Abstract. Understanding the interannual variability of surface mass balance (SMB) and surface melting in Antarctica is key to quantify the signal-to-noise ratio in climate trends, identify opportunities for multi-year climate predictions and assess the ability of climate models to respond to climate variability. Here we simulate summer SMB and surface melting from 1979 to 2017 using the Regional Atmosphere Model (MAR) at 10 km resolution over the drainage basins of the Amundsen Sea glaciers in West Antarctica. Our simulations reproduce the mean present-day climate in terms of near-surface temperature (mean overestimation of 0.10 ^∘C), near-surface wind speed (mean underestimation of 0.42 m s⁻¹), and SMB (relative bias <20 % over Thwaites glacier). The simulated interannual variability of SMB and melting is also close to observation-based estimates.

For all the Amundsen glacial drainage basins, the interannual variability of summer SMB and surface melting is driven by two distinct mechanisms: high summer SMB tends to occur when the Amundsen Sea Low (ASL) is shifted southward and westward, while high summer melt rates tend to occur when ASL is shallower (i.e. anticyclonic anomaly). Both mechanisms create a northerly flow anomaly that increases moisture convergence and cloud cover over the Amundsen Sea and therefore favors snowfall and downward longwave radiation over the ice sheet. The part of interannual summer SMB variance explained by the ASL longitudinal migrations increases westward and reaches 40 % for Getz. Interannual variation in the ASL relative central pressure is the largest driver of melt rate variability, with 11 % to 21 % of explained variance (increasing westward). While high summer SMB and melt rates are both favored by positive phases of El Niño–Southern Oscillation (ENSO), the Southern Oscillation Index (SOI) only explains 5 % to 16 % of SMB or melt rate interannual variance in our simulations, with moderate statistical significance. However, the part explained by SOI in the previous austral winter is greater, suggesting that at least a part of the ENSO–SMB and ENSO–melt relationships in summer is inherited from the previous austral winter. Possible mechanisms involve sea ice advection from the Ross Sea and intrusions of circumpolar deep water combined with melt-induced ocean overturning circulation in ice shelf cavities. Finally, we do not find any correlation with the Southern Annular Mode (SAM) in summer.

Abstract. The Antarctic continent is a vast desert and is the coldest and the most unknown area on Earth. It contains the Antarctic ice sheet, the largest continental water reservoir on Earth that could be affected by the current global warming, leading to sea level rise. The only significant supply of ice is through precipitation, which can be observed from the surface and from space. Remote-sensing observations of the coastal regions and the inner continent using CloudSat radar give an estimated rate of snowfall but with uncertainties twice as large as each single measured value, whereas climate models give a range from half to twice the space–time-averaged observations. The aim of this study is the evaluation of the vertical precipitation rate profiles of CloudSat radar by comparison with two surface-based micro-rain radars (MRRs), located at the coastal French Dumont d'Urville station and at the Belgian Princess Elisabeth station located in the Dronning Maud Land escarpment zone. This in turn leads to a better understanding and reassessment of CloudSat uncertainties. We compared a total of four precipitation events, two per station, when CloudSat overpassed within 10 km of the station and we compared these two different datasets at each vertical level. The correlation between both datasets is near-perfect, even though climatic and geographic conditions are different for the two stations. Using different CloudSat and MRR vertical levels, we obtain 10 km space-scale and short-timescale (a few seconds) CloudSat uncertainties from −13 % up to +22 %. This confirms the robustness of the CloudSat retrievals of snowfall over Antarctica above the blind zone and justifies further analyses of this dataset.

Abstract. Snow accumulation is the main positive component of the mass balance in Antarctica. In contrast to the major efforts deployed to estimate its overall value on a continental scale – to assess the contribution of the ice sheet to sea level rise – knowledge about the accumulation process itself is relatively poor, although many complex phenomena occur between snowfall and the definitive settling of the snow particles on the snowpack. Here we exploit a dataset of near-daily surface elevation maps recorded over 3 years at Dome C using an automatic laser scanner sampling 40–100 m² in area. We find that the averaged accumulation is relatively regular over the 3 years at a rate of +8.7 cm yr⁻¹. Despite this overall regularity, the surface changes very frequently (every 3 d on average) due to snow erosion and heterogeneous snow deposition that we call accumulation by “patches”. Most of these patches (60 %–85 %) are ephemeral but can survive a few weeks before being eroded. As a result, the surface is continuously rough (6–8 cm root-mean-square height) featuring meter-scale dunes aligned along the wind and larger, decameter-scale undulations. Additionally, we deduce the age of the snow present at a given time on the surface from elevation time series and find that snow age spans over more than a year. Some of the patches ultimately settle, leading to a heterogeneous internal structure which reflects the surface heterogeneity, with many snowfall events missing at a given point, whilst many others are overrepresented. These findings have important consequences for several research topics including surface mass balance, surface energy budget, photochemistry, snowpack evolution, and the interpretation of the signals archived in ice cores.

Abstract. Surface snow density is an important variable for the surface mass balance and energy budget. It evolves according to meteorological conditions, in particular, snowfall, wind, and temperature, but the physical processes governing atmospheric influence on snow are not fully understood. A reason is that no systematic observation is available on a continental scale. Here, we use the passive microwave observations from AMSR-E satellite to retrieve the surface snow density at Dome C on the East Antarctic Plateau. The retrieval method is based on the difference of surface reflections between horizontally and vertically polarized brightness temperatures at 37 GHz, highlighted by the computation of the polarization ratio, which is related to surface snow density. The relationship has been obtained with a microwave emission radiative transfer model (DMRT-ML). The retrieved density, approximately representative of the topmost 3 cm of the snowpack, compares well with in situ measurements. The difference between mean in situ measurements and mean retrieved density is 26.2 kg m⁻³, which is within typical in situ measurement uncertainties. We apply the retrieval method to derive the time series over the period 2002–2011. The results show a marked and persistent pluri-annual decrease of about 10 kg m⁻³ yr⁻¹, in addition to atmosphere-related seasonal, weekly, and daily density variations. This trend is confirmed by independent active microwave observations from the ENVISAT and QuikSCAT satellites, though the link to the density is more difficult to establish. However, no related pluri-annual change in meteorological conditions has been found to explain such a trend in snow density. Further work will concern the extension of the method to the continental scale.

Abstract. We evaluate modelled Antarctic ice sheet (AIS) near-surface climate, surface mass balance (SMB) and surface energy balance (SEB) from the updated polar version of the regional atmospheric climate model, RACMO2 (1979–2016). The updated model, referred to as RACMO2.3p2, incorