Predicting the ocean state in a reliable and interoperable way, while ensuring high-quality products, requires forecasting systems that synergistically combine science-based methodologies with advanced technologies for timely, user-oriented solutions. Achieving this objective necessitates the adoption of best practices when implementing ocean forecasting services, resulting in the proper design of system components and the capacity to evolve through different levels of complexity. The vision of OceanPrediction Decade Collaborative Center, endorsed by the UN Decade of Ocean Science for Sustainable Development 2021-2030, is to support this challenge by developing a “predicted ocean based on a shared and coordinated global effort” and by working within a collaborative framework that encompasses worldwide expertise in ocean science and technology. To measure the capacity of ocean forecasting systems, the OceanPrediction Decade Collaborative Center proposes a novel approach based on the definition of an Operational Readiness Level (ORL). This approach is designed to guide and promote the adoption of best practices by qualifying and quantifying the overall operational status. Considering three identified operational categories - production, validation, and data dissemination - the proposed ORL is computed through a cumulative scoring system. This method is determined by fulfilling specific criteria, starting from a given base level and progressively advancing to higher levels. The goal of ORL and the computed scores per operational category is to support ocean forecasters in using and producing ocean data, information, and knowledge. This is achieved through systems that attain progressively higher levels of readiness, accessibility, and interoperability by adopting best practices that will be linked to the future design of standards and tools. This paper discusses examples of the application of this methodology, concluding on the advantages of its adoption as a reference tool to encourage and endorse services in joining common frameworks.
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RBINS Staff Publications 2023
Abstract The present study describes the responses of summer phytoplankton biomass to changes in top-down forcing (expressed as zooplankton mortality) in three ecosystems (the North Sea, the Baltic Sea and the Nordic Seas) across different 3D ecosystem models. In each of the model set-ups, we applied the same changes in the magnitude of mortality (±20%) of the highest trophic zooplankton level (Z1). Model results showed overall dampened responses of phytoplankton relative to \Z1\ biomass. Phytoplankton responses varied depending on the food web structure and trophic coupling represented in the models. Hence, a priori model assumptions were found to influence cascades and pathways in model estimates and, thus, become highly relevant when examining ecosystem pressures such as fishing and climate change. Especially, the different roles and parameterizations of additional zooplankton groups grazed by Z1, and their importance for the outcome, emphasized the need for better calibration data. Spatial variability was high within each model indicating that physics (hydrodynamics and temperature) and nutrient dynamics also play vital roles for ecosystem responses to top-down effects. In conclusion, the model comparison indicated that changes in top-down forcing in combination with the modelled food-web structure affect summer phytoplankton biomass and, thereby, indirectly influence water quality of the systems.
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RBINS Staff Publications 2018
