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IOCAS ICM及其ENSO实时预测试验和改进
高川1,2, 王宏娜1,2, 陶灵江1,3, 张荣华1,2,3
1.中国科学院海洋研究所海洋环流与波动重点实验室 青岛 266071;2.青岛海洋科学与技术国家实验室 青岛 266237;3.中国科学院大学 北京 100049
摘要:
厄尔尼诺和南方涛动(ENSO)是仅次于季节变化的最强年际气候变率信号,对全球气候和天气产生重要影响。准确、及时、有效地预报ENSO事件的发生和演变具有重大的实用意义。以中国科学院海洋研究所冠名的中等复杂程度海气耦合模式(IOCAS ICM),每月定期进行ENSO实时预报试验。IOCAS ICM实时预报结果目前收录于美国哥伦比亚大学国际气候研究所(IRI),以作进一步的集成分析和应用。该模式的大气部分是一个描述对海表温度(SST)年际异常响应的风应力异常经验模式,海洋部分包括了动力海洋模块、SST距平模块(嵌套于动力海洋模块中)和次表层上卷海温(Te)距平模块三部分。IOCAS ICM的特点之一是开发了次表层海温反算优化这一创新技术,可有效改进热带太平洋SST异常的模拟和预报。IOCAS ICM和其他海气耦合模式的最新预报结果(以2017年9月为初条件)表明,2017年年末热带太平洋会处于一个SST冷异常态,最大变冷中心集中在赤道东太平洋,但并不足以达到拉尼娜(La Niña)事件的水平,SST冷异常可能会在2018年春季逐渐减弱,转化为中性状态。此外,本文还对四维变分资料同化方法(4D-Var)以及条件非线性最优扰动方法(CNOP)在IOCAS ICM中的应用进行了讨论。
关键词:  IOCAS ICM  ENSO实时预报试验  资料同化  CNOP技术
DOI:10.11693/hyhz20170500132
分类号:P732
基金项目:中国科学院战略性先导科技专项(A类)项目,XDA11010105号,XDA11020306号;国家自然科学基金项目,41705082号,41690122号,41690120号,41475101号。
THE IOCAS ICM AND ITS IMPROVEMENTS IN REAL-TIME ENSO PREDICTIONS
GAO Chuan1,2, WANG Hong-Na1,2, TAO Ling-Jiang1,3, ZHANG Rong-Hua1,2,3
1.Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China;2.Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China;3.University of Chinese Academy of Sciences, Beijing 100049, China
Abstract:
El Niño and Southern Oscillation (ENSO), the strongest signal of interannual variability on the Earth, and have great impacts on climate and weather worldwide. It is of great practical significance to accurately and effectively make real-time ENSO prediction. An intermediate coupled model (ICM) is used at the Institute of Oceanology, Chinese Academy of Sciences (IOCAS), named the IOCAS ICM, to predict the sea surface temperature (SST) evolution in the tropical Pacific. Recently, the IOCAS ICM has been routinely used to predict the real-time ENSO events, whose result are collected by the International Research Institute for Climate and Society (IRI) for further analyses and application. The atmosphere component of the IOCAS ICM is an empirically statistical wind stress anomaly model, and represents the response of wind stress anomaly to the SST anomaly field. The IOCAS ICM includes three modules of a dynamic ocean model, an SST anomaly model incorporated into the dynamic ocean model and a Te (the temperature of subsurface water entrained into the mixed layer) model. One distinguishing feature of the IOCAS ICM is an empirical parameterization of Te. This novel approach can effectively improve the simulation and prediction of the SST evolution in the tropical Pacific. Recent prediction results (predicted from September 2017) with IOCAS ICM and other coupled model show that the SST in tropical Pacific gradually evolves into cold condition, with its large anomaly being centered in the eastern Pacific. But the strength of the coldness may not reach the level of La Niña event. The cold SST condition may be gradually weakened in the spring 2018 and become a normal condition. Additionally, the applications of four dimensional variational (4D-Var) data assimilation method and the conditional nonlinear optimal perturbation (CNOP) approach into the IOCAS ICM are also discussed.
Key words:  IOCAS ICM  real-time ENSO prediction  data assimilation  CNOP approach
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