The processes driving the seasonal variability of the mixed layer salinity in the Arctic Ocean are investigated using a simulation performed with regional ocean – sea ice model at high resolution. While the seasonal variations of the mixed layer depth remain small, in particular under the perennial sea ice (O(30m)), the mean salinity of the mixed layer varies largely, with a seasonal cycle as high as 3 pss. On the shelves, where the sea ice is seasonal, the mixed layer is much fresher but exhibits a seasonal cycle with a similar amplitude. Overall, the seasonal variability of the mixed layer salinity results largely from a 1D vertical balance between the freshwater flux at the surface arising from the sea ice melt and freezing processes, and vertical mixing and entrainment occurring at the base of the mixed layer. The largest variations are found in summer, when the mixed layer is the thinnest. Over the shelves, this simple 1D balance is complexified due to the role of advection and river runoff that can locally affect the mixed layer depth and salinity. Interestingly, the largest variations are found less than 100km on each side of the sea ice edge, where all the processes affecting the mixed layer are amplified. This suggests the need to better observed and understand the ocean-sea ice-atmosphere exchanges in these regions.

The Arctic sea ice, in particular the ice pack, acts as an insulator between the atmosphere and the ocean. Leads, commonly found in the Arctic, facilitate ocean-atmosphere flux exchanges. Local observations have captured heat fluxes through some leads one order of magnitude larger than those outside of the leads, leading to the speculation that air-sea exchanges through leads contribute significantly to the Arctic Ocean surface buoyancy forcing. Here, we quantify the magnitude and impact on the ocean surface of the leads using SEDNA, a subkilometer pan-Arctic hindcast. Leads account for 22% of the sea ice cover surface, and within them, there is approximately 25% of the total surface water mass transformation. In other words, the water mass transformation in leads is similar to those underneath the surrounding ice-covered oceans. Thus, the present estimate indicates that leads have a small contribution to Arctic Ocean dynamics, contrary to previous hypotheses.

The seasonality of Arctic sea ice cover significantly influences heat, salt, buoyancy fluxes, ocean-ice stresses, and the potential and kinetic energy stored in the ocean mixed layer. This study examines the seasonal variability of oceanic scales and cross-scale flux of kinetic energy in the seasonally ice-covered Arctic, using a high-resolution, idealized coupled ocean-sea ice model. Our simulations demonstrate pronounced seasonality in the scales of oceanic motion within the mixed layer, governed by distinct mechanisms during summer and winter. In summer, an inverse energy cascade sustains mesoscale dynamics and enhances kinetic energy. In winter, ice-induced dissipation suppresses kinetic energy and mesoscale, allowing only the persistence of submesoscale processes. These results underscore the critical role of sea ice in modulating the seasonal dynamics of oceanic motion and their dominant scales, a behavior markedly different from that in the open ocean. Thus, understanding these coupled processes is essential for improving predictions of the ocean's energy evolution as the Arctic transitions toward a summer ice-free regime.

Mesoscale eddies, generated by lateral gradients in salinity and temperature in the Arctic marginal ice zone, are known to modulate the melting of sea ice. Yet, it remains unclear if eddies modify sea ice growth during the freezing season. Here, we use idealized spin-down simulations of a front to explore the sea ice growth above an eddying ocean. In the presence of eddies, mixing of the sea surface temperature and salinity induces spatial variability in the heat and salt fluxes at the ice-ocean interface, imprinting spatial variability on the sea ice thickness. Sea ice thickness shows an order of magnitude more spatial variability in our simulations with strong eddies compared to those without. Increased spatial heterogeneity in the sea ice could make it more brittle and affect its evolution. This effect may become more pronounced as the Arctic transitions to a summer open-ocean regime and the eddy field intensifies.

