In the context of Canada’s Ocean Protection Plan (OPP), improved coastal and near-shore modelling is needed to enhance marine safety and emergency response capacity in the aquatic environment. In this study, the Nucleus for European Modelling of the Ocean (NEMO) is adopted to develop an ocean forecasting system for Saint John harbour in the Bay of Fundy, on the east coast of Canada. The challenging regional oceanography is characterized by the presence of some of the world’s strongest tides, significant river runoff and complicated geometry. A three-level one-way nesting approach is used to downscale from a 1/12° North Atlantic-Arctic regional model to very-high-resolution port-scale around Saint John harbour. The three nested grids cover the outer shelf, the Bay of Fundy and finally the approach to the harbour with resolutions of 2.5 km, 500 m and 100 m respectively. Due to the lack of accurate runoff data at the Saint John River outlet, the model’s lateral open boundary condition is modified to introduce the river forcing with the observed time series of water level near the mouth of the river. Evaluation with observational data demonstrates the model’s accuracy for the simulation of tidal elevation and currents, non-tidal water level and currents, temperature and salinity. Comparison with the observed sea surface temperature demonstrates the improved model accuracy through increasing the horizontal resolution. Virtual Lagrangian trajectories computed using the modelled surface currents and including wind effects show good agreement with the observed trajectories of different types of surface drifters. This study demonstrates the capability of the NEMO modelling framework to provide very-high-resolution modelling at port-scale resolution for the Saint John harbour.

The oceanography sub-initiative of Canada’s Oceans Protection Plan was tasked to develop high-resolution nearshore ocean models for enhanced marine safety and emergency response, fitting into the multi-scale, multi-level nested operational ocean forecasting systems. For decision making on eventual 24/7 operational support, two ocean models (a structured grid model, NEMO (Nucleus for European Modelling of the Ocean); and an unstructured grid model, FVCOM (Finite Volume Coastal Ocean Model), were evaluated. The evaluation process includes the selection of the study area, the requirements for model setup, and the evaluation metrics. The chosen study area, Saint John Harbour in the Bay of Fundy, features strong tides, significant river runoff and a narrow tidal-river channel. Both models were configured with the same sources of bathymetry and forcing data. FVCOM achieved 50-100 m horizontal resolution in the inner harbour and included wetting/drying. NEMO achieved 100 m resolution in the harbour with a three-level one-way nesting configuration. Statistical metrics showed that one-year simulations with both models achieved comparable accuracies against the observed tidal and non-tidal water levels and currents, temperature and salinity, and the trajectories of surface drifters, but the computational cost of FVCOM was significantly less than that of NEMO.

The deployment of 206 surface drifters over 3 years in a fjord system in northern British Columbia allows examination of drift and dispersion in complex coastal regions on time scales up to 10 days. The surface drift is found to be seasonally variable, with stronger dispersion and outflows in the spring and fall, and negligible outflow in the summer. Dispersion at time scales less than 10 hr is well described by fractional Brownian motion, where the drifter tracks exhibit fractal characteristics with a dimension of 1.34 over scales of 2 to 13 km. Drifters are found to reach less energetic nearshore regions within 12–15 hr, which slows along‐channel dispersion. The comparison of the drifter statistics (from 2014–2016) with observations of the spatial distribution of oil sheen following an oil spill in 2006 shows that the drifter results provide a reasonable proxy for oil drift in this area. A statistical model for the extent of along‐channel transport of spilled oil is proposed for use in planning emergency response activities in the area.

