Artificial intelligence (AI) is currently transforming data analysis and modelling of the Earth system. While statistical and data-driven models have been used for a long time, recent advances now allow for encoding non-linear, spatio-temporal relationships robustly without sacrificing interpretability. This has the potential to accelerate climate science by providing new physics-based modelling approaches; improving our understanding of the underlying processes; reducing and better quantifying climate signals, variability, and uncertainty; and even making predictions directly from observations across different spatio-temporal scales. We encourage participants to discuss the limitations of AI methods (including their often substantial training-data requirements, susceptibility to data leakage, poor generalisation, and physical inconsistencies) to help ensure that these methods are applied where they are fit for purpose and add value.


This session aims to provide a venue to present the latest progress in the use of AI applied to all aspects of climate science and we welcome abstracts focussed on, but not limited to:
- Causal discovery and inference: causal impact assessment, interventions, counterfactual analysis
- Learning processes, equations, and feature representations across models and observations
- Hybrid physics-AI and differentiable models, climate model emulators, data-model integration
- AI-based detection and attribution approaches, including for extreme events
- Probabilistic modelling and uncertainty quantification
- AI-based climate downscaling, including super-resolution and bias-correction
- Climate model analysis in the AI era, including benchmarking and stress-testing
- Explainable AI applications to climate data science and climate modelling
- Distributional robustness and transfer learning, including out-of-distribution generalisation tasks
- AI reasoning and agent-based workflows for analysing climate data, developing, improving, testing, and porting scientific code, and climate information