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一种新的模式倾向误差估计算法及其在ENSO模拟中的应用
何群,高艳秋,唐佑民,张继才
1.浙江大学海洋学院;2.自然资源部第二海洋研究所卫星海洋环境动力学国家重点实验室;3.河海大学海洋学院
摘要:
气候模式是我们理解、模拟和预报气候演变的重要工具。然而即使是目前最先进的耦合模式,其模拟和预报与大气/海洋的真实状态相比,仍存在较大偏差,这是由于在模式的倾向方程中不可避免地存在系统性的误差(倾向误差)。因此,减少模式倾向误差对改进模式的模拟和预报效果具有重要意义。该研究首先发展了一种新的计算模式倾向误差的估计算法——基于局地集合变换卡尔曼滤波器(Local Ensemble Transform Kalman Filter, LETKF)同化技术的倾向误差估计算法。在此基础上,将新发展的算法应用到Zebiak-Cane(ZC)模式,通过同化海表面温度异常(Sea Surface Temperature Anomaly, SSTA)数据,估计随时空变化的倾向误差。进一步分析发现,倾向误差和ZC模式的模拟偏差具有高度相关性。使用计算得到的倾向误差订正模式,进行积分模拟,结果显示订正后的模式改善了对厄尔尼诺—南方涛动(El Nino-Southern Oscillation,ENSO)的一些重要特征的模拟,表明新发展的模式倾向误差估计算法十分有效且在ENSO模拟中具有较好的应用价值。此外,这种新的模式倾向误差算法,计算高效简便,可便捷地应用于各模式中,利于推广。
关键词:  模式倾向误差  参数估计  局地集合变换Kalman滤波器  Zebiak-Cane模式
DOI:10.11693/hyhz20220100009
分类号:
基金项目:国家重点基础研究发展计划(973计划)
A NEW ESTIMATION ALGORITHM FOR MODEL TENDENCY ERRORS AND THE APPLICATION IN ENSO
HE Qun1, GAO Yan-Qiu2, TANG You-Min3, ZHANG Ji-Cai4
1.Ocean College, Zhejiang University;2.State Key Laboratory of Satellite Ocean Environment Dynamics,Second Institute of Oceanography,Ministry of Natural Resources;3.College of Oceanography, Hohai University;4.Ocean College,Zhejiang University
Abstract:
Climate models are important tools for us to understand, simulate and forecast the evolution of the climate. However, even with the current state-of-the-art coupled models, due to the inevitable systematic errors in the tendency equations of model (model tendency error), the simulations and forecasts are still far from the true state of the atmosphere/ocean. Therefore, reducing the model tendency error is of great significance to improve the simulation and forecasting effect of the model. The paper firstly develops a new estimation algorithm for calculating the tendency error of the model based on the Local Ensemble Transform Kalman Filter (LETKF) assimilation technique. On this basis, the newly developed algorithm is applied to the Zebiak-Cane (ZC) model to estimate the space-time dependent tendency error by assimilating the observed data of Sea Surface Temperature Anomaly (SSTA). Further analysis found that there is a high correlation between the tendency error and the simulation error of the ZC model. Using the calculated tendency error to correct the model, an integral simulation is carried out. The results show that the revised model improves some important characteristics of El Nino-Southern Oscillation (ENSO), indicating that the newly developed model tendency error estimation algorithm is very effective and has good application value in ENSO simulation. In addition, this new model tendency error algorithm is computationally efficient and simple, and can be easily applied to various models, which is beneficial to popularization.
Key words:  model tendency error  parameter estimation  LETKF  Zebiak-Cane model
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