{
  "abstract": "This work investigates the incidence of rapid ice-loss events in the Arctic, in which a substantial fraction of September sea-ice is lost in only a few years. Such events are seen to occur in climate model simulations forced with increasing greenhouse gas concentrations, but not as yet in the relatively short observational record. We train a convolutional neural network (CNN) using six large climate model ensembles to diagnose vulnerability to rapid ice-loss events. The trained model is shown to separate higher and lower risk states in out-of-sample climate models via a leave-one-out cross-validation. The model emphasises the role of persistently thin sea-ice, remaining sea-ice extent and sustained high upper ocean heat content as predictors of rapid ice-loss. The CNN is applied to observational data over the satelite-era showing a signal for rapid ice-loss prior to a drop around 2007 and an increasing risk from around 2015 to present. Such rapid-ice loss events would likely have dramatic consequences for Arctic ecosystems, coastal communities and global shipping.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.16584",
  "author_total": 4,
  "authors": [
    {
      "name": "Matthew Patterson"
    },
    {
      "name": "Ioana Colfescu"
    },
    {
      "name": "Nathan Kirk"
    },
    {
      "name": "Graeme MacGhilchrist"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cs6wj82qw8b7w.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.16584",
  "fulltext_url": null,
  "id": "cs6wj82qw8b7w",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.16584",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.ao-ph"
    ],
    [
      "openalex",
      "1902|Arctic and Antarctic ice dynamics"
    ]
  ],
  "openalex": "W7221801640",
  "pdf_url": "https://arxiv.org/pdf/2610.16584",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cs6wj82qw8b7w.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:06Z",
      "native_id": "2610.16584",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.16584"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    }
  ],
  "title": "Identifying Arctic states vulnerable to rapid ice loss with a convolutional neural network",
  "title_text": "Identifying Arctic states vulnerable to rapid ice loss with a convolutional neural network",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:06Z",
  "url": "https://research.draconitic.com/paper/cs6wj82qw8b7w",
  "venue": "arXiv",
  "versions": []
}