南海夏季风爆发日期与海温的多尺度关系及最优子集回归预测

采用小波变换,Lanczos时间滤波器,相关分析研究了南海夏季风爆发日期的多尺度特征及与海温场的关系,并研制了南海夏季风爆发日期的短期气候预测新方法——多尺度最优子集回归预测方法.结果表明,1958-2008年南海夏季风爆发的平均日期为5月18日,标准差约10 d,具有弱的偏早爆发趋势,存在15.4a的年代际变化和5....

Multi-scale correlation of onset date of South China Sea summer monsoon with sea surface temperature and optimal subset regression prediction

Using wavelet transform, Lanczos filter, correlation analysis, Multi-scale characteristics of onset date of South China Sea summer monsoon (SCSSM) and its relationship with sea surface temperature (SST) on interannual and decadal time scale are explored. The optimal subset regression prediction method with the view of multiple time scales is developed as the new method of short-term climate prediction for onset date of SCSSM. The results show that the mean onset date of SCSSM is about 18 May and the mean square deviation is about 10 days during 1958-2008. The SCSSM seems to break out little early trend. It exhibits decadal variation with 15.4 years period and interannual variation with 5.3 years period. On interannual time scale, there are four regions with significant correlation between SSCSM onset dates and SST during previous winter, which occur in tropical southern Indian Ocean, tropical northwestern Pacific east of Philippine, equatorial central and eastern Pacific, and tropical eastern Pacific. On decadal time scale, besides tropical western Pacific, which is consistent with the result on interannual time scale, there are additional five regions with significant correlation between onset dates of SSCSM and SST during previous year (from last March to February), which occur in southwestern Indian Ocean, southeastern Indian Ocean, north Pacific, tropical southeastern Pacific and southeastern Pacific. The test of independent samples from 1999 to 2008 shows that the maximal prediction error of multi-scale optimal subset regression prediction is 8.5 days. The probability of difference less than 5 days between prediction and actual value is accounted for 50 percent. The anomalous early and late onset date of SSCSM can be better predicted. The multi-scale optimal subset regression prediction is significantly better than optimal subset regression prediction and single/multiple variable linear regression prediction. So, the prediction from multi-scale optimal subset regression prediction can offer important reference to operational prediction of short tern climate prediction for onset date of SSCSM.