G 3. AN ELECTRONIC JOURNAL OF THE EARTH SCIENCES Published by AGU and the Geochemical Society

Transcription

1 Geosystems G 3 AN ELECTRONIC JOURNAL OF THE EARTH SCIENCES Published by AGU and the Geochemical Society Data Brief Volume 9, Number 6 17 June 2008 Q06006, doi: /2007gc ISSN: New airborne laser altimetry over the Thwaites Glacier catchment, West Antarctica Duncan A. Young, Scott D. Kempf, Donald D. Blankenship, John W. Holt, and David L. Morse Institute for, Jackson School of Geosciences, University of Texas at Austin, Austin, Texas, USA (duncan@ig.utexas.edu) [1] A new airborne altimetry data set collected over Thwaites Glacier, one of Antarctica s most active ice streams, demonstrates the improvement in publicly available digital elevation models (DEMs) of the Antarctic ice sheet. The airborne altimetry comprises 35,000 line km sampled at 20 m along track. The full data set has a relative error of ±20 cm; a reference subset has an error of ±8 cm. These data are offset from ICESat observations by +20 cm. We find that a recently released ICESat DEM provides a good model of the surface of Thwaites Glacier, despite cloud cover and wide track spacings. However, the ICESat DEM s accuracy is an order of magnitude less than that of the ICESat profile data. Our airborne data will serve as an additional temporal reference for the evolution of Thwaites Glacier s surface as well as aid the construction of future high-resolution DEMs. Components: 5518 words, 5 figures. Keywords: Antarctica; airborne altimetry; digital elevation models; Thwaites Glacier; satellite altimetry. Index Terms: 0730 Cryosphere: Ice streams; 0994 Exploration : Instruments and techniques; 0794 Cryosphere: Instruments and techniques. Received 24 December 2007; Revised 31 March 2008; Accepted 11 April 2008; Published 17 June Young, D. A., S. D. Kempf, D. D. Blankenship, J. W. Holt, and D. L. Morse (2008), New airborne laser altimetry over the Thwaites Glacier catchment, West Antarctica, Geochem. Geophys. Geosyst., 9, Q06006, doi: /2007gc Introduction [2] Ice sheet dynamics are strongly represented in ice surface topography. The gravitational stresses that drive ice flow are controlled by ice surface slopes several ice thicknesses in scale. Ice surface slopes also largely dictate basal hydraulic gradients that control basal water systems and at smaller scales reflect stresses transmitted laterally within the ice column and local variations in accumulation. Because of the importance of ice topography, the advent of space-based altimetry has revolutionized our understanding of the Antarctic ice sheet [Ridley et al., 1993; Bamber, 1994; Zwally et al., 2005]. However, current space-based altimeters have limitations which affect accuracy in certain regions. One of these regions is the Amundsen Sea Embayment (ASE) of the West Antarctic Ice Sheet, a margin of the ice sheet thought to be actively responding to climate change [Shepherd et al., 2004; Payne et al., 2004]. [3] The incoherent radar altimeters on the European Space Agency s European Remote Sensing satellites have a 16 km wide pulse-limited footprint that induces systematic errors in sloped regions, as typically found at the margin of the ice sheet Copyright 2008 by the American Geophysical Union 1 of 11

2 [Bamber and Gomez-Dans, 2005]. The 60-m footprint of the Geoscience Laser Altimeter System (GLAS) instrument mounted on the ICESat greatly reduces slope effects. Clouds, however, are a nearconstant feature in the ASE and may limit the performance of this system [Abshire et al., 2005]. Laser life issues limit the track spacing of GLAS to approximately 8 km in the ASE [Schutz et al., 2005]. Both of these systems have been used to generate digital elevation models (DEMs) of the Antarctic ice sheet [DiMarzio et al., 2007; Liu et al., 2001; Bamber and Bindschadler, 1997]. [4] During the austral summer, a joint aerogeophysical survey of the embayment was carried out by teams from the University of Texas Institute for (UTIG) and the British Antarctic Survey (BAS), for the Airborne Geophysical Survey of the Amundsen Sea Embayment, Antarctica (AGASEA), and the Basin Balance Assessment and Synthesis (BBAS) projects, respectively. Both aircraft were outfitted with icepenetrating radar, gravimeters, and magnetometers. In addition, the UTIG aircraft was equipped with a nadir-pointing laser distance meter for measuring surface elevations. [5] In this paper, we report on these new altimetry data and use them to validate publicly available digital elevation models currently used to constrain ice dynamics in West Antarctica. 