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  Complex networks reveal oceanic drivers and extended predictability of terrestrial heat extremes

Qiu, H., Zhang, T., Yang, S., Cai, F. (2026): Complex networks reveal oceanic drivers and extended predictability of terrestrial heat extremes. - Environmental Research Letters, 21, 12, 124045.
https://doi.org/10.1088/1748-9326/ae7b5b

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https://creativecommons.org/licenses/by/4.0/

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 ???ViewItemFull_lblCreators???:
Qiu, Huayan1, ???ENUM_CREATORROLE_AUTHOR???
Zhang, Tuantuan1, ???ENUM_CREATORROLE_AUTHOR???
Yang, Song1, ???ENUM_CREATORROLE_AUTHOR???
Cai, Fenying2, ???ENUM_CREATORROLE_AUTHOR???           
???ViewItemFull_lblAffiliations???:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 ???ViewItemFull_lblAbstract???: While sea surface temperature (SST) anomalies are known as important drivers of terrestrial heat extremes, a unified quantitative understanding of the SST’s impacts across seasons, ocean basins, and lead times has not been ascertained. Here, we apply a complex network framework to systematically investigate these ocean-to-land linkages at a global scale. It is found that the global impacts of different ocean basins emerge at distinct time lags. In particular, the Indian Ocean exerts near-instantaneous effects, whereas the predominant influences of tropical Pacific can persist for up to six months. The impacts of the North Atlantic and western Pacific peak at lead times of approximately 3–6 months. Robust SST-terrestrial heat extreme teleconnections span >90% of the land areas even at a lead time of 12 months, exposing a potentially substantial source of long-term predictability that has been overlooked by traditional statistical analysis. Climate models demonstrate skillful simulations of these lag-dependent teleconnections, linking stronger SST variability to enhanced modulations on extreme terrestrial high temperature. This framework provides a systematic, data-driven approach for elucidating ocean-to-land modulations, which deepens our knowledge of the predictability of the climate system.

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???ViewItemFull_lblLanguages???: eng - English
 ???ViewItemFull_lblDates???: 2026-06-102026-06-252026-06-25
 ???ViewItemFull_lblPublicationStatus???: ???ViewItem_lblPublicationState_published-in-print???
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.1088/1748-9326/ae7b5b
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???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_WORKINGGROUP???: Time Series Analysis
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Complex Networks
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Climate impacts
???ENUM_IDENTIFIERTYPE_REGIONALK???: Global
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Quantitative Methods
???ENUM_IDENTIFIERTYPE_OATYPE???: Gold Open Access
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???ViewItemFull_lblSourceTitle???: Environmental Research Letters
???ViewItemFull_lblSourceGenre???: ???ENUM_GENRE_JOURNAL???, SCI, Scopus, p3, oa
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???ViewItemFull_lblPages???: ???lbl_noEntry??? ???ViewItemFull_lblSourceVolumeIssue???: 21 (12) ???ViewItemFull_lblSourceSequenceNo???: 124045 ???ViewItemFull_lblSourceStartEndPage???: ???lbl_noEntry??? ???ViewItemFull_lblSourceIdentifier???: ???ENUM_IDENTIFIERTYPE_CONE???: https://publications.pik-potsdam.de/cone/journals/resource/150326
???ENUM_IDENTIFIERTYPE_PUBLISHER???: IOP Publishing