  Mesoscale eddies are poorly represented in climate ocean models, and
therefore their effects on the large scale circulation must be parameterized.
Classical parameterizations, which represent the bulk effect of the unresolved
eddies, can be improved with new subgrid models learned directly from data.
Zanna and Bolton (2020) (ZB20) applied an equation-discovery algorithm to
reveal an interpretable expression parameterizing the subgrid mesoscale fluxes
through the components of the velocity-gradient tensor. In this work, we
implement the ZB20 parameterization into the primitive-equation GFDL MOM6 ocean
model and test it in two idealized configurations with significantly different
stratification and topography. In addition, we propose an approach based on
spatial filtering to improve the representation of large-scale energy
backscatter and numerical properties of the parameterization. The ZB20
parameterization led to improved climatological mean flow and energy
distributions, compared to the current state-of-the-art energy backscatter
parameterizations. The ZB20 is scale-aware and can be used with a single value
of the non-dimensional scaling coefficient for a range of resolutions. The
successful application of the ZB20 to parameterize mesoscale eddies in two
idealized configurations offers a promising opportunity to reduce long-standing
biases in global ocean simulations in future studies.
