A simple, but general, simulation model is specified according to the state-of-the-art within phytoplankton modelling: Process representations are based upon prevailing theoretical and empirical representations given in the literature, and a set of earlier published values of model coefficients that have demonstrated good fit to reliable observations was selected. The emerging phytoplankton model was then validated against data obtained from enclosure experiments with light-, N-, P- and Si-limitations. We applied no tuning of the coefficients as the purpose of this test was to estimate the predictive power of the proposed model. The general standard deviations between model predictions and observations were on the range 0.04-0.36 and 0.13-0.42 for the nutrient and phytoplankton state variables respectively. Not surprisingly, these values are higher than those obtained in tuned simulations. Nevertheless, several characteristics, such as the balance between diatoms and flagellates, were predicted by the model. The phytoplankton model was set up and driven by a 3-dimensional physical model for the North Sea. The period February-June 1988 was simulated and forced with realistic topography, meteorological data, riverine freshwater and nutrient input. Simulated developments in nutrients, diatoms and flagellates are presented with references to actual observations and the Chrysochromulina polylepis bloom in 1988. Several important characteristics, such as the timing of two diatom blooms in March and April and one flagellate bloom in May together with vertical and horizontal distributions of nutrients, were simulated without tuning of the model to the actual observations. The present simulations support the general idea that flagellates in the coastal areas of the North Sea are stimulated by anthropogenic nutrients, but more specifically that a strong flagellate bloom in the Kattegat-Skagerrak area, corresponding to the C. polylepis bloom, was stimulated by such nutrients in May 1988. Although the model should be improved before it is applied in a management context, the great potential of using such models in environmental management is demonstrated.
