Small-scale sea ice deformation in the Beaufort Sea seasonal ice zone

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113,, doi: /2006jc003971, 2008 Small-scale sea ice deformation in the Beaufort Sea seasonal ice zone J. K. Hutchings 1 and W. D. Hibler III 1 Received 20 October 2006; revised 16 January 2008; accepted 15 April 2008; published 19 August [1] During spring 2003, the deformation of a single lead in the Beaufort Sea was investigated using four Global Positioning System recording stations. The lead was situated in first-year ice, in the transition zone between multiyear, and seasonal ice packs. We discuss the opening rate and shear of this lead in the context of weather system forcing of the Beaufort Sea sea ice. It is found that in an opening mode, the lead displays semidiurnal fluctuations in divergence. It was also observed that the lead predominately ridged first-year ice in closing. The volume of ice ridged was the same order of magnitude as ice growth within the study region during the time period studied. Further work is required to fully understand the implications of the subdiurnal forcing of the ice pack on deformation in the shear zone at the edge of the perennial ice pack in the Beaufort Sea. This study provides information about the temporal and spatial scales that must be resolved in these future investigations. Citation: Hutchings, J. K., and W. D. Hibler III (2008), Small-scale sea ice deformation in the Beaufort Sea seasonal ice zone, J. Geophys. Res., 113,, doi: /2006jc Introduction [2] In winter pack ice deformation occurs through the propagation of fractures (initially cracks), and subsequent working of these. Cracks may become leads, that are linear fractures in the ice 10s to 100s of meters wide, and kilometers long. Once a fracture forms it will often work for weeks, producing ridges while closing, and allowing new ice growth on opening. In analysis of satellite data it has been observed that leads often align into systems that extend hundreds of kilometers [Kwok, 2003]; and that, regionally, leads are often aligned to each other [Marco and Thomson, 1977; Overland et al., 1995]. It is these lead systems that control the exchange of heat and moisture from the central ocean to the atmosphere during the Arctic Winter. New ice growth in these leads impacts the Arctic ice mass budget, as ice growth rate is greater for open water and thin ice compared to thicker ice. Model studies indicate that semidiurnal fluctuations in lead divergence enhances ice growth rate, and might account for between 10 and 20% of Arctic-wide seasonal ice growth [Heil and Hibler, 2002; Kwok et al., 2003]. Ridging of sea ice occurs at leads and cracks, modifying the thickness distribution of sea ice. [3] As the winter time evolution of the sea ice thickness distribution is controlled by localized deformation events, there is a need to understand the mechanisms controlling sea ice deformation at the lead scale. Models of pack ice relate opening and ridging rates to surface stresses on pack ice, typically on scales of 100 km 2 and days. Sea ice models are now being developed is to resolve higher resolutions, 10 km 2 [Maslowski and Lipscomb, 2003] and subdiurnal 1 International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, Alaska, USA. Copyright 2008 by the American Geophysical Union /08/2006JC motion [Hibler et al., 2006]. At these high resolutions few leads are simulated in each grid cell and the assumption of isotropy breaks down. Satellite image analysis [Kwok, 2003] reveals anisotropic behavior of the winter ice pack over hundreds of kilometer scales. Anisotropic models are being developed to capture the oriented nature of sea ice deformation due to preferred lead orientations over large scales [Coon et al., 1998; Hibler, 2001; Feltham and Wilchinsky, 2004]. Hence the understanding of lead scale dynamics is becoming increasingly important for sea ice model development. [4] We need to determine how pack ice fractures and subsequently deforms in responses to atmospheric and oceanographic stresses on the ice surface (surface wind and surface ocean currents). There is a wealth of literature regarding failure mechanisms for sea ice, models and laboratory observations of crack propagation; for example, Shen and Dempsey [2001]. However, there are few in situ observations reported regarding the formation and working of active leads in the sea ice packs. Analysis of ice drift indicates that the motion and deformation of sea ice has power at synoptic and semidiurnal time scales [Hibler et al., 1974; Pease et al., 1995; Geiger et al., 1998; Heil and Hibler, 2002]. However, such analysis has not previously been applied to a single lead. Difficulty in measuring how stress propagation in the ice pack relates to deformation has led to a gap between the theory of pack ice failure, observations and large-scale models of pack ice deformation. In situ investigations of the response of leads to changes in weather patterns could provide insight into whether models correctly simulate temporal and small-scale evolution of the ice pack deformation in winter. Investigating the relationship between large-scale wind patterns and lead dynamics will shed light on how ridging rate and new ice growth varies with synoptic conditions. 1of10

