Network-based forecasting of climate phenomena

Josef Ludescher, Maria Martin, Niklas Boers, Armin Bunde, Catrin Ciemer, Jingfang Fan, Shlomo Havlin, Marlene Kretschmer, Jürgen Kurths, Jakob Runge, Veronika Stolbova, Elena Surovyatkina, Hans Joachim Schellnhuber

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

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.

Original languageEnglish
Article number1922872118
JournalProceedings of the National Academy of Sciences of the United States of America
Volume118
Issue number47
DOIs
StatePublished - 23 Nov 2021

Bibliographical note

Publisher Copyright:
© 2021 National Academy of Sciences. All rights reserved.

Funding

We thank the anonymous reviewers for their very constructive comments and suggestions. J.L., J.F., and E.S. acknowledge the support of the East Africa Peru India Climate Capacities project funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (Grant 18_II_149_Global_A_Risikovorhersage). N.B. acknowledges funding by the Volkswagen foundation and the European Union’s Horizon 2020 research and innovation program (EU H2020) under Grant Agreement 820970 (Tipping Points in the Earth System). C.C. acknowledges funding by the German Research Foundation/São Paulo Research Foundation (International Research Training Group 1740/Thematic Research Project 2015/50122-0). S.H. thanks the Israel Science Foundation (Grant 189/19), the joint China-Israel Science Foundation (Grant 3132/19), the Bar-Ilan University Center for Research in Applied Cryptography and Cyber Security, NSF-US–Israel Binational Science Foundation Grant 2019740, the EU H2020 project Real-time earthquake risk reduction for a resilient Europe, and Defense Threat Reduction Agency Grant HDTRA-1-19-1-0016 for financial support. M.K. has received funding from the EU H2020 under Marie Skłodowska-Curie Grant Agreement 841902. J.K. acknowledges support from the Russian Ministry of Science and Education Agreement 13.1902.21.0026. V.S. acknowledges support from the Russian Foundation for Basic Research (Grant 20-07-01071).

FundersFunder number
China-Israel Science Foundation3132/19
German Research Foundation/São Paulo Research Foundation2015/50122-0
Russian Ministry of Science and Education Agreement13.1902.21.0026
Defense Threat Reduction AgencyHDTRA-1-19-1-0016
Horizon 2020 Framework Programme841902, 820970
Volkswagen Foundation
United States-Israel Binational Science Foundation2019740
Russian Foundation for Basic Research20-07-01071
Israel Science Foundation189/19
Bundesministerium für Umwelt, Naturschutz und nukleare Sicherheit18_II_149_Global_A_Risikovorhersage

    Keywords

    • Climate networks
    • Climate phenomena
    • Forecasting
    • Network theory

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