Forecasting the magnitude and onset of El Nino based on climate network

Jun Meng, Jingfang Fan, Yosef Ashkenazy, Armin Bunde, Shlomo Havlin

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


El Ninois probably the most influential climate phenomenon on inter-annual time scales. It affects the global climate system and is associated with natural disasters; it has serious consequences in many aspects of human life. However, the forecasting of the onset and in particular the magnitude of El Ninoare still not accurate enough, at least more than half a year ahead. Here, we introduce a new forecasting index based on climate network links representing the similarity of low frequency temporal temperature anomaly variations between different sites in the Nino 3.4 region. We find that significant upward trends in our index forecast the onset of El Nino approximately 1 year ahead, and the highest peak since the end of last El Nino in our index forecasts the magnitude of the following event. We study the forecasting capability of the proposed index on several datasets, including, ERA-Interim, NCEP Reanalysis I, PCMDI-AMIP 1.1.3 and ERSST.v5.

Original languageEnglish
Article number043036
JournalNew Journal of Physics
Issue number4
StatePublished - Apr 2018

Bibliographical note

Publisher Copyright:
© 2018 The Author(s). Published by IOP Publishing Ltd on behalf of Deutsche Physikalische Gesellschaft.


We thank Kai Xu for helpful discussions and suggestions. We acknowledge the Israel-Italian collaborative project NECST, the Israel Science Foundation, ONR, Japan Science Foundation, BSF-NSF, and DTRA (Grant No. HDTRA-1-10-1-0014) for financial support. JF thanks the fellowship program funded by the Planning and Budgeting Committee of the Council for Higher Education of Israel.

FundersFunder number
Japan Science Foundation
Office of Naval Research
Seventh Framework Programme317532
Israel Science Foundation
Council for Higher Education


    • ENSO
    • climate networks
    • complex systems
    • dynamic networks


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