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

doi:10.1073/pnas.1922872118

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 journal › Article › peer-review

33 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 language	English
Article number	1922872118
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	118
Issue number	47
DOIs	https://doi.org/10.1073/pnas.1922872118
State	Published - 23 Nov 2021
Externally published	Yes

Keywords

Climate networks
Climate phenomena
Forecasting
Network theory

ASJC Scopus subject areas

General

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1073/pnas.1922872118

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Ludescher, J., Martin, M., 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, 118(47), Article 1922872118. https://doi.org/10.1073/pnas.1922872118

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T1 - Network-based forecasting of climate phenomena

AU - Ludescher, Josef

AU - Martin, Maria

AU - Boers, Niklas

AU - Bunde, Armin

AU - Ciemer, Catrin

AU - Fan, Jingfang

AU - Havlin, Shlomo

AU - Kretschmer, Marlene

AU - Kurths, Jürgen

AU - Runge, Jakob

AU - Stolbova, Veronika

AU - Surovyatkina, Elena

AU - Schellnhuber, Hans Joachim

PY - 2021/11/23

Y1 - 2021/11/23

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AB - 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.

KW - Climate networks

KW - Climate phenomena

KW - Forecasting

KW - Network theory

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M1 - 1922872118

ER -

Network-based forecasting of climate phenomena

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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