海洋生态系统动力学模型伴随同化研究及应用
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摘要
海洋生态系统具有典型的非线性特征，微小的扰动通过非线性作用得以放大，因此在海洋生态系统动力学数值模拟中，参数的取值可以显著影响模型的模拟结果。然而模型中的参数很难精确确定，不仅因为生态模型中某些参数之间具有很高的相关性，还因为模型中很多参数不是常数，在空间大尺度上，由于温度、光照等环境因素的不同，生态模型中的参数在不同海区的取值也不尽相同；即使是同一海区，温度、光照等环境因素也会随时间发生变化，从而导致参数的取值发生变化。在以往的研究中，参数通常取常数，不随空间、时间发生变化，导致模型的模拟结果与观测结果存在较大差异，且该问题不能通过增加模型复杂性得到解决。
在本文中，首先在渤黄海建立了一个典型的三维营养盐-浮游植物-浮游动物-碎屑（NPZD）生态系统动力学模型及伴随模型，模型的背景流场由POM(Princeton Ocean Model)模式提供，只考虑背景流场对生态变量的作用，而未考虑生态变量对背景流场的反作用，根据已有的SeaWiFS叶绿素资料，利用伴随同化方法对模型中的全部12个参数进行优化，研究发现模型中的某些参数之间具有很高的相关性，且优化后的参数都具有明显的季节变化，大部分参数的季节变化可以在生物学上得到很好的解释。与参数取常数相比，随时间变化的参数可以显著提高模型的模拟能力。
对生态模型中随空间变化的参数进行反演时，首先通过敏感性分析，找出模型中对模拟结果影响最大的5个参数作为研究对象。为了保证参数空间分布的连续性，使得模拟结果更加合理，选取一些网格点作为独立网格点，只需对独立网格点的参数值进行调整，其它网格点的参数值通过Cressman插值得到，利用此方法通过孪生数值实验得到了最优的影响半径。给定两种形式的空间分布，孪生数值实验表明只对模型中的一个参数进行反演时，对于给定的两种空间分布，每个参数都可以得到很好的反演，且参数的空间分布越符合实际情况越容易反演；同时对5个参数进行反演时，只有当参数空间分布的搭配与它们在生态系统中引起浮游植物生物量变化的作用相一致时，5个参数才能得到较准确的反演。实验表明海洋生态系统动力学模型中参数空间变化是合理可行的，可将此方法应用到实际问题，从而更好地模拟叶绿素等生态变量的空间分布特征。
渤海是我国唯一的内海，陆源排污量很大，但水交换能力低下，一旦遭到污染将很难得到改善，因此准确模拟渤海污染物（总氮、总磷、COD等）的时空变化特征，对实现经济的可持续发展具有重要意义。对污染物的时空分布进行数值模拟时，初始场对模拟结果的影响很大，本文中将污染物当作保守物质，只考虑污染物一种状态变量的输运扩散过程，同时借鉴参数空间分布中“独立网格点”的思想，利用伴随同化方法对渤海区污染物的初始场进行反演。孪生数值实验表明：给定旋转抛物面和圆锥面两种形式的污染物初始分布，不论污染物浓度中间高、四周低还是四周高、中间低，均可得到较好的反演结果。与传统的插值方法相比，伴随同化方法可有效减少模拟结果与观测值的误差，模拟结果能更好的反映污染物的全场分布特征，验证了模型的稳定性与可靠性。最后把该模型应用到实际实验中，利用已有常规监测数据，对渤海污染物的初始分布及时间变化情况进行了准确模拟，从而求得渤海污染物的月平均分布。该方法可用于海洋环境质量的监测与评价，具有重要的现实意义。
One typical feature of ecosystems is nonlinearity. Even small perturbations can beamplified through nonlinear interactions. Therefore, parameter values cansignificantly affect the model results in the simulation of marine ecosystem dynamics.However, it is very difficult to estimate parameter values accurately. Not only becauseare there very high correlations between some parameters, but also that someparameters are not constant. Parameter values in different sea areas are not same dueto different environmental factors, such as temperature, light intensity and so on. Evenwithin the same sea area, environmental factors such as temperature, light intensitymay change over time, indicating that the values of the parameter changes over time,too. In previous studies, there always has been a large discrepancy between simulationresults and observations because most researchers treated ecological parameters asconstant, and this difficult problem can not be solved by increasing the complexity ofthe model.
In this paper, a typical3-dimension nutrient-phytoplankton-zooplankton-detritus(NPZD) marine ecosystem dynamical model and the corresponding adjoint model areconstructed in the Bohai Sea and the Yellow Sea. The three-dimensional PrincetonOcean Model (POM) is used to calculate the ambient physical velocities, thetemperature, and the eddy diffusivities. Only the impact of background field onecological state variables is considered while the impact of ecological state variableon background field is neglected. The variational adjoint method is applied to estimatevalues of all the12parameters by utilizing real chlorophyll data from theSea-Viewing Wide Field-of-view Sensor (SeaWiFS). The seasonal variabilities ofthese parameters are reconstructed. It looks that that some parameters are highly correlated and the time variances of most parameters seem reasonable. The simulatedresults show that using time varing parameters values accord much better with theobservations than by using constant parameters.
In the study of estimating spatially varying parameters, five parameters whichhave greatest impact on simulation results are selected by a conventional sensitivityanalysis. In order to guarantee the continuity of parameter values and make thecalculated results more reasonable, several grids are selected as independent grids,and only the parameter value of these independent grids need to be optimized whilethe other grids are calculated by interpolation method. Based on this method, weconfirmed the optimal influence radius by a twin experiment. In the followingexperiments, when the five parameters are inversed respectively, spatial variations ofthe two given types can be reproduced very well. But when all the five parameters areestimated simultaneously, the collocation of the changing trend of each parameterinfluences the estimation results remarkably. Only when this collocation coincideswith the ecological mechanisms which influence the growth of the phytoplankton inthe model, can the five parameters be estimated accurately. The result demonstratesthat it is reasonable to take the spatial variation of parameters into account in theecosystem dynamical model. This method can be applied to real experiments toestimate the distributions of ecological control variables such as chlorophyll.
The Bohai Sea is the only inland sea of China，and its organic pollutants aretremendous. Unfortunately, water exchange of the Bohai Sea is very weak and itsphysical self-clean capacity is poor due to its special geographical position, so it ishard to recover if the Bohai Sea is polluted. Therefore, an accurate simulation of thetime varying pollutant (e.g. total Nitrate, total Phosphate, COD) distribution is neededif we want to achieve sustainable development of economy. The initial condition hasdramatic influence on the results when simulating the time varying pollutantdistribution. In this paper, the pollution is treated as conservative substance, and onlythe transporting diffusion process of pollution is considered. The adjoint assimilationmethod is firstly applied to estimate the pollutant initial field as far as I know, and some independent grids are also selected as before to guarantee the continuity ofpollutant distribution. No matter a parabolic or conical surface the initial distributionof pollutant concentration shows, and regardless the pollutant concentration is higherin the center or not, the initial distribution can be inversed successfully. Comparedwith the traditional interpolation methods, the adjoint assimilation method caneffectively reduce the errors between simulation results and observations, which provethe stability and reliability of this model. Therefore, this model could be applied inreal experiment to simulate the initial field and the distribution of pollution in anytime step by using the regular monitoring observations, and then the monthly meandistribution of pollution can be calculated by the statistical method. The results showthat this method has important practical significance because it can be used for themonitoring and evaluation of the marine environmental quality.
在本文中，首先在渤黄海建立了一个典型的三维营养盐-浮游植物-浮游动物-碎屑（NPZD）生态系统动力学模型及伴随模型，模型的背景流场由POM(Princeton Ocean Model)模式提供，只考虑背景流场对生态变量的作用，而未考虑生态变量对背景流场的反作用，根据已有的SeaWiFS叶绿素资料，利用伴随同化方法对模型中的全部12个参数进行优化，研究发现模型中的某些参数之间具有很高的相关性，且优化后的参数都具有明显的季节变化，大部分参数的季节变化可以在生物学上得到很好的解释。与参数取常数相比，随时间变化的参数可以显著提高模型的模拟能力。
对生态模型中随空间变化的参数进行反演时，首先通过敏感性分析，找出模型中对模拟结果影响最大的5个参数作为研究对象。为了保证参数空间分布的连续性，使得模拟结果更加合理，选取一些网格点作为独立网格点，只需对独立网格点的参数值进行调整，其它网格点的参数值通过Cressman插值得到，利用此方法通过孪生数值实验得到了最优的影响半径。给定两种形式的空间分布，孪生数值实验表明只对模型中的一个参数进行反演时，对于给定的两种空间分布，每个参数都可以得到很好的反演，且参数的空间分布越符合实际情况越容易反演；同时对5个参数进行反演时，只有当参数空间分布的搭配与它们在生态系统中引起浮游植物生物量变化的作用相一致时，5个参数才能得到较准确的反演。实验表明海洋生态系统动力学模型中参数空间变化是合理可行的，可将此方法应用到实际问题，从而更好地模拟叶绿素等生态变量的空间分布特征。
渤海是我国唯一的内海，陆源排污量很大，但水交换能力低下，一旦遭到污染将很难得到改善，因此准确模拟渤海污染物（总氮、总磷、COD等）的时空变化特征，对实现经济的可持续发展具有重要意义。对污染物的时空分布进行数值模拟时，初始场对模拟结果的影响很大，本文中将污染物当作保守物质，只考虑污染物一种状态变量的输运扩散过程，同时借鉴参数空间分布中“独立网格点”的思想，利用伴随同化方法对渤海区污染物的初始场进行反演。孪生数值实验表明：给定旋转抛物面和圆锥面两种形式的污染物初始分布，不论污染物浓度中间高、四周低还是四周高、中间低，均可得到较好的反演结果。与传统的插值方法相比，伴随同化方法可有效减少模拟结果与观测值的误差，模拟结果能更好的反映污染物的全场分布特征，验证了模型的稳定性与可靠性。最后把该模型应用到实际实验中，利用已有常规监测数据，对渤海污染物的初始分布及时间变化情况进行了准确模拟，从而求得渤海污染物的月平均分布。该方法可用于海洋环境质量的监测与评价，具有重要的现实意义。
One typical feature of ecosystems is nonlinearity. Even small perturbations can beamplified through nonlinear interactions. Therefore, parameter values cansignificantly affect the model results in the simulation of marine ecosystem dynamics.However, it is very difficult to estimate parameter values accurately. Not only becauseare there very high correlations between some parameters, but also that someparameters are not constant. Parameter values in different sea areas are not same dueto different environmental factors, such as temperature, light intensity and so on. Evenwithin the same sea area, environmental factors such as temperature, light intensitymay change over time, indicating that the values of the parameter changes over time,too. In previous studies, there always has been a large discrepancy between simulationresults and observations because most researchers treated ecological parameters asconstant, and this difficult problem can not be solved by increasing the complexity ofthe model.
In this paper, a typical3-dimension nutrient-phytoplankton-zooplankton-detritus(NPZD) marine ecosystem dynamical model and the corresponding adjoint model areconstructed in the Bohai Sea and the Yellow Sea. The three-dimensional PrincetonOcean Model (POM) is used to calculate the ambient physical velocities, thetemperature, and the eddy diffusivities. Only the impact of background field onecological state variables is considered while the impact of ecological state variableon background field is neglected. The variational adjoint method is applied to estimatevalues of all the12parameters by utilizing real chlorophyll data from theSea-Viewing Wide Field-of-view Sensor (SeaWiFS). The seasonal variabilities ofthese parameters are reconstructed. It looks that that some parameters are highly correlated and the time variances of most parameters seem reasonable. The simulatedresults show that using time varing parameters values accord much better with theobservations than by using constant parameters.
