This approach provided a unique Lagrangian view of the winter-to-summer transition of sea ice breakup and floe size distribution at each cluster between March and August. The results show the critical timings of a temporary breakup of winter sea ice coinciding with strong wind events and b spring breakup during surface melt, melt ponding and drainage leading to distinctive summer ice floes.
Importantly our results suggest that summer sea ice floe distribution is potentially affected by the state of winter sea ice, including the composition and fracturing caused by deformation events of winter sea ice, and that substantial mid-summer breakup of sea ice floes is likely linked to the timing of thermodynamic melt of sea ice in the area. As the rate of deformation and thermodynamic melt of sea ice has been increasing in the MIZ in the Beaufort Sea, our results suggest that these elevated factors would promote faster and more enhanced breakup of sea ice, leading to a higher melt rate of sea ice and thus a more rapid advance of the summer MIZ.
Sea ice breakup is an important process that characterizes the winter-to-summer transition of sea ice in the marginal ice zone MIZ. During winter, Arctic sea ice forms a continuous heterogeneous sheet that consists of ice of different ages and thickness, from multiyear ice MYI, second-year ice or older to newly formed ice within leads. Large floes break into smaller floes either by floe-floe interactions, through a flexural failure by ocean surface waves Langhorne et al. The production of smaller floes is important, as a group of smaller floes is more susceptible to lateral melting than a larger floe Steele, This effect in turn allows increased radiative warming in the upper ocean, thus accelerating the melting of the floes Perovich et al.
This positive feedback influences the evolution of the sea ice in the summer MIZ. The size distribution of sea ice floes, commonly known as floe size distribution FSD , characterizes the sub-grid-scale composition of the sea ice field. If logarithms are taken on both n x and x , i. The power-law distribution is also expressed as the complementary cumulative floe size number distribution CFND , i. Identification of these transition points is important to understand the underlying physical processes that control the breakup of the ice pack. A recent study by Clauset et al. In the LSF based method, one relies on a subjective judgment of where the straight line lies and how valid it is as a power-law distribution.
It is important to note that the Virkar and Clauset method VC14 is an effective statistical estimate of a power-law fit to the FND tail. It should not be seen as verifying whether the FND is scale-invariant. The procedure identifies the most appropriate range of sizes above which the tail of the FND is plausibly fit by a power law.
Often this range covers less than one tenth of observed floe sizes, and excludes a significant number of small floes and consequently a large percentage of the total ice area may be missing. The question of how to understand the FSD evolution and the potential emergence of power-law scaling features in the tail is an area of active research Stern et al.
Sea ice floe size x has been previously defined as the mean caliper diameter hereafter d of the floe e. The choice of x depends on the geometry or shape of the floes that can be inferred from floe properties i. The ratios of those floe properties, reported in previous studies, show some variability compared with the values from a disc or a square Rothrock and Thorndike, ; Hudson, ; Toyota and Enomoto, ; Toyota et al. If the floe shapes are irregular, the mean caliper diameter d provides the most realistic representation of the floe size. Previous studies on sea-ice breakup and FSD transition have been conducted over a relatively short period of time and focused on particular events.
Toyota et al. The study by Wang et al. So far, no study has been conducted to examine a full winter-to-summer transition of sea ice breakup and FSD. During the U. Office of Naval Research ONR MIZ program, four separate clusters separated by about km of autonomous multi-sensor buoy systems, that measure a wide range of ocean, atmosphere and sea ice parameters, were deployed Lee et al. This approach provides a unique view of a full winter-to-summer transition of sea ice breakup and FSD at the drifting clusters. The purpose of this study is to provide a comprehensive overview of the winter-to-summer transition of sea ice breakup and FSD, by analyzing the TS-X derived parameters and their relationships to oceanic, atmospheric and sea ice parameters measured by clusters of multi-sensor buoys.
One of each buoy type was deployed at the center, and the remaining four IMBs and WBs were deployed at the corners of a 5-km grid around the center, known as a 5-dice array. Trajectories of MIZ buoy clusters and characteristic floe size distribution.ilpoglidinboo.tk
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Trajectories are shown in solid lines for a selected reference buoy that was initially located at the centre of the buoy cluster. Empty square symbols mark the location of TS-X images used for the retrieval of open water fraction, and filled square symbols mark the location of TS-X images analyzed for floe size distribution FSD in this study. Star symbols mark the location of the buoys on March 20, Plots in panels b — d show FSD results in a log-log plot of cumulative floe number density for selected dates.
The wind data were extracted for the nearest data grid to the location of the reference buoy at each cluster. Open water fraction was calculated from SIC data for the data points that were extracted within the km radius from the location of the reference buoy at each cluster. The image data in GeoTiff format, containing flag for five different surface types at the 4-km pixel size, were used for this study.
The nearest open water pixel was identified for each cluster, and then the distance between the cluster and the corresponding open water pixel was calculated. Small openings of open water within sea ice area can cause erroneous detection of the nearest ice edge. To avoid such errors, the nearest open water pixel that was connected to a large body of open water at least pixels or at least 1, km 2 was selected. The acquisition of satellite data was near continuous in order to follow the evolution of ice conditions in the area of the MIZ buoy clusters.
