Data Driven Parameterization of Mesoscale Eddies and Comparison with Kinetic Energy Backscatter Parameterizations in NEMO Ocean Model

Abstract:

Kinetic energy backscatter (KEB) parameterizations of subgrid 2d turbulence have shown their efficiency in ocean models at eddy-permitting resolution as they restore activity of mesoscale eddies. Modern KEBs utilize only two properties of partly resolved inverse energy cascade: spatial scale of KEB parameterization should be larger than that of the eddy viscosity and amount of returning energy should compensate energy loss due to eddy viscosity. Typical operators used to construct KEB tendency are rather simplest ones such as Laplace operator with negative viscosity coefficient or stochastic process. Application of artificial neural networks (ANN) to approximate subgrid forces may give rise to new KEB models. The main challenge in this direction is to preprocess subgrid forces in such a way to reveal a part corresponding to returning of energy from subgrid scales. In this work, we propose to define subgrid forces as a nudging, which drives coarse-resolution model solution towards high-resolution one. This force is energy-generating and may be approximated with ANN, which uses coarse-resolution fields as inputs. As soon as KEB tendency was generated using ANN, it is adjusted in amplitude as usual energetically-consistent KEBs, which makes computations stable. Conventional KEBs and ANN model are compared in Double-Gyre configuration of NEMO ocean model.

The work was supported by the Russian Foundation for Basic Research (projects 19-35-90023, 18-05-60184) and world-class research center “Moscow Center for Fundamental and Applied Mathematics”.