Separating Epistemic and Aleatoric Uncertainties in Weather and Climate Models

Representing and quantifying uncertainty in physical parameterisations is a central challenge in weather and climate modelling, and approaches are often developed separately for different timescales. Here, we consider the separation of uncertainty by source using machine learning frameworks for subgrid-scale parameterisations. In this context, aleatoric uncertainty arises from internal variability in the training data, and epistemic uncertainty, arises from poorly constrained parameters during training. Using the Lorenz 1996 system as a testbed for simplified chaotic dynamics, we deal with uncertainties through a unified framework using Bayesian Neural Networks, to explore how the different sources of uncertainty evolve over different prediction timescales.