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Services based on Ecosystem data AssiMiLation: Essential Science and Solutions

Periodic Reporting for period 1 - SEAMLESS (Services based on Ecosystem data AssiMiLation: Essential Science and Solutions)

Okres sprawozdawczy: 2021-01-01 do 2021-12-31

The ocean provides us with vital climate and food services by absorbing 30% of anthropogenic carbon emissions and supplying 17% of animal proteins to the world’s population. However, state-of-the-art operational centers such as the Copernicus Marine Service are still far from accurately estimating these services using Monitoring and Forecasting Centres (MFCs).
The main aim of SEAMLESS is to provide CMEMS Monitoring and Forecasting Centres (MFCs) with unprecedented capabilities to deliver indicators of climate-change impacts and food security in ocean ecosystems, such as particulate carbon export and plankton phenology.
The central hypothesis of SEAMLESS is that new ensemble data assimilation methods can better estimate crucial ecosystem indicators by integrating the new generation of European Copernicus satellite observations and in-situ ocean data. Specifically, our approach is linking coherently biogeochemical and hydrodynamic simulations.
SEAMLESS is developing a new assimilation prototype that expands simulations to plankton dynamics and related biogeochemical processes, e.g. plankton phenology and carbon export. This prototype is being disseminated to CMEMS stakeholders and the wider oceanographic community.
On project completion, six CMEMS MFCs will be able to add new and improved products on water quality, carbon cycle and trophic webs to their portfolios, ultimately allowing users to exploit more sustainably ocean ecosystem services.
The work performed in the first Reporting Period can be summarized as:
• Support the project activities by developing common code repositories and management tools
• Establish a SEAMLESS brand identity
• Development of the new SEAMLESS assimilative prototype for the Copernicus Marine Service (CMEMS), including:
I. the coupling of the CMEMS biogeochemical models “PISCES” and “BFM” to the Framework for Aquatic Biogeochemistry Modelling (FABM)
II. adding the data Assimilation (DA) functionality of PDAF to the SEAMLESS prototype;
III. addition of three new variational DA algorithms to the prototypes
IV. Configuration of the prototype system for data-rich sites in all the six CMEMS Monitoring and Forecasting Centres (MFCs) included in the project
V. Documentation of the prototype.
• Application of the SEAMLESS assimilative prototype in the screening analysis of the observability and controllability of the target indicators in all the six CMEMS MFCs included in the project
• Transform deterministic physical-BGC models used by MFCs into numerically efficient probabilistic systems to estimate uncertainties
• Establish plans for future dissemination and exploitation of SEAMLESS products

Major results of this first project period include the delivery of the core system of the SEAMLESS assimilative prototype. Now this incorporates five operational biogeochemical models of CMEMS, as well as ensemble data assimilation methods upgraded by SEAMLESS. The prototype was applied successfully in model intercomparison experiments at two long-term monitoring ocean sites during a project workshop. These experiments led to the identification of the observations and biogeochemical parameters most useful to estimate the ecosystem indicators via ensemble simulations. Ensemble generation methods based on stochastic approaches were also advanced and proved effective in large scale simulations in the North Atlantic. The performance of such simulations was evaluated with ensemble validation methods that were coded and made available to the scientific community in a public repository.
Currently, the generation of adequate model ensembles and the choice of the assimilated variables are challenging steps in the use of several data assimilation methods in research and operational systems, including CMEMS. The spread of ensembles should provide a reliable measure of the system’s prior uncertainty and should account for initial errors propagated by the system dynamics, and uncertainty in the atmospheric and radiative forcings, physical and biological model parameters, sub-grid scale processes, numerical scheme, and inappropriate BGC model structure. On the other hand, the choice of the observable variables to be assimilated is crucial to ensure that the target ecosystem indicators can be observed and constrained effectively in the analysis, in relation to the spatial-temporal resolution of the observations and the complexity of the biogeochemical models.

The adequacy of the ensemble distributions and the effectiveness of the observations have rarely been evaluated in biogeochemical data assimlation applications so far.
SEAMLESS has already progressed biogeochemical data assimilation beyond the state-of-the-art by advancing methods of generation and evaluation of stochastic ensembles, thus representing more realistically the uncertainty of biogeochemical models. Also, SEAMLESS has performed an unprecedented excercise of intercomparison of biogeochemical model sensitivities. This provided an optimal list of model parameters and observations to be used to generate ensembles and the estimate the SEAMLESS indicators.

The SEAMLESS indicators were selected because of their relevance to marine policies and blue growth activities, e.g. the United Nations Sustainable Development Goals and aquaculture farms. These policies and economical activities will be advantaged by the provision of more reliable indicators once the SEAMLESS methods will be uptaken by CMEMS. To support CMEMS‘ uptake of the methods and impact generation, SEAMLESS has engaged the CMEMS Entrusted Entity, core developers, and stakeholders by organizing 2 workshops, contributing to 14 workshop, delivering 33 Social Media communications.
SEASMLESS researchers exploring the new prototype at a hand-ons workshop in Triest, Italy.