Explore the vast range of titles available.

To describe the evolution of sea ice at scales of ∼100 km over days to months, the AIDJEX group proposed a framework based on an isotropic, plastic continuum approach (Coon et al. 1974), whose validity relies upon statistical averages taken over a large number of floes (Gray and Morland 1994; Feltham 2008). Many studies demonstrate the ability of the continuum (E)VP models to reasonably simulate key properties of the sea ice: the large-scale distribution of sea ice thickness, concentration and circulation (e.g., Kreyscher et al. 2000); relationships between sea ice concentration, thickness and velocity (Docquier et al. 2017); long-term trends in winter sea ice velocity (Tandon et al. 2018). Early evaluations with synthetic aperture radar estimates of drift and deformation (Kwok and Cunningham 2002) challenged continuum sea ice models’ representation of spatiotemporal deformation, particularly in terms of localization and intermittency (Girard et al. 2009; Kwok et al. 2008). Current approaches to model initialization and data assimilation also need to be rethought. [...]a considerable amount of time and development is needed before DEMs become usable by a large community.

Deformation within and between sea ice floes creates pressure ridges, which compose a considerable volume fraction of the Arctic sea ice cover and complicate sea ice volume measurements. An important parameter for quantifying the ice volume contained in these deformation features is the keel shape. We use ice draft data collected by submarine sonar between 1960 and 2005 to classify keel geometry based on the cross‐sectional area, keel depth, and keel width to show that geometry varies with draft relative to the surrounding undeformed ice. Small, bummock‐sized features are more likely to be cusp‐shaped, while larger ridge keels are more likely trapezoids. This is consistent across decades, regions, and between first‐ and multi‐year ice. Though comparatively rare, the deepest keels (≥ ${\\ge} $8 m) are more likely to be triangular or cusped with increasing maximum keel depth. These findings are valuable for quantifying the impact of sea ice ridging on volume retrievals.

Pressure ridges, formed by sea ice deformation, affect momentum transfer in the Arctic Ocean and support a larger biomass than the surrounding-level ice. Although trends in Arctic sea ice thickness and concentration are well documented, changes in ridge morphology remain unclear. This study provides airborne-based evidence of a shift towards a smoother ice surface, with fewer pressure ridges and reduced surface drag, attributed to the loss of old ice. Furthermore, an increase in seasonal ice cover enhances overall deformation in the Arctic and acts as a negative feedback mechanism on pan-Arctic ridge morphology: the greater the proportion of seasonal ice, the higher the pan-Arctic mean ridge rate, dampening an overall decline in ridges with age. While thinner and less frequent ridges benefit industries such as shipping, these changes are likely to have profound impacts on the energy and mass balance and the ecosystem of the Arctic Ocean. Pressure ridges, a characteristic feature of Arctic sea ice, play an important role in the ecosystem but pose challenges to shipping. Here the authors use aircraft measurements to document a decline in both the frequency and height of these pressure ridges in recent decades.

Sea ice outflow through the Transpolar Drift (TPD) is essential in Arctic sea ice loss. Twenty-four buoys deployed in the Arctic Ocean during the summer of 2021 were used to analyse sea ice kinematics and deformation across the pack ice zone (PIZ) and marginal ice zone (MIZ), mainly focusing on the TPD region. Three stages were identified as sea ice transitions from melt to growth and to melt again. In Stage 1, sea ice exhibited active internal motion, with a high deformation rate (5.7 d −1 ) determined using the buoy trajectory-stretching exponents. In Stage 2, ice consolidation reduced wind response and deformation rates (2.3 d −1 ), but still with intermittently enhanced ice deformation over 6.0 d −1 caused by severe storms. In Stage 3, the combined impacts of a super cyclone, MIZ ice and oceanic conditions, and tidal dynamics north of Svalbard remarkably altered the ice kinematic regime. Variations in sea ice kinematics along the TPD region support the MIZ definition by the threshold of certain sea ice concentration variability. This study demonstrates how seasonal transitions, spatial heterogeneities of sea ice conditions, atmospheric or oceanic forcings, and extreme cyclones collectively shape sea ice dynamics in the TPD region, amplifying its seasonal changes relative to those in the central Arctic Ocean.

The speed‐up of glaciers following ice shelf collapse can accelerate ice mass loss dramatically. Investigating the deformation of landfast sea ice enables studying its resistive (buttressing) stresses and mechanisms driving ice collapse. Here, we apply offset tracking to Sentinel‐1A/B synthetic aperture radar data to obtain a 2014–2022 time‐series of horizontal velocity and strain rate fields of landfast ice filling the embayment formerly covered by the Larsen B Ice Shelf, Antarctic Peninsula until 2002. The landfast ice disintegrated in 2022, and we find that it was precipitated by a few large opening rifts. Grounded glaciers did not accelerate instantaneously after the collapse, which implies little buttressing effect from landfast ice, a conclusion also supported by the near‐zero correlation between glacier velocity and landfast ice area. Our observations suggest that buttressing stresses are unlikely to be recovered by landfast sea ice over sub‐decadal timescales following the collapse of an ice shelf. Plain Language Summary The Antarctic Ice Sheet is a potentially major contributor to sea‐level rise due to glaciers' dynamic response to changing oceanic and atmospheric conditions. Its floating extensions, ice shelves, play a critical role in stabilizing the ice sheet by resisting the flow of glaciers that feed into them. However, ice shelves can collapse rapidly. In 2002, a Rhode Island‐sized section of the Larsen B Ice Shelf disintegrated, causing adjacent glaciers to speed up. In 2011, landfast sea ice replaced the ice shelf in the Larsen B embayment, but it broke up in 2022. We use remote sensing data to investigate why the landfast ice collapsed and whether it resisted glacier flow as the ice shelf did. We show that opening rifts may be responsible for ice disintegration. We find no detectable buttressing effect from the landfast ice because glaciers did not speed up after removing landfast ice, and seasonal change of landfast ice extent did not affect the grounded glacier velocities. It may be because landfast ice is thinner and easier to deform than the ice shelf. Our observations suggest a possible precursor to ice collapse and highlight the limited role that landfast ice plays in slowing down ice mass loss. Key Points We produce time‐dependent velocity and strain rate fields over Larsen B landfast sea ice from 2014 to 2022 Opening rifts within the landfast sea ice may contribute to its disintegration in 2022 Landfast sea ice provides no apparent buttressing to the upstream grounded glaciers

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.