2 [5] In this paper we present a case study of the deformation of a single lead observed in the Beaufort Sea during Spring This case study provides information about the temporal scales and spatial accuracy of an array of position measurements required to resolve the deformation of a single lead. The role this lead plays, under varying synoptic conditions, in the local sea ice mass balance is discussed. We discuss these results in the context of synoptic activity in the Beaufort Sea, and the limitations of currently available sea ice deformation data. Suggestions are made for future work to investigate the local- to regional-scale lead response to weather and variability in ocean surface currents on sea ice thickness distribution. 2. Field Campaign [6] The Applied Physics Laboratory Ice Station (APLIS) 2003 occurred during late Spring In this paper we present a case study of data collected between March 29 and April 7, The camp was located in the seasonal ice zone of the Beaufort Sea, within the boundary region between seasonal and perennial pack ice. The ice camp itself was surrounded by regions of first year ice mixed with multiyear ice. On 26 March 26 a crack opened in first year ice, 2 km to the south of the camp. Four Geographical Positioning System (GPS) receivers were placed in an 3.2 km by 6.4 km array about this crack. The crack opened to approximately 20 m and reclosed several times until 3 April, then developed into a lead, opening to between m wide after 7 April. We refer to this evolving crack/lead as the APLIS lead in this paper. During the case study time period, the APLIS lead was approximately 8 km long and worked, continuously producing ridges on the south side of the lead up until 3 April. After 3 April, the APLIS lead opened and continued to fluctuate, never completely closing. 3. Synoptic Conditions and Large-Scale Deformation [7] A variety of sources provide information about the synoptic conditions, position of large-scale, O(100 km) long, lead systems and deformation of the ice pack about the APLIS camp. We use mean sea level surface pressure data from the NCEP/NCAR reanalysis (Figure 1) to identify weather patterns in the region. In MODIS imagery, at 81.5 GHz, large open lead systems show up with highbrightness temperatures due to increased humidity, fog or cloud over these leads. We use cloud-free MODIS imagery, when available, to identify the location of major lead systems in the Beaufort Sea. Lead systems associated with the coastal slip zone and point Barrow failure system (Figure 2) show up clearly in MODIS images recorded during the case study time period. RADARSAT ScanSAR images where collected at the highest possible frequency during the ice camp. Coverage was daily with a gap every three days. These images were used to calculate sea ice divergence at approximately 5km resolution, using the RADARSAT Geophysical Processing System [Kwok et al., 1990]. [8] The timing of RADARSAT and MODIS satellite overpasses do not correspond. To determine if similar features are observed in both sets of images, we examined RGPS divergence fields spanning the time of each MODIS images, an example is shown in Figures 2 and 3. We find that the lead systems visible in the MODIS images correspond to opening linear features in the RGPS sea ice deformation. Hence we are confident that the linear features in the MODIS images indicate the opening of major lead systems that correspond to Linear Kinematic Features in the RGPS data. These linear kinematic features display shear and divergence an order of magnitude higher than sea ice deformation elsewhere. In this paper we refer to the linear kinematic features observed in RGPS data as RGPS LKFs, so we can distinguish large spatial scale, higher-energy deformation from the localized APLIS lead deformation. The ice camp was not located in a RGPS LKF at any time during the case study, and we find that the GPS array deformation rates are the same order of magnitude as RGPS deformation rates calculated in the ice camp vicinity. However, cross-correlation analysis of buoy and RGPS strain rates in the vicinity of the ice camp do not indicate agreement between the two data sets. Error estimates of the RGPS divergence rate [Lindsay and Stern, 2003], at 0.5% per day, are larger than the observed APLIS lead strain, hence