In the study of estimating spatially varying parameters, five parameters whichhave greatest impact on simulation results are selected by a conventional sensitivityanalysis. In order to guarantee the continuity of parameter values and make thecalculated results more reasonable, several grids are selected as independent grids,and only the parameter value of these independent grids need to be optimized whilethe other grids are calculated by interpolation method. Based on this method, weconfirmed the optimal influence radius by a twin experiment. In the followingexperiments, when the five parameters are inversed respectively, spatial variations ofthe two given types can be reproduced very well. But when all the five parameters areestimated simultaneously, the collocation of the changing trend of each parameterinfluences the estimation results remarkably. Only when this collocation coincideswith the ecological mechanisms which influence the growth of the phytoplankton inthe model, can the five parameters be estimated accurately. The result demonstratesthat it is reasonable to take the spatial variation of parameters into account in theecosystem dynamical model. This method can be applied to real experiments toestimate the distributions of ecological control variables such as chlorophyll.
The Bohai Sea is the only inland sea of China，and its organic pollutants aretremendous. Unfortunately, water exchange of the Bohai Sea is very weak and itsphysical self-clean capacity is poor due to its special geographical position, so it ishard to recover if the Bohai Sea is polluted. Therefore, an accurate simulation of thetime varying pollutant (e.g. total Nitrate, total Phosphate, COD) distribution is neededif we want to achieve sustainable development of economy. The initial condition hasdramatic influence on the results when simulating the time varying pollutantdistribution. In this paper, the pollution is treated as conservative substance, and onlythe transporting diffusion process of pollution is considered. The adjoint assimilationmethod is firstly applied to estimate the pollutant initial field as far as I know, and some independent grids are also selected as before to guarantee the continuity ofpollutant distribution. No matter a parabolic or conical surface the initial distributionof pollutant concentration shows, and regardless the pollutant concentration is higherin the center or not, the initial distribution can be inversed successfully. Comparedwith the traditional interpolation methods, the adjoint assimilation method caneffectively reduce the errors between simulation results and observations, which provethe stability and reliability of this model. Therefore, this model could be applied inreal experiment to simulate the initial field and the distribution of pollution in anytime step by using the regular monitoring observations, and then the monthly meandistribution of pollution can be calculated by the statistical method. The results showthat this method has important practical significance because it can be used for themonitoring and evaluation of the marine environmental quality.
引文
[1]樊伟.海洋生态系统动力学数值模拟与同化研究：[博士学位论文].青岛：中国海洋大学海洋环境学院，2008.
[2]樊伟，吕咸青.基于参数空间分布的海洋生态系统数值模拟.海洋科学进展，2009，27（1）：24~33.
[3]高会旺，冯士筰，管玉平.海洋浮游生态系统动力学模式的研究.海洋与湖沼，2000，31（3）：341~347.
[4]韩桂军，何柏荣，马继瑞，等．利用伴随法优化非线性潮汐模型的开边界条件I：伴随方程的建立及“孪生”数值试验．海洋学报，2000，22（6）：134~140.
[5]韩桂军，何柏荣，马继瑞，等．利用伴随法优化非线性潮汐模型的开边界条件II：黄海、东海潮汐资料的同化试验．海洋学报，2001，23（2）：25~31.
[6]刘桂梅.关键物理过程对黄、渤海浮游生物影响的现象分析与模式研究：[博士学位论文].青岛：中国科学院海洋研究所，2002.
[7]刘桂梅，孙松，王辉，等.春秋季黄海海洋锋对中华哲水蚤分布的影响.自然科学进展，2002，12（11）：1150~1154.
[8]刘桂梅，孙松，王辉.海洋生态系统动力学模型及其研究进展.地球科学进展，2003，18（3）：427~432.
[9]刘浩，潘伟然．营养盐负荷对浮游植物水华影响的模型研究.水科学进展，2008，19（3）：345~351.
[10]吕咸青．数据同化反演风应力拖曳系数以及垂向涡动黏性系数的分布．海洋学报，2001，23（1）：13~20.
[11]吕咸青，田纪伟，吴自库．渤、黄海的底摩擦系数．力学学报，2003a，35（4）：465~468.
[12]吕咸青，吴自库，殷忠斌，田纪伟．渤、黄、东海潮汐开边界的1种反演方法．青岛海洋大学学报，2003b，33（2）：155~172.
[13]吕咸青，张杰．如何利用水位资料反演开边界条件(1)．水动力学研究与进展，1999，14：92~102.
[14]马继瑞，韩桂军，李冬．变分伴随数据同化在海表面温度预报中的应用研究．海洋学报，2002，24（5）：1~7.
[15]逄勇，李学灵．珠江三角洲河网入伶仃洋污染物通量计算研究．水利学报，2001，9：40~44.
[16]任湘湘，李海，吴辉碇.海洋生态系统动力学模型研究进展.海洋预报，2012，29（1）：65~72.
[17]唐启升，苏纪兰.海洋生态系统动力学研究与海洋生物资源可持续利用.地球科学进展，2001，16（10）：5~11.
[18]唐启升，苏纪兰，等.中国海洋生态系统动力学研究：I关键科学问题与研究发展战略.北京：科学出版社，2000，第252页.
[19]王保栋，单宝田，战闰，等.黄、渤海无机氮的收支模式初探．海洋科学，2002，26（2）：33-36.
[20]王辉，刘桂梅，万莉颖.数据同化在海洋生态模型中的应用和研究进展.地球科学进展，2007，22（10）：989~996.
[21]徐青，刘玉光，程永存，等.伴随同化技术在渤黄海生态模式中的应用：控制变量的选取与孪生实验.高技术通讯，2006，16（1）：78~83.
[22]闫菊，鲍献文，王海，等.胶州湾污染物COD的三维扩散与输运研究.环境科学研究，2001，12（2）：14~17.
[23]尹增强，李九奇，倪雪朋.海洋生态系统动力学理论在渔业上的应用.海洋开发与管理，2008，8：83~86.
[24]曾庆存，周广庆，浦一芬，等.地球系统动力学模式及模拟研究.大气科学，2008，32（4）：653~690.
[25]张继才.三维正压潮汐潮流伴随同化模型数值建模及应用研究：[博士学位论文].青岛：中国海洋大学海洋环境学院，2008.
[26]张继才，吕咸青．空间分布底摩擦系数的伴随法反演研究．自然科学进展，2005，15（9）：1086~1093.
[27]赵强.一个简单海洋生态系统动力学模型的伴随同化应用研究：[硕士学位论文].青岛：中国海洋大学海洋环境学院，2006.
[28]张书文，夏长水，袁业立.黄海冷水团水域物理－生态耦合数值模式研究.自然科学进展，2002,12（3）：315~319.
[29]张素香，李瑞杰，罗峰，朱文谨.海洋生态动力学模型的研究进展.海洋湖沼通报，2006，4：121~127.
[30]朱江，曾庆存，郭冬建，刘卓．利用伴随算法从岸边潮位站资料估计近岸模式的开边界条件．中国科学(D辑)，1997，279（5）：462~468.
[31] Allen C T, Young S G, Haupt S E. Improving pollutant source characterization by betterestimating wind direction with a genetic algorithm. Atmospheric Environment,2007,41:2283~2289.
[32] Allen J I, Eknes M, Evensen G. An ensemble Kalman filter with a complex marineecosystem model: hindcasting phytoplankton in the Cretan Sea. Annales Geophysicae,2002,20:1~13.
[33] Anderson D L T, Sheinbaum J, Haines K. Data assimilation in ocean models. Reports onProgress in Physics,1996,59:1209~1266.
[34] Anderson L A, Robinson A R, Lozano C J. Physical and biological modeling in the GulfStream region: I. Data assimilation methodology. Deep-Sea Research. Part1.Oceanographic Research Papers,2000,47:1787~1827.
[35] Anderson T R. Plankton functional type modeling: running before we can walk? Journal ofplankton research,2005,27(11):1073~1081.
[36] Anderson T R. Progress in marine ecosystem modelling and the “unreasonableeffectiveness of mathematics”. Journal of Marine Systems,2010(81):4~11.
[37] Armstrong R A, Sarmiento J L, Slater, R D. Monitoring ocean productivity by assimilatingsatellite chlorophyll into ecosystem models. In: Powell, Steele (Eds.), Ecological TimeSeries. Chapman and Hall, London,1995, pp:371~390.
[38] Baretta J W. The European regional seas ecosystem model, a complex marine ecosystemmodel. Netherlands Journal of Sea Research,1995,33(3-4):233~246.
[39] Bennett A F, Budgell W P. Ocean data assimilation and the Kalman filter, spatial regularity.Journal of Physical Oceanography,1987,17:1583~1601.
[40] Besiktepe S T, Lermusiaux P F J, Robinson A R. Coupled physical and biogeochemicaldata-driven simulations of Massachusetts Bay in late summer: real-time and postcruise dataassimilation. Journal of Marine Systems,2003,40:171~212.
[41] Bissett W P, Walsh J J, Dieterle D A, et al.. Carbon cycling in the upper waters of theSargasso Sea: I. Numerical simulation of differential carbon and nitrogen fluxes, Deep-SeaResearch. Part1,1999,46:205~269.
[42] Bratseth A M. Statistical interpolation by means of successive corrections. Tellus,1986,38A:439~447.
[43] Carmillet V, Brankart J M, Brasseur P, Drange H, et al.. A singular evolutive extendedKalman filter to assimilate ocean color data in a coupled physical-biochemical model of theNorth Atlantic ocean. Ocean Modelling,2001,3:167~192.
[44] Carlotti F. Growth and egg production of female Calanus finmarchicus: An individual-based physiological model and experimental validation. Marine Ecology Progress Series,1997,149:91~104.
[45] Chai F, Dugdale R C, Peng T H, et al.. One-dimensional ecosystem model of the equatorialPacific upwelling system. Part I: Model development and silicon and nitrogen cycle.Deep-Sea Research. Part2,2002,49:2713~2745.
[46] Chen C, Beardsley R C, Franks P J S. A3-D prognostic model study of the ecosystem overGeorges Bank and adjacent coastal regions.Part1: Physical model. Deep-Sea Research,2001,48:419~456.
[47] Chen C, Denis A, Wiesenburg, et al.. Influences of river discharge on biological productionin the inner shelf: A coupled biological and physical model of the Louisiana-Texas Shelf.Journal of Marine Research,1997,55(2):293~320.
[48] Chen Q, Tan K, Zhu C, Li R. Development and application of a two-dimensional waterquality model for the Daqinghe River Mouth of the Dianchi Lake. Journal ofEnvironmental Sciences,2009,21(3):313~318.
[49] Cloern J E, Grenz C, Vidergar-Lucas L. An empirical model of the phytoplanktonchlorophyll: carbon ratio-the conversion factor between productivity and growth rate.Limnology and Oceanography,1995,40:1313~1321.
[50] Cressman G P. An operational objective analysis system. Monthly Weather Review,1959,87:367~374.
[51] Daley R. Atmospheric Data Analysis. New York: Cambridge Atmospheric and SpaceScience Series, Cambridge University Press,1991,363~402.
[52] Daniel R L, Wendy, C G, Dennis, J M, et al.. Biological/physical simulations of calanusfinmarchicus population dynamics in the Gulf of Maine. Marine Ecology Progress Series,1998,169:189~210.
[53] Das S K, Lardner R W. Variational parameter estimation for a two dimensional numericaltidal model. International Journal for Numerical Methods in Fluids,1992,15:313~327.
