The global distribution of high Remote-sensing reflectance (Rrs) waters visible from satellite, likely associated with coccolithophore blooms, has changed markedly over the past 40 years. Over that period there has globally been an overall decrease in bloom area of 1.15 million km2 but with notable Rrs increases in the Barents Sea and the Antarctic Ocean. The primary drivers of these fundamental changes to ocean biogeochemistry have been investigated using Machine Learning techniques together with contemporaneous global multi-decadal time-series of sea-surface temperature (SST); wind speed and stress; sea level anomaly (SLA); photosynthetically available radiation (PAR) and; mixed layer depth (MLD). When split into ocean provinces different drivers of positive and negative trends in Rrs were found to dominate in different regions, but generally increases were found to coincide with changes to SST, PAR and reductions to wind-speed.

Oceanography has entered an era of new observing platforms, such as biogeochemical Argo floats and gliders, some of which will provide three-dimensional maps of essential ecosystem variables on the North-West European (NWE) Shelf. In a foreseeable future operational centres will use multi-platform assimilation to integrate those valuable data into ecosystem reanalyses and forecast systems. Here we address some important questions related to glider biogeochemical data assimilation and introduce multi-platform data assimilation in a (pre)operational model of the NWE Shelf-sea ecosystem. We test the impact of the different multi-platform system components (glider vs satellite, physical vs biogeochemical) on the simulated biogeochemical variables. To characterize the model performance we focus on the period around the phytoplankton spring bloom, since the bloom is a major ecosystem driver on the NWE Shelf. We found that the timing and magnitude of the phytoplankton bloom is insensitive to the physical data assimilation, which is explained in the study. To correct the simulated phytoplankton bloom one needs to assimilate chlorophyll observations from glider or satellite Ocean Color (OC) into the model. Although outperformed by the glider chlorophyll assimilation, we show that OC assimilation has mostly desirable impact on the sub-surface chlorophyll. Since the OC assimilation updates chlorophyll only in the mixed layer, the impact on the sub-surface chlorophyll is the result of the model dynamical response to the assimilation. We demonstrate that the multi-platform assimilation combines the advantages of its components and always performs comparably to its best performing component.