Greg Munday
DPhil Research Student at the University of Oxford. Interested in all things hybrid climate modelling.
Sessions
Hybrid climate modelling combines numerical models with machine-learned components. We present the development of multiple machine-learned surface climate processes and their integration into the climate model SpeedyWeather.jl using PyTorch and Lux.jl. Despite the offline training, the hybrid model is designed to generalise in space and to different climates. We address speed vs. accuracy tradeoffs using SymbolicRegression.jl and discuss online learning with Enzyme.jl.
Climate models rely on parameterizations that are traditionally tuned manually. We present a differentiable calibration framework using Enzyme.jl to compute exact reverse mode gradients of energy-balance diagnostics in SpeedyWeather.jl. By batching single- timestep gradients across chaotic dynamics, we enable systematic, reproducible optimization of shortwave radiation parameters, establishing an extensible workflow for objective calibration in Earth system models.