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.
Funder
Bundesministerium für Umwelt, Naturschutz, Bau und Reaktorsicherheit
EC | Horizon 2020
Volkswagen Foundation
Israel Science Foundation
Joint China-Israel Science Foundation
DTRA
Russian Ministry of Science and Education
Russian Foundation for Basic Research
United States - Israel Binational Science Foundation
Bar Ilan University Center for Research in Applied Cryptography and Cyber Security
Deutsche Forschungsgemeinschaft
Publisher
Proceedings of the National Academy of Sciences
Cited by
34 articles.
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