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An operational ocean and sea ice forecast model, Barents-2.5, is implemented for short-term forecasting at the coast off northern Norway, the Barents Sea, and the waters around Svalbard. Primary forecast parameters are sea ice concentration (SIC), sea surface temperature (SST), and ocean currents. The model also provides input data for drift modeling of pollutants, icebergs, and search-and-rescue applications in the Arctic domain. Barents-2.5 has recently been upgraded to include an ensemble prediction system with 24 daily realizations of the model state. SIC, SST, and in situ hydrography are constrained through the ensemble Kalman filter (EnKF) data assimilation scheme executed in daily forecast cycles with a lead time up to 66 h. Here, we present the model setup and validation in terms of SIC, SST, in situ hydrography, and ocean and ice velocities. In addition to the model's forecast capabilities for SIC and SST, the performance of the ensemble in representing the model's uncertainty and the performance of the EnKF in constraining the model state are discussed. [ABSTRACT FROM AUTHOR]

An operational ocean and sea ice forecast model, Barents-2.5, is implemented at MET Norway for short-term forecasting at the coast off Northern Norway, the Barents Sea, and waters around Svalbard. Primary forecast parameters are the sea ice concentration (SIC), sea surface temperature (SST), and ocean currents. The model is also a substantial input for drift modeling of pollutants, ice berg, and in search-and-rescue pertinent applications in the Arctic domain. Barents-2.5 has recently been upgraded to include an Ensemble Prediction System with 24 daily realizations of the model state. SIC, SST and in-situ hydrography are constrained through the Ensemble Kalman Filter (EnKF) data assimilation scheme executed in daily forecast cycles with lead time up to 66 hours. While the ocean circulation is not directly constrained by assimilation of ocean currents, the model ensemble represents the given uncertainty in the short-term current field by retaining the current state for each member throughout forecast cycles. Here we present the model setup and a validation in terms of SIC, SST and in-situ hydrography. The performance of the ensemble to represent the models uncertainty, and the performance of the EnKF to constrain the model state are discussed, in addition to the model's forecast capabilities for SIC and SST. [ABSTRACT FROM AUTHOR]

The northward flow of warm and saline Atlantic Water through the eastern Nordic Seas sustains a spring-bloom ecosystem that hosts some of the world's largest commercial fish stocks. Abrupt climatic changes, or changes beyond species-specific thresholds, may have severe effects on species abundance and distribution. Here, we utilize a numerical ocean model hindcast to explore the similarities and differences between large-scale anomalies, such as great salinity anomalies, and along-shelf hydrographic anomalies of regional origin, which represent abrupt changes at subannual time scales. The large-scale anomalies enter the Nordic Seas to the south and propagate northward at a speed one order of magnitude less than the Atlantic Water current speed. On the contrary, wind-generated along-shelf anomalies appear simultaneously along the Norwegian continental shelf and propagate northward at speeds associated with topographically trapped Kelvin waves. This process involves changes in the vertical extent of the Atlantic Water along the continental slope. Such a dynamic oceanic response both affects thermal habitats and has the potential to ventilate shelf waters by modifying the cross-shelf transport of nutrients and key prey items for early stages of fish. [ABSTRACT FROM AUTHOR]

Operational forecasting systems routinely assimilate daily means of sea ice concentration (SIC) from microwave radiometers in order to improve the accuracy of the forecasts. However, the temporal and spatial averaging of the individual satellite swaths into daily means of SIC entails two main drawbacks: (i) the spatial resolution of the original product is blurred (especially critical in periods with strong sub-daily sea ice movement), and (ii) the sub-daily frequency of passive microwave observations in the Arctic are not used, providing less temporal resolution in the data assimilation (DA) analysis and, therefore, in the forecast. Within the SIRANO (Sea Ice Retrievals and data Assimilation in NOrway) project, we investigate how challenges (i) and (ii) can be avoided by assimilating individual satellite swaths (level 3 uncollated) instead of daily means (level 3) of SIC. To do so, we use a regional configuration of the Barents Sea (2.5 km grid) based on the Regional Ocean Modeling System (ROMS) and the Los Alamos Sea Ice Model (CICE) together with the ensemble Kalman filter (EnKF) as the DA system. The assimilation of individual swaths significantly improves the EnKF analysis of SIC compared to the assimilation of daily means; the mean absolute difference (MAD) shows a 10 % improvement at the end of the assimilation period and a 7 % improvement at the end of the 7 d forecast period. This improvement is caused by better exploitation of the information provided by the SIC swath data, in terms of both spatial and temporal variance, compared to the case when the swaths are combined to form a daily mean before assimilation. [ABSTRACT FROM AUTHOR]

Abstract: A high-resolution nested WAM/SWAN wave model suite aimed at rapidly establishing nearshore wave forecasts as well as a climatology and return values of the local wave conditions with Rapid Environmental Assessment (REA) in mind is described. The system is targeted at regions where local wave growth and partial exposure to complex open-ocean wave conditions makes diagnostic wave modelling difficult. SWAN is set up on 500 m resolution and is nested in a 10 km version of WAM. A model integration of more than 1 year is carried out to map the spatial distribution of the wave field. The model correlates well with wave buoy observations (0.96) but overestimates the wave height somewhat (18%, bias 0.29 m). To estimate wave height return values a much longer time series is required and running SWAN for such a period is unrealistic in a REA setting. Instead we establish a direction-dependent transfer function between an already existing coarse open-ocean hindcast dataset and the high-resolution nested SWAN model. Return values are estimated using ensemble estimates of two different extreme-value distributions based on the full 52 years of statistically downscaled hindcast data. We find good agreement between the downscaled wave height and wave buoy observations. The cost of generating the statistically downscaled hindcast time series is negligible and can be redone for arbitrary locations within the SWAN domain, although the sectors must be carefully chosen for each new location. The method is found to be well suited to rapidly provide detailed wave forecasts as well as hindcasts and return values estimates of partly sheltered coastal regions. [Copyright &y& Elsevier]

