Variability of sea ice deformation rates in the Arctic and their relationship with basin-scale wind forcing

Abstract

The temporal variability of the moments of probability distribution functions (pdfs) of total sea ice deformation rates in the Arctic is analyzed in the context of the basin-scale wind forcing acting on the ice. The pdfs are estimated for 594 satellite-derived sea ice deformation maps from 11 winter seasons between 1996/1997 and 2007/2008, provided by the RADARSAT Geophysical Processor System. The temporal scale analyzed equals 3 days. The moments of the pdfs, calculated for a range of spatial scales (12.5–900 km), have two dominating components of variability: a seasonal cycle, with deformation rates decreasing throughout winter towards a minimum in March; and a short-term, synoptic variability, strongly correlated with the area-averaged magnitude of the wind stress over the Arctic, estimated based on the NCEP-DOE Reanalysis-2 data (correlation coefficient of 0.71 for the mean deformation rate). Due to scaling properties of the moments, logarithms of higher moments are strongly correlated with the wind stress as well. Exceptions are observed only at small spatial scales, as a result of extreme deformation events, not directly associated with large-scale wind forcing. By repeating the analysis within regions of different sizes and locations, we show that the wind–ice deformation correlation is largest at the basin scale and decreases with decreasing size of the area of study. Finally, we suggest that a positive trend in seasonally averaged correlation between sea ice deformation rates and the wind forcing, present in the analyzed data, may be related to an observed decrease in the multi-year ice area in the Arctic, indicating possibly even stronger correlations in the future

Scaling Properties of Arctic Sea Ice Deformation in a High‐Resolution Viscous‐Plastic Sea Ice Model and in Satellite Observations

“…Except for Herman and Glowacki (2012), who used gridded Eulerian data in a spatial scaling analysis, scaling analyses of sea ice deformation are based on Lagrangian trajectories, either derived from satellite images (Marsan et al, 2004;Stern & Lindsay, 2009), recorded by buoys (Hutchings et al, 2011(Hutchings et al, , 2012Rampal et al, 2008), or modeled in a Lagrangian framework (Rampal et al, 2016). Both Lagrangian and Eulerian approach should, in theory, lead to the same spatial scaling results (if small timescales are considered where the advection of ice between two time steps is negligible), but the temporal scaling properties depend on the deformation history of individual ice flows.…”

“…Scaling analyses are a useful tool for evaluating small-scale sea ice deformation produced by sea ice models (Bouillon & Rampal, 2015a;Girard et al, 2009;Rampal et al, 2016) because they quantify the strong localization of deformation in space (heterogeneity) and in time (intermittency), which can then be compared to the observed localization in satellite (Herman & Glowacki, 2012;Marsan et al, 2004;Stern & Lindsay, 2009) and buoy data (Hutchings et al, 2011(Hutchings et al, , 2012Oikkonen et al, 2017;Rampal et al, 2008). The scaling characteristics and multifractality of deformation rates in a VP model with 12 km horizontal grid spacing have been found to significantly disagree with the scaling laws that were estimated from satellite data and buoy trajectories (Girard et al, 2009), even though VP models can realistically represent the large-scale sea ice drift velocity fields (Kwok et al, 2008;.…”

Linking scales in sea ice mechanics

“…1000 km) [44][45][46]. The structure function β(q) is quadratic, β(q) = aq 2 + bq, and convex (a > 0, b > 0).…”

Sea ice leads in the Arctic Ocean: Model assessment, interannual variability and trends

“…This may partly explain why there has been a quest for new sea ice model rheologies in recent years [see, e.g., Girard et al , ; Tsamados et al , ; Bouillon and Rampal , ]. The lack of existing modeling capacity has meant that our understanding of linear kinematics of sea ice is mainly based on buoys and satellite observations of ice drift [e.g., Kwok et al , ; Lindsay , ; Weiss and Marsan , ; Marsan et al , ; Rampal et al , ; Stern and Lindsay , ; Hutchings et al , ; Herman and Glowacki , ] and satellite as well as airborne measurements for sea ice leads [ Fily and Rothrock , ; Stone and Key , ; Lindsay and Rothrock , ; Miles and Roger , ; Tschudi et al , ; Onana et al , ; Broehan and Kaleschke , ; Willmes and Heinemann , , ]. Here we exploit the fact that lead area fraction data sets for the last decade have become available [ Roehrs and Kaleschke , ; Wernecke and Kaleschke , ; Willmes and Heinemann , , ; Ivanova et al , ], which can be used to evaluate sea ice models.…”