Network-based forecasting of climate phenomena

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...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2021-11, Vol.118 (47), p.1-10
Main Authors: Ludescher, Josef, Martin, Maria, Boers, Niklas, Bunde, Armin, Ciemer, Catrin, Fan, Jingfang, Havlin, Shlomo, Kretschmer, Marlene, Kurths, Jürgen, Runge, Jakob, Stolbova, Veronika, Surovyatkina, Elena, Schellnhuber, Hans Joachim
Format: Article
Language:English
Subjects:
Climate change
Complex systems
Drought
El Nino
Extreme weather
Global warming
Mathematical models
PERSPECTIVE
Physical Sciences
Polar vortex
Rainfall
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Summary: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.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1922872118