Taking a look at both sides of the ice: comparison of ice thicknessand drift speed as observed from moored, airborne and

shore-based instruments near Barrow, Alaska

Andrew R. MAHONEY,1 Hajo EICKEN,1 Yasushi FUKAMACHI,2 Kay I. OHSHIMA,2

Daisuke SIMIZU,3 Chandra KAMBHAMETTU,4 M.V. ROHITH,4 Stefan HENDRICKS,5

Joshua JONES1

1Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USAE-mail: mahoney@gi.alaska.edu

2Institute of Low Temperature Science, Hokkaido University, Sapporo, Japan3National Institute of Polar Research, Tachikawa, Japan

4University of Delaware, Newark, DE, USA5Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany

ABSTRACT. Data from the Seasonal Ice Zone Observing Network (SIZONet) acquired near Barrow,Alaska, during the 2009/10 ice season allow novel comparisons between measurements of ice thicknessand velocity. An airborne electromagnetic survey that passed over a moored Ice Profiling Sonar (IPS)provided coincident independent measurements of total ice and snow thickness and ice draft at a scaleof 10 km. Once differences in sampling footprint size are accounted for, we reconcile the respectiveprobability distributions and estimate the thickness of level sea ice at 1.48�0.1m, with a snow depth of0.12�0.07m. We also complete what we believe is the first independent validation of radar-derivedice velocities by comparing measurements from a coastal radar with those from an under-ice acousticDoppler current profiler (ADCP). After applying a median filter to reduce high-frequency scatter in theradar-derived data, we find good agreement with the ADCP bottom-tracked ice velocities. Withincreasing regulatory and operational needs for sea-ice data, including the number and thickness ofpressure ridges, coordinated observing networks such as SIZONet can provide the means of reducinguncertainties inherent in individual datasets.

KEYWORDS: aerogeophysical measurements, Arctic glaciology, remote sensing, sea ice, sea-icedynamics

1. INTRODUCTIONWith ongoing retreat and thinning of Arctic sea ice (Wangand Overland, 2009; Stroeve and others, 2012) and growingcommercial interest in resource extraction and marinenavigation (Arctic Council, 2009; Schmidt, 2011), there isan increasing demand for observational data of ice thicknessand velocity. Although a growing number of pan-Arctic andregional sea-ice thickness datasets are becoming publiclyavailable (e.g. Kurtz and others, 2009; Laxon and others,2013), their usefulness for regulatory and operationalpurposes is limited by spatial resolution and error char-acteristics. Altimetry-derived ice thicknesses suffer fromlarge errors, primarily due to uncertainties in the snow depthand the densities of the sea ice and snow. Kwok andCunningham (2008) estimate that the uncertainties indensities alone account for 10–20% of the variance inArctic sea-ice thickness calculated using Ice Cloud and landElevation Satellite (ICESat) data. Moreover, the spatialresolution of these satellite-derived products is too coarseto resolve pressure ridges, which comprise the thickestelements of the ice cover.Airborne and submarine platforms provide the best

means of obtaining sea-ice thickness data at intermediatespatial scales that sample enough ice to obtain usefulthickness statistics while also resolving individual ridges.Airborne thickness surveys use altimetric techniques similarto those used from space or employ a combination of

altimetry and electromagnetic induction (Haas and others,2009, 2010) to calculate total snow and ice thicknesswithout requiring knowledge of their densities. Upward-looking sonar on naval submarines travelling beneath theice has provided a wealth of data on sea-ice thicknessesdating back to the early 1970s (Thorndike and others, 1975),but in recent years it has become more common to usemoored Ice Profiling Sonars (IPSs) to observe the ice as itpasses overhead (e.g. Melling and others, 1995; Hansen andothers, 2013). Submarine methods also use a form ofaltimetry to determine ice thickness, but instead of measur-ing freeboard they measure the draft of ice and are thereforeless sensitive to uncertainties in the densities of snow, iceand water and to snow depth (Forsström and others, 2011).The measurement of ice velocity is essential for a proper

analysis of ice thickness data collected by IPSs and it iscommon practice to deploy acoustic Doppler currentprofilers (ADCPs) alongside each IPS to measure the driftof ice. Ice velocity is also a key constraint, together with icethickness, for the design of Arctic offshore structures (ISO,2010). Observation of ice motion may be either Eulerian (asin the case of a mooring measuring ice drift at a fixed point)or Lagrangian (such as using a GPS-tracked buoy to recordthe path of an ice floe). Here we focus on Eulerianmeasurements of ice velocity, which can also be derivedat a grid of points using sequences of images of sea ice (e.g.Fowler, 2003; Kwok and others, 2003).

Annals of Glaciology 56(69) 2015 doi: 10.3189/2015AoG69A565 363

In this paper, we combine data collected as part of theSeasonal Ice Zone Observing Network (SIZONet; sizonet.org) to make novel comparisons between coincident andco-located observations of sea ice from above and below.Using airborne electromagnetic (AEM) data collected alonga flight that passed over a moored IPS, we compare twocompletely independent measurements of the local icethickness distribution around the mooring. This comparisonalso allows us to estimate the thickness of the snow on top of

the ice. We also make a comparison between ice velocitiesrecorded by an upward-looking ADCP and those deter-mined from sequences of imagery acquired by a coastal-based radar system. To our knowledge, this is the first suchvalidation of surface radar-derived ice velocities and itdemonstrates the suitability of such systems for real-time iceand hazard monitoring in the Arctic coastal regions.

