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The El Nio Southern Oscillation (ENSO) is the most consequential driver of interannual global climate variability and can lead to extreme weather events like drought or flooding in various parts of the world. Current operational forecasts are hampered by the so-called spring predictability barrier (SPB), which makes forecasts before or during the boreal spring particularly challenging.In recent years, we developed several methods based on complex system science that can provide reliable El Nio forecasts well before the SPB, thus about doubling the pre-warning time. The first of these methods is based on a dynamical climate network (CN) consisting of nodes that are reanalysis grid points in the Pacific, and links between them, whose strength is characterized by the cross-correlations of the atmospheric surface temperatures at the grid points. In the calendar year before an El Nio event, the links between the eastern equatorial Pacific and the rest of the tropical Pacific tend to strengthen such that the average link strength exceeds a certain threshold. This property serves as a precursor to forecast the onset of El Nio events. In particular, the CN-based method has already provided 12 real-time forecasts, 11 of which turned out to be correct (p = 5.1*10-3). Here, we discuss an improvement of the CN method as well as the combination with other El Nio forecasting methods.Approaches based on information entropy and the zonal temperature gradient in the western Pacific provide additional forecasts with about 1 year lead time for the magnitude and the type of an upcoming El Nio event, respectively. Combining the three methods provides not only more information about an upcoming El Nio, particularly about the risk exposure of a given geographical location, but concurring forecasts can support each other and lead to higher overall confidence in the forecast. This was the case, for instance, at the end of 2022, when the combined method correctly forecasted a moderate-to-strong El Nio of eastern Pacific type for 2023.
Ludescher et al. (Fri,) studied this question.