Deep Learning Regional Climate Model Emulators: A Comparison of Two Downscaling Training Frameworks

Author:

van der Meer Marijn123ORCID,de Roda Husman Sophie3ORCID,Lhermitte Stef34ORCID

Affiliation:

1. Laboratory of Hydraulics, Hydrology, and Glaciology (VAW) ETH Zürich Zurich Switzerland

2. Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL) Birmensdorf Switzerland

3. Department of Geoscience & Remote Sensing Delft University of Technology Delft The Netherlands

4. Department of Earth & Environmental Sciences KU Leuven Leuven Belgium

Abstract

AbstractRegional climate models (RCMs) have a high computational cost due to their higher spatial resolution compared to global climate models (GCMs). Therefore, various downscaling approaches have been developed as a surrogate for the dynamical downscaling of GCMs. This study assesses the potential of using a cost‐efficient machine learning alternative to dynamical downscaling by using the example case study of emulating surface mass balance (SMB) over the Antarctic Peninsula. More specifically, we determine the impact of the training framework by comparing two training scenarios: (a) a perfect and (b) an imperfect model framework. In the perfect model framework, the RCM‐emulator learns only the downscaling function; therefore, it was trained with upscaled RCM (UPRCM) features at GCM resolution. This emulator accurately reproduced SMB when evaluated on UPRCM, but its predictions on GCM data conserved RCM‐GCM inconsistencies and led to underestimation. In the imperfect model framework, the RCM‐emulator was trained with GCM features and downscaled the GCM while exposed to RCM‐GCM inconsistencies. This emulator predicted SMB close to the truth, showing it learned the underlying inconsistencies and dynamics. Our results suggest that a deep learning RCM‐emulator can learn the proper GCM to RCM downscaling function while working directly with GCM data. Furthermore, the RCM‐emulator presents a significant computational gain compared to an RCM simulation. We conclude that machine learning emulators can be applied to produce fast and fine‐scaled predictions of RCM simulations from GCM data.

Funder

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Environmental Chemistry,Global and Planetary Change

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