Author

Peer Nowack

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#machine learning Preprint Sep 2026

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

Machine learning (ML) emulators provide a fast and cost-effective method to simulate climate change scenarios after being trained on Earth System Models projections. However, the black-box nature of those data-driven approaches limit the usability and trustworthiness of their outputs and in particular their use as caus...

Shan Zhao, Ilija Trajković, Julia Kaltenborn et al. · 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