we believe that RGPS strain rate data for the region about the ice camp is unable to resolve the lead motion. [9] Initially a large-scale fracture pattern formed in the Beaufort Sea, with a RGPS LKF extending out from Point Barrow. This lead system is clearly visible in the MODIS image in Figure 2. At this time an anticyclonic weather system was located over the Beaufort Sea coast. Later, on 26 March, a system of leads running north-west opened, while the Point Barrow slip line is still working. This corresponded to the time when the anticyclone moved south toward Alaska. On 28 March, the anticyclone moves northnorth-east, and the APLIS lead opened as the seasonal ice pack was pushed toward the perennial pack. The anticyclone remains centered to the east of the ice camp and continues to strengthen until 1 April, labeled as A in Figure 1. During this time the APLIS lead closed and ridged, and a coastal slip zone was created along the Beaufort Sea coast. On 1 April the large lead system extending from Point Barrow reduced, and closed by 5 April. This happened as the ice pack was forced away from the Canadian Archipelago, opening lead systems in the eastern Beaufort, as the anticyclone moved eastward. A cyclone with south westerly winds developed to the west of the ice camp. As this moved over the ice camp, on 1 April, the APLIS lead closed. During the following 2 days, no new large lead systems appeared, and the APLIS lead divergence and shear was minimal, until on 3 April, labeled as B in Figure 1, the APLIS lead opened. The open APLIS lead no longer closed completely to form ridges, and the diurnal character of divergence and shear was amplified compared to the earlier time periods when the lead underwent closing and ridging. [10] The switch between the lead system running from Point Barrow and the coastal slip zone is a common phenomena in the Spring time Beaufort Sea. With this knowledge, we have some confidence that the mechanical behavior, and internal stress state, of sea ice in the springtime Beaufort Sea is often represented by two characteristically different stress states. One state, with the Point Barrow lead system, creates a slightly closing and shearing 2of10

3 Figure 1. (first panel) Mean sea-level pressure, from NCEP/NCAR reanalysis, for cell following ice camp location. (second panel) Filtered divergence rate and (third panel) filtered shear rate along lead. A spectral filter with Hanning window was applied to remove frequencies above 3 cycles per day. (fourth panel) Ridge volume calculated from observed GPS array divergence using a simple model. state at the APLIS camp site. The other situation, with coastal slip zone, corresponds to divergence and shearing at the APLIS site, and occurs as the ice pack opened through compression toward the Canadian Archipelago. 4. Analysis of GPS Data [11] The 12 channel GPS receivers (Garmin, model 17N) calculate GPS position internally using up to 12 satellites within the receivers view. The receivers view was masked to not include satellites lower that 15 in the sky, and the receivers were positioned so that ridges fell within this mask. Position and time were recorded every 10 seconds, at the four locations. As we are interested in the relative motion between GPS receivers, we believed it was not necessary to use differential GPS methods to correct for uniform biases in the position data. The array dimensions are sufficiently small, such that the GPS bias due to atmospheric correction will be similar for each location, and we expect position biases to be uniform across the array. Biases may exist between receivers if each receiver observes different satellite constellations at a given time. We 3of10

4 Figure 2. Modis image, channel 32, of the Beaufort Sea from 28 March, 22:25Z. The scale is in W m 2 micrometer 1 steradian 1. reduce the chance of this by ensuring that each receiver has a full sky view within its 15 mask. [12] As the GPS positions at the four locations are not recorded at the same time, we average positions over 1 minute windows. The minute averaged position data is then used to calculate strain rate tensor components, following the method of Kwok et al. [2003]. The strain rates are rotated to a coordinate system following the direction of the lead, and we investigate the strain rate along the lead, shear rate, and opening of the lead, divergence. [13] The GPS manufacture quotes an absolute position error of 15 m at 95% confidence, and the relative position error should be 2 m if all GPS are viewing the same satellite constellation at the same time. The satellite constellation was not recorded by each GPS receiver, hence we cannot be certain that random biases do not occur between receivers. To estimate the error in our analysis we examined the spectral characteristics of