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Abstract |
Melt on the surface of Antarctic ice shelves can potentially lead to their disintegration, accelerating the flow of grounded ice to the ocean and raising global sea levels. However, the current understanding of the processes driving surface melt is incomplete, increasing uncertainty in predictions of ice shelf stability and thus of Antarctica's contribution to sea-level rise. Previous studies of surface melt in Antarctica have usually focused on either a process-level understanding of melt through energy-balance investigations or used metrics such as the annual number of melt days to quantify spatiotemporal variability in satellite observations of surface melt. Here, we help bridge the gap between work at these two scales. Using daily passive microwave observations from the AMSR-E and AMSR-2 sensors and the machine learning approach of a self-organising map, we identify nine representative spatial distributions (“patterns”) of surface melt on the Shackleton Ice Shelf in East Antarctica from 2002/03–2020/21. Combined with output from the RACMO2.3p3 regional climate model and surface topography from the REMA digital elevation model, our results point to a significant role for surface air temperatures in controlling the interannual variability in summer melt and also reveal the influence of localised controls on melt. In particular, prolonged melt along the grounding line shows the importance of katabatic winds and surface albedo. Our approach highlights the necessity of understanding both local and large-scale controls on surface melt and demonstrates that self-organising maps can be used to investigate the variability in surface melt on Antarctic ice shelves. |
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