[54] Delhez E J M. and Martin G.3D modelling of hydrodynamic and ecohydrodynamicprocesses on the north-western European continental shelf. Bulletin de la Société royale dessciences de Liège,1994,63(1-2):5~64.
[55] DeMaria M, Jones R W. Optimization of a hurricane track forecast model with the adjointmodel equations. Monthly Weather Review,1993,121(6):1730~1745.
[56] Denman K L. Modelling planktonic ecosystems: Parameterizing complexity. Progress inoceanography,2003,57:429~452.
[57] Duan J, Nanda S K. Two-dimensional depth-averaged model simulation of suspendedsediment concentration distribution in a groyne field. Journal of Hydrology,2006,327(3-4):426~437.
[58] Eknes M, Evensen, G. An ensemble Kalman filter with a1-D marine ecosystem model.Journal of Marine Systems,2002,36:75~100.
[59] Eliassen A. Provisional report on calculation of spatial covariance and autocorrelation ofthe pressure field. Oslo: Inst. Weather and Climate Research, Academy of Sciences,1954,Report No.5.
[60] Evans G T. The role of local models and data sets in the Joint Global Ocean Flux Study.Deep-Sea Research. Part1. Oceanography Research,1999,46:1369~1389.
[61] Evensen G. Sequential data assimilation with a nonlinear quasi-geostrophic model usingMontre Carlo methods to forecast error statistics. Journal of Geophysical Research,1994,99(10):143~162.
[62] Fan W, Lv X Q. Data assimilation in a simple marine ecosystem model based on spatialbiological parameterizations. Ecological Modelling,2009,220(17):1997~2008.
[63] Fang G H. Tide and Tidal current charts for the marginal seas adjacent to China. ChineseJournal of Oceanology and Limnology.1986,4(1):1-16.
[64] Fasham M J R, Ducklow H W, McKelvie S M. A nitrogen-based model of planktondynamics in the oceanic mixed layer. Journal of Marine Research,1990,48:591~639.
[65] Fasham M J R, Boyd P W, Savidge G. Modeling the relative contributions of utotrophs andheterotrophs to carbon flow at a Lagrangian JGOFS station in the Northeast Atlantic: theimportance of DOC. Limnology and Oceanography,1999,44:80~94.
[66] Fasham M J R., Evans G T., Kiefer D A, et al.. The use of optimization techniques to modelmarine ecosystem dynamics at the JGOFS station at47degrees N20degrees W.Philosophical Transactions of the Royal Society of London. Series B,1995,348:203~209.
[67] Faugeras B, Bernard O, Sciandra A, et al.. A mechanistic modeling and data assimilationapproach to estimate the carbon/chlorophyll and carbon/nitrogen ratios in a coupledhydrodynamical–biological model. Nonlinear Processes in Geophysics,2004,11:515~533.
[68] Faugeras B, Levy M, Memery L, et al.. Can biogeochemical fluxes be recovered fromnitrate and chlorophyll data? A case study assimilating data in the NorthwesternMediterranean Sea at the JGOFS-DYFAMED station. Journal of Marine Systems,2003,40–41:99~125.
[69] Fennel K, Losch M, Schroter J, et al.. Testing a marine ecosystem model: sensitivityanalysis and parameter optimization. Journal of Marine Systems,2001:28,45~63.
[70] Fletcher B, Reeves C M. Function minimization by conjugate gradients. The computerjournal,1964,7(2):149~154.
[71] Flynn K J. Castles built on sand: dysfunctionality in plankton models and the inadequacy ofdialogue between biologists and modelers. Journal of plankton research,27(12):1205~1210.
[72] Franks P J S. NPZ model of plankton dynamics: their construction, coupling to physics, andapplication. Journal of Physical Oceanography,2002,58:379~387.
[73] Franks P J S, Chen C. Plankton production in tidal fronts: A model of Georges bank insummer. Journal of Marine Research,1996,54:631~651.
[74] Franks P J S, Chen C S. A3-D Prognostic numerical model study of the Georges Bankecosystem, Part II: biological–physical model. Deep-Sea Research. Part2. Topical Studiesin Oceanography,2001,48:457~482.
[75] Friedrichs M A M. A data assimilative marine ecosystem model of the central equatorialPacific: numerical twin experiments. Journal of Marine Research,2001,59:859~894.
[76] Friedrichs M A M. Assimilation of JGOFS EqPac and SeaWiFS data into a marineecosystem model of the central equatorial Pacific Ocean. Deep-Sea Research. Part2.Topical Studies in Oceanography,2002,49:289~320.
[77] Friedrichs M A M, Dusenberry J D, Anderson L A, et al.. Assessment of skill and portabilityin regional marine biogeochemical models: Role of multiple planktonic groups, Journal ofGeophysical Research,2007,112:1~22.
[78] Friedrichs M A M, Hood R, Wiggert J. Ecosystem model complexity versus physicalforcing: Quantification of their relative impact with assimilated Arabian Sea data. Deep-SeaResearch. Part2. Topical Studies in Oceanography,2006,53:576~600.
[79] Frost B. W. Effects of the size and concentration of food particles on the feeding behaviorof the marine planktonic copepod Calanus pacificus. Limnology and Oceanography,1972,17:805~815.
[80] Gandin L. Objective Analysis of Meteorological Fields (translated from Russian).Jerusalem: Israel Program for Scientific Translation,1965.
[81] Garcia-Gorriz E, Hoepffner N, Ouberdous M. Assimilation of SeaWiFS data in a coupledphysical–biological model of the Adriatic Sea. Journal of Marine Systems,2003,40–41:233~252.
[82] Gentleman W. A chronology of plankton dynamics in silico: how computer models havebeen used to study marine ecosystems. Hydrobiologia,2002,480:69~85.
[83] George L, Mellor. Users Guide for a Three-Dimensional Primitive Equation NumericalOcean Model. New Jersey: Princeton University,2004,56.
[84] Gilchrist B, Cressman G. An experiment in Objective Analysis. Tellus,1954,6:309~318.
[85] Gupta I, Dhage S, Chandorkar A A, Srivastav A. Numerical modeling for Thane creek.Environmental Modelling&Software,2004,19(6):571~579.
[86] Gregg W W. Assimilation of SeaWiFS ocean chlorophyll data into a three-dimensionalglobal ocean model. Journal of Marine Systems,2008,69:205~225.
[87] Gregg W W, Friedrichs M A M., Robinson A R, et al.. Skill assessment in ocean biologicaldata assimilation. Journal of Marine Systems,2009,76:16~33.
[88] Gregg W W, Ginoux P, Schopf P S, et al.. Phytoplankton and iron: validation of a globalthree-dimensional ocean biogeochemical model. Deep-Sea Research, Part2, TopicalStudies in Oceanography,2003,50:3143~3169.
[89] Greiner E, Arnault S, Morliaère A. Twelve-monthly experiments of4D-variationalassimilation in the tropical Atlantic during1987, Part1: Method and statistical results.Progress in Oceanography,1998a,41:141~202.
[90] Greiner E, Arnault S, Morliaère A. Twelve-monthly experiments of4D-variationalassimilation in the tropical Atlantic during1987, Part2: Oceanographic interpretation.Progress in Oceanography,1998b,41:203~247.
[91] Greiner E, Perigaud C. Assimilation of Geo-sat altimetric data in a nonlinearreduced-gravity model of the Indian Ocean, Part I: adjoint approach and model-dataconsistency. Journal of Physical Oceanography,1994,24(8):1783~1804.
[92] Greiner E, Perigaud C. Assimilation of Geo-sat altimetric data in a nonlinearreduced-gravity model of the Indian Ocean, Part II: Some Validation and Interpretation ofthe assimilated results. Journal of Physical Oceanography,1996,26:1735~1746.
[93] Gunson J, Oschlies A, Garcon V. Sensitivity of ecosystem parameters to simulated satelliteocean color data using a coupled physical–biological model of the North Atlantic. Journalof Marine Research,1999,57:613~639.
[94] Gunson J R, Malanotte-Rizzoli P. Assimilation studies of open-ocean flows, Part I:Estimation of initial and boundary conditions, Journal of Geophysical Research,1996a,101:28457~28472.
[95] Gunson J R, Malanotte-Rizzoli P. Assimilation studies of open-ocean flows, Part II: Errormeasures with strongly nonlinear dynamics. Journal of Geophysical Research,1996b,101:28473~28488.
[96] Guo X Y, Yanagi T. Three-dimensional structure of tidal current in the East China Sea andthe Yellow Sea. Journal of Oceanography.,1998,54:651~668.
[97] Gupta I, Dhage S, Chandorkar A A, Srivastav A. Numerical modeling for Thane creek.Environmental Modelling&Software,2004,19(6):571~579.
[98] Harmon R, Challenor P. A Markov chain Monte Carlo model for estimation andassimilation into models. Ecological Modelling,1997,101:41~59.
[99] Harms I H, Karcher M J, Dethleff D. Modelling Siberian river runoff-implications forcontaminant transport in the Arctic Ocean. Journal of Marine Systems,2005,27(1-3):95~115.
[100] Hemmings J C P, Srokosz M A, Challenor P, et al.. Assimilating satellite ocean-colourobservations into oceanic ecosystem models. Philosophical Transactions of the RoyalSociety of London A-Mathematics, Physics and Engineering Science,2003,361:33~39.
[101] Hemmings J C P, Srokosz M A, Challenor P, et al.. Split-domain calibration of anecosystem model using satellite ocean colour data. Journal of Marine Systems,2004,50:141~179.
[102] Hestenes M R, Stiefel E. Methods of conjugate gradients for solving linear systems.1952,49(6):409~436.
[103] Holfort J, Siedler G. The meridional oceanic transports of heat and nutrients in the SouthAtlantic. Journal of Physical Oceanography,2001,31:5~29.
[104] Hood R. Functional group modeling: progress, challenges and prospects, Deep-SeaResearch. Part2. Topical Studies in Oceanography,2006,53:459~512.
[105] Hoteit I, Triantafyllou G, Petihakis G, et al.. A singular evolutive extended Kalman filter toassimilate real in situ data in a1-D marine ecosystem model. Annales Geophysicae,2003,21:389~397.
[106] Hu J, Fennel K, Mattern J P, et all. Data assimilation with a local Ensemble Kalman Filterapplied to a three-dimensional biological model of the Middle Atlantic Bight. Journal ofMarine Systems,2012,94:145~156.
[107] Huang H Q, Chen G, Zhang Q F. The distribution characteristics of pollutants released atdifferent cross-sectional positions of a river. Environmental Pollution,2010,158:1327~1333.
[108] Hurtt G C, Armstrong R A. A pelagic ecosystem model calibrated with BATS data.Deep-Sea Research. Part2. Topical Studies in Oceanography,1996,43:653~683.
[109] Hurtt G C, Armstrong RA. A pelagic ecosystem model calibrated with BATS and OWSIdata. Deep-Sea Research. Part2. Topical Studies in Oceanography,1999,46:27~61.
[110] Ibrahim H, George T, George P. Towards a data assimilation system for the Cretan Seaecosystem using a simplified Kalman filter. Journal of Marine Systems,2004,45:159~171.
[111] Ishizaka J. Coupling of Coastal Zone Color Scanner data to a physical–biological model ofthe southeastern United-States continental-shelf ecosystem.3. Nutrient and phytoplanktonfluxes and CZCS data assimilation. Journal of Geophysical Research,1990,95:20201~20212.
[112] Kalnay E. AtmosphericModeling, Data Assimilation and Predictability.University Press,Cambridge.2003,341pp.
