JI Xuanliang, LIU Guimei, GAO Shan, WANG Hui. Parameter sensitivity study of the biogeochemical model in the China coastal seas[J]. Acta Oceanologica Sinica, 2015, 34(12): 51-60. doi: 10.1007/s13131-015-0762-0
Citation: JI Xuanliang, LIU Guimei, GAO Shan, WANG Hui. Parameter sensitivity study of the biogeochemical model in the China coastal seas[J]. Acta Oceanologica Sinica, 2015, 34(12): 51-60. doi: 10.1007/s13131-015-0762-0

Parameter sensitivity study of the biogeochemical model in the China coastal seas

doi: 10.1007/s13131-015-0762-0
  • Received Date: 2015-07-20
  • Rev Recd Date: 2015-09-21
  • In order to develop a coupled basin scale model of ocean circulation and biogeochemical cycling, we present a biogeochemical model including 12 components to study the ecosystem in the China coastal seas (CCS). The formulation of phytoplankton mortality and zooplankton growth are modified according to biological characteristics of CCS.The four sensitivity biological parameters, zooplankton assimilation efficiency rate (ZooAE_N), zooplankton basal metabolism rate (ZooBM), maximum specific growth rate of zooplankton (μ20) and maximum chlorophyll to carbon ratio (Chl2C_m) are obtained in sensitivity experiments for the phytoplankton, and experiments about the parameter μ20, half-saturation for phytoplankton NO3 uptake (KNO3) and remineralization rate of small detritusN (SDeRRN) are conducted. The results demonstrate that the biogeochemical model is quite sensitive to the zooplankton grazing parameter when it ranges from 0.1 to 1.2 d-1. The KNO3 and SDeRRN also play an important role in determining the nitrogen cycle within certain ranges.The sensitive interval of KNO3 is from 0.1 to 1.5(mmol/m3)-1, and interval of SEdRRN is from 0.01 and 0.1 d-1. The observational data from September 1998 to July 2000 obtained at SEATS station are used to validate the performance of biological model after parameters optimization. The results show that the modified model has a good capacity to reveal the biological process features, and the sensitivity analysis can save computational resources greatly during the model simulation.
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