mlsanityData to 14 Oct 2026

Causal Climate Emulation with Bayesian Filtering

arXiv 2506.09891 ↗First posted 11 Jun 2025cs.LG cs.AI cs.CEcited by 5

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Abstract (arXiv)

Traditional models of climate change use complex systems of coupled equations to simulate physical processes across the Earth system. These simulations are highly computationally expensive, limiting our predictions of climate change and analyses of its causes and effects. Machine learning has the potential to quickly emulate data from climate models, but current approaches are not able to incorporate physically-based causal relationships. Here, we develop an interpretable climate model emulator based on causal representation learning. We derive a novel approach including a Bayesian filter for stable long-term autoregressive emulation. We demonstrate that our emulator learns accurate climate dynamics, and we show the importance of each one of its components on a realistic synthetic dataset and data from two widely deployed climate models.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 35 of its 79 references that are in the map. First posted on arXiv 11 Jun 2025.

Builds on

35 of 79 references.

References · 35

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23 more
  1. 2023Spherical Fourier Neural Operators: Learning Stable Dynamics on the Spherecited by 233
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026How Can Machine Learning Emulators Best Support Climate Science?cited by 0
  2. 2026Emulating the Forced Response of Climate Models with Generative Machine Learningcited by 0
  3. 2026Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Modelscited by 0
  4. 2026Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systemscited by 0
  5. 2026Artificial intelligence pathways from weather to climatecited by 0