Spatial and temporal variability in summer snow pack in Dronning Maud Land, Antarctica

Abstract

Abstract.To quantify the spatial and temporal variability in the snow pack, field measurements were carried out during four summers in Dronning Maud Land, Antarctica. Data from a 310-km-long transect revealed the largest horizontal gradients in snow density, temperature, and hardness in the escarpment region. On the local scale, day-to-day temporal variability dominated the standard deviation of snow temperature, while the diurnal cycle was of second significance, and horizontal variability on the scale of 0.4 to 10 m was least important. In the uppermost 0.2 m, the snow temperature was correlated with the air temperature over the previous 6-12 h, whereas at the depths of 0.3 to 0.5 m the most important time scale was 3 days. Cloud cover and radiative fluxes affected the snow temperature in the uppermost 0.30 m and the snow density in the uppermost 0.10 m. Both on the intra-pit and transect scales, the ratio of horizontal to temporal variability increased with depth. The horizontal standard deviation of snow density increased rapidly between the scales of 0.4 and 2 m, and more gradually from 10 to 100 m. Inter-annual variations in snow temperature and density were due to interannual differences in air temperature and the timing of the precipitation events.

Measurements and modelling of snow particle size and shortwave infrared albedo over a melting Antarctic ice sheet

“…2b). The highest r snow exceeding 500 kg m −3 were due to the presence of ice layers formed via refreezing of meltwater; such high ρ snow values were not observed during our previous campaigns at the same site in 2006-2008, when the summers were colder (Vihma et al, 2011).…”

“…Similarly to our campaigns in (Vihma et al, 2011, snow temperature and density profiles were measured in the uppermost 50 cm of the snow pits, but here we only present and utilize data from the uppermost 20 cm. Snow temperature (T snow ) was measured at the surface and at the depths of 2.5, 5, 10, 15, and 20 cm with the handheld temperature probe Ebro TFX 410, which is equipped with a 30 cm probe and has a nominal accuracy of ±0.3 • C. The vertical snow density (ρ snow ) profiles were measured with a steel cylinder (volume 247 cm 3 ) pushed horizontally in the snow pit wall.…”

Cloud and precipitation properties from ground-based remote-sensing instruments in East Antarctica

“…Clouds are among the key components of the hydrological cycle, serving as the agents linking water vapour transport into Antarctica with precipitation, determining its amount and spatial distribution. Correct representation of cloud condensate amount and phase partitioning in climate models is crucial for simulation of Antarctic precipitation timing and spatial distribution, especially when orographic effects are present (Wacker et al, 2009). Via their radiative forcing, clouds also play a significant role in the Antarctic surface energy balance affecting air and surface temperatures and heat flux exchange of the snow surface with the air above and deeper snow layers (Bintanja and Van den Broeke, 1996;Van den Broeke et al, 2004;Van den Broeke et al, 2006;Vihma et al, 2011).…”

“…Correct representation of cloud condensate amount and phase partitioning in climate models is crucial for simulation of Antarctic precipitation timing and spatial distribution, especially when orographic effects are present (Wacker et al, 2009). Via their radiative forcing, clouds also play a significant role in the Antarctic surface energy balance affecting air and surface temperatures and heat flux exchange of the snow surface with the air above and deeper snow layers (Bintanja and Van den Broeke, 1996;Van den Broeke et al, 2004;Van den Broeke et al, 2006;Vihma et al, 2011). Bennartz et al (2013) demonstrated a dramatic example of cloud influence on the ice sheet surface energy balance, where radiative forcing of the liquidcontaining clouds coupled with warm air advection was responsible for the surface melt on top of the Greenland ice sheet in July 2012.…”

“…The combination of the ground-based remote-sensing instruments with an AWS allows for the study of cloud radiative forcing and attributing snow accumulation to precipitation with a potential to distinguish between local accumulation due to snowfall or clear-sky drifting snow. The goal of collecting these data is to perform detailed process-based model evaluation and improve cloud and precipitation parameterisations, required for Antarctic climate simulations (Gallée and Gorodetskaya, 2008;Wacker et al, 2009;Bromwich et al, 2012). Furthermore, the data can be used to evaluate and complement satellite data from similar sensors (e.g.…”