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

18 Scopus citations


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
Issue number47
StatePublished - 23 Nov 2021
Externally publishedYes


  • Climate networks
  • Climate phenomena
  • Forecasting
  • Network theory

ASJC Scopus subject areas

  • General


Dive into the research topics of 'Network-based forecasting of climate phenomena'. Together they form a unique fingerprint.

Cite this