This paper investigates the impacts of different turbulence models on the biological state at an ocean station in the northern Adriatic sea, named S3, comparing them with other uncertainties inherent to coupled physical-biological simulations. The numerical tool is a 1-D model resulting from the coupling of two advanced numerical models. The hydrodynamic part is modelled using the General Ocean Turbulence Model (www.gotm.net), in a version adopting state-of-the-art second-moment Turbulence Closure Models (TCMs). Marine biogeochemistry is parameterized with the Biogeochemical Flux Model (http://www.bo.ingv.it/bfm), which is a direct descendant of ERSEM (European Regional Sea Ecosystem Model). Results, obtained by forcing the model with hourly wind and solar radiation data and assimilating salinity casts, are compared against monthly observations made at the station during 2000-2001. Provided that modern second-moment TCMs are employed, the comparisons indicate that both the physical and the biological dynamics are relatively insensitive to the choice of the particular scheme adopted, suggesting that TCMs have finally 'converged' in recent years. As a further example, the choice of the nutrient boundary conditions has an impact on the system evolution that is more significant than the choice of the specific TCM, therefore representing a possible limitation of the 1-D model applied to stations located in a Region of Freshwater Influence. The 1-D model simulates the onset and intensity of the spring-summer bloom quite well, although the duration of the bloom is not as prolonged as in the data. Since local dynamics appears unable to sustain the bloom conditions well into summer, phytoplankton at the station was most likely influenced by river input or advection processes, an aspect that was not found when the S3 behaviour was adequately modelled using climatological forcings. When the focus is in predicting high-frequency dynamics, it is more likely that lateral advection cannot be neglected. While the physical state can be satisfactorily estimated at these short time scales, the accurate estimation of the biological state in coastal regions still appears as rather elusive.