{
  "abstract": "Surface ocean circulation shapes climate, air–sea exchange, and the transport of heat, carbon, and other tracers, yet most long-term global datasets rely primarily on geostrophic balance and therefore miss important ageostrophic motions. Here, we reconstruct global daily sea surface circulation at 0.25° resolution from 1993 to 2023 using a physics-informed framework that integrates satellite altimetry with momentum constraints from geostrophic balance, wind stress, and nonlinear advection (Cui, 2026; https://doi.org/10.5281/zenodo.19966716). The resulting dataset is dynamically consistent, with a mean momentum balance error below 1.5 %, and reduces the median velocity error against independent mooring observations across the tropical Pacific, Atlantic, and Indian Oceans to 0.07 m s⁻¹, compared with 0.20 m s⁻¹ for geostrophic currents and 0.10 m s⁻¹ for Ekman corrected currents. We show that geostrophic flow sets the large-scale circulation, whereas Ekman and nonlinear contributions are smaller but comparable in magnitude to each other. Although nonlinear advection contributes little to relative vorticity, it strongly shapes surface divergence and the fine scale structure of eddy kinetic energy. Our results show that nonlinear ageostrophic flow is an essential component of global surface circulation and that neglecting it limits our ability to resolve surface transport and variability on climatically relevant scales.",
  "abstract_shown": true,
  "affiliations": [
    "State Key Laboratory of Internet of Things for Smart City, Department of Ocean Science and Technology, University of Macau, Macau, China",
    "Center for Ocean Research in Hong Kong and Macau (CORE), Hong Kong, China",
    "State Key Laboratory of Internet of Things for Smart City, Department of Civil and Environmental Engineering, University of Macau, Macau, China",
    "Department of Ocean Science and Engineering and Center for Complex Flows and Soft Matter Research, Southern University of Science and Technology, Shenzhen, China",
    "State Key Laboratory of Internet of Things for Smart City, Department of Computer and Information Science, University of Macau, Macau, China",
    "State Key Laboratory of Physical Oceanography, Institute of Oceanographic Instrumentation, Shandong Academy of Sciences, Qingdao, China",
    "Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "G. Cui"
    },
    {
      "affs": [
        1
      ],
      "name": "G. Cui"
    },
    {
      "affs": [
        2
      ],
      "name": "K.-V. Yuen"
    },
    {
      "affs": [
        0
      ],
      "name": "Y. Chen"
    },
    {
      "affs": [
        1
      ],
      "name": "Y. Chen"
    },
    {
      "affs": [
        3
      ],
      "name": "Z. Liu"
    },
    {
      "affs": [
        4
      ],
      "name": "D. Yang"
    },
    {
      "affs": [
        5
      ],
      "name": "G. Liu"
    },
    {
      "affs": [
        6
      ],
      "name": "G. Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Z. Cai"
    },
    {
      "affs": [
        1
      ],
      "name": "Z. Cai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/113htj0ccpre6.bib",
  "cited_by": 0,
  "doi": "10.5194/essd-18-8149-2026",
  "fulltext_url": null,
  "id": "113htj0ccpre6",
  "issns": [
    "1866-3508",
    "1866-3516"
  ],
  "landing_url": "https://doi.org/10.5194/essd-18-8149-2026",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "GE1-350"
    ],
    [
      "lcc",
      "QE1-996.5"
    ],
    [
      "openalex",
      "1910|Oceanographic and Atmospheric Processes"
    ]
  ],
  "openalex": "W7160988089",
  "pdf_url": "https://essd.copernicus.org/articles/18/8149/2026/essd-18-8149-2026.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": "Copernicus Publications",
  "ris": "https://research.draconitic.com/paper/113htj0ccpre6.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T14:21:06Z",
      "native_id": "5c4c65014d2045eb9f3ab4259c26cb46",
      "source": "doaj",
      "url": "https://doi.org/10.5194/essd-18-8149-2026"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:1910|Oceanographic and Atmospheric Processes",
      "subfield": "oceanography"
    }
  ],
  "title": "Daily global sea surface ageostrophic current dataset from 1993–2023 via physics-informed deep learning",
  "title_text": "Daily global sea surface ageostrophic current dataset from 1993–2023 via physics-informed deep learning",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T14:20:33Z",
  "url": "https://research.draconitic.com/paper/113htj0ccpre6",
  "venue": "Earth System Science Data",
  "versions": []
}