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 causal attribution tools. Here, we develop a hierarchical causal representation learning framework applied to sea surface temperature fields from a state-of-the-art global climate model. As a key advance over previous work, our framework explicitly models both atmospheric dynamical interactions arising from internal climate variability and forced responses due to changes in atmo...
| Authors | Shan Zhao, Ilija Trajkovic, Julia Kaltenborn et al. |
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| Primary category | cs.LG |
| Published | 25 September 2026 |
| Last updated at source | 6 October 2026 |
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