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Preprints, Working Papers, ... Year : 2022

Stochastic data-driven parameterization of unresolved mesoscale eddies

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.
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hal-03727820 , version 1 (19-07-2022)

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  • HAL Id : hal-03727820 , version 1

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Long Li, Bruno Deremble, Noé Lahaye, Etienne Mémin. Stochastic data-driven parameterization of unresolved mesoscale eddies. 2022. ⟨hal-03727820⟩
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