani
Edouard L. Davin
Marie-Estelle Demory
Alessandro Dosio
Hendrik Feldmann
Barbara Früh
Beate Geyer
Klaus Keuler
Donghyun Lee
Delei Li
Nicole P. M. van Lipzig
Seung-Ki Min
Hans-Jürgen Panitz
Burkhardt Rockel
Christoph Schär
Christian Steger
Wim Thiery
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coupling interfacesrepresenting the feedbacks between the distinct models used for contribution. PARASO is stable and ready to use but is still characterized by significant biases.
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Inaccuracies in air–sea heat fluxes severely degrade the accuracy of ocean numerical simulations. Here, we use artificial neural networks to correct air–sea heat fluxes as a function of oceanic and atmospheric state predictors. The correction successfully improves surface and subsurface ocean temperatures beyond the training period and in prediction experiments.
FINAM is not a model), a new coupling framework written in Python to dynamically connect independently developed models. Python, as the ultimate glue language, enables the use of codes from nearly any programming language like Fortran, C++, Rust, and others. FINAM is designed to simplify the integration of various models with minimal effort, as demonstrated through various examples ranging from simple to complex systems.
This study introduces a new 3D lake–ice–atmosphere coupled model that significantly improves winter climate simulations for the Great Lakes compared to traditional 1D lake model coupling. The key contribution is the identification of critical hydrodynamic processes – ice transport, heat advection, and shear-driven turbulence production – that influence lake thermal structure and ice cover and explain the superior performance of 3D lake models to their 1D counterparts.