Continuously accelerating ice loss over Amundsen Sea catchment, West Antarctica, revealed by integrating altimetry and GRACE data

Earth and Planetary Science Letters 321-322 (2012) 74–80

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Continuously accelerating ice loss over Amundsen Sea catchment, West Antarctica, revealed by integrating altimetry and GRACE data Hyongki Lee a,⁎, C.K. Shum b, c, Ian M. Howat b, c, Andrew Monaghan d, Yushin Ahn e, Jianbin Duan b, Jun-Yi Guo b, Chung-Yen Kuo f, Lei Wang b a

Department of Civil and Environmental Engineering, University of Houston, Houston, TX, USA School of Earth Sciences, The Ohio State University, Columbus, OH, USA Byrd Polar Research Center, The Ohio State University, Columbus, OH, USA d National Center for Atmospheric Research, Boulder, CO, USA e School of Technology, Michigan Technological University, Houghton, MI, USA f Department of Geomatics, National Cheng Kung University, Taiwan, ROC b c

a r t i c l e

i n f o

Article history: Received 21 December 2010 Received in revised form 22 December 2011 Accepted 28 December 2011 Available online 3 February 2012 Editor: P. Shearer Keywords: Amundsen Sea sector ice mass loss Envisat GRACE firn density

a b s t r a c t Satellite altimetry and Gravity Recovery and Climate Experiment (GRACE) measurements have provided contemporary, but substantially different Antarctic ice sheet mass balance estimates. Altimetry provides no information about firn density while GRACE data is significantly impacted by poorly constrained glacial isostatic adjustment signals. Here, we combine Envisat radar altimetry and GRACE data over the Amundsen Sea (AS) sector, West Antarctica, to estimate the basin-wide averaged snow and firn column density over a seasonal time scale. Removing the firn variability signal from Envisat-observed ice-sheet elevation changes reveals more rapid dynamic thinning of underlying ice. We report that the net AS sector mass change rates are estimated to be − 47 ± 8 Gt yr − 1 between 2002 and 2006, and − 80 ± 4 Gt yr − 1 between2007 and 2009, equivalent to a sea level rise of 0.13 and 0.22 mm yr − 1, respectively. The acceleration is due to a combination of decreased snowfall accumulation (+ 13 Gt yr− 1 in 2002–2006, and − 6 Gt yr − 1 in 2007– 2009) and enhanced ice dynamic thinning (− 60 ± 10 Gt yr − 1 in 2002–2006, and − 74 ± 11 Gt yr− 1 in 2007–2009) after 2007. Because there is no significant snowfall trend over the past 21 yr (1989–2009) and an increase in ice flow speed (2003–2010), the accelerated mass loss is likely to continue. © 2011 Elsevier B.V. All rights reserved.

1. Introduction The West Antarctic ice sheet contains enough water to raise global sea level by up to 5 m if the ice were to completely melt (Bamber et al., 2009; Lythe et al., 2001). Much of the West Antarctic ice sheet is grounded well below sea level (Rignot and Jacobs, 2002). Hence, thinning will cause glaciers flowing into the Amundsen Sea (AS) to float off the bed, easing resistive forces acting on upstream ice and thus leading to further glacier acceleration and rapid ice sheet collapse (Joughin et al., 2010; Thomas et al., 2004). A mechanism for such a retreat may be increased by ice shelf melting in the AS driven by warm circumpolar deep water (CDW) intrusions onto the continental shelf (Jenkins et al., 2010; Rignot et al., 2008; Shepherd et al., 2002). The AS sector (or basin G-H) contains enough ice to raise sea level by 1.3 m (Rignot, 2001), and has currently the largest rate of ice mass loss in Antarctica (Rignot et al., 2008). ⁎ Corresponding author at: Department of Civil and Environmental Engineering, University of Houston, N107 Engineering Building 1, Houston, TX 77204-4003, USA. Tel.: +1 713 743 4685; fax: + 1 713 743 0186. E-mail address: [email protected] (H. Lee). 0012-821X/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.epsl.2011.12.040

