ENSO simulation and prediction in a hybrid coupled model with data assimilation
ENSO simulation and prediction in a hybrid coupled model with data assimilation
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DOI：
10.2151/jmsj.81.1
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发表时间：
2003-02
影响因子：
3.1
通讯作者：
Youmin Tang;W. Hsieh
中科院分区：
文献类型：
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作者：
Youmin Tang;W. Hsieh
利用三维变分同化方案，将几种类型的观测资料（海表温度（SST）、海平面高度距平（SLHA）和上层海洋400米深度平均热含量距平（HCA））同化到一个热带太平洋混合耦合模式中。本文介绍了1980-998年海洋分析和同化各类观测资料的海温异常（SSTA）预报技巧。SST同化除了改善了SSTA的模拟结果外，还对赤道太平洋HCA和SLHA的模拟结果有一定的改善，尤其是在赤道太平洋东部。同化海平面高度场的海洋分析改善了赤道太平洋SSTA、海平面高度场和HCA的模拟，同化HCA改善了海平面高度场和HCA的模拟。对于ENSO的预测，同化SST产生了最好的预测技能，为Nino 3区域SSTA在3个月或更短的前置时间，但严重退化的预测在较长的前置时间。最好的Nino 3 SSTA预测的提前时间超过3个月来自初始化同化的HCA和SLHA数据。同化SLHA产生的预测技能Nino 3 SSTA几乎一样好同化HCA，表明相当大的潜力，提高ENSO预测测高数据。在这项研究中，神经网络（NN）的方法被用来找到模式变量之间的非线性统计关系的同化HCA和SLHA。使用神经网络产生了更好的预测技能比使用多元线性回归。
With a 3D Var assimilation scheme, several types of observations—sea surface temperatures (SST), sea level height anomalies (SLHA), and the upper ocean 400 meter depth-averaged heat content anomalies (HCA)—were assimilated into a hybrid coupled model of the tropical Pacific. The ocean analyses, and prediction skills of the SST anomalies (SSTA) from the assimilation of each type of observation, were presented for 1980-998. SST assimilation, besides improving the simulation of SSTA, also slightly improved the HCA and SLHA simulations in the equatorial Pacific, especially in the east. The ocean analyses with the assimilation of SLHA improved the simulations of SSTA, SLHA and HCA in the equatorial Pacific, while the assimilation of HCA improved the SLHA and HCA simulations. For ENSO predictions, assimilating SST yielded the best prediction skills for the Nino3 region SSTA at lead times of 3 months or shorter, but severely degraded the predictions at longer lead times. The best Nino3 SSTA predictions for lead times longer than 3 months came from the initializations with the assimilation of HCA and SLHA data. Assimilating SLHA yielded prediction skills for the Nino3 SSTA almost as good as assimilating HCA, indicating considerable potential for improving ENSO predictions from altimetry data. In this study, a neural network (NN) approach was used to find the nonlinear statistical relations among model variables for the assimilation of HCA and SLHA. Using NN yielded better prediction skills than using multiple linear regression.