Project title: Developing trustworthy machine learning parameterisations with uncertainty quantification for climate models
PI: Hannah Christensen
In climate science, a key area of interest is the application of machine learning to subgrid-scale modelling or ‘parameterisations’. Traditional physics-based parameterisations often rely on assumptions and can introduce errors and computational bottlenecks. Machine learning approaches have been explored, but these introduce a source of uncertainty that must be thoroughly understood. This research aims to quantify uncertainties arising from multiple sources with the ultimate aim of revolutionising the next generation of climate models.
