海洋生态系统动力学数值模拟与同化研究
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摘要
随着人类对气候变化的日益重视,如何更加精确地、合理地模拟气候的演变规律及评估其对人类生存环境带来的影响成为一个关键的科学问题。作为这一问题中的重要一环,海洋生态系统数值模拟成为一个研究热点,这也对海洋生态系统动力学模型提出了更高的要求。在这种背景下,海洋生态系统动力学模型在模型维数、应用范围与模型复杂性程度等方面得到快速发展。但是,在以往大部分海洋生态模拟工作中,研究者们忽略了或者没有考虑关键生态参数在空间上存在变化这一特征,并认为在模拟区域中存在普适的一组参数,导致模拟结果与观测资料之间存在较大的区域性误差,并且这种误差不能通过改进背景物理环境或增加新的生态机制而得到有效控制。这个问题在大洋尺度甚至全球尺度的海洋生态系统模拟中表现地尤为突出,为了弥补这一缺陷,一些研究者根据实测数据估计局地参数或者是分区优化参数,并采用一些方法对参数进行平滑,虽然这些做法在一定程度上实现了参数的空间变化,却没有将模型与有限的观测作为一个整体来考虑,不能优化参数至全局最优,因此不能准确地描述模型参数在空间上的变化。本文使用一种全新的方法即参数的空间分布来实现参数的空间变化,并深入研究了是否能通过此方法改进模拟与同化结果。
在气候耦合模式FOAM的气候态背景场下,本文建立了一个简单的全球尺度上的三维海洋生态系统动力学模型及其伴随模型,在500米深度内进行数值模拟与同化研究。通过在底层添加营养盐修正项的方式解决了模拟过程中营养盐不断耗散的问题,保障了模型的平稳运行。
结合考察敏感性函数与代价函数关于模型参数的梯度量级两种方法,对各模型参数进行了敏感性分析,挑选出对模拟结果敏感且不相关的五个参数作为控制变量。在孪生数值实验中,通过同化模型产生的‘观测数据’来反演控制变量。首先通过反演无空间变化的控制变量验证了伴随模型的正确性,继而基于参数的空间分布,利用伴随方法反演了给定的空间变化的控制变量,证明了参数空间分布的有效性以及在模型中利用伴随同化方法反演空间变化的参数的可行性。
在实际实验中,本文在全球尺度上通过伴随方法同化SeaWiFS叶绿素资料。在使用参数空间分布后,表层浮游植物生物量(氮)的平均误差下降至0.0584mmolN·m-3,与不考虑参数空间变化的同化实验相比下降了62.4%。反演出的参数在空间上呈现显著的变化特征,证明了空间变化的参数比参数取常数更合理。通过使用参数的空间分布,SeaWiFS图像上大部分的区域特征都能被很好地模拟出来,证明模拟与观测之间的区域性误差得到了有效控制。本文发现同化结果与独立点的数量以及影响半径的选取有很大关系:独立点数量越多,同化结果就越好;影响半径应根据独立点的设置来选取,过大或过小的影响半径都对同化结果不利。然而,由于缺少必要的数据支持,模型没有考虑一些海区存在的铁限制因素,导致模型不能正确模拟南大洋等海域的营养盐水平,这表明了在全球尺度上进行海洋生态系统数值模拟时,铁限制等机制是应当考虑的。
People has paid more attention to the global climate change in recently years, thus to improve numerical modeling ability on climate change and to evaluate its influence on human living become significantly important. As a crucial role in the issue, more researchers are focusing on marine ecosystem modeling and more robust models are urgent in demand. Under such circumstances, the number of model dimensions, application area and model complexity are undergoing quickly development. However, in most previous studies, the spatial variations of key parameters are always ignored or not taken into account at all, and researchers always assumes that a set of optimized parameters is suitable for the whole study area. It results in big spatial differences between modeled values and the observations, and these differences can not be effectively reduced by improving physical background or adding new ecological mechanisms, especially when modeling on basin or global scales. To solve the problem, spatially variable parameters are needed. Traditional ways are optimizing local parameters or estimating parameters in each sub-area independently, and then smoothing the parameter values. In such a way, spatially variable parameters are obtained, but the model and the observations are not taken as a whole and the parameters are not optimized globally, thus these spatially variable parameters can not represent the reality accurately. In this study, a new method (spatial parameterization) is utilized to realize spatial variations of the parameters. The article tested if the new method can help us improve marine ecosystem modeling.
In this effort, a simple 3-D marine ecosystem model and the corresponding adjoint model are constructed on a global scale under climatological physical environment provided by FOAM. The numerical study and assimilation experiments are implemented within a depth of 500m. A relaxed term of nutrient in the bottom layer is designed to prevent nutrient from dissipation, which insures the positive simulation.
Five uncorrelated and sensitive parameters are selected as control variables by a conventional sensitivity analysis and investigating the gradients of the cost function with respect to each parameter, respectively. In twin experiments, model-generated data are served as fictitious observations, and they are assimilated into the model to estimate the control variables. Firstly, the spatially-constant control variables are estimated, which validates the adjoint model. Furthermore, based on spatial parameterization, the spatially varying control variables are estimated by the adjoint method, which indicates the validity of spatial parameterization and the feasibility of estimating spatially varying parameters by the adjoint method.
