More specifically, SEAMLESS will focus on the following eight ocean indicators: oxygen concentration, pH, phytoplankton concentration and phenology, trophic efficiency, primary production and particulate organic carbon. Our project will cover, directly or indirectly, all the CMEMS ecosystem models and marine systems. We will advance stochastic ensemble data assimilation methods, encompassing Kalman and particle filters and variational approaches. These methods will be used to assimilate biogeochemical and physical data from both satellites and in situ platforms (e.g. gliders and biogeochemical-Argo floats).
At the end of the project, our new methods will have improved the CMEMS capability to deliver better simulations of the past (“reanalysis”) and better predictions of the future (“forecasts”) of the state of the ocean. These reanalyses and forecasts will be used by a large range of stakeholders, including policymakers, coastal planners, institutional monitoring, aquaculture farmers and climate-change scientists. SEAMLESS will also develop an open-source, user-friendly assimilative modelling tool (“prototype”) and will train stakeholders on how to use it.
Download a brief summary
of the project for stakeholders (200KB, PDF)