Many sea‐ice models formulate sea‐ice rheology by a viscous‐plastic approach with an elliptical yield curve and a normal flow rule. However, it remains uncertain whether this formulation is suitable for finer‐resolution climate models in the warming Arctic. We analyze sea‐ice deformation using half‐hourly Global Positioning System (GPS) data initially spaced approximately 100 m to 10 km apart from March–July 2020 and 2022 in the Beaufort Gyre. Our findings show the prevalence of shear‐dominated deformation and the greater capacity for persistence in the convergence‐dominated deformation compared to divergence‐dominated deformation. The former supports the sine‐lens yield curve, while the latter supports the teardrop yield curve. Our results examine the validity of the elliptical yield curve during sea‐ice breakup and advocate the need for a combination of sine‐lens and teardrop yield curves from data‐supported arguments. Plain Language Summary Using GPS arrays, we tracked sea ice deformation in the Pacific Arctic during the melting season. We found that shear was the most common type of deformation, with occasional instances of the ice either compressing (converging) or spreading out (diverging). Based on these findings, we discuss how our observations align with the three common sea‐ice rheology models and suggest that each model needs an improvement when ice diverges. Our study offers valuable insights for developing a more accurate, data‐driven model for sea‐ice rheology. Key Points Shear is the most common type of deformation, with intermittent occurrences of convergence‐ and divergence‐dominated deformation The strong convergence deformation is more likely to continue converging than the strong divergence deformation is to keep diverging A high‐frequency oscillation in the ratio of the divergence to shear rate occurs before the crack formations seen from satellite imagery

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.

Results from a numerical model simulation show significant changes in the dynamic properties of Arctic sea ice during 2007–2011 compared to the 1979–2006 mean. These changes are linked to a 33% reduction in sea ice volume, with decreasing ice concentration, mostly in the marginal seas, and decreasing ice thickness over the entire Arctic, particularly in the western Arctic. The decline in ice volume results in a 37% decrease in ice mechanical strength and 31% in internal ice interaction force, which in turn leads to an increase in ice speed (13%) and deformation rates (17%). The increasing ice speed has the tendency to drive more ice out of the Arctic. However, ice volume export is reduced because the rate of decrease in ice thickness is greater than the rate of increase in ice speed, thus retarding the decline of Arctic sea ice volume. Ice deformation increases the most in fall and least in summer. Thus the effect of changes in ice deformation on the ice cover is likely strong in fall and weak in summer. The increase in ice deformation boosts ridged ice production in parts of the central Arctic near the Canadian Archipelago and Greenland in winter and early spring, but the average ridged ice production is reduced because less ice is available for ridging in most of the marginal seas in fall. The overall decrease in ridged ice production contributes to the demise of thicker, older ice. As the ice cover becomes thinner and weaker, ice motion approaches a state of free drift in summer and beyond and is therefore more susceptible to changes in wind forcing. This is likely to make seasonal or shorter‐term forecasts of sea ice edge locations more challenging. Key Points Arctic sea ice volume during 2007‐2011 is reduced by 33% Sea ice speed and deformation increase by 13% and 17% Ice volume export is reduced

Optical methods deployed for studying motion and deformation of objects often struggle to distinguish small displacements hidden behind observational noise. In geophysical applications, this has limited analysis to lower spatial and temporal resolutions, while reliable extraction of high‐resolution data is required for understanding material deformation and failure. In this work, we propose a novel method for determining deformation for noisy observational data using deep learning‐based optical flow. To enable higher estimate accuracy, we introduce a novel initialization technique considering contextual information. This allows an unprecedentedly high‐resolution description of motion in radar imagery. We use the proposed technique on verification cases to compare with the currently used methodologies and on ship radar observations on sea ice deformation. The outcome of our work is an open‐source end‐to‐end tool for determining full‐field Lagrangian deformation fields for data sets with small pixel displacements and high observational noise. Plain Language Summary Estimating motion and deformation from radar imagery is a common task in geophysical sciences. High‐resolution description of material dynamics are required for accurate analytical solutions and models. Current methods for determining deformation from radar data have relied on tradition optical methods, resulting in lower resolutions and decreased accuracy. We develop a deep learning based tool to provide a highly accurate full‐field description of deformation in radar data. We further introduce a method to increase the tool's accuracy with small displacements and intensified noise. We verify the accuracy of the tool against the current methods used for sea ice radar imagery as well as against state‐of‐the‐art deep learning methods. To highlight the abilities of the novel tool, we apply it on ship radar imagery for description of sea ice deformation. Key Points A deep learning‐based tool is developed for determining motion and deformation from radar imagery in geophysical applications A novel temporal multiresolution tree is introduced to enhance accuracy in optical flow applications The method outperforms previously used approaches and is tested with high‐resolution ship radar observations on sea ice