Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations

Uncertainties in ocean-mixing parameterizations are primary sources for ocean and climate modeling biases.Due to lack of process understanding,traditional physics-driven parameterizations perform unsatisfactorily in the tropics.Recent advances in the deep-learning method and the new availability of long-term turbulence measurements provide an opportunity to explore data-driven approaches to parameterizing oceanic vertical-mixing processes.Here,we describe a novel parameterization based on an artificial neural network trained using a decadal-long time record of hydrographic and turbulence observations in the tropical Pacific.This data-driven parameterization achieves higher accuracy than current parameterizations,demonstrating good generalization ability under physical constraints.When integrated into an ocean model,our parameterization facilitates improved simulations in both ocean-only and coupled modeling.As a novel application of machine learning to the geophysical fluid,these results show the feasibility of using limited observations and well-understood physical constraints to construct a physics-informed deep-learning parameterization for improved climate simulations.