{
  "abstract": "Machine-learning-based parameterizations (i.e. representation of sub-grid processes) of global climate models have recently been proposed as a powerful alternative to physical, but empirical, representations, offering a lower computational cost and higher accuracy. Yet, those approaches still suffer from a lack of generalization and extrapolation beyond the high-fidelity training data, which is however critical to projecting climate change. Here we show that a multi-fidelity approach can provide the best of both worlds: it generalizes to a warmer climate for which high fidelity data are not available, guided by abundant low-fidelity data from an even warmer climate, while retaining the accuracy of high-fidelity data, by learning a nonlinear mapping from low-to high-fidelity. In an application to climate modeling, the multi-fidelity framework yields more accurate climate projections without requiring major increase in computational resources, while providing uncertainty estimates that increase coherently with prediction error. Even though the multi-fidelity model was only tested offline, our approach paves the way for the use of machine-learning based methods that can optimally leverage historical observations or high-fidelity simulations and extrapolate to unseen regimes such as warmer climates.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Blanka Balogh"
    },
    {
      "name": "Mohamed Aziz Bhouri"
    },
    {
      "name": "Liran Peng"
    },
    {
      "name": "Michael S Pritchard"
    },
    {
      "name": "Pierre Gentine"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d993sww91hcn4.bib",
  "cited_by": 0,
  "doi": "10.1088/3049-4753/aea999",
  "fulltext_url": null,
  "id": "d993sww91hcn4",
  "issns": [
    "3049-4753",
    "3049-4753"
  ],
  "landing_url": "https://meteofrance.hal.science/meteo-05796490v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.math"
    ],
    [
      "hal",
      "0.phys"
    ],
    [
      "openalex",
      "2306|Climate variability and models"
    ]
  ],
  "openalex": "W7213597378",
  "pdf_url": "https://meteofrance.hal.science/meteo-05796490/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d993sww91hcn4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:55Z",
      "native_id": "meteo-05796490",
      "source": "hal",
      "url": "https://meteofrance.hal.science/meteo-05796490v1"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2306|Climate variability and models",
      "subfield": "climate"
    }
  ],
  "title": "Probabilistic multi-fidelity climate model parameterization for better generalization",
  "title_text": "Probabilistic multi-fidelity climate model parameterization for better generalization",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:55Z",
  "url": "https://research.draconitic.com/paper/d993sww91hcn4",
  "venue": "Machine Learning: Earth",
  "versions": []
}