2. DATASETS AND METHODS2.1. Ice draft and velocity measurements from under-ice mooringsTwo moorings (B1 and B2) were deployed near Barrow,Alaska, USA, as part of SIZONet (Fig. 1). Mooring B1 wasdeployed at 71.32698°N, 156.87663°W on 5 August 2009and retrieved on 29 July 2010. Mooring B2 was deployed at71.23471° N, 157.65271°W on 7 August 2009 andretrieved on 29 July 2010. These moorings each comprisedan ASL Environmental Science Ice Profiler IPS and aTeledyne RDI Workhorse Sentinel ADCP as well as aSeabird SBE-37 conductivity–temperature (C-T) recorderand a Seabird SBE-39 temperature–pressure (T-P) recorder(Fig. 2). The IPSs are used to measure the draft of the sea icepassing overhead while the ADCPs measure current velocityprofile of the overlying water column and, of particularrelevance here, the velocity of the ice through bottomtracking.The calculation of ice draft from raw IPS data is an

involved process, described in detail by Melling and others(1995). In brief, the distance from the sonar to the ice oropen-water surface is determined from the travel time ofechoes, with adjustments made for instrument tilt. Correc-tions for sound-speed variations over time are made byidentifying open water above the sonar and reconciling themeasured echo travel time with the depth determined from

Fig. 1. AEM flight path over mooring B2 on 12 April 2010 near Barrow, Alaska. Also shown are the locations of mooring B1, an ice mass-balance site (MBS) and the approximate range of a coastal sea-ice radar system installed on a building in Barrow. The background is a WideSwath Envisat ASAR image acquired 1 hour after the AEM flight passed over mooring B2.

Fig. 2. Configuration of SIZONet moorings deployed near Barrowin 2009/10. Distances indicate approximate rope lengths betweenmooring components.

Mahoney and others: Ice thickness and drift speed near Barrow364

an onboard pressure sensor. Through this approach, thedraft of the level ice can be measured to an estimatedaccuracy and precision of �0.05m (Fukamachi and others,2006). Ice draft measurements are made at 1 s intervals.A moored IPS generates a time series of ice draft at a fixed

location as the sea ice drifts overhead. Since the driftvelocity of the ice is not constant over time, these datacannot be used to derive distance-referenced probabilitydistributions. It is therefore necessary to transform the timeseries into a pseudo-spatial series using ice velocity data. Inour case we use ice velocities calculated from the ADCPbottom track data. The ice velocity is determined from theDoppler shifts of acoustic signals returned from the bottomof the ice. This is similar to the method used to determinethe water velocity, but a separate longer-pulse signal is usedto achieve accuracies of a few mms–1 (Gordon, 1996).The bottom track data are recorded every 15min and so

must be interpolated to match the 1 s time series recorded bythe IPS. Each interpolated velocity measurement thusrepresents an effective sampling distance for each ice draftmeasurement. We then use a cubic spline interpolation tocreate a regularly spaced pseudo-spatial series of ice draftwith 1m spacing, approximately matching the footprint ofthe sonar beam on the underside of the ice (Williams andothers, 2008). The measurement of ice draft can be relatedto ice thickness by invoking Archimedes’ principle, with thetotal weight of the ice and snow equal to the weight of thewater displaced. If we assume that the ice at eachmeasurement is in isostatic equilibrium, then this can beexpressed as

�iZi þ �sZs ¼ �wD ð1Þ

where �i, �s and �w are the densities of ice, snow and water,respectively. Zi and Zs are the thicknesses of ice and snow,respectively, and D is the ice draft.

2.2. Airborne electromagnetic (AEM) ice thicknessmeasurementsAEM sounding uses electromagnetic (EM) induction todetermine the distance from the towed instrument, knownas an EM-bird, to the water surface (Haas and others, 2009,2010). The technique involves emitting a primary EM field(in this case at 4.09 kHz), which induces a secondary field inthe conductive sea water. Using a one-dimensional modelin which the sea-water and sea-ice conductivities arespecified (Pfaffling and others, 2007), the distance to theunderside of the ice can be determined from the relativestrength of the in-phase component of the secondary field.At the same time, the distance to the upper surface of the ice(or snow if present) is measured using a laser altimetermounted in the EM-bird. The combined thickness of snowand sea ice is determined by subtracting these two distances(Fig. 3). In comparison with field measurements, thistechnique is found to have an accuracy of better than0.1m over level ice (Haas and others, 2009).In April 2010, two AEM flights were made over the sea

ice near Barrow as part of SIZONet activities. On 12 April2010, the flight path passed twice over mooring B2 (Fig. 1).A helicopter was used for these flights, allowing us to makecontrolled, tight turns over the mooring location. The EM-bird was flown at an altitude of �15m, giving an effectivesampling footprint of �70m. Each AEMmeasurement is thusa mean value of ice and snow thickness over this area. TheEM-bird will therefore tend to underestimate the maximum

thickness of ice ridges, though it can be expected to give anaccurate measure of the overall ice volume (Pfaffling andothers, 2007).

