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The contribution of sea‐ice biology and impact of Arctic warming on overall primary production in a Pan‐Arctic ocean model are investigated in a 57 year (1950–2006) simulation at coarse resolution using a simple ecosystem model. The ice biology model formally represents the growth and aggregation of micro algae into an ice‐water interface, nearly undisturbed by surface mixed layer dynamics. The importance of this so‐called ‘ice‐algae’ stems from their significant contribution to the total primary production (up to 50% depending on the locations, according to observations described in Gosselin et al. (1997). Simple 1D tests reveal that, depending on their initial biomass and light availability, ice algae can affect the temporal variation of surface nutrients, while they marginally impact the total primary production, or the long term position of the nutricline. The sea‐ice primary production is found in the model to be as high as 40% of the total primary production depending on the location and 7.5% for the whole Arctic. The modeled primary production of the ocean is negatively correlated to the September ice cover whereas the production in the ice is more weakly positively correlated. Because of the negative correlation between sea ice cover and pelagic primary production, the short term response to the continuing ice decline will be an increased total production as seen in the model, while the ice algae production would decline. Key Points Sea‐ice biology contributes significantly to the Arctic primary production Marginal impact of ice algae on total primary production or nutricline Overall production will increase with declining ice but longer term is uncertain

The second version of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2) was implemented operationally at Environment and Climate Change Canada (ECCC) in July 2019. Like its predecessors, CanSIPSv2 applies a multimodel ensemble approach with two coupled atmosphere–ocean models, CanCM4i and GEM-NEMO. While CanCM4i is a climate model, which is upgraded from CanCM4 of the previous CanSIPSv1 with improved sea ice initialization, GEM-NEMO is a newly developed numerical weather prediction (NWP)-based global atmosphere–ocean coupled model. In this paper, CanSIPSv2 is introduced, and its performance is assessed based on the reforecast of 30 years from 1981 to 2010, with 10 ensemble members of 12-month integrations for each model. Ensemble seasonal forecast skill of 2-m air temperature, 500-hPa geopotential height, precipitation rate, sea surface temperature, and sea ice concentration is assessed. Verification is also performed for the Niño-3.4, the Pacific–North American pattern (PNA), the North Atlantic Oscillation (NAO), and the Madden–Julian oscillation (MJO) indices. It is found that CanSIPSv2 outperforms the previous CanSIPSv1 system in many aspects. Atmospheric teleconnections associated with the El Niño–Southern Oscillation (ENSO) are reasonably well captured by the two CanSIPSv2 models, and a large part of the seasonal forecast skill in boreal winter can be attributed to the ENSO impact. The two models are also able to simulate the Northern Hemisphere teleconnection associated with the tropical MJO, which likely provides another source of skill on the subseasonal to seasonal time scale.

Ocean–sea ice reanalyses are crucial for assessing the variability and recent trends in the Arctic sea ice cover. This is especially true for sea ice volume, as long-term and large scale sea ice thickness observations are inexistent. Results from the Ocean ReAnalyses Intercomparison Project (ORA-IP) are presented, with a focus on Arctic sea ice fields reconstructed by state-of-the-art global ocean reanalyses. Differences between the various reanalyses are explored in terms of the effects of data assimilation, model physics and atmospheric forcing on properties of the sea ice cover, including concentration, thickness, velocity and snow. Amongst the 14 reanalyses studied here, 9 assimilate sea ice concentration, and none assimilate sea ice thickness data. The comparison reveals an overall agreement in the reconstructed concentration fields, mainly because of the constraints in surface temperature imposed by direct assimilation of ocean observations, prescribed or assimilated atmospheric forcing and assimilation of sea ice concentration. However, some spread still exists amongst the reanalyses, due to a variety of factors. In particular, a large spread in sea ice thickness is found within the ensemble of reanalyses, partially caused by the biases inherited from their sea ice model components. Biases are also affected by the assimilation of sea ice concentration and the treatment of sea ice thickness in the data assimilation process. An important outcome of this study is that the spatial distribution of ice volume varies widely between products, with no reanalysis standing out as clearly superior as compared to altimetry estimates. The ice thickness from systems without assimilation of sea ice concentration is not worse than that from systems constrained with sea ice observations. An evaluation of the sea ice velocity fields reveals that ice drifts too fast in most systems. As an ensemble, the ORA-IP reanalyses capture trends in Arctic sea ice area and extent relatively well. However, the ensemble can not be used to get a robust estimate of recent trends in the Arctic sea ice volume. Biases in the reanalyses certainly impact the simulated air–sea fluxes in the polar regions, and questions the suitability of current sea ice reanalyses to initialize seasonal forecasts.

