Abstract
Abstract. The Greenland Ice Sheet (GrIS) rapid mass loss is primarily driven by an increase in meltwater runoff, which highlights the importance of understanding the formation, evolution, and impact of meltwater features on the ice sheet. Buried lakes are meltwater features that contain liquid water and exist under layers of snow, firn, and/or ice. These lakes are invisible in optical imagery, challenging the analysis of their evolution and implication for larger GrIS dynamics and mass change. Here, we present a method that uses a convolutional neural network, a deep learning method, to automatically detect buried lakes across the GrIS. For the years 2018 and 2019 (which represent low- and high-melt years, respectively), we compare total areal extent of both buried and surface lakes across six regions, and we use a regional climate model to explain the spatial and temporal differences. We find that the total buried lake extent after the 2019 melt season is 56 % larger than after the 2018 melt season across the entire ice sheet. Northern Greenland has the largest increase in buried lake extent after the 2019 melt season, which we attribute to late-summer surface melt and high autumn temperatures. We also provide evidence that different processes are responsible for buried lake formation in different regions of the ice sheet. For example, in southwest Greenland, buried lakes often appear on the surface during the previous melt season, indicating that these meltwater features form when surface lakes partially freeze and become insulated as snowfall buries them. Conversely, in southeast Greenland, most buried lakes never appear on the surface, indicating that these features may form due to downward percolation of meltwater and/or subsurface penetration of shortwave radiation. We provide support for these processes via the use of a physics-based snow model. This study provides additional perspective on the potential role of meltwater on GrIS dynamics and mass loss.
Highlights
The Greenland Ice Sheet (GrIS), which holds enough ice to raise sea level globally by more than 7 m (Smith et al, 2020), has experienced net mass loss every year since 1998 (Mouginot et al, 2019)
In this study we develop a convolutional neural network (CNN), a deep learning technique for automatic detection of features from images, to detect buried lakes across the GrIS
We find that the total number of surface lakes and their total areal extent is much lower during the 2018 melt season than during the 2019 melt season
Summary
The Greenland Ice Sheet (GrIS), which holds enough ice to raise sea level globally by more than 7 m (Smith et al, 2020), has experienced net mass loss every year since 1998 (Mouginot et al, 2019). Meltwater often pools in surface lakes in the ablation zone during the summer from May to October (McMillan et al, 2007; Banwell et al, 2012). Water in these lakes runs off the ice sheet, drains via hydrofracture (Das et al, 2008; Tedesco et al, 2013; Williamson et al, 2018b), or refreezes in the firn (Bell et al, 2018).
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