Remote sensing provides the only estimates of total ice sheet mass balance and contribution to present-day sea level rise. Satellite radar altimetry has been used to measure ice sheet elevation change rate (żs) at satellite crossover points (Davis et al., 2005; Helsen et al., 2008; Wingham et al., 1998). Data from the Geoscience Laser Altimeter (GLAS) onboard the Ice Cloud and land Elevation Satellite (ICESat) have also been used to estimate żs (Gunter et al., 2009; Howat et al., 2008; Pritchard et al., 2009). Although GLAS provided high accuracy with smaller footprints (Magruder et al., 2007), measurements were limited to 30-day campaigns every ~90 days during its lifespan, hampering the accurate determination of secular trends (Howat et al., 2008; Riva et al., 2009). Derivation of mass change from repeat altimetry requires constraint on the variability in the density of the firn column due to accumulation of snow and compaction of firn. Previous researchers have used snowpack and meteorological models to estimate this contribution (Helsen et al., 2008; Zwally and Li, 2002) or have applied a single density value based on the assumed mechanism of thinning (i.e., ice density for increased discharge or snow density for decreased accumulation) (Wingham et al., 2006; Pritchard et al., 2009), or some combination of both approaches (Riva et al., 2009). Temporal gravity solutions from the Gravity Recovery and Climate

H. Lee et al. / Earth and Planetary Science Letters 321-322 (2012) 74–80

Experiment (GRACE) satellites have also been used to derive monthly ice mass balance estimates in previous studies. While these studies agree that the Antarctic ice sheet is losing mass, there is a 47 Gt yr − 1, or 0.13 mm yr − 1 equivalent sea level rise, difference between the estimates of Chen et al. (2009) and Velicogna (2009), despite the fact that these studies used the same data product over a time interval differing by only 1 month. This discrepancy may be due to two causes. The first is that GRACE-derived Antarctic ice sheet mass balance estimates are highly dependent on the fidelity of Glacial Isostatic Adjustment (GIA) forward modeling (Shum et al., 2008). The second is that different post-processing methods do lead to quite different results. There are two common steps in GRACE data post-processing. The first step is to apply a high-pass filter to the GRACE geopotential coefficient data to remove correlated errors (e.g., Duan et al., 2009). However, the mass changes obtained still include remaining unacceptably large errors, which should be further alleviated by smoothing. This smoothing as well as the finite spectral resolution of GRACE causes signal leakage. Hence, a critical step is to recover the original signal from the results corrupted by the leakage (e.g., Baur et al., 2009; Guo et al., 2010). Here, we employ a novel approach for combining Environmental Satellite (Envisat) measurements of elevation changes with GRACEobserved surface mass changes, in terms of equivalent water height (EWH) changes, in order to constrain changes in the basin-wide, average snow and firn column densities (ρaf) over the AS sector, West Antarctica. We then use this density estimate to obtain an improved mass change rate from Envisat żs over this rapidly changing region between September 2002 and December 2009. By using GRACE only to constrain density variations, and not mass change directly, we obtain a more confident and much higher resolution map of mass loss over the AS sector. Through the combination of altimeter and GRACE, we are also able to gain insight into the portioning of mass change between changes in snowfall accumulation and ice dynamics. 2. Data and methodology 2.1. Envisat data processing In this study, Envisat ice-sheet elevation changes are estimated from collinear analysis using the regional stackfile method (Lee et al., 2008, 2009). In contrast to the crossover analysis, collinear analysis yields vastly more individual measurements, and thus requires less interpolation to obtain maps of surface elevation change. In addition, unlike crossover analysis, repeat-track collinear analysis is not sensitive to large (>1 m) systematic biases induced by antenna polarization and reflecting surface anisotropy (Remy et al., 2006). We use Envisat Geophysical Data Record (GDR) data, retracked with ICE-1 retracker optimized for general continental ice sheets altimeter data acquired over each 35-day orbital cycle between September 2002 and December 2009. The Envisat 18-Hz regional stackfile over the AS sector is created by a similar procedure as described in Lee et al. (2008) with the difference that latitudes of 18-Hz stackfile bins are computed by linearly interpolating latitudes of the 1-Hz stackfile bins. A 1-km resolution Antarctic Digital Elevation Model (DEM) that combines ICESat and European Remote Sensing (ERS) geodetic phase data (Bamber and Gomez-Dans, 2005) is used to correct for the surface gradient. The DEM is also used as a mean surface profile to compute the ice sheet surface anomalies. The anomalies at each 1-Hz stackfile bins are computed by averaging twenty anomalies at 18-Hz stackfile bins. Time series of elevation change are then generated for individual 1° × 1° regions covering 80°S to 74°S and 240°E to 270°E. A composite basin-wide time series over the AS sector is formed by using areaweighted averages of elevation changes over each 1° × 1° region. Finally, elevation change rates (żs) are estimated by simultaneously

