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[1] The seasonal cycle of water masses and sea ice in the Gulf of St. Lawrence is examined using a three-dimensional coastal ice-ocean model with realistic tidal, atmospheric, hydrologic, and oceanic forcing. The model includes a level 2.5 turbulent kinetic energy equation. A model simulation over 1997-1998 is verified against available data on sea ice, temperature, and salinity. The results demonstrate a consistent seasonal cycle in atmosphere-ocean exchanges and the formation and circulation of water masses and sea ice. The accuracy of radiative, momentum, and sensible heat exchanges at the sea surface, and the production of turbulent kinetic energy from winds and tides, are critical to the accuracy of the modeled circulation. The analysis of the mean error on near-surface temperature and salinity in the late summer and fall using standard bulk exchange coefficients and radiation (about 1°C too cold and 1 salinity unit too fresh) shows the tradeoff between tidal mixing at the head of the Laurentian Channel, and winddriven circulation and mixing in the surface waters. The results suggest year-long stratification in the estuary and northwestern Gulf, with little mixing except near the head region, where relatively deep warmer waters are mixed to the surface during winter, and cold intermediate waters are efficiently withdrawn during summer. The results suggest that the summer cold waters found at intermediate depths in the estuary and northwestern Gulf are not formed in situ. A significant fraction of these waters enters through the Strait of Belle Isle in wintertime, eventually reaching the estuary within about 6 months.

In some coastal regions of the Arctic Ocean, grounded ice ridges contribute to stabilizing and maintaining a landfast ice cover. Recently, a grounding scheme representing this effect on sea ice dynamics was introduced and tested in a viscous‐plastic sea ice model. This grounding scheme, based on a basal stress parameterization, improves the simulation of landfast ice in many regions such as in the East Siberian Sea, the Laptev Sea, and along the coast of Alaska. Nevertheless, in some regions like the Kara Sea, the area of landfast ice is systematically underestimated. This indicates that another mechanism such as ice arching is at play for maintaining the ice cover fast. To address this problem, the combination of the basal stress parameterization and tensile strength is investigated using a 0.25° Pan‐Arctic CICE‐NEMO configuration. Both uniaxial and isotropic tensile strengths notably improve the simulation of landfast ice in the Kara Sea but also in the Laptev Sea. However, the simulated landfast ice season for the Kara Sea is too short compared to observations. This is especially obvious for the onset of the landfast ice season which systematically occurs later in the model and with a slower build up. This suggests that improvements to the sea ice thermodynamics could reduce these discrepancies with the data.

Despite the availability of several atmospheric reanalyses (e.g. ERA-Interim) there exists both considerable uncertainty in surface forcing fields for ice/ocean modelling and sensitivity to the choice of product used. Here we introduce a relatively high-resolution alternative forcing dataset for ice-ocean models derived from the Canadian Meteorological Centre's (CMC) global deterministic prediction system (GDPS). A set of daily 30 h reforecasts is produced using the GDPS 33 km resolution model providing hourly atmospheric forcing fields for the period 2002-2011. The CMC GDPS reforecasts (CGRF) are compared with ERA-Interim and several observational datasets to evaluate their suitability for forcing ocean models. In particular, the surface temperature, humidity and winds of the CGRF show equivalent biases to those found in ERA-interim. Moreover, the higher resolution of the CGRF permit a more detailed representation of atmospheric structures and topographic steering, resulting in finer-scale coastal features and wind-stress curl. Although the CGRF dataset is not a reanalysis and thus is expected to be less well constrained by available observations, its higher resolution and small bias make it an attractive alternative for forcing ice-ocean models.