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

The paper presents a hierarchical causal representation learning framework that models both internal climate variability and forced responses in sea surface temperature fields from a global climate model. By training on future climate change scenarios, the method accurately predicts long‑term global mean and regional temperature evolution and reproduces realistic responses to perturbations in greenhouse gas and aerosol concentrations on unseen scenarios. This demonstrates the potential of causal representation learning to improve climate model emulation.

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