For the retrieval of open water fraction, sea ice type and FSD, the algorithm described in Hwang et al. The algorithm is composed of various image analysis methods, including Kernel Graph Cuts KGC to segment SAR imagery into water-ice regions, the distance transformation and watershed transform to split touching ice floes, as well as a rule-based post-processing for the revalidation of segmentation boundaries.
At the reduced resolution, the derived FSD results significantly underestimated the number of small floes less than m in diameter Hwang et al. The filtered images were then applied to the KGC algorithm to segment into water and ice regions in summer, or water, first-year ice FYI and multi-year ice MYI regions in winter Hwang et al.
Open water fraction was then calculated as a fraction of total open water pixels. For this effort, a combination of the distance transformation, watershed and post-boundary revalidation methods was used Hwang et al. The algorithm-produced floe split image was then manually scrutinized, by us, to check and correct the errors from the algorithm. At the end of this process, the processed image was used to calculate FSD.
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In the final error-corrected image, each ice floe pixel detected as floe boundaries was reassigned to a water pixel, and then a standard blob analysis was used to calculate floe properties for each floe. The calculated floe properties include the number of ice pixels, floe area, perimeter, major and minor axes, and the mean, maximum and minimum caliper diameters.
In this study, the mean caliper diameter d is used to define floe size in FSD. Figure 1b shows a typical example of such cases. The lower bound x min marks the lower limit of the straight line reasonably well. The failure of the test is associated with two situations. Figure 1c shows an example for the first situation. This type of situation occurs mostly in mid-August when the number of small floes suddenly increased following a substantial breakup event see Section 5. Figure 1d shows an example for the second situation. In this example, the CFND data points exhibit a curved-down line in the log-log plot.
This type of situation occurs mostly in late August, when only a few large ice floes existed in a finite size of TS-X images. The mean area fraction of MYI varies between The lowest MYI fraction The largest MYI fraction Some variability up to 4. TS-X images showing the transition of sea ice during winter.
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Red symbols are the locations of buoys, and red lines are the baselines between buoys. The scale bar is 3 km; north is up in the image. In winter, sea ice fracturing and deformation are observed throughout the period. First, a number of openings suddenly appear at C1 Figure 2A — May 8 , which are likely caused by sea ice fracturing. Second, the buoy array at C3 is stretched and its surrounding sea ice was re-arranged Figure 2C — May 8 , indicating strong deformation events at C3.
Between April and early May, two strong wind events are observed w1 and w2 in Figure 3. During wind event w1, a strong wind was felt across all the clusters, with the peak wind speed up to During wind event w2, a similar opening of the ice did not occur Figure 3a , although TS-X image at C1 shows a large number of openings in the May 8 image Figure 2A. The strong deformation at C3 Figure 2C likely occurred during wind events w1 and w2, based on the analysis of temporal evolution of the displacement of the buoys at C3 not shown here ; i.
Evolution of sea ice and atmospheric conditions during winter-to-spring transition. The evolution of a open water fraction, b wind speed, c air temperature and d the distance from clusters to the nearest ice edge during winter-to-spring transition.
In panel a , the solid lines and closed circles are the open water fractions derived from AMSR-2 and TS-X data, respectively, at each cluster. In panel b , solid lines are the wind speeds from ERA interim data at each cluster.
In panel c , solid lines are the air temperatures from AWS at each cluster air temperature data from C2 are not shown here. In all panels, black, blue, red and green colors represent C1, C2, C3 and C4, respectively. Wind events such as w1, w2… are marked in the plots. Next, fracturing of sea ice at the clusters is observed during early June still before surface melt June images in Figure 2. During this time, another strong wind event w3 occurred. The peak wind speed is Note that the fracturing is a locally forced event, as the nearest ice edge is more than km away from the clusters in early June Figure 3d , corresponding to small openings of open water areas in the Mackenzie Bay and in the coast of the Banks Island see Video S1.
These small openings have the potential for wave development and hence wave-induced break-up; however, we can confirm that at this time no waves were felt by any of the WBs in the clusters. Red and blue symbols represents the location of center buoy and surrounding buoys. The date of the image is shown on the top of each image i. Scale bar is m.
TS-X images showing the transition of sea ice during spring melt. Red star symbol with yellow rectangle represents the location of the buoy at the centre of each cluster. Four red symbols surrounding the centre buoy represent the location of the buoys deployed at four corners of the cluster northeast, northwest, southwest and southeast.
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Scale bar is 3 km; north is up in the image. During this surface melt transition, the wind speed was moderate compared to other earlier wind events; e. Very little change in open water fraction from TS-X is observed across the clusters; i. Despite the very small change in open water fraction, surface snow melt marks a very important transition. As a result, sea ice, especially FYI formed in leads or cracks in later winter, is more susceptible to breakup with the same internal pressure.
Melt ponding weakens the ice because of increased solar absorption leading to localized thinning and degradation of the internal structure, so the ice is more susceptible to fracture. To demonstrate this point, a sequence of TS-X images zoomed into a floe are shown in Figure 6.
Related Detection of Melt Ponds on Arctic Sea Ice with Optical Satellite Data
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