position, divergence and shear for an array of 4 GPS receivers placed in Fairbanks, Alaska, at 64.5N. This allows us to characterize the shape of the noise spectral density, which we use in estimating the signal to noise ratio for the spectral density of deformation. We estimate that position error (two standard deviations, or 95% confidence) for our GPS systems is 3.4 m and this is reduced to 3.0 m by averaging over 6 positions recorded within a minute. In reality the distribution of GPS position has a high 4th statistical moment (greater than 1000), and more than 95% of positions fall within 2 standard deviations of the mean. The standard error of divergence rate for a stationary buoy array is %. The APLIS array is not stationary, so we performed an analysis of error propagation of our estimated 3 m position error through velocity, strain component and divergence calculations. The errors are proportional to the logarithm of position measurement accuracy, time between observations, and ice velocity. Velocity errors are less than 10% for sampling intervals greater than one hour. The divergence signal to noise ratio is between 100 and 0.01 for typical ice velocities. Figure 4 indicates that buoy derived deformation is unresolved for temporal resolutions less than three hours and for ice velocity less than 0.02 m/s. [14] Comparing the stationary Fairbanks array and the APLIS03 data, Figure 5, we find GPS error is the same magnitude as the ice deformation signal for frequencies higher than 3.5 cycles per day. It is well known that GPS accuracy decreases the further north the receiver is placed, due to the horizontal dilution of precision effects of reduced satellite coverage. Hence the precision estimates at 64N might be higher than at 73N. We do know that GPS error is correlated over 10 to 20 minute times, due to progression of the GPS satellite constellation over a fixed location. Analysis of divergence and shear for the stationary Fairbanks array, Figure 5, show that the error on GPS position and strain rate is dominated by coherent noise at around the 10 minute time scale. This suggests that a low pass filter method is appropriate to remove the effects of GPS noise in our analysis of buoy array position data. [15] Power spectral density of both divergence and along lead shear is shown in Figure 5. There is a peak in both divergence and shear at the 12 hour period. To determine if this peak is significant, we consider the power spectra of GPS noise, estimated from the Fairbanks GPS array. We find that the spectral peaks at frequencies lower than 3 cycles per day are significant, at the 99% chi-squared confidence level. 4of10

5 Figure 3. Sea ice divergence rate on 28 March between 03:09Z and 16:45Z, for the subregion boxed in Figure 2, estimated with the RADARSAT Geophysical Processing System. RGPS data was provided by Ron Kwok. [16] Knowing the noise characteristics of our GPS estimated deformation allows us to build a spectral filter for divergence and shear. As Hibler [1971] we filter with a Hanning window to remove frequencies higher than 3 cycles per day. Time series of filtered divergence and along lead shear are shown in Figure 1. We observe several divergence and shear events in the filtered time series with strain rates of O(10 7 ), which correspond to area changes of to 0.01% and mean GPS station displacements of between 3 and 20 m. Hence we are confident that the filtered time series data displays ridging and opening events that are detectable above the GPS noise. [17] There is semidiurnal periodic behavior in the lead divergence (Figure 5A), which is most likely attributed to tides and/or inertial motion of the ice-ocean boundary layer. There is also a peak in the spectrum at lower frequency, which corresponds to the synoptic time period or frequency of weather systems moving over the ice camp. In the time series plots, we see that semidiurnal fluctuations are prevalent when the lead is opening. During other times the lead was closing and shearing. There is no significant semidiurnal fluctuation in the shear along the lead. The sharp peaks in the shear time series appear to occasionally have a diurnal character that might be related to tidal fluctuations, though the time series is not long enough to determine this. 5of10

