{
  "abstract": "El Niño-Southern Oscillation (ENSO) is the primary source of climate predictability on seasonal and longer timescales over much of the globe, but predicting its phase beyond 9 months has remained challenging. Here, we leverage an underappreciated factor in ENSO forecasting: the substantial variation in forecast skill based on the ENSO phase at the time a forecast is issued. Our analysis of transitions between ENSO phases in a dynamical hindcast dataset demonstrates that this variation markedly exceeds intermodel differences in predictive capability. Forecasts initialized during La Niña conditions perform best, with three models correctly predicting transitions to El Niño 1 year in advance over 70% of the time. We present our findings in a simple framework that allows decision-makers to assess the likelihood of a forecast being correct on the basis of the current ENSO state. Applied effectively, these findings could improve long-lead ENSO forecasting strategies, enhancing global risk management.",
  "abstract_shown": true,
  "affiliations": [
    "Natural Systems Modelling Group, Technology, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney 2145, Australia.",
    "School of Mathematics and Statistics, University of New South Wales, Sydney 2052, Australia.",
    "School of Geography, Earth and Atmospheric Sciences, University of Melbourne, Melbourne 3053, Australia.",
    "ARC Centre of Excellence for the Weather of the 21st Century, University of Melbourne, Melbourne 3053, Australia.",
    "Climate Variability and Hazards, Environment, CSIRO, Hobart 7004, Australia.",
    "ACCESS-NRI, Australian National University, Canberra 2601, Australia."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Nandini Ramesh",
      "orcid": "0000-0002-9538-2042"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Mandy B. Freund",
      "orcid": "0000-0002-8839-5494"
    },
    {
      "affs": [
        4
      ],
      "name": "Damien B. Irving",
      "orcid": "0000-0003-1258-5002"
    },
    {
      "affs": [
        5
      ],
      "name": "Dougal T. Squire",
      "orcid": "0000-0003-3271-6874"
    },
    {
      "affs": [
        4
      ],
      "name": "Carly R. Tozer",
      "orcid": "0000-0001-8605-5907"
    },
    {
      "affs": [
        4
      ],
      "name": "Didier P. Monselesan",
      "orcid": "0000-0002-0310-8995"
    },
    {
      "affs": [
        4
      ],
      "name": "Tess Parker",
      "orcid": "0000-0003-4201-3929"
    },
    {
      "affs": [
        4
      ],
      "name": "Annette Stellema",
      "orcid": "0000-0001-8370-2662"
    },
    {
      "affs": [
        4
      ],
      "name": "James S. Risbey",
      "orcid": "0000-0003-3202-9142"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5mjfsz18trt75.bib",
  "cited_by": 0,
  "doi": "10.1126/sciadv.aeg8654",
  "fulltext_url": "https://europepmc.org/articles/PMC13693681",
  "id": "5mjfsz18trt75",
  "issns": [
    "2375-2548"
  ],
  "landing_url": "https://www.science.org/doi/10.1126/sciadv.aeg8654",
  "language": "eng",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2306|Climate variability and models"
    ]
  ],
  "openalex": "W7223401788",
  "pdf_url": "https://europepmc.org/articles/PMC13693681?pdf=render",
  "pmcid": "PMC13693681",
  "pmid": "42912991",
  "published": "2026-10-23",
  "publisher": "American Association for the Advancement of Science (AAAS)",
  "ris": "https://research.draconitic.com/paper/5mjfsz18trt75.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:41Z",
      "native_id": "10.1126/sciadv.aeg8654",
      "source": "crossref",
      "url": "https://www.science.org/doi/10.1126/sciadv.aeg8654"
    },
    {
      "first_seen": "2026-10-30T12:20:03Z",
      "native_id": "MED:42912991",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42912991"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2306|Climate variability and models",
      "subfield": "climate"
    }
  ],
  "title": "Using initial states to identify successful El Niño–Southern Oscillation forecasts 1 year in advance",
  "title_text": "Using initial states to identify successful El Niño–Southern Oscillation forecasts 1 year in advance",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:03Z",
  "url": "https://research.draconitic.com/paper/5mjfsz18trt75",
  "venue": "Science Advances",
  "versions": []
}