Improved El Niño forecasting by cooperativity detection

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 110; no. 29; pp. 11742 - 11745
• Main Authors: Ludescher, Josef, Gozolchiani, Avi, Bogachev, Mikhail I., Bunde, Armin, Havlin, Shlomo, Schellnhuber, Hans Joachim
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 16.07.2013; National Acad Sciences
• Subjects: Algorithms; Climate change; Climate models; Climatology; Correlation analysis; Developing countries; El Nino; El Nino-Southern Oscillation; False alarms; Forecasting - methods; Forecasting models; Global climate models; Hyperlinks; La Nina; LDCs; Ocean currents; Pacific Ocean; Physical Sciences; Sensitivity and Specificity; Statistical forecasts; Temperature; Time Factors; Weather forecasting; Pacific Ocean; spring barrier; climate; cross-correlations; ENSO; dynamic networks
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.1309353110

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.