垂向湍流扩散系数的不确定性对深层叶绿素最大值现象模拟的影响
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摘要
深层叶绿素最大值(Deep Chlorophyll Maximum,DCM)现象的数值模拟是研究海洋表层生态系统和全球碳循环的重要组成部分之一。但是由于自身的复杂性和观测的局限性,数值模式中物理参数的不确定性给模拟结果带来了一定程度的误差。其中,垂向湍流扩散系数(vertical turbulence diffusion)是模式所包含的物理参数中很难直接通过观测来确定的参数,它在模式中的来源和取值往往具有很大的不确定性。本文通过条件非线性最优(参数)扰动(Conditional nonlinear optimal perturbation related to parameter,CNOP-P)方法,研究了垂向湍流扩散系数的不确定性对模式模拟结果的影响。我们发现,垂向湍流扩散系数对DCM模拟产生最大影响的CNOP型扰动位于生产力层的上半部分。并且,去掉生产力层内湍流扩散系数的误差,模式模拟的改进程度最高达到了80%。可见,垂向湍流扩散对生态系统的发展和保持起着极其重要的作用,改进垂向湍流扩散系数的不确定性,对DCM的数值模拟有着重要意义。
The simulation of deep chlorophyll maximum(DCM) is one of the most important parts in the study of the ocean surface ecosystem and global carbon cycle. However, the complexity and limited observation of the physical parameters in the numerical model usually cause various errors in the output results. Among such parameters, the coefficient of vertical turbulence diffusion is difficult to be directly determined by observation; therefore,its uncertainty in the model is very large. This study investigates the influence of turbulence diffusion on the numerical model output results using the conditional nonlinear optimal perturbation method(related to parameters).We determined that the strongest perturbation of the vertical turbulence diffusion occurred in the productivity layer,and proved that eliminating this perturbation resulted in significant improvement of the model output. This illustrates that the physical condition is very important for the development and maintenance of the ocean ecosystem model.
The simulation of deep chlorophyll maximum(DCM) is one of the most important parts in the study of the ocean surface ecosystem and global carbon cycle. However, the complexity and limited observation of the physical parameters in the numerical model usually cause various errors in the output results. Among such parameters, the coefficient of vertical turbulence diffusion is difficult to be directly determined by observation; therefore,its uncertainty in the model is very large. This study investigates the influence of turbulence diffusion on the numerical model output results using the conditional nonlinear optimal perturbation method(related to parameters).We determined that the strongest perturbation of the vertical turbulence diffusion occurred in the productivity layer,and proved that eliminating this perturbation resulted in significant improvement of the model output. This illustrates that the physical condition is very important for the development and maintenance of the ocean ecosystem model.
引文
[1]Klausmeier C A and Litchman E.Algal games:The vertical distribution of phytoplankton in poorly mixed water columns[J].Limnology Oceanography,2001,46(8):1998-2007.
[2]Huisman J,Thi N N P,Karl D M,et al.Reduced mixing generates oscillations and chaos in the oceanic deep chlorophyll maximum[J].Nature,2006,439(7074):322-325.
[3]倪晓波,黄大吉.海洋次表层叶绿素最大值的分布和形成机制研究[J].海洋科学,2006,30(5):60-66,72.Ni Xiaobo,Huang Daji.Subsurface chlorophyll maximum:its temporal-spatial distribution and formation mechanism in the ocean[J].Marine Sciences,2006,30(5):60-66,72.
[4]Chai F,Dugdale R C,Peng T H,et al.One-dimensional ecosystem model of the equatorial Pacific upwelling system.Part I:model development and silicon and nitrogen cycle[J].Deep-Sea Research Part II,2002,49(13):2713-2745.
[5]Fennel K and Boss E.Subsurface maxima of phytoplankton and chlorophyll:Steady-state solutions from a simple model[J].Limnology Oceanography,2003,48(4):1521-1534.
[6]Liccardo A,Fierro A,Ludicone D,et al.Response of the deep chlorophyll maximum to fluctuations in vertical mixing intensity[J].Progress in Oceanography,2013,109(1):33-46.
[7]Hodges B A and Rudnick D L.Simple models of steady deep maxima in chlorophyll and biomass[J].Deep-Sea Research PartⅠ,2004,51(8):999-1015.
[8]Ryabov A B,Rudolf L,Blasius B.Vertical distribution and composition of phytoplankton under the influence of an upper mixed layer[J].Journal of Theoretical Biology,2010,263(1):120-133.
[9]Navarro G and Ruiz J.Hysteresis conditions the vertical position of deep chlorophyll maximum in the temperate ocean[J].Global Biogeochemical Cycles,2013,27(4):1013-1022.
[10]Wang Qiang and Mu Mu.Responses of the ocean planktonic ecosystem to finite-amplitude perurbations[J].Journal of Geophysical Research:Oceans,2014,119(12):8454-8471.
[11]Mu Mu,Duan Wansuo,Wang Bin.Conditional nonlinear optimal perturbation and its applications[J].Nonlinear Processes in Geophysics,2003,10(6):493-501.
[12]Mu Mu,Duan Wansuo,Wang Qiang,et al.An extension of conditional nonlinear optimal perturbation approach and its applications[J].Nonlinear Processes in Geophysics,2010,17(2):211-220.
[13]谢东东,孙国栋,邵爱梅,等.草原生态系统模式中参数不确定性导致的模拟结果不确定性研究[J].气候与环境研究,2013,18(3):375-386.Xie Dongdong,Sun Guodong,Shao Aimei,et al.Astudy of simulation uncertainties caused by parameter uncertainties in a grassland ecosystem model[J].Climate and Environmental Research(in Chinese),18(3):375-386.
