张雅乐,俞永强,段晚锁. 2012. 四个耦合模式ENSO后报试验的“春季预报障碍”[J]. 气象学报, 70(3):506-519, doi:10.11676/qxxb2012.042
四个耦合模式ENSO后报试验的“春季预报障碍”
The spring prediction barrier of ENSO in retrospective prediction experiments as shown by the four coupled ocean atmosphere models
投稿时间：2010-07-14  修订日期：2011-03-17
DOI：10.11676/qxxb2012.042
中文关键词:  ENSO事件回报试验，春季可预报性障碍，预报误差，海-气相互作用
英文关键词:Retrospective ENSO prediction, Spring prediction barrier, Prediction error, Air sea interaction
基金项目:国家自然科学基金面上项目（40975065和40821092）、中国科学院知识创新工程重要方向项目（KZCX2-YW-QN203）
作者单位
张雅乐 中国气象局气象干部培训学院, 北京，100081
中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室，北京，100029 
俞永强 中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室，北京，100029 
段晚锁 中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室，北京，100029 
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中文摘要:
      用CliPAS计划中3个气候模式和中国科学院大气物理研究所耦合模式FGOALS-g短期气候异常回报试验结果，将动力和统计方法相结合，考察了1982—2003年厄尔尼诺/拉尼娜事件发展期和衰减期海表温度春季可预报性障碍现象。结果表明，所考察的耦合模式对ENSO事件预报的误差发展存在明显的季节依赖性，最大误差增长通常发生在春季，发生显著的可预报性障碍现象。进一步分析发现厄尔尼诺事件和拉尼娜事件在发展期的季节预报障碍现象比衰减期明显，以厄尔尼诺事件发展期春季可预报性障碍现象最为显著，拉尼娜事件衰减期季节预报障碍现象不显著。研究还发现，预报误差的增长在ENSO事件冷暖位相具有显著的非对称性，发展期暖位相预报误差强于冷位相，而衰减期冷位相的预报误差比暖位相大。通过回归分析，诊断了海 气相互作用的强度，发现耦合系统在春季最不稳定，使预报误差最易在春季发展，从而导致可预报性障碍。
英文摘要:
      Based on the dynamic and statistical analysis methods, this study analyzes recent 22-year (1982-2003) retrospective ENSO prediction performed by the three coupled GCMs that participate in the Climate Prediction and its Application to Society (CliPAS) project and by a coupled GCM namely FGOALS g developed at LASG/IAP. The seasonal dependence of prediction error growth for both the growing and decaying phases of El Nino/La Nina events is presented. All the four coupled models show considerable ENSO prediction skill, and the so called “Spring Prediction Barrier” (SPB) is also very evident for the each retrospective prediction experiment. The further analysis suggests that SPB is strongly associated with the prediction error growth during the spring, in particular, the growth rate of prediction error is the strongest during the spring for El Nino events and the growing phase of La Nino a evens, but it does not depend on the season for the decaying phase of La Nina events. We have also found significant asymmetry in the growth rate of prediction error of SST anomaly between El Nino and La Nino a events. By analyzing the regression, we found that the air-sea interaction is the most unstable in the spring, which favors rapid growth of prediction error in this season and then results in SPB in the retrospective ENSO prediction experiments.
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