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

ABSTRACT: 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. W

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC9385460 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature