{
  "abstract": "The rapid expansion of solar and wind energy across the United States is increasing the demand for reliable seasonal forecasts to guide energy planning and grid operations. Here we show high seasonal prediction skill for springtime solar energy resources over the southwestern United States using a state-of-the-art seasonal forecast system. We identify two leading sources of solar predictability: a dominant component linked to primary El Niño-Southern Oscillation conditions, and a secondary component associated with El Niño-Southern Oscillation diversity and tropical Atlantic variability. Together, these components explain the spatial structure of forecast skill and its underlying physical drivers. State-level evaluations show robust solar prediction skill in high-resource states. The forecast system also captures the compound behavior of wind-solar variability and historical co-fluctuations associated with seasonal energy droughts and surpluses. These results provide a physically interpretable foundation for integrating seasonal forecasts into wind-solar energy planning and compound risk management.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Xiaosong Yang",
      "orcid": "0000-0003-3154-605X"
    },
    {
      "name": "Thomas L. Delworth",
      "orcid": "0000-0003-4865-5391"
    },
    {
      "name": "Liwei Jia"
    },
    {
      "name": "Nathaniel C. Johnson",
      "orcid": "0000-0003-4906-178X"
    },
    {
      "name": "Colleen E. McHugh"
    },
    {
      "name": "William F. Cooke",
      "orcid": "0000-0003-4550-3210"
    },
    {
      "name": "Andrew T. Wittenberg",
      "orcid": "0000-0003-1680-8963"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7w2gyd5cw694f.bib",
  "cited_by": 0,
  "doi": "10.1038/s43247-026-04114-6",
  "fulltext_url": null,
  "id": "7w2gyd5cw694f",
  "issns": [
    "2662-4435"
  ],
  "landing_url": "https://www.nature.com/articles/s43247-026-04114-6",
  "language": null,
  "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": [
    [
      "openalex",
      "2306|Climate variability and models"
    ]
  ],
  "openalex": "W7219061375",
  "pdf_url": "https://www.nature.com/articles/s43247-026-04114-6_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-03",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/7w2gyd5cw694f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:54Z",
      "native_id": "10.1038/s43247-026-04114-6",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s43247-026-04114-6"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2306|Climate variability and models",
      "subfield": "climate"
    }
  ],
  "title": "Seasonal predictability of spring solar resources and wind-solar variability across the United States",
  "title_text": "Seasonal predictability of spring solar resources and wind-solar variability across the United States",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:54Z",
  "url": "https://research.draconitic.com/paper/7w2gyd5cw694f",
  "venue": "Communications Earth & Environment",
  "versions": []
}