(Machine) Learning Multiscale Ocean Interaction

The oceans are a crucial component of the climate system. They store and redistribute most of the excess heat from anthropogenic emissions. Climate simulations have been essential for understanding and predicting ocean warming. However, uncertainty remains regarding the causes and pace of future ocean warming due to inadequate representations of unresolved processes, such as ocean turbulence or clouds, in global climate models. In this talk, I will discuss the potential for machine learning to accelerate the discovery of physics principles and governing equations for multiscale climate processes such as turbulence. I will show examples of how these discoveries can help improve climate models and the simulations of ocean heat transport. I will discuss the promise and challenges for climate science and modeling in the age of data, computing, and artificial intelligence.