2023 Spring Lectures in Climate Data Science

Physics to Machine Learning and Machine Learning Back to Physics
Over the last couple of years, we have witnessed an explosion in the use of machine learning for Earth system science applications ranging from Earth monitoring to modeling. Machine learning has shown tremendous success in emulating complex physics such as atmospheric convection or terrestrial carbon and water fluxes using satellite or high-fidelity simulations in a supervised framework. However, machine learning, especially deep learning, is opaque (the so-called black box issue) and thus a question remains: what (new) understanding have we really developed?
I will here illustrate the value of machine learning to understand or discover new processes in climate, with an application to rainfall organization. I will also present new tools merging causal discovery and machine learning to improve the trustworthiness and interpretability of machine learning for climate and physics applications.