Advertisement

Acta Oceanologica Sinica

, Volume 30, Issue 1, pp 7–14 | Cite as

Numerical study on spatially varying control parameters of a marine ecosystem dynamical model with adjoint method

  • Ping Qi
  • Chunhui Wang
  • Xiaoyan Li
  • Xianqing Lv
Article

Abstract

Based on the simulation of a marine ecosystem dynamical model in the Bohai Sea, the Yellow Sea and the East China Sea, chlorophyll data are assimilated to study the spatially varying control parameters (CPs) by using the adjoint method. In this study, the CPs at some grid points are selected as the independent CPs, while the CPs at other grid points can be obtained through linear interpolation with the independent CPs. The independent CPs are uniformly selected from each 30′× 30′ area, and we confirm that the optimal influence radius is 1.2° by a twin experiment. In the following experiments, when only the maximum growth rate of phytoplankton (V m ) is estimated by two given types of spatially varying CPs, the mean relative errors of V m are 1.22% and 0.94% while the decrease rates of the mean error of chlorophyll in the surface are 94.6% and 95.8%, respectively. When the other four CPs are estimated respectively, the results are also satisfactory, which indicates that the adjoint method has a strong ability of optimizing the prescribed CP with spatial variations. However, when all these five most important CPs are estimated simultaneously, the collocation of the changing trend of each parameter influences the estimation results remarkably. Only when the collocation of the changing trend of each parameter is consistent with the ecological mechanisms which influence the growth of the phytoplankton in marine ecosystem, could the five most important CPs be estimated more accurately.

Key words

marine dynamical ecosystem adjoint method influence radius spatially varying parameters 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Evans G T. 1999. The role of local models and data sets in the Joint Global Ocean Flux Study. Deep-Sea Research I, 46(8): 1369–1389CrossRefGoogle Scholar
  2. Fan W, Lv X Q. 2009. Data assimilation in a simple marine ecosystem model based on spatial biological parameterizations. Ecological Modelling, 220(17): 1997–2008CrossRefGoogle Scholar
  3. Franks P J S, Chen C S. 1996. Plankton production in tidal fronts: A model of Georges Bank in summer. Journal of Marine Research, 54: 631–651CrossRefGoogle Scholar
  4. Fennel K, Losch M, Schroter J, et al. 2001. Testing a marine ecosystem model: sensitivity analysis and parameter optimization. Journal of Marine Systems, 28(1–2): 45–63CrossRefGoogle Scholar
  5. Friedrichs M A M. 2002. Assimilation of JGOFS EqPac and SeaWiFS data into a marine ecosystem model of the central equatorial Pacific Ocean. Deep-Sea Research, 49: 289–319Google Scholar
  6. Hemmings J C P, Srokosz M A, Challenor P, et al. 2004. Split-domain calibration of an ecosystem model us ing satellite ocean colour data. Journal of Marine Systems, 50: 141–179CrossRefGoogle Scholar
  7. Losa S N, Kivman G A, Ryabchenko V A. 2004. Weak constraint parameter estimation for a simple ocean ecosystem model: what can we learn about the model and data. Journal of Marine Systems, 45(1–2): 1–20CrossRefGoogle Scholar
  8. Losa S N, Vzina A, Wright D, et al. 2006. 3D ecosystem modelling in the North Atlantic: Relative impacts of physical and biological parameterizations. Journal of Marine Systems, 61(3–4): 230–245Google Scholar
  9. Lv X Q, Fan W. 2009. Numerical study on estimating spatially varying parameters in a simple marine ecosystem model. Periodical of Ocean University of China, 39(5): 846–854Google Scholar
  10. Lv X Q, Zhang J C. 2006. Numerical study on spatially varying bottom friction coefficient of a 2D tidal model with adjoint method. Continental Shelf Research, 26(16): 1905–1923CrossRefGoogle Scholar
  11. Matear R J. 1995. Parameter optimization and analysis of ecosystem models using simulated annealing: a case study at Station P. Journal of Marine Research, 53: 571–607CrossRefGoogle Scholar
  12. Xu Q, Lin H, Liu Y G, et al. 2008. Data assimilation in a coupled physical-biological model for the Bohai sea and the Northern Yellow Sea. Marine and Freshwater Research, 59(6): 529–539CrossRefGoogle Scholar
  13. Zhao Q, Lv X Q. 2008. Parameter estimation in a three dimensional marine ecosystem model using the adjoint technique. Journal of Marine Systems, 74(122): 443–452CrossRefGoogle Scholar

Copyright information

© The Chinese Society of Oceanography and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ping Qi
    • 1
  • Chunhui Wang
    • 2
  • Xiaoyan Li
    • 2
  • Xianqing Lv
    • 2
  1. 1.School of EnvironmentBeijing Normal UniversityBeijingChina
  2. 2.Laboratory of Physical OceanographyOcean University of ChinaQingdaoChina

Personalised recommendations