In real experiments, SeaWiFS chlorophyll-a data are assimilated into the model based on the adjoint method on a global scale. After spatial parameterization is utilized, the mean error of phytoplankton in the surface layer (nitrogen) reduced to 0.0584 mmolN·m-3, which is a reduction of 62.4% compared with the assimilation experiment without spatial parameterization. The assimilation results show that spatially varying parameters are more reasonable than a set of constants, since the selected parameters exhibit great spatial variations. By utilizing spatial parameterization, most regional features in SeaWiFS imagery can be reproduced. The assimilation results are sensitive to the amount of independent grids and the selected influencing radius: the more independent grids we use, the better the assimilation results are; the influencing radius should be suitable for the configuration of the independent grids, since either big or small influencing radius is unfavorable for the assimilations. Because of the deficiency of the model (iron-limitation is not considered), the model fails to simulate high nitrate zones in southern ocean, which indicates that when modeling on a global scale, some important mechanisms such as iron-limitation should be included in.
在气候耦合模式FOAM的气候态背景场下,本文建立了一个简单的全球尺度上的三维海洋生态系统动力学模型及其伴随模型,在500米深度内进行数值模拟与同化研究。通过在底层添加营养盐修正项的方式解决了模拟过程中营养盐不断耗散的问题,保障了模型的平稳运行。
结合考察敏感性函数与代价函数关于模型参数的梯度量级两种方法,对各模型参数进行了敏感性分析,挑选出对模拟结果敏感且不相关的五个参数作为控制变量。在孪生数值实验中,通过同化模型产生的‘观测数据’来反演控制变量。首先通过反演无空间变化的控制变量验证了伴随模型的正确性,继而基于参数的空间分布,利用伴随方法反演了给定的空间变化的控制变量,证明了参数空间分布的有效性以及在模型中利用伴随同化方法反演空间变化的参数的可行性。
在实际实验中,本文在全球尺度上通过伴随方法同化SeaWiFS叶绿素资料。在使用参数空间分布后,表层浮游植物生物量(氮)的平均误差下降至0.0584mmolN·m-3,与不考虑参数空间变化的同化实验相比下降了62.4%。反演出的参数在空间上呈现显著的变化特征,证明了空间变化的参数比参数取常数更合理。通过使用参数的空间分布,SeaWiFS图像上大部分的区域特征都能被很好地模拟出来,证明模拟与观测之间的区域性误差得到了有效控制。本文发现同化结果与独立点的数量以及影响半径的选取有很大关系:独立点数量越多,同化结果就越好;影响半径应根据独立点的设置来选取,过大或过小的影响半径都对同化结果不利。然而,由于缺少必要的数据支持,模型没有考虑一些海区存在的铁限制因素,导致模型不能正确模拟南大洋等海域的营养盐水平,这表明了在全球尺度上进行海洋生态系统数值模拟时,铁限制等机制是应当考虑的。
People has paid more attention to the global climate change in recently years, thus to improve numerical modeling ability on climate change and to evaluate its influence on human living become significantly important. As a crucial role in the issue, more researchers are focusing on marine ecosystem modeling and more robust models are urgent in demand. Under such circumstances, the number of model dimensions, application area and model complexity are undergoing quickly development. However, in most previous studies, the spatial variations of key parameters are always ignored or not taken into account at all, and researchers always assumes that a set of optimized parameters is suitable for the whole study area. It results in big spatial differences between modeled values and the observations, and these differences can not be effectively reduced by improving physical background or adding new ecological mechanisms, especially when modeling on basin or global scales. To solve the problem, spatially variable parameters are needed. Traditional ways are optimizing local parameters or estimating parameters in each sub-area independently, and then smoothing the parameter values. In such a way, spatially variable parameters are obtained, but the model and the observations are not taken as a whole and the parameters are not optimized globally, thus these spatially variable parameters can not represent the reality accurately. In this study, a new method (spatial parameterization) is utilized to realize spatial variations of the parameters. The article tested if the new method can help us improve marine ecosystem modeling.
In this effort, a simple 3-D marine ecosystem model and the corresponding adjoint model are constructed on a global scale under climatological physical environment provided by FOAM. The numerical study and assimilation experiments are implemented within a depth of 500m. A relaxed term of nutrient in the bottom layer is designed to prevent nutrient from dissipation, which insures the positive simulation.
Five uncorrelated and sensitive parameters are selected as control variables by a conventional sensitivity analysis and investigating the gradients of the cost function with respect to each parameter, respectively. In twin experiments, model-generated data are served as fictitious observations, and they are assimilated into the model to estimate the control variables. Firstly, the spatially-constant control variables are estimated, which validates the adjoint model. Furthermore, based on spatial parameterization, the spatially varying control variables are estimated by the adjoint method, which indicates the validity of spatial parameterization and the feasibility of estimating spatially varying parameters by the adjoint method.
In real experiments, SeaWiFS chlorophyll-a data are assimilated into the model based on the adjoint method on a global scale. After spatial parameterization is utilized, the mean error of phytoplankton in the surface layer (nitrogen) reduced to 0.0584 mmolN·m-3, which is a reduction of 62.4% compared with the assimilation experiment without spatial parameterization. The assimilation results show that spatially varying parameters are more reasonable than a set of constants, since the selected parameters exhibit great spatial variations. By utilizing spatial parameterization, most regional features in SeaWiFS imagery can be reproduced. The assimilation results are sensitive to the amount of independent grids and the selected influencing radius: the more independent grids we use, the better the assimilation results are; the influencing radius should be suitable for the configuration of the independent grids, since either big or small influencing radius is unfavorable for the assimilations. Because of the deficiency of the model (iron-limitation is not considered), the model fails to simulate high nitrate zones in southern ocean, which indicates that when modeling on a global scale, some important mechanisms such as iron-limitation should be included in.
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