Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks

Abstract

Vertical mixing parameterizations in ocean models are formulated on the basis of the physical principles that govern turbulent mixing. However, many parameterizations include ad hoc components that are not well constrained by theory or data. One such component is the eddy diffusivity model, where vertical turbulent fluxes of a quantity are parameterized from a variable eddy diffusion coefficient and the mean vertical gradient of the quantity. In this work, we improve a parameterization of vertical mixing in the ocean surface boundary layer by enhancing its eddy diffusivity model using data-driven methods, specifically neural networks. The neural networks are designed to take extrinsic and intrinsic forcing parameters as input to predict the eddy diffusivity profile and are trained using output data from a second moment closure turbulent mixing scheme. The modified vertical mixing scheme predicts the eddy diffusivity profile through online inference of neural networks and maintains the conservation principles of the standard ocean model equations, which is particularly important for its targeted use in climate simulations. We describe the development and stable implementation of neural networks in an ocean general circulation model and demonstrate that the enhanced scheme outperforms its predecessor by reducing biases in the mixed-layer depth and upper ocean stratification. Our results demonstrate the potential for data-driven physics-aware parameterizations to improve global climate models.

References in corpus (6)

• Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
• Are General Circulation Models obsolete?
• Interpreting and Stabilizing Machine-learning Parametrizations of Convection
• A posteriori learning for quasi-geostrophic turbulence parametrization
• Deep learning of systematic sea ice model errors from data assimilation increments
• Variability as a better characterization of Shannon entropy

Cited by in corpus (6)

• Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
• Machine learning for online sea ice bias correction within global ice-ocean simulations
• Samudra: An AI Global Ocean Emulator for Climate
• A stable implementation of a data-driven scale-aware mesoscale parameterization
• Advancing global sea ice prediction capabilities using a fully-coupled climate model with integrated machine learning
• NORi: An ML-Augmented Ocean Boundary Layer Parameterization