[113] Kishi M J, Okunishi T, Yamanaka Y. A comparison of simulated particle fluxes usingNEMURO and other ecosystem models in the Western North Pacific. Journal of PhysicalOceanography,2004,60:63~73.
[114] Kuo Y H, Zou X, Guo Y R. Variational assimilation of precipitable water using anonhydrostatic mesoscale adjoint model. Part I: moisture retrieval and sensitivityexperiments. Monthly Weather Review,1996,124(1):122~147.
[115] Kuroda H, Kishi M J. A data assimilation technique applied to estimate parameters for theNEMURO marine ecosystem model. Ecological Modelling,2004,172:69~85.
[116] Lancelot C, Spitz Y H, Gypens N, et al.. Modelling diatom-Phaeocystis blooms and nutrientcycles in the Southern Bight of the North Sea: the MIRO model, Mar. Ecol. Prog. Ser.,2005,289:63~78.
[117] Lardner R W. Optimal control of open boundary conditions for a numerical tidal model.Computer Methods in Applied Mechanics and Engineering,1993,102:367~387.
[118] Lawson L M, Spitz Y H, Hofmann E E, Long R B. A data assimilation technique applied toa predator-prey model. Bulletin of Mathematical Biology,1995,57(4):593~617.
[119] Lawson L M, Hofmann E E, Spitz Y H. Time series sampling and data assimilation in asimple marine ecosystem model. Deep-Sea Research. Part2. Topical Studies inOceanography,1996,43:625~65.
[120] Le Quéré C, Harrison S P, Prentice I C, et al. Ecosystem dynamics based on planktonfunctional types for global ocean biogeochemistry models, Global Change Biology,2005,11:2016~2040.
[121] LeDimet F X, Talagrand O. Variational algorithms for analysis and assimilation ofmeteorological observations: Theoretical aspects. Tellus,1986,38A:97~110.
[122] Lee M E, Seo M. Analysis of pollutant transport in the Han River with tidal current using a2D finite element model. Journal of Hydro-environment Research,2007,1(1):30~42.
[123] Lefevre F, Provost C L, Lyard F H. How can we improve a global ocean tide model at a regionalscale? A test on the Yellow Sea and the East China Sea. Journal of Geophysical Research,2000,105:8707~8725.
[124] Lenartz F, Raick C, Soetaert K, et al.. Application of an Ensemble Kalman filter to a1-Dcoupled hydrodynamic-ecosystem model of the Ligurian Sea. Journal of Marine Systems,2007,68:327~348.
[125] Lenhart H J, Radach G, Ruardij P. The effects of river input on the ecosystem dynamics inthe continental coastal zone of the North Sea using a box refined ecosystem model ERSEM.Journal of Sea Research,1997,38,249~274.
[126] Leredde Y, Lauer-Leredde C, Diaz C. On the variational data assimilation by a marineecosystem model of NPZ type. Comptes Rendus Geoscience,2005,337:1055~1064.
[127] Liu X, Peng W, He G, Liu J, Wang, Y. A coupled model of hydrodynamics and water qualityfor Yuqiao reservoir in Haihe river basin. Journal of Hydrodynamics,2008,20(5):574~582.
[128] Losa S N, Kivman G A, Ryabchenko V A. Weak constraint parameter estimation for asimple ocean ecosystem model: what can we learn about the model and data? Journal ofMarine Systems,2004,45:1~20.
[129] Losa S N, Kivman G A, Schroter J, et al.. Sequential weak constraint parameter estimationin an ecosystem model. Journal of Marine Systems,2003,43:31~49.
[130] Losa S N, Vézina A, Wright D, et al.3D ecosystem modelling in the North Atlantic:Relative impacts of physical and biological parameterizations. Journal of Marine Systems,2006,61:230~245.
[131] Luyten P J, Jones J E, Proctor R, et al.. COHERENS-A coupled hydrodynamical ecologicalmodel for regional and shelf seas: User Documentation. Belgium: MUMM Report,Management Unit of the Mathematical Models of the North Sea.1999.
[132] Lv X Q, Wu Z K, Gu Y., et al.. Study on the adjoint method in data assimilation and therelated problems. Applied Mathematics and Mechanics,2004,25(6):636~646.
[133] Lv X Q, Zhang J C. Numerical study on spatially varying bottom friction coefficient of a2D tidal model with adjoint method. Continental Shelf Research,2006,26:1905~1923.
[134] Magri S, Brasseur P, Lacroix G. Data assimilation in a marine ecosystem model of theLigurian Sea. Comptes Rendus Geoscience,2005,337:1065~1074.
[135] Matear R J. Parameter estimation and analysis of ecosystem models using simulatedannealing: a case study at Station P. Journal of Marine Research,1995,53:571~607.
[136] Mattern J P, Fennel K, Dowd M. Sequential data assimilation applied to aphysical–biological model for the Bermuda Atlantic time series station. Journal of Marinesystems,2010a,79:144~156.
[137] Mattern J P, Fennel K, Dowd M. Introduction and assessment of measures for quantitativemodel-data comparison using satellite image,2010a,2:794~818.
[138] Mattern J P, Fennel K, Dowd M. Estimating time-dependent parameters for a biologicalocean model using an emulator approach,2012,96-97:32~47.
[139] McGillicuddy D J, Lynch D R, Moore, A.M., er al.. An adjoint data assimilation approachto diagnosis of physical and biological controls on Pseudocalanus spp. in the Gulf ofMaine-Georges Bank region. Fisheries Oceanography,1998,7(3-4):205~218.
[140] Mellor G L, Yamada T. Development of a turbulence closure model for geophysical fluidproblem. Reviews of Geophysics and space physics,1982:851~875.
[141] Miller A J, DiLorenzo E, Neilson D J, et al.. Modeling CalCOFI observations during ElNino: fitting physics and biology. CalCOFI Report,2000,41, pp:11.
[142] Miller R N. Toward the application of the Kalman filter to regional open ocean modeling.Journal of Physical Oceanography,1986,16:72~86.
[143] Moll A, Radach G. Review of three-dimensional ecological modeling related to the NorthSea Shelf system Part1: models and their results. Progress in Oceanography,2003,57:175~217.
[144] Moore A M. Data assimilation in a quasi-geostrophic open ocean model of the Gulf Streamregion using the adjoint method. Journal of Physical Oceanography,1991,21:398~427.
[145] Moore J K, Doney S C, Glover, D M, et al.. Iron cycling and nutrient-limitation patterns insurface waters of the World Ocean. Deep-Sea Research. Part2. Topical Studies inOceanography,2002,49:463~507.
[146] Natvik L J, Eknes M, Evensen G. A weak constraint inverse for zero-dimensional marineecosystem model. Journal of Marine Systems,2001,28:19~44.
[147] Natvik L J, Evensen, G. Assimilation of ocean colour data into a biochemical model of theNorth Atlantic. Part1. Data assimilation experiments. Journal of Marine Systems,2003a,40–41:127~153.
[148] Natvik L J, Evensen, G. Assimilation of ocean colour data into a biochemical model of theNorth Atlantic. Part2. Statistical analysis. Journal of Marine Systems,2003b,40–41:155~169.
[149] Oschlies A, Schartau M. Basin-scale performance of a locally optimized marine ecosystemmodel. Journal of Marine Research,2005,63:335~358.
[150] Panofsky H A. Objective weather map analysis. Journal of Meteorology,1949,6:386~392.
[151] P tsch J, Radach G. Long-term simulation of the eutrophication of the North Sea: Temporaldevelopment of nutrients, chlorophyll and primary production in comparison toobservations. Journal of Sea Research,1997,38,275~310.
[152] Peng S Q, Xie L. Effect of determining initial conditions by four-dimensional variationaldata assimilation on storm surge forecasting. Ocean Modelling,2006,14:1~18.
[153] Perianez R. A particle-tracking model for simulating pollutant dispersion in the Strait ofGibraltar. Marine Pollution Bulletin,2004,49(7-8):613~623.
[154] Perianez R. GISPART: a numerical model to simulate the dispersion of contaminants in theStrait of Gibraltar. Enviromental Modelling&Software,2005a,20(6):797~802.
[155] Perianez R. Modelling the transport of suspended particulate matter by the Rhone Riverplume (France): implications for pollutant dispersion. Environmental Pollution,2005b,133(2):351~364.
[156] Polak B, Ribiere G. Note surla convergence des methodes de directions conjuguees. RevueFrancaise D’informatique et de recherche operationnelle, serie rouge.1969,16:35~43.
[157] Polyak B T. The conjugate gradient method in extreme problems. USSR computationalmathematics and mathematical physics,1969,9(4):94~112.
[158] Popova E E, Lozano C J, Srokosz M A, et al.. Coupled3D physical and biologicalmodeling of the mesoscale variability observed in North-East Atlantic in Spring1997:biological processes. Deep-Sea Research. Part1. Oceanographic Research Papers,2002,49:1741~1768.
[159] Price N M, Andeson L G, Morel F M M. Iron and nitrogen nutrition of equatorial Pacificplankton. Deep Sea Research. Part A,1991,38:1361~1378.
[160] Prunet P, Minster J F, RuizPino D, et al.. Assimilation of surface data in a one-dimensionalphysical–biogeochemical model of the surface ocean.1. Method and preliminary results.Global Biogeochemical Cycles,1996,10:111~138.
[161] Qi P, Wang C H, Li X Y, Lv X Q. Numerical study on spatially varying control parametersof a marine ecosystem dynamical model with adjoint method. Acta Oceanologica Sinica,2010,30(1):7~14.
[162] Qiu Z F, He Y J, Lv X Q. Tidal constituents in the Bohai and Yellow Seas from an adjointnumerical model with T/P data. Journal of Hydrodynamics (B),2005,17(3):275~282.
[163] Radach G, Moll A. Estimation of the variability of production by simulating annual cyclesof phytoplankton in the central North Sea. Progress in Oceanography,1993,31(4),339~419.
[164] Raick C, Soetaert K, Grégoire M. Model complexity and performance: How far can wesimplify? Progress in Oceanography,2006,70:27~57.
[165] Rajar R, Matjaz Cetina. Hydrodynamic and water quality modelling: An experience.Ecological Modelling,1997,101(2-3):195~207.
[166] Rajar R, Matjaz Cetina, Andrej Sirca. Hydrodynamic and water quality modelling: casestudies. Ecological Modelling,1997,101(2-3):209~228.
[167] Redfield A C, Ketchum B H, Richards F A. The influence of organisms on the compositionof sea water. The Sea, vol.2. Inter-Science, New York,1963,26~77pp.
[168] Riley G A. Factors controlling phytoplankton populations on Georges Bank. Journal ofMarine Research,1946,6:54~73.
[169] Riley G A. A theoretical analysis of the zooplankton population of Georges Bank. Journalof Marine Research,1947,6:104~113.
[170] Riley G A. Theory of growth and competition in natural populations. Journal of theFisheries Board of Canada,1953,10:211~223.
[171] Robinson A R, Lermusiaux P F J. Overview of Data Assimilation. Cambridge: HarvardReports in Physical/Interdisciplinary Ocean Science,2000, Number62.
[172] Roy R, Broomhead D S, Platt T, et al.. Sequential variations of phytoplankton growth andmortality in an NPZ model:A remote-sensing-based assessment. Journal of Marine Systems,2012,92:16~29.
[173] Sasaki Y. Some basic formalisms in numerical variational analysis. Monthly WeatherReview,1970,98:875~883.
[174] Saltelli A, Marivoet J. Non-parametric statistics in sensitivity analysis for model output:comparison of selected techniques. Reliability Engineering and System Safety,1990,28(2):229-253.
