Breakthrough in El Nino Forecasting


The following is a public news release by the Potsdam Institute for Climate Impact Research dated 7/1/13 and with the title above:

In order to extend forecasting from six months to one year or even more, scientists have now proposed a novel approach based on advanced connectivity analysis applied to the climate system. The scheme builds on high-quality data of air temperatures and clearly outperforms existing methods. The study will be published this week in the Proceedings of the National Academy of Sciences.

“Enhancing the preparedness of people in the affected regions by providing more early-warning time is key to avoiding some of the worst effects of El Niño,” says Hans Joachim Schellnhuber, director of the Potsdam Institute for Climate Impact Research and co-author of the study by Josef Ludescher et al (Justus-Liebig Universität Giessen). The new approach employs network analysis which is a cutting-edge methodology at the crossroads of physics and mathematics. Data from more than 200 measurement points in the Pacific, available from the 1950s on, were crucial for studying the interactions between distant sites that cooperate in bringing about the warming.

According to Schellnhuber a new algorithm was developed and tested which does not only extend the forecasting time but also enhances the reliability. In fact, the novel method correctly predicted the absence of an El Niño-event in the last year. This forecast was made in 2011 already, whereas conventional approaches kept on predicting a significant warming far into 2012.

El Niño is part of a more general oscillation of the Pacific ocean-atmosphere system called ENSO, which also embraces anomalous cold episodes dubbed La Niña which can inflict severe damages as well. The present study focuses on the warming events only. However, an El Niño-year is followed by a La Niña-year, as a rough rule.

“It is still unclear to which extent global warming caused by humankind’s emissions of greenhouse gases will influence the ENSO pattern,” says Schellnhuber. “Yet the latter is often counted among the so-called tipping elements in the Earth system, meaning that at some level of climate change it might experience a relatively abrupt transformation.” Certain data from the Earth’s past suggest that higher mean global temperatures could increase the amplitude of the oscillation, so correct forecasting would become even more important.

Article: Ludescher, J., Gozolchiani, A., Bogachev, M.I., Bunde, A., Havlin, S., Schellnhuber, H.J. (2013): Improved El Niño forecasting by cooperativity detection. Proceedings of the National Academy of Sciences (early online edition) [DOI:10.1073/pnas.1309353110].

Image 1 for article titled "Breakthrough in El Nino Forecasting"
The “climate network”. Each node inside the El Niño basin (solid red symbols) is linked to each node outside the basin (open symbols). The nodes are characterized by their air temperature at the surface level (SAT), and the link strength between the nodes is determined from their cross-correlation. The red rectangle denotes the area where the NINO3.4 index is measured. For the definition of the El Niño basin, we have followed refs. 32 and 33. In SI Appendix, we provide a sensitivity test for this choice and show, for example, that the inclusion of the two nodes south of the Equator is not essential for our results (Ludescher et al., op. cit.)

Image 2 for article titled "Breakthrough in El Nino Forecasting"
The forecasting algorithm. We compare the average link strength S(t) in the climate network (red curve) with a decision threshold Θ (horizontal line,
here Θ = 2.82) (left scale) with the standard NINO3.4 index (right scale), between January 1, 1950 and December 31, 2011. When the link strength crosses the threshold from below, outside an El Niño episode, we give an alarm and predict that an El Niño episode will start in the following calendar year. The El Niño episodes (when the NINO3.4 index is above 0.5 °C for at least 5 mo) are shown by the solid blue areas. The first half of the record (A) is the learning phase where we optimize the decision threshold. In the second half (B), we use the threshold obtained in A to predict El Niño episodes. Correct predictions are marked by green arrows and false alarms by dashed arrows. The index n marks a nonpredicted El Niño episode (Ibid.)

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