Abstract. The broadband albedo of surface snow is determined both by the near-surface profile of the physical and chemical properties of the snowpack and by the spectral and angular characteristics of the incident solar radiation. Simultaneous measurements of the physical and chemical properties of snow were carried out at Summit Camp, Greenland (72°36´ N, 38°25´ W, 3210 m a.s.l.) in May and June 2011, along with spectral albedo measurements. One of the main objectives of the field campaign was to test our ability to predict snow spectral albedo by comparing the measured albedo to the albedo calculated with a radiative transfer model, using measured snow physical and chemical properties. To achieve this goal, we made daily measurements of the snow spectral albedo in the range 350–2200 nm and recorded snow stratigraphic information down to roughly 80 cm. The snow specific surface area (SSA) was measured using the DUFISSS instrument (DUal Frequency Integrating Sphere for Snow SSA measurement, Gallet et al., 2009). Samples were also collected for chemical analyses including black carbon (BC) and dust, to evaluate the impact of light absorbing particulate matter in snow. This is one of the most comprehensive albedo-related data sets combining chemical analysis, snow physical properties and spectral albedo measurements obtained in a polar environment. The surface albedo was calculated from density, SSA, BC and dust profiles using the DISORT model (DIScrete Ordinate Radiative Transfer, Stamnes et al., 1988) and compared to the measured values. Results indicate that the energy absorbed by the snowpack through the whole spectrum considered can be inferred within 1.10%. This accuracy is only slightly better than that which can be obtained considering pure snow, meaning that the impact of impurities on the snow albedo is small at Summit. In the near infrared, minor deviations in albedo up to 0.014 can be due to the accuracy of radiation and SSA measurements and to the surface roughness, whereas deviations up to 0.05 can be explained by the spatial heterogeneity of the snowpack at small scales, the assumption of spherical snow grains made for DISORT simulations and the vertical resolution of measurements of surface layer physical properties. At 1430 and around 1800 nm the discrepancies are larger and independent of the snow properties; we propose that they are due to errors in the ice refractive index at these wavelengths. This work contributes to the development of physically based albedo schemes in detailed snowpack models, and to the improvement of retrieval algorithms for estimating snow properties from remote sensing data.

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.