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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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Principais autores: Björn Lütjens, Raffaele Ferrari, Duncan Watson‐Parris, Noelle E. Selin
Format: Artigo
Sprog:Inglês
Udgivet: American Geophysical Union (AGU) 2025-08-01
Serier:Journal of Advances in Modeling Earth Systems
Fag:
emulation
benchmark
machine learning
internal variability
pattern scaling
climate
Online adgang:https://doi.org/10.1029/2024MS004619
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