Blowing snow impacts Antarctic ice sheet surface mass balance by snow redistribution and sublimation. However, numerical models poorly represent blowing snow processes, while direct observations are limited in space and time. Satellite retrieval of blowing snow is hindered by clouds and only the strongest events are considered. Here, we develop a blowing snow detection (BSD) algorithm for ground-based remote-sensing ceilometers in polar regions and apply it to ceilometers at Neumayer III and Princess Elisabeth (PE) stations, East Antarctica. The algorithm is able to detect (heavy) blowing snow layers reaching 30ĝ€m height. Results show that 78ĝ€% of the detected events are in agreement with visual observations at Neumayer III station. The BSD algorithm detects heavy blowing snow 36ĝ€% of the time at Neumayer (2011-2015) and 13ĝ€% at PE station (2010-2016). Blowing snow occurrence peaks during the austral winter and shows around 5ĝ€% interannual variability. The BSD algorithm is capable of detecting blowing snow both lifted from the ground and occurring during precipitation, which is an added value since results indicate that 92ĝ€% of the blowing snow is during synoptic events, often combined with precipitation. Analysis of atmospheric meteorological variables shows that blowing snow occurrence strongly depends on fresh snow availability in addition to wind speed. This finding challenges the commonly used parametrizations, where the threshold for snow particles to be lifted is a function of wind speed only. Blowing snow occurs predominantly during storms and overcast conditions, shortly after precipitation events, and can reach up to 1300ĝ€mĝ€†a. g. l. in the case of heavy mixed events (precipitation and blowing snow together). These results suggest that synoptic conditions play an important role in generating blowing snow events and that fresh snow availability should be considered in determining the blowing snow onset.@en

Surface mass balance (SMB) strongly controls spatial and temporal variations in the Antarctic Ice Sheet (AIS) mass balance and its contribution to sea level rise. Currently, the scarcity of observational data and the challenges of climate modeling over the ice sheet limit our understanding of the processes controlling AIS SMB. Particularly, the impact of blowing snow on local SMB is not yet constrained and is subject to large uncertainties. Tho assess the impact of blowing snow on local SMB, we investigate the 15-sec attenuated backscatter profiles from 910 nm ceilometers at two East Antarctic locations in Dronning Maud Land. Ceilometers are robust ground-based remote sensing instruments that can withstand harsh conditions unmanned and produce data continuously. In addition to yielding information on cloud base height and vertical structure, these instruments also provide information on the particles present in the boundary layer. We developed a new algorithm to detect blowing snow (snow particles lifted by the wind from the surface to substantial height) from the ceilometer attenuated backscatter. The algorithm routinely detects the presence of blowing snow if 1) a certain threshold is be exceeded at the range bin closest to the ground (signaling a large concentration of scatterers), and 2) if the intensity of the signal decreases with height (signature of the presence of a blowing snow layer). The algorithm successfully allows to detect strong blowing snow signal from layers thicker than 15 m at the Princess Elisabeth (PE, 72S, 23E) and Neumayer (70S, 8 W) stations in Dronning Maud Land, East Antarctica. Moreover, we combined the ceilometer with automatic weather stations to understand key conditions for blowing snow at the study locations. Results show a very good match between the blowing snow events detected by the new algorithm and visual observations at Neumayer station. Applying the algorithm to PE station, we retrieve the frequency and annual cycle of blowing snow. Finally, the analysis of the automatic weather station data enables to distinguish key meteorological parameters for blowing snow conditions: wind speed, wind direction, relative humidity and temperature inversion (boundary layer stability). These parameters can then be used as input in snow models resolving blowing snow and for validation of the blowing snow schemes in climate models.@en