Machine learning is reshaping the modelling of many physical processes in Earth system models, offering new routes for parameterisation, emulation, and hybrid modelling. This session focuses on the use of machine learning to emulate computationally expensive and unresolved processes, accelerate physical models, simulate across weather and climate, and improve representation across domains such as convection, turbulence, radiation, hydrology, sea ice, and other components of the Earth system.

Topics include (but are not limited to):

- Subgrid-scale parameterization via machine learning (for example those related to air-sea & land-atmosphere interactions)
- Emulators of physical processes, model components, or whole weather and climate models (including end-to-end learning)
- Hybrid ML-physics modelling frameworks
- Foundation Models
- Reinforcement learning (such as for ensuring physical consistency, stability, optimising model behaviour and improving time-series modelling)
- Physics-informed neural networks, neural operators, and differentiable programming
- Verification of data-driven models (including AI forecasting)
- Physical behaviour, encoding and analysis of AI parametrisations, emulators and whole models (such as through feature-based evaluation/conditional vs unconditional evaluation)
- Calibration and parameter optimization using ML
- Coupling of ML models with physical models
- Cross-domain applications (atmosphere, ocean, cryosphere, land).

This session provides a critical overview of current progress and emerging directions in the application of ML across parametrisations, emulation and hybrid modelling.