Abstract

Abstract Through its atmospheric teleconnections, El Niño–Southern Oscillation (ENSO) shifts and disrupts weather and climate patterns far beyond the equatorial Pacific where it occurs often resulting in catastrophic consequences in many countries of the world. It is also the largest source of seasonal and interannual climate predictability. Despite its huge importance, ENSO forecasting is still not performed operationally at longer leads than about 6 months ahead. At the same time, there is mounting scientific evidence that forecasts are possible even more than a year in advance. Early warning of ENSO events could substantially mitigate some of the most damaging impacts, such as floods, droughts, and harvest failure and help avoid famine, migration, and disease outbreaks. Here, we present forecasts from a statistical ENSO model of the next El Niño predicted to occur in the winter of 2023/24, at lead times between 11 and 17 months ahead of an expected peak in December 2023. We use a statistical unobserved dynamic components model (EDCM) based on subsurface ocean temperatures as well as sea surface temperatures and zonal wind stress. EDCM has been previously validated through hindcasts of the major El Niños since 1970 and through real-time forecasts of the 2015/16 and 2018/19 El Niños. Our statistical framework and results indicate that there is potential for doubling the operational predictive lead time of ENSO to at least 12 months, with additional promise for even earlier anticipation of 19 months. Such longer-lead forecasts could be of high value, because decision-making and management in a number of key socioeconomic sectors could be greatly improved.

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