[175] Schartau M, Oschlies A, Willebrand J. Parameter estimates of a zero-dimensionalecosystem model applying the adjoint method. Deep-Sea Research. Part2. Topical Studiesin Oceanography,2001,48:1769~1800.
[176] Schartau M, Oschlies A. Simultaneous data-based optimization of a2D-ecosystem model atthree locations in the North Atlantic: Part I—method and parameter estimates. Journal ofMarine Research,2003,61:765~793.
[177] Schiller A. The mean circulation of the Atlantic Ocean north of30S determined with theadjoint method applied to an ocean general circulation model. Journal of Marine Research,1995,53(3):453~497.
[178] Schlitzer R. Carbon export fluxes in the Southern Ocean: results from inverse modeling andcomparison with satellite-based estimates. Deep-Sea Research. Part2. Topical Studies inOceanography,2002,49:1623~1644.
[179] Seiler U. Estimation of open boundary conditions with the adjoint method. Journal ofGeophysical Research,1993,98:22855~22870.
[180] Smagorinsky J. General circulation experiments with the primitive equations:1. the basicexperiment. Monthly Weather Review,91(3):99~164.
[181] Smedstad O M, O’Brien J J. Variational data assimilation and parameter estimation in anequatorial Pacific Ocean model. Progress in Oceanography,1991,26:179~241.
[182] Sobol I M. Sensitivity estimates for nonlinear mathematical models. Math Modelling&Computational Experiment,1993(1):407~414.
[183] Spitz Y H, Moisan J R, Abbott M R, et al.. Data assimilation and a pelagic ecosystem model:parameterization using time series observations. Journal of Marine Systems,1998,16:51~68.
[184] Spitz Y H, Moisan J R, Abbott M R. Configuring an ecosystem model using data from theBermuda Atlantic Time Series (BATS). Deep-Sea Research. Part2. Topical Studies inOceanography,2001,48:1733~1768.
[185] Steele J H. Plant production in the northern North Sea. Marine Research,1958,7:1~36.
[186] Steele J H. The quantitative ecology of marine phytoplankton. Biological Reviews,1959,34:129~158.
[187] Stow C A, Jolliff Jason, McGillicuddy J, et al.. Skill assessment for coupledbiological/physical models of marine systems, Journal of Marine Systems,2009,76:4~15.
[188] Talagrand O, Courtier P. Variational assimilation of meteorological observations with theadjoint vorticity equation Part I: Theory.Quarterly. Journal of the Royal MeteorologicalSociety,1987,113:1311~1328.
[189] Thacker W C. The role of the Hessian in fitting models to measurements. Journal ofGeophysical Research C,1989,94:6177~6196.
[190] Thacker W C, Long, R B. Fitting dynamics to data. Journal of Geophysical Research,1988,93:1227~1240.
[191] Tian T, Wei H, Su J, et al.. Simulations of annual cycle of phytoplankton production and theutilization of nitrogen in the Yellow Sea. Journal of Physical Oceanography,2005,61:343~357.
[192] Torres R, Allen, J I, Figueiras F G. Sequential data assimilation in an upwelling influencedestuary. Journal of Marine Systems,2006,60:317~329.
[193] Triantafyllou G, Hoteit I, Petihakis G. A singular evolutive interpolated Kalman filter forefficient data assimilation in a3-D complex physical–biogeochemical model of the CretanSea. Journal of Marine Systems,2003,40:213~231.
[194] Ullman D S, Wilson R E. Model parameter estimation from data assimilation modelling:temporal and spatial variability of the bottom drag coefficient. Journal of GeophysicalResearch,1998,103:5531~5549.
[195] Vallino J J. Improving marine ecosystem models: use of data assimilation and mesocosmexperiments. Journal of Marine Research,2000,58:117~164.
[196] Van Leeuwea M A, Scharekb R, De Baara, H J W., et al.. Iron enrichment experiments inthe Southern Ocean: physiological responses of plankton communities. Deep-Sea Research.Part2. Topical Studies in Oceanography,1997,44:189~208.
[197] Volterra V. Fluctuations in the abundance of a species considered mathematically. Nature,1926,118:558–560.
[198] Ward B A, Friedrichs M A M, Anderson T R, et al. Parameter optimisation techniques andthe problem of underdetermination in marine biogeochemical models. Journal of MarineSystems,2010,81:4~43.
[199] Weber L, Volker C, Schartau M, et al.. Modeling the speciation and biogeochemistry of ironat the Bermuda Atlantic Time-Series study site. Global Biogeochemical Cycles,2005,19,GB1019. doi:10.1029/2004GBC002340.
[200] Xu Q, Lin H, Liu Y G, et al.. Data assimilation in a coupled physical-biological model forthe Bohai Sea and the Northern Yellow Sea. Marine and Freshwater Research,2008,59:529~539.
[201] Yamanaka Y, Yoshie N, Fujii M, et al.. An ecosystem Model Coupled withNitrogen-Silicon-Carbon cycles applied to Station A7in the Northwestern Pacific. Journalof Physical Oceanography,2004,60:227~241.
[202] Yu L, Malanotte-Rizzoli P. Inverse modeling of seasonal variations in the North AtlanticOcean. Journal of Physical Oceanography,1998,28(5):902~922.
[203] Yu L S, O'Brien J J. On the initial condition parameter estimation. Journal of PhysicalOceanography,1992,22:1361~1364.
[204] Yu L S, O'Brien J J. Variational data assimilation for determining the seasonal net surfaceheat flux using a tropical Pacific Ocean model. Journal of Physical Oceanography,1995,25:2319~2343.
[205] Yuan D L, Rienecker M M. Inverse estimation of sea surface heat flux over the equatorialPacific Ocean: Seasonal cycle. Journal of Geophysical Research,2003,108(C8):32~47.
[206] Zhang A J, Parker, B B, Wei, E. Assimilation of water level data into a coastalhydrodynamic model by an adjoint optimal technique. Continental Shelf Research,2002,22:1909~1934.
[207] Zhang A J, Wei E, Parker, B B. Optimal estimation of tidal open boundary conditions usingpredicted tides and adjoint data assimilation technique. Continental Shelf Research,2003,23:1055~1070.
[208] Zhang J C, Lv X Q, Wang P, Wang Y P. Study on linear and nonlinear bottom frictionparameterizations for regional tidal models using data assimilation. Continental ShelfResearch,2011,31:555~573.
[209] Zhang J C, Zhu J G, Lv X Q. Numerical Study on the Bottom Friction Coefficient of theBohai Sea, the Yellow Sea and the East China Sea. Chinese Journal of ComputationalPhysics,2006,23(6):731~737.
[210] Zhao Q, Hu X M, Lv X Q, Xiong X J, Yang B. Study on the Transport of COD in the SeaArea Around Maidao off Qingdao Coast Using Data Assimilation. Journal of OceanUniversity of China,2007,6(4):339-344.
[211] Zhao L, Wei H, Xu Y F, et al.. An adjoint data assimilation approach for estimatingparameters in a three-dimensional ecosystem model. Ecological Modelling,2005,186:234~249.
[212] Zhao Q, Lv X Q. Parameter estimation in a three-dimensional marine ecosystem modelusing the adjoint technique. Journal of Marine Systems,2008,74:443~452.
[2]樊伟，吕咸青.基于参数空间分布的海洋生态系统数值模拟.海洋科学进展，2009，27（1）：24~33.
[3]高会旺，冯士筰，管玉平.海洋浮游生态系统动力学模式的研究.海洋与湖沼，2000，31（3）：341~347.
[4]韩桂军，何柏荣，马继瑞，等．利用伴随法优化非线性潮汐模型的开边界条件I：伴随方程的建立及“孪生”数值试验．海洋学报，2000，22（6）：134~140.
[5]韩桂军，何柏荣，马继瑞，等．利用伴随法优化非线性潮汐模型的开边界条件II：黄海、东海潮汐资料的同化试验．海洋学报，2001，23（2）：25~31.
[6]刘桂梅.关键物理过程对黄、渤海浮游生物影响的现象分析与模式研究：[博士学位论文].青岛：中国科学院海洋研究所，2002.
[7]刘桂梅，孙松，王辉，等.春秋季黄海海洋锋对中华哲水蚤分布的影响.自然科学进展，2002，12（11）：1150~1154.
[8]刘桂梅，孙松，王辉.海洋生态系统动力学模型及其研究进展.地球科学进展，2003，18（3）：427~432.
[9]刘浩，潘伟然．营养盐负荷对浮游植物水华影响的模型研究.水科学进展，2008，19（3）：345~351.
[10]吕咸青．数据同化反演风应力拖曳系数以及垂向涡动黏性系数的分布．海洋学报，2001，23（1）：13~20.
[11]吕咸青，田纪伟，吴自库．渤、黄海的底摩擦系数．力学学报，2003a，35（4）：465~468.
[12]吕咸青，吴自库，殷忠斌，田纪伟．渤、黄、东海潮汐开边界的1种反演方法．青岛海洋大学学报，2003b，33（2）：155~172.
[13]吕咸青，张杰．如何利用水位资料反演开边界条件(1)．水动力学研究与进展，1999，14：92~102.
[14]马继瑞，韩桂军，李冬．变分伴随数据同化在海表面温度预报中的应用研究．海洋学报，2002，24（5）：1~7.
[15]逄勇，李学灵．珠江三角洲河网入伶仃洋污染物通量计算研究．水利学报，2001，9：40~44.
[16]任湘湘，李海，吴辉碇.海洋生态系统动力学模型研究进展.海洋预报，2012，29（1）：65~72.
[17]唐启升，苏纪兰.海洋生态系统动力学研究与海洋生物资源可持续利用.地球科学进展，2001，16（10）：5~11.
[18]唐启升，苏纪兰，等.中国海洋生态系统动力学研究：I关键科学问题与研究发展战略.北京：科学出版社，2000，第252页.
[19]王保栋，单宝田，战闰，等.黄、渤海无机氮的收支模式初探．海洋科学，2002，26（2）：33-36.
[20]王辉，刘桂梅，万莉颖.数据同化在海洋生态模型中的应用和研究进展.地球科学进展，2007，22（10）：989~996.
[21]徐青，刘玉光，程永存，等.伴随同化技术在渤黄海生态模式中的应用：控制变量的选取与孪生实验.高技术通讯，2006，16（1）：78~83.
[22]闫菊，鲍献文，王海，等.胶州湾污染物COD的三维扩散与输运研究.环境科学研究，2001，12（2）：14~17.
[23]尹增强，李九奇，倪雪朋.海洋生态系统动力学理论在渔业上的应用.海洋开发与管理，2008，8：83~86.
[24]曾庆存，周广庆，浦一芬，等.地球系统动力学模式及模拟研究.大气科学，2008，32（4）：653~690.
[25]张继才.三维正压潮汐潮流伴随同化模型数值建模及应用研究：[博士学位论文].青岛：中国海洋大学海洋环境学院，2008.
[26]张继才，吕咸青．空间分布底摩擦系数的伴随法反演研究．自然科学进展，2005，15（9）：1086~1093.
[27]赵强.一个简单海洋生态系统动力学模型的伴随同化应用研究：[硕士学位论文].青岛：中国海洋大学海洋环境学院，2006.
[28]张书文，夏长水，袁业立.黄海冷水团水域物理－生态耦合数值模式研究.自然科学进展，2002,12（3）：315~319.
[29]张素香，李瑞杰，罗峰，朱文谨.海洋生态动力学模型的研究进展.海洋湖沼通报，2006，4：121~127.
[30]朱江，曾庆存，郭冬建，刘卓．利用伴随算法从岸边潮位站资料估计近岸模式的开边界条件．中国科学(D辑)，1997，279（5）：462~468.
