Bathymetry-Aware Mesoscale Eddy Parameterizations across Upwelling Slope Fronts: A Machine Learning–Augmented Approach

Abstract

Abstract Mesoscale eddy buoyancy fluxes across continental slopes profoundly modulate the boundary current dynamics and shelf–ocean exchanges but have yet to be appropriately parameterized via the Gent–McWilliams (GM) scheme in predictive ocean models. In this work, we test the prognostic performance of multiple GM variants in noneddying simulations of upwelling slope fronts that are commonly found along the subtropical continental margins. The tested GM variants range from a set of constant eddy buoyancy diffusivities to recently developed energetically constrained, bathymetry-aware diffusivities, whose implementation is augmented by an artificial neural network (ANN) serving to predict the mesoscale eddy energy based on the topographic and mean flow quantities online. In addition, an ANN is employed to parameterize the cross-slope eddy momentum flux (EMF) that maintains a barotropic flow field analogous to that in an eddy-resolving model. Our tests reveal that noneddying simulations employing the bathymetry-aware forms of the Rhines scale–based scheme and GEOMETRIC scheme can most accurately reproduce the heat contents and along-slope baroclinic transports as those in the eddy-resolving simulations. Further analyses reveal certain degrees of physical consistency in the ANN-inferred eddy energy, which tends to grow (decay) as isopycnal slopes are steepened (flattened), and in the parameterized EMF, which exhibits the correct strength of shaping the flow baroclinicity if a bathymetry-aware GM variant is jointly used. These findings provide a recipe of GM variants for use in noneddying simulations with continental slopes and highlight the potential of machine learning techniques to augment physics-based mesoscale eddy parameterization schemes. Significance Statement This study evaluates the predictive skill of parameterization schemes of water mass transports induced by ocean mesoscale eddies across continental slopes. Correctly parameterizing these transports in noneddying ocean models (e.g., ocean climate models) is crucial for predicting the ocean circulation and shelf–ocean exchanges. This work highlights the importance of bathymetric effects on eddy transports, as parameterization schemes that account for the influence of a sloping seafloor outperform those developed specifically for a flat-bottomed ocean. This work also highlights the efficacy of machine learning techniques to augment physics-based mesoscale eddy parameterization schemes, for instance, by estimating the mesoscale eddy energy online to realize energy-dependent parameterization schemes in noneddying simulations.