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Shan Zhao

zhaoshanuestc@gmail.com
Collaborating researcher - Technical University of Munich
Supervisor
David Rolnick
GitHub

Publications

Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models
Shan Zhao
Ilija Trajković
Julia Kaltenborn
Yaniv Gurwicz
Peer Nowack
David Rolnick
Julien Boussard
Machine learning (ML) emulators provide a fast and cost-effective method to simulate climate change scenarios after being trained on Earth S… (see more)ystem 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 atmospheric greenhouse gas and aerosol concentrations. When trained on future climate change scenarios, our method accurately predicts the long-term global mean and regional temperature evolution and shows physically realistic responses to perturbations in greenhouse gas and aerosol concentrations when evaluated on unseen scenarios. Our results underline the potential of causal representation learning frameworks for advancing climate model emulation.
2026-09-24
arXiv (preprint)
doi.org
arxiv.org