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Current large-scale sea ice models represent very crudely or are unable to simulate the formation, maintenance and decay of coastal landfast ice. We present a simple landfast ice parameterization representing the effect of grounded ice keels. This parameterization is based on bathymetry data and the mean ice thickness in a grid cell. It is easy to implement and can be used for two-thickness and multithickness category models. Two free parameters are used to determine the critical thickness required for large ice keels to reach the bottom and to calculate the basal stress associated with the weight of the ridge above hydrostatic balance. A sensitivity study was conducted and demonstrates that the parameter associated with the critical thickness has the largest influence on the simulated landfast ice area. A 6 year (2001)(2002)(2003)(2004)(2005)(2006)(2007) simulation with a 20 km resolution sea ice model was performed. The simulated landfast ice areas for regions off the coast of Siberia and for the Beaufort Sea were calculated and compared with data from the National Ice Center. With optimal parameters, the basal stress parameterization leads to a slightly shorter landfast ice season but overall provides a realistic seasonal cycle of the landfast ice area in the East Siberian, Laptev and Beaufort Seas. However, in the Kara Sea, where ice arches between islands are key to the stability of the landfast ice, the parameterization consistently leads to an underestimation of the landfast area.

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.

[1] We investigate the convergence properties of the nonlinear solver used in viscousplastic (VP) sea ice models. More specifically, we study the nonlinear solver that is based on an implicit solution of the linearized system of equations and an outer loop (OL) iteration (or pseudo time steps). When the time step is comparable to the forcing time scale, a small number of OL iterations leads to errors in the simulated velocity field that are of the same order of magnitude as the mean drift. The slow convergence is an issue at all spatial resolution but is more severe as the grid is refined. The metrics used by the sea ice modeling community to assess convergence are misleading. Indeed, when performing 10 OL iterations with a 6 h time step, the average kinetic energy of the pack is always within 2% of the fully converged value. However, the errors on the drift are of the same order of magnitude as the mean drift. Also, while 40 OL iterations provide a VP solution (with stress states inside or on the yield curve), large parts of the domain are characterized by errors of 0.5-1.0 cm s À1 . The largest errors are localized in regions of large sea ice deformations where strong ice interactions are present. To resolve those deformations accurately, we find that more than 100 OL iterations are required. To obtain a continuously differentiable momentum equation, we replace the formulation of the viscous coefficients with capping with a tangent hyperbolic function. This reduces the number of OL iterations required to reach a certain residual norm by a factor of $2.

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.

Recent increases in marine traffic in the Arctic have amplified the demand for reliable ice and marine environmental predictions. This article presents the verification of ice forecast skill from a new system implemented recently at the Canadian Meteorological Centre called the Global Ice Ocean Prediction System (GIOPS). GIOPS provides daily global ice and ocean analyses and 10-day forecasts on a 1/4• -resolution grid. GIOPS includes a multivariate ocean data assimilation system that combines satellite observations of sealevel anomaly and sea-surface temperature (SST) together with in situ observations of temperature and salinity. Ice analyses are produced using a 3D-Var method that assimilates satellite observations from SSM/I and SSMIS together with manual analyses from the Canadian Ice Service. Analyses of total ice concentration are projected onto the thickness categories used in the ice model using spatially and temporally varying weighting functions derived from ice model tendencies. This method may reduce deleterious impacts on the ice thickness distribution when assimilating ice concentration, as it can directly modulate (and reverse) nonlinear processes such as ice deformation. An objective verification of sea ice forecasts is made using two methods: analysis-based error assessment focusing on the marginal ice zone, and a contingency table approach to evaluate ice extent as compared to an independent analysis. Together the methods demonstrate a consistent picture of skilful medium-range forecasts in both the Northern and Southern Hemispheres as compared to persistence. Using the contingency table approach, GIOPS forecasts show a significant open-water bias during spring and summer. However, this bias depends on the choice of threshold used. Ice forecast skill is found to be highly sensitive to the assimilation of SST near the ice edge. Improved observational coverage in these areas (including salinity) would be extremely valuable for further improvement in ice forecast skill.

Abstract. As part of the CONCEPTS (Canadian Operational Network of Coupled Environmental PredicTion Systems) initiative, a high-resolution (1/12 • ) ice-ocean regional model is developed covering the North Atlantic and the Arctic oceans. The long-term objective is to provide Canada with short-term ice-ocean predictions and hazard warnings in ice-infested regions. To evaluate the modelling component (as opposed to the analysis -or data-assimilation -component, which is not covered in this contribution), a series of hindcasts for the period 2003-2009 is carried out, forced at the surface by the Canadian GDPS reforecasts (Smith et al., 2014). These hindcasts test how the model represents upper ocean characteristics and ice cover. Each hindcast implements a new aspect of the modelling or the ice-ocean coupling. Notably, the coupling to the multi-category ice model CICE is tested. The hindcast solutions are then assessed using a verification package under development, including in situ and satellite ice and ocean observations. The conclusions are as follows: (1) the model reproduces reasonably well the time mean, variance and skewness of sea surface height; (2) the model biases in temperature and salinity show that while the mean properties follow expectations, the Pacific Water signature in the Beaufort Sea is weaker than observed; (3) the modelled freshwater content of the Arctic agrees well with observational estimates; (4) the distribution and volume of the sea ice are shown to be improved in the latest hindcast due to modifications to the drag coefficients and to some degree to the ice thickness distribution available in CICE; (5) nonetheless, the model still overestimates the ice drift and ice thickness in the Beaufort Gyre.