Uncertainty Quantification of a Machine Learning Subgrid-Scale Parameterization for Atmospheric Gravity Waves

Subgrid-scale processes, such as atmospheric gravity waves, play a pivotal role in shaping the Earth’s climate but cannot be explicitly resolved in climate models due to limitations on resolution. Instead, subgrid-scale parameterizations are used to capture their effects. Recently, machine learning has emerged as a promising approach to learn parameterizations. In this study, we explore uncertainties associated with a machine learning parameterization for atmospheric gravity waves. Focusing on the uncertainties in the training process (parametric uncertainty), we use an ensemble of neural networks to emulate an existing gravity wave parameterization. We estimate both offline uncertainties in raw neural network output and online uncertainties in climate model output, after the neural networks are coupled. We find that online parametric uncertainty contributes a significant source of uncertainty in climate model output that must be considered when introducing neural network parameterizations. This uncertainty quantification provides valuable insights into the reliability and robustness of machine learning-based gravity wave parameterizations, thus advancing our understanding of their potential applications in climate modeling.