SEAMLESS provided improved ocean model data to:
Monitor and assess marine ecosystem health in policy frameworks
Implement marine spatial planning
Operate aquaculture and fisheries
Investigate climate change impacts on ocean ecosystems
Advanced Data Assimilation
More specifically, SEAMLESS focused on the following eight ocean indicators.
|Particulate organic carbon
|CMEMS (Copernicus Marine Environment Monitoring Service)
Our project covered, directly or indirectly, all the CMEMS ecosystem models and marine systems. We advanced stochastic ensemble data assimilation methods, encompassing Kalman and particle filters and variational approaches. These methods were 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 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.