Downscaling multi-model climate projection ensembles with deep learning (DeepESD): contribution to CORDEX EUR-44

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Abstract ∘) domain, based on eight global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). To our knowledge, this is the first time that CNNs have been used to produce downscaled multi-model ensembles based on the perfect-prognosis approach, allowing us to quantify inter-model uncertainty in climate change signals. The results are compared with those corresponding to an EUR-44 ensemble of regional climate models (RCMs) showing that DeepESD reduces distributional biases in the historical period. Moreover, the resulting climate change signals are broadly comparable to those obtained with the RCMs, with similar spatial structures. As for the uncertainty of the climate change signal (measured on the basis of inter-model spread), DeepESD preserves the uncertainty for temperature and results in a reduced uncertainty for precipitation. To facilitate further studies of this downscaling approach, we follow FAIR principles and make publicly available the code (a Jupyter notebook) and the DeepESD dataset. In particular, DeepESD is published at the Earth System Grid Federation (ESGF), as the first continental-wide PP dataset contributing to CORDEX (EUR-44).

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Downscaling multi-model climate projection ensembles with deep learning (DeepESD): contribution to CORDEX EUR-44 ; volume:15 ; number:17 ; year:2022 ; pages:6747-6758 ; extent:12
Geoscientific model development ; 15, Heft 17 (2022), 6747-6758 (gesamt 12)

Creator: Baño-Medina, Jorge
Manzanas, Rodrigo
Cimadevilla, Ezequiel
Fernández, Jesús
González-Abad, Jose
Cofiño, Antonio S.
Gutiérrez, José Manuel

DOI: 10.5194/gmd-15-6747-2022

URN: urn:nbn:de:101:1-2022090805165305161689

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 11:15 PM CEST

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Baño-Medina, Jorge
Manzanas, Rodrigo
Cimadevilla, Ezequiel
Fernández, Jesús
González-Abad, Jose
Cofiño, Antonio S.
Gutiérrez, José Manuel

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