2. Data Acquisition 2.1. Survey Design [6] The ASE survey was divided into two sections: BAS focused on the catchment of Pine Island Glacier [Vaughan et al., 2006], while UTIG flew a grid with 15 km spacing over the Thwaites Glacier and Smith Glacier catchments [Holt et al., 2006; Diehl et al., 2008]. Flights were conducted out of two camps. The primary UTIG camp (THW) was located 20 km south of the ASE Ross Sea Embayment ice divide at ( 78.5 S, W); a second camp, operated by BAS (hereinafter called PNE) ( S, W), was located in the Pine Island catchment to the west (Figure 1). The relatively steep surface topography of the ice sheet demanded that the AGASEA grid be broken into eight subsections flown at different elevations, ranging from 1400 m above sea level (asl) to 3000 m asl, in order to maintain a terrain clearance of at least 500 m. In practice, the average terrain clearance was 844 m. In addition, targeted profiles were also flown, including three reflights of ICESat ground tracks Aerogeophysical System [7] The aerogeophysical platform used was a modified ski-equipped de Havilland Canada DHC-6 Twin Otter operated by Kenn Borek Air, which has been used by UTIG in Antarctica for many years [Blankenship et al., 2001, 2003]. The aircraft typically flew at 70 m s 1. The core acquisition system comprised several elements, including a phase-coherent high-capability airborne radar sounder [Peters et al., 2005] that sampled at 200 Hz. Also included were a towed magnetometer (10 Hz), gravimeter (1 Hz), inertial navigation system (INS) (8 Hz), laser altimeter (3.5 Hz), and real-time Global Positioning System (GPS) (1 Hz) [Diehl et al., 2008; Blankenship et al., 2001]. All of these systems recorded discrete data packets synchronized to a common GPS-derived time base Altimeter [8] The laser altimeter was a Riegl LD HiP-LR Distance Meter, using a 3.5 mw diode laser operating at 905 nm wavelength. The Riegl replaced the azimuth system used in previous UTIG surveys [Spikes et al., 2003; Blankenship et al., 2001]. The distance meter operated at 2000 Hz with a range resolution of 2 mm; the greatest range from each 575 pulse block was recorded at a rate of 3.5 Hz, along with the deviation and maximum amplitude of the detected pulse echoes. The typical ground spot size was 1-m wide; the pulse blocks resulted in an along-track resolution of 20 m Navigation and Positioning Instrumentation [9] Two onboard dual-carrier-phase GPS receivers (an Ashtech Z-Surveyor and a Trimble 5700) recorded at 2 Hz for the entire flight plus 30 min both before takeoff and after landing; two additional GPS receivers were located at each camp (a Z-Surveyor and a 5700 at THW and a Z-Surveyor and a Leica at PNE) to provide references for differential GPS processing. Altitude and ground tracks of the aircraft were controlled by an autopilot (Collins APS-65) coupled to the aircraft s navigational GPS receiver and pressure altimeter. A Litton 92 ring laser gyro INS provided attitude data, with a quoted angular resolution of 0.05 [Vaughn et al., 1996]. 2of11

3 Geosystems G 3 young et al.: thwaites airborne laser altimetry /2007GC Figure 1. (a) The AGASEA survey lines, superposed on RAMP DEM contours [Liu et al., 2001]. Lines are classified by the final source of their positioning: differential GPS referenced either to THW camp (the green triangle) or to PNE camp (the blue triangle) or using precise point positioning (red lines). Dotted lines are not used to constrain line leveling. Purple lines are not included owing to a lack of validating crossovers, or loss of altimetry data. The top inset shows the location in Antarctica; the bottom inset shows the survey in the context of West Antarctic balance velocities [Le Brocq et al., 2006]. (b) AGASEA lines compared to GLAS reference tracks for a portion of the interior Operations [10] Flights were conducted from THW and to and from PNE between 12 December 2004 and 30 January GPS quality was assessed in the field using numbers of cycle slips and calculation of differential pseudorange elevations. UTIG acquired 43,500 line km of data on 75 flights. Laserderived surface elevations were returned for 87% of the flight lines, with low-level clouds causing some data gaps. 3. Data Reduction [11] In the absence of precise topographic reference points in central Antarctica, our primary tool for validating our elevation data was to establish the internal consistency of the survey lines using the derived elevation differences at transect crossings. We compared pairs of elevation footprints from separate lines with samples within 40 m of each other. Crossover difference minimization was used to validate the altimeter orientation on the aircraft, to select high-quality GPS positioning solutions, and to level transects Positioning the Altimeter in the Field [12] The altimeter was installed to point nadir when flying; therefore, to account for the aircraft s nose-up flight attitude, the laser was pointed rearward relative to the aircraft s landed attitude by a 3of11

