基于逐步回归模态投影方法的BCC气候系统模式ENSO预报订正

Statistical Correction of ENSO Prediction in BCC_CSM1.1m Based on Stepwise Pattern Projection Method

本文利用国家气候中心气候系统模式(Beijing climate center climate System Model,BCC_CSM1.1m)提供的1991—2014年海表温度回报数据,将逐步回归模态投影方法(stepwise Pattern Projection Method,SPPM)应用到改进BCC_CSM1.1m模式El Nino和南方涛动(ENSO)预报研究。SPPM是一种经验性模式误差订正方法,其主要思路是在大尺度模式预报因子场中找寻出与格点观测预报变量相关性高的信号,通过投影将这种信号反演出来,然后建立回归方程得到订正后的预报结果。本文交叉检验和滚动独立样本检验的结果表明,利用SPPM可以有效地提高BCC_CSM1.1m气候系统模式的预报技巧,尤其是在热带太平洋地区以及印度洋海区,24年交叉检验Nino3.4指数提前6个月预报的相关系数技巧可以提高8%~10%,预报误差得到显著降低。不同季节SPPM订正效果略有不同,其中对秋季的预报技巧提升最为显著。与此同时,交叉检验结果还显示,SPPM对El Nino中心纬向位置的预报也有一定程度的改进。

Using the sea surface temperature （SST） hindcast datasets produced by the climate system model of Beijing Climate Center （BCC_CSM1. lm） from 1991 to 2014, the Stepwise Pattern Projection Method （SPPM） is employed to statistically correct El Nino-South Oscillation （ENSO） prediction. The main idea of the SPPM is to produce a prediction at the predictand grid by projecting the predictor field onto its co- variance pattern with the one-point predictand after selecting the predictor domain. The SPPM significantly improves the performance of the prediction over the equatorial Pacific and Indian Ocean. The temporal correlation score has increased 8%-10% in terms of Nino3.4 SST anomaly index with a 6-month lead in the cross validation. The spatial anomaly correlation coefficients for El Nino event predictions also increase obviously by the SPPM at most lead months, particularly in autumn. Besides, the prediction for the location of warming center also can be improved, compared with that of the original BCC_CSM1.1m.