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fitting the trend and an annual term. They are further corrected for vertical basal motion, which is assumed to be entirely due to GIA, by forward modeling with the Ivins and James (2005) (hereafter, IJ05) model (lithosphere thickness = 65 km, upper mantle viscosity = 5 × 1020 Pa s, lower mantle viscosity = 1022 Pa s). Uncertainty in the GIA correction is estimated as the difference between the values predicted from IJ05 and ICE-5G (VM2) (Peltier, 2004) GIA models. The difference is 0.9 ± 0.9 mm yr − 1 over the AS sector, which is small compared to other uncertainties. Fig. 1(a) and (b) shows the spatial distributions of the standard deviations of the Envisat elevation change time series for all 1°× 1° regions before and after the surface gradient correction to demonstrate the effectiveness of the correction. It is clear to see that spurious elevation changes presumably caused by satellite orbital drift are generally reduced following the gradient correction, especially near the coastal regions. Fig. 1(c) and (d) shows estimated żs and their formal uncertainties. There are decreasing żs near the coast, and either zero or increasing żs in the interior regions. There are also elevation increases in the area adjacent to the peninsular basins (upper left of Fig. 1c). Higher formal uncertainties are observed in the coastal regions, which may be due in part to higher surface slopes. As summarized in Table 1, the basin-wide żs compares well with previous studies (Davis et al., 2005; Helsen et al., 2008; Wingham et al., 2006). Elevation in the AS sector decreased at a rate of about −10 cm yr− 1 between 1992 and 2007. We find, however, a tripling in the rate of elevation decrease after 2007, reaching a basin-wide average of −22.2 cm yr− 1. 2.2. GRACE data processing This study uses Release 4 (RL04) GRACE data processed by Center for Space Research (CSR) (April 2002–February 2010, 92 months), Jet Propulsion Laboratory (JPL; April 2002–February 2010, 91 months), and GeoForschungsZentrum (GFZ; August 2002–February 2010, 86 months). To reduce the GRACE South–North striping (Swenson and Wahr, 2006), we used an improved decorrelation method by Duan et al. (2009). Because our study area is at the polar region, the decorrelation filter reduced the noise effectively and we did not apply any further spatial smoothing. Since GRACE is a twin-satellite system and is thus insensitive to the geocenter motion (variations of the degree one harmonics in the Stokes coefficients), we add degree-one terms using a solution determined by Satellite Laser Ranging (SLR) to Lageos-1 and Lageos-2 satellites (J. Ries, personal communication, 2010). Furthermore, as in Velicogna and Wahr (2006), hydrologic contamination by signals outside Antarctica is removed using the change in monthly global water storage, in the form of model-decomposed Stokes coefficients, using the Variable Infiltration Capacity (VIC) hydrologic model (Liang et al., 1994). As explained below, we do not apply a GIA correction. Finally, the surface mass changes (in EWH changes) are computed as in Wahr et al. (1998) at the 1° × 1° regions over the AS sector from each of the monthly gravity fields. Similar to the Envisat observation, GRACE EWH changes also reveal more rapid negative trends after 2007 over the AS sector (Fig. 2). This similarity between these two independent datasets suggests that the ice mass loss over the AS sector has increased in recent years. Rather than to derive GRACE mass change rates from the EWH changes, these EWH changes are used to estimate ρaf after de-trending (Section 3.1). Accordingly, we do not remove the GIA signal from GRACE observation, which is manifest as a secular trend in the time series. 3. Results and discussion 3.1. Firn column density estimates Previous studies observe that the ice speed varies on inter-annual timescales, with no substantial seasonal oscillation (Joughin et al.,

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H. Lee et al. / Earth and Planetary Science Letters 321-322 (2012) 74–80

Fig. 1. Spatial distributions of the standard deviations of the Envisat time series from September 2002 to December 2009 for all 1° × 1° regions (a) before and (b) after the surface gradient correction using the Antarctic DEM. The AS sector is shown with dashed lines. Spatial plots of elevation change rates (c) and their formal uncertainties (d) are also shown.

Table 1 Comparison of ice sheet elevation changes and their published uncertainties (95% confidence interval for this study) observed from satellite radar altimeter over the AS sector.

Wingham et al. (1998) Shepherd et al. (2002) Davis et al. (2005) Wingham et al. (2006) Helsen et al. (2008) This study

Observation /GIA model used

Elevation changes (cm yr− 1)

ERS-1/2 (1992–1996)/ICE-3G Tushingham and Peltier (1991) ERS-1/2 (1992–2000)/no correction ERS-1/2 (1992–2003)/Ivins et al. (2001) ERS-1/2 (1992–2003)/Nakada et al. (2000) ERS-2 (1995–2003)/Ivins et al. (2001) Envisat (2002–006)/IJ05 Envisat (2007–009)/IJ05