6 Figure 4. Divergence error estimated by propagation of position error through strain rate calculation. Errors greater than 100% are shaded white. Contours are shown for 10% errors (light grey), 1% errors (dark grey), and less than 0.1 error (black). [18] Both divergence and shear vary on the synoptic time scale. For example, two time periods with markedly different behavior are days 91/92 and days 94/95. An anticyclone sat over the ice camp during the first time period, and the second time period coincides with a cyclone. Under increased convergence, due to anticyclonic wind forcing, the amplitude of subdiurnal oscillations, in divergence, is greatly reduced compared to the time period after day 93, with cyclonic wind, when the lead was opening. During the anticyclone, shear rate increased continuously and did not fluctuate. Visual observations of lead motion recorded during this time document that the lead was in continuous shear, and appeared to oscillate between slipping and sticking with a 10 minute time period. During the cyclone opening rate and shear rate are not well correlated, though particular opening and closing events correspond to particular shearing events. This indicates that subdiurnal fluctuations in the forcing on the ice pack where significant in controlling the ice pack stress during this period of cyclonic wind forcing. [19] Finally, shear was predominantly in the westward direction, such that the ice camp to the north of the lead moved westward relative to the southern edge of the lead. The ice camp drifted approximately 1 westward during the 9 days, Figure 6, and was clearly part of the shear zone between the multiyear and seasonal ice zones. There were 4 events when shear reversed direction, all of which occurred during times when a cyclone traversed the ice camp. 5. Driver of Semidiurnal Strain Behavior: Tides or Inertial Motion? [20] As is clear from the analysis presented in the sections above, the magnitude of deformation observed is small. In Figure 5. Power spectra of (A) divergence and (B) shear along the lead. Red lines show APLIS03 data; yellow lines show data collected in Fairbanks. Dashed lines are the 99% chi-squared confidence level. 6of10

7 Figure 6. Drift of ice camp. fact, it was necessary to filter out the GPS noise, as the observed deformation was a similar magnitude to the noise. There were only two days in the time series when the semidiurnal fluctuation in strain was clearly discernible above GPS noise. During these two days, the 5 to 6 April, the lead was open and not ridging. We find that the rotation was anticlockwise, with a twelve hour period during this time (Figure 7). [21] It is not entirely obvious which direction the strain tensor should rotate under the tidal forcing in the Beaufort Sea. Models of Arctic tides show the M2 tide rotating in a anticlockwise direction in the Beaufort Sea [Kowalik and Proshutinsky, 1994; Hibler et al., 2006]. A coupled iceocean model shows that the sea ice strain tensor rotates in an anticlockwise direction under this tidal forcing [Hibler et al., 2006]. This model suggests that the semidiurnal fluctuation observed in our case study is driven by tides rather than inertial motion. [22] Although the time period of analysis is short, the semidiurnal period in lead opening is significant. A longer time series is required for a statistically robust analysis. Hence we can only hypothesis that tidal interaction influences lead dynamics while leads are open in the vicinity of the Beaufort Sea shelf break. 6. Ice Growth and Ridging [23] As leads open, new ice forms. On closing this new ice, and ice adjacent to the lead, may be redistributed to thicker ice through ridging or rafting. In situ observations of the APLIS lead indicated that the deformation associated with the lead was resulting in the ridging of thick first year ice. The ridge is composed of blocks of first year ice, and a series of these formed on the south side of the working lead. The lead worked itself toward the ice camp, eventually encroaching on the camp s runway. New ice forming in the lead was measured to be less than 6 inches thick throughout the time period. Figure 7. Principle direction plotted as a function of time in a 12-hour cycle, for data between the 5 and 6 of April. The least squares fit line to this data is shown in blue. During this time rotation of the strain ellipse was anticlockwise. 7of10

8 [24] With a simple model of ice growth and ridging, we are able to estimate the volume of new ice growth and volume of ice ridged during the case study time period. In the model we assume that the ice the lead is embedded in is 1.5 m first year ice. This corresponds to in situ measurements of ice thickness taken from first year ice at the ice camp. Ice growth rate for 1.5 m ice and open water or thin ice were taken from Maykut and Untersteiner [1971]. From the filtered divergence, we can calculate area of open water within the GPS array. To do this, we assume that the initial area of the lead is zero. Given that the lead was closing on March 29th, this is a reasonable assumption for the order of magnitude estimates we present. The amount of ice ridged by the lead is calculated as the volume of new ice ridged and volume of first year ice ridged when the GPS array is converging. Ridging under shear was assumed