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

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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.

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Acknowledgements

We acknowledge the World Climate Research Program’s Working Group on Coupled Modelling, which is responsible for the CMIP. We thank Dr. Tom Hurst at AFSC, NOAA for reviewing the manuscript.

Funding

MB received funding from the European Union’s Horizon Europe Research and Innovation Programme under grant agreement No 101060072 (ACTNOW).

Author information

Authors and Affiliations

  1. Farallon Institute, Petaluma, CA, USA

    Trond Kristiansen

  2. Actea Inc, San Francisco, CA, USA

    Trond Kristiansen & Jordan Miller

  3. CMCC Foundation – Euro-Mediterranean Center on Climate Change, Bologna, Italy

    Momme Butenschön

Authors

  1. Trond Kristiansen
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  2. Jordan Miller
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  3. Momme Butenschön
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Contributions

We declare that all authors have contributed to the preparation of the manuscript. TK and JM prepared the downscaled data, analyzed the data, prepared the figures, and wrote the manuscript. MB wrote the manuscript, contributed ideas, and analyzed the data.

Corresponding author

Correspondence to
Trond Kristiansen.

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Competing interests

TK and JM are co-founders of Actea Inc, which performed the downscaling of the data, and did the analysis. The statistical downscaling implementation is the patent-pending intellectual property of Actea Inc, while the methodology is fully available in a peer-review publication. All other authors declare that they have no competing interests.

Data availability

The GLORYS12V1 and ERA5 datasets are available through the Copernicus Marine Service Center (marine.copernicus.eu), the statistically downscaled CMCC-CM2-SR5 dataset will be made available upon request , and the code used to create the figures in this manuscript is available on Github: https://github.com/Actea-Earth/downscaled_currents. The datasets were distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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Kristiansen, T., Miller, J. & Butenschön, M. Statistical downscaling reproduces high-resolution ocean transport for particle tracking in the Bering Sea.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-37904-1

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  • DOI: https://doi.org/10.1038/s41598-026-37904-1

Keywords

  • Statistical downscaling
  • Lagrangian particle tracking
  • Bering Sea ocean currents
  • Mesoscale ocean dynamics
  • Ocean reanalysis

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