2.3. Gridded ice velocities from coastal sea-ice radardataThe University of Alaska Fairbanks (UAF) has operated acoastal sea-ice radar discontinuously since the 1970s(Shapiro and Metzner, 1989; Mahoney and others, 2007;Druckenmiller and others, 2009; Jones, 2013; Rohith andothers, 2013). Data from the current system are available innear real time from http://seaice.alaska.edu/gi/observatories/barrow_radar. Figure 4 shows an image from the radar on12 April 2010 coinciding with the Envisat AdvancedSynthetic Aperture Radar (ASAR) image shown in Figure 1.The coastal radar has a considerably lower grazing anglethan space-based systems and is reliant on rough surfaceswith higher local incidence angles to act as naturalreflectors. The coastal radar is therefore mostly sensitive toridges and floe edges, with little or no energy returned fromareas of level ice in between. As a result, images from thecoastal radar often contain ‘empty’ regions without featuresthat can be tracked through commonly used techniquesbased upon cross-correlation of image pairs. To overcomethese challenges, we use a combination of dense and sparseoptical flow methods to generate gridded ice velocities(Rohith and others, 2013)The radar data are recorded in range-azimuth space with

512 samples per range line and up to 4096 lines perrotation. The calculation of velocity in physical unitsrequires accurate geolocation of the radar imagery. Wedetermined the correct range resolution and orientation ofthe imagery using linear ground control features such aspipelines, roads and snow fences that were recognizable inboth the radar imagery and high-resolution satellite dataavailable through the Geographic Information Network ofAlaska (GINA). At a nominal range setting of 6 nauticalmiles, we determined the range resolution to be21.5�0.5m, which is the pixel size chosen for reprojectionof the data to a Cartesian plane. The radar system recordsimages every 120 rotations, which at a rotation speed of�0.5Hz corresponds to �4min between images althoughthis interval is variable due to small changes in rotationspeed of the radar antenna. Since the file creation times foreach radar image are only preserved to an accuracy of1min, it is therefore difficult to precisely determine the time

Fig. 3. From Haas and others (2009). Principle of AEM thicknesssounding, using a bird with transmitter and receiver coils and alaser altimeter. Ice thickness Zi is obtained from the differencebetween measurements of the bird’s height above the water and icesurface, hw and hi, respectively.

Mahoney and others: Ice thickness and drift speed near Barrow 365

interval over which motion is observed. However, over thewhole record for the 2009/10 season, we calculate anaverage interval between consecutive images of 231�9 s.Together, these uncertainties in spatial scale and timeinterval amount to a 5% error in the radar-derived velocities.The velocity vectors shown in Figure 4 are calculated on

a 20�20 pixel (438m � 438m) grid and have beenmedian-filtered in time to remove erroneous values (thisprocedure is discussed in more detail in Section 3).Gridpoints with zero velocity are shown by white dots andindicate the extent of landfast ice at the time of dataacquisition. Gridpoints where no velocity measurementcould be determined are blank. Velocity determinationtypically fails due to one of three causes: (1) a lack ofreflectors; (2) excessive ice motion; or (3) rapid changes inreflector orientation or shape due to ice movement ordeformation. For the purposes of comparing radar-derivedice velocities with the bottom track data recorded by theADCP, we calculate the mean velocity recorded at the fourgridpoints surrounding mooring B1.

3. RESULTS

3.1. Ice thickness over mooring B2Figure 5 shows the path of the AEM flight on 12 April 2010(in white) over mooring B2 together with a pseudo-track ofice motion (in gray) derived by integrating the bottom trackvelocity recorded by the ADCP forward and backwards intime from the time of the AEM overpass. The continuouswhite lines indicate portions of the flight made at measure-ment altitude within a 10 km radius of the mooring (shownby the black dashed circle). The white dots indicate thecalculated 6 hourly positions along the pseudo-track. Thehelicopter made two separate overpasses, which areindicated by the labeled arrows. Table 1 lists the time anddistance of the closest point on each overpass together withthe AEM-derived ice thickness and the IPS-measured icedraft at the times. The background is the Envisat ASAR imageshown in Figure 1, which was acquired at 21:26:59 UTC(coordinated universal time) on 12 April, just 1 hour after thefirst overpass. The black cross indicates the location of icethat was at the mooring at the time of overpass 1, based onthe pseudo-track data.Table 1 shows significant differences between the

coincident AEM and IPS measurements at the time of eachoverpass. In both cases the IPS-measured draft is greaterthan the AEM-measured combined snow and ice thickness.In some cases such differences can be accounted for by thelarger sampling footprint of the EM-bird, if there happenedto be a narrow ridge keel above the IPS at the time of theoverpass, the thickness of which would be underestimatedin the AEM data. However, examination of the IPS beforeand after each overpass indicates this is not the case.

