The tropical Pacific–Indian Ocean associated mode simulated by LICOM2.0
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Oceanic general circulation models have become an important tool for the study of marine status and change. This paper reports a numerical simulation carried out using LICOM2.0 and the forcing field from CORE. When compared with SODA reanalysis data and ERSST.v3b data, the patterns and variability of the tropical Pacific–Indian Ocean associated mode (PIOAM) are reproduced very well in this experiment. This indicates that, when the tropical central–western Indian Ocean and central–eastern Pacific are abnormally warmer/colder, the tropical eastern Indian Ocean and western Pacific are correspondingly colder/warmer. This further confirms that the tropical PIOAM is an important mode that is not only significant in the SST anomaly field, but also more obviously in the subsurface ocean temperature anomaly field. The surface associated mode index (SAMI) and the thermocline (i.e., subsurface) associated mode index (TAMI) calculated using the model output data are both consistent with the values of these indices derived from observation and reanalysis data. However, the model SAMI and TAMI are more closely and synchronously related to each other.
Key wordsocean general circulation model numerical simulation tropical Pacific–Indian Ocean associated mode subsurface ocean temperature anomaly
大洋环流模式已成为研究海洋状态及其变化的重要工具. 本文利用中科院大气所的LICOM2.0模式和来自CORE的强迫场进行了一个数值模拟, 并重点分析了热带太平洋-印度洋联合模(以下简称联合模)特征在模式中的表现. 与SODA再分析资料和ERSST. V3b等观测资料进行对比发现, 数值试验很好地再现了联合模的形态和变率. 即, 当热带中西印度洋和中东太平洋异常偏暖/冷时, 热带东印度和西太平洋相应地偏冷/暖. 这进一步证实了联合模是热带太平洋—印度洋非常重要的一个模态, 不仅在表层海温异常场上显著, 而且在次表层海温异常场上表现更为明显. 研究还发现, 模式海温资料计算得到的表层联合模指数和温跃层(次表层)联合模指数与从观测资料中计算得到的都较为一致. 然而, 模式中这两个指数相关性更好而且更加同步.
关键词大洋环流模式 数值模拟 热带太平洋-印度洋联合模 次表层海温异常
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Two anonymous reviewers provided careful comments on the submitted manuscript, which helped improve the article, and for which we are grateful. The authors also sincerely thank Professor Hailong LIU for his help with LICOM2.0. This work was supported by the National Basic Research Program of China (Grant No. 2013CB956203), the National Natural Science Foundation of China (Grant Nos. 41490642 and 41575062), and the Open Fund of LASG.
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