Asymmetric Influences of ENSO Phases on the Predictability of North Pacific Sea Surface Temperature

The North Pacific sea surface temperature (SST) has a profound climatic influence. The El Nino-Southern Oscillation (ENSO) significantly impacts the North Pacific SST; however, the influence of the distinct phases of ENSO on SST predictability remains unclear. To overcome the model limitations, this study assessed SST predictability under diverse ENSO phases using reanalysis. The predictability limit of the North Pacific SST under La Nina (8.4 months) is longer than that under Neutral (5.9 months) and El Nino (5.5 months) conditions, which unveils asymmetry. This asymmetry mirrors contemporary multimodal prediction skills. Error growth dynamics reveal La Nina's robust signal strength with a slow error growth rate, in contrast to El Nino's weaker signal and faster error growth. There exhibits intermediate signal strength and elevated error growth in Neutral condition. Physically, predictability signal strength aligns with SST variability, whereas the error growth rate correlates with atmospheric-ocean heating anomalies. La Nina, which induces positive heating anomalies, minimizes the impact of atmospheric noise, resulting in lower error growth. The result is beneficial for improving North Pacific SST predictions. The North Pacific sea surface temperature (SST) significantly impacts global climate, particularly in East Asia and the United States. Accurate prediction of North Pacific SST, which is crucial for seasonal and interannual forecasting, remains a focal point in atmospheric and oceanic research. This study investigates how the El Nino-Southern Oscillation (ENSO) influences SST predictability in the North Pacific during different phases (El Nino, La Nina, Neutral). Using reanalysis data, this research introduces the concept of predictability limit (PL), revealing an asymmetry in the PL response: La Nina extends the PL to 8.4 months, El Nino shortens it to 5.5 months, and Neutral falls in 5.9 months. This asymmetry aligns with the existing prediction skills. Examining the error growth dynamics, La Nina exhibits a robust SST signal with slower error growth, whereas El Nino displays a weaker signal with faster error growth. Neutral, meanwhile, shows a high signal strength but large error growth. This study sheds light on these differences in SST predictability and provides valuable insights for refining North Pacific SST predictions through model advancements. El Nino-Southern Oscillation phases asymmetrically impact North Pacific sea surface temperature (SST) predictability: El Nino (5.5 months), La Nina (8.4 months), Neutral (5.9 months) Error growth dynamics vary: La Nina displays strong signals with slow growth, El Nino exhibits weak signals and rapid growth A positive heating anomaly from La Nina reduces atmospheric noise impact, resulting in a smaller error growth rate of the North Pacific SST