This study evaluates monthly sea levels during 1993–2023 from four ocean models using tide gauge and altimeter data in the Northwest Atlantic with its shelf seas, including the Gulf of Maine, Scotian Shelf, Gulf of St. Lawrence, and the Newfoundland and Labrador Shelf. The evaluation is carried out for four different aspects: the multi-decadal mean and linear trend, seasonal cycle, and the de-trended and de-seasonalized anomalies. Overall, the high-resolution model with advanced data assimilation (GLORYS12v1) possesses skills in all four aspects. The other three models show different discrepancies in reproducing the observed sea level variations relative to GLORYS12v1. They possess low or no skills for the timing (despite reasonable standard deviations) of sea level anomalies at time scales longer than 20 months along the coast, and at all time scales on the shelf, over the shelf break, and in the deep ocean. Without data assimilation, the models with high and medium resolutions show biases in the time-mean sea levels in the Labrador Sea that can be attributed to the simulated stronger and weaker deep convection (deeper and shallower mixed layer depth), respectively. The medium-resolution model, using a different data assimilation approach than GLORYS12v1, shows biases in the seasonal amplitude and multi-decadal trends.

Icepack (v1.1.0) – the column thermodynamics model of the Community Ice CodE (CICE) version 6 – is used to assess how changing the thermodynamics from the Bitz and Lipscomb (1999) physics (hereafter BL99) to the mushy-layer physics impacts the model performance in reproducing in situ landfast ice observations from two ice mass balance (IMB) buoys co-deployed in the landfast ice close to Nain (Labrador) in February 2017. To this end, a new automated surface retrieval algorithm is used to determine the in situ ice thickness, snow depth, basal ice congelation and snow-ice formation from the measured vertical temperature profiles. Icepack simulations are run to reproduce these observations using each thermodynamics scheme, with a particular interest in how the different physics influence the representation of snow-ice formation and ice congelation. Results show that the BL99 parameterization represents well the ice congelation but underrepresents the snow-ice contribution to the ice mass balance. In particular, defining snow-ice formation based on the hydrostatic balance alone does not reproduce the negative freeboards observed for several days in the IMB observations, resulting in an earlier snow-flooding onset, a positive ice thickness bias and reduced snow depth variations. We find that the mushy-layer thermodynamics with default parameters significantly degrades the model performance, overestimating both the congelation growth and snow-ice formation. The simulated thermodynamics response to flooding, however, better represents the observations, and the best results are obtained when allowing for negative freeboards in the mushy-layer physics. We find that the mushy-layer thermodynamics produces a larger variability in congelation rates at the ice bottom interface, alternating between periods of exceedingly fast growth and periods of unrealistic basal melt. This pattern is related to persistent brine dilution in the lowest ice layer by the congelation and brine drainage parameterizations. We also show that the mushy-layer congelation parameterization produces significant frazil formation, which is not expected in a landfast ice context. This behavior is attributed to the congelation parameterization not fully accounting for the conductive heat flux imbalance at the ice–ocean boundary. We propose a modification of the mushy-layer congelation scheme that largely reduces the frazil formation and allows for better tuning of the congelation rates to match the observations. Our results demonstrate that the mushy-layer physics and its parameters can be tuned to closely match the in situ observations, although more observations are needed to better constrain them.

Landfast ice is a common coastal feature in the Arctic Ocean and around the Antarctic continent. One contributing and stabilizing mechanism is the grounding of sea ice ridges in shallow water. Recently, a grounding scheme representing this effect on sea ice dynamics was developed in order to improve the simulation of landfast ice by continuum-based sea ice models. This parameterization assumes that the ridged keel thickness is proportional to the mean thickness. Results demonstrated that this simple parameterization notably improves the simulation of landfast ice in many regions such as in the East Siberian Sea, the Laptev Sea and along the Alaskan coast. Nevertheless, a weakness of this approach is that it is based solely on the mean properties of sea ice. Here, we extend the parameterization by taking into account subgrid-scale ice thickness distribution and bathymetry distribution, which are generally non-normal, and by computing the maximum seabed stress as a joint probability interaction between the sea ice and the seabed. The probabilistic approach shows a reasonably good agreement with observations and with the previously proposed grounding scheme while potentially offering more physical insights into the formation of landfast ice.