Fig. 4. Coastal radar image acquired at 21:25 on 12 April 2010 (UTC), coincident with the Envisat ASAR image in Figure 1. Vectors show icevelocities determined from consecutive images.

Table 1. Time, closest distance and coincident measurements forAEM overpasses 1 and 2

Overpass Time (UTC) Spatial offset Ice + snowthickness

Ice draft

m m m

1 20:26:50 345 1.54 4.932 20:38:05 269 1.96 3.99

Mahoney and others: Ice thickness and drift speed near Barrow366

Instead, it is more likely the difference is due to the spatialoffset between the actual measurement locations. This issupported by the SAR image in Figure 5, which shows highbackscatter in the region of the mooring at the time of theoverpass (marked by black cross), indicating rough, hetero-geneous ice.Neither of the overpasses was aligned with the drift of ice

at the time, which means it is not feasible to attempt to co-locate the measurements more accurately. We thereforecompare AEM and IPS measurements by calculating theirprobability distributions using all data that fall within 10 kmof mooring B2 (indicated by the black dashed circle inFig. 5). Figure 6 shows the distributions of AEM-derived iceand snow thickness and IPS-derived ice draft, binned into0.05m intervals. Both distributions have pronouncedmodes, which represent the thickness and draft of levelundeformed ice. The AEM data indicate a modal combinedthickness of ice and snow of 1.6� 0.025m while the IPSdata show a modal ice draft of 1.35�0.025m. These valuesand their relationship with density and snow depth arediscussed in more detail below.

3.2. Ice velocities near Barrow during 2009/10winter seasonA comparison of radar-derived and ADCP bottom-trackedice velocities was carried out for mooring B1 for the period1 November 2009 to 30 June 2010 (Fig. 7). We binned theradar-derived values every 15min to match the samplinginterval of the ADCP. We have also excluded data fromperiods with a significant open-water fraction and when theinstrument tilt exceeded 20°. The presence of open water

can be inferred from increased magnitude and variability ofthe bottom track error recorded by the ADCP due to thepresence of surface waves (Belliveau and others, 1990). Weapplied a 2 hour running mean to the bottom track errorvalues and discarded data from periods with error valuesgreater than 0.1m s–1.The radar-derived velocities show significant scatter and

that the optical flow algorithm tends to overestimate icespeed as compared to bottom-tracked ice velocities.However, we see considerably better agreement when we

Fig. 5. Map showing the AEM flight path over mooring B2. The gray line indicates a pseudo-track of ice drift calculated by integrating thebottom track velocity over time. White dots indicate the 6 hourly pseudo-positions of the ice before and after the overpass. Only those at �6and 12 hours are labeled, to reduce clutter in the figure. The black cross indicates ice that was at the mooring at the time of overpass 1.

Fig. 6. Probability distribution of combined ice and snow thickness(AEM) and ice draft (IPS) derived from all measurements within10 km of mooring B2.

Mahoney and others: Ice thickness and drift speed near Barrow 367

apply a 2 hour running median filter to the radar-deriveddata, with tighter clustering around the line y= x and animprovement in the root-mean-square (rms) difference invelocity magnitudes from 0.24m s–1 to 0.12m s–1. The closeagreement in both alongshore and offshore componentsindicates that both datasets are well aligned geographically.Examining the radar-derived ice velocities and ADCP

bottom-tracked velocities as time series (Fig. 8) confirms theoverall good agreement between these two independentobservations of ice motion while also allowing closerscrutiny of those occasions when the results differ. The grayboxes indicate periods of open water inferred from thebottom track error as described above. It is clear that these

periods correspond to the highest velocities and alsocoincide with many of the gaps in the coastal radar velocityrecord. Examination of the radar imagery during these datagaps reveals an absence of reflectors over the mooring site.We remind the reader that, due to the insensitivity of thecoastal radar system to areas of smooth ice, the absence ofreflectors in the imagery does not necessarily imply anabsence of ice on the ocean, but in those cases where thereis sufficient daylight we are able to confirm the presence ofopen water through examination of images from the Barrowsea-ice webcam (http://seaice.alaska.edu/gi/observatories/barrow_webcam), which is co-located with the radar.Despite the gaps in the radar velocity record, there are

Fig. 7. Scatter plots comparing ADCP- and radar-derived ice velocities for winter 2009/10: (a) velocity magnitude; (b) alongshore velocitycomponent; (c) offshore velocity component.

Fig. 8. Time series of (a) ice velocity magnitude and (b) alongshore and (c) offshore components derived from the ice radar (black dots) andADCP bottom track data (gray dots). Open-water periods are shown by gray shading.

Mahoney and others: Ice thickness and drift speed near Barrow368

occasions when the radar was able to detect and track iceduring periods of inferred open water. For these cases theoverall rms difference between the bottom track data andmedian-filtered radar-derived ice velocity is 0.48m s–1, witha tendency for the radar to underestimate the ice velocityrelative to the ADCP.

