By Aakash Sane, Princeton University

Abstract 

The ocean surface boundary layer (OSBL) plays a crucial role in the ocean by modulating the exchange of mass and energy between the atmosphere and ocean interior via vertical turbulent mixing. The processes driving this mixing cannot be resolved in ocean models, necessitating the use of parameterizations that are uncertain. I will describe improvements in an existing energetics based parameterization of vertical mixing for the OSBL in the NOAA-GFDL's model. I will demonstrate how neural networks, trained to predict the eddy diffusivity profile from high-fidelity and expensive turbulence schemes, enhances the mixing scheme in the model. The enhanced scheme reduces biases in the mixed-layer depth and modestly improves the tropical upper ocean stratification in ocean-only global simulations. Interpretable equations that replace neural networks achieve similar improvements at lower computational cost, demonstrating the successful application of machine learning to improve a sub-grid parameterization of turbulent mixing in ocean climate models.