As the sea-ice modeling community is shifting to advanced numerical frameworks, developing new sea-ice rheologies, and increasing model spatial resolution, ubiquitous deformation features in the Arctic sea ice are now being resolved by sea-ice models. Initiated at the Forum for Arctic Modelling and Observational Synthesis (FAMOS), the Sea Ice Rheology Experiment (SIREx) aims at evaluating current state-of-the-art sea-ice models using existing and new metrics to understand how the simulated deformation fields are affected by different representations of sea-ice physics (rheology) and by model configuration. Part I of the SIREx analysis is concerned with evaluation of the statistical distribution and scaling properties of sea-ice deformation fields from 35 different simulations against those from the RADARSAT Geophysical Processor System (RGPS). For the first time, the Viscous-Plastic (and the Elastic-Viscous-Plastic variant), Elastic-Anisotropic-Plastic, and Maxwell-Elasto-Brittle rheologies are compared in a single study. We find that both plastic and brittle sea-ice rheologies have the potential to reproduce the observed RGPS deformation statistics, including multi-fractality. Model configuration (e.g. numerical convergence, atmospheric forcing, spatial resolution) and physical parameterizations (e.g. ice strength parameters and ice thickness distribution) both have effects as important as the choice of sea-ice rheology on the deformation statistics. It is therefore not straightforward to attribute model performance to a specific rheological framework using current deformation metrics. In light of these results, we further evaluate the statistical properties of simulated Linear Kinematic Features (LKFs) in a SIREx Part II companion paper.

Simulating sea-ice drift and deformation in the Arctic Ocean is still a challenge because of the multi-scale interaction of sea-ice floes that compose the Arctic sea ice cover. The Sea Ice Rheology Experiment (SIREx) is a model intercomparison project formed within the Forum of Arctic Modeling and Observational Synthesis (FAMOS) to collect and design skill metrics to evaluate different recently suggested approaches for modeling linear kinematic features (LKFs) and provide guidance for modeling small-scale deformation. In this contribution, spatial and temporal properties of LKFs are assessed in 36 simulations of state-of-the-art sea ice models and compared to deformation features derived from RADARSAT Geophysical Processor System (RGPS). All simulations produce LKFs, but only very few models realistically simulate at least some statistics of LKF properties such as densities, lengths, or growth rates. All SIREx models overestimate the angle of fracture between conjugate pairs of LKFs and LKF lifetimes pointing to inaccurate model physics. The temporal and spatial resolution of a simulation and the spatial resolution of atmospheric forcing affect simulated LKFs as much as the model’s sea ice rheology and numerics. Only in very high resolution simulations (≤2\,km) the concentration and thickness anomalies along LKFs are large enough to affect air-ice-ocean interaction processes.

The marginal sea ice zone (MIZ) is a complex interface between the open ocean and the pack ice, where ocean-ice-atmosphere interactions are extremely complex. With a width of about 100 km, similar to the grid size of many climate models, the MIZ is not resolved in CMIP5 climate scenarios. In recent years, coupled climate models have been developed with ocean components at higher resolution, such as the high resolution version of the Met Office Global Coupled Model GC3 based on the GO6 configuration of the ORCA12 1/12° ocean model. We compare the MIZ representation in a coupled simulation with a simulation using the same ocean component forced by observed atmospheric data. Biases in the MIZ position and width are of the same order of magnitude in the coupled and forced model. The sea ice edge is strongly influenced by the ocean circulation and is often found at the wrong location, even when an observed atmospheric state is used to force the model. Despite a possible mismatch between atmosphere and ice/ocean in the forced model, due to the absence of feedback between sea ice and atmospheric temperature, surface heat fluxes in the MIZ are similar in amplitude in the coupled and forced simulations.  Our analysis focuses on the Greenland Sea because it is region of deep water formation, a major control of the Atlantic Meridional Overturning Circulation and thus very important for climate scenarios. The strong interannual variability of sea ice in the Greenland Sea is examplified by the Odden tongue, a protrusion of sea ice extending northeastward away from the Greenland continental slope. The coupled model exhibits such interannual variability, with a sea ice concentration larger than observed on average. The relationship between atmosphere, ice concentration and mixed layer depth is analyzed to assess the performance of both coupled and forced 1/12° models to represent deep water formation in the Nordic seas.