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 multi-scale 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.

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.

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 Modeling and Observational Synthesis, the Sea Ice Rheology Experiment (SIREx) aims at evaluating 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 1 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 representation, 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 in a SIREx Part 2 companion paper.

We present a new brittle rheology and an accompanying numerical framework for large‐scale sea‐ice modeling. This rheology is based on a Bingham‐Maxwell constitutive model and the Maxwell‐Elasto‐Brittle (MEB) rheology, the latter of which has previously been used to model sea ice. The key strength of the MEB rheology is its ability to represent the scaling properties of simulated sea‐ice deformation in space and time. The new rheology we propose here, which we refer to as the brittle Bingham‐Maxwell rheology (BBM), represents a further evolution of the MEB rheology. It is developed to address two main shortcomings of the MEB rheology and numerical implementation we were unable to address previously: excessive thickening of the ice in model runs longer than about one winter and a relatively high computational cost. In the BBM rheology and numerical framework these shortcomings are addressed by demanding that the ice deforms under convergence in a purely elastic manner when internal stresses lie below a given compressive threshold. Numerical performance is improved by introducing an explicit scheme to solve the ice momentum equation. In this paper, we introduce the new rheology and numerical framework. Using an implementation of BBM in version two of the neXtSIM sea‐ice model (neXtSIMv2), we show that it gives reasonable long term evolution of the Arctic sea‐ice cover and very good deformation fields and statistics compared to satellite observations.

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.

Simulating sea ice drift and deformation in the Arctic Ocean is still a challenge because of the multiscale interaction of sea ice floes that compose the Arctic Sea ice cover. The Sea Ice Rheology Experiment (SIREx) is a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). In SIREx, skill metrics are designed to evaluate different recently suggested approaches for modeling linear kinematic features (LKFs) to provide guidance for modeling small‐scale deformation. These LKFs are narrow bands of localized deformation that can be observed in satellite images and also form in high resolution sea ice simulations. 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 the RADARSAT Geophysical Processor System. 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 boundary condition 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.