Early signs of strong El Niño-Southern Oscillation episodes.

The El Niño-Southern Oscillation (ENSO) is the Earth's most prominent source of year-to-year climate variability, occurring in the tropical Pacific but with global impacts. Therefore, it is essential to improve our ability to forecast ENSO in order to better understand the mechanisms that govern global climate. ENSO modeling based on Artificial Intelligence (AI) technology has shown promising results and has garnered attention from the scientific community. AI technology has highlighted the importance of sea surface salinity in obtaining accurate ENSO predictions, even beyond the spring predictability barrier (SPB), which is commonly understood as a prediction barrier that occurs during the boreal spring. Here, we present evidence that ENSO exhibits distinct precondition patterns in the early boreal spring. These patterns are caused by an eastward shift of the Westerly/Easterly (WW/EW) winds convergence during the early boreal spring, which results from an intensification of the WW or weakening of the EW in the preceding months. These patterns include changes in the surface and sub-surface salinity distribution and the premature well-known eastward migration of the region with the highest amount of rainfall. Furthermore, during the early boreal spring in ENSO years, the eastward shift of the WW/EW convergence leads to a significant increase in internal, downwelling Kelvin wave (KW) activity near the date line compared to neutral years. Additionally, we find that the spatial standard deviations of the sea surface temperature anomaly in Niño-3 and Niño-4 region, rapidly decrease in the first half of ENSO developing year. This feature is fundamentally distinct from that in neutral years, suggesting that the cycle of ENSO persistence goes beyond the SPB and sea surface temperature anomalies in those two Niño regions are the most predictable.