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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. 

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.