[31] Allen C T, Young S G, Haupt S E. Improving pollutant source characterization by betterestimating wind direction with a genetic algorithm. Atmospheric Environment,2007,41:2283~2289.
[32] Allen J I, Eknes M, Evensen G. An ensemble Kalman filter with a complex marineecosystem model: hindcasting phytoplankton in the Cretan Sea. Annales Geophysicae,2002,20:1~13.
[33] Anderson D L T, Sheinbaum J, Haines K. Data assimilation in ocean models. Reports onProgress in Physics,1996,59:1209~1266.
[34] Anderson L A, Robinson A R, Lozano C J. Physical and biological modeling in the GulfStream region: I. Data assimilation methodology. Deep-Sea Research. Part1.Oceanographic Research Papers,2000,47:1787~1827.
[35] Anderson T R. Plankton functional type modeling: running before we can walk? Journal ofplankton research,2005,27(11):1073~1081.
[36] Anderson T R. Progress in marine ecosystem modelling and the “unreasonableeffectiveness of mathematics”. Journal of Marine Systems,2010(81):4~11.
[37] Armstrong R A, Sarmiento J L, Slater, R D. Monitoring ocean productivity by assimilatingsatellite chlorophyll into ecosystem models. In: Powell, Steele (Eds.), Ecological TimeSeries. Chapman and Hall, London,1995, pp:371~390.
[38] Baretta J W. The European regional seas ecosystem model, a complex marine ecosystemmodel. Netherlands Journal of Sea Research,1995,33(3-4):233~246.
[39] Bennett A F, Budgell W P. Ocean data assimilation and the Kalman filter, spatial regularity.Journal of Physical Oceanography,1987,17:1583~1601.
[40] Besiktepe S T, Lermusiaux P F J, Robinson A R. Coupled physical and biogeochemicaldata-driven simulations of Massachusetts Bay in late summer: real-time and postcruise dataassimilation. Journal of Marine Systems,2003,40:171~212.
[41] Bissett W P, Walsh J J, Dieterle D A, et al.. Carbon cycling in the upper waters of theSargasso Sea: I. Numerical simulation of differential carbon and nitrogen fluxes, Deep-SeaResearch. Part1,1999,46:205~269.
[42] Bratseth A M. Statistical interpolation by means of successive corrections. Tellus,1986,38A:439~447.
[43] Carmillet V, Brankart J M, Brasseur P, Drange H, et al.. A singular evolutive extendedKalman filter to assimilate ocean color data in a coupled physical-biochemical model of theNorth Atlantic ocean. Ocean Modelling,2001,3:167~192.
[44] Carlotti F. Growth and egg production of female Calanus finmarchicus: An individual-based physiological model and experimental validation. Marine Ecology Progress Series,1997,149:91~104.
[45] Chai F, Dugdale R C, Peng T H, et al.. One-dimensional ecosystem model of the equatorialPacific upwelling system. Part I: Model development and silicon and nitrogen cycle.Deep-Sea Research. Part2,2002,49:2713~2745.
[46] Chen C, Beardsley R C, Franks P J S. A3-D prognostic model study of the ecosystem overGeorges Bank and adjacent coastal regions.Part1: Physical model. Deep-Sea Research,2001,48:419~456.
[47] Chen C, Denis A, Wiesenburg, et al.. Influences of river discharge on biological productionin the inner shelf: A coupled biological and physical model of the Louisiana-Texas Shelf.Journal of Marine Research,1997,55(2):293~320.
[48] Chen Q, Tan K, Zhu C, Li R. Development and application of a two-dimensional waterquality model for the Daqinghe River Mouth of the Dianchi Lake. Journal ofEnvironmental Sciences,2009,21(3):313~318.
[49] Cloern J E, Grenz C, Vidergar-Lucas L. An empirical model of the phytoplanktonchlorophyll: carbon ratio-the conversion factor between productivity and growth rate.Limnology and Oceanography,1995,40:1313~1321.
[50] Cressman G P. An operational objective analysis system. Monthly Weather Review,1959,87:367~374.
[51] Daley R. Atmospheric Data Analysis. New York: Cambridge Atmospheric and SpaceScience Series, Cambridge University Press,1991,363~402.
[52] Daniel R L, Wendy, C G, Dennis, J M, et al.. Biological/physical simulations of calanusfinmarchicus population dynamics in the Gulf of Maine. Marine Ecology Progress Series,1998,169:189~210.
[53] Das S K, Lardner R W. Variational parameter estimation for a two dimensional numericaltidal model. International Journal for Numerical Methods in Fluids,1992,15:313~327.
[54] Delhez E J M. and Martin G.3D modelling of hydrodynamic and ecohydrodynamicprocesses on the north-western European continental shelf. Bulletin de la Société royale dessciences de Liège,1994,63(1-2):5~64.
[55] DeMaria M, Jones R W. Optimization of a hurricane track forecast model with the adjointmodel equations. Monthly Weather Review,1993,121(6):1730~1745.
[56] Denman K L. Modelling planktonic ecosystems: Parameterizing complexity. Progress inoceanography,2003,57:429~452.
[57] Duan J, Nanda S K. Two-dimensional depth-averaged model simulation of suspendedsediment concentration distribution in a groyne field. Journal of Hydrology,2006,327(3-4):426~437.
[58] Eknes M, Evensen, G. An ensemble Kalman filter with a1-D marine ecosystem model.Journal of Marine Systems,2002,36:75~100.
[59] Eliassen A. Provisional report on calculation of spatial covariance and autocorrelation ofthe pressure field. Oslo: Inst. Weather and Climate Research, Academy of Sciences,1954,Report No.5.
[60] Evans G T. The role of local models and data sets in the Joint Global Ocean Flux Study.Deep-Sea Research. Part1. Oceanography Research,1999,46:1369~1389.
[61] Evensen G. Sequential data assimilation with a nonlinear quasi-geostrophic model usingMontre Carlo methods to forecast error statistics. Journal of Geophysical Research,1994,99(10):143~162.
[62] Fan W, Lv X Q. Data assimilation in a simple marine ecosystem model based on spatialbiological parameterizations. Ecological Modelling,2009,220(17):1997~2008.
[63] Fang G H. Tide and Tidal current charts for the marginal seas adjacent to China. ChineseJournal of Oceanology and Limnology.1986,4(1):1-16.
[64] Fasham M J R, Ducklow H W, McKelvie S M. A nitrogen-based model of planktondynamics in the oceanic mixed layer. Journal of Marine Research,1990,48:591~639.
[65] Fasham M J R, Boyd P W, Savidge G. Modeling the relative contributions of utotrophs andheterotrophs to carbon flow at a Lagrangian JGOFS station in the Northeast Atlantic: theimportance of DOC. Limnology and Oceanography,1999,44:80~94.
[66] Fasham M J R., Evans G T., Kiefer D A, et al.. The use of optimization techniques to modelmarine ecosystem dynamics at the JGOFS station at47degrees N20degrees W.Philosophical Transactions of the Royal Society of London. Series B,1995,348:203~209.
[67] Faugeras B, Bernard O, Sciandra A, et al.. A mechanistic modeling and data assimilationapproach to estimate the carbon/chlorophyll and carbon/nitrogen ratios in a coupledhydrodynamical–biological model. Nonlinear Processes in Geophysics,2004,11:515~533.
[68] Faugeras B, Levy M, Memery L, et al.. Can biogeochemical fluxes be recovered fromnitrate and chlorophyll data? A case study assimilating data in the NorthwesternMediterranean Sea at the JGOFS-DYFAMED station. Journal of Marine Systems,2003,40–41:99~125.
[69] Fennel K, Losch M, Schroter J, et al.. Testing a marine ecosystem model: sensitivityanalysis and parameter optimization. Journal of Marine Systems,2001:28,45~63.
[70] Fletcher B, Reeves C M. Function minimization by conjugate gradients. The computerjournal,1964,7(2):149~154.
[71] Flynn K J. Castles built on sand: dysfunctionality in plankton models and the inadequacy ofdialogue between biologists and modelers. Journal of plankton research,27(12):1205~1210.
[72] Franks P J S. NPZ model of plankton dynamics: their construction, coupling to physics, andapplication. Journal of Physical Oceanography,2002,58:379~387.
[73] Franks P J S, Chen C. Plankton production in tidal fronts: A model of Georges bank insummer. Journal of Marine Research,1996,54:631~651.
[74] Franks P J S, Chen C S. A3-D Prognostic numerical model study of the Georges Bankecosystem, Part II: biological–physical model. Deep-Sea Research. Part2. Topical Studiesin Oceanography,2001,48:457~482.
[75] Friedrichs M A M. A data assimilative marine ecosystem model of the central equatorialPacific: numerical twin experiments. Journal of Marine Research,2001,59:859~894.
[76] Friedrichs M A M. Assimilation of JGOFS EqPac and SeaWiFS data into a marineecosystem model of the central equatorial Pacific Ocean. Deep-Sea Research. Part2.Topical Studies in Oceanography,2002,49:289~320.
[77] Friedrichs M A M, Dusenberry J D, Anderson L A, et al.. Assessment of skill and portabilityin regional marine biogeochemical models: Role of multiple planktonic groups, Journal ofGeophysical Research,2007,112:1~22.
[78] Friedrichs M A M, Hood R, Wiggert J. Ecosystem model complexity versus physicalforcing: Quantification of their relative impact with assimilated Arabian Sea data. Deep-SeaResearch. Part2. Topical Studies in Oceanography,2006,53:576~600.
[79] Frost B. W. Effects of the size and concentration of food particles on the feeding behaviorof the marine planktonic copepod Calanus pacificus. Limnology and Oceanography,1972,17:805~815.
[80] Gandin L. Objective Analysis of Meteorological Fields (translated from Russian).Jerusalem: Israel Program for Scientific Translation,1965.
[81] Garcia-Gorriz E, Hoepffner N, Ouberdous M. Assimilation of SeaWiFS data in a coupledphysical–biological model of the Adriatic Sea. Journal of Marine Systems,2003,40–41:233~252.
[82] Gentleman W. A chronology of plankton dynamics in silico: how computer models havebeen used to study marine ecosystems. Hydrobiologia,2002,480:69~85.
[83] George L, Mellor. Users Guide for a Three-Dimensional Primitive Equation NumericalOcean Model. New Jersey: Princeton University,2004,56.
[84] Gilchrist B, Cressman G. An experiment in Objective Analysis. Tellus,1954,6:309~318.
[85] Gupta I, Dhage S, Chandorkar A A, Srivastav A. Numerical modeling for Thane creek.Environmental Modelling&Software,2004,19(6):571~579.
[86] Gregg W W. Assimilation of SeaWiFS ocean chlorophyll data into a three-dimensionalglobal ocean model. Journal of Marine Systems,2008,69:205~225.
[87] Gregg W W, Friedrichs M A M., Robinson A R, et al.. Skill assessment in ocean biologicaldata assimilation. Journal of Marine Systems,2009,76:16~33.
[88] Gregg W W, Ginoux P, Schopf P S, et al.. Phytoplankton and iron: validation of a globalthree-dimensional ocean biogeochemical model. Deep-Sea Research, Part2, TopicalStudies in Oceanography,2003,50:3143~3169.
[89] Greiner E, Arnault S, Morliaère A. Twelve-monthly experiments of4D-variationalassimilation in the tropical Atlantic during1987, Part1: Method and statistical results.Progress in Oceanography,1998a,41:141~202.
[90] Greiner E, Arnault S, Morliaère A. Twelve-monthly experiments of4D-variationalassimilation in the tropical Atlantic during1987, Part2: Oceanographic interpretation.Progress in Oceanography,1998b,41:203~247.