4 couple of degrees. The orientation of the distance finder and INS within the aircraft was measured by leveling numerous points within the aircraft itself and on the instrument body. The INS was then run for half an hour while the aircraft was parked to find its reference frame with respect to the aircraft Finding the Range to the Surface [13] The time-stamped raw range data output from the distance finder was corrected to a common GPS-derived time base used for all acquisition. Abrupt range discontinuities were used to filter out early returns due to clouds. INS data were used to calculate the heading, roll, and pitch of the aircraft at 8 Hz. These data were then interpolated to the 3.5 Hz range sampling rate. Finally, we used the method described by Spikes et al. [2003] to project the laser range vector with respect to the aircraft s baseplate Positioning the Aircraft [14] The GPS raw data were reduced to position solutions using two independent software packages: Kinematic and Rapid Static (KARS) [Mader, 1992] and GPS Inferred Positioning System/Orbit Analysis Simulation Software (GIPSY/OASIS II) [Lichten et al., 1995]. KARS is a differential GPS technique that combined base station receiver data with aircraft receiver data using dual-carrier-phase processing to find the relative position of the aircraft every half second. Solutions were initiated from both ends of the flight. To find the base station positions, static precise point positioning solutions (PPP) [Zumberge et al., 1997] were used. The PPP method uses post hoc GPS satellite clock and orientation data to position the receiver with respect to the GPS constellation. Geographic positions were computed using the GIPSY package for all base stations for each flight period, and a linear fit was used to account for ice motion over the season. The fit position for the start of each flight was used as the absolute position of the base station and thus as the reference point for the aircraft. [15] As an alternate method, kinematic PPP was used to find aircraft position solutions without reference to a base station [Gao and Wojciechowski, 2004]. Zhang and Forsberg [2007] used PPP methods to successfully obtain airborne laser altimetry with RMS errors of 10 cm in the North Atlantic. We again used GIPSY/OASIS II, using higher-rate satellite data, and seeded the solutions with the coarse acquisition code real-time GPS used for navigation. [16] The World Geodetic System 1984 (WGS 84) ellipsoid, tied to the International Terrestrial Reference Frame 94 [National Imagery and Mapping Agency, 2000], was used as a datum. After generation, each position solution was converted to the Scientific Committee on Antarctic Research standard projection for Antarctica (polar stereographic with the latitude of true scale at 71 S) and was corrected for the height of the GPS antennas from the base of the aircraft fuselage. With multiple receivers on the aircraft and both base camps, in addition to the two modes of processing, we obtained up to 18 different position solutions for each transect. For each aircraft solution, the range vectors were applied to find a corresponding surface elevation solution. [17] Crossover analysis of all surface elevation solutions revealed that for short and moderate baseline distances (up to 300 km) the KARS solutions with base reference data (RMS ± 18 cm without outliers) were better than the GIPSY solutions (RMS ± 29 cm, Figure 2). Kinematic GIPSY positions were more reliable farther from the camp as KARS solutions degraded to an RMS of 31 cm; however, we discovered a 17 cm vertical bias between kinematic KARS (using static GIPSY for the base) and kinematic GIPSY. We believe that this offset is due to a lack of an explicit delay model for the Antarctic troposphere in the tropospheric approximation used in kinematic GIPSY [Niell, 1996] and, to a lesser extent, to the effect