Seasonal evolution of the sea-ice floe size distribution in the Beaufort and Chukchi seas

Abstract

The size distribution of ice floes in the polar seas affects the dynamics and thermodynamics of the ice cover, and ice-ocean models are beginning to include the floe size distribution (FSD) in their simulations. The FSD has previously been reported to follow a power law of the form x−α, where x is the floe size and –α characterizes how steeply the number of floes decreases as x increases. Different studies have found different values of α and different ranges of x over which the power law applies. We found that a power law describes the FSD in the Beaufort and Chukchi seas reasonably well over floe sizes from 2 to 30 km, based on 187 visible-band satellite images (resolution 250 m) acquired during spring through fall of 2013 and 2014. The mean power-law exponent goes through a seasonal cycle in which α increases in spring, peaks in July or August, and decreases in fall. June is the transition month from spring FSD to summer FSD. This cycle is consistent with the processes of floe break-up in spring followed by preferential melting of smaller floes in summer and the return of larger floes after fall freeze-up. We also analyzed 12 high-resolution satellite images acquired near the low-resolution images in space and time. We found that the FSDs from the high-resolution images follow power laws over floe sizes from 10 m to 3 km. While the power-law exponents of the corresponding high- and low-resolution images do not always match in a strict statistical sense, they suggest the plausibility that the FSD follows a single power law over a wide range of floe sizes. This study covers a larger spatial and temporal sampling space and is based on more satellite images than previous studies. Results have been used for model calibration and validation.

Advances in Modeling Interactions Between Sea Ice and Ocean Surface Waves

“…Perovich () made observational estimates of floe perimeter per unit ice area from aerial photographs captured during the 1998 Surface Heat Budget of the Arctic Ocean (SHEBA) field campaign, on 10 dates between June and September. These estimates represent areas covering a 50 km square centered on the SHEBA site, a Lagrangian drifter, and resolve floes to 10 m—larger than the smallest floes in our model but at finer scales than many other observational estimates (e.g., Stern, Schweiger, Stark, et al, ).…”

“…Sea ice floe sizes can be estimated from images captured by cameras carried by aerial vehicles, visible‐band satellites (e.g., Landsat, MODIS, and MEDEA), and synthetic aperture radar (e.g., TerraSAR‐X and RADARSAT‐2) using image processing tools to separate individual floes. The spatial and temporal coverage of existing observations is limited (Stern, Schweiger, Zhang, et al, ), and the range of floe sizes resolved varies between studies (Stern, Schweiger, Stark, et al, ). Naturally, observational uncertainty is introduced by (and not limited to) (1) lack of distinct floe edges, (2) floes at sizes below the image resolution, (3) floes extending beyond the image frame, and (4) visual interference such as cloud cover in visible‐band imagery.…”

“…Naturally, observational uncertainty is introduced by (and not limited to) (1) lack of distinct floe edges, (2) floes at sizes below the image resolution, (3) floes extending beyond the image frame, and (4) visual interference such as cloud cover in visible‐band imagery. Some studies use similar methods (Stern, Schweiger, Stark, et al, ), but a comprehensive intercomparison of image processing methods for floes has not yet been carried out.…”

“…Nearly all studies fit power laws to either the cumulative or noncumulative floe number density distribution (Stern, Schweiger, Stark, et al, ). In most cases, the value of the exponent is obtained from a least squares regression on a log‐log plot, which may lead to erroneous overconfidence in the applicability of the power law hypothesis, and depends on the binning of the data (Clauset et al, ; Stern, Schweiger, Stark, et al, ).…”

“…Nearly all studies fit power laws to either the cumulative or noncumulative floe number density distribution (Stern, Schweiger, Stark, et al, ). In most cases, the value of the exponent is obtained from a least squares regression on a log‐log plot, which may lead to erroneous overconfidence in the applicability of the power law hypothesis, and depends on the binning of the data (Clauset et al, ; Stern, Schweiger, Stark, et al, ). Although power law exponents are often discussed, the data do not universally show straight lines in log‐log space (Herman, ; Horvat et al, ; Steer et al, ; Wang et al, ); and very few have been assessed using statistical tests of the power law hypothesis (Stern, Schweiger, Stark, et al, ).…”