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GPS/GNSS campaign: Short-term occupations at multiple locations (Closed: No longer collecting data) 

Abstract Ice shelves regulate ice sheet dynamics, with their stability influenced by horizontal flow and vertical flexure. MacAyeal and others (2021) developed the theoretical foundation for a coupled flow-flexure model (the “M21 model”), combining the Shallow Shelf Approximation with thin-beam flexure, providing a computationally efficient tool for studying phenomena like ice shelf rumpling and lake drainage. However, the M21 model relies on proprietary software, is unstable under compressive flow conditions, and does not incorporate fracture processes critical for capturing ice-shelf damage evolution. We present an open-source version of the M21 model addressing these limitations. Using the free Python librariesFiredrakeandicepack, we introduce a plastic failure mechanism, effectively limiting bending stresses and thereby stabilizing the model. This enhancement expands the viscous M21 model into a viscoplastic flow-flexure-fracture (3F) framework. We validate the 3F model through test cases replicating key ice shelf phenomena, including marginal rumpling and periodic surface meltwater drainage. By offering this tool as open-source software, we aim to enable broader adoption, with the ultimate aim of representing surface meltwater induced flow-flexure-fracture processes in large-scale ice sheet models. 

Abstract. Over recent decades, the Greenland Ice Sheet (GrIS) has lost mass through increased melting and solid ice discharge into the ocean. Surface meltwater features such as supraglacial lakes (SGLs), channels and slush are becoming more abundant as a result of the former and are implicated as a control on the latter when they drain. It is not yet clear, however, how these different surface hydrological features will respond to future climate changes, and it is likely that GrIS surface melting will continue to increase as the Arctic warms. Here, we use Sentinel-2 and Landsat 8 optical satellite imagery to compare the distribution and evolution of meltwater features (SGLs, channels, slush) in the Russell–Leverett glacier catchment, southwest Greenland, in relatively high (2019) and low (2018) melt years. We show that (1) supraglacial meltwater covers a greater area and extends further inland to higher elevations in 2019 than in 2018; (2) slush – generally disregarded in previous Greenland surface hydrology studies – is far more widespread in 2019 than in 2018; (3) the supraglacial channel system is more interconnected in 2019 than in 2018; (4) a greater number and larger total area of SGLs drained in 2019, although draining SGLs were, on average, deeper and more voluminous in 2018; (5) small SGLs (≤0.0495 km2) – typically disregarded in previous studies – form and drain in both melt years, although this behaviour is more prevalent in 2019; and (6) a greater proportion of SGLs refroze in 2018 compared to 2019. This analysis provides new insight into how the ice sheet responds to significant melt events, and how a changing climate may impact meltwater feature characteristics, SGL behaviour and ice dynamics in the future. 

Abstract Global Navigation Satellite System (GNSS) observations and ground-based timelapse photography obtained over the record-high 2019/2020 melt season are combined to characterise the flexure and fracture behaviour of a previously formed doline on George VI Ice Shelf, Antarctica. The GNSS timeseries shows a downward vertical displacement of the doline centre with respect to the doline rim of ~60 cm in response to loading from a central meltwater lake. The GNSS data also show a tens-of-days episode of rapid-onset, exponentially decaying horizontal displacement, where the horizontal distance between the doline rim and its centre increases by ~70 cm. We interpret this event as the initiation and/or widening of a fracture, aided by stress perturbations associated with meltwater loading in the doline basin. Viscous flexure modelling indicates that the meltwater loading generates tensile surface stresses exceeding 75 kPa. This, together with our timelapse photos of circular fractures around the doline, suggests the first such documentation of meltwater-loading-induced ‘ring fracture’ formation on an ice shelf, equivalent to the fracture type proposed as part of the chain-reaction lake drainage process involved in the 2002 breakup of the Larsen B Ice Shelf. 

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; cool and warm melt seasons respectively. We develop a novel time series classification method to categorize lakes into four classes: 1) refreezing, 2) rapidly draining, 3) slowly draining, and 4) buried. Our findings reveal significant interannual variability between the two melt seasons, with a notable increase in the proportion of 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. However, we also observe that non-draining lakes are deeper during the cooler 2018 melt season, suggesting that additional factors may predispose lakes to drain earlier in a warmer year. Our automatic classification approach and the resulting two-year ice-sheet-wide dataset provide unprecedented insights into GrIS supraglacial lake dynamics and evolution, offering a valuable resource for future research. 

Supraglacial lakes form on the surface of the Greenland Ice Sheet during the summer months and can directly impact ice sheet mass balance by removing mass via drainage and runoff or indirectly impact mass balance by influencing ice sheet dynamics. Here, 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, a cool and warm melt season respectively. We use a machine learning time series classification approach to categorize lakes into four different categories: lakes that 1) refreeze, 2) rapidly drain, 3) slowly drain, and 4) become buried lakes at the end of the melt season. We find that during the warmer 2019, not only was the number of lake drainage events higher than in 2018, but also the proportion of lakes that drained was greater. By investigating mean lake depths for these four categories, we show that drained lakes were, on average, 22% deeper than lakes that refroze or became buried lakes. Interestingly, drained lakes had approximately the same maximum depth in 2018 and 2019; however, lakes that did not drain were 29% deeper in 2018, a cooler year. Our unique two-year dataset describing the fate of every Greenland supraglacial lake provides novel insight into lake drainage and refreeze in a relatively warm and cool year, which may be increasingly relevant in a warming climate.