to be zero. Given that ridging of the APLIS lead was observed only during compressive event, and the lead was linear without a significant number of kinks in the study region, ridging under shear along the lead was negligible. [25] We estimate that new ice growth in the APLIS lead during 29 March 7 April is about 50 m 3. The mean lead spacing in the southern Beaufort in late spring is 10km, and to gauge the effect lead deformation has on local ice mass balance we consider the APLIS lead as representative of other leads around the ice camp. Given an estimate of the rate of first year basal ice growth [Maykut and Untersteiner, 1971] we calculate that the lead ice growth is 0.01% of the basal ice growth during the case study time period. We estimate the total volume of ice ridged was about m 3,which is the same order of magnitude as the basal ice growth. This corresponds to an average ridge area, running parallel to the lead, of approximately 2000 m 2. We do not know the shape of the ridges, but can estimate that the mean height of a single triangular ridge built along the lead during the case study time period was 30 to 40 meters. Alternatively 3 4 ridges of 10 meter height might be formed, which corresponds well with the 0.5 to 2 meter sails observed forming on three different occasions on the south side of the lead. [26] Considering the time series of ridging rate, Figure 1, we see that ridging events occurred up until 3 April. On 3 April, the GPS array diverged and lead remained open for the rest of the case study time period. There are step changes in the ridging rate, that occur as synoptic conditions change (as described in section 3). Each step change is separated by a time when the lead was opening or shearing and not ridging. During the first two days of the time period, there were three distinct ridging events. These three events coincide with changes in synoptic conditions that probably brought about large changes in the pack ice internal stress field (as is demonstrated by changes in large failure zone orientations, shown in RGPS data, and lead opening, indicated in MODIS images). Between 1 and 3 April there appears to be a semidiurnal component to ridging, which could be associated with tidal motion. However, the length of time series is insufficient to determine if motion is tidal. 7. Discussion [27] The case study presented in this paper considers a single lead, which is not located in an RGPS LKF, and whose deformation is an order of magnitude smaller that leads in the failure zone orientated off Point Barrow or coastal slip zone. Visual observations, during camp resupply flights, of the lead systems paralleling the APLIS lead in the seasonal ice zone suggest that these lead systems were synchronized in their opening and closing behavior, on daily time scales. Furthermore, it appears that these lead systems opened and closed in unison with the APLIS lead. Hence we believe that the behavior displayed by this lead may be indicative of the response of similar small leads in the Beaufort Sea seasonal ice zone. This hypothesis requires further testing, as satellite observations of lead systems in the seasonal ice zone where too infrequent to resolve semidiurnal motion of the pack, and not accurate enough to determine whether deformation was truly synchronized across the seasonal ice zone. The SAR RGPS data set is not of sufficient accuracy to study individual leads similar to the APLIS lead. Throughout most of the Arctic, SAR does not sample motion frequently enough to represent the semidiurnal signal in deformation. Hence the case study presented in this paper is a unique insight into the dynamical behavior of a lead that is unresolved in remote sensing and International Arctic Buoy Program data. [28] We find that the lead deformation fluctuated on a twelve hour time period, and this response evolved with synoptic conditions. Changes in wind stress resulted in opening or closing events. The lead continued to shear throughout the case study, though the direction and periodicity of shear rate changed with synoptic conditions. When the lead closed during cyclonic weather, the shearing was continuous in one direction. Anticyclonic weather corresponded with fluctuating opening/closing and shear of the APLIS lead. When the lead was open, divergence fluctuated semidiurnally, and we hypothesis this may be tidally driven. [29] During closure, the lead was observed to form ridges. It was predominantly first year ice, and not new ice, that was ridged. The ridging events were linked to synoptic activity. During the case study we find that the volume of ice ridged by the lead was three orders of magnitude larger than the new ice growth in the lead. New ice growth rate was found to be exceptionally low, and inconsistent with other studies [Heil and