the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
HCLIM38: a flexible regional climate model applicable for different climate zones from coarse to convection-permitting scales
Hylke de Vries
Andreas Dobler
Oskar Landgren
Petter Lind
David Lindstedt
Rasmus A. Pedersen
Juan Carlos Sánchez-Perrino
Erika Toivonen
Bert van Ulft
Fuxing Wang
Ulf Andrae
Yurii Batrak
Erik Kjellström
Geert Lenderink
Grigory Nikulin
Joni-Pekka Pietikäinen
Ernesto Rodríguez-Camino
Patrick Samuelsson
Erik van Meijgaard
Minchao Wu
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Our results show that while both methods lead to similar conclusions for two recent weather events in Sweden, the commonly used method risks underestimating the strength of the connection between the event and changes to the climate.
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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.
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