A study of the large scale circulation and water mass formation in the Nordic seas and Arctic ocean

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, and the Woods Hole Oceanographic Institution, 1994.Includes bibliographical references (leaves 205-212).by Cecilie Mauritzen.Ph.D

Set-up of the Nordic and Barents Seas (NoBa) Atlantis model

End-to-end models are important tools when moving towards an ecosystem based approach to fisheries management. Atlantis is one such end-to-end model. Atlantis has been developed forseveral areas, including Australia, U.S., and European waters, and models for other areas are under development, The models give unique opportunities to explore spatial impact of climate and fisheries, and includes all levels from physical forcing to top predators in the system, including bacteria, phytoplankton, zooplankton, fish, benthos and marine mammals. Atlantis for the Nordic and Barents Seas (NoBa) has been built with the aim of representing the key species and processes in the areas, where the main objective is to explore combined climate and fisheries scenarios. In setting up the model several thousand parameters need to be defined This report provides an overview and explanations of key parameters used to initialize the model

Set-up of the Nordic and Barents Seas (NoBa) Atlantis model

End-to-end models are important tools when moving towards an ecosystem based approach to fisheries management. Atlantis is one such end-to-end model. Atlantis has been developed forseveral areas, including Australia, U.S., and European waters, and models for other areas are under development, The models give unique opportunities to explore spatial impact of climate and fisheries, and includes all levels from physical forcing to top predators in the system, including bacteria, phytoplankton, zooplankton, fish, benthos and marine mammals. Atlantis for the Nordic and Barents Seas (NoBa) has been built with the aim of representing the key species and processes in the areas, where the main objective is to explore combined climate and fisheries scenarios. In setting up the model several thousand parameters need to be defined This report provides an overview and explanations of key parameters used to initialize the model

Rapid response of the Norwegian Atlantic Slope Current to wind forcing

Under embargo until: 2023-07-11We explore drivers of variability in the Norwegian Atlantic Slope Current, which carries relatively warm Atlantic Water toward the Barents Sea and Arctic Ocean, using Copernicus Marine Environment Monitoring Service (CMEMS) satellite altimetry data and TOPAZ4 ocean reanalysis data. Previous studies have pointed to a variety of causes, on a variety of time scales. We use data with daily resolution to investigate day-to-day changes in ocean transport across three sections crossing the shelf-slope of Norway (Svinøy, Gimsøy, and the Barents Sea Opening). The highest (lowest) extremes in transport at all sections develop over two days as a cyclonic (anticyclonic) atmospheric pressure system approaches from the southwest, piling up (extracting) water at the coast of Norway. The actual peak is reached when the pressure system passes the site of measurement, and the transport then relaxes for the next two days as the system continues northward along the coast. Other sources of short-term variability, such as propagating continental shelf waves and baroclinic instability, are unlikely to yield covariability over large separations. Monthly variability in the current can also be explained by passing weather systems since their numbers and intensity vary greatly from month to month. Many studies of longer-term variability, especially in the Barents Sea Opening, have pointed to the North Atlantic Oscillation (NAO) as the main cause of variability. Our results show that passing weather systems offer a better explanation of month-to-month variability.publishedVersio

Sensitivity of the Norwegian and Barents Sea Atlantis end-to-end ecosystem model to parameter perturbations of key species

Using end-to-end models for ecosystem-based management requires knowledge of the structure, uncertainty and sensitivity of the model. The Norwegian and Barents Seas (NoBa) Atlantis model was implemented for use in ‘what if’ scenarios, combining fisheries management strategies with the influences of climate change and climate variability. Before being used for this purpose, we wanted to evaluate and identify sensitive parameters and whether the species position in the foodweb influenced their sensitivity to parameter perturbation. Perturbing recruitment, mortality, prey consumption and growth by +/- 25% for nine biomass-dominating key species in the Barents Sea, while keeping the physical climate constant, proved the growth rate to be the most sensitive parameter in the model. Their trophic position in the ecosystem (lower trophic level, mid trophic level, top predators) influenced their responses to the perturbations. Top-predators, being generalists, responded mostly to perturbations on their individual life-history parameters. Mid-level species were the most vulnerable to perturbations, not only to their own individual life-history parameters, but also to perturbations on other trophic levels (higher or lower). Perturbations on the lower trophic levels had by far the strongest impact on the system, resulting in biomass changes for nearly all components in the system. Combined perturbations often resulted in non-additive model responses, including both dampened effects and increased impact of combined perturbations. Identifying sensitive parameters and species in end-to-end models will not only provide insights about the structure and functioning of the ecosystem in the model, but also highlight areas where more information and research would be useful—both for model parameterization, but also for constraining or quantifying model uncertainty.publishedVersio

Potential sources of marine plastic from survey beaches in the Arctic and Northeast Atlantic