Hibler, 2002; Kwok et al., 2003]. Given the short-time period studied, and the fact that the lead was predominantly closing and ridging during the greater part of the time period, this is perhaps not surprising. [30] It was not possible to observe subdiurnal fluctuations in deformation during the time the APLIS lead was closing with GPS data. This is because the magnitude and period of strain fluctuations during this time were in-distinguishable from GPS error. To determine if divergence and shear during ridging is influenced by inertial motion or shorter time perturbations, a measurement system with much great spatial position accuracy is required. The use of the differential GPS method in future field studies could provide the insight required. [31] It is interesting to note that the characteristic periods of shear and divergence differ, whereby it appears the divergence is dominated by semidiurnal motion. For the shear, it is not clear whether semidiurnal motion, diurnal tides, synoptic or smaller variability dominate the fluctuations. A semidiurnal fluctuation in divergence and motion occurs throughout the Arctic, having been discovered in 8of10

9 both buoy [Hibler et al., 1974; Heil and Hibler, 2002] and SAR [Kwok et al., 2003] data. Heil and Hibler [2002] argue that the semidiurnal peak in large (of order 100 km) buoy deformation corresponds to a cascade of kinetic energy to higher frequencies, fluctuating with ice-ocean inertial motion, due to highly non-linear ice mechanics. The case study presented in this paper suggests that semidiurnal motion becomes an important driver of the pack stress state after the ice pack has opened and leads are not completely closed. Once the pack is closed the stress state becomes characterized by continuous shearing of reduced amplitude. At least for this case study, changes in wind correspond with dramatic changes in the strain characteristics for a single lead. The influence of inertial motion on deformation is limited in a closing ice pack, as the ice strength increases significantly once leads close. The pack maintains weakness to tensile stress, perpendicular to the lead direction, allowing opening in this direction which is followed by weakening of the ice strength to compression. This supports the view that ice strength must be treated anisotropically, at scales on 0.1 to 10 km and is related to the history of divergence or open water fraction. [32] It is also interesting to note that the observed semidiurnal fluctuation in motion and divergence could be tidal, rather than related to inertial motion. It is generally considered that inertial motion and deformation of sea ice is ubiquitous across the Arctic. This case study was in a unique location, on the shelf break of the Beaufort Sea, where the M2 tide might be amplified. It is impossible to determine whether the tidal activity in our case study is a localized phenomena, or coordinated with similar motion throughout the Beaufort Sea seasonal ice zone. [33] Future work is required to understand the influence of large-frequency motion on localized deformation. We can only infer that leads parallel to the APLIS lead responded similarly to changing wind patterns, as over daily time scales they were opening and closing in concert with the APLIS lead. It is uncertain if the subdiurnal fluctuations were coordinated for parallel leads between the Alaskan coast and multiyear ice, or if the subdiurnal motion is a local phenomena. Dense arrays of drift measurements in the seasonal ice zone, with position accuracy better than 10 m, and hourly temporal sampling, are required to extend localized studies of ice deformation to larger scale behaviour. Error propagation analysis indicates that subdiurnal divergence fluctuations are only observable in data products with such position accuracy when the ice drift is greater than 0.5 km/day. To further investigate the motion of small leads, such as the one studied in this paper, that are closing and shearing, finer temporal resolution and position accuracy are required of the GPS data. We estimate that position accuracies of better than 10 cm, and 2 minute sampling would be required to resolve the fine stick-slip like motion that was visually observed when this lead was predominantly shearing. [34] We find that the deformation of the APLIS lead was significant in modifying the local ice thickness distribution. During cyclonic conditions, when the lead closed, ice thickness redistribution through ridging was of comparable magnitude to regional basal ice growth. If this lead is representative of other leads in the seasonal ice zone of the Beaufort Sea in late Spring, our results suggest that deformation unresolved by current ice drifting buoys and satellite ice motion products has a significant impact on ice mass balance and