Author

Peer Nowack

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#machine learning Sep 2026

Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models

A hierarchical causal representation learning framework applied to sea surface temperature fields from a state-of-the-art global climate model is developed that accurately predicts the long-term global mean and regional temperature evolution and shows physically realistic responses to perturbations in greenhouse gas an...

Shan Zhao, Ilija Trajković, Julia Kaltenborn et al. · arXiv.org · 0 citations

#machine learning Preprint May 2026

Emulating the Forced Response of Climate Models with Generative Machine Learning

This research demonstrates that the model, ArchesClimate -- SSP, does not simply imitate scenarios seen during training, but is actually capable of modeling the response of a climate state to diverse forcings, an important step towards reliable and rapid climate model scenario generation.

Graham Clyne, Julia Kaltenborn, Peer Nowack et al. · 0 citations