doi:  10.3878/j.issn.1006-9895.1703.16256
BCC二代气候系统模式的季节预测评估和可预报性分析

Evaluation and Predictability Analysis of Seasonal Prediction by BCC Second-Generation Climate System Model
摘要点击 323  全文点击 192  投稿时间:2016-11-02  
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基金:  公益性行业(气象)科研专项GYHY201506013,国家重点研发计划项目2016YFA0602100、国家自然科学基金项目41405080、气象预报业务关键技术发展专项YBGJXM (2017)05和7B
中文关键词:  季节预测  确定性预报  概率预报  可预报性  BCC二代模式
英文关键词:  Seasonal prediction  Deterministic forecast  Probabilistic forecast  Predictability  BCC Second-Generation Climate System Model
              
作者中文名作者英文名单位
吴捷WU Jie中国气象局国家气候中心气候研究开放实验室, 北京 100081;南京信息工程大学大气科学学院, 南京 210044;南京大学大气科学学院/中国气象局-南京大学气候预测研究联合实验室, 南京 210023
任宏利REN Hongli中国气象局国家气候中心气候研究开放实验室, 北京 100081;南京大学大气科学学院/中国气象局-南京大学气候预测研究联合实验室, 南京 210023
张帅ZHANG Shuai南京信息工程大学地理与遥感学院, 南京 210044
刘颖LIU Ying中国气象局国家气候中心气候研究开放实验室, 北京 100081
刘向文LIU Xiangwen中国气象局国家气候中心气候研究开放实验室, 北京 100081
引用:吴捷,任宏利,张帅,刘颖,刘向文.2017.BCC二代气候系统模式的季节预测评估和可预报性分析[J].大气科学,41(6):1300-1315,doi:10.3878/j.issn.1006-9895.1703.16256.
Citation:WU Jie,REN Hongli,ZHANG Shuai,LIU Ying,LIU Xiangwen.2017.Evaluation and Predictability Analysis of Seasonal Prediction by BCC Second-Generation Climate System Model[J].Chinese Journal of Atmospheric Sciences (in Chinese),41(6):1300-1315,doi:10.3878/j.issn.1006-9895.1703.16256.
中文摘要:
      本文利用国家气候中心(BCC)第二代季节预测模式系统历史回报数据,从确定性预报和概率预报两个方面系统地评估了该模式对气温、降水和大气环流的季节预报性能,并与BCC一代气候预测模式的结果进行了对比,重点分析了二代模式的季节可预报性问题。结果显示,BCC二代模式对全球气温、降水和环流的预报性能整体上优于一代模式,特别在热带中东太平洋、印度洋和海洋大陆地区的温度和降水的预报效果改进尤为明显。这些热带地区降水预报的改进,可以通过激发太平洋—北美型(PNA)、东亚—太平洋型(EAP)等遥相关波列提升该模式在中高纬地区的季节预报技巧。分析表明,厄尔尼诺和南方涛动(ENSO)信号在热带和热带外地区均是模式季节可预报性的重要来源,BCC二代模式能够较好把握全球大气环流对ENSO信号的响应特征,从而通过对ENSO预报技巧的改进有效地提升了模式整体的预报性能。从概率预报来看,BCC二代模式对我国冬季气温和夏季降水具备一定的预报能力,特别是对我国东部大部分地区冬季气温正异常和负异常事件预报的可靠性和辨析度相对较高。因此,进一步提高模式对热带大尺度异常信号和大气主要模态的预报能力、加强概率预报产品释用对提高季节气候预测水平具有重要意义。
Abstract:
      Based on the hindcast data of Beijing Climate Center (BCC) second-generation seasonal prediction model system (BCCv2), the seasonal prediction performance of 2-m temperature, precipitation and circulation was evaluated by employing deterministic and probabilistic forecast verification methods. BCCv2 simulations were compared with that of BCC first-generation prediction system (BCCv1) to further analyze the seasonal climate predictability. The results show that the performance of the BCCv2 is significantly improved compared to that of the BCCv1 especially in the tropical eastern Pacific Ocean, the Indian Ocean and the Maritime Continent areas. The improvements of precipitation prediction in the tropics are the major reason for the improvements of the forecast skill in the mid-high latitudes through realistic description of the atmospheric teleconnection patterns, such as the Pacific-North American (PNA) and East Asian -Pacific (EAP) patterns. The El Niño and South Oscillation (ENSO) signal is the dominant source of predictability for both the tropical and extra-tropical regions. The global atmosphere circulation in response to ENSO signal is accurately described in BCCv2, which improves its overall prediction performance by advancing ENSO prediction skill. From the perspective of probabilistic prediction, the BCCv2 showed useful prediction skills for the prediction of China surface air temperature anomalies in winter and precipitation anomalies in summer especially for the above normal (AN) and below normal (BN) events of winter temperature in eastern China with relatively high reliability and resolution. Therefore, further improvements of the capability of the BCCv2 in predicting tropical large-scale anomalies and primary climate variability modes and the application of probabilistic prediction products of this model are two key issues for improving seasonal climate prediction in China.
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