【作者】 尹训强；

【导师】 袁业立；

【作者基本信息】 中国海洋大学 ， 物理海洋学， 2015， 博士

【摘要】 海洋模式和气候系统模式在实际应用过程中往往存在较大的偏差,亟需利用较为成熟的数据同化方法在数值模拟过程中有效结合观测信息,得到更合理的模拟结果或通过改进初值场提高预测的精度。在目前海洋与气候研究的主要的数据同化方法中,集合调整Kalman滤波(EAKF)同化方法不需要扰动观测,可以充分保留数值模式的先验信息,其计算和存储方面的需求也相对较少,适合用于开展海洋和气候系统模式的数据同化。本文从方法实现的角度详细阐述了EAKF方法的基本理论和相关假定,讨论了EAKF同化方法的串行实现、并行实现、观测数据处理和集合样本处理等过程,建立了EAKF同化模块,随后将其应用于区域海洋模式、全球海洋模式和海气耦合模式中。本文首先在基于POM建立的西北太平洋环流模式中,通过EAKF同化模块开展了2005年到2009年的Argo资料的集合滤波同化实验。为对比分析区域海洋模式数据同化的效果,本研究设计了3组数值实验：控制实验(单模式运行,无数据同化)、集合自由发散实验(集合运行,无数据同化)和EAKF同化实验(集合运行,Argo数据同化)。该区域海洋模式将不同年份的初始场作为2005年不同模式样本的初始场,实现集合模式运行,开展集合自由发散实验和EAKF同化实验。自由发散实验的集合样本分布情况的分析表明：由于模式对初始场的适应过程,集合模拟结果在开始几个月内出现集合样本分布有所减少,但随后稳定在一定范围。这种构造集合初始场的方法应用在区域模式中,所有集合样本具有一定的发散性,可用来开展准确的集合滤波同化。经过EAKF同化后的集合样本分布相比无同化的自由发散实验略小,但仍保持了一定的量值,对后续的滤波同化过程不会造成不良影响。通过分析SST的集合模拟结果相对特定参考点的相关系数,模式背景误差协方差表现出较强的各向异性特征。为考察同化的效果,所有实验的结果与卫星观测SST、GTSPP温盐剖面数据和卫星高度计观测数据等进行了细致的对比。误差统计结果显示：同化结果相对卫星SST的误差比同化前在整体上减少,平均减少量为10%左右：相比独立于Argo数据的GTSPP温盐剖面观测,同化后的温度和盐度误差比同化前均有大幅减小,相对控制实验和自由发散实验的误差减少最大百分比分别达到85%和80%。同化前后的模拟结果与卫星高度计观测海面高度数据的对比显示：同化过程还增强了模式对海洋中尺度活动能力模拟能力,这一改进在黑潮及其延伸体附近,以及10°N断面上尤为突出。在基于MOM4建立的全球大洋环流模式中,开展了2008年的Argo浮标数据的EAKF同化,对比分析了4组同化实验与控制实验(未同化)的实验结果。初步同化实验(实验1)中初始温度场的扰动采用了1.0℃的振幅对上层海洋进行扰动,且无集合样本扩展,其实验结果表明：通过Argo数据同化后的温度(盐度)在上400m(500m)水层偏差显著减小,然而这些偏差在更深水层增大;SST的误差在上半年的减小值明显高于其余时段。为了考察同化的改善作用在不同深度和不同时段的差异,本文设计了3个敏感性实验。其中2个实验用于分析不同垂向扰动的敏感性：扰动深度(实验2)和扰动振幅(实验3)。实验2采用了整层水柱的扰动,扰动振幅仍为1.0℃,实验结果表明：模拟温度、盐度的偏差在整个水体中均得到减小。实验3采用较小扰动振幅(0.1℃),相比实验2说明合适的扰动振幅也非常重要。实验4采用了集合样本扩展,其扩展系数则是通过一系列的数值实验的敏感性分析所得到的5%。与其它3个实验相比,实验4的同化性能有了较大的提高。综合上述实验结果,我们认为：对于初始场的扰动应考虑模式的所有分层：合适的扰动振幅对EAKF同化具有重要作用;最优集合样本扩展系数的选择有助于提高EAKF同化的效果。基于国家海洋局第一海洋研究所地球系统模式(FIO-ESM),采用微扰动法构建了集合初始场,开展了卫星SST和SLA等数据的EAKF同化实验。本研究利用气候系统模式数据同化后的海洋模式、大气模式、海冰模式、陆面模式和海浪模式等分量模式的同化结果重构了1992-2013年的气候再分析数据,并从整体上对重构的再分析数据进行了评估。本研究采用了ERA-Interim再分析数据集、EN4温盐再分析数据集、GPCP降水数据集、AVISO沿轨道观测海浪有效波高等多种数据,对FIO-ESM同化再分析数据行了对比分析,结果显示：重构的再分析数据可以成功再现1992-2013年间上层海洋、大气运动和水汽分布、海冰变化、海浪气候态分布等方面的气候特征。在进一步研究中,该数据将用于开展气候分析和未来气候预测,提高我们对气候变化的认知水平更多还原

