Services based on Ecosystem data AssiMiLation: Essential Science and Solutions (SEAMLESS)

About SEAMLESS EAT View Publications


SEAMLESS aims at improving the current European capability to simulate and predict the state of marine ecosystems. The project focuses on state indicators that are linked to the ocean “health” (e.g. to oxygenation, acidification, eutrophication), “services” (e.g. to sustainable aquaculture) and “response” to climate change (e.g. to the transfer of carbon in the ocean depths). Currently, these indicators are monitored and/or simulated routinely by observatories and models of the European Copernicus Marine Services (“CMEMS”). SEAMLESS will improve the current CMEMS methods that integrate the information from monitored and simulated indicators, i.e. “data assimilation” methods.

This infographic shows the the SEAMLESS evolution of the CMEMS core system. SEAMLESS will develop novel ensemble data assimilation approaches that jointly assimilate physics and biogeochemistry observations, from both satellite and in situ platforms. This new prototype system will more coherently link the biogeochemical and physical simulations that control the evolution of the ecosystem indicators.

SEAMLESS provided improved ocean model data to:

Underwater scene showing seaweed
Monitor and assess marine ecosystem health in policy frameworks

Offshore wind farms
Implement marine spatial planning

Aquaculture farm
Operate aquaculture and fisheries

Shoals of fish underwater
Investigate climate change impacts on ocean ecosystems

Advanced Data Assimilation


More specifically, SEAMLESS focused on the following eight ocean indicators.

Oxygen concentration pH Phytoplankton concentration Phytoplankton phenology
Trophic efficiency Primary production 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.

For Stakeholders

Stakeholders can download a brief project summary (200KB, PDF).
Download project summary