Evaluation of the Real-time El Niño Forecasts by the Climate Network Approach between 2011 and Present (2402.05911v1)

Abstract: El Ni~no episodes are part of the El Ni~no-Southern Oscillation (ENSO), which is the strongest driver of interannual climate variability, and can trigger extreme weather events and disasters in various parts of the globe. Previously we have described a network approach that allows to forecast El Ni~no events about 1 year ahead. Here we evaluate the real-time forecasts of this approach between 2011 and 2022. We find that the approach correctly predicted (in 2013 and 2017) the onset of both El Ni~no periods (2014-2016 and 2018-2019) and generated only 1 false alarm in 2019. In June 2022, the approach correctly forecasted the onset of an El Ni~no event in 2023. We show how to determine the $p$-value of the 12 real-time forecasts between 2011 and 2022 and find $p\cong 0.005$, this way strongly rejecting the null hypothesis that the same quality of the forecast can be obtained by random guessing. We also discuss how the algorithm can be further improved by reducing the number of false alarms in the network model forecast. When combined with other statistical methods, a more detailed forecast, including the magnitude of the event and its type, can be obtained. For 2024, the method indicates the absence of a new El Ni~no event.