德国气候预测系统中东亚冬季风的季节预测及可预报性  

Seasonal Prediction and Predictability of the East Asian Winter Monsoon by the German Climate Forecast System

作　　者：吴昱树 陈权亮[2] 龚海楠 周涛 皇彦 WU Yushu;CHEN Quanliang;GONG Hainan;ZHOU Tao;HUANG Yan(Ulanqab Meteorological Bureau,Ulanqab,Inner Mongolia 012000;College of Atmospheric Sciences,Chengdu University of Information Technology,Chengdu 610225;Center for Monsoon System Research,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100190;Yuncheng Meteorological Bureau,Yuncheng,Shanxi 044000)

机构地区：[1]乌兰察布市气象局,内蒙古乌兰察布012000 [2]成都信息工程大学大气科学学院,成都61022 [3]中国科学院大气物理研究所季风系统研究中心,北京100190 [4]运城市气象局,山西运城044000

出　　处：《大气科学》2024年第3期1027-1042,共16页Chinese Journal of Atmospheric Sciences

基　　金：国家重点研发计划项目2018YFC1507103;国家自然科学基金项目42005057、41925020。

摘　　要：东亚冬季风(EAWM)作为北半球冬季最强的中纬度环流系统之一,主导着东亚的冬季气候。因此,开展东亚冬季风季节预测和可预报性研究具有十分重要的意义。本研究使用德国气候预测系统(German Climate Forecast System,简称GCFS2)输出的回报数据(1993~2016年)对EAWM的预测性能进行全面评估。GCFS2很好地预测了EAWM气候态的主要特征,包括西伯利亚高压、阿留申低压、东亚大槽、东亚高空急流及东亚上空的地表气温和降水,并可以熟练地预测东亚大槽及东亚地表气温的年际变化。GCFS2对一个海平面气压定义的EAWM指数(EAWMI)显示出了预测技巧,同时可以很好地预测与EAWM相关的位于海洋上的大气环流、地表气温及降水异常。GCFS2中EAWM的预测技巧主要得益于对观测中的EAWM–ENSO关系及ENSO遥相关的成功再现,模式中增强的EAWM–ENSO[强于观测,观测中整个24年(1993~2016)EAWM与ENSO的相关系数为-0.46]关系,有助于提前2个月或更长时间预测EAWM。GCFS2中12月初始化的EAWMI在去除ENSO信号后仍有0.42的预测技巧,说明有另一预测源,为冬季巴伦支—喀拉海区域海冰覆盖度(BK_SIC)。观测中BK_SIC减少,增强西伯利亚高压,EAWM从而增强;模式中BK_SIC的变化可以增加西伯利亚高压东北部的可预测性,使得12月初始化的EAWM预测技巧增加。As one of the strongest atmospheric circulations during boreal winter,the East Asian winter monsoon(EAWM)dominates the East Asia winter climate.Therefore,it is of great significance to carry out research on the seasonal prediction and predictability of the EAWM.In this study,the prediction performance of the East Asian winter monsoon(EAWM)is assessed using the seasonal hindcast data(1993–2016)from the German Climate Forecast System(GCFS2).The main features of the EAWM,including the Siberian High(SH),East Asian trough,East Asian jet stream,and surface air temperature and precipitation over East Asia,are predicted well using GCFS2.The interannual variations of the East Asian trough and surface air temperature are skillfully predicted by GCFS2,along with the EAWM index(EAWMI),which is defined by the sea level pressure.Moreover,the EAWM-related atmospheric circulation,surface air temperature,and precipitation anomalies over oceans are well predicted.The good EAWM prediction skills of GCFS2 are mainly ascribed to the successful reproduction of the EAWM–El Niño southern oscillation(ENSO)relationship and ENSO teleconnection.The correlation coefficient between the EAWM and ENSO is−0.46(1993–2016),which is higher than the observed value.This indicates that the enhanced EAWM–ENSO relationship in GCFS2 is a useful parameter in predicting EAWM at two months leading or longer.The EAWMI initialized in GCFS2 for December has a prediction skill of 0.42 after removing the ENSO signal,indicating that the sea-ice coverage in the Barents–Karabakh region in winter(BK_SIC)is another source for prediction works.The weakened BK_SIC enhances the observed SH and EAWM.Moreover,the changes of BK_SIC in the model can increase the predictability of the northeastern SH,thereby improving the EAWM prediction initialized for December.

关 键 词：德国气候预测系统(GCFS2) 季节预测 东亚冬季风(EAWM) ENSO 海冰 

分 类 号：P466[天文地球—大气科学及气象学]