of solid Earth tides. Given a lack of base station data distributed over a range of elevations, an explicit correction for errors in tropospheric delay was not implemented as suggested by Shan et al. [2007]; instead, we leveled the GIPSY data to successful KARS solutions, as described in section 3.4. [18] The best quality surface elevation solutions were decided using an iterative RMS deviation determination. Transects with fewer than three crossovers were immediately rejected. For each of the remaining transects, solutions with the highest RMS crossover deviation were eliminated, and then all the crossovers for the survey were recomputed. This process was repeated until a single solution remained for each transect, provided that the last solution s RMS deviation was below a chisquare threshold RMS crossover of 67.8 cm (based on a 90% probability that the GPS solution is valid). A third of the transects did not meet this criterion. The remaining two thirds of the transects, 4of11

5 Figure 2. Cumulative histograms of different altimetry crossover combinations. Curves that are closer to the left axis are better. Differential pseudorange results (black curve) lack clock corrections and were used only for field quality assessment. PPP (red curve) performed better than distant relative GPS (dark green curve) but performed notably worse than relative solutions near the base station (bright green curve). The combination of the two methods (gray curve) was worse than each separately, indicating systematic biases. Solutions filtered by crossovers were much improved (dark blue solid curve); leveling improved the statistics further (blue dashed curve). Including a subset of lines with poorer solutions increased coverage but slightly diluted the final survey quality (thick blue curve). each with a unique surface elevation solution, became a reference set of surface elevations with an RMS error of ±13 cm. All of these solutions used the KARS processing Leveling the Data [19] We assumed that the primary component of vertical error in convergent GPS solutions is longperiod linear drift. We iteratively found the best linear correction for all lines within the reference set that minimizes the RMS crossover deviation. After leveling, the RMS deviation was reduced to ±8 cm for the reference set. Transects outside the reference set were compared with this leveled set, and the solution that produced the lowest RMS was selected. A linear correction that best fit the reference elevations was applied to each of these secondary transects. The final data set had an RMS deviation of ±20 cm. [20] No tide correction was applied to the data. The differential Earth tides between the base station and the aircraft have a mean amplitude of 1 mm, and ocean tides are poorly known in the Amundsen Sea Sector. Crossovers located over floating ice and open water were removed from the crossover analysis Data Release [21] The final Mb data set will be released as an ASCII text file at both the AGASEA project Web site ( and the National Snow and Ice Data Center (NSIDC; html). 4. Comparison With GLAS Elevation Profiles [22] We first compared our data to version 28 of the GLAS12 Ice Surface Elevation product generated by the ICESat project. We used data from operational periods 2A (September 2003) to 3I (November 2007), excluding the as yet unreleased 2C data. The GLAS elevation data were saturation corrected [Fricker et al., 2005], and all footprints with detector gains greater than 50 were rejected under the assumption that they were contaminated with clouds. The data were projected from their native Topex ellipsoid to WGS 84. We searched our entire airborne data for footprints within 40 m of the remaining 5of11

6 Figure 3. Maps of differences between AGASEA altimetry and publicly available Antarctic topographic data. The 0.3 surface slope contour is shown in black. A normalized histogram of the difference within the red box is shown in the top right corner of each map. See text for discussion. GLAS footprint centers and differenced the elevations (Figure 3, top left). [23] The resulting 3386 crossings indicated that the two systems have an RMS difference of 1.28 m, which may be driven by the large observed ice loss in this area. The mode of AGASEA surface elevations is +27 cm higher than the GLAS observations. Restricting the temporal range of crossovers tends to decrease the RMS differences but to increase the bias. An interior subset restricted to a flat portion of the ASE ice divide, with 331 points, had an RMS difference of 40 cm and had a modal elevation offset of +20 cm. The source of this modal offset is unclear. 5. Comparison With Existing Antarctic DEMs [24] We compared our airborne altimetry data to three largely satellite-derived DEMs, all available online from the NSIDC. The first is the 1997 ERS- 1 DEM (ERS-1) [Bamber and Bindschadler, 1997], the second is the compilation DEM created by the Radarsat Antarctic Mapping Project (RAMP) [Liu et al., 2001], and the third is the 6of11

