An appropriate model for phytoplankton distribution patterns is critical for understanding biogeochemical cycles and trophic interactions in the oceans and seas. Because phytoplankton dynamics in coastal waters are more complex due to shallow depth and proximity to land, more accurate models applied to the correct spatial and temporal scales are needed. Our study investigates the role of the atmosphere and hydrosphere in pelagic habitat by modelling phytoplankton assemblages at two Long Term Ecological Research sites in the northern Adriatic Sea using niche-forming environmental variables (wind, temperature, salinity, river discharge, rain, and water column stratification). To study the synchronization between the phytoplankton community and these environmental variables at the two LTER sites, we applied current linear and nonlinear numerical methods for ecological modelling. The aim was to use periodic and/or non-periodic properties of the environmental variables to classify the phytoplankton assemblages at one LTER site (Gulf of Trieste - Slovenia) and then predict them at another LTER site 100 km away (Gulf of Venice - Italy). We found that periodicity played a role in the explanatory and predictive power of the environmental variables and that it was more important than non-periodic events in defining the common structure of the two pelagic habitats. The non-linear classification functions of the neural networks further increased the predictive power of these variables. We observed partial synchronization of communities at the mesoscale and differences between the original and predicted assemblages under similar environmental conditions. We conclude that mesoscale connectivity plays an important role in phytoplankton communities in the northern Adriatic. However, the loss of periodicity of niche-forming variables due to more frequent extreme meteorological and hydrological events could loosen these connections and affect the temporal succession of phytoplankton assemblages.