European Global Ocean Observing System

The EuroGOOS Coastal working group examines the entire coastal value chain from coastal observations to services for coastal users. The main objective of the working group is to review the status quo, identify gaps and future steps needed to secure and improve the sustainability of the European coastal service provision. Within this framework, our white paper defines a EuroGOOS roadmap for sustained “community coastal downstream service” provision, provided by a broad EuroGOOS community with focus on the national and local scale services. After defining the coastal services in this context, we describe the main components of coastal service provision and explore community benefits and requirements through sectoral examples (aquaculture, coastal tourism, renewable energy, port, cross-sectoral) together with the main challenges and barriers to user uptake. Technology integration challenges are outlined with respect to multiparameter observations, multi-platform observations, the land-coast-ocean continuum, and multidisciplinary data integration. Finally, the technological, financial, and institutional sustainability of coastal observing and coastal service provision are discussed. The paper gives special attention to the delineation of upstream and downstream services, public-private partnerships and the important role of Copernicus in better covering the coastal zone. Therefore, our white paper is a policy and practice review providing a comprehensive overview, in-depth discussion and actionable recommendations (according to key short-term or medium-term priorities) on the envisaged elements of a roadmap for sustained coastal service provision. EuroGOOS, as an entity that unites European national operational oceanography centres, research institutes and scientists across various domains within the broader field of operational oceanography, offers to be the engine and intermediary for the knowledge transfer and communication of experiences, best practices and information, not only amongst its members, but also amongst the different (research) infrastructures, institutes and agencies that have interests in coastal oceanography in Europe.

This paper reports on the Hackathon Sessions organised at the Polar Data Forum IV (PDF IV) (20–24 September 2021), during which 351 participants from 50 different countries discussed collaboratively about the latest developments in polar data management. The 4th edition of the PDF hosted lively discussions on (i) best practices for polar data management, (ii) data policy, (ii) documenting data flows into aggregators, (iv) data interoperability, (v) polar federated search, (vi) semantics and vocabularies, (vii) Virtual Research Environments (VREs), and (viii) new polar technologies. This paper provides an overview of the organisational aspects of PDF IV and summarises the polar data objectives and outcomes by describing the conclusions drawn from the Hackathon Sessions.

Polar sciences are crucial to understand the effects of climate change. 6 out of 9 eco-tipping points identified by the IPCC are situated in the polar regions. Potential rising sea levels, altered weather patterns and changes in sea-currents are all connected to environmental change in the polar regions.<br> Although polar science is necessary to understand climate change, like any sector or industry, it is important to identify actions <br> to reduce the impact of polar science on the environment. This synthesis report aims to define best practices in limiting the environmental impacts of polar research. It synthesises best practices from both polar regions on how to conduct polar research while limiting its impact.

This paper aims to quantify data uncertainties in marine microplastic measurements, including spatiotemporal sampling error and sample volume estimation error, identify impacts of varying mesh sizes, sampling and analysis methods, and evaluate consistency in multiple microplastic observation datasets. Twenty-seven datasets on surface marine microplastics with particle size &amp;gt;100 µm in the Baltic Sea are compiled. Results show that the trawl datasets have a spatiotemporal sampling error of 25% for microlitter concentration, 36% for microplastic fiber concentrations and 40-56% for microplastic particle concentration. By taking surface currents and wave-induced Stokes drift into account, the sample volume of the trawl measurements is corrected, leading to a mean microplastic concentration correction of 12%. The differences of microplastic concentration between datasets with varying mesh sizes from 100 – 500 µm are not statistically significant. Analysis methods, however, can lead to significant differences in microplastic datasets. The dataset consistency is further examined among the three dataset categories using trawl, pump and bulk sampling techniques. It is found that an individual dataset is often self-consistent. Most of the datasets within one monitoring category are more consistent than those from different categories. More than 70% of the datasets within individual categories are consistent, which have mean microplastic concentration significantly smaller than the rest of the datasets. Significant inconsistencies are identified between different data categories. Six out of eight highest relative standard deviations are found in the pump and bulk datasets. The median value of the mean microplastic concentration from the 10 pump datasets is about 4.5 times as much as that of the 14 trawl datasets, both for fiber and non-fiber particles. Significant differences are also identified on microplastic fiber fraction in different dataset categories. Two thirds of the 13 bulk and pump datasets have a microplastic fiber fraction &amp;gt;85% while the 14 trawl datasets show much lower microplastic fiber fractions between 45-70%. In addition, the particle collection efficiency, potential leakage of particles with irregular shapes, clogging, the false zero samples and related lower limit of the detectable microplastic concentration for given sampling methods and water environment, are also discussed.

Coastal observing systems are typically nationally funded and built around national priorities. As a result, there are presently significant differences between countries in terms of sustainability, observing capacity and technologies, as well as methods and research priorities. Ocean observing systems in coastal areas must now move toward an integrated, multidisciplinary and multiscale system of systems, where heterogeneity should be exploited to deliver fit-for-purpose products that answer the diversity and complexity of the requirements from stakeholders and end-users. Essential elements of such distributed observation systems are the use of machine-to-machine communication, data fusion and processing applying recent technological developments for the Internet of Things (IoT) toward a common cyberinfrastructure. This perspective paper illustrates some of the challenges for sustained coastal observations and provides details on how to address present gaps. We discuss the role of collaborative robotics between unmanned platforms in coastal areas and the methods to benefit from IoT technologies. Given present trends in cost-effective solutions in ocean sensors and electronics, and methods for marine automation and communication, we consider that a distributed observation system can effectively provide timely information in coastal regions around the world, including those areas that are today poorly observed (e.g., developing countries). Adaptation in space and time of the sensing nodes, and the flexibility in handling different sensing platforms can provide to the system the ability to quickly respond to the rapid changes in oceanic and climatic processes, as well as to promptly respond to evolving stakeholder and end-user requirements.

An overview of the current European capacity in terms of operational modelling of marine and coastal systems is presented. This overview is compiled from a survey conducted in 2018-2019 among members of EuroGOOS and its related network of Regional Operational Oceanographic Systems, addressing the purposes, context and technical specificities of operational modelling systems. Contributions to the survey were received from 38 organizations around Europe, which represent 84 operational model systems simulating mostly hydrodynamics, biogeochemistry and sea waves. The analysis of contributions highlights the strengths and weaknesses of the current capacity from an operational point of view, and leads to the formulation of recommendations towards the improvement of marine operational modelling services in Europe. In particular, this study highlights the heterogeneity of the European operational modelling capacity in terms of atmospheric and land boundary conditions, its limited deployment for biogeochemical phenomena, and a restricted use of data assimilation methods. In order to improve the accuracy of their simulations, model operators aim towards a further refinement of spatial resolution, and identify the quality and accessibility of forcing data and the suitability of observations for data assimilation as restricting factors. The described issues call for institutional integration efforts and promotion of good practices to homogenize operational marine model implementations, and to ensure that external forcing datasets, observation networks and process formulations and parameterizations are adequately developed to enable the deployment of high-level operational marine and coastal modelling services across Europe.