Abstract. Many ice shelves in Antarctica experience surface melt each summer, with potentially severe consequences for sea level rise. However, large interannual and regional variability in surface melt increases uncertainty in predictions of how ice shelves will react to climate change. Previous studies of surface melt have usually focused on either a process-level understanding of surface melt through energy balance investigations, or used regional melt metrics to quantify interannual variability in satellite observations of surface melt. Here, we use an approach that helps 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, East Antarctica, over the previous two decades (2002/03–2020/21). Our results point to a significant role for surface air temperatures in controlling the interannual variability of summer melt, and also reveal the influence of local controls on driving melt. In particular, prolonged melt in the south-east of the shelf and 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 of surface melt on Antarctic ice shelves.

Abstract. Surface melting on the Antarctic Ice Sheet has been monitored by satellite microwave radiometery for over 40 years. Despite this long perspective, our understanding of the microwave emission from wet snow is still limited, preventing the full exploitation of these observations to study supraglacial hydrology. Using the Snow Microwave Radiative Transfer (SMRT) model, this study investigates the sensitivity of microwave brightness temperature to snow liquid water content at frequencies from 1.4 to 37 GHz. We first determine the snowpack properties for 8 selected coastal sites by retrieving profiles of density, grain size and ice layers from microwave observations when the snowpack is dry during winter time. Second, a series of brightness temperature simulations is run with added water. The results show that: i) a small amount of liquid water (≈0.5 kg m^-2 can be detected, but the actual amount can not be retrieved in the full range of possible water contents, ii) the detection of a buried wet layer is possible up to a maximum 1 to 6 m depth depending on the frequency (6–37 GHz) and on the site, iii) surface ponds and water-saturated areas may prevent melt detection, but the current coverage of these water bodies in the large satellite field of view is presently too small in Antarctica to have noticeable effects, iv) at 1.4 GHz, while the simulations are less reliable, we found a weaker sensitivity to liquid water and the maximal depth of detection is relatively shallow (<10 m) compared to the typical radiation penetration depth in dry firn (≈1000 m) at this low frequency. These numerical results pave the way for the development of improved multi-frequency algorithms to detect melt intensity and depth in the Antarctic snowpack.