Multi-year predictability of hydrography and circulation on the  U.S. Northeast Shelf: a dynamical downscaling perspective

Guo, Yiming; Castillo-Trujillo, Alma Carolina; Chen, Ke; Kwon, Young-Oh; Perkins, Sydney; Seo, Hyodae; Fratantoni, Paula; Alexander, Michael; Saba, Vincent

doi:10.5194/os-22-3693-2026

Articles | Volume 22, issue 6

https://doi.org/10.5194/os-22-3693-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/os-22-3693-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 6

Research article

|

13 Nov 2026

Research article |

| 13 Nov 2026

Multi-year predictability of hydrography and circulation on the U.S. Northeast Shelf: a dynamical downscaling perspective

Yiming Guo, Alma Carolina Castillo-Trujillo, Ke Chen, Young-Oh Kwon, Sydney Perkins, Hyodae Seo, Paula Fratantoni, Michael Alexander, and Vincent Saba

Data sets

Global Ocean Gridded L4 Sea Surface Heights And Derived Variables Reprocessed 1993 Ongoing CMEMS https://doi.org/10.48670/moi-00148

Global Ocean Physics Reanalysis CMEMS https://doi.org/10.48670/moi-00021

Data for "Assessing the Multi-Year Predictability of Ocean Conditions on the U.S. Northeast Shelf Using Dynamical Downscaling" Yiming Guo et al. https://doi.org/10.5281/zenodo.20115945

Short summary

The U.S. Northeast Shelf supports productive fisheries, but global models struggle to predict its ocean conditions years in advance due to poor representation of shelf processes. Using a high-resolution ocean model downscaled from a global system, we show that dynamical downscaling reduces biases and improves multi-year prediction skill by better resolving shelf-slope processes. This work demonstrates how downscaling translates large-scale climate information into actionable coastal predictions.