Plastic litter is accumulating on pristine northern European beaches, including the European Arctic, and questions remain about the exact origins and sources. Here we investigate plausible fishery and consumer-related sources of beach littering, using a combination of information from expert stakeholder discussions, litter observations and a quantitative tool - a drift model - for forecasting and backtracking likely pathways of pollution. The numerical experiments were co-designed together with practice experts. The drift model itself was forced by operational ocean current, wave and weather forecasts. The model results were compared to a database of marine litter on beaches, collected every year according to the standardized monitoring program of the Oslo/Paris Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR). By comparing the heterogeneous beach observations to the model simulations, we are able to highlight probable sources. Two types of plastic are considered in the simulations: floating plastic litter and submerged, buoyant microplastics. We find that the model simulations are plausible in terms of the potential sources and the observed plastic litter. Our analysis results in identifiable sources of plastic waste found on each beach, providing a basis for stakeholder actions.publishedVersio

Bias Correction of Operational Storm Surge Forecasts Using Neural Networks

Storm surges can give rise to extreme floods in coastal areas. The Norwegian Meteorological Institute produces 120-hour regional operational storm surge forecasts along the coast of Norway based on the Regional Ocean Modeling System (ROMS), using a model setup called Nordic4-SS. Despite advances in the development of models and computational capabilities, forecast errors remain large enough to impact response measures and issued alerts, in particular, during the strongest events. Reducing these errors will positively impact the efficiency of the warning systems while minimizing efforts and resources spent on mitigation. Here, we investigate how forecasts can be improved with residual learning, i.e., training data-driven models to predict the residuals in forecasts from Nordic4-SS. A simple error mapping technique and a more sophisticated Neural Network (NN) method are tested. Using the NN residual correction method, the Root Mean Square Error in the Oslo Fjord is reduced by 36% for lead times of one hour and 9% for 24 hours. Therefore, the residual NN method is a promising direction for correcting storm surge forecasts, especially on short timescales. Moreover, it is well adapted to being deployed operationally, as i) the correction is applied on top of the existing model and requires no changes to it, ii) all predictors used for NN inference are already available operationally, iii) prediction by the NNs is very fast, typically a few seconds per station, and iv) the NN correction can be provided to a human expert who may inspect it, compare it with the model output, and see how much correction is brought by the NN, allowing to capitalize on human expertise as a quality validation of the NN output. While no changes to the hydrodynamic model are necessary to calibrate the neural networks, they are specific to a given model and must be recalibrated when the numerical models are updated

Multiple stakeholders’ perspectives of marine social ecological systems, a case study on the Barents Sea

The Barents Sea ecosystem components and services are under pressure from climate change and other anthropogenic impacts. Following an Ecosystem-based management approach, multiple simultaneous pressures are addressed by using integrative strategies, but regular prioritization of key issues is needed. Identification of such priorities is typically done in a ‘scoping’ phase, where the characterization of the social-ecological system is defined and discussed. We performed a scoping exercise using an open and flexible multi-stakeholder approach to build conceptual models of the Barents Sea social-ecological system. After standardizing vocabulary, a com plex hierarchical model structure containing 155 elements was condensed to a simpler model structure con taining a maximum of 36 elements. To capture a common understanding across stakeholder groups, inputs from the individual group models were compiled into a collective model. Stakeholders’ representation of the Barents Sea social-ecological system is complex and often group specific, emphasizing the need to include social scientific methods to ensure the identification and inclusion of key stakeholders in the process. Any summary or simpli fication of the stakeholders’ representation neglects important information. Some commonalities are highlighted in the collective model, and additional information from the hierarchical model is provided by multicriteria analysis. The collective conceptual stakeholder model provides input to an integrated overview and strengthens prioritization in Ecosystem-based management by supporting the development of qualitative network models. Such models allow for exploration of perturbations and can inform cross-sectoral management trade-offs and prioritiespublishedVersio

Highly mixed impacts of near-future climate change on stock productivity proxies in the North East Atlantic

Impacts of climate change on ocean productivity sustaining world fisheries are predominantly negative but vary greatly among regions. We assessed how 39 fisheries resources—ranging from data-poor to data-rich stocks—in the North East Atlantic are most likely affected under the intermediate climate emission scenario RCP4.5 towards 2050. This region is one of the most productive waters in the world but subjected to pronounced climate change, especially in the northernmost part. In this climate impact assessment, we applied a hybrid solution combining expert opinions (scorings)—supported by an extensive literature review—with mechanistic approaches, considering stocks in three different large marine ecosystems, the North, Norwegian and Barents Seas. This approach enabled calculation of the directional effect as a function of climate exposure and sensitivity attributes (life-history schedules), focusing on local stocks (conspecifics) across latitudes rather than the species in general. The resulting synopsis (50–82°N) contributes substantially to global assessments of major fisheries (FAO, The State of World Fisheries and Aquaculture, 2020), complementing related studies off northeast United States (35–45°N) (Hare et al., PLoS One, 2016, 11, e0146756) and Portugal (37–42°N) (Bueno-Pardo et al., Scientific Reports, 2021, 11, 2958). Contrary to prevailing fisheries forecasts elsewhere, we found that most assessed stocks respond positively. However, the underlying, extensive environmental clines implied that North East Atlantic stocks will develop entirely different depending upon the encountered stressors: cold-temperate stocks at the southern and Arctic stocks at the northern fringes appeared severely negatively impacted, whereas warm-temperate stocks expanding from south were found to do well along with cold-temperate stocks currently inhabiting below-optimal temperatures in the northern subregion.publishedVersio