Network-based forecasting of climate phenomena
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 15, 2021
- Source ID
- 10.1073/pnas.1922872118
Entities
People
- Armin Bunde
- Catrin Ciemer
- Elena Surovyatkina
- Hans Joachim Schellnhuber
- Jakob Runge
- Jingfang Fan
- Josef Ludescher
- Jürgen Kurths
- Maria Martin
- Marlene Kretschmer
- Niklas Boers
- Shlomo Havlin
- Veronika Stolbova
Organizations
- Bar-Ilan University
- Beijing Normal University
- Defense Threat Reduction Agency
- ETH Zurich
- Federal Ministry for the Environment, Climate Protection, Nature Conservation and Nuclear Safety
- German Aerospace Center
- German Research Foundation
- Israel Science Foundation
- N. I. Lobachevsky State University of Nizhny Novgorod
- Potsdam Institute for Climate Impact Research
- Russian Center for Science Information
- Technical University of Munich
- United States – Israel Binational Science Foundation
- University of Exeter
- University of Giessen
- University of Reading
- Volkswagen Foundation