Coupling of coastal zone color scanner data to a physical‐biological model of the southeastern U.S. continental shelf ecosystem: 2. An Eulerian model

Abstract

A four‐component (nutrient, phytoplankton, zooplankton, and detritus) physical‐biological model was constructed for the southeastern U.S. continental shelf ecosystem for a horizontal plane at a nominal depth of 17 m. An optimal interpolation procedure was applied to current meter measurements made in this region to obtain the flow and temperature fields that were used for the model. The physical‐biological model was used to simulate the horizontal chlorophyll distributions at 17 m on the southeastern U.S. continental shelf for several days in April 1980. These simulated phytoplankton fields were compared statistically with the chlorophyll distributions obtained from Coastal Zone Color Scanner (CZCS) measurements for this region in April 1980. Model simulations using various physical‐biological dynamics and various boundary conditions show that the variability of chlorophyll distributions on the outer southeastern U.S. continental shelf is controlled primarily by horizontal advection. The biological processes associated with nutrient input by upwelling create the across‐shelf gradient of chlorophyll concentration observed on the midshelf to outer shelf and also maintain the chlorophyll concentration in this region. The simple biological model that included horizontal advection and upwelling processes contained all the conditions necessary to reproduce the chlorophyll patterns on the outer southeastern U.S. continental shelf. However, the technique used to estimate upwelling processes may be a source of error for the simulated distributions. Statistical comparisons between the simulated and CZCS‐derived chlorophyll fields are useful for evaluating a large number of model simulations and for determining the pattern of the error associated with the simulated distributions. Also, statistical comparisons allow determination of values for an optimal parameter set for the biological model. However, implementation of the optimal parameter set should be done carefully. The best approach is to optimize the physical model with some other constraints and then use the CZCS chlorophyll distributions to improve the choice of values for the biological parameters.