7 recently released GLAS DEM (ICESat) [DiMarzio et al., 2007]. Bamber and Gomez-Dans [2005] compared the first two DEMs to GLAS laser altimetry and found systematic offsets in the ERS-1 DEM related to surface slope and large errors in the coastal regions of the RAMP DEM. We reassessed these DEMs in light of our higherresolution data. We subtracted each DEM from every point in the entire AGASEA data set, including flights near the coast and over volcanoes as well as the interior flat area as in the analysis in section 4. We used bicubic interpolation to determine DEM elevations between nodes [Wessel and Smith, 1991]. Comparisons were made using the WGS 84 vertical datum. In all cases, the magnitude of the variability of the AGASEA-DEM differences was more than an order higher than the magnitude of the internal error estimate for the airborne laser data and that of a direct comparison of GLAS and AGASEA footprints ERS-1 DEM [25] The ERS-1 DEM used data from a low-trackspacing 336-day geodetic campaign in This DEM used data from the incoherent radar altimeter on board ERS-1, which had a pulselimited footprint of 16 km. As such, the system was affected by off-nadir echoes in regions sloping more than 0.8. However, data coverage was dense, with tracks spaced at 3 km at the latitudes of interest here. A grid with a cell size of 5 km was released. [26] For all points we found an RMS difference of 27.4 m and a modal offset between AGASEA and ERS-1 of 1.4 m. For the interior area, we found an RMS difference of 5.2 m and a modal offset of 0.46 m (Figure 3, bottom left) RAMP DEM [27] The intent of the RAMP DEM was to fill in gaps in extended ERS-1 coverage [Zwally et al., 1997] due to slopes above 0.8 by using digitized cartographic data from the Antarctic Digital Database [Antarctic Digital Database Consortium, 2000]. The specific data used in the AGASEA survey regions came from 1:500,000-scale maps compiled from U.S. Navy tricamera aerial photography in 1966, controlled by the U.S. Geological Survey observations in Significant errors in the cartographic data were indicated by Bamber and Gomez-Dans [2005]. Here we compare our data with the 400-m product. For the entire DEM, we found an RMS difference of 55 m and a modal offset of +0.4 m; for the interior only, the RMS difference dropped to 5.7 m, and there was a modal offset of m (Figure 3, bottom right) ICESat DEM [28] The ICESat project recently released a DEM compiled from GLAS data acquired between 2003 and A variable size window (up to 20 km across) was used to extrapolate GLAS data to a regular grid. NSIDC recently released a 500-m version of the ICESat DEM projected in polar stereographic with a latitude of true scale at 70 S (compared to the 71 S used for most other Antarctic data sets). We converted our data to this projection and compared these data to the DEM. [29] We found an RMS difference of 18.4 m, with a modal offset of 20 cm for the entire AGASEA data set; for the interior subset, the RMS difference was 4.4 m, with a modal offset of +40 cm (Figure 3, top right). As the RMS difference is much higher than that seen using crossovers with actual observations, in this case, much of the discrepancy is likely due to the limitations of interpolation. Our observations differ from that of DiMarzio et al. [2007]; they compared a similar GLAS-derived DEM for Greenland to Airborne Topographic Mapper [Krabill et al., 2002] observations and found a mean bias of 41 cm and an RMS difference of 88 cm Causes for Deviations [30] To understand the cause of the deviations between our data and the DEMs, we examined the following three hypotheses: (1) that larger footprints for the satellite-based systems cause biases in sloping surfaces, (2) that biases are due to rough surfaces at scales less than that of either the altimeter footprint or the interpolation window, and (3) that the ice surface has changed between observations Slope [31] To examine the first hypothesis, we smoothed each DEM using a 15 km cosine arch curve, extracted long-wavelength slope values for each laser spot, and compared them with our deviation data. Our work (Figure 4) concurs with the work of Bamber and Gomez-Dans [2005]; we find that mismatches are dependent on surface slope in the ERS-1 DEM that is consistent with systematic mislocation of ERS footprints on steep surfaces, 7of11

