Improved El niño forecasting by cooperativity detection

Josef Ludescher, Avi Gozolchiani, Mikhail I. Bogachev, Armin Bunde, Shlomo Havlin, Hans Joachim Schellnhuber

Research output: Contribution to journalArticlepeer-review

132 Scopus citations

Abstract

Although anomalous episodic warming of the eastern equatorial Pacific, dubbed El Niño by Peruvian fishermen, has major (and occasionally devastating) impacts around the globe, robust forecasting is still limited to about 6 mo ahead. A significant extension of the prewarning time would be instrumental for avoiding some of the worst damages such as harvest failures in developing countries. Here we introduce a unique avenue toward El Niño prediction based on network methods, inspecting emerging teleconnections. Our approach starts from the evidence that a large-scale cooperative mode-linking the El Niño basin (equatorial Pacific corridor) and the rest of the ocean-builds up in the calendar year before the warming event. On this basis, we can develop an efficient 12-mo forecasting scheme, i.e., achieve some doubling of the earlywarning period. Our method is based on high-quality observational data available since 1950 and yields hit rates above 0.5, whereas false-Alarm rates are below 0.1.

Original languageEnglish
Pages (from-to)11742-11745
Number of pages4
JournalProceedings of the National Academy of Sciences of the United States of America
Volume110
Issue number29
DOIs
StatePublished - 16 Jul 2013

Keywords

  • Climate
  • Cross-correlations
  • Dynamic networks
  • ENSO
  • Spring barrier

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