海洋学研究 ›› 2019, Vol. 37 ›› Issue (4): 1-13.DOI: 10.3969/j.issn.1001-909X.2019.04.001.

• 研究论文 •    下一篇

海洋再分析数据集中印度洋季节内信号的评估

蒙泽1,2,3, 周磊*3, 秦箭煌3, 付红丽4, 王关锁5   

  1. 1.自然资源部 第二海洋研究所,浙江 杭州 310012;
    2.卫星遥感海洋环境动力学国家重点实验室,浙江 杭州 310012;
    3.上海交通大学 海洋学院,上海 200030;
    4.国家海洋信息中心,天津 300171;
    5.自然资源部 第一海洋研究所 海洋环境与数值模拟研究室,山东 青岛 266061
  • 收稿日期:2019-03-26 修回日期:2019-05-22 出版日期:2019-12-15 发布日期:2022-11-10
  • 通讯作者: *周磊(1979-),男,研究员,主要从事海-气相互作用方面的研究。E-mail:zhoulei1588@sjtu.edu.cn
  • 作者简介:蒙泽(1992-),男,浙江杭州市人,主要从事海-气相互作用方面的研究。E-mail:mengze@sio.org.cn
  • 基金资助:
    国家自然科学基金重大研究计划项目资助(41690121);国家自然科学基金重点项目资助(41530961);国家自然科学基金创新研究群体项目资助(41621064)

Assessment of intraseasonal variabilities over Indian Ocean based on oceanic reanalysis datasets

MENG Ze1,2,3, ZHOU Lei*3, QIN Jian-huang3, FU Hong-li4, WANG Guan-suo5   

  1. 1. Second Institute of Oceanography, MNR, Hangzhou 310012, China;
    2. State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou 310012, China;
    3. School of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, China;
    4. National Marine Data and Information Service, Tianjin 300171, China;
    5. Lab of Marine Science and Numerical Modeling, First Institute of Oceanography, MNR, Qingdao 266061, China
  • Received:2019-03-26 Revised:2019-05-22 Online:2019-12-15 Published:2022-11-10

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摘要： 季节内变化是热带气候的重要影响因素。以北半球夏、冬两季为例,对比了海洋再分析数据集(ECCO2、SODA3和CORA)与卫星观测海表面温度(SST)以及海表面高度(SSH)在印度洋区域季节内信号的差异;还以冬季MJO(Madden Julian Oscillation)事件和夏季季风事件为例,对比分析了不同强迫过程中再分析数据与观测数据的差异与原因。结果表明,再分析产品在近岸海域具有与观测数据相当的季节内波动,但是在大洋内部,除CORA在赤道中印度洋略强于观测数据,其他再分析数据季节内波动明显弱于观测数据20%以上,SODA3甚至达到60%以上。具体来说,在东传MJO和北传CIO (Central Indian Ocean mode) 事件期间,再分析数据中热力强迫下的季节内SST变化呈现较好,仅ECCO2和CORA在相位上滞后观测数据5～10 d。在夏季CIO的SST异常西传期间,再分析数据中动力强迫下的季节内变化呈现较差,ECCO2和CORA仅在85°E～95°E呈现出微弱的西传信号,SODA3的SST异常并未西传;而且强度上,90°E～100°E附近所有再分析SST异常均弱于观测数据50%以上。通过对比东印度洋上混合层季节内流速发现,CIO事件期间再分析流速振幅弱于观测数据51.42%,且CIO波峰期间与观测数据的相对偏差达到65.16%。再分析数据中动力强迫的信号呈现较弱,可能是导致季节内变化在大洋中部明显弱于观测数据的原因。因此,要提升再分析产品中季节内信号,不仅需要关注热力与动力强迫的修正,更需要扩大海洋观测以增加数据同化,尤其是在赤道印度洋海域需要加强对海流的观测。

关键词: 印度洋, 季节内振荡, MJO, CIO, 海洋再分析数据集

Abstract: Intraseasonal variabilities (ISVs) are important factors in tropical climate. The ISVs like sea surface temperature (SST) and sea surface height (SSH) from oceanic reanalysis ECCO2, SODA3 and CORA were compared with the satellite observations during boreal summer and winter. Intraseasonal SST anomalies were also further compared during MJO and CIO cases. Results show that the ISVs from reanalysis data have the same clear variabilities nearshore as the observations, while within the ocean, the ISVs standard deviation (STD) of reanalysis data is weaker than that of the observation data for at least 20%, SODA3 even faces the differences up to 60%. During the eastward propagation of MJO and northward propagation of CIO, the SST anomalies induced by thermal force is well simulated by the reanalysis data, only ECCO2 and CORA encounter a phase lag of 5-10 days. During the westward propagation of SST anomalies at CIO, reanalysis data have an awful simulation for this dynamic forced ISVs. ECCO2 and CORA propagate to the west in a small area from 85°E-95°E feebly, and even that in SODA3 doesn’t show its westward signals. Temperature in all reanalysis data is a half weaker than the observations at eastern Indian Ocean (90°E-100°E). Through comparison in intraseasonal velocity anomalies, the STD of reanalysis data is far less than RAMA (51.42%), the averages among the peaks of velocity are also weaker than observations for 65.16%. This suggests that improper simulation of dynamically-forcing ISVs may lead to the weaker variabilities within Indian Ocean. Therefore, in order to upgrade the ISVs in reanalysis, it is necessary to modify the heat forcing as well as dynamic forcing in atmospheric modeling and, more importantly, add up the oceanic assimilation. Equatorial Indian Ocean, as the prevalent area for ISV events and the area with clear difference in STD, is surely a region for more observation plans with oceanic variabilities like currents.

Key words: Indian Ocean, intraseasonal variability, Madden-Julian Oscillation, Central Indian Ocean mode, ocean reanalysis datasets

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