− 11.7 ± 1.0

2010; Rignot, 2008). We therefore assume that the seasonal amplitude of mass anomalies observed by GRACE is due to seasonality in SMB. Consequently, the seasonal amplitude of mass anomaly can be a constraint on the mass change due to surface accumulation and ablation. Thus, by comparing seasonal changes in surface height, from altimetry, and mass change, from GRACE, we can estimate the annual effective, basin-wide, average density, ρaf, associated with the seasonal mass change as:

−9.0 ± 2.0 − 10.0 ± 0.4 − 6.8 ± 0.3 − 11.5 ± 0.4 (uncorrected) −7.7 ± 2.0 − 22.2 ± 2.7

ρaf ¼

Rm ρ Rh w

ð1Þ

where Rm and Rh are the seasonal amplitudes in the detrended GRACE basin-wide averaged EWH changes and Envisat basin-wide averaged elevation changes, respectively, and ρw is density of water (1000 kg m − 3). Due to the large change in trend after 2007, we detrend the 2002–2006 and 2007–2009 data separately. The annual

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and then subtracting the 1989–2009 mean. While we do not have in situ observational data with which to validate these model estimates, the fact that the three datasets, each generated with different atmospheric models and assimilation systems, have similar monthly fluctuations and trends (none are statistically different from zero for 1989–2009), suggests that they are reasonably capturing the SMB variability in the AS sector. Likewise, Bromwich et al. (2011) noted that simulated SMB was similar across West Antarctica among the five reanalyses they evaluated (including the three employed in this study), and that temporal trends were statistically insignificant over most regions of West Antarctica. Between September 2002 and December 2006, δSMB are + 3.6, + 2.1, and +4.0 cm yr − 1 for ERAinterim, JRA-25, and MERRA, respectively, indicating higher annual snowfall in the AS sector, compared to the 1989–2009 mean. Above average accumulation for 2002–2006 is consistent with the higher amplitudes of the Envisat elevation changes in 2002–2006 (Fig. 2a) and the resultant density values for the same period (Table 2), which are near the value for snow. Conversely, between January 2007 and December 2009, δSMB are −1.4, − 3.7, and + 0.3 cm yr − 1 from ERA-interim, JRA-25, and MERRA, respectively, indicating a decrease in accumulation to near or below the 21-yr mean. Therefore, a decrease in snow accumulation would explain the lower amplitudes in Envisat elevation changes in 2007–2009 (Fig. 2a), and correspondingly higher density values for the same period (Table 2). 3.2. Ice mass loss estimates

Fig. 2. (a) Time series of Envisat dh, and (b) GRACE EWH anomalies over the AS sector. The shading in (b) illustrates the range of estimates from CSR, JPL, and GFZ solutions. The solid line indicates the mean of the three estimates.

ρaf averaged over years 2002–2006, and averaged over years 2007– 2009, are summarized in Table 2. The estimated densities for years 2002–2006 using three different GRACE solutions are close to or slightly larger than that of snow (320–510 kg m − 3). On the other hand, we obtained higher density values for years 2007–2009 ranging from 480 to 930 kg m − 3. In order to assess the extent to which changes in accumulation can explain the variability in ρaf, we examine surface mass balance (SMB; precipitation minus evaporation) using the output from three independent atmospheric reanalysis models: The European Centre for Medium-Range Weather Forecasts Interim reanalysis (ERA-interim; Simmons et al., 2007), the Japanese 25-year Reanalysis Project (JRA25; Onogi et al., 2007), and NASA's Modern Era Retrospectiveanalysis for Research and Applications (MERRA; Bosilovich, 2008). We choose these reanalysis datasets because they are all recent and because using the three in combination provides a proxy of uncertainty in SMB estimates. Fig. 3 compares the monthly running total annual SMB anomalies (δSMB). The anomalies are computed by calculating the 12-month centered running totals of monthly SMB between January 1989–April 2010 (except the tail months),

The AS sector has been thinning dynamically due to accelerating flow. Thus, it is necessary to differentiate changes in firn and ice thickness using the appropriate densities for each (Li and Zwally, 2011). As described in Zwally and Li (2002), the ice-sheet surface elevation change rate, żs is given by: z_ s ¼ h_ a þ h_ f þ h_ i þ z_ b

ð2Þ

where h_ a is the thickness change due to accumulation, equivalent to the SMB rate divided by the density of surface snow, h_ f is the change in firn thickness due to compaction driven by both accumulation and temperature, h_ i is the change in ice column thickness due to flux divergence (i.e., dynamic thinning), and żb is the change in bed elevation due to GIA, which is removed from żs as described in Section 2.1. We can thus simplify Eq. (2) by combining h_ a and h_ f into a single term h_ af such as: z_ s ¼ h_ af þ h_ i :