Calculating and plotting the normalized states of stress for viscous–plastic sea ice models is a common diagnostic for evaluating the numerical convergence and the physical consistency of a numerical solution. Researchers, however, usually do not explain how they calculate the normalized stresses. Here, we argue that care must be taken when calculating and plotting the normalized states of stress. A physically consistent and numerically converged solution should exhibit normalized stresses that are inside (viscous) or on (plastic) the normalized yield curve. To do so, two possible mistakes need to be avoided. First, when using an implicit solver, normalized stresses should be computed from viscous coefficients and replacement pressure calculated using the previous numerical iterate and the strain rates at the numerator calculated from the latest iterate. Calculating the stresses only from the latest iterate falsely indicates that the solution has numerically converged. Second, for both implicit and explicit (i.e., the EVP) solvers, the stresses should be normalized by the ice strength and not by the replacement pressure. Using the latter, normalized states of stress only lie on the yield curve (i.e., falsely indicating there are no viscous states of stress).

Results from a high-resolution ice–ocean model are analyzed to understand the physical processes responsible for the interannual variability of ocean convection over the Irminger Sea. The modeled convection in the open Irminger Sea for the winters of 2007/08 and 2008/09 is in good agreement with observations. Deep convection is caused by strong atmospheric forcing that increases the ocean heat loss through latent and sensible heat fluxes. Greenland tip jets are found to be the only strong wind events that directly affect the deep convection area and explain up to 53% of the total turbulent heat loss during active convection years. Deep convection is modeled where there is favorable preconditioning of the water column due to isopycnal doming inside the semienclosed Irminger Gyre. The region of deep convection is also characterized by weak eddy kinetic energy. Finally, an estimation of the surface-forced water mass transformation confirms the Irminger Sea as a region of intermittent production of Labrador Sea Water, with annual averages between 0.9 and 1.9 Sverdrups (Sv; 1 Sv ≡ 10 6 m 3 s −1 ) of water denser than 27.7 kg m −3 for years of active convection.

This article presents the C-grid implementation of the CICE sea ice model, including the C-grid discretization of the momentum equation, the boundary conditions (BCs), and the modifications to the code required to use the incremental remapping transport scheme. To validate the new C-grid implementation, many numerical experiments were conducted and compared to the B-grid solutions. In idealized experiments, the standard advection method (incremental remapping with C-grid velocities interpolated to the cell corners) leads to a checkerboard pattern. A modal analysis demonstrates that this computational noise originates from the spatial averaging of C-grid velocities at corners. The checkerboard pattern can be eliminated by adjusting the departure regions to match the divergence obtained from the solution of the momentum equation. We refer to this novel approach as the edge flux adjustment (EFA) method. The C-grid discretization with edge flux adjustment allows for transport in channels that are one grid cell wide – a capability that is not possible with the B-grid discretization nor with the C-grid and standard remapping advection. Simulation results match the predicted values of a novel analytical solution for one-grid-cell-wide channels.

Deep convection in the Labrador Sea is an important component of the global ocean ventilation. The associated loss of heat to the atmosphere from the interior of the sea is thought to be mostly supplied by mesoscale eddies, generated either remotely or as a result of convection itself—processes that are not resolved by low-resolution ocean climate models. The authors first employ a high-resolution (°) ocean model forced with high-resolution (33 km, 3 h) atmospheric fields to further elaborate on the role of mesoscale eddies in maintaining the balance of heat and buoyancy in the Labrador Sea. In general agreement with previous studies, it is found that eddies remove heat along the coast and supply it to the interior. Some of the eddies that are generated because of the barotropic instability off the west coast of Greenland are recaptured by the boundary current. In the region of deep convection, the convergence of heat and buoyancy by eddies significantly increases with the deepening of the winter mixed layer. In addition, the vertical eddy flux plays an important part in the heat budget of the upper Labrador Sea, accounting for up to half of the heat loss to the atmosphere north of 60°N. A low-resolution (1°) model with parameterized eddies is then applied to show that it does capture, qualitatively, the general structure of eddy buoyancy advection along the Labrador Current. However, the 1° model is deficient in this regard in the most eddy active region off the west coast of Greenland, although some improvements can be made by forcing it with the high-resolution atmospheric fields.