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Zebiak-Cane数值模式的可预报性分析 被引量:3

Predictability Analysis of Zebiak-Cane Model
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摘要 用Zebiak-Cane(ZC)模式关于热带太平洋海表温度距平(SSTA)的预报资料,从预报误差发展的角度,考察了该模式关于El Nio/La Nia事件生长期和衰减期以及正常年份SSTA的预报技巧。结果表明,ZC模式关于ENSO预报的一个最突出的特征是:El Nio事件生长期SSTA的预报误差存在着明显的季节依赖性,最大预报误差增长通常出现在春季和夏初(AMJ),存在显著的春季可预报性障碍(SPB);对于El Nio事件的衰减期、La Nia事件的生长期和衰减期以及正常年份,SSTA预报误差没有明显的季节依赖性。作者比较了ZC模式关于ENSO事件不同发展阶段(生长期和衰减期)以及正常年份SSTA的可预报性。结果表明,La Nia生长期SSTA的预报最困难;在El Nio事件、La Nia事件和正常年份3种情形中,ZC模式关于La Nia事件的预报技巧最低。用上述动力学方法,进一步考察了ZC模式预报技巧的年代际变化。结果表明,ZC模式在20世纪80年代SPB现象相对较弱,有较高的预报技巧;而在70和90年代,ZC模式预报技巧明显低于80年代,同时伴有显著的SPB现象。 Using the equatorial Pacific sea surface temperature anomaly(SSTA) forecasts of the Zebiak-Cane model,the SSTA prediction skill for neutral years and the growing/decaying phase of El Nio/La Nia events are investigated by analyzing the behavior of forecast error growth.The results show that there is a most prominent signature for the ENSO forecast of ZC model and the evolution of SSTA forecast error associated with the growing phase of El Nio events depends remarkably on season with the fastest growth occurring during boreal spring(AMJ),having prominent SPB.For the decaying phase of El Nio events,the growing/decaying phase of La Nia events and neutral years,the seasonal dependence of SSTA forecast error evolution is not prominent.We compare the SSTA predictability of neutral year and the growing/decaying phase of ENSO event for ZC model,the results show that the SSTA forecast of growing phase of La Nia event is most difficult,and for the El Nio and La Nia event and neutral year,the prediction skill of La Nia event is the lowest.The decadal change of the ZC model forecast skill is also investigated by using the same dynamical method.It is found that the ZC model forecast skill is higher in 1980s,and relatively weak SPB,the ZC model forecast skill is lower in 1970s and 1990s,and having prominent SPB.
出处 《气候与环境研究》 CSCD 北大核心 2008年第2期134-148,共15页 Climatic and Environmental Research
基金 中国科学院知识创新工程重要方向项目KZCX3-SW-230 国家重点基础研究发展规划项目2006CB403606 国家自然科学基金40505013、40675030和40221503
关键词 ENSO事件 可预报性 预报误差 春季可预报性障碍 年代际变化 ENSO event,predictability,forecast error,spring predictability barrier,decadal change
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