ð3Þ

Neglecting the contribution of temperature variability to the height changes of the firn layer because of the dominance of the accumulation in forcing h_ f in Antarctica (Helsen et al., 2008), h_ af then represents total change caused by accumulation only, including both the addition of new snowfall and that associated with the compaction rate (Li and Zwally, 2011). We further neglect variability in total

Table 2 Basin-wide estimated snow and firn column density (ρaf, kg m− 3), ice thickness change rates (h_ i , cm yr− 1) with ice-only mass change rates (Gt yr− 1) in parentheses, and the net mass change rates (Gt yr− 1) over the AS sector. GRACE solution used for density estimates

Density (ρaf)

CSR

510 860 360 930 320 480

JPL GFZ

2002–2006 2007–2009 2002–2006 2007–2009 2002–2006 2007–2009

_ i) h_ i (m

Net mass change rate

ERA-interim

JRA-25

MERRA

ERA-interim

JRA-25

MERRA

−14.8 −20.6 − 17.7 − 20.7 − 19.0 − 19.3

− 11.8 (− 44) − 17.9 (− 66) − 13.5 (− 50) − 18.2 (− 67) − 14.3 (−53) − 14.5 (− 54)

− 15.5 − 22.5 − 18.8 − 22.5 −20.2 − 22.8

− 40 − 82 − 51 −82 − 55 −77

− 35 − 81 − 42 − 82 − 44 − 69

− 41 −82 − 54 − 82 − 59 − 83

(− 55) (− 76) (− 66) (− 77) (− 70) (− 72)

(− 57) (− 83) (−70) (−83) (− 75) (− 85)

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SMB anomaly (cm year-1)

25 20 15 10 5 0 -5 -10 -15 -20 -25 1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

Year ERA-Interim

JRA-25

MERRA

Fig. 3. Time series of monthly running annual SMB anomalies for 1989–2009, averaged over the AS sector. The green (magenta) box highlights the 2002–2006 (2007–2009) period. Units are in water equivalent cm yr− 1.

firn column thickness on time scales longer than the 8-year Envisat altimeter observation period. Therefore, h_ af can be estimated by dividing the SMB anomalies (from Section 3.1) by ρaf, and h_ i can then be calculated as:

where ρi is ice density (917 kg m − 3), and Arg is the total area of the grounded ice in the AS sector (~ 404,000 km 2). As summarized in Table 2, the overall averaged ice-only mass losses are −60 ± 10 (3σ) Gt yr − 1 during 2002–2006, and − 74 ± 11 (3σ) Gt yr − 1 during 2007–2009, indicating accelerated ice loss by 23% after 2007. Finally, the net mass change rates can be estimated as:

The timing of accelerated mass loss at the end of 2006 is consistent with an observed increase in ice flow speed across the grounding line. Using automated repeat-image feature tracking methods (Ahn and Howat, 2011), we obtained a time series of ice flow speed at the grounding line of PIG from eight pairs of Landsat 7 ETM+ images acquired between 2003 and 2010 (Fig. 4). These data show that velocities of the PIG increased from approximately 3000 m yr − 1 during 2003–2005 to 3500 m yr − 1 during 2006–2007, to 4000 m yr − 1 during 2008–2010. This acceleration in flow speed agrees with our altimeter and GRACE observations. It has been reported that PIG and several other glaciers draining from the AS sector accelerated over the past decade, increasing the total outflow (ice discharge) by −43 Gt yr − 1 between 2000 and 2007, with −19 Gt yr − 1 of this increase occurring between 2006 and 2007 (Rignot, 2008). Furthermore, although they have different time spans, we compare _ i with the changes in total ice discharge our estimated basin-wide m from the glaciers draining into the AS from Rignot (2008). For the period September 2002–December 2006, our estimated ice-only mass loss is −60 ± 10 (3σ) Gt yr − 1 while the increase in total ice discharge is −43 ± 11 (σ) Gt yr − 1 (237 Gt yr − 1–280 Gt yr − 1 from Table 1 in Rignot, 2008). Over the period January 2007–December 2009, we estimate the increase in total ice discharge of −49 Gt yr − 1 by scaling the ice flow speed increase over PIG (Fig. 4), assuming constant glacier width and thickness over the time span and equivalent ice speed increase for the other glaciers in the basin while our iceonly mass loss estimate is −74 ± 11 (3σ) Gt yr − 1. Although our esti_ i values provide larger ice mass losses than the increases in mated m ice discharges, they are overall in good agreement considering their error bounds.

