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Mesoscale eddies are poorly represented in climate ocean models, andtherefore their effects on the large scale circulation must be parameterized.Classical parameterizations, which represent the bulk effect of the unresolvededdies, can be improved with new subgrid models learned directly from data.Zanna and Bolton (2020) (ZB20) applied an equation-discovery algorithm toreveal an interpretable expression parameterizing the subgrid mesoscale fluxesthrough the components of the velocity-gradient tensor. In this work, weimplement the ZB20 parameterization into the primitive-equation GFDL MOM6 oceanmodel and test it in two idealized configurations with significantly differentstratification and topography. In addition, we propose an approach based onspatial filtering to improve the representation of large-scale energybackscatter and numerical properties of the parameterization. The ZB20parameterization led to improved climatological mean flow and energydistributions, compared to the current state-of-the-art energy backscatterparameterizations. The ZB20 is scale-aware and can be used with a single valueof the non-dimensional scaling coefficient for a range of resolutions. Thesuccessful application of the ZB20 to parameterize mesoscale eddies in twoidealized configurations offers a promising opportunity to reduce long-standingbiases in global ocean simulations in future studies.

Implementation of a data-driven equation-discovery mesoscale parameterization into an ocean model