Phytoplankton is an essential component in the functioning of marine ecosystems and in the carbon cycle. It is therefore essential to assess its variability and its main drivers. However, unlike seasonal and interannual variations, fluctuations of phytoplanktonic biomass and communities on decadal to multi-decadal timescales remain hampered by the lack of long-term observations at global scale and the uncertainties related to the complex balance of the processes that control their fate. These processes are imperfectly and diversely parameterized in biogeochemical models, limiting their use to document long-term phytoplankton variability. Yet, it is crucial to detect natural low-frequency cycles in phytoplankton biomass (and thus carbon fluxes) because they can enhance, weaken or even mask climate-related trends. In this context, the inter/transdisciplinary DREAM project proposes to investigate and benchmark different deep learning (DL) frameworks (learned from satellite and in situ observations) to emulate past and future multi-decadal time-series of surface phytoplankton biomass and communities. This approach will allow us to assess the relative contribution of the different processes (e.g. physical, predation, community structures) involved in phytoplankton dynamics over the last decades in response to natural climate low-frequency variability but also to past and future anthropogenic forcing. Ultimately, DREAM will also contribute to characterizing and better constraining the uncertainties in the climate projections of the different Earth System Models gathered in the Coupled Model Intercomparison Project Phase 6 (CMIP6).

Since 2011 pelagic Sargassum strandings in the Caribbean basin have had significant environmental, health, economic and socio-political consequences and a substantial impact on tourism. Research so far tends to confirm the existence of a new ecosystem subjected to movements of water masses across the equatorial Atlantic. The phenomenon occurred in 2011 and the 2012-2018 period, along with increases in Sargassum biomass. Satellite imagery is used to detect Sargassum rafts in the equatorial Atlantic at medium resolution. Sea surface current data for the same area are used to infer annual passive drifting. The combination of these two data sources have made it possible to design coarse-scale approximate prediction tools that remain, however, difficult to operate at the scale of insular coastal zones. This context makes it more difficult to manage Sargassum influxes. The CESAR program will describe and explain the dynamics of coastal Sargassum flows at local spatial scales, their impacts on Caribbean coasts and the various paths explored by political institutions to address this environmental issue. This implies improving knowledge on local forcing parameters and Sargassum dynamics in the Lesser Antilles. The accumulation of Sargassum on the coastline impacts also the morphosedimentary dynamics of beaches and the biogeomorphic functioning of coastal marine ecosystems, especially seagrass beds. The characterization of these impacts at the local and regional level is a major challenge for the development of governance adaptation strategies within the Caribbean context, linking environmental, economic, health, regulatory and decision-making policies. CESAR will deliver operational systems to prepare communities and governments to the apparent increasing future risk of Sargassum washing ashore.