Coastal phytoplankton bloom dynamics in the Tyrrhenian Sea: Advantage of integrating in situ observations, large-scale analysis and forecast systems

Abstract Coastal systems represent the most dynamic natural systems on Earth, making their study particularly challenging. A holistic approach that integrates a set of monitoring tools for data collection (i.e., satellite imagery, numerical models and in situ observations) may provide different information about coastal ecosystems at different spatial and temporal scales. Of course, none of these tools are perfect, being that each is characterized by intrinsic errors and therefore specific uncertainties, which is also an important subject of investigation. Long-term high-resolution in situ observations of the phytoplankton biomass at a coastal site (Civitavecchia, Tyrrhenian Sea), provided by the Civitavecchia Coastal Environment Monitoring System (C-CEMS) observational platform, are presented, discussed and integrated with data from the Copernicus Marine Environment Monitoring Services (CMEMS) for the Mediterranean Sea, which are generated by the MedBFM model system, and satellite observations from the CMEMS Ocean Colour database. The analysis of the time series of phytoplankton provided by in situ, satellite and model data show the typical dynamics of coastal temperate systems, which are characterized by spring and autumn blooms and significant interannual variability. The empirical orthogonal function (EOF) analysis highlights the consistency among the multiplatform datasets, whereas integrating the local in situ time series with a spatial analysis from model and satellite data provides information about the extent of coastal bloom phenomena and the relevance of the observation location with respect to surroundings. Our study of the dynamics of coastal blooms in the Civitavecchia coastal system allows us to propose a best practice framework that may be of general interest, and potentially applied to any multiplatform monitoring system (MPMS). The MPMS approach allows us to investigate the interannual variability over different horizontal and vertical scales, reflecting the variability in natural drivers (i.e., atmospheric forcings, coastal currents, upwelling, and land inputs), as typically expected in coastal areas.