Ernst P. A., Subrahmanyam B., Morel Y., Trott C. B., Chaigneau Alexis. (2023). Subsurface eddy detection optimized with potential vorticity from models in the Arabian Sea. Journal of Atmospheric and Oceanic Technology, 40 (6), p. 677-700. ISSN 0739-0572.
Titre du document
Subsurface eddy detection optimized with potential vorticity from models in the Arabian Sea
Ernst P. A., Subrahmanyam B., Morel Y., Trott C. B., Chaigneau Alexis
Source
Journal of Atmospheric and Oceanic Technology, 2023,
40 (6), p. 677-700 ISSN 0739-0572
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 algo-rithm 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 re -scaled isopycnal potential vorticity (PV) is best for subsurface eddy detection. We proceed to demonstrate that our new al-gorithm 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 subsur-face 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.SIGNIFICANCE STATEMENT: Eddies are a key part of ocean circulation both at the surface and in the subsurface. The purpose of our study was to design the first detection method comprehensively optimized for subsurface eddy detection from numerical simulations. We demonstrate that potential vorticity (PV) is the best field to use when algo-rithmically tracking eddies in subsurface water masses, using our new method to identify and track eddies in the Red Sea Water (RSW). Additionally, our method allows us to efficiently evaluate the dynamics of eddies through potential vorticity diagnostics, exemplified with a previously undescribed eddy near the Chagos Archipelago. Our methodology can be used by future researchers to study the eddy dynamics hidden within subsurface water masses around the world.
