Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models
cs.LG
Submitted: 2026-09-25
Updated: 2026-09-25
Terminology
Sources
- DeCaFlow: A deconfounding causal generative model
- A Foundation Model for the Earth System
- Towards Causal Representations of Climate Model Data
- Causal Representation Learning in Temporal Data via Single-Parent Decoding
- Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution
- ArchesClimate: Probabilistic Decadal Ensemble Generation With Flow Matching
- CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal models
- Causal Climate Emulation with Bayesian Filtering
- GraphCast: Learning skillful medium-range global weather forecasting
- ACE: A fast, skillful learned global atmospheric model for climate prediction
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