双月刊

ISSN 1006-9895

CN 11-1768/O4

德国气候预测系统中东亚冬季风的季节预测及可预报性
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1.乌兰察布市气象局,乌兰察布;2.成都信息工程大学大气科学学院,成都;3.中国科学院大气物理研究所季风系统研究中心;4.运城市气象局,运城

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国家重点研发计划 2018YFC1507103,国家自然科学基金 42005057、41925020


Seasonal prediction and Predictability of East Asian winter monsoon in German Climate Forecast System
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1.YunchengMeteorological Bureau;2.Ulanqab Meteorological Bureau

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    摘要:

    本研究使用德国气候预测系统(German Climate Forecast System,GCFS2)输出的回报数据(1993~2016年)对东亚冬季风(EAWM)的预测性能进行全面评估。GCFS2很好的预测了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,同时可以很好的预测与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。

    Abstract:

    This study assesses the prediction performance of the East Asian Winter Monsoon (EAWM) using seasonal hindcast data(1993-2016) from the German Climate Forecast System(GCFS2). Main features of the EAWM are well predicted by the GCFS2,including the Siberian High, the East Asian trough , the East Asian jet stream, and the surface air temperature, and precipitation over East Asia.The interannual variations of East Asian trough and East Asian surface air temperature are skillfully predicted by GCFS2. GCFS2 shows prediction skills for the EAWM index (EAWMI) defined by sea level pressure. At the same time,the EAWM-related atmospheric circulation, surface air temperature, and precipitation anomalies over oceans are also well predicted .The high prediction skills of EAWM in GCFS2 are mainly due to the successful reproductions of the EAWM-ENSO relationship and the ENSO teleconnection. The correlation coefficient between EAWM and ENSO is -0.46 (1993 – 2016), which is stronger than that of observation. This means that the enhanced EAWM-ENSO relationship in GCFS2 is helpful to predict EAWM 2 months leading or longer. The EAWMI initialized in December GCFS2 still has 0.42 prediction skills after removing the ENSO signal, which indicates another source of prediction – the sea ice coverage in the Barents-Karabakh region in winter (BK_SIC) – works. The weaken of BK_SIC leads to the enhanced Siberian high pressure (SH) and the enhanced EAWM in the observation. The change of BK_SIC in the model can increase the predictability of northeastern SH, resulting in an increase in the EAWM prediction skills for December initialized.

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  • 收稿日期:2022-04-28
  • 最后修改日期:2022-06-27
  • 录用日期:2022-08-01
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