_ i ¼ δSMB⋅Arg þ m _ i: _ ¼ h_ af ρaf Arg þ m m

4. Conclusions

  h_ i ¼ z_ s −h_ af ¼ z_ s − δSMB=ρaf :

ð4Þ

It should be noted that the SMB anomalies with respect to the 1989–2009 mean are used in Eq. (4) because contemporary fluctuations in accumulation can lead to changes in ice sheet volume only if they depart from the long-term mean (Wingham et al., 2006). Estimated ice thickness change rates are summarized in Table 2. Overall, we observe more rapid dynamic ice thinning in the AS sector during 2007–2009. A study by Wingham et al. (2009) reported an accelerated thinning of the Pine Island Glacier (PIG), the largest ice stream in West Antarctica, by examining ERS-2 and Envisat radar altimeter data during the period 1995–2006. Therefore, our results indicate a further acceleration in thinning after 2007. The ice-only mass change _ i ) from the ice thickness change can then be estimated as: rates (m _ i ¼ h_ i ρi Ar g m

ð5Þ

ð6Þ

The overall averaged net mass change rates are −47 ± 8 (3σ) Gt yr − 1 during 2002–2006, and −80 ± 4 (3σ) Gt yr − 1during 2007– 2009, indicating that the AS sector has undergone accelerated mass loss by 70% after 2007. Therefore, our results indicate that the positive accumulation anomaly (+13 Gt yr − 1 basin-wide SMB fluctuation averaged using the three reanalysis models) during 2002–2006 compensated the ice dynamic thinning whereas the negative accumulation anomaly (−6 Gt yr − 1) during 2007–2009 enhanced the net mass loss. This demonstrates that our methodology allows for partitioning of the causality of mass change between SMB and ice flux. Despite different time spans, our result for 2002–2006 generally agrees with the Helsen et al. (2008) mass balance estimate of − 55 + 7/ −2 Gt yr − 1 (different upper and lower bound error estimate) during 1995–2003 using ERS-2 altimeter measurements, and with the Zwally et al. (2005) estimate of −63 ± 2 Gt yr − 1 during 1992–2002 using ERS-1/2 altimeter measurements.

For the period September 2002–December 2006, the AS sector thinned by −7.7 cm yr − 1, which increased to −22.2 cm yr − 1 during January 2007–December 2009. Using our basin-wide averaged snow and firn column density, estimated using the amplitudes of Envisat elevation changes and GRACE mass changes, we estimate the firn thickness changes trends over Envisat observational period due to the contemporary accumulation variability from three reanalysisbased SMB datasets, and separate it from the underlying ice thickness _ depend on quality of the reanalysis _ i and m change. Hence, both m models. We report that the mass loss rate increased from −47 ± 8 Gt yr − 1 between 2002 and 2006 to − 80 ± 4 Gt yr − 1 between 2007 and 2009, equivalent to a sea level rise of 0.13 to 0.22 mm yr − 1 (using 362 Gt mm − 1 Sea Level Equivalent), over the AS sector. This abrupt increase is due to a combination of decreased snowfall accumulation (+ 13 Gt yr − 1 in 2002–2006, and −6 Gt yr − 1in 2007–2009) and enhanced ice dynamic thinning

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Fig. 4. Velocity profiles (m yr− 1) over the Pine Island Glacier (PIG) using repeat Landsat 7 ETM+ images.

