Network-based forecasting of climate phenomena

doi:10.1073/pnas.1922872118

GSTDTAP > 资源环境科学

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 and Hans J. Schellnhuber
	2021
出版年	2021
国家	瑞士
领域	资源环境
英文摘要	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. © 2021 National Academy of Sciences
URL	查看原文
来源平台	Centre for Energy Policy and Economics
引用统计	被引频次：27[WOS] [WOS记录] [WOS相关记录]
文献类型	科技报告
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/344902
专题	资源环境科学
推荐引用方式 GB/T 7714	Josef Ludescher, Maria Martin, Niklas Boers, Armin Bunde, Catrin Ciemer, Jingfang Fan, Shlomo Havlin, Marlene Kretschmer, Jürgen Kurths, Jakob Runge, Veronika Stolbova, Elena Surovyatkina and Hans J. Schellnhuber. Network-based forecasting of climate phenomena,2021.