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

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 Abstract: 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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Language(s): eng - English
 Dates: 2024-06-172024-07-122024-07-12
 Publication Status: Finally published
 Pages: 8
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41612-024-00701-6
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Organisational keyword: FutureLab - Artificial Intelligence in the Anthropocene
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
MDB-ID: pending
 Degree: -

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Title: npj Climate and Atmospheric Science
Source Genre: Journal, SCI, Scopus, oa
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Pages: - Volume / Issue: 7 Sequence Number: 165 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/npj-climate-atmospheric-science
Publisher: Nature