(−60 ± 10 Gt yr − 1 in 2002–2006, and −74 ± 11 Gt yr − 1 in 2007– 2009) after 2007. Considering that SMB has changed negligibly over the past 21 yr (1989–2009) and the ice flow speed has increased continuously over the past 7 yr (2003–2009), the acceleration in mass loss we observe over the AS sector is not likely to be a shortterm phenomenon. Rather, near-future mass balance fluctuations will likely be driven by changes in the ice discharge and thus may continue to accelerate. Acknowledgments This research is partially supported by grants from NASA's Polar Program (Grant No. NNX10AG31G and NNX11AR47G), University of Houston, Ohio State University's Climate, Water, and Carbon (CWC) Program, and Chinese Academy of Sciences/SAFEA International Partnership Program for Creative Research Teams. We thank Michiel Helsen at Utrecht University and Duncan Wingham at University College London for helpful discussions on firn compaction and density estimation. We also thank Jonathan Bamber for providing Antarctic DEM and two anonymous reviewers for their constructive comments. Some of the figures in this paper are generated using the Generic Mapping Tool (GMT). References Ahn, Y., Howat, I.M., 2011. Efficient, automated glacier surface velocity measurements from repeat images using multi-image/multi-chip (MIMC) feature tracking. IEEE Trans. Geosci. Remote. Sens. 49, 2838–2846. Bamber, J., Gomez-Dans, J.L., 2005. The accuracy of digital elevation models of the Antarctic continent. Earth Planet. Sci. Lett. 237, 516–523. Bamber, J.L., Riva, R.E.M., Vermeersen, B.L.A., LeBroq, A.M., 2009. Reassessment of the potential sea-level rise from a collapse of the West Antarctic Ice Sheet. Science 324, 901–903. Baur, O., Kuhn, M., Featherstone, W.E., 2009. GRACE-derived ice-mass variations over Greenland by accounting for leakage effects. J. Geophys. Res. 114, B06407. doi:10.1029/ 2008JB006239. Bosilovich, M., 2008. NASA's Modern Era Retrospective-Analysis for Research and Applications: Integrating Earth Observations, Earthzine. http://www.earthzine. org/2008/09/26/nasas-modern-era-retrospective-analysis/. Bromwich, D.H., Nicolas, J.P., Monaghan, A.J., 2011. An assessment of precipitation changes over Antarctica and the Southern Ocean since 1989 in contemporary global reanalyses. J. Clim. 24. doi:10.1175/2011JCLI4074.1. Chen, J.L., Wilson, C.R., Blankenship, D., Tapley, B.D., 2009. Accelerated Antarctic ice loss from satellite gravity measurements. Nat. Geosci. 2, 859–862. Davis, C.H., Li, Y., McConnell, J.R., Frey, M.M., Hanna, E., 2005. Snowfall-driven growth in East Antarctic ice sheet mitigates recent sea-level rise. Science 308, 1,898–1,901. Duan, X.J., Guo, J.Y., Shum, C.K., van der Wal, W., 2009. On the postprocessing removal of correlated errors in GRACE temporal gravity field solutions. J. Geod. 83, 1,095–1,106. Gunter, B., et al., 2009. A comparison of coincident GRACE and ICESat data over Antarctica. J. Geod. 83, 1,051–1,060. Guo, J.Y., Duan, X.J., Shum, C.K., 2010. Non-isotropic filtering and leakage reduction for determining mass changes over land and ocean using GRACE data. Geophys. J. Int. 181, 290–302. doi:10.1111/j.1365-246X.2010.04534.x. Helsen, M.M., et al., 2008. Elevation changes in Antarctica mainly determined by accumulation variability. Science 320, 1,626–1,629.

