Journalartikel
Autorenliste: Ludescher, Josef; Martin, Maria; Boers, Niklas; Bunde, Armin; Ciemer, Catrin; Fan, Jingfang; Havlin, Shlomo; Kretschmer, Marlene; Kurths, Juergen; Runge, Jakob; Stolbova, Veronika; Surovyatkina, Elena; Schellnhuber, Hans Joachim
Jahr der Veröffentlichung: 2021
Zeitschrift: Proceedings of the National Academy of Sciences
Bandnummer: 118
Heftnummer: 47
ISSN: 0027-8424
eISSN: 1091-6490
Open Access Status: Green
DOI Link: https://doi.org/10.1073/pnas.1922872118
Verlag: National Academy of Sciences
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 Nino 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.
Zitierstile
Harvard-Zitierstil: Ludescher, J., Martin, M., Boers, N., Bunde, A., Ciemer, C., Fan, J., et al. (2021) Network-based forecasting of climate phenomena, Proceedings of the National Academy of Sciences, 118(47), Article e1922872118. https://doi.org/10.1073/pnas.1922872118
APA-Zitierstil: 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. (2021). Network-based forecasting of climate phenomena. Proceedings of the National Academy of Sciences. 118(47), Article e1922872118. https://doi.org/10.1073/pnas.1922872118
Schlagwörter
climate networks; climate phenomena; DRIVEN; forecasting; network theory; SKILL
