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

25 Scopus citations

Abstract

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
Volume20
Issue number4
DOIs
StatePublished - 1 Apr 2018

Keywords

  • ENSO
  • climate networks
  • complex systems
  • dynamic networks

ASJC Scopus subject areas

  • Physics and Astronomy (all)

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