{
  "abstract": "Climate models have traditionally been developed in Fortran, due to its long-standing use in scientific computing and its excellent computational performance. While Python offers substantial advantages in terms of code readability, maintainability, and the availability of libraries and tools, the performance gap between Python and Fortran has historically limited Python's use in large-scale climate modeling. This performance gap can be mitigated by using the JAX library, which significantly improves execution speed of Python code. We use JAX as a backend for Veris, a new sea ice model implemented in Python. Veris builds upon the Fortran-based sea ice component of the general circulation model MITgcm. Benchmark experiments show that Veris exhibits scaling behavior with increasing process counts comparable to the Fortran reference implementation. For small CPU process counts, Veris outperforms the MITgcm, showing the great potential that JAX has for climate modeling, particularly as further improvements in inter-process communication are anticipated. When executed on a high-end GPU, a single-process Veris simulation matches the performance of the parallelized Fortran reference running on hundreds of CPU cores, but at a fraction of the energy cost. These results demonstrate the potential of Veris for large-scale HPC-based simulations.",
  "abstract_shown": true,
  "affiliations": [
    "Alfred-Wegener-Institut, Helmholtz Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany",
    "Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jan P. Gärtner",
      "orcid": "0009-0008-8460-7358"
    },
    {
      "affs": [
        0
      ],
      "name": "Martin Losch",
      "orcid": "0000-0002-3824-5244"
    },
    {
      "affs": [
        0
      ],
      "name": "Suvarchal K. Cheedela"
    },
    {
      "affs": [
        1
      ],
      "name": "Markus Jochum"
    },
    {
      "affs": [
        1
      ],
      "name": "Roman Nuterman",
      "orcid": "0000-0003-1774-3460"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/86rp6m9w8v875.bib",
  "cited_by": 0,
  "doi": "10.5194/gmd-19-5225-2026",
  "fulltext_url": "https://gmd.copernicus.org/articles/19/5225/2026/",
  "id": "86rp6m9w8v875",
  "issns": [
    "1991-959X",
    "1991-9603"
  ],
  "landing_url": "https://doi.org/10.5194/gmd-19-5225-2026",
  "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",
      "QE1-996.5"
    ],
    [
      "openalex",
      "1702|Computational Physics and Python Applications"
    ]
  ],
  "openalex": "W4407429975",
  "pdf_url": "https://gmd.copernicus.org/articles/19/5225/2026/gmd-19-5225-2026.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-01",
  "publisher": "Copernicus Publications",
  "ris": "https://research.draconitic.com/paper/86rp6m9w8v875.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T14:21:03Z",
      "native_id": "0ab9fc641c5640589ee2f42e5218dfc8",
      "source": "doaj",
      "url": "https://doi.org/10.5194/gmd-19-5225-2026"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "lcc:QE1-996.5",
      "subfield": "geology-geochemistry"
    }
  ],
  "title": "Veris: fast & efficient sea-ice modeling in Python with GPU acceleration",
  "title_text": "Veris: fast & efficient sea-ice modeling in Python with GPU acceleration",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T14:20:32Z",
  "url": "https://research.draconitic.com/paper/86rp6m9w8v875",
  "venue": "Geoscientific Model Development",
  "versions": []
}