ITS1.1/CL0.1.17 | Machine Learning for Climate Science
Machine Learning for Climate Science
Convener: Duncan Watson-Parris | Co-conveners: Marlene KretschmerECSECS, Gustau Camps-Valls, Peer NowackECSECS, Sebastian SippelECSECS

Machine learning (ML) is transforming data analysis and modelling of the Earth system. While statistical and data-driven models have been used for a long time, recent advances in ML and deep learning now allow for encoding non-linear, spatio-temporal relationships robustly without sacrificing interpretability. This has the potential to accelerate climate science through new approaches for modelling and understanding the climate system. For example, ML is now used in the detection and attribution of climate signals, to merge theory and Earth observations in innovative ways, and to directly learn predictive models from observations. The limitations of machine learning methods also need to be considered, such as requiring, in general, rather large training datasets, data leakage, and/or poor generalisation abilities so that 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 ML applied to all aspects of climate science, and we welcome abstracts focussed on, but not limited to:

More accurate, robust and accountable ML models:
- Hybrid models (physically informed ML, parameterizations, emulation, data-model integration)
- Novel detection and attribution approaches
- Probabilistic modelling and uncertainty quantification
- Uncertainty quantification and propagation
- Distributional robustness, transfer learning and/or out-of-distribution generalisation tasks in climate science
- Green AI

Improved understanding through data-driven approaches:
- Causal discovery and inference: causal impact assessment, interventions, counterfactual analysis
- Learning (causal) process and feature representations in observations or across models and observations
- Explainable AI applications
- Discover governing equations from climate data with symbolic regression approaches

Enhanced interaction:
- The human in the loop - active learning & reinforcement learning for improved emulation and simulations
- Large language models and AI agents - exploration and decision making, modeling regional decision-making
- Human interaction within digital twins