The Greenland Ice Sheet is losing mass at accelerated rates in the 21st century, making it the largest single contributor to rising sea levels. Faster flow of outlet glaciers has substantially contributed to this loss, with the cause of speedup, and potential for future change, uncertain. Here we combine more than three decades of remotely sensed observational products of outlet glacier velocity, elevation, and front position changes over the full ice sheet. We compare decadal variability in discharge and calving front position and find that increased glacier discharge was due almost entirely to the retreat of glacier fronts, rather than inland ice sheet processes, with a remarkably consistent speedup of 4-5% per km of retreat across the ice sheet. We show that widespread retreat between 2000 and 2005 resulted in a stepincrease in discharge and a switch to a new dynamic state of sustained mass loss that would persist even under a decline in surface melt.

Abstract. There is significant uncertainty regarding the spatiotemporal distribution of seasonal snow on glaciers, despite being a fundamental component of glacier mass balance. To address this knowledge gap, we collected repeat, spatially extensive high-frequency ground-penetrating radar (GPR) observations on two glaciers in Alaska during the spring of 5 consecutive years. GPR measurements showed steep snow water equivalent (SWE) elevation gradients at both sites; continental Gulkana Glacier's SWE gradient averaged 115 mm 100 m−1 and maritime Wolverine Glacier's gradient averaged 440 mm 100 m−1 (over > 1000 m). We extrapolated GPR point observations across the glacier surface using terrain parameters derived from digital elevation models as predictor variables in two statistical models (stepwise multivariable linear regression and regression trees). Elevation and proxies for wind redistribution had the greatest explanatory power, and exhibited relatively time-constant coefficients over the study period. Both statistical models yielded comparable estimates of glacier-wide average SWE (1 % average difference at Gulkana, 4 % average difference at Wolverine), although the spatial distributions produced by the models diverged in unsampled regions of the glacier, particularly at Wolverine. In total, six different methods for estimating the glacier-wide winter balance average agreed within ±11 %. We assessed interannual variability in the spatial pattern of snow accumulation predicted by the statistical models using two quantitative metrics. Both glaciers exhibited a high degree of temporal stability, with ∼85 % of the glacier area experiencing less than 25 % normalized absolute variability over this 5-year interval. We found SWE at a sparse network (3 stakes per glacier) of long-term glaciological stake sites to be highly correlated with the GPR-derived glacier-wide average. We estimate that interannual variability in the spatial pattern of winter SWE accumulation is only a small component (4 %–10 % of glacier-wide average) of the total mass balance uncertainty and thus, our findings support the concept that sparse stake networks effectively measure interannual variability in winter balance on glaciers, rather than some temporally varying spatial pattern of snow accumulation.

The original version of this Article contained an error in the spelling of the author Seongsu Jeong, which was incorrectly given as Seonsgu Jeong. This has now been corrected in both the PDF and HTML versions of the Article.

Abstract. There is significant uncertainty regarding the spatiotemporal distribution of seasonal snow on glaciers, despite being a fundamental component of glacier mass balance. To address this knowledge gap, we collected repeat, spatially extensive high-frequency ground-penetrating radar (GPR) observations on two glaciers in Alaska for five consecutive years. GPR measurements showed steep snow water equivalent (SWE) elevation gradients at both sites; continental Gulkana Glacier’s SWE gradient averaged 115 mm 100 m−1 and maritime Wolverine Glacier’s gradient averaged 440 mm 100 m−1 (over >1000 m). We extrapolated GPR point observations across the glacier surface using terrain parameters derived from digital elevation models as predictor variables in two statistical models (stepwise multivariable linear regression and regression trees). Elevation and proxies for wind redistribution had the greatest explanatory power, and exhibited relatively time-constant coefficients over the study period. Both statistical models yielded comparable estimates of glacier-wide average SWE (1 % average difference at Gulkana, 4 % average difference at Wolverine), although the spatial distributions produced by the models diverged in unsampled regions of the glacier, particularly at Wolverine. In total, six different methods for estimating the glacier-wide average agreed within ±11 %. We assessed interannual variability in the spatial pattern of snow accumulation predicted by the statistical models using two quantitative metrics. Both glaciers exhibited a high degree of temporal stability, with ~ 85 % of the glacier area experiencing less than 25 % normalized absolute variability over this five-year interval. We found SWE at a sparse network (3 stakes per glacier) of long-term glaciological stake sites to be highly correlated with the GPR-derived glacier-wide average. We estimate that interannual variability in the spatial pattern of SWE is only a small component (4–10 % of glacier-wide average) of the total mass balance uncertainty and thus, our findings support the concept that sparse stake networks effectively measure interannual variability in winter balance on glaciers, rather than some spatially varying pattern of snow accumulation.

Supplementary Material1 Table S1. Observed density and calculated radar velocity at Gulkana and Wolverine glaciers. 2 Density uncertainty is the standard deviation of all density observations collected that year, 3 density velocity error is uncertainty introduced by uncertainty in density observations. Probe 4 velocity uncertainty is the standard error of the least-squares regression line. 5 6 Gulkana Density [kg m -3 ] Density Velocity [m ns -1 ] Probe velocity [m ns -1 ] Mean Velocity [m ns -1 ] 2013 365 ± 37 0.229 ± 0.005 0.216 ± 0.003 0.223 ± 0.003 2014 380 ± 29 0.227 ± 0.004 0.215 ± 0.003 0.221 ± 0.003 2015 328 ± 13 0.235 ± 0.002 0.228 ± 0.009 0.231 ± 0.005 2016 370 ± 27 0.229 ± 0.004 0.194 ± 0.005 0.211 ± 0.003 2017 366 ± 8 0.229 ± 0.001 0.227 ± 0.002 0.228 ± 0.001 Mean 362 ± 11 0.230 ± 0.002 0.216 ± 0.002 0.223 ± 0.001 Wolverine 2013 446 ± 23 0.218 ± 0.003 0.193 ± 0.003 0.207 ± 0.002 2014 445 ± 43 0.218 ± 0.006 0.210 ± 0.002 0.214 ± 0.003 2015 414 ± 14 0.222 ± 0.002 0.235 ± 0.002 0.229 ± 0.001 2016 456 ± 6 0.216 ± 0.001 0.229 ± 0.003 0.223 ± 0.002 2017 438 ± 12 0.219 ± 0.002 0.217 ± 0.002 0.218 ± 0.001 Mean 440 ± 10 0.219 ± 0.001 0.217 ± 0.001 0.218 ± 0.001 7 8