This paper describes the Coastal Ice Ocean Prediction System for the East Coast of Canada (CIOPS-E) running operationally at Environment and Climate Change Canada (ECCC). CIOPS-E uses a one-way downscaling technique on a 1/36° horizontal grid (~2 km) to simulate high-resolution ice and ocean conditions over the northwest Atlantic Ocean and the Gulf of St. Lawrence (GSL). CIOPS-E is forced at its lateral boundaries with ECCC’s Regional Ice-Ocean Prediction System (RIOPS) and tidal conditions from the Oregon State University TPXO model. The three-dimensional temperature and salinity fields are spectrally nudged towards the RIOPS solution offshore of the 1500 m isobath to, effectively constrain mesoscale features in the Gulf Stream area. Over the continental shelf and the GSL, the CIOPS-E solution is free to develop fully according to model dynamics. CIOPS-E is evaluated over one year from March 2019 to February 2020. Overall, the CIOPS-E improves the representation of tides compared to ECCC’s lower resolution systems: RIOPS (1/12°) and the Regional Marine Prediction System – Gulf of St. Lawrence (RMPS-GSL, 5 km). The accuracy of the tides are comparable to the TPXO at most coastal tide gauges. Sub-tidal water levels from CIOPS-E agree well with the observed seasonal variability and show improved errors statistics at all stations compared to RIOPS and RMPS-GSL. Improvements are especially noted for the GSL. Sea surface temperatures (SSTs) from CIOPS-E are lower (higher) in spring (fall) over most of the GSL compared to satellite-derived analyses and RIOPS. Comparison with in-situ observations of SST show significant improvement in CIOPS-E with respect to the RMPS-GSL. Lastly, sea ice conditions in the GSL are compared with the Canadian Ice Service (CIS) charts and the RMPS-GSL model. The sea ice cover and thickness from the pseudo-analysis component (without data assimilation) shows an overestimation compared to the CIS estimates, which is subsequently corrected in the forecast phase through the direct insertion of a Radarsat image analysis product.

This paper proposes a fuzzy number—based framework for quantifying and propagating uncertainties through a model for the trajectories of objects drifting at the ocean surface. Various sources of uncertainty that should be considered are discussed. This model is used to explore the effect of parameterizing direct wind drag on the drifting object based on its geometry, and using measured winds to parameterize shear and rotational dynamics in the ocean surface currents along with wave-driven circulation and near-surface wind shear. Parameterizations are formulated in a deterministic manner that avoids the commonly required specification of empirical leeway coefficients. Observations of ocean currents and winds at Ocean Station Papa in the northeast Pacific are used to force the trajectory model in order to focus on uncertainties arising from physical processes, rather than uncertainties introduced by the use of atmospheric and hydrodynamic models. Computed trajectories are compared against observed trajectories from five different types of surface drifters, and optimal combinations of forcing parameterizations are identified for each type of drifter. The model performance is assessed using a novel skill metric that combines traditional assessment of trajectory accuracy with penalties for overestimation of uncertainty. Comparison to the more commonly used leeway method shows similar performance, without requiring the specification of empirical coefficients. When using optimal parameterizations, the model is shown to correctly identify the area in which drifters are expected to be found for the duration of a seven day simulation.

This paper describes the Coastal Ice Ocean Prediction System for the East Coast of Canada (CIOPS-E) running operationally at Environment and Climate Change Canada (ECCC). CIOPS-E uses a one-way downscaling technique on a 1/36° horizontal grid (~2 km) to simulate high-resolution ice and ocean conditions over the northwest Atlantic Ocean and the Gulf of St. Lawrence (GSL). CIOPS-E is forced at its lateral boundaries with ECCC’s Regional Ice-Ocean Prediction System (RIOPS) and tidal conditions from the Oregon State University TPXO model. The three-dimensional temperature and salinity fields are spectrally nudged towards the RIOPS solution offshore of the 1500 m isobath to, effectively constrain mesoscale features in the Gulf Stream area. Over the continental shelf and the GSL, the CIOPS-E solution is free to develop fully according to model dynamics. CIOPS-E is evaluated over one year from March 2019 to February 2020. Overall, the CIOPS-E improves the representation of tides compared to ECCC’s lower resolution systems: RIOPS (1/12°) and the Regional Marine Prediction System – Gulf of St. Lawrence (RMPS-GSL, 5 km). The accuracy of the tides are comparable to the TPXO at most coastal tide gauges. Sub-tidal water levels from CIOPS-E agree well with the observed seasonal variability and show improved errors statistics at all stations compared to RIOPS and RMPS-GSL. Improvements are especially noted for the GSL. Sea surface temperatures (SSTs) from CIOPS-E are lower (higher) in spring (fall) over most of the GSL compared to satellite-derived analyses and RIOPS. Comparison with in-situ observations of SST show significant improvement in CIOPS-E with respect to the RMPS-GSL. Lastly, sea ice conditions in the GSL are compared with the Canadian Ice Service (CIS) charts and the RMPS-GSL model. The sea ice cover and thickness from the pseudo-analysis component (without data assimilation) shows an overestimation compared to the CIS estimates, which is subsequently corrected in the forecast phase through the direct insertion of a Radarsat image analysis product.