arXiv:2501.12500v3 Announce Type: replace Abstract: Understanding climate dynamics requires going beyond correlations in observational data to uncover the underlying causal process. Latent drivers such as atmospheric processes play a central role in temporal dynamics, while direct causal influences also exist among geographically proximate observed variables. Traditional Causal Representation Learning (CRL) typically focuses on latent factors but overlooks such observable-to-observable causal relations, which limits its applicability to climate analysis. In this paper, we introduce a unified f
The increasing availability of climate data and advances in AI, particularly Causal Representation Learning, are converging to allow more sophisticated analyses of complex systems like climate dynamics.
Improved causal models in climate analysis can lead to more accurate predictions, better policy decisions, and more effective interventions, moving beyond correlation to understanding drivers.
The ability to integrate latent dynamics with observable causal relations in climate models enhances the fidelity and utility of AI in environmental science, potentially improving our understanding of climate change.
- · Climate scientists
- · AI researchers (causal inference)
- · Environmental policy makers
- · Climate tech sector
- · Traditional correlation-based climate models
- · Sectors reliant on outdated or inaccurate climate projections
More precise identification of anthropogenic and natural drivers of climate change.
Development of targeted and effective climate mitigation and adaptation strategies.
Potential for new climate engineering or geo-engineering approaches based on a deeper understanding of Earth systems.
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Read at arXiv cs.LG