资源环境科技发展态势分析平台

A cross-validated statistical model has been developed to produce hindcasts for the 1980-2016 November-December-January (NDJ; assumed El Nino peak) mean Nino-3.4 sea surface temperature anomalies (SSTA). A linear combination of two parameters is sufficient to successfully predict the peak SSTA: 1) the 5 degrees N-5 degrees S, 130 degrees E-180 degrees, 5-250-m oceanic potential temperature anomalies in February and 2) the 58N-58S, 140 degrees E-160 degrees W cumulative zonal wind anomalies (ZWA), integrated from November (one year before) up to the prediction month. This model is simple but is comparable to, or even outperforms, many NOAA Climate Prediction Center's statistical models during the boreal spring predictability barrier. In contrast to most statistical models, the predictand Nino-3.4 SSTA is not used as a predictor. The explained variance between observed and predicted NDJ Nino-3.4 SSTA at a lead time of 8 months is 57% using 5 yr for cross validation and 63% in full hindcast mode. Predictive skill is lower after 2000 when the mean climate state is more La Nina-like because of stronger equatorial easterly ZWA. Strengthened Pacific subtropical highs are observed, with weaker westerly ZWA that emerge at a later time during El Nino. The western Pacific is more recharged, with stronger upwelling over the eastern Pacific. The resulting strong zonal subsurface temperature gradient provides a high potential for Kelvin waves being triggered without strong westerly ZWA. However, the persistent easterly ZWA lead to more central Pacific-like El Ninos. These are more difficult to predict because the contribution of the thermocline feedback is reduced. Overall, the authors find that the importance of the recharge state for ENSO prediction has increased after 2000, contradicting some previous studies.