Improved El Niño forecasting by cooperativity detection
- aInstitut für Theoretische Physik, Justus-Liebig-Universität Giessen, 35392 Giessen, Germany;
- bDepartment of Physics, Bar-Illan University, Ramat Gan 52900, Israel;
- cRadio Systems Department, St. Petersburg Electrotechnical University, St. Petersburg 197376, Russia;
- dPotsdam Institute for Climate Impact Research, 14412 Potsdam, Germany; and
- eSanta Fe Institute, Santa Fe, NM 87501
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Contributed by Hans Joachim Schellnhuber, May 30, 2013 (sent for review March 12, 2013)
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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 early-warning 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.
Footnotes
- ↵1To whom correspondence should be addressed. E-mail: john{at}pik-potsdam.de.
Author contributions: A.B., S.H., and H.J.S. designed research; J.L., A.G., and M.I.B. performed research; A.G. and M.I.B. contributed new reagents/analytic tools; J.L. analyzed data; and A.B., S.H., and H.J.S. wrote the paper.
The authors declare no conflict of interest.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1309353110/-/DCSupplemental.














