doi:  10.3878/j.issn.1006-9895.1610.16194
CMIP5多模式模拟两类El Niño海表盐度分布及与降水的关系

Comparison of Sea Surface Salinity-Distribution and Its Relationship with Precipitation between the Two Types of El Niño in CMIP5 Model
摘要点击 226  全文点击 240  投稿时间:2016-07-14  最后修改时间:2016-11-18
查看HTML全文  查看全文  查看/发表评论  下载PDF阅读器
基金:  中国科学院战略性先导科技专项“热带西太平洋海洋系统物质能量交换及其影响”XDA11010304,国家自然科学基金项目41576026、41376039、41376019,江苏高校优势学科(PAPD)建设工程
中文关键词:  CMIP5模式  两类El Niño  海表温度  海表盐度  降水
英文关键词:  CMIP5 model  Two types of El Niño  SST (Sea surface temperature)  SSS (Sea surface salinity)  Precipitation
        
作者中文名作者英文名单位
白文蓉BAI Wenrong南京信息工程大学大气科学学院, 南京 210044
智海ZHI Hai南京信息工程大学大气科学学院, 南京 210044
林鹏飞LIN Pengfei中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室, 北京 100029
引用:白文蓉,智海,林鹏飞.2017.CMIP5多模式模拟两类El Niño海表盐度分布及与降水的关系[J].大气科学,41(3):629-647,doi:10.3878/j.issn.1006-9895.1610.16194.
Citation:BAI Wenrong,ZHI Hai,LIN Pengfei.2017.Comparison of Sea Surface Salinity-Distribution and Its Relationship with Precipitation between the Two Types of El Niño in CMIP5 Model[J].Chinese Journal of Atmospheric Sciences (in Chinese),41(3):629-647,doi:10.3878/j.issn.1006-9895.1610.16194.
中文摘要:
      利用CMIP5提供的25个工业革命前控制试验(piControl)模拟数据评估了热带太平洋两类El Niño(即东部EP和中部CP型El Niño)的海表盐度(SSS)空间结构差异及其与海表温度(SST)和降水的关系。结果表明:(1)大部分模式能够模拟出EP和CP型空间结构,两类El Niño中的SST、降水和SSS的空间技巧评分依次减小,其中,EP型SST和降水水平分布的模拟能力强于CP型,SSS则为CP型强于EP型,CP型模拟的SST、SSS和降水异常中心位置较EP型偏西且强度偏弱;(2)CP型SST、降水和SSS三者空间分布的线性一致性比EP型好,即在CP型中,SST影响降水,进而影响SSS,同时SSS对SST调制的反馈机制较显著,而对于EP型,由于海洋水平平流和非局地效应等因素,使得SST与SSS空间对应较差;(3)依据多模式模拟的SSS空间技巧评分高低将CMIP5模式分为两类,技巧评分低(高)的模式模拟的SST、SSS和降水异常值的中心位置偏西(偏东),引起中心位置偏移的原因与模式模拟赤道太平洋冷舌的位置有关,即赤道太平洋冷舌西伸显著,导致发生El Niño时SST异常变暖西伸显著,进而使得降水异常和SSS异常位置偏西。同时,技巧评分低的模式还易出现向东南延伸的负SSS异常,原因是双赤道辐合带的东南分支过于明显,即降水偏多,导致SSS偏淡。SSS变化会影响ENSO的发生发展。因此,探讨两类El Niño盐度分布的差异及相关物理场的关系,为提高模式的气候模拟和预测提供有益的借鉴。
Abstract:
      Based on the 25 models in the Phase 5 of Coupled Model Intercomparison Project (CMIP5) piControl simulation, the present study assessed the spatial distribution of the sea surface salinity (SSS) for the Eastern-Pacific (EP) and the Central Pacific (CP) El Niño in the tropical Pacific Ocean and explored the relationships among SSS, the sea surface temperature (SST) and precipitation. The results illustrate that: (1) Most of CMIP5 models can realistically reproduce the features of the two types of El Niño. The spatial skill scores of SST simulation show the best performance, followed by those of precipitation and SSS. The simulated horizontal distributions of SST and precipitation anomalies for the EP El Niño were better than those for the CP El Niño, but the results were opposite for SSS simulations. During the CP El Niño period, the positions of maximal SST, precipitation and SSS anomalies were clearly shifted to the west and slightly weaker compared with that during the EP El Niño. (2) The correlations among SST, precipitation and SSS in the CP El Niño were higher than that in the EP El Niño, which showed that SST directly affected precipitation, which then subsequently affected SSS significantly. In addition, SSS had an obvious feedback on SST. Compared with that in EP El Niño, the interaction between SST and SSS might be weaker because of the horizontal advection, the nonlocal effects and other related ocean physics. (3) Based on each SSS skill score simulated, CMIP5 models were divided into two groups. It was found that the maximal position of the variability with the low (high) scores models were located westward (eastward) in the equatorial Pacific, probably due to the position of the equatorial Pacific cold tongue. When the Pacific cold tongue extended westward, the warm SST anomalies moved westward remarkably during the El Niño events. This resulted in westward shift of precipitation and SSS anomalies at the same time. Meanwhile, the negative SSS anomalies extended southeastward in the low skill score models, which were probably attributed to the effects of the southeastern branch of the double ITCZ that caused more precipitation and freshen SSS. The SSS variability showed a close relationship with SST associated with the evolution of ENSO. Furthermore, the present study in the variation of simulated SSS spatial distribution and related physical fields can provide some information for improving climate prediction in the future.
主办单位:中国科学院大气物理研究所 单位地址:北京市9804信箱
联系电话: 010-82995051,010-82995052传真:010-82995052 邮编:100029 Email:dqkx@mail.iap.ac.cn
本系统由北京勤云科技发展有限公司设计
京ICP备09060247号