8 Figure 4. Slope dependence of AGASEA-DEM differences. The RAMP DEM s cartography, used to fill in regions steeper than 0.8 [Liu et al., 2001], disagrees strongly with our altimetry. The ERS-1 DEM, based on observations, shows a degradation with slope that may be due to both slope dependence and true surface lowering; the ICESat DEM is most successful in recovering topography at all slopes. Dashed curves indicate the results of Bamber and Gomez-Dans [2005], who examined DEMs of the entire ice sheet using GLAS profile data. resulting in a derived DEM higher in elevation than in reality. A complication in this interpretation is that steep areas are flowing rapidly at Thwaites Glacier [Lang et al., 2004] and thus may be sensitive to changes in ice dynamics; this point will be discussed further in section A larger slope-related issue in the RAMP DEM is likely due to absolute error in the photogrammetry-based cartography that was used for steep regions. The ICESat DEM showed the least dependence on slope, as expected Roughness [32] For each of our lines, we calculated the alongtrack RMS slope at an 800-m length scale after detrending the profile elevations. This statistic is a good measure of roughness at a scale relevant to both radar altimeter footprints and the interpolation window used for the ICESat DEM. As seen in Figure 5, for the ERS-1 DEM, there is a clear correlation between roughness, as measured in the airborne data, and difference between the airborneand satellite-derived data. The DEM is consistently higher than the altimetry data in rougher areas. This is consistent with the course spatial resolution of the ERS-1 grid and thus with the failure of the ERS-1 altimeter to capture narrow troughs in the ice surface. Despite the wide scatter in the comparison with the RAMP data set (Figure 5, middle), we find no correlation of the sign of the deviation when compared with roughness. This is likely due to the use of cartographic data in morphologically complex areas, with effectively random level biases. The ICESat DEM also shows little dependence of the sign of deviation on roughness at the 800-m scale. The low scatter implies that this DEM captures much of the kilometer-scale structure of the ice sheet; deviation between data sets is due to the DEM interpolation method Elevation Change [33] Various analyses of surface elevation change rates using satellite-derived data have shown that the surfaces of Thwaites Glacier and its neighbors are lowering rapidly [Davis et al., 2005; Zwally et al., 2005; Wingham et al., 2006]. The decade separating the ERS-1 geodetic campaign from the AGASEA survey implies that significant drawdown should be observable between the two sets of observations. Indeed, Figure 3 shows tens of meters of apparent surface lowering in the comparison with the ERS-1 DEM, near the coast and along the main trunks of Thwaites and Smith glaciers. However, the sign of the apparent lowering is the same as that expected from slope- and roughness-dependent errors in the ERS data, both 8of11

9 Figure 5. Density plots of along-track RMS slope (at an 800-m baseline) as a function of DEM difference. The ERS-1 DEM shows a clear trend toward higher DEM values in areas of rough terrain; the RAMP DEM is scattered, as the rough areas are derived from photogrammetry. Errors in the ICESat DEM are not strongly dependent on roughness. of which should be enhanced in the areas of fast flow. The regions of fast flow in the RAMP DEM are dominated by older cartographic data and show significant positive and negative offsets. These offsets, as discussed in section 5.2, likely reflect the vintage of the datums used for the cartographic data and not real changes in surface elevation. The GLAS data used in the ICESat data set were contemporaneous with the AGASEA survey, and therefore time-dependent differences between the airborne data and the DEM should be minor. 6. Conclusions [34] We present a new surface elevation data set for a poorly surveyed but actively changing portion of the West Antarctic Ice Sheet. Comparisons of these new data with satellite-derived digital elevation models reveal the improvement of the GLAS laser 9of11

