A New 1-D Physical-biological Coupled Model With Application To Ocean Stations

As the dominant feature of the biogeochemical cycling in the ocean,the marine physical processes are often associated with the biological processes to effectively depict the circulation of physical/biological factors under the action of marine dynamics.In this study,a new coupled one dimensional physical-biological ocean model was developed that is based on modified Gill-Turner mixed layer model(GT model)and Schmittner biological model to explore surface heat and energy exchange,ocean circulation,and the biogeochemical cycling within the upper ocean at two ocean time-series stations:the Bermuda Atlantic Time-series Station(BATS)and the Hawaii Ocean Time-series Station(HOT).In this study,I also used this coupled model to simulate catch per unit effort(CPUE)of tuna caught by the longline fishery in waters of the Hawaiian Islands.The GT model was improved by adding advection and diffusion modules as well as considering the influence of temperature gradient on diffusivity to make the model more descriptive of the mixing dynamics in the upper ocean.The original Schmittner model was also enhanced in two ways.First,I uncoupled the cycling of nitrogen and phosphate by using different remineralization rates for two nutrients.Second,I improved the calculation of phytoplankton growth rate by considering all three environmental variables:light intensity,temperature limitation and nutrients limitation.Moreover,variable phytoplankton chlorophyll-to-nitrogen ratios were introduced into the biological component as a significant part to catch the unique features of the distribution of both chlorophyll and primary production more accurately.Combining the modified GT model with the Schmittner model,yielded a new physical-biological coupled model(PBCM)that was implemented in the Mathematica 10.0 platform.In BATS,the PBCM accurately simulates the general time-varying regularity of the distributions of physical and biological factors,such as temperature,dissolved nutrients,dissolved oxygen,chlorophyll concentration and primary production.The vertical distribution of these variables are largely determined by variations of mixed layer depth,and these patterns clearly reflect the effect of strong vertical mixing on the biogeochemical cycling.From the perspective of hydrodynamics,the mechanism of the varied nitrogen-to-phosphate ratio is reasonably revealed,as well as the noteworthy seasonal variation of the depths of nitracline.In addition,the PBCM successfully reproduces the formation of the subsurface chlorophyll maximum layers(SCMLs),and well explains the mechanism by which the SCML appears at the upper boundary of nitracline.The PBCM simulation of the HOT station near the subtropical North Pacific Ocean shows that the biogeochemical cycling in the upper euphotic layer of the ocean is mainly controlled by the vertical mixing process,but that in the underlying aphotic layer the major features of planktonic system are more affected by the complex flow movements,such as mesoscale eddies and Rossby waves.The model also captures the weak seasonal variation in physical and biological variables at HOT relative to that at BATS.The model reveals that nitrogen is the limited nutrient for phytoplankton in the Pacific Ocean and explains the reasons for the lack of seasonal fluctuations in the depth of the nitracline.Finally,the HOT simulation supports the concept that seasonal variations in the nitrogen-to-phosphate ratio and the primary production are due to the growth of nitrogen-fixing phytoplankton in surface waters during the summer.In this study,I demonstrate how the PBCM has broad application prospects.I have extracted information on the habitat preference of bigeye(Thunnus obesus)and yellowfin(Thunnus albacares)in the Eastern Tropical Pacific Ocean(ETPO)by developing Generalized Additive Models(GAMs)based on the CPUE data of tuna as well as oceanographic conditions.I then combined the PBCM and GAMs to reasonably simulate the monthly distribution of CPUE of bigeye and yellowfin tuna near Hawaii islands.The simulation could be applied as scientific basis for the further management of tuna fisheries.