Statistical downscaling reproduces high-resolution ocean transport for particle tracking in the Bering Sea

TL;DR

This paper shows that statistical downscaling can accurately recreate detailed ocean transport patterns in the Bering Sea, reducing the need for expensive high-resolution simulations.

Contribution

The study introduces a validated statistical downscaling method for ocean transport modeling that matches high-resolution simulations at lower computational cost.

Findings

Statistically downscaled ocean currents and winds showed strong correlations (r = 0.87 and r = 0.98) with reanalysis models.

Downscaled data produced comparable vorticity, shear, and Lagrangian Cohesive Structures to high-resolution models.

Particle tracking using downscaled data showed consistent dispersal patterns with a mean Bhattacharyya coefficient of 0.720 ± 0.133.

Abstract

Understanding ocean transport is critical for applications ranging from fisheries to chemical plume tracking and carbon dioxide removal modeling. However, available hydrodynamic data often lack the spatial resolution needed for effective transport simulations. We apply statistical downscaling to coarse-resolution ocean reanalysis and atmospheric wind data, reconstructing fine-scale fields validated against high-resolution dynamic models in the Bering Sea. This enables the prediction of transport patterns without the need to run high resolution physics simulations, saving computational costs and time. We examined five years of high-resolution, statistically downscaled ocean currents and surface winds and found that the correlation of ocean current and wind components with GLORYS and ERA5 reanalysis models were r = 0.87 and r = 0.98. The Liu-mean skill score was 0.75 for ocean current velocity. 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 downscaled and reanalysis forcing showed consistent relative separation distances with mean Bhattacharyya coefficient of 0.720 ± 0.133. 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.

The online version contains supplementary material available at 10.1038/s41598-026-37904-1.

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