“春季预报障碍”（SPB）现象能导致El Niño事件预报结果产生较大的不确定性。前人用订正的Zebiak-Cane模式探讨了两类El Niño事件预报的目标观测敏感区，并确定了热带太平洋上的SSTA最优观测网。鉴于Zebiak-Cane模式较为简单，未能用于考察次表层和副热带太平洋的影响。本项目拟采用全球海气耦合模式（GFDL CM2p1），首先从理想回报试验出发，针对模式中的两类El Niño事件，从误差增长的角度，考察太平洋上易导致两类事件发生显著SPB，尤其是导致最大预报误差的初始误差。在此基础上，确定两类El Niño事件预测的目标观测敏感区。其次，从实际预测思路出发，针对真实的两类El Niño事件进行观测系统试验，对初值采用集合最优插值同化方法，考察敏感区内初始场精度的提高对提高两类El Niño事件实际预测的作用，进而为太平洋上优化实际观测网的布局提供指导。

The “Spring Predictability Barrier” (SPB) phenomenon could cause the large uncertainties of the prediction results for El Niño events. Within the revised Zebiak-Cane model, previous studies have discussed the sensitive areas of targeted observation associated with two types of El Niño events, and have determined the optimal observational array of SSTA for the target observation on the tropical pacific. However, Zebiak-Cane model is an anomaly coupled model of intermediate-complexity and cannot be used to explore the influence of subsurface and the extratropical pacific. In view of this, using the Geophysical Fluid Dynamics Laboratory Climate Model version 2p1(GFDL CM2p1), this project will firstly identify the characteristics and the growth mechanisms of the initial errors in the Pacific that cause a significant SPB, especially the largest prediction errors for two types of El Niño events (including both CP-El Niño and EP-El Niño events) most probably from the perspective of initial error growth. On the basis of these initial errors, the project will further identify the sensitive areas of targeted observation associated with two types of El Niño events, and examined the validity of sensitive areas. In addition, the Ensemble Optimal Interpolation will be adopted to conduct observation system experiment for realistic CP- and EP- El Niño events, in order to investigate the role of sensitive areas in improving realistic two types of El Niño predictions. These studies may be helpful for optimizing the Pacific observation network.