10 altimeter over the ERS-1 radar altimeter. Our results are consistent with earlier assessments of compilation DEMs [Bamber and Gomez-Dans, 2005] as well as observations of surface elevation lowering [Zwally et al., 2005; Davis et al., 2005], although further quantification of these differences is not possible without a complete reconciliation of the very different types of altimetry data [e.g., Shepherd and Wingham, 2007]. However, the combination of GLAS and AGASEA altimetry should provide a broad spatial spectrum benchmark for future studies of ice elevation and slope changes for Thwaites Glacier and its neighbors. Acknowledgments [35] We thank two anonymous reviewers for suggestions that improved this paper. This work was funded by NSF grants OPP to J. Holt, D. Blankenship, and D. Morse and OPP to Blankenship and Holt and by the G. Unger Vetlesen Foundation. Support for this work was provided in part by the John A. and Katherine G. Jackson School of Geosciences and the Geology Foundation at the University of Texas at Austin. We thank UNAVCO for providing the Trimble GPS revivers used in this survey and NSIDC for providing access to the DEMs and GLAS data. We also thank Kenn Borek Air, the 109th NAG, and BAS for supporting this survey. This is UTIG contribution References Abshire, J. B., X. Sun, H. Riris, J. M. Sirota, J. F. McGarry, S. Palm, D. Yi, and P. Liiva (2005), Geoscience Laser Altimeter System (GLAS) on the ICESat mission: On-orbit measurement performance, Geophys. Res. Lett., 32, L21S02, doi: /2005gl Antarctic Digital Database Consortium (2000), Antarctic Digital Database, Version 3.0: Database, Manual and Bibliography, 4.1 ed., Sci. Comm. on Antarct. Res., Cambridge, U. K. Bamber, J. L. (1994), A digital elevation model of the Antarctic ice sheet derived from ERS-1 altimeter data and comparison with terrestrial measurements, Ann. Glaciol., 20, Bamber, J. L., and R. A. Bindschadler (1997), An improved elevation dataset for climate and ice-sheet modelling: Validation with satellite imagery, Ann. Glaciol., 25, Bamber, J., and J. L. Gomez-Dans (2005), The accuracy of digital elevation models of the Antarctic continent, Earth Planet. Sci. Lett., 237(3 4), , doi: / jepsl Blankenship, D. D., D. Morse, C. A. Finn, R. E. Bell, M. E. Peters, S. D. Kempf, S. M. Hodge, M. Studinger, J. C. Behrendt, and J. M. Brozena (2001), Geological controls on the initiation of rapid basal motion for West Antarctic ice streams: A geophysical perspective including new airborne radar sounding and laser altimetry results, in The West Antarctic Ice Sheet: Behavior and Environment, Antarct. Res. Ser., vol. 77, edited by R. B. Alley and R. A. Bindschadler, pp , AGU, Washington, D. C. Blankenship, D. D., J. W. Holt, D. L. Morse, and I. W. D. Dalziel (2003), The scientific and technical evolution of Antarctic over-ice aerogeophysics using an instrumented Twin Otter, in REVEAL: REmote Views and Exploration of Antarctic Lithosphere Workshop: The Future of Antarctic Airborne Geophysical Capabilities Workshop Report, edited by C. Finn et al., pp , U.S. Geol. Surv. Open File Rep., Davis, C. H., Y. Li, J. R. McConnell, M. M. Frey, and E. Hanna (2005), Snowfall-driven growth in East Antarctic Ice Sheet mitigates recent sea-level rise, Science, 308(5730), , doi: /science Diehl, T. M., J. W. Holt, D. D. Blankenship, D. A. Young, T. A. Jordan, and F. Ferraccioli (2008), First airborne gravity results over the Thwaites Glacier catchment, West Antarctica, Geochem. Geophys. Geosyst., 9, Q04011, doi: / 2007GC DiMarzio, J., A. Brenner, R. Schutz, C. A. Shuman, and H. J. Zwally (2007), GLAS/ICESat 500 m laser altimetry digital elevation model of Antarctica, nsidc-0304, Natl. Snow and Ice Data Cent., Boulder, Colo. Fricker, H. A., A. Borsa, B. Minster, C. Carabajal, K. Quinn, and B. Bills (2005), Assessment of ICESat performance at the salar de Uyuni, Bolivia, Geophys. Res. Lett., 32, L21S06, doi: /2005gl Gao, Y., and A. Wojciechowski (2004), High precision kinematic positioning using single dual-frequency GPS receiver, Int. Arch. Photogramm. Remote Sens., 34, 1 5. Holt, J. W., D. D. Blankenship, D. L. Morse, D. A. Young, M. E. Peters, S. D. Kempf, T. G. Richter, D. G. Vaughan, and H. F. J. Corr (2006), New boundary conditions for the West Antarctic Ice Sheet: Subglacial topography of the Thwaites and Smith glacier catchments, Geophys. Res. Lett., 33, L09502, doi: /2005gl Krabill, W. B., W. Abdalati, E. B. Frederick, S. S. Manizade, C. F. Martin, J. G. Sonntag, R. N. Swift, R. H. Thomas, and J. G. Yungel (2002), Aircraft laser altimetry measurement of elevation changes of the Greenland ice sheet: Technique and accuracy assessment, J. Geodyn., 34(3 4), Lang, O., B. T. Rabus, and S. W. Dech (2004), Velocity map of the Thwaites Glacier catchment, West Antarctica, J. Glaciol., 50(168), Le Brocq, A. M., A. J. Payne, and M. J. Siegert (2006), West Antarctic balance calculations: Impact of flux-routing algorithm, smoothing algorithm and topography, Comput. Geosci., 32(10), Lichten, S., Y. Bar-Sever, W. Bertiger, M. Heflin, K. Hurst, R. J. Muellersehoen, S.