4. DISCUSSION4.1. Reconciling thickness and draft measurementsTo our knowledge, the AEM flight that twice passed within350m of mooring B2 allowed the first direct comparisonbetween airborne and submarine measurements of icethickness at a scale larger than that of an individual floe(e.g. Doble and others, 2011). To compare AEM and IPSdata it is important to understand the measurements thateach instrument makes and how these relate to each other.Primarily it is important to recall that the EM-bird measuresthe combined thickness of snow and ice while the IPSmeasures just the draft of the ice. Rearranging Eqn (1) andsubstituting a thickness-weighted mean density of snow andice, �*, we can express the expected relationship betweenthe AEM and IPS measurements as

Zi þ Zsð Þ ¼�w

��D ð2Þ

where

�� ¼�iZi þ �sZsZi þ Zs

ð3Þ

At the time of the AEM overpass, the temperature andsalinity at mooring B2 were –1.686°C and 31.69, respect-ively, which yields a sea-water density, �w, of 1025 kgm–3.Substituting this and the modal values derived from ourmeasurements shown in Figure 6 (Zi +Zs = 1.6�0.025mand D=1.35�0.025m), we derive a value of �� of 860�30 kgm–3. Assuming a sea-ice density of 910�20 kgm–3

(Timco and Frederking, 1996) and a snow density of300� 100 kgm–3, taken from data for March reported byWarren and others (1999), we can use Eqn (3) to estimatethat the level ice in the vicinity of mooring B2 on 12 April2010 was 1.48� 0.09m thick, with a snow depth of0.12�0.09m. Here we assume the uncertainties arenormally distributed and uncorrelated, and we use the

Gaussian method to propagate errors. Although the largestuncertainty, both in relative and absolute terms, is that forsnow depth, the uncertainty in the value of �� has thebiggest effect on the derived values. This in turn isdependent on the uncertainties in the densities of waterand ice and our ability to determine the modes in the AEMand IPS data.For comparison, sea ice at the UAF mass-balance site

(Fig. 1) on 12 April was 1.24m thick and the snow depthmeasured by three sonic altimeters ranged between 0.29 and0.43m, with a mean of 0.35m. Although these values are notin direct agreement, the differences in ice thickness and snowdepth are consistent with each other. Although measure-ments of snow on drifting sea ice are rare, we expect snow tobe thicker on landfast ice along the Alaska Chukchi Coastthan on drifting ice offshore. Shorefast ice near Barrow istypically among the first ice to form in the Chukchi Sea andcollects snow drifting in from a broad catchment carried byprevailing northeasterly winds. In contrast, these sameprevailing winds create a semi-persistent coastal polynya atthe seaward edge of the landfast ice (Eicken and others, 2006;Mahoney and others, 2012) that may reduce the amount ofsnow advected onto drifting sea ice downwind. With lesssnow to insulate the ice surface, the offshore ice can thereforebe expected to be thicker.Closer examination of the two distributions (Fig. 6) shows

that they differ not only in the position of their modes, butalso in the shape of the tail, most noticeably for icethicknesses less than 4m. This difference cannot beaccounted for by a simple isostatic assumption, so insteadwe consider the differing footprints of the two instruments.To better match the footprints of the two instruments, weapplied a 70m boxcar smoothing filter to the IPS data. Thissmoothing changes the shape of the tail of the IPS draftdistribution to more closely resemble that of the AEM data(Fig. 9). A Gaussian filter was also tried, but resulted in apoorer fit. Having reconciled the sampling footprints of theIPS and AEM, we then applied a stretching to the smoothedIPS draft distribution that minimized the rms differencebetween it and the AEM distribution. Using this approachwe find a conversion factor from ice draft to total thicknessof 1.20� 0.01m (Fig. 10), which corresponds to a distri-bution-wide mean value of ��of 850�0.30 kgm–3. Within10 km of mooring B2, the mean thickness of ice and snowmeasured by the EM-bird is 2.66m. Our mean value��therefore corresponds to a mean ice thickness of2.40�0.14m and a mean snow depth of 0.26�0.14m.Although there is good agreement between the modes of

the AEM and smoothed, shifted IPS (Fig. 10), there aredifferences in the two distributions that warrant furthercomment. We expect the distributions to differ simplybecause the AEM flight path and IPS pseudo-track do notoverlap and the two sensors did not observe exactly the sameice. We believe this explains why the AEM data show agreater amount of thin ice (<1m) than the IPS data. There arealso differences in the tail such that the AEM data indicatemore ice between 1.4 and 4.0m, and less ice >4m, than theIPS data. This may derive from the different sampling areas,but it also probably indicates that deformed ice must betreated differently than level ice when it comes to assump-tions concerning the effective mean ice density or electricalconductivity. This is discussed further in Section 5. Therelative over- and under-observation of ice thinner andthicker than �4m, respectively, might also be explained if

Fig. 9. Probability distribution of combined ice and snow thickness(AEM) and 70m boxcar smoothed ice draft (IPS) derived from allmeasurements within 10 km of mooring B2.

