{
  "abstract": "Mechanistic attribution in Earth system science requires quantifying uncertainty and communicating its implications. Here, we develop an uncertainty-aware mechanistic attribution pipeline that constructs probabilistic fingerprints from ensemble estimates, including covariance-aware separation of competing mechanisms, and an evidence ladder that systematically varies the information provided to language models (LLMs). We evaluate this framework in an idealized stochastic system and a coupled El Niño model using controlled interventions and ensemble data assimilation. In the idealized system, numerical separation increases GPT-4o's intervention-label agreement from 0.80 to 0.98, but not GPT-4o-mini's. In the coupled system, both LLMs identify seven of eight fingerprint-ambiguous events when given full fingerprints with verbal interpretations; GPT-4o's false ambiguity reports decrease from 10/82 to 1/82, without consistent gains in intervention-label agreement. For eight historical ENSO events, the uncertainty-aware condition agrees with the model-inferred mechanism labels in seven cases. These findings distinguish uncertainty assessment from its communication.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.30501",
  "author_total": 2,
  "authors": [
    {
      "name": "Pouria Behnoudfar"
    },
    {
      "name": "Nan Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/awnpr3s9tnm1c.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.30501",
  "fulltext_url": null,
  "id": "awnpr3s9tnm1c",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.30501",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math-ph"
    ],
    [
      "arxiv",
      "math.DS"
    ],
    [
      "arxiv",
      "math.MP"
    ],
    [
      "openalex",
      "2306|Climate variability and models"
    ]
  ],
  "openalex": "W7223319568",
  "pdf_url": "https://arxiv.org/pdf/2610.30501",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/awnpr3s9tnm1c.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:34Z",
      "native_id": "2610.30501",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.30501"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.DS",
      "subfield": "dynamical-systems"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:math-ph",
      "subfield": "mathematical-physics"
    }
  ],
  "title": "When Language Models Misinterpret Uncertainty in Earth System Mechanism Attribution",
  "title_text": "When Language Models Misinterpret Uncertainty in Earth System Mechanism Attribution",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:38Z",
  "url": "https://research.draconitic.com/paper/awnpr3s9tnm1c",
  "venue": "arXiv",
  "versions": []
}