The El Niño-Southern Oscillation (ENSO) is the dominant mode of climate variability on interannual time scales, and its temporal evolution can be summarized using the Niño 3.4 index. Here we used EOF analysis to construct low-dimensional representations of the 12-month evolution of Niño 3.4 during different times of the year. The leading EOF explains more than 90% of the variance of the Niño 3.4 evolution from June to May, which means that the differences in evolution from one year to another are essentially differences in amplitude. Two EOFs explained 94% or more of the evolution variance for other 12-month periods of the year. The two-dimensional nature of the Niño 3.4 trajectories is a direct expression of the spring persistence barrier since the first EOF describes wintertime ENSO events, and the second EOF describes independent behavior during the antecedent spring. A periodic second-order autoregressive (AR2) model reproduced the observed properties, but a first-order model did not. Niño 3.4 EOFs in ocean-atmosphere coupled model forecasts matched observed EOFs with varying levels of fidelity depending on model and time of year. Forecast models with more accurate climatological covariance also have lower mean-squared error (MSE). Low-dimensional EOF-based statistical corrections reduced forecast MSE.

厄尔尼诺-南方涛动（ENSO）是年际尺度上气候变化的主要模式，其时间演变可以用尼诺3.4指数来概括。在这里，我们使用EOF分析来构建Niño3.4在一年中不同时间的12个月演变的低维表示。领先的EOF解释了从6月到5月Niño3.4演变的90％以上的变化，这意味着从一年到另一年的演变差异本质上是振幅差异。两个EOF解释了一年中其他12个月期间94％或更多的演化方差。Niño3.4轨迹的二维性质直接反映了春天的持久性障碍，因为第一个EOF描述了冬季ENSO事件，第二个EOF描述了先前弹簧期间的独立行为。周期性的二阶自回归（AR2）模型重现了观察到的特性，但一阶模型则没有。海洋-大气耦合模型预测中的Niño3.4 EOF与所观测到的EOF相匹配，保真度取决于模型和一年中的时间。气候协方差更准确的预测模型的均方误差（MSE）也较低。基于低维EOF的统计校正减少了预测的MSE。气候协方差更准确的预测模型的均方误差（MSE）也较低。基于低维EOF的统计校正减少了预测的MSE。气候协方差更准确的预测模型的均方误差（MSE）也较低。基于低维EOF的统计校正减少了预测的MSE。