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Abstract Surface melting occurs across many of Antarctica’s ice shelves, mainly during the austral summer. The onset, duration, area and fate of surface melting varies spatially and temporally, and the resultant surface meltwater is stored as ponded water (lakes) or as slush (saturated firn or snow), with implications for ice-shelf hydrofracture, firn air content reduction, surface energy balance and thermal evolution. This study applies a machine-learning method to the entire Landsat 8 image catalogue to derive monthly records of slush and ponded water area across 57 ice shelves between 2013 and 2021. We find that slush and ponded water occupy roughly equal areas of Antarctica’s ice shelves in January, with inter-regional variations in partitioning. This suggests that studies that neglect slush may substantially underestimate the area of ice shelves covered by surface meltwater. Furthermore, we found that adjusting the surface albedo in a regional climate model to account for the lower albedo of surface meltwater resulted in 2.8 times greater snowmelt across five representative ice shelves. This extra melt is currently unaccounted for in regional climate models, which may lead to underestimates in projections of ice-sheet melting and ice-shelf stability. 

Abstract Supraglacial lakes on the Greenland Ice Sheet (GrIS) can impact both the ice sheet surface mass balance and ice dynamics. Thus, understanding the evolution and dynamics of supraglacial lakes is important to provide improved parameterizations for ice sheet models to enable better projections of future GrIS changes. In this study, we utilize the growing inventory of optical and microwave satellite imagery to automatically determine the fate of Greenland‐wide supraglacial lakes during 2018 and 2019; low and high melt seasons respectively. We develop a novel time series classification method to categorize lakes into four classes: (a) Refreezing, (b) rapidly draining, (c) slowly draining, and (d) buried. Our findings reveal significant interannual variability between the two melt seasons, with a notable increase in the proportion of draining lakes, and a particular dominance of slowly draining lakes, in 2019. We also find that as mean lake depth increases, so does the percentage of lakes that drain, indicating that lake depth may influence hydrofracture potential. We further observe rapidly draining lakes at higher elevations than the previously hypothesized upper‐elevation hydrofracture limit (1,600 m), and that non‐draining lakes are generally deeper during the lower melt 2018 season. Our automatic classification approach and the resulting 2‐year ice‐sheet‐wide data set provide new insights into GrIS supraglacial lake dynamics and evolution, offering a valuable resource for future research. 

Abstract Antarctic ice‐shelf stability is threatened by surface melt, which has been implicated in several ice‐shelf collapse events over recent decades. Here, we first analyze cumulative days of wet snow/ice status (“melt days”) for melt seasons from 1980 to 2021 over Antarctica's ice shelves using passive and active microwave satellite observations. As these observations do not directly reveal meltwater volumes, we calculate these using the physics‐based multi‐layer snow model SNOWPACK, driven by the global climate‐reanalysis model Modern‐Era Retrospective analysis for Research and Applications Version 2. We find a strong non‐linear relationship between melt days and meltwater production volume. SNOWPACK's calculation of melt days shows agreement with observations of both cumulative days, and spatial and interannual variability. Highest melt rates are found on the Peninsula ice shelves, particularly in the 1992/1993 and 1994/1995 austral summers. Over all ice shelves, SNOWPACK calculates a small, but significant, decreasing trend in both annual melt days and meltwater production volume over the 41 years. 

Abstract Increasing surface melt has been implicated in the collapse of several Antarctic ice shelves over the last few decades, including the collapse of Larsen B Ice Shelf over a period of just a few weeks in 2002. The speed at which an ice shelf disintegrates strongly determines the subsequent loss of grounded ice and sea level rise, but the controls on collapse speed are not well understood. Here we show, using a novel cellular automaton model, that there is an intrinsic speed limit on ice shelf collapse through cascades of interacting melt pond hydrofracture events. Though collapse speed increases with the area of hydrofracture influence, the typical flexural length scales of Antarctic ice shelves ensure that hydrofracture interactions remain localized. We argue that the speed at which Larsen B Ice Shelf collapsed was caused by a season of anomalously high surface meltwater production.