[91] Greiner E, Perigaud C. Assimilation of Geo-sat altimetric data in a nonlinearreduced-gravity model of the Indian Ocean, Part I: adjoint approach and model-dataconsistency. Journal of Physical Oceanography,1994,24(8):1783~1804.
[92] Greiner E, Perigaud C. Assimilation of Geo-sat altimetric data in a nonlinearreduced-gravity model of the Indian Ocean, Part II: Some Validation and Interpretation ofthe assimilated results. Journal of Physical Oceanography,1996,26:1735~1746.
[93] Gunson J, Oschlies A, Garcon V. Sensitivity of ecosystem parameters to simulated satelliteocean color data using a coupled physical–biological model of the North Atlantic. Journalof Marine Research,1999,57:613~639.
[94] Gunson J R, Malanotte-Rizzoli P. Assimilation studies of open-ocean flows, Part I:Estimation of initial and boundary conditions, Journal of Geophysical Research,1996a,101:28457~28472.
[95] Gunson J R, Malanotte-Rizzoli P. Assimilation studies of open-ocean flows, Part II: Errormeasures with strongly nonlinear dynamics. Journal of Geophysical Research,1996b,101:28473~28488.
[96] Guo X Y, Yanagi T. Three-dimensional structure of tidal current in the East China Sea andthe Yellow Sea. Journal of Oceanography.,1998,54:651~668.
[97] Gupta I, Dhage S, Chandorkar A A, Srivastav A. Numerical modeling for Thane creek.Environmental Modelling&Software,2004,19(6):571~579.
[98] Harmon R, Challenor P. A Markov chain Monte Carlo model for estimation andassimilation into models. Ecological Modelling,1997,101:41~59.
[99] Harms I H, Karcher M J, Dethleff D. Modelling Siberian river runoff-implications forcontaminant transport in the Arctic Ocean. Journal of Marine Systems,2005,27(1-3):95~115.
[100] Hemmings J C P, Srokosz M A, Challenor P, et al.. Assimilating satellite ocean-colourobservations into oceanic ecosystem models. Philosophical Transactions of the RoyalSociety of London A-Mathematics, Physics and Engineering Science,2003,361:33~39.
[101] Hemmings J C P, Srokosz M A, Challenor P, et al.. Split-domain calibration of anecosystem model using satellite ocean colour data. Journal of Marine Systems,2004,50:141~179.
[102] Hestenes M R, Stiefel E. Methods of conjugate gradients for solving linear systems.1952,49(6):409~436.
[103] Holfort J, Siedler G. The meridional oceanic transports of heat and nutrients in the SouthAtlantic. Journal of Physical Oceanography,2001,31:5~29.
[104] Hood R. Functional group modeling: progress, challenges and prospects, Deep-SeaResearch. Part2. Topical Studies in Oceanography,2006,53:459~512.
[105] Hoteit I, Triantafyllou G, Petihakis G, et al.. A singular evolutive extended Kalman filter toassimilate real in situ data in a1-D marine ecosystem model. Annales Geophysicae,2003,21:389~397.
[106] Hu J, Fennel K, Mattern J P, et all. Data assimilation with a local Ensemble Kalman Filterapplied to a three-dimensional biological model of the Middle Atlantic Bight. Journal ofMarine Systems,2012,94:145~156.
[107] Huang H Q, Chen G, Zhang Q F. The distribution characteristics of pollutants released atdifferent cross-sectional positions of a river. Environmental Pollution,2010,158:1327~1333.
[108] Hurtt G C, Armstrong R A. A pelagic ecosystem model calibrated with BATS data.Deep-Sea Research. Part2. Topical Studies in Oceanography,1996,43:653~683.
[109] Hurtt G C, Armstrong RA. A pelagic ecosystem model calibrated with BATS and OWSIdata. Deep-Sea Research. Part2. Topical Studies in Oceanography,1999,46:27~61.
[110] Ibrahim H, George T, George P. Towards a data assimilation system for the Cretan Seaecosystem using a simplified Kalman filter. Journal of Marine Systems,2004,45:159~171.
[111] Ishizaka J. Coupling of Coastal Zone Color Scanner data to a physical–biological model ofthe southeastern United-States continental-shelf ecosystem.3. Nutrient and phytoplanktonfluxes and CZCS data assimilation. Journal of Geophysical Research,1990,95:20201~20212.
[112] Kalnay E. AtmosphericModeling, Data Assimilation and Predictability.University Press,Cambridge.2003,341pp.
[113] Kishi M J, Okunishi T, Yamanaka Y. A comparison of simulated particle fluxes usingNEMURO and other ecosystem models in the Western North Pacific. Journal of PhysicalOceanography,2004,60:63~73.
[114] Kuo Y H, Zou X, Guo Y R. Variational assimilation of precipitable water using anonhydrostatic mesoscale adjoint model. Part I: moisture retrieval and sensitivityexperiments. Monthly Weather Review,1996,124(1):122~147.
[115] Kuroda H, Kishi M J. A data assimilation technique applied to estimate parameters for theNEMURO marine ecosystem model. Ecological Modelling,2004,172:69~85.
[116] Lancelot C, Spitz Y H, Gypens N, et al.. Modelling diatom-Phaeocystis blooms and nutrientcycles in the Southern Bight of the North Sea: the MIRO model, Mar. Ecol. Prog. Ser.,2005,289:63~78.
[117] Lardner R W. Optimal control of open boundary conditions for a numerical tidal model.Computer Methods in Applied Mechanics and Engineering,1993,102:367~387.
[118] Lawson L M, Spitz Y H, Hofmann E E, Long R B. A data assimilation technique applied toa predator-prey model. Bulletin of Mathematical Biology,1995,57(4):593~617.
[119] Lawson L M, Hofmann E E, Spitz Y H. Time series sampling and data assimilation in asimple marine ecosystem model. Deep-Sea Research. Part2. Topical Studies inOceanography,1996,43:625~65.
[120] Le Quéré C, Harrison S P, Prentice I C, et al. Ecosystem dynamics based on planktonfunctional types for global ocean biogeochemistry models, Global Change Biology,2005,11:2016~2040.
[121] LeDimet F X, Talagrand O. Variational algorithms for analysis and assimilation ofmeteorological observations: Theoretical aspects. Tellus,1986,38A:97~110.
[122] Lee M E, Seo M. Analysis of pollutant transport in the Han River with tidal current using a2D finite element model. Journal of Hydro-environment Research,2007,1(1):30~42.
[123] Lefevre F, Provost C L, Lyard F H. How can we improve a global ocean tide model at a regionalscale? A test on the Yellow Sea and the East China Sea. Journal of Geophysical Research,2000,105:8707~8725.
[124] Lenartz F, Raick C, Soetaert K, et al.. Application of an Ensemble Kalman filter to a1-Dcoupled hydrodynamic-ecosystem model of the Ligurian Sea. Journal of Marine Systems,2007,68:327~348.
[125] Lenhart H J, Radach G, Ruardij P. The effects of river input on the ecosystem dynamics inthe continental coastal zone of the North Sea using a box refined ecosystem model ERSEM.Journal of Sea Research,1997,38,249~274.
[126] Leredde Y, Lauer-Leredde C, Diaz C. On the variational data assimilation by a marineecosystem model of NPZ type. Comptes Rendus Geoscience,2005,337:1055~1064.
[127] Liu X, Peng W, He G, Liu J, Wang, Y. A coupled model of hydrodynamics and water qualityfor Yuqiao reservoir in Haihe river basin. Journal of Hydrodynamics,2008,20(5):574~582.
[128] Losa S N, Kivman G A, Ryabchenko V A. Weak constraint parameter estimation for asimple ocean ecosystem model: what can we learn about the model and data? Journal ofMarine Systems,2004,45:1~20.
[129] Losa S N, Kivman G A, Schroter J, et al.. Sequential weak constraint parameter estimationin an ecosystem model. Journal of Marine Systems,2003,43:31~49.
[130] Losa S N, Vézina A, Wright D, et al.3D ecosystem modelling in the North Atlantic:Relative impacts of physical and biological parameterizations. Journal of Marine Systems,2006,61:230~245.
[131] Luyten P J, Jones J E, Proctor R, et al.. COHERENS-A coupled hydrodynamical ecologicalmodel for regional and shelf seas: User Documentation. Belgium: MUMM Report,Management Unit of the Mathematical Models of the North Sea.1999.
[132] Lv X Q, Wu Z K, Gu Y., et al.. Study on the adjoint method in data assimilation and therelated problems. Applied Mathematics and Mechanics,2004,25(6):636~646.
[133] Lv X Q, Zhang J C. Numerical study on spatially varying bottom friction coefficient of a2D tidal model with adjoint method. Continental Shelf Research,2006,26:1905~1923.
[134] Magri S, Brasseur P, Lacroix G. Data assimilation in a marine ecosystem model of theLigurian Sea. Comptes Rendus Geoscience,2005,337:1065~1074.
[135] Matear R J. Parameter estimation and analysis of ecosystem models using simulatedannealing: a case study at Station P. Journal of Marine Research,1995,53:571~607.
[136] Mattern J P, Fennel K, Dowd M. Sequential data assimilation applied to aphysical–biological model for the Bermuda Atlantic time series station. Journal of Marinesystems,2010a,79:144~156.
[137] Mattern J P, Fennel K, Dowd M. Introduction and assessment of measures for quantitativemodel-data comparison using satellite image,2010a,2:794~818.
[138] Mattern J P, Fennel K, Dowd M. Estimating time-dependent parameters for a biologicalocean model using an emulator approach,2012,96-97:32~47.
[139] McGillicuddy D J, Lynch D R, Moore, A.M., er al.. An adjoint data assimilation approachto diagnosis of physical and biological controls on Pseudocalanus spp. in the Gulf ofMaine-Georges Bank region. Fisheries Oceanography,1998,7(3-4):205~218.
[140] Mellor G L, Yamada T. Development of a turbulence closure model for geophysical fluidproblem. Reviews of Geophysics and space physics,1982:851~875.
[141] Miller A J, DiLorenzo E, Neilson D J, et al.. Modeling CalCOFI observations during ElNino: fitting physics and biology. CalCOFI Report,2000,41, pp:11.
[142] Miller R N. Toward the application of the Kalman filter to regional open ocean modeling.Journal of Physical Oceanography,1986,16:72~86.
[143] Moll A, Radach G. Review of three-dimensional ecological modeling related to the NorthSea Shelf system Part1: models and their results. Progress in Oceanography,2003,57:175~217.
[144] Moore A M. Data assimilation in a quasi-geostrophic open ocean model of the Gulf Streamregion using the adjoint method. Journal of Physical Oceanography,1991,21:398~427.
[145] Moore J K, Doney S C, Glover, D M, et al.. Iron cycling and nutrient-limitation patterns insurface waters of the World Ocean. Deep-Sea Research. Part2. Topical Studies inOceanography,2002,49:463~507.
[146] Natvik L J, Eknes M, Evensen G. A weak constraint inverse for zero-dimensional marineecosystem model. Journal of Marine Systems,2001,28:19~44.
[147] Natvik L J, Evensen, G. Assimilation of ocean colour data into a biochemical model of theNorth Atlantic. Part1. Data assimilation experiments. Journal of Marine Systems,2003a,40–41:127~153.
[148] Natvik L J, Evensen, G. Assimilation of ocean colour data into a biochemical model of theNorth Atlantic. Part2. Statistical analysis. Journal of Marine Systems,2003b,40–41:155~169.
[149] Oschlies A, Schartau M. Basin-scale performance of a locally optimized marine ecosystemmodel. Journal of Marine Research,2005,63:335~358.
[150] Panofsky H A. Objective weather map analysis. Journal of Meteorology,1949,6:386~392.
[151] P tsch J, Radach G. Long-term simulation of the eutrophication of the North Sea: Temporaldevelopment of nutrients, chlorophyll and primary production in comparison toobservations. Journal of Sea Research,1997,38,275~310.
[152] Peng S Q, Xie L. Effect of determining initial conditions by four-dimensional variationaldata assimilation on storm surge forecasting. Ocean Modelling,2006,14:1~18.
[153] Perianez R. A particle-tracking model for simulating pollutant dispersion in the Strait ofGibraltar. Marine Pollution Bulletin,2004,49(7-8):613~623.
[154] Perianez R. GISPART: a numerical model to simulate the dispersion of contaminants in theStrait of Gibraltar. Enviromental Modelling&Software,2005a,20(6):797~802.
[155] Perianez R. Modelling the transport of suspended particulate matter by the Rhone Riverplume (France): implications for pollutant dispersion. Environmental Pollution,2005b,133(2):351~364.
[156] Polak B, Ribiere G. Note surla convergence des methodes de directions conjuguees. RevueFrancaise D’informatique et de recherche operationnelle, serie rouge.1969,16:35~43.
[157] Polyak B T. The conjugate gradient method in extreme problems. USSR computationalmathematics and mathematical physics,1969,9(4):94~112.
[158] Popova E E, Lozano C J, Srokosz M A, et al.. Coupled3D physical and biologicalmodeling of the mesoscale variability observed in North-East Atlantic in Spring1997:biological processes. Deep-Sea Research. Part1. Oceanographic Research Papers,2002,49:1741~1768.
[159] Price N M, Andeson L G, Morel F M M. Iron and nitrogen nutrition of equatorial Pacificplankton. Deep Sea Research. Part A,1991,38:1361~1378.
[160] Prunet P, Minster J F, RuizPino D, et al.. Assimilation of surface data in a one-dimensionalphysical–biogeochemical model of the surface ocean.1. Method and preliminary results.Global Biogeochemical Cycles,1996,10:111~138.
[161] Qi P, Wang C H, Li X Y, Lv X Q. Numerical study on spatially varying control parametersof a marine ecosystem dynamical model with adjoint method. Acta Oceanologica Sinica,2010,30(1):7~14.
[162] Qiu Z F, He Y J, Lv X Q. Tidal constituents in the Bohai and Yellow Seas from an adjointnumerical model with T/P data. Journal of Hydrodynamics (B),2005,17(3):275~282.
[163] Radach G, Moll A. Estimation of the variability of production by simulating annual cyclesof phytoplankton in the central North Sea. Progress in Oceanography,1993,31(4),339~419.
[164] Raick C, Soetaert K, Grégoire M. Model complexity and performance: How far can wesimplify? Progress in Oceanography,2006,70:27~57.
[165] Rajar R, Matjaz Cetina. Hydrodynamic and water quality modelling: An experience.Ecological Modelling,1997,101(2-3):195~207.
[166] Rajar R, Matjaz Cetina, Andrej Sirca. Hydrodynamic and water quality modelling: casestudies. Ecological Modelling,1997,101(2-3):209~228.
[167] Redfield A C, Ketchum B H, Richards F A. The influence of organisms on the compositionof sea water. The Sea, vol.2. Inter-Science, New York,1963,26~77pp.
[168] Riley G A. Factors controlling phytoplankton populations on Georges Bank. Journal ofMarine Research,1946,6:54~73.
[169] Riley G A. A theoretical analysis of the zooplankton population of Georges Bank. Journalof Marine Research,1947,6:104~113.
[170] Riley G A. Theory of growth and competition in natural populations. Journal of theFisheries Board of Canada,1953,10:211~223.
[171] Robinson A R, Lermusiaux P F J. Overview of Data Assimilation. Cambridge: HarvardReports in Physical/Interdisciplinary Ocean Science,2000, Number62.
[172] Roy R, Broomhead D S, Platt T, et al.. Sequential variations of phytoplankton growth andmortality in an NPZ model:A remote-sensing-based assessment. Journal of Marine Systems,2012,92:16~29.
[173] Sasaki Y. Some basic formalisms in numerical variational analysis. Monthly WeatherReview,1970,98:875~883.
[174] Saltelli A, Marivoet J. Non-parametric statistics in sensitivity analysis for model output:comparison of selected techniques. Reliability Engineering and System Safety,1990,28(2):229-253.
[175] Schartau M, Oschlies A, Willebrand J. Parameter estimates of a zero-dimensionalecosystem model applying the adjoint method. Deep-Sea Research. Part2. Topical Studiesin Oceanography,2001,48:1769~1800.
[176] Schartau M, Oschlies A. Simultaneous data-based optimization of a2D-ecosystem model atthree locations in the North Atlantic: Part I—method and parameter estimates. Journal ofMarine Research,2003,61:765~793.
[177] Schiller A. The mean circulation of the Atlantic Ocean north of30S determined with theadjoint method applied to an ocean general circulation model. Journal of Marine Research,1995,53(3):453~497.
[178] Schlitzer R. Carbon export fluxes in the Southern Ocean: results from inverse modeling andcomparison with satellite-based estimates. Deep-Sea Research. Part2. Topical Studies inOceanography,2002,49:1623~1644.
[179] Seiler U. Estimation of open boundary conditions with the adjoint method. Journal ofGeophysical Research,1993,98:22855~22870.
[180] Smagorinsky J. General circulation experiments with the primitive equations:1. the basicexperiment. Monthly Weather Review,91(3):99~164.
[181] Smedstad O M, O’Brien J J. Variational data assimilation and parameter estimation in anequatorial Pacific Ocean model. Progress in Oceanography,1991,26:179~241.
[182] Sobol I M. Sensitivity estimates for nonlinear mathematical models. Math Modelling&Computational Experiment,1993(1):407~414.
[183] Spitz Y H, Moisan J R, Abbott M R, et al.. Data assimilation and a pelagic ecosystem model:parameterization using time series observations. Journal of Marine Systems,1998,16:51~68.
[184] Spitz Y H, Moisan J R, Abbott M R. Configuring an ecosystem model using data from theBermuda Atlantic Time Series (BATS). Deep-Sea Research. Part2. Topical Studies inOceanography,2001,48:1733~1768.
[185] Steele J H. Plant production in the northern North Sea. Marine Research,1958,7:1~36.
[186] Steele J H. The quantitative ecology of marine phytoplankton. Biological Reviews,1959,34:129~158.
[187] Stow C A, Jolliff Jason, McGillicuddy J, et al.. Skill assessment for coupledbiological/physical models of marine systems, Journal of Marine Systems,2009,76:4~15.
[188] Talagrand O, Courtier P. Variational assimilation of meteorological observations with theadjoint vorticity equation Part I: Theory.Quarterly. Journal of the Royal MeteorologicalSociety,1987,113:1311~1328.
[189] Thacker W C. The role of the Hessian in fitting models to measurements. Journal ofGeophysical Research C,1989,94:6177~6196.
[190] Thacker W C, Long, R B. Fitting dynamics to data. Journal of Geophysical Research,1988,93:1227~1240.
[191] Tian T, Wei H, Su J, et al.. Simulations of annual cycle of phytoplankton production and theutilization of nitrogen in the Yellow Sea. Journal of Physical Oceanography,2005,61:343~357.
[192] Torres R, Allen, J I, Figueiras F G. Sequential data assimilation in an upwelling influencedestuary. Journal of Marine Systems,2006,60:317~329.
[193] Triantafyllou G, Hoteit I, Petihakis G. A singular evolutive interpolated Kalman filter forefficient data assimilation in a3-D complex physical–biogeochemical model of the CretanSea. Journal of Marine Systems,2003,40:213~231.
[194] Ullman D S, Wilson R E. Model parameter estimation from data assimilation modelling:temporal and spatial variability of the bottom drag coefficient. Journal of GeophysicalResearch,1998,103:5531~5549.
[195] Vallino J J. Improving marine ecosystem models: use of data assimilation and mesocosmexperiments. Journal of Marine Research,2000,58:117~164.
[196] Van Leeuwea M A, Scharekb R, De Baara, H J W., et al.. Iron enrichment experiments inthe Southern Ocean: physiological responses of plankton communities. Deep-Sea Research.Part2. Topical Studies in Oceanography,1997,44:189~208.
[197] Volterra V. Fluctuations in the abundance of a species considered mathematically. Nature,1926,118:558–560.
[198] Ward B A, Friedrichs M A M, Anderson T R, et al. Parameter optimisation techniques andthe problem of underdetermination in marine biogeochemical models. Journal of MarineSystems,2010,81:4~43.
[199] Weber L, Volker C, Schartau M, et al.. Modeling the speciation and biogeochemistry of ironat the Bermuda Atlantic Time-Series study site. Global Biogeochemical Cycles,2005,19,GB1019. doi:10.1029/2004GBC002340.
[200] Xu Q, Lin H, Liu Y G, et al.. Data assimilation in a coupled physical-biological model forthe Bohai Sea and the Northern Yellow Sea. Marine and Freshwater Research,2008,59:529~539.
[201] Yamanaka Y, Yoshie N, Fujii M, et al.. An ecosystem Model Coupled withNitrogen-Silicon-Carbon cycles applied to Station A7in the Northwestern Pacific. Journalof Physical Oceanography,2004,60:227~241.
[202] Yu L, Malanotte-Rizzoli P. Inverse modeling of seasonal variations in the North AtlanticOcean. Journal of Physical Oceanography,1998,28(5):902~922.
[203] Yu L S, O'Brien J J. On the initial condition parameter estimation. Journal of PhysicalOceanography,1992,22:1361~1364.
[204] Yu L S, O'Brien J J. Variational data assimilation for determining the seasonal net surfaceheat flux using a tropical Pacific Ocean model. Journal of Physical Oceanography,1995,25:2319~2343.
[205] Yuan D L, Rienecker M M. Inverse estimation of sea surface heat flux over the equatorialPacific Ocean: Seasonal cycle. Journal of Geophysical Research,2003,108(C8):32~47.
[206] Zhang A J, Parker, B B, Wei, E. Assimilation of water level data into a coastalhydrodynamic model by an adjoint optimal technique. Continental Shelf Research,2002,22:1909~1934.
[207] Zhang A J, Wei E, Parker, B B. Optimal estimation of tidal open boundary conditions usingpredicted tides and adjoint data assimilation technique. Continental Shelf Research,2003,23:1055~1070.
[208] Zhang J C, Lv X Q, Wang P, Wang Y P. Study on linear and nonlinear bottom frictionparameterizations for regional tidal models using data assimilation. Continental ShelfResearch,2011,31:555~573.
[209] Zhang J C, Zhu J G, Lv X Q. Numerical Study on the Bottom Friction Coefficient of theBohai Sea, the Yellow Sea and the East China Sea. Chinese Journal of ComputationalPhysics,2006,23(6):731~737.
[210] Zhao Q, Hu X M, Lv X Q, Xiong X J, Yang B. Study on the Transport of COD in the SeaArea Around Maidao off Qingdao Coast Using Data Assimilation. Journal of OceanUniversity of China,2007,6(4):339-344.
[211] Zhao L, Wei H, Xu Y F, et al.. An adjoint data assimilation approach for estimatingparameters in a three-dimensional ecosystem model. Ecological Modelling,2005,186:234~249.
[212] Zhao Q, Lv X Q. Parameter estimation in a three-dimensional marine ecosystem modelusing the adjoint technique. Journal of Marine Systems,2008,74:443~452.