3 papers
cs.LG2026
No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation
Bradley Stanley-Clamp, Anson Lei, Hannah M. Christensen +1
Climate emulation is an out-of-distribution (OOD) projection task. This is precisely the challenge where modern Machine Learning (ML) methods are most prone to failure. Consequentl…
physics.ao-ph2026
Crowdsourcing the Frontier: Advancing Hybrid Physics-ML Climate Simulation via a $50,000 Kaggle Competition
Jerry Lin, Zeyuan Hu, Tom Beucler +24
Subgrid machine-learning (ML) parameterizations have the potential to introduce a new generation of climate models that incorporate the effects of higher-resolution physics without…
physics.ao-ph2025
Epistemic and Aleatoric Uncertainty Quantification in Weather and Climate Models
Laura A. Mansfield, Hannah M. Christensen
Representing and quantifying uncertainty in physical parameterisations is a central challenge in weather and climate modelling, and approaches are often developed separately for di…