SEAMLESS: Services based on Ecosystem data AssiMiLation: Essential Science and Solutions
The overall objective of SEAMLESS is to provide CMEMS with new capabilities to deliver indicators of climate-change impacts and food security in marine ecosystems.
Objectives
The specific objective 1 of SEAMLESS is to enrich the existing CMEMS portfolio with novel or improved operational products and associated uncertainty for indicators of carbon cycle, water quality and marine food web.
The specific objective 2 of SEAMLESS is to develop new ensemble generation and data assimilation methods that maximize the flow of information from the new observing networks towards the controllable ecosystem indicators.
The specific objective 3 of SEAMLESS is to develop an innovative modelling/assimilative prototype, consisting in an open-source software that includes CMEMS MFC biogeochemical and physical models, coupled to the ensemble DA tools that will be applied, advanced or newly developed in SEAMLESS.
The specific objective 4 of SEAMLESS is to enable CMEMS MFCs to assimilate physical and biogeochemical data consistently, to link better the biogeochemical and physical simulations to the ecosystem indicators.
The specific objective 5 of SEAMLESS is to enable CMEMS MFCs to assimilate biogeochemical observations from the Copernicus space element and in situ platforms consistently, to link better the surface and subsurface ecosystem dynamics to the ecosystem indicators.
The specific objective 6 of SEAMLESS is to improve CMEMS MFC models through better parameterization of the biogeochemical processes that influence the ecosystem indicators
Project Summary
To achieve this objective, we will address the central hypothesis of SEAMLESS: new ensemble methods that jointly assimilate the new generation of satellite and in situ observations can control and improve the estimates of key ecosystem indicators, such as particulate carbon export and phytoplankton phenology. This hypothesis is based on our previous work and pilot studies, which demonstrated that the joint assimilation of biogeochemical and physical data, from satellite sensors, biogeochemical-Argo floats and autonomous gliders improved the MFCs’ model simulations of the plankton stocks at the base of the marine food web. SEAMLESS will expand simulations to plankton dynamics and related biogeochemical processes, e.g., plankton phenology and particulate carbon export, by developing a new ensemble assimilation prototype transferable to CMEMS MFCs. On completion of SEAMLESS, six CMEMS MFCs will be able to deliver quality-assessed simulations of carbon cycle and food security indicators required by the CMEMS user community. This will be achieved by a team of institutions with outstanding expertise and track records for all the key project components. It brings together core developers of CMEMS MFCs and CMEMS working group chairs (Biogeochemical Data Assimilation and Biogeochemical Argo), four research-intensive institutes/universities and two Small Medium Enterprises (SMEs). Representatives of the CMEMS “Entrusted Entity” and of their user communities are part of the project Steering Group and Advisory Board.
