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  Network-based Forecasting of Climate Phenomena

Ludescher, J., Martin, M. A., 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 (PNAS), 118, 47, e1922872118.
https://doi.org/10.1073/pnas.1922872118

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 ???ViewItemFull_lblCreators???:
Ludescher, Josef1, ???ENUM_CREATORROLE_AUTHOR???           
Martin, Maria A.1, ???ENUM_CREATORROLE_AUTHOR???           
Boers, Niklas1, ???ENUM_CREATORROLE_AUTHOR???           
Bunde, Armin2, ???ENUM_CREATORROLE_AUTHOR???
Ciemer, Catrin1, ???ENUM_CREATORROLE_AUTHOR???           
Fan, Jingfang1, ???ENUM_CREATORROLE_AUTHOR???           
Havlin, Shlomo2, ???ENUM_CREATORROLE_AUTHOR???
Kretschmer, Marlene2, ???ENUM_CREATORROLE_AUTHOR???
Kurths, Jürgen1, ???ENUM_CREATORROLE_AUTHOR???           
Runge, Jakob2, ???ENUM_CREATORROLE_AUTHOR???
Stolbova, Veronica2, ???ENUM_CREATORROLE_AUTHOR???
Surovyatkina, Elena1, ???ENUM_CREATORROLE_AUTHOR???           
Schellnhuber, Hans Joachim1, ???ENUM_CREATORROLE_AUTHOR???           
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1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 ???ViewItemFull_lblAbstract???: 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.

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???ViewItemFull_lblLanguages???: eng - English
 ???ViewItemFull_lblDates???: 2021-08-082021-11-232021-11-23
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???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD1 - Earth System Analysis
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???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: FutureLab - Artificial Intelligence in the Anthropocene
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: Director Emeritus Schellnhuber
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: Director Emeritus / Executive Staff / Science & Society
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Complex Networks
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???ENUM_IDENTIFIERTYPE_DOI???: 10.1073/pnas.1922872118
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???ViewItemFull_lblSourceTitle???: Proceedings of the National Academy of Sciences of the United States of America (PNAS)
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