Subsurface Eddy Detection Optimized with Potential Vorticity from Models in the Arabian Sea

Coherent ocean vortices, or eddies, are usually tracked on the surface of the ocean. However, tracking subsurface eddies is important for a complete understanding of deep ocean circulation. In this study, we develop an algorithm designed for the detection of subsurface eddies in the Arabian Sea using Nucleus for European Modelling of the Ocean (NEMO) model simulations. We optimize each parameter of our algorithm to achieve favorable results when compared with an algorithm using sea surface height (SSH). When compared to similar methods, we find that using the rescaled isopycnal potential vorticity (PV) is best for subsurface eddy detection. We proceed to demonstrate that our new algorithm can detect eddies successfully between specific isopycnals, such as those that define the Red Sea Water (RSW). In doing so, we showcase how our method can be used to describe the properties of eddies within the RSW and even identify specific long-lived subsurface eddies. We conduct one such case study by discerning the structure of a completely subsurface RSW eddy near the Chagos Archipelago using Lagrangian particle tracking and PV diagnostics. We conclude that our rescaled PV method is an efficient tool for investigating eddy dynamics within the ocean’s interior, and publicly provide our optimization methodology as a way for other researchers to develop their own subsurface detection algorithms with optimized parameters for any spatiotemporal model domain.