Uncertainty Quantification of Machine Learning Parameterisations

Machine learning (ML) parameterisations for climate models are emerging as a promising approach for capturing subgrid-scale processes, which are not explicitly resolved in climate models due to limitations on resolution. These ML parameterisations are typically trained on datasets generated by high resolution climate models or existing parameterisations (“offline”), but evaluated based on their performance when coupled into an existing climate model (“online”). Quantifying uncertainties associated with ML parameterisations is crucial for gaining insights into the reliability of hybrid ML-climate models.

I will discuss uncertainties associated with an ML parameterisation for atmospheric GWs, focusing on the parametric uncertainties which originate during the training process. I will show how these can propagate when coupled online, becoming a significant source of uncertainty in climate model circulation that we must consider carefully when building ML parameterisations.