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  Key propagation pathways of extreme precipitation events revealed by climate networks

Li, K., Huang, Y., Liu, K., Wang, M., Cai, F., Zhang, J., Boers, N. (2024): Key propagation pathways of extreme precipitation events revealed by climate networks. - npj Climate and Atmospheric Science, 7, 165.
https://doi.org/10.1038/s41612-024-00701-6

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 ???ViewItemFull_lblCreators???:
Li, Kaiwen1, ???ENUM_CREATORROLE_AUTHOR???           
Huang, Yu1, ???ENUM_CREATORROLE_AUTHOR???           
Liu, Kai2, ???ENUM_CREATORROLE_AUTHOR???
Wang, Ming2, ???ENUM_CREATORROLE_AUTHOR???
Cai, Fenying1, ???ENUM_CREATORROLE_AUTHOR???           
Zhang, Jianxin1, ???ENUM_CREATORROLE_AUTHOR???           
Boers, Niklas1, ???ENUM_CREATORROLE_AUTHOR???           
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1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 ???ViewItemFull_lblAbstract???: The comprehensive understanding of propagation patterns of extreme precipitation events (EPEs) is essential for early warning of associated hazards such as floods and landslides. In this study, we utilize climate networks based on an event synchronization measure to investigate the propagation patterns of EPEs over the global land masses, and identify 16 major propagation pathways. We explain them in association with regional weather systems, topographic effects, and travelling Rossby wave patterns. We also demonstrate that the revealed propagation pathways carry substantial EPE predictability in certain areas, such as in the Appalachian, the Andes mountains. Our results help to improve the understanding of key propagation patterns of EPEs, where the global diversity of the propagated patterns of EPEs and corresponding potential predictability provide prior knowledge for predicting EPEs, and demonstrate the power of climate network approaches to study the spatiotemporal connectivity of extreme events in the climate system.

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???ViewItemFull_lblLanguages???: eng - English
 ???ViewItemFull_lblDates???: 2024-06-172024-07-122024-07-12
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.1038/s41612-024-00701-6
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: FutureLab - Artificial Intelligence in the Anthropocene
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Nonlinear Dynamics
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Machine Learning
???ENUM_IDENTIFIERTYPE_MDB_ID???: pending
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???ViewItemFull_lblSourceTitle???: npj Climate and Atmospheric Science
???ViewItemFull_lblSourceGenre???: ???ENUM_GENRE_JOURNAL???, SCI, Scopus, oa
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???ENUM_IDENTIFIERTYPE_PUBLISHER???: Nature