A machine learning-based approach to quantify ENSO sources of predictability

A machine learning method is used to identify sources of long-term ENSO predictability in the ocean (sea surface temperature (SST) and heat content) and the atmosphere (near-surface zonal wind (U10)). The interplay between predictors and the geographical regions contributing to the skill are determined. Tropical SST represents the primary source of predictability skill up to 1 year ahead, while U10 plays an essential role between 11-21 months in advance, from late fall up to late spring. The long-lead signal originates from coupled wind-SST interactions across the Indian Ocean (IO) and propagates across the Pacific via an atmospheric bridge mechanism. A linear correlation analysis supports this mechanism, suggesting a precursor link between anomalies in SST in the western and wind in the eastern IO. Our results have important implications for ENSO predictions beyond one year ahead and identify the critical role of U10 over the IO.