Abstract. The Arctic sea ice cover has changed drastically over the last decades. Associated with these changes is a shift in dynamical regime seen by an increase of extreme fracturing events and an acceleration of sea ice drift. The highly non-linear dynamical response of sea ice to external forcing makes modelling these changes and the future evolution of Arctic sea ice a challenge for current models. It is, however, increasingly important that this challenge be better met, both because of the important role of sea ice in the climate system and because of the steady increase of industrial operations in the Arctic. In this paper we present a new dynamical/thermodynamical sea ice model called neXtSIM that is designed to address this challenge. neXtSIM is a continuous and fully Lagrangian model, whose momentum equation is discretised with the finite-element method. In this model, sea ice physics are driven by the combination of two core components: a model for sea ice dynamics built on a mechanical framework using an elasto-brittle rheology, and a model for sea ice thermodynamics providing damage healing for the mechanical framework. The evaluation of the model performance for the Arctic is presented for the period September 2007 to October 2008 and shows that observed multiscale statistical properties of sea ice drift and deformation are well captured as well as the seasonal cycles of ice volume, area, and extent. These results show that neXtSIM is an appropriate tool for simulating sea ice over a wide range of spatial and temporal scales.

Abstract. In this paper, we evaluate the neXtSIM sea ice model with respect to the observed scaling invariance properties of sea ice deformation in the spatial and temporal domains. Using an Arctic setup with realistic initial conditions, state-of-the-art atmospheric reanalysis forcing and geostrophic currents retrieved from satellite data, we show that the model is able to reproduce the observed properties of this scaling in both the spatial and temporal domains over a wide range of scales, as well as their multi-fractality. The variability of these properties during the winter season is also captured by the model. We also show that the simulated scaling exhibits a space–time coupling, a suggested property of brittle deformation at geophysical scales. The ability to reproduce the multi-fractality of this scaling is crucial in the context of downscaling model simulation outputs to infer sea ice variables at the sub-grid scale and also has implications for modeling the statistical properties of deformation-related quantities, such as lead fractions and heat and salt fluxes.

The Copernicus Marine Environment Monitoring Service (CMEMS) Ocean State Report (OSR) provides an annual report of the state of the global ocean and European regional seas for policy and decision-makers with the additional aim of increasing general public awareness about the status of, and changes in, the marine environment. The CMEMS OSR draws on expert analysis and provides a 3-D view (through reanalysis systems), a view from above (through remote-sensing data) and a direct view of the interior (through in situ measurements) of the global ocean and the European regional seas. The report is based on the unique CMEMS monitoring capabilities of the blue (hydrography, currents), white (sea ice) and green (e.g. Chlorophyll) marine environment. This first issue of the CMEMS OSR provides guidance on Essential Variables, large-scale changes and specific events related to the physical ocean state over the period 1993–2015. Principal findings of this first CMEMS OSR show a significant increase in global and regional sea levels, thermosteric expansion, ocean heat content, sea surface temperature and Antarctic sea ice extent and conversely a decrease in Arctic sea ice extent during the 1993–2015 period. During the year 2015 exceptionally strong large-scale changes were monitored such as, for example, a strong El Niño Southern Oscillation, a high frequency of extreme storms and sea level events in specific regions in addition to areas of high sea level and harmful algae blooms. At the same time, some areas in the Arctic Ocean experienced exceptionally low sea ice extent and temperatures below average were observed in the North Atlantic Ocean

We explore the main drivers of seasonal and long-term variations in basin-scale Arctic sea-ice drift speed. To do so, we examine the relationship between the observed time-varying area-mean ice drift speed in the central Arctic and observed thickness and concentration as well as surface wind stress. Drift speeds are calculated from the positions of drifting buoys, thickness is based on submarine observations, concentration on satellite observations, and the wind stress comes from a global reanalysis. We find that seasonal changes in drift speed are correlated primarily with changes in concentration when concentration is low and with changes in thickness otherwise. The correlation between drift speed and concentration occurs because changing concentration changes how readily the ice responds to the synoptic-scale forcing of the atmosphere. Drift speed is correlated with neither concentration nor thickness in April and May. We show this behavior to be correlated with a decrease in the localization of deformation. This indicates that the increase in drift speed is caused by newly formed fractures not refreezing, leading to an overall reduced ice-cover strength without a detectable change in ice concentration. We show that a strong long-term trend exists in months of relatively low ice concentration. Using our analysis of the seasonal cycle, we show that the trend in concentration drives a significant portion of the drift-speed trend, possibly reinforced by a trend in cyclone activity. Hence, the trend in drift speed in this period is primarily caused by increased synoptic-scale movement of the ice pack.

This paper introduces modifications to the traditional viscous‐plastic sea‐ice dynamical model, which are necessary to model land‐fast ice in the Kara Sea in a realistic manner. The most important modifications are an increase in the maximum viscosity from the standard value of ζmax=(2.5×108s)P to ζmax=(1013s)P, and to use a solver for the momentum equation capable of correctly solving for small ice velocities (the limit here is set to 10−4 m/s). Given these modifications, a necessary condition for a realistic fast‐ice simulation is that the yield curve give sufficient uniaxial compressive strength. This is consistent with the idea that land‐fast ice in the Kara Sea forms primarily via static arching. The modified model is tested and tuned using forcing data and observations from 1997 and 1998. The results show that it is possible to model land‐fast ice using this model with the modifications mentioned above. The model performs well in terms of modeled fast‐ice extent, but suffers from unrealistic break‐ups during the start and end of the fast‐ice season. The main results are that fast ice in the Kara Sea is supported by arching of the ice, the arches footers resting on a chain of islands off shore.