A learned surface roughness scheme for climate prediction

In weather and climate models, momentum, heat, humidity and tracer fluxes between the Earth’s surface and atmosphere strongly depend on surface roughness. The roughness length depends on space and time-dependent surface properties over ocean, sea-ice and land. For example, surface winds impact wave height over sea-ice free oceans; vegetation and orography determine roughness length over land, where its effect on near-surface turbulence strongly impacts the surface fluxes. Here, we present a set of machine learning models trained on reanalysis data to predict surface roughness over both land and ocean grid cells in SpeedyWeather, a Julia-based climate model. More accurately representing the surface roughness has been shown to significantly improve model bias against observations over a range of variables such as surface air temperatures and near-surface wind speed. We explore the downstream impacts of using this parameterisation in the climate model, and test the generalisability of an offline-learned surface roughness scheme in future climates with reduced sea ice and land-use change. Spatial generalisation is achieved through surface roughness being a function of local variables only. We discuss efficient inference on CPU and GPU for every grid cell on each integration time-step. So-called model distillation via symbolic regression minimises the trade-off between speed versus accuracy, enabling another route to rapid inference on a grid-cell basis. Further, we investigate online learning through differentiable physics parameterisations to calibrate the learned parameterisation to surface variables from ERA5 reanalysis. We generally propose machine-learned schemes of individual climate processes towards interpretable, data-driven climate modelling.