| 影响江南春雨年际变化的前期海洋信号及可能机理 |
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| 引用本文： | 胡雅君,刘屹岷,吴琼,王晓春.影响江南春雨年际变化的前期海洋信号及可能机理[J].大气科学,2017,41(2):395-408. |
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| 作者姓名： | 胡雅君 刘屹岷 吴琼 王晓春 |
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| 作者单位： | 1.中国科学院大气物理研究所大气科学与地球流体力学数值模拟国家重点实验室, 北京 100029 |
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| 基金项目： | 国家自然科学基金项目41328006，海洋专项XDA11010402，南京信息工程大学启动经费项目S8113046001，2015江苏双创团队，江苏高校优势学科建设工程资助项目（PAPD） |
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| 摘 要： | 基于诊断，本文计算了1982~2014年江南春雨的开始时间、结束时间和总降水量，分析了江南春雨的气候特征和年际变化，探讨了前冬Nino3.4区域海温异常与江南春雨的联系及可能机理。结果表明，江南春雨的起止时间和总降水量都具有显著的年际变化，前冬赤道东太平洋海温与江南春雨总量存在显著的正相关。前冬Nino3.4指数为正时，一方面通过Walker环流在赤道120°E附近区域激发出异常下沉运动以及低层异常反气旋，增强了南海地区低层西南气流以及水汽输送，另一方面与东太平洋海温变化相联系的印度洋增暖在赤道印度洋引发低层东风和孟加拉湾北部反气旋环流异常，进一步增强了江南地区的水汽输送；高层南亚地区则存在西风异常，对应江南上空辐散和抽吸作用加强，导致上升运动进一步增强，使得江南春雨总量增加；前冬Nino3.4指数为负时则次年春雨偏少；并且前冬El Ni?o事件的强度对春雨异常也有影响，前冬El Ni?o强（弱）的年份，海温异常的信号能（不能）持续到春季，江南春雨总量通常偏多（偏少）。另外，加入了前冬南极涛动指数和印度洋海盆一致模所建立的江南春雨总量的多元线性回归方程，其回归结果比基于单独的Nino3.4指数能更好地反映江南春雨的异常，可用于季节预测。
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| 关 键 词： | 江南春雨 年际变化 Nino3.4指数 El Ni?o强度 季节预测 |
| 收稿时间： | 2016/2/5 0:00:00 |
Preceding Oceanic Influences on the Inter-annual Variation of Spring Persistent Rain in Jiangnan of China and the Possible Mechanism |
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| HU Yajun,LIU Yimin,WU Qiong and WANG Xiaochun.Preceding Oceanic Influences on the Inter-annual Variation of Spring Persistent Rain in Jiangnan of China and the Possible Mechanism[J].Chinese Journal of Atmospheric Sciences,2017,41(2):395-408. |
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| Authors: | HU Yajun LIU Yimin WU Qiong and WANG Xiaochun |
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| Institution: | 1.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 1000292.University of Chinese Academy of Sciences, Beijing 1000493.School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044 |
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| Abstract: | Based on analysis of observations, we defined the starting and ending time and the total rainfall of the spring persistent rainfall in Jiangnan of China (SPRJ) from 1982 to 2014, and investigated the climatic characters of the SPRJ as well as its interannual variation. The relationship between the SST anomaly of Nino3.4 region in the preceding winter and the SPRJ and its physical mechanism were further studied. Results show that there is a significant positive correlation between the preceding winter Nino3.4 index and total SPRJ. The warm water can trigger an anomalous Walker circulation that leads to significant abnormal descending motions and corresponding low-level anticyclonic circulation near 120°E at the equator. The strengthened southwesterly winds of the anticyclone in the low level of the South China Sea facilitate more water vapor transport from the South China Sea to Jiangnan of China. On the other hand, the Indian Ocean SST anomaly associated with El Niño events can induce abnormal low level easterly winds in tropical Indian Ocean and anticyclone in the north of the Bay of Bengal, which also promotes the water vapor transportation. Meanwhile, the westerly anomalies in the upper troposphere above South Asia enhances divergence and pumping above Jiangnan of China, and thus are favorable for ascending motions and more SPRJ. In contrast, there is less SPRJ following a La Niña event. Moreover, the influence of El Niño on the SPRJ changes with its original intensity. With a strong El Niño in the preceding winter, the SSTA in the Pacific can persist to the following spring and there will be more SPRJ rainfall; with a weak El Niño in the preceding winter, however, the SSTA in the Pacific cannot persist to the following spring and the SPRJ total rainfall will decrease. Besides, when considering the combined effects of Nino3.4 index and Antarctic Oscillation Index and Indian Ocean Basin Mode of the preceding winter, the seasonal prediction is improved. Thus the multiple linear regression of the three predictors is useful for the prediction of the SPRJ. |
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| Keywords: | Spring persistent rainfall in Jiangnan of China Interannual variation Nino3 4 index El Niño intensity Seasonal prediction |
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