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Network-based forecasting of climate phenomena

Ludescher, J., Martin, M. ORCID:, Boers, N. ORCID:, Bunde, A., Ciemer, C. ORCID:, Fan, J. ORCID:, Havlin, S. ORCID:, Kretschmer, M. ORCID:, Kurths, J., Runge, J., Stolbova, V. ORCID:, Surovyatkina, E. ORCID: and Schellnhuber, H. J. (2021) Network-based forecasting of climate phenomena. Proceedings of the National Academy of Sciences of the United States of America, 118 (47). e1922872118. ISSN 0027-8424

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To link to this item DOI: 10.1073/pnas.1922872118


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.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:101629
Uncontrolled Keywords:climate networks, forecasting, climate phenomena, network theory
Publisher:National Academy of Sciences


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