Dispersal tracking in the future ocean combining statistical downscaling and dynamic modeling approaches

Understanding transport of substances and particles in the ocean has applications from search and rescue to fisheries biology and chemical plume tracking, including carbon dioxide removal modeling. Such movement requires hydrodynamic fields at sufficiently fine spatial scales. Often, available hydrodynamic fields for periods of interest are too coarse to model transport effectively. This study demonstrates that historic statistical relationships between coarse and fine scale hydrodynamic fields can recreate fine scale fields over novel temporal domains with sufficient fidelity to simulate transport comparable to direct fine scale field simulations. This enables understanding transport patterns without running high resolution physics simulations, saving computational costs and time. We examined five years of high-resolution, statistically downscaled ocean currents and surface winds, correlating them with GLORYS and ERA5 reanalysis models (r = 0.87 and r = 0.98). Okubo–Weiss analyses showed comparable vorticity and shear between downscaled and dynamical models. The Finite-time Layupanov Exponent analysis showed consistent Lagrangian Cohesive Structures across datasets. Multi-year particle tracking using both approaches showed consistent relative separation distances. The demonstrated parity in dispersal patterns indicates statistically downscaled approaches can substitute dynamical models for large-scale applications. Future work should validate these results across diverse oceanographic regimes and incorporate biogeochemical feedback mechanisms.