The Impact of Internal Variability on Benchmarking Deep Learning Climate Emulators

Abstract Full‐complexity Earth system models (ESMs) are computationally very expensive, limiting their use in exploring the climate outcomes of multiple emission pathways. More efficient emulators that approximate ESMs can directly map emissions onto climate outcomes, and benchmarks are being used t...

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Hlavní autoři:	Björn Lütjens, Raffaele Ferrari, Duncan Watson‐Parris, Noelle E. Selin
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	American Geophysical Union (AGU) 2025-08-01
Edice:	Journal of Advances in Modeling Earth Systems
Témata:	emulation benchmark machine learning internal variability pattern scaling climate
On-line přístup:	https://doi.org/10.1029/2024MS004619
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