ocean-atmospheric surface fluxes in the Beaufort Sea. [35] Acknowledgments. Frontier Systems for Global Change and JAMSTEC funded this study. The ice camp was supported by the US Navy, and we wish to thank the Arctic Submarine Laboratory for providing the opportunity for this field study. NCAR/NCEP reanalysis data was obtained from the NOAA-CIRES Climate Diagnostics Center. Aqua Modis data was provided through the EOS Data Gateway and thanks to the Goddard DAAC Modis Data Support Team for providing visualization software. During the field campaign we used sea ice analyzes obtained from the National Ice Center. We also used quick-look ERS-2 and RADARSAT- 1 SAR images provided by the Alaska Satellite Facility. Thanks to Joe Lovick for processing the SAR quick looks in a format that was acceptable for transmission to the Prudhoe Bay and the ice camp. Ron Kwok provided the RADARSAT scansar imagery. Ron Kwok and Lisa Nguyen processed this data with the RADARSAT Geophysical Processing System and provided the RGPS deformation products used in this study. Many thanks to Tohru Saito, Tina Tin, Diane Bentley, Andy Anderson, Pat McKeown, Dan Steele, Jorg Haarpainter, and Jeff Dixon for assistance in deploying and recovering the GPS receiver array. Thanks also to the APLIS03 staff and the APLIS03 ERA Helicopter crew, for the recovery of the GPS data from several stations. Jennifer Hutchings is particularly indebted to Jorg Haarpainter, without whom the GPS data from one of the stations would have been lost to the Arctic Ocean. References Coon, M., G. Knoke, D. Echert, and R. Pritchard (1998), The architecture of an anisotropic elastic-plastic sea ice mechanics constitutive law, J. Geophys. Res., 103(C3), 21,915 21,925. Feltham, D., and A. Wilchinsky (2004), A continuum anisotropic model of sea-ice dynamics, Proc. R. Soc. London, Ser. A, 406(2047), Geiger, C., W. Hibler III, and S. Ackley (1998), Large-scale sea ice drift and deformation: comparison between models and observations in the western Weddell Sea during 1992, J. Geophys. Res., 103(C10), 21,893 21,913. Heil, P., and W. Hibler III (2002), Modelling the high-frequency component of Arctic sea-ice drift and deformation, J. Phys. Oceanogr., 32, Hibler, W., III (1971), Removal of aircraft altitude variation from laser profiles of the Arctic ice pack, J. Geophys. Res., 77(36), Hibler, W., III (2001), Modeling the formation and evolution of oriented fractures in sea ice, Ann. Glaciol., 33, Hibler, W., W. Weeks, A. Kovacs, and S. Ackley (1974), Differential sea ice drift I: spatial and temporal variations in sea ice deformation, J. Glaciol., 31, Hibler, W., III, A. Roberts, P. Heil, A. Proshutinsky, H. Simmons, and J. Lovick (2006), Modeling m2 tidal variability in Arctic sea-ice drift and deformation, Ann. Glaciol. 44, Kowalik, Z., and A. Proshutinsky (1994), The Arctic Ocean tides, in The Polar Oceans and Their Role in Shaping the Global Environment, edited by O. M. Johannessen, R. D. Muench, and J. E. Overland, Geophysical Monograph, 85 pp , AGU, Washington, D. C. Kwok, R. (2003), RGPS Arctic Ocean sea ice deformation from SAR ice motion: Linear kinematic features winter , winter , summer 1998, Tech. Rep. JPL D-21524, Polar Remote Sensing Group, Jet Propulsion Laboratory, Pasadena, California. Kwok, R., J. Curlander, R. McConnell, and S. Pang (1990), An ice-motion tracking system at the Alaska SAR facility, IEEE J. Oceanic Eng., 15, Kwok, R., G. Cunningham, and W. Hibler III (2003), Sub-daily sea ice motion and deformation from RADARSat observations, Geophys. Res. Lett., 30(23), 2218, doi: /2003gl Lindsay, R., and H. Stern (2003), The RADARsat geophysical processor system: Quality of sea ice trajectory and deformation estimates, J. Atmos. Ocean. Technol., 20, Marco, J., and R. Thomson (1977), Rectilinear leads and internal motions in the ice pack of the western Arctic Ocean, J. Geophys. Res., 82(6), Maslowski, W., and W. Lipscomb (2003), High resolution simulations of Arctic sea ice , Polar Res., 22(1), Maykut, G., and N. Untersteiner (1971), Some results from a time-dependent thermodynamic model of sea ice, J. Geophys. Res., 76(6), Overland, J., B. Walter, T. B. Curtin, and P. Turet (1995), Hierarchy and sea ice mechanics: A case study from the Beaufort Sea, J. Geophys. Res., 100(C3), of10

10 Pease, C., P. Turet, and R. Pritchard (1995), Barents Sea tidal and inertial motions from ARGOS ice buoys during the Coordinated Eastern Arctic Experiment, J. Geophys. Res., 100(C12), 24,705 24,718. Shen, H., and J. Dempsey (Eds.) (2001), IUTAM Symposium on Scaling Laws in Ice Mechanics and Ice Dynamics, Kluwer Academic Publishers, Dordrecht, The Netherlands. W. D. Hibler III and J. K. Hutchings, International Arctic Research Center, University of Alaska Fairbanks, 930 Koyukuk Drive, Fairbanks, AK 99777, USA of 10