梁萍,陈丽娟,丁一汇,何金海,周兵. 2018. 长江梅雨的长期变率与海洋的关系及其可预报性研究[J]. 气象学报, 76(3):379-393, doi:10.11676/qxxb2018.009
长江梅雨的长期变率与海洋的关系及其可预报性研究
Relationship between long-term variability of Meiyu over the Yangtze River and ocean and Meiyu's predictability study
投稿时间:2017-06-20  修订日期:2017-12-20
DOI:10.11676/qxxb2018.009
中文关键词:  梅雨  多尺度变化  可预报性  海温
英文关键词:Meiyu  Multi-scale variation  Predictability  Sea surface temperature
基金项目:国家重点研发计划"重大自然灾害监测预警与防范"专项(2017YFC1502301)、国家自然科学基金项目(41775047、41275073)、国家重点基础研究发展计划项目(2015CB453203)、上海市气象局研究型专项(YJ201604)、公益性行业(气象)科研专项(GYHY201406001)、全球变化研究国家重大科学研究计划项目(2015CB953900)、国家气象科技创新工程"次季节至季节气候预测和气候系统模式"、中国气象局青年英才专项。
作者单位
梁萍 上海区域气候中心, 上海, 200030 
陈丽娟 国家气候中心, 北京, 100081
南京信息工程大学气象灾害预报预警与评估协同创新中心, 南京, 210044 
丁一汇 国家气候中心, 北京, 100081 
何金海 南京信息工程大学气象灾害预报预警与评估协同创新中心, 南京, 210044
南京信息工程大学大气科学学院, 南京, 210044 
周兵 国家气候中心, 北京, 100081 
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中文摘要:
      采用最新发布的梅雨国家标准资料,以长江区域梅雨为代表,在分析区域梅雨的多时间尺度变化特征的基础上,从海洋外强迫影响因子角度探讨了梅雨的可预报性来源,进一步综合海洋背景变率和预测模型回报试验讨论梅雨异常的可预报性。结果表明:(1)长江梅雨呈现周期为3-4、6-8、12-16、32、64 a的多时间尺度变化分量和长期减少趋势。其中,3-4 a准周期变化是梅雨异常变化的主要分量。梅雨的干湿位相转变受12-16 a的准周期变化调制,极端涝年易出现在12-16 a准周期变化湿位相和3-4 a变化分量峰值位相叠加的情况。(2)长江梅雨的各准周期变化分量有不同的海洋外强迫背景,是梅雨可预报性的重要来源。与时间尺度较短的年际变化分量相关联的海温关键区主要分布于热带,而与时间尺度较长的年代际或多年代际变化分量相联系的海温关键区则来自中高纬度。3-4 a准周期变化分量的海洋外强迫强信号随季节变化由前冬的ENSO (厄尔尼诺-南方涛动)转为春末夏初的印度洋偶极子(IOD)。6-8和12-16 a年准周期变化分量的海洋强迫关键区主要位于太平洋。准32和准64 a周期振荡则受北太平洋多年代际变化(PDO)和北大西洋多年代际变化(AMO)的共同影响。梅雨的长期变化趋势则与全球变暖背景及以PDO为代表的年代际海洋外强迫因子相联系。(3)尽管梅雨异常与ENSO的正相关关系呈现减弱趋势,但20世纪70年代以后的梅雨异常年际变化分量的可预报性有所增大。(4)将梅雨各变化分量作为预测对象分别建模,进一步构建梅雨异常预测统计模型。采用该模型对近5年梅雨预测进行独立样本检验,有较好的回报效果,验证了梅雨异常年际分量可预报性的稳定性以及基于多时间尺度分离建立梅雨预测模型的优越性。
英文摘要:
      By using the latest Meiyu data developed based on national Meiyu monitoring criteria and taking Meiyu over the Yangtze River as an example, the multi-scale variation of Meiyu is analyzed and the predictability of Meiyu from the perspective of external forcing of sea surface temperature (SST) is investigated. The predictability of Meiyu anomalies on the interannual time scale is discussed by combining the variability of background SST and the hindcast of a predictive model. Results suggest that Meiyu over the Yangtze River shows a long-term decreasing trend and multi-scale quasi-periodic oscillations including 3-4-year, 6-8-year, 12-16-year, 32-year and 64-year. The 3-4-year quasi-periodic variation is the main component of Meiyu anomaly. Conversion between dry and wet phases of Meiyu is modulated by the 12-16-year quasi-periodic oscillation. The extreme flood Meiyu usually occurs simultaneously with the wet phase of the 12-16 year oscillation and the peak phase of the 3-4 year oscillation. Different components of Meiyu over the Yangtze River correspond to different SST external forcing background. Key regions of SST associated with interannual variation of Meiyu are located in the tropics. While Meiyu variations on longer time scales including inter-decadal and multi-decadal are related to SST in the middle and high latitudes. The SST signal of the 3-4-year quasi-periodic component converts from ENSO (El Niño-Southern Oscillation) in the preceding winter to IOD (Indian Ocean Dipole) in late spring and early summer. SST key regions of 6-8-year and 12-16-year oscillations are mainly located in the Pacific. 32-year and 64-year oscillations are influenced by multi-decadal changes of the North Pacific (Pacific Decadal Oscillation, PDO) and the Atlantic (Atlantic Multi-decadal Oscillation, AMO). The long-term changing trend of Meiyu is associated with both the global warming and the decadal change of SST especially PDO. Although the positive correlation between Meiyu anomaly and ENSO shows a decreasing trend, the predictability of the interannual variation of Meiyu anomaly has improved since the 1970s. Finally, a prediction model of Meiyu anomaly is established by combining models of multi-scale components. Independent samples test of Meiyu anomalies in the latest 5 years exhibits an encouraging hindcast performance, which verifies the stability of predictability of interannual component of Meiyu anomaly and the superiority of Meiyu prediction model based on multiple time scale separation.
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