Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 18, issue 11
Articles | Volume 18, issue 11
https://doi.org/10.5194/essd-18-8149-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-8149-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 11
Data description article
 | 
06 Nov 2026
Data description article |  | 06 Nov 2026

Daily global sea surface ageostrophic current dataset from 1993–2023 via physics-informed deep learning

Guangxi Cui, Ka-Veng Yuen, Ying Chen, Zhiqiang Liu, Dingqi Yang, Guangliang Liu, and Zhongya Cai

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Short summary
Most long-term satellite-based global current datasets capture only large-scale flows driven by pressure differences, missing important nonlinear motions. We used satellite data and a physics-informed deep learning method to create a new global dataset, including these previously missing components. Our currents are more accurate than traditional products, and we show that motions driven by nonlinear effects strongly influence the transport of heat, carbon, and nutrients in ocean.
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