NINO区SST与SOI的耦合振荡信号及其预测试验

应用奇异交叉谱（ＳＣＳＡ）分析方法，提取Ｎｉｎｏ 海区各区的平均海温（ＳＳＴ）和南方涛动指数（ＳＯＩ）之间的耦合振荡信号，由此描述其年际和年代际的时变特征。基于ＳＣＳＡ，重建耦合振荡分量序列（ＲＣＣＳ），并与回归分析相结合，对Ｎｉｎｏ 各海区平均的ＳＳＴ月际序列作短期气候预测试验。结果表明，各海区ＳＳＴ与ＳＯＩ的显著耦合振荡周期各有特色，其年际或１０ 年际变化不尽相同，从而构成了ＥＮＳＯ信号在时空演变型态上的复杂性。ＳＣＳＡ基础上的回归预报模型的预报技巧绝大部分优于ＳＳＡ－ＡＲ预报模型，实际预报试验证明效果优良

COUPLED-OSCILLATION SIGNALS BETWEEN SST OVER THE NINO OCEANIC REGIONS AND SOI WITH ITS PREDICTION EXPERIMENT

The singular cross spectrum analysis(SCSA) is used to extract the coupled oscillation signals between SST over Nino oceanic regions and SOI,therefrom describing their interannual and decadal variation features.The short term climatic prediction experiment is made for SST over Nino oceanic regions based on reconstructed coupled oscillation components series(RCCS) together with regression model. Results show that there is different coupled oscillation periods between SST over different Nino oceanic regions and SOI,as well as different interannual and decadal variation features,thereby forming ENSO's complexity in its space/time evolution. The forecast skill of SCSA combing regression model is mostly superior to the SSA AR prediction model,demonstrating its advantages in actual prediction experiment.