Mahoney and others: Ice thickness and drift speed near Barrow 369

the sensitivity of the EM-bird was reduced to the noise levelof the receiver at this equivalent range. However, theoreticalconsiderations of the EM response show that signal-to-noiseratios are not critical until a range of 30–35m, correspondingto an ice thickness of 15–20m at a survey altitude of 15m.

4.2. Coastal ice motion observed from above andbelowFigure 8 shows the variability of ice motion at one point inthe coastal zone near Barrow over a full ice season. Zero icemotion indicates the ice above the mooring was landfast.The record shows landfast ice forming over the mooring asearly as mid-November, with several attachment anddetachment events occurring throughout the year. Ingeneral, the periods of landfast ice lengthen over the courseof the year before final break-up over the mooring aroundthe beginning of June. Both the ADCP and the coastal radarsystem identify each cessation and recommencement of icemotion, though in some cases the occurrence of landfast icecoincides with data gaps in the radar-derived ice velocityrecord (e.g. the latter part of April and most of May).Examination of the radar imagery on these occasionsindicates these gaps are due to a lack of reflectors over themooring. However, the presence of ice can be confirmedfrom the presence of stationary reflectors in the neighbor-hood. On other occasions, the radar data show apparent icemotion while the ADCP data continue to indicate landfastice (e.g. 21 April and 28 May). In these cases we find thatthe ice motion algorithm was confused by the passage ofsnow squalls and migrating birds.In identifying the onset of ice motion at the end of land-

fast periods, the ADCP and radar-derived ice velocityrecords provide accurate timings of detachment events.The detachment of landfast ice represents a significanthazard to anyone on the ice when it begins to move. At thesame time, such events are important to communities alongthe Alaska Chukchi Coast during the spring whaling season,since any open water created provides access to hunt thewhales migrating north along the coast (George and others,2004; Druckenmiller and others, 2010). Previous studies ofcoastal ice dynamics using surface radars have noted that itmay be possible to detect precursor events leading up to

detachments. Shapiro and Metzner (1989) and Mahoneyand others (2007) report the occurrence of ‘flickering’ in theradar imagery prior to breakout events. Rohith and others(2013) have taken this further to develop an algorithm basedon Hidden Markov Models that has successfully detectedsome breakout events based on ‘hidden’ characteristics ofthe gridded flow field. A more detailed study of landfast icedetachments, including an analysis of ice deformation fromradar-derived gridded ice velocities, is provided byJones (2013).

5. CONCLUSIONSBy assembling a number of different SIZONet datasetsacquired in the 2009/10 ice season near Barrow, we havebeen able to perform unique comparisons between coin-cident measurements of sea ice from above and below.Once differences in sampling footprint size between the EM-bird and the IPS had been accounted for, the probabilitydistributions of ice thickness and draft within 10 km ofmooring B2 on 12 April 2010 could be reconciled byassuming mean density of the combined snow and icecover. Moreover, this value can be used to estimate therelative proportions of snow and ice comprising thethickness measured by the EM-bird. Assuming sea-ice andsnow densities of 910�20 kgm–3 and 300� 10 kgm–3,respectively, we estimate the thickness of level sea ice nearmooring B2 at 1.48� 0.09m, with a snow depth of0.12�0.09m. Applying this method to the whole thicknessdistribution, including thick deformed ice, we estimate amean ice thickness and snow depth of 2.40� 0.14m and0.26� 0.14m, respectively. However, by including de-formed ice in the latter calculation, we may be over-estimating the effective mean density of the ice, which inturn will lead to an underestimation of ice thickness and anoverestimation of snow depth.The inhomogeneous composition of deformed ice creates

significant uncertainty in the thickness of ridges derived fromboth IPS and AEM measurements. Pressure ridges are notnecessarily in isostatic equilibrium on a point-to-point basis,and field observations indicate that the maximum keel depthis typically 3–5.5 times greater than the sail height(e.g. Melling and others, 1993; Bowen and Topham, 1996).Ridge-specific values of �* are therefore necessary to avoidoverestimation of the thickness of ridges from IPS draftmeasurements. At the same time, the AEM data may alsounderestimate the thickness of deformed ice by assuminguniform ice conductivity and neglecting voids below thewaterline that may interact with the secondary field (Reid andothers, 2003; Pfaffling and others, 2007). With the thicknessof pressure ridges gaining greater attention, primarily due tothe hazard they pose to maritime operations, reducing theseuncertainties will become increasingly important. Compar-isons between coincident airborne and submarine measure-ments of ice thickness, in particular with the inclusion ofaccurate altimetry from an EM-bird, will likely be of greatvalue in constraining more sophisticated models for treatingdeformed ice. This underscores the importance of coordin-ated observing networks such as SIZONet.Through direct comparison of coincident and co-located

time series, we show that there is good agreement betweenice velocities measured through acoustic bottom trackingwith an upward-looking ADCP and those determinedthrough optical flow analysis of imagery of the upper

Fig. 10. Probability distribution of combined ice and snowthickness (AEM) and 70m boxcar smoothed, isostatically adjustedice draft (IPS*1.20) derived from all measurements within 10 km ofmooring B2.

