Egg mortality of Northeast Arctic cod (Gadus morhua) and haddock (Melanogrammus aeglefinus)

This is a pre-copyedited, author-produced PDF of an article accepted for publication in ICES Journal of Marine Science following peer review. The definitive publisher-authenticated version ICES J. Mar. Sci. (2013) is available online at: http://dx.doi.org/10.1093/icesjms/fst00

Nearshore wave forecasting and hindcasting by dynamical and statistical downscaling

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 Enviromental 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 one 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 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 providing detailed wave forecasts as well as hindcasts and return values estimates of partly sheltered coastal regions.Comment: 20 pages, 7 figures and 2 tables, MREA07 special issue on Marine rapid environmental assessmen

Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling system

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.Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling systempublishedVersio

Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling system

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.Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling systempublishedVersio

Spirituality through art education

Bibliography: p. 203-212

Combining hydrographical particles-tracking models with spatial analyses to evaluate spatial dynamics of cod larvae and 0-group in the Barents Sea

Recruitment ecology of cod has been an important focus within the framework of GLOBEC. A large part of this work focused on understanding the spatiotemporal component of cod populations. For the early pelagic life stages, studies based on an individual-based platform have provided highly valuable insights combining oceanographic, behavioural and modelling approaches. The spatial modelling of observational data often fails to include densitydependent covariates, which for early pelagic life stages, originate from the combination of circulation patterns and eggs coming from the spawning aggregations. We performed this task combining a hydrographical particle-tracking model with spatial statistical analyses to investigate the relative contribution of hydrographical variables on the spatial distribution of cod larvae in the Barents Sea under two short-term climatic regimes in the period 1986-1991. The cod larvae distribution is modelled using eggs drifting from the spawning aggregations in the Lofoten Islands. We found that inter-annual variability in the spatial aggregations of the spawners influenced the distribution of larvae drifted. We have also shown how the spatial distribution of passive-drifting larvae can change over the two regimes (1986-1988 and 1989- 1991), being more upstream in cold periods. Though the currents pattern is the main hydrographical factor shaping the spatial distribution of larvae, the temperature modifies such distribution by affecting larvae survival. However, our study highlights the geographic extension of the temperature effect changed between warm and cold periods, with clear ecological implications in terms of growth and survival. This approach can be useful for other fish populations to further understand the underlying processes shaping the seascape of early life stages. Keywords: Barents Sea cod, larvae, hydrographical particles-tracking models, spatial analyses

A combination of hydrodynamical and statistical modeling reveals non-stationary climate effects on fish larvae distributions

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