Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations

Abstract

Abstract. An unusual, large polynya opened and then closed by freezing and convergence north of the coast of Greenland in late winter 2018. The closing corresponded to a natural, but well-constrained, full-scale ice deformation experiment. We have observed the closing of and deformation within the polynya with satellite synthetic-aperture radar (SAR) imagery, and measured the accumulated effects of dynamic and thermodynamic ice growth 5 with an airborne electromagnetic (AEM) ice thickness survey one month after the closing began. During that time strong ice convergence decreased the area of the former polynya by a factor of 2.5. The AEM survey showed mean and modal thicknesses of the one-month old ice of 1.96 ± 1.5 m and 0.95 m, respectively.We show that this is in close agreement with the modeled thermodynamic growth and with the dynamic thickening expected from the polynya area decrease during that time. In addition,we found characteristic differences in the shapes of ice thickness distributions in different regions of the closing polynya. These closely corresponded to different deformation histories of the surveyed ice that were derived from the high-resolution SAR imagery by drift tracking along Lagrangian backward trajectories. Results show a linear proportionality between convergence and thickness change that agrees well with ice thickness redistribution theory. In addition, the e-folding of the tails of the different ice thickness distributions is proportional to the magnitude of the total deformation experienced by the ice. Lastly, we developed a simple volume-conserving model to derive dynamic ice thickness change from high-resolution SAR deformation tracking. The model has a spatial resolution of 1.4 km and reconstructs thickness profiles in reasonable agreement with the AEM observations. The computed ice thickness distribution resembles main characteristics like mode, e-folding, and width of the observed distribution. This demonstrates that high-resolution SAR deformation observations are capable of producing realistic ice thickness distributions. The MYI surrounding the polynya had a mean and modal total thickness (snow + ice) of 2.1 ± 1.4 m and 2.0 m, respectively. The similar first- and multi-year ice mean thicknesses elude to the large amount of deformation experienced by the closing polynya.

Fast Ice Thickness Distribution in the Western Ross Sea in Late Spring

“…(2010) and Von Albedyll et al. (2021). We find the absolute value of the point‐by‐point, unsmoothed, vertical, in‐phase thickness gradient, Δ h I /Δ x , and smooth it over 101 points (approximately 600 m of profile length) using a centered moving median filter.…”

“…Level ice is defined where the filtered gradient is smaller than 0.006, a value chosen by Von Albedyll et al. (2021). Allowing for small variations in flight speed, we conservatively take the minimum possible separation between along‐profile samples, Δ x , as 5 m, while Δ h I is the difference in unsmoothed, in‐phase apparent thickness between adjacent EM measurements.…”

A Volume‐Conserved Approach to Estimating Sea‐Ice Production in Antarctic Polynyas

“…For the average SIT (unit: cm) in each pixel, following von Albedyll et al. (2021), the evolution equation is integrated as: $$$\begin{array}{c}\frac{\partial \overline{\mathrm{S}\mathrm{I}\mathrm{T}}}{\partial t}+\overline{\boldsymbol{V}}\cdot \nabla \overline{\mathrm{S}\mathrm{I}\mathrm{T}}+\overline{\mathrm{S}\mathrm{I}\mathrm{T}}\nabla \cdot \overline{\boldsymbol{V}}=\overline{\mathrm{S}\mathrm{I}{\mathrm{P}}_{\mathrm{v}\mathrm{c}}},\end{array}$$$ where $$V$$ is ice drift (unit: cm d −1 ), and $$\mathrm{S}\mathrm{I}{\mathrm{P}}_{\mathrm{v}\mathrm{c}}$$ is SIP (unit: cm d −1 ). Equation indicates that the local variation in SIT in one pixel ($$\frac{\partial \overline{\mathrm{S}\mathrm{I}\mathrm{T}}}{\partial t}$$, Figures 1a and 1b) results from ice advection $$\left(\overline{V}\cdot \nabla \overline{\mathrm{S}\mathrm{I}\mathrm{T}}\right.$$), divergence ($$\overline{\mathrm{S}\mathrm{I}\mathrm{T}}\nabla \cdot \overline{V}$$), and thermodynamic growth/melting ($$\overline{\mathrm{S}\mathrm{I}{\mathrm{P}}_{\mathrm{v}\mathrm{c}}}$$, Figures…”

Deformation lines in Arctic sea ice: intersection angles distribution and mechanical properties

“…We compute ice drift fields based on a pattern-matching ice tracking algorithm introduced by Thomas et al (2008Thomas et al ( , 2011 with substantial modifications by Hollands and Dierking (2011). We retrieve divergence, shear, total deformation, and vorticity from the regularly gridded sea-ice drift output at 1.4 km resolution following the approach described in von Albedyll et al (2020) and Krumpen et al (2021b).…”

“…LKFs play a primary role in the mass and energy budget of the Arctic ocean. First, the creation of thicker ice (ridges) or open water (leads), where sea ice growth is enhanced in winter, takes place along LKFs (Stern et al, 1995;Hopkins, 1994;von Albedyll et al, 2020von Albedyll et al, , 2022. Second, shear motion and brine injection from sea-ice growth along LKFs influence the halocline (McPhee et al, 2005;Itkin et al, 2015;Nguyen et al, 2012).…”