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  Network-based Forecasting of Climate Phenomena

Ludescher, J., Martin, M. A., Boers, N., Bunde, A., Ciemer, C., Fan, J., Havlin, S., Kretschmer, M., Kurths, J., Runge, J., Stolbova, V., Surovyatkina, E., Schellnhuber, H. J. (2021): Network-based Forecasting of Climate Phenomena. - Proceedings of the National Academy of Sciences of the United States of America (PNAS), 118, 47, e1922872118.
https://doi.org/10.1073/pnas.1922872118

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 Creators:
Ludescher, Josef1, Author              
Martin, Maria A.1, Author              
Boers, Niklas1, Author              
Bunde, Armin2, Author
Ciemer, Catrin1, Author              
Fan, Jingfang1, Author              
Havlin, Shlomo2, Author
Kretschmer, Marlene2, Author
Kurths, Jürgen1, Author              
Runge, Jakob2, Author
Stolbova, Veronica2, Author
Surovyatkina, Elena1, Author              
Schellnhuber, Hans Joachim1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: Network theory, as emerging from complex systems science, can provide critical predictive power for mitigating the global warming crisis and other societal challenges. Here we discuss the main differences of this approach to classical numerical modeling and highlight several cases where the network approach substantially improved the prediction of high-impact phenomena: 1) El Niño events, 2) droughts in the central Amazon, 3) extreme rainfall in the eastern Central Andes, 4) the Indian summer monsoon, and 5) extreme stratospheric polar vortex states that influence the occurrence of wintertime cold spells in northern Eurasia. In this perspective, we argue that network-based approaches can gainfully complement numerical modeling.

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Language(s): eng - English
 Dates: 2021-08-082021-11-232021-11-23
 Publication Status: Finally published
 Pages: 10
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: MDB-ID: No data to archive
PIKDOMAIN: RD1 - Earth System Analysis
Organisational keyword: RD1 - Earth System Analysis
Working Group: Earth System Modes of Operation
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Organisational keyword: FutureLab - Artificial Intelligence in the Anthropocene
Organisational keyword: Director Emeritus Schellnhuber
PIKDOMAIN: Director Emeritus / Executive Staff / Science & Society
Research topic keyword: Complex Networks
Research topic keyword: Monsoon
Regional keyword: South America
Model / method: Nonlinear Data Analysis
DOI: 10.1073/pnas.1922872118
 Degree: -

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Project name : EPICC
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Project name : TiPES
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Title: Proceedings of the National Academy of Sciences of the United States of America (PNAS)
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 118 (47) Sequence Number: e1922872118 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals410
Publisher: National Academy of Sciences (NAS)