The EMSO-ERIC Pan-European Consortium: Data Benefits and Lessons Learned as the Legal Entity Forms

The European Multidisciplinary Seafloor and water-column Observatory (EMSO) European Research Infrastructure Consortium (ERIC) provides power, communications, sensors, and data infrastructure for continuous, high-resolution, (near-)real-time, interactive ocean observations across a multidisciplinary and interdisciplinary range of research areas including biology, geology, chemistry, physics, engineering, and computer science, from polar to subtropical environments, through the water column down to the abyss. Eleven deep-sea and four shallow nodes span from the Arctic through the Atlantic and Mediterranean, to the Black Sea. Coordination among the consortium nodes is being strengthened through the EMSOdev project (H2020), which will produce the EMSO Generic Instrument Module (EGIM). Early installations are now being upgraded, for example, at the Ligurian, Ionian, Azores, and Porcupine Abyssal Plain (PAP) nodes. Significant findings have been flowing in over the years; for example, high-frequency surface and subsurface water-column measurements of the PAP node show an increase in seawater pCO2 (from 339 μatm in 2003 to 353 μatm in 2011) with little variability in the mean air-sea CO2 flux. In the Central Eastern Atlantic, the Oceanic Platform of the Canary Islands open-ocean canary node (aka ESTOC station) has a long-standing time series on water column physical, biogeochemical, and acidification processes that have contributed to the assessment efforts of the Intergovernmental Panel on Climate Change (IPCC). EMSO not only brings together countries and disciplines but also allows the pooling of resources and coordination to assemble harmonized data into a comprehensive regional ocean picture, which will then be made available to researchers and stakeholders worldwide on an open and interoperable access basis.

Climate Change Influences Carrying Capacity in a Coastal Embayment Dedicated to Shellfish Aquaculture

A spatially explicit coupled hydrodynamic-biogeochemical model was developed to study a coastal ecosystem under the combined effects of mussel aquaculture, nutrient loading and climate change. The model was applied to St Peter's Bay (SPB), Prince Edward Island, Eastern Canada. Approximately 40 % of the SPB area is dedicated to mussel (Mytilus edulis) longline culture. Results indicate that the two main food sources for mussels, phytoplankton and organic detritus, are most depleted in the central part of the embayment. Results also suggest that the system is near its ultimate capacity, a state where the energy cycle is restricted to nitrogen-phytoplankton-detritus-mussels with few resources left to be transferred to higher trophic levels. Annually, mussel meat harvesting extracts nitrogen (N) resources equivalent to 42 % of river inputs or 46.5 % of the net phytoplankton primary production. Under such extractive pressure, the phytoplankton biomass is being curtailed to 1980's levels when aquaculture was not yet developed and N loading was half the present level. Current mussel stocks also decrease bay-scale sedimentation rates by 14 %. Finally, a climate change scenario (year 2050) predicted a 30 % increase in mussel production, largely driven by more efficient utilization of the phytoplankton spring bloom. However, the predicted elevated summer temperatures (> 25 A degrees C) may also have deleterious physiological effects on mussels and possibly increase summer mortality levels. In conclusion, cultivated bivalves may play an important role in remediating the negative impacts of land-derived nutrient loading. Climate change may lead to increases in production and ecological carrying capacity as long as the cultivated species can tolerate warmer summer conditions.

Uncertainties in projecting climate-change impacts in marine ecosystems

Projections of the impacts of climate change on marine ecosystems are a key prerequisite for the planning of adaptation strategies, yet they are inevitably associated with uncertainty. Identifying, quantifying, and communicating this uncertainty is key to both evaluating the risk associated with a projection and building confidence in its robustness. We review how uncertainties in such projections are handled in marine science. We employ an approach developed in climate modelling by breaking uncertainty down into (i) structural (model) uncertainty, (ii) initialization and internal variability uncertainty, (iii) parametric uncertainty, and (iv) scenario uncertainty. For each uncertainty type, we then examine the current state-of-the-art in assessing and quantifying its relative importance. We consider whether the marine scientific community has addressed these types of uncertainty sufficiently and highlight the opportunities and challenges associated with doing a better job. We find that even within a relatively small field such as marine science, there are substantial differences between subdisciplines in the degree of attention given to each type of uncertainty. We find that initialization uncertainty is rarely treated explicitly and reducing this type of uncertainty may deliver gains on the seasonal-to-decadal time-scale. We conclude that all parts of marine science could benefit from a greater exchange of ideas, particularly concerning such a universal problem such as the treatment of uncertainty. Finally, marine science should strive to reach the point where scenario uncertainty is the dominant uncertainty in our projections.

Developing European operational oceanography for Blue Growth, climate change adaptation and mitigation, and ecosystem-based management

Operational approaches have been more and more widely developed and used for providing marine data and information services for different socio-economic sectors of the Blue Growth and to advance knowledge about the marine environment. The objective of operational oceanographic research is to develop and improve the efficiency, timeliness, robustness and product quality of this approach. This white paper aims to address key scientific challenges and research priorities for the development of operational oceanography in Europe for the next 5-10 years. Knowledge gaps and deficiencies are identified in relation to common scientific challenges in four EuroGOOS knowledge areas: European Ocean Observations, Modelling and Forecasting Technology, Coastal Operational Oceanography and Operational Ecology. The areas "European Ocean Observations" and "Modelling and Forecasting Technology" focus on the further advancement of the basic instruments and capacities for European operational oceanography, while "Coastal Operational Oceanography" and "Operational Ecology" aim at developing new operational approaches for the corresponding knowledge areas.