The Los Alamos Sea Ice Model (CICE) is used by several Earth system models where sea ice boundary conditions are not necessary, given their global scope. However, regional and local implementations of sea ice models require boundary conditions describing the time changes of the sea ice and snow being exchanged across the boundaries of the model domain. The physical detail of these boundary conditions regarding, for example, the usage of different sea ice thickness categories or the vertical resolution of thermodynamic properties, must be considered when matching them with the requirements of the sea ice model. Available satellite products do not include all required data. Therefore, the most straightforward way of getting sea ice boundary conditions is from a larger-scale model. The main goal of our study is to describe and evaluate the implementation of time-varying sea ice boundaries in the CICE model using two regional coupled ocean–sea ice models, both covering a large part of the Barents Sea and areas around Svalbard: the Barents-2.5 km​​​​​​​, implemented at the Norwegian Meteorological Institute (MET), and the Svalbard 4 km (S4K) model, implemented at the Norwegian Polar Institute (NPI). We use the TOPAZ4 model and a Pan-Arctic 4 km resolution model (A4) to generate the boundary conditions for the sea ice and the ocean. The Barents-2.5 km model is MET's main forecasting model for ocean state and sea ice in the Barents Sea. The S4K model covers a similar domain but it is used mainly for research purposes. Obtained results show significant improvements in the performance of the Barents-2.5 km model after the implementation of the time-varying boundary conditions. The performance of the S4K model in terms of sea ice and snow thickness is comparable to that of the TOPAZ4 system but with more accurate results regarding the oceanic component because of using ocean boundary conditions from the A4 model. The implementation of time-varying boundary conditions described in this study is similar regardless of the CICE versions used in different models. The main challenge remains the handling of data from larger models before its usage as boundary conditions for regional/local sea ice models, since mismatches between available model products from the former and specific requirements of the latter are expected, implying case-specific approaches and different assumptions. Ideally, model setups should be as similar as possible to allow a smoother transition from larger to smaller domains. [ABSTRACT FROM AUTHOR]

The Los Alamos Sea Ice Model (CICE) is used by several Earth System Models where sea ice boundary conditions are not necessary, given their global scope. However, regional and local implementations of sea ice models require boundary conditions describing the time changes of the sea ice and snow being exchanged across the boundaries of the model domain. These boundary conditions include but are not limited to: (i) drift direction and velocity; (ii) concentration; (iii) thickness (of the ice and snow); (iv) thermodynamic conditions (with emphasis on sea ice and snow temperature or enthalpy); (v) salinity. The physical detail of these boundary conditions regarding, for example, the usage of different sea ice size categories or the vertical resolution of thermodynamic properties, must also be taken into account when matching them with the requirements of a specific implementation of a sea ice model. Available satellite products do not include all required fields described above. Therefore, the most straightforward way of getting sea ice boundary conditions is from a larger scale model. The main goal of our study is to describe and evaluate the implementation of time-varying sea ice boundaries in the CICE model using two regional coupled ocean-sea ice models, covering a large part of the Barents Sea and areas around Svalbard: the Barents-2.5 km, implemented at the Norwegian Meteorological Institute (MET), and the S4K, implemented at the Norwegian Polar Institute (NPI). We use the TOPAZ4 model and a Pan-Arctic 4 km-resolution model (A4) model to generate the boundary conditions for the sea ice and the ocean. The Barents-2.5 km model is MET Norway's main forecasting model for ocean state and sea ice in the Barents Sea. The S4K model covers a similar domain but it is used mainly for research purposes. Obtained results show significant improvements in the performance of the Barents-2.5 km model after the implementation of the time-varying boundary conditions. The performance of the S4K model in terms of sea ice and snow thickness is comparable to that of the TOPAZ4 system but with more accurate results regarding the oceanic component. The implementation of time-varying boundary conditions described in this study is similar regardless of the CICE versions used in different models. [ABSTRACT FROM AUTHOR]

High and variable mortality during the egg and larval stages is thought to be an important source of interannual variability in stock size in many marine fish. However, quantitative information about the mortality during these life stages, especially interannual variability, is sparse. Here, we used a time-series covering 35 years (1959–1993) of survey data to estimate mortality during the egg stages of northeast Arctic cod (Gadus morhua) and Northeast Arctic haddock (Melanogrammus aeglefinus). Using a regression approach based on the numbers of eggs in different developmental stages, we calculated the mean instantaneous mortality rate of cod eggs to be 0.17 d−1 (95% CI: 0.15–0.19), which is significantly higher than that for haddock, 0.09 d−1 (95% CI: 0.07–0.12). Interannual variability in egg mortality ranges from ∼0.12 to ∼0.22 d−1 for cod and from ∼0.04 to ∼0.12 d−1 for haddock. The accuracy of these estimates was evaluated by the analysis of synthetic data constructed from a coupled physical–biological model, suggesting that mean mortality and the magnitude of interannual variability were estimated reliably, but not mortality for any given year. [ABSTRACT FROM PUBLISHER]