{
  "abstract": "Accurate and physically realistic ocean forecasting at high spatial and temporal resolution is essential for resolving fine-scale dynamical features, with profound implications for hazard prediction, maritime navigation, and sustainable ocean management. Conventional numerical models can produce sub-daily, eddy-resolving forecasts but require substantial computational resources and often struggle to maintain predictive skill at such fine scales. Deep learning offers a promising alternative with significantly higher computational efficiency. However, most existing models operate at daily resolution, averaging out sub-daily variability and thus struggling to learn the complex and diverse diurnal cycle characteristics across different variables and depth layers. This limits their ability to capture rapidly evolving ocean processes, and they remain heavily dependent on atmospheric forcing from numerical weather prediction (NWP) models. Here, we introduce FuXi-Ocean, a global deep learning-based ocean forecasting model that generates 6-hourly predictions at 1/12 ∘ mesoscale-eddy-resolving resolution, extending to 1500 m depth. Unlike previous approaches, FuXi-Ocean operates independently of NWP forecasts, using near-surface atmospheric variables only for initialization, thereby establishing a fully independent, coupled ocean-atmosphere prediction framework. The model adaptively integrates forecasts across multiple temporal windows, capturing variable-specific evolution rates in ocean dynamics, while effectively addressing the technical challenge of cumulative error amplification in autoregressive forecasting. Through its innovative architectural design, FuXi-Ocean maintains stable predictive performance despite requiring four times more autoregressive steps than daily-resolution models. Based on our evaluation for 2022, FuXi-Ocean demonstrates improved performance over the latest deep learning-based GOFS (WenHai) in 10-day forecasts of temperature, salinity, zonal and meridional currents, sea surface temperature, and sea level anomaly. By advancing global ocean forecasts from daily to sub-daily resolution through independent deep learning-based ocean-atmospheric coupling, FuXi-Ocean represents a significant step forward in high-resolution ocean prediction.",
  "abstract_shown": true,
  "affiliations": [
    "School of Marine Science and Technology, Tianjin University, Tianjin, China",
    "Shanghai Academy of Artificial Intelligence for Science, Shanghai, China",
    "Artificial Intelligence Innovation and Incubation Institute , Fudan University, Shanghai, China",
    "Shanghai Innovation Institute, Shanghai, China",
    "FuXi Intelligent Computing Technology Co. Ltd, Shanghai, China",
    "Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai, China",
    "Institute for Big Data, Fudan University, Shanghai, China",
    "MOE Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, China",
    "School of Data Science, Fudan University, Shanghai, China",
    "Yazhou Bay Innovation Institute, College of Marine Science and Technology, Hainan Tropical Ocean University, Sanya, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Yuan Niu"
    },
    {
      "affs": [
        2,
        1,
        3
      ],
      "name": "Qiusheng Huang"
    },
    {
      "affs": [
        2,
        4
      ],
      "name": "Xiaohui Zhong",
      "orcid": "0000-0002-7506-1276"
    },
    {
      "affs": [
        5
      ],
      "name": "Anboyu Guo",
      "orcid": "0009-0006-1188-9329"
    },
    {
      "affs": [
        2,
        1
      ],
      "name": "Lei Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Dianjun Zhang"
    },
    {
      "affs": [
        5
      ],
      "name": "Z Zhou",
      "orcid": "0009-0001-7321-9869"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaoyan Jia"
    },
    {
      "affs": [
        6,
        7,
        8
      ],
      "name": "Libo Wu",
      "orcid": "0000-0002-3953-526X"
    },
    {
      "affs": [
        5
      ],
      "name": "Renhe Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiawei Qi",
      "orcid": "0000-0001-8379-3293"
    },
    {
      "affs": [
        9
      ],
      "name": "Xuefeng Zhang"
    },
    {
      "affs": [
        2,
        4,
        1,
        3
      ],
      "name": "Hui Li",
      "orcid": "0000-0001-9198-3951"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/12sr3wtra5caz.bib",
  "cited_by": 8,
  "doi": "10.1038/s41612-026-01444-2",
  "fulltext_url": null,
  "id": "12sr3wtra5caz",
  "issns": [
    "2397-3722"
  ],
  "landing_url": "https://doi.org/10.1038/s41612-026-01444-2",
  "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": [
    [
      "openalex",
      "1910|Oceanographic and Atmospheric Processes"
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  "openalex": "W7162855188",
  "pdf_url": "https://www.nature.com/articles/s41612-026-01444-2_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-05-30",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/12sr3wtra5caz.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:21:01Z",
      "native_id": "W7162855188",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s41612-026-01444-2"
    }
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  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:1910|Oceanographic and Atmospheric Processes",
      "subfield": "oceanography"
    }
  ],
  "title": "A deep learning global ocean forecasting model with sub-daily and eddy-resolving resolution",
  "title_text": "A deep learning global ocean forecasting model with sub-daily and eddy-resolving resolution",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T13:21:01Z",
  "url": "https://research.draconitic.com/paper/12sr3wtra5caz",
  "venue": "npj Climate and Atmospheric Science",
  "versions": []
}