Network-based Forecasting of Climate Phenomena

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

doi:10.1073/pnas.1922872118

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学術論文

Network-based Forecasting of Climate Phenomena

Authors

/persons/resource/Josef.Ludescher

Ludescher, Josef
Potsdam Institute for Climate Impact Research;

/persons/resource/Maria.Martin

Martin, Maria A.
Potsdam Institute for Climate Impact Research;

/persons/resource/Niklas.Boers

Boers, Niklas
Potsdam Institute for Climate Impact Research;

Bunde, Armin
External Organizations;

/persons/resource/Catrin.Ciemer

Ciemer, Catrin
Potsdam Institute for Climate Impact Research;

/persons/resource/Jingfang.Fan

Fan, Jingfang
Potsdam Institute for Climate Impact Research;

Havlin, Shlomo
External Organizations;

Kretschmer, Marlene
External Organizations;

/persons/resource/Juergen.Kurths

Kurths, Jürgen
Potsdam Institute for Climate Impact Research;

Runge, Jakob
External Organizations;

Stolbova, Veronica
External Organizations;

/persons/resource/Elena.Surovyatkina

Surovyatkina, Elena
Potsdam Institute for Climate Impact Research;

/persons/resource/emdir

Schellnhuber, Hans Joachim
Potsdam Institute for Climate Impact Research;

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引用

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):. doi:10.1073/pnas.1922872118.

引用: https://publications.pik-potsdam.de/pubman/item/item_26041

要旨

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.