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, we compare total areal extent of both buried and surface lakes across six regions, and 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 observes 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 western Greenland, buried lakes often appear on the surface during the previous melt season, indicating that these features form when surface lakes partially freeze and become insulated as snowfall buries them. In contrast, in southeast Greenland, most buried lakes never appear on the surface, signifying that these features may form due to subsurface penetration of shortwave radiation and/or downward percolation of meltwater. This study helps to provide additional perspective on the potential role of meltwater on GrIS dynamics and mass loss.;
