Proposed best practices for the integration of in situ observations and large scale analysis and forecast systems: case study of the phytoplankton blooming off a Tyrrhenian coastal site

The coastal systems are among the most dynamic natural systems, being extreme complex zones in which chemical, physical and biological processes interact at different spatial and temporal scales. A holistic approach, based on the integration of multiple 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 is perfect, being each characterized by intrinsic errors and therefore specific uncertainty, the latter also considered as an important subject of investigation.

In this context, our goal is to understand the spatial and temporal distribution of phytoplanktonic biomass in coastal waters in order to evaluate the phytoplankton dynamics in a polluted coastal area located in the northern Tyrrhenian Sea. Long-term high-resolution observations (weekly sampling from 2015 to 2017) of phytoplankton biomass at a coastal site from the C-CEMS observing system (central Tyrrhenian Sea offshore Civitavecchia) are presented, discussed and integrated with the analysis data provided by the Copernicus Marine Environment Monitoring Services (CMEMS) for the Mediterranean Sea, generated by the MedBFM model system, and with satellite observations (from CMEMS Ocean Colour database). The focus of this work is twofold: on one side, to analyse the phytoplankton bloom dynamics of the Civitavecchia coastal ecosystem by adopting a multi-platform approach which integrates CMEMS products and C-CEMS in situ data, on the other side, to propose best practices to integrate multi-platform data streams that may be adopted also in other similar contexts of coastal ecosystems.

The analysis of the time series of phytoplankton provided by in situ, satellite and model data show the typical dynamics of temperate climate, characterized by spring and autumn blooms, together with a significant interannual variability. The EOF analysis has shown consistency among multi-platform datasets. Notwithstanding the incongruences, specifically related to the chlorophyll model outputs, which underestimate the in situ and satellite data and that may be related to some representativeness error (i.e. river nutrient inputs based on climatological information and grid resolution), the intercomparison is beneficial to provide information at different temporal and spatial scales of the phytoplankton dynamics.