Mahoney and others: Ice thickness and drift speed near Barrow370

surface of the ice acquired by a coastal radar system (Figs 7and 8). This is the first independent validation of radar-derived ice velocities that we are aware of and itdemonstrates that surface radar can be an effective tool forquantitatively observing ice motion in the coastal zone.With the potential for greater temporal resolution, surfaceradar may provide a suitable alternative to bottom-mooredADCPs for measuring ice velocity in places where necessaryinfrastructure exists. Moreover, since they are able toprovide data in real time, coastal radars represent aneffective means of identifying certain ice-related hazardsas they are happening and possibly before they occur.

ACKNOWLEDGEMENTSThis work was supported by the US National Science Found-ation (awards ARC 0632398 and 0856867), the US Depart-ment of Homeland Security Center for Island, Maritime andExtreme Environment Security (CIMES) and by a Grant in Aidfor Scientific Research from the Japanese Ministry ofEducation, Culture, Sports, Science and Technology (MEXT)(awards 20221001 and 23654163).We also thank the staff ofCH2M HILL Polar Services, the Barrow Arctic ScienceConsortium and UMIAQ for field support in Barrow. Weare also grateful to the North Slope Borough Department ofWildlife Management for use of their boat and assistancefrom their staff.

REFERENCESArctic Council (2009) Arctic marine shipping assessment 2009

report (AMSA). Arctic Council, TrømsøBelliveau DJ, Bugden GL, Eid BM and Calnan CJ (1990) Sea icevelocity measurements by upward-looking Doppler currentprofilers. J. Atmos. Ocean. Technol., 7(4), 596–602 (doi:10.1175/1520-0426(1990)007<0596:SIVMBU>2.0.CO;2)

Bowen RG and Topham DR (1996) A study of the morphology of adiscontinuous section of a first year arctic pressure ridge. ColdReg. Sci. Technol., 24(1), 83–100 (doi: 10.1016/0165-232X(95)00002-S)

Doble MJ, Skouroup H, Wadhams P and Geiger CA (2011) Therelation between Arctic sea ice surface elevation and draft: acase study using coincident AUV sonar and airborne scanninglaser. J. Geophys. Res., 116(C8), C00E03 (doi: 10.1029/2011JC007076)

Druckenmiller ML, Eicken H, Johnson MA, Pringle DJ and WilliamsCC (2009) Toward an integrated coastal sea-ice observatory:system components and a case study at Barrow, Alaska. ColdReg. Sci. Technol., 56(2–3), 61–72 (doi: 10.1016/j.coldregions.2008.12.003)

Druckenmiller ML, Eicken H, George JC and Brower L (2010)Assessing the shorefast ice: Iñupiat whaling trails off Barrow,Alaska. In Krupnik I, Aporta C, Gearheard S, Laidler GJ andHolm LK eds SIKU: knowing our ice: documenting Inuit sea iceknowledge and use. Springer, Dordrecht, 203–228

Eicken H, Shapiro LH, Gaylord AG, Mahoney A and Cotter PW(2006) Mapping and characterization of recurring spring leadsand landfast ice in the Beaufort and Chukchi seas. (OCS StudyMMS 2005-068) Minerals Management Service, US Departmentof the Interior, Anchorage, AK

Forsström S, Gerland S and Pedersen CA (2011) Thickness anddensity of snow-covered sea ice and hydrostatic equilibriumassumption from in situ measurements in Fram Strait, theBarents Sea and the Svalbard coast. Ann. Glaciol., 52(57 Pt 2),261–270 (doi: 10.3189/172756411795931598)

Fowler C (2003) Polar Pathfinder Daily 25km EASE-Grid Sea IceMotion Vectors. National Snow and Ice Data Center, Boulder,CO. Digital media: nsidc.org/data/nsidc-0116

Fukamachi Y, Mizuta G, Ohshima KI, Toyota T, Kimura N andWakatsuchiM (2006) Sea ice thickness in the southwestern Sea ofOkhotsk revealed by a moored ice-profiling sonar. J. Geophys.Res., 111(C9), C09018 (doi: 10.1029/2005JC003327)

George JC, Huntington HP, Brewster K, Eicken H, Norton DW andGlenn R (2004) Observations on shorefast ice dynamics inArctic Alaska and the responses of the Iñupiat huntingcommunity. Arctic, 57(4), 363–374

Gordon RL (1996) Acoustic Doppler current profiler principles ofoperation: a practical primer, 2nd edn. RD Instruments, SanDiego, CA

Haas C, Lobach J, Hendricks S, Rabenstein L and Pfaffling A (2009)Helicopter-borne measurements of sea ice thickness, using asmall and lightweight, digital EM system. J. Appl. Geophys.,67(3), 234–241 (doi: 10.1016/j.jappgeo.2008.05.005)

Haas C, Hendricks S, Eicken H and Herber A (2010) Synopticairborne thickness surveys reveal state of Arctic sea icecover. Geophys. Res. Lett., 37(9), L09501 (doi: 10.129/2010GL042652)

Hansen E and 7 others (2013) Thinning of Arctic sea ice observedin Fram Strait: 1990–2011. J. Geophys. Res., 118(C10),5202–5221 (doi: 10.1002/jgrc.20393)