【Abstract】 Since there is always exist some bias during the simulation of ocean and climate system, data assimilation is urgently needed to properly absorb the observed information into the numerical model in order to increase the precision of numerical simulation or forecast. Among all the methods of data assimilation used in the study of ocean and climate, ensemble adjustment Kalman filter (EAKF) is more suitable for application due to its significant advantage. In this method, perturbing of observation is avoided, the prior information from numerical model can be well preserved, and the cost of computation and requirement of storage are also relatively smaller. In this study, the basic theory and hypothesis of EAKF method is descripted in detail on the aspect of its implementation. After the discussion on the designing EAKF program in serial and parallel way, treatments of observations and ensemble sampling, the EAKF assimilation module is developed.Using the EAKF assimilation module, the Argo temperature and salinity profiles in 2005-2009 have been assimilated into a regional ocean general circulation model of the Northwest Pacific Ocean based on Princeton Ocean Model. Three numerical tests, including the control run (without data assimilation, which serves as the reference experiment; CTL), ensemble free run (without data assimilation; EnFR) and EAKF experiment (with Argo data assimilation using EAKF), are carried out to examine the performance of this system. Using the restarts of different years as the initial conditions of the ensemble integrations, the ensemble spreads from EnFR and EAKF are all kept at a finite value after a sharp decreasing in the first few months because of the sensitive of the model to the initial conditions, and the reducing of the ensemble spread due to Argo data assimilation is not much. The distribution of the correlation, which defined as the correlation between the SST referred to the point (135°E,25°N), shows that significant correlation occurs near the referred point and a character of high anisotropy can be found in the magnified distribution. The ensemble samples obtained in this way can well represent the probabilities of the real ocean states and no ensemble inflation is necessary for this EAKF experiment. Different experiment results were compared with satellite sea surface temperature (SST) data and the Global Temperature-Salinity Profile Program (GTSPP) data. The comparison of SST shows that modeled SST errors are reduced after data assimilation; the error reduction percentage after assimilating the Argo profiles is about 10% on average. The comparison against the GTSPP profiles, which are independent of the Argo profiles, shows improvements in both temperature and salinity. The comparison results indicated that a great error reduction in all vertical layers relative to CTL and the ensemble mean of EnFR; the maximum value for temperature and salinity reaches to 85% and 80% respectively. The standard deviations of sea surface height were employed to examine the simulation ability and it indicated that the mesoscale variability is improved after Argo data assimilation, especially in the Kuroshio extension area and along the section of 10°N. All these results suggest that this system is potentially useful for improving the simulation ability of oceanic numerical models.The EAKF assimilation module is used to assimilate Argo profiles of 2008 in a global version of the Modular Ocean Model version 4 (MOM4). Four assimilation experiments are carried out to compare with the simulation without data assimilation, which serves as the control experiment. All experiment results are compared with dataset of GTSPP and satellite SST. The first experiment (Exp 1) is implemented by perturbing temperature of upper layers in the initial conditions (ICs) with an amplitude of 1.0℃ and no ensemble inflation. The results from Exp 1 show that the simulated temperature (salinity) deviation in the upper 400 m (500 m) is reduced through Argo data assimilation; however, these deviations are increased in deeper layers. The error reduction in SST is much greater during January to June than during the rest of the year. Three more experiments are designed to understand the responses in different layers and months. Two of them test model sensitivities to ICs by perturbing them vertically:one over the vertical extent of the whole water column (Exp 2) and the other employs smaller perturbation amplitude of 0.1℃ (Exp 3). Exp 2 shows that the simulated temperature and salinity deviations are systematically improved in the whole water column. Comparison between Exps 2 and 3 suggests that perturbation amplitude is important. Exp 4 tests the influence of the optimal inflation factor of 5%, which is determined by other set of numerical tests. Exp 4 improves assimilation performance much more than the other three experiments without inflation. Therefore, we conclude that the perturbation should be introduced to all model layers, proper perturbation amplitude is important for Ocean data assimilation using EAKF, and the ensemble inflation by an optimal inflation is critical to improve the skill of the EAKF analysis.Based on the Earth System Model (ESM) of FIO-ESM, a method of perturbing the initial ocean temperature by a tiny random value is used to set up the restarts for ensemble runs. The satellite SST and SLA data have been assimilated into FIO-ESM model. A climate system reanalysis data covering 1992-2013 have been reconstructed from the assimilated results in element models of ocean, atmosphere, ice, land surface, and ocean waves followed by an overall comparison of this dataset. The comparison of the experiments before and after assimilation of FIO-ESM indicated that the results are more reliable and capable to reproduce the evolution of climate system. In order to assessing the reconstructed dataset, the reanalyzing data of ERA-Interim, EN4 and GPCP, and the wave measurements by satellite altimeter provided by AVISO are employed to compare with the climate reanalysis data reconstructed by EAKF assimilation. The assessment shows that our reanalysis data can provide the reliable climate character for the upper ocean, motions and vapor distribution in atmosphere, ice evolution, and distribution of significant wave height. This dataset can be further used in the study of climate events, climate prediction and it is potentially helpful to improve our understanding on climate change.更多还原