Impact of Parameterized Isopycnal Diffusivity on Shelf‐Ocean Exchanges Under Upwelling‐Favorable Winds: Offline Tracer Simulations Augmented by Artificial Neural Network

Abstract

AbstractIsopycnal eddy mixing across continental slopes profoundly modulates the ocean circulation and biogeochemistry. Yet this process must be parameterized in coarse‐resolution ocean models via an isopycnal eddy diffusivity prescribed with the Redi scheme. In this work, we evaluate the skill of physics‐based and data‐driven Redi variants in predicting the cross‐slope exchanges using a suite of offline‐mode parameterized tracer simulations for wind‐driven upwelling continental slope fronts, which commonly arise around the margins of subtropical gyres. The tested physics‐based Redi variants range from a constant eddy diffusivity to a recently proposed, bathymetry‐aware diffusivity augmented by the artificial neural network (ANN) that infers the mesoscale eddy kinetic energy from the mean flow and topographic quantities. Moreover, a purely data‐driven eddy diffusivity is learned by the ANN from the output data set of an eddy‐resolving model, whose solutions serve as the ground truth against which the parameterized tracer simulations are compared. Among all tested Redi variants, the ANN‐learned diffusivity and the bathymetry‐aware diffusivity outperform others in reproducing the tracer solutions of the eddy‐resolving model. However, a physics‐based Redi variant with local deficiencies can introduce global errors in the predicted tracer distribution, which calls for ongoing efforts in constraining the shelf‐to‐ocean transition of the isopycnal eddy diffusivity. A purely data‐driven diffusivity can nearly reproduce the diagnosed diffusivity from the eddy‐resolving model, which highlights the efficacy of machine learning techniques for parameterizing eddy processes across steep topography. This work serves as a key step toward parameterizing the isopycnal eddy mixing in ocean models with continental slopes.