International Organization for Standardization (ISO) (2010) Petro-leum and natural gas industries – Arctic offshore structures. (ISO/DIS 19906) International Organization for Standardization,Geneva

Jones JM (2013) Landfast sea ice formation and deformation nearBarrow, Alaska: variability and implications for ice stability. (MSthesis, University of Alaska Fairbanks)

Kurtz NT and 6 others (2009) Estimation of sea ice thicknessdistributions through the combination of snow depth andsatellite laser altimetry data. J. Geophys. Res., 114(C10),C10007 (doi: 10.1029/2009JC005292)

Kwok R and Cunningham GF (2008) ICESat over Arctic sea ice:estimation of snow depth and ice thickness. J. Geophys. Res.,113(C8), C08010 (doi: 10.1029/2008JC004753)

Kwok R, CunninghamGF andHiblerWD III (2003) Sub-daily sea icemotion and deformation from RADARSAT observations. Geo-phys. Res. Lett., 30(23), 2218 (doi: 10.1029/2003GL018723)

Laxon SW and 14 others (2013) CryoSat-2 estimates of Arctic seaice thickness and volume. Geophys. Res. Lett., 40(4), 732–737(doi: 10.1002/grl.50193)

Mahoney A, Eicken H and Shapiro L (2007) How fast is landfast seaice? A study of the attachment and detachment of nearshore iceat Barrow, Alaska. Cold Reg. Sci. Technol., 47(3), 233–255 (doi:10.1016/j.coldregions.2006.09.005)

Mahoney AR and 6 others (2012) Mapping and characteriz-ation of recurring spring leads and landfast ice in theBeaufort and Chukchi seas. (OCS Study BOEM 2012-067)Washington DC

Melling H, Topham DR and Riedel D (1993) Topography of theupper and lower surfaces of 10 hectares of deformed sea ice.Cold Reg. Sci. Technol., 21(4), 349–369 (doi: 10.1016/0165-232X(93)90012-W)

Melling H, Johnston PH and Riedel DA (1995) Measurements of theunderside topography of sea ice by moored subsea sonar.J. Atmos. Ocean. Technol., 12(3), 589–602 (doi: 10.1175/1520-0426(1995)012<0589:MOTUTO>2.0.CO;2)

Pfaffling A, Haas C and Reid JE (2007) A direct helicopter EM seaice thickness inversion, assessed with synthetic and field data.Geophysics, 72(4), F127–F137 (doi: 10.1190/1.2732551)

Postberg F, Schmidt J, Hillier J, Kempf S and Srama R (2011) A salt-water reservoir as the source of a compositionally stratifiedplume on Enceladus. Nature, 474(7353), 620–622 (doi:10.1038/nature10175)

Reid JE, Worby AP, Vrbancich J and Munro AIS (2003)Shipborne electromagnetic measurements of Antarctic

Mahoney and others: Ice thickness and drift speed near Barrow 371

sea-ice thickness. Geophysics, 68(5), 1537–1546 (doi: 10.1190/1.1620627)

Rohith MV, Jones J, Eicken H and Kambhamettu C (2013) Extractingquantitative information on coastal ice dynamics and icehazard events from marine radar digital imagery. IEEE Trans.Geosci. Remote Sens., 51(5), 2556–2570 (doi: 10.1109/TGRS.2012.2217972)

Schmidt C (2011) Despite data gaps, US moves closer to drilling inArctic Ocean. Science, 333(6044), 812–813

Shapiro LH and Metzner RC (1989) Nearshore ice conditions fromradar data, Point Barrow, Alaska. (Rep. UAG-R312) Universityof Alaska Fairbanks, Fairbanks, AK

Stroeve JC, Serreze MC, Holland MM, Kay JE, Maslanik J andBarrett AP (2012) The Arctic’s rapidly shrinking sea ice cover: aresearch synthesis. Climatic Change, 110(3–4), 1005–1027 (doi:10.1007/s10584-011-0101-1)

Thorndike AS, Rothrock DA, Maykut GA and Colony R (1975)The thickness distribution of sea ice. J. Geophys. Res., 80(33),4501–4513 (doi: 10.1029/JC080i033p04501)

Timco GW and Frederking RMW (1996) A review of sea icedensity. Cold Reg. Sci. Technol., 24(1), 1–6 (doi: 10.1016/0165-232X(95)00007-X)

Wang M and Overland JE (2009) A sea ice free summer Arcticwithin 30 years? Geophys. Res. Lett., 36(7), L07502 (doi:10.1029/2009GL037820)

Warren SG and 6 others (1999) Snow depth on Arctic sea ice.J. Climate, 12(6), 1814–1829 (doi: 10.1175/1520-0442(1999)012<1814:SDOASI>2.0.CO;2)

Williams SB and 6 others (2008) AUV-assisted surveying of relicreef sites. Proceedings of OCEANS 2008, 15–18 September2008, Quebec City, QC. Institute of Electrical and ElectronicsEngineers, Piscataway, NJ, 1–7

Mahoney and others: Ice thickness and drift speed near Barrow372