Howat, I., Smith, B.E., Joughin, I., Scambos, T.A., 2008. Rates of southeast Greenland ice volume loss from combined ICESat and ASTER observation. Geophys. Res. Lett. 35, L17505. doi:10.1029/2008GL034496. Ivins, E.R., James, T.S., 2005. Antarctic glacial isostatic adjustment: a new assessment. Antarct. Sci. 17, 541–553. Ivins, E.R., Wu, X., Raymond, C.A., Yoder, C.F., James, T.S., 2001. Temporal geoid of a rebounding Antarctica and potential measurement by the GRACE and GOCE satellites. Int. Assoc. Geodesy Symp. 123, 361. Jenkins, A., et al., 2010. Observations beneath Pine Island Glacier in West Antarctica and implications for its retreat. Nat. Geosci. 3, 468–472. Joughin, I., Smith, B.E., Holland, D.M., 2010. Sensitivity of 21st century sea level to ocean-induced thinning of Pine Island Glacier, Antarctica. Geophys. Res. Lett. 37, L20502. doi:10.1029/2010GL044819. Lee, H., Shum, C.K., Yi, Y., Braun, A., Kuo, C.Y., 2008. Laurentia crustal uplift observed using satellite radar altimetry. J. Geodyn. 46, 182–193. Lee, H., et al., 2009. Louisiana wetland water level monitoring using retracked TOPEX/ POSEIDON altimetry. Mar. Geod. 32, 284–302. Li, J., Zwally, H.J., 2011. Modeling of firn compaction for estimating ice-sheet mass change from observed ice-sheet elevation change. Ann. Glaciol. 52, 1–7. Liang, X., Lettenmaier, D.P., Wood, E.F., Burges, S.J., 1994. A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res. 99 (D7), 14,415–14,428. Lythe, M., Vaughan, D., the BEDMAP Group, 2001. BEDMAP: a new ice thickness and subglacial topographic model of Antarctica. J. Geophys. Res. 106 (B6), 11,335–11,351. Magruder, L., Webb, C., Urban, T., Silverberg, E., Schutz, B., 2007. ICESat altimetry data product verification at White Sands space harbor. IEEE Trans. Geosci. Remote Sens. 45, 147–155. Nakada, M., et al., 2000. Late Pleistocene and Holocene melting history of the Antarctic ice sheet derived from sea-level variations. Mar. Geol. 167, 85–103. Onogi, K., et al., 2007. The JRA-25 re-analysis. J. Meteor. Soc. Jpn. 85, 369–432. Peltier, W.R., 2004. Global glacial isostasy and the surface of the ice-age Earth: the ICE5G (VM2) model and GRACE. Annu. Rev. Earth Planet. Sci. 32, 111–149. Pritchard, H.D., Arthen, R.J., Vaughan, D.G., Edwards, L.A., 2009. Extensive dynamic thinning on the margins of the Greenland and Antarctic ice sheets. Nature 461, 971–975. Remy, F., Legresy, B., Benveniste, J., 2006. On the azimuthally anisotropy effects of polarization for altimetric measurements. IEEE Trans. Geosci. Remote. Sens. 44, 3,289–3,296. Rignot, E., 2001. Evidence for rapid retreat and mass loss of Thwaites Glacier, West Antarctica. J. Glaciol. 47, 213–222. Rignot, E., 2008. Changes in West Antarctic ice stream dynamics observed with ALOS PALSAR data. Geophys. Res. Lett. 35, L12505. doi:10.1029/2008GL033365. Rignot, E., Jacobs, S., 2002. Rapid bottom melting widespread near Antarctic ice-sheet grounding lines. Science 296, 2,020–2,023. Rignot, E., et al., 2008. Recent Antarctic ice mass loss from radar interferometry and regional climate modeling. Nat. Geosci. 1, 106–110. Riva, R.E.M., et al., 2009. Glacial isostatic adjustment over Antarctica from combined ICESat and GRACE satellite data. Earth Planet. Sci. Lett. 288, 516–523. Shepherd, A., Wingham, D.J., Mansley, J.A.D., 2002. Inland thinning of the Amundsen Sea sector, West Antarctica. Geophys. Res. Lett. 29. doi:10.1029/2001GL014183. Shum, C.K., Kuo, C.-Y., Guo, J.-Y., 2008. Role of Antarctic ice mass balance in present-day sea level change. Polar Sci. 2, 149–161. Simmons, A., Uppala, S., Dee, D., Kobayashi, S., 2007. ERA-Interim: New ECMWF reanalysis products from 1989 onwards, Newsletter 110-Winter 2006/07, ECMWF. 11 pp. Swenson, S., Wahr, J., 2006. Post-processing removal of correlated errors in GRACE data. Geophys. Res. Lett. 33, L08402. doi:10.1029/2005GL025285. Thomas, R., et al., 2004. Accelerated sea-level rise from West Antarctica. Science 306, 255–258. Tushingham, A.M., Peltier, W.R., 1991. ICE-3G: a new model of Pleistocene deglaciation based upon geophysical predictions of postglacial relative sea level change. J. Geophys. Res. 96, 4,497–4,523. Velicogna, I., 2009. Increasing rates of ice mass from the Greenland and Antarctic ice sheets revealed by GRACE. Geophys. Res. Lett. 36, L19503. doi:10.1029/ 2009GL040222.

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H. Lee et al. / Earth and Planetary Science Letters 321-322 (2012) 74–80

Velicogna, I., Wahr, J., 2006. Measurements of time-variable gravity show mass loss in Antarctica. Science 311, 1,754–1,756. Wahr, J., Molenaar, M., Bryan, F., 1998. Time variability of the Earth's gravity field: hydrologic and oceanic effects and their possible detection using GRACE. J. Geophys. Res. 103 (B12), 30,205–30,229. Wingham, D., Ridout, A., Scharroo, R., Arthern, R., Shum, C., 1998. Antarctic elevation change from 1992 to 1996. Science 282, 456–458. Wingham, D., Shepherd, A., Muir, A., Marshall, G., 2006. Mass balance of the Antarctic ice sheet. Philos. Trans. R. Soc. A 364, 1,627–1,635. doi:10.1098/rsta.2006.1792.

Wingham, D., Wallis, D.W., Shepherd, A., 2009. Spatial and temporal evolution of Pine Island Glacier thinning, 1995–2006. Geophys. Res. Lett. 36, L17501. doi:10.1029/ 2009GL039126. Zwally, H., Li, J., 2002. Seasonal and interannual variations of firn densification and ice-sheet surface elevation at the Greenland summit. J. Glaciol. 48, 199–207. Zwally, H., et al., 2005. Mass changes of the Greenland and Antarctic ice sheets and shelves and contributions to sea-level rise: 1992–2002. J. Glaciol. 51, 509–527.