Stochastic data-driven parameterization of unresolved mesoscale eddies

TL;DR

This paper introduces a stochastic, energy-preserving parameterization method for unresolved mesoscale eddies in ocean models, improving the simulation of large-scale circulation and variability.

Contribution

It presents a novel stochastic framework based on physical principles that conserves energy and enhances ocean model accuracy by calibrating noise from simulation data.

Findings

Energy conservation in stochastic eddy representation

Improved simulation of eastward jets in double-gyre circulation

Enhanced low-frequency variability in large-scale currents

Abstract

In this work, a stochastic representation based on a physical transport principle is proposed to account for mesoscale eddy effects on the large-scale oceanic circulation. This stochastic framework arises from a decomposition of the Lagrangian velocity into a smooth-in-time component and a highly oscillating noise term. One important characteristic of this random model is that it conserves the total energy of the resolved flow for any realization. Such an energy-preserving representation is successfully implemented in a well established multi-layered quasi-geostrophic dynamical core. The empirical spatial correlation of the unresolved noise is calibrated from the eddy-resolving simulation data. In particular, a stationary correction drift can be introduced in the noise through Girsanov transformation. This non intuitive term appears to be important in reproducing on a coarse mesh the eastwards jet of the wind-driven double-gyre circulation. In addition, a projection method has been proposed to constrain the noise living along the iso-surfaces of the vertical stratification. The resulting noise enables us to improve the intrinsic low-frequency variability of the large-scale current.