Leveraging Interpretable Machine Learning for Climate Physics

In this presentation, I will describe the complex and multiscale nature of the climate system and how machine learning can be leveraged to deepen our understanding of key physical climate processes. I will focus on advances in interpretable and physics-aware machine learning methods that have the potential to accelerate scientific discovery in climate physics and modeling. In particular, I will discuss examples of interpretable and generalizable machine learning models that capture ocean turbulence processes (horizontal scale of 10 km-100 km) and how these turbulent features can impact large-scale ocean currents (1000’s of kms). The machine-learned models of turbulent processes are shown to improve coarse-resolution climate simulations by faithfully capturing the complex multiscale dynamical properties in the climate system.