-C. Wu, T. Yunck, and J. Zumberge (1995), Gipsy-Oasis II: A high precision GPS data processing system and general satellite orbit, Tech. Rep , Jet Propul. Lab., Calif. Inst. of Technol., Pasadena, Calif. Liu, H., K. C. Jezek, B. Li, and J. L. Zhao (2001), Radarsat Antarctic Mapping Project digital elevation model version 2, nsidc-0082, Natl. Snow and Ice Data Cent., Boulder, Colo. Mader, G. L. (1992), Rapid static and kinematic Global Positioning System solutions using the ambiguity function technique, J. Geophys. Res., 97(B3), National Imagery and Mapping Agency (2000), Department of Defence World Geodetic System 1984: Its definition and relationship with local geodetic systems, Tech. Rep. TR8350.2, Bethesda, Md. Niell, A. (1996), Global mapping functions for the atmospheric delay at radio wavelengths, J. Geophys. Res., 101(B2), Payne, A. J., A. Vieli, A. P. Shepherd, D. J. Wingham, and E. Rignot (2004), Recent dramatic thinning of largest West Antarctic ice stream triggered by oceans, Geophys. Res. Lett., 31, L23401, doi: /2004gl of 11

11 Peters, M. E., D. D. Blankenship, and D. L. Morse (2005), Analysis techniques for coherent airborne radar sounding: Application to West Antarctic ice streams, J. Geophys. Res., 110, B06303, doi: /2004jb Ridley, J. K., S. Laxon, C. G. Rapley, and D. Mantripp (1993), Antarctic ice sheet topography mapped with the ERS-1 radar altimeter, Int. J. Remote Sens., 14(9), , doi: / Schutz, B. E., H. J. Zwally, C. A. Shuman, D. Hancock, and J. P. DiMarzio (2005), Overview of the ICESat mission, Geophys. Res. Lett., 32, L21S01, doi: /2005gl Shan, S., M. Bevis, E. Kendrick, G. L. Mader, D. Raleigh, K. Hudnut, M. Sartori, and D. Phillips (2007), Kinematic GPS solutions for aircraft trajectories: Identifying and minimizing systematic height errors associated with atmospheric propagation delays, Geophys. Res. Lett., 34, L23S07, doi: / 2007GL Shepherd, A., and D. Wingham (2007), Recent sea-level contributions of the Antarctic and Greenland ice sheets, Science, 315(5818), , doi: /science Shepherd, A., D. Wingham, and E. Rignot (2004), Warm ocean is eroding West Antarctic Ice Sheet, Geophys. Res. Lett., 31, L23402, doi: /2004gl Spikes, V. B., B. M. Csathó, and I. M. Whillans (2003), Laser profiling over Antarctic ice streams: Methods and accuracy, J. Glaciol., 49(165), Vaughan, D. G., H. F. J. Corr, F. Ferraccioli, N. Frearson, A. O Hare,D.Mach,J.W.Holt,D.D.Blankenship,D.L. Morse, and D. A. Young (2006), New boundary conditions for the West Antarctic Ice Sheet: Subglacial topography beneath Pine Island Glacier, Geophys. Res. Lett., 33, L09501, doi: /2005gl Vaughn, C. R., J. L. Button, W. B. Krabill, and D. Rabine (1996), Georeferencing of airborne laser altimeter measurements, Int. J. Remote Sens., 17(11), , doi: / Wessel, P., and W. H. F. Smith (1991), Free software helps map and display data, Eos Trans. AGU, 72(41), 441. Wingham, D., A. Shepherd, A. Muir, and G. Marshall (2006), Mass balance of the Antarctic ice sheet, Philos. Trans. R. Soc. Ser. A, 364(1844), Zhang, X., and R. Forsberg (2007), Assessment of long-range kinematic GPS positioning errors by comparison with airborne laser altimetry and satellite altimetry, J. Geod., 81(3), , doi: /s Zumberge, J. F., M. B. Heflin, D. C. Jeferson, M. M. Watkins, and F. H. Webb (1997), Precise point positioning for the efficient and robust analysis of GPS data from large networks, J. Geophys. Res., 102(B3), Zwally, H. J., A. C. Brenner, J. P. DiMarzio, and M. Giovinetto (1997), Ice sheet topography, slopes and flow directions from ERS altimetry, paper presented at 3rd ERS Symposium, Eur. Space Agency, Florence, Italy. Zwally, H. J., M. B. Giovinetto, J. Li, H. G. Cornejo, M. A. Beckley, A. C. Brenner, J. L. Saba, and D. Yi (2005), Mass changes of the Greenland and Antarctic ice sheets and shelves and contributions to sea-level rise: , J. Glaciol., 51(175), of 11