[14]Sun Guodong and Mu Mu.A Preliminary Application of the Differential Evolution Algorithm to Calculate the CNOP[J].Atmospheric and Oceanic Science Letters,2009,2(6):381-385.
[15]Sun Guodong and Mu Mu.A new approach to identify the sensitivity an importance of physical parameters combination within numerical models using the LundPotsdam-Jena(LPJ)model as an example[J].Theoretical and applied Climatology,2017,128(3-4):587-601.
[16]Birgin E G,Martinez M J,Raydan M.Nonmonotone spectral projected gradient methods on convex sets[J].SIAM Journal on control and Optimization,2000,10(4):1196-1211.
[17]Powell M J D.VMCWD:A FORTRAN subroutine for constrained optimization[J].Acm Sigmap Bulletin,1983,32(32):4-16.
[18]Storn R and Price K.Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization,1997,11:341-359.
[19]Sun Guodong and Mu Mu.The analyses of the net primary production due to regional and seasonal temperature differences in eastern China using the LPJ model[J].Ecological Modelling,2014,289:66-76.
[20]宫响,史洁,高会旺.海洋次表层叶绿素最大值的特征因子及其影响因素[J].地球科学进展,2012,27(5):539-548.Gong Xiang,Shi Jie,Gao Huiwang.Subsurface chlorophyll maximum in ocean:Its characteristics and influencing factors[J].Advances in Earth Science,2012,27(5):539-548.
[2]Huisman J,Thi N N P,Karl D M,et al.Reduced mixing generates oscillations and chaos in the oceanic deep chlorophyll maximum[J].Nature,2006,439(7074):322-325.
[3]倪晓波,黄大吉.海洋次表层叶绿素最大值的分布和形成机制研究[J].海洋科学,2006,30(5):60-66,72.Ni Xiaobo,Huang Daji.Subsurface chlorophyll maximum:its temporal-spatial distribution and formation mechanism in the ocean[J].Marine Sciences,2006,30(5):60-66,72.
[4]Chai F,Dugdale R C,Peng T H,et al.One-dimensional ecosystem model of the equatorial Pacific upwelling system.Part I:model development and silicon and nitrogen cycle[J].Deep-Sea Research Part II,2002,49(13):2713-2745.
[5]Fennel K and Boss E.Subsurface maxima of phytoplankton and chlorophyll:Steady-state solutions from a simple model[J].Limnology Oceanography,2003,48(4):1521-1534.
[6]Liccardo A,Fierro A,Ludicone D,et al.Response of the deep chlorophyll maximum to fluctuations in vertical mixing intensity[J].Progress in Oceanography,2013,109(1):33-46.
[7]Hodges B A and Rudnick D L.Simple models of steady deep maxima in chlorophyll and biomass[J].Deep-Sea Research PartⅠ,2004,51(8):999-1015.
[8]Ryabov A B,Rudolf L,Blasius B.Vertical distribution and composition of phytoplankton under the influence of an upper mixed layer[J].Journal of Theoretical Biology,2010,263(1):120-133.
[9]Navarro G and Ruiz J.Hysteresis conditions the vertical position of deep chlorophyll maximum in the temperate ocean[J].Global Biogeochemical Cycles,2013,27(4):1013-1022.
[10]Wang Qiang and Mu Mu.Responses of the ocean planktonic ecosystem to finite-amplitude perurbations[J].Journal of Geophysical Research:Oceans,2014,119(12):8454-8471.
[11]Mu Mu,Duan Wansuo,Wang Bin.Conditional nonlinear optimal perturbation and its applications[J].Nonlinear Processes in Geophysics,2003,10(6):493-501.
[12]Mu Mu,Duan Wansuo,Wang Qiang,et al.An extension of conditional nonlinear optimal perturbation approach and its applications[J].Nonlinear Processes in Geophysics,2010,17(2):211-220.
[13]谢东东,孙国栋,邵爱梅,等.草原生态系统模式中参数不确定性导致的模拟结果不确定性研究[J].气候与环境研究,2013,18(3):375-386.Xie Dongdong,Sun Guodong,Shao Aimei,et al.Astudy of simulation uncertainties caused by parameter uncertainties in a grassland ecosystem model[J].Climate and Environmental Research(in Chinese),18(3):375-386.
[14]Sun Guodong and Mu Mu.A Preliminary Application of the Differential Evolution Algorithm to Calculate the CNOP[J].Atmospheric and Oceanic Science Letters,2009,2(6):381-385.
[15]Sun Guodong and Mu Mu.A new approach to identify the sensitivity an importance of physical parameters combination within numerical models using the LundPotsdam-Jena(LPJ)model as an example[J].Theoretical and applied Climatology,2017,128(3-4):587-601.
[16]Birgin E G,Martinez M J,Raydan M.Nonmonotone spectral projected gradient methods on convex sets[J].SIAM Journal on control and Optimization,2000,10(4):1196-1211.
[17]Powell M J D.VMCWD:A FORTRAN subroutine for constrained optimization[J].Acm Sigmap Bulletin,1983,32(32):4-16.
[18]Storn R and Price K.Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization,1997,11:341-359.
[19]Sun Guodong and Mu Mu.The analyses of the net primary production due to regional and seasonal temperature differences in eastern China using the LPJ model[J].Ecological Modelling,2014,289:66-76.
[20]宫响,史洁,高会旺.海洋次表层叶绿素最大值的特征因子及其影响因素[J].地球科学进展,2012,27(5):539-548.Gong Xiang,Shi Jie,Gao Huiwang.Subsurface chlorophyll maximum in ocean:Its characteristics and influencing factors[J].Advances in Earth Science,2012,27(5):539-548.