Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm

• Published in: The cryosphere Vol. 13; no. 2; pp. 627 - 645
• Main Authors: Hutter, Nils, Zampieri, Lorenzo, Losch, Martin
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 20.02.2019; Copernicus Publications
• Subjects: Algorithms; Analysis; Arctic cyclones; Arctic sea ice; Computer simulation; Cyclones; Data; Data processing; Data processing services; Datasets; Deformation; Detection; Distribution; Drift; Drift estimation; Evaluation; Geophysics; Heat loss; Ice; Ice cover; Ice drift; Ice environments; Kinematics; Mathematical models; Microprocessors; Optimization; Orientation; Physics; Pixels; Polar environments; Probability distribution functions; Radarsat; Remote sensing; Ridges; Sea ice; Sea ice deformation; Sea ice models; Segments; Source code; Surface drag; Tracking; Winter storms; Arctic; Arctic region
• ISSN: 1994-0424; 1994-0416; 1994-0424; 1994-0416
• DOI: 10.5194/tc-13-627-2019

Leads and pressure ridges are dominant features of the Arctic sea ice cover. Not only do they affect heat loss and surface drag, but they also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred to as linear kinematic features (LKFs). This paper introduces two methods that detect and track LKFs in sea ice deformation data and establish an LKF data set for the entire observing period of the RADARSAT Geophysical Processor System (RGPS). Both algorithms are available as open-source code and applicable to any gridded sea ice drift and deformation data. The LKF detection algorithm classifies pixels with higher deformation rates compared to the immediate environment as LKF pixels, divides the binary LKF map into small segments, and reconnects multiple segments into individual LKFs based on their distance and orientation relative to each other. The tracking algorithm uses sea ice drift information to estimate a first guess of LKF distribution and identifies tracked features by the degree of overlap between detected features and the first guess. An optimization of the parameters of both algorithms, as well as an extensive evaluation of both algorithms against handpicked features in a reference data set, is presented. A LKF data set is derived from RGPS deformation data for the years from 1996 to 2008 that enables a comprehensive description of LKFs. LKF densities and LKF intersection angles derived from this data set agree well with previous estimates. Further, a stretched exponential distribution of LKF length, an exponential tail in the distribution of LKF lifetimes, and a strong link to atmospheric drivers, here Arctic cyclones, are derived from the data set. Both algorithms are applied to output of a numerical sea ice model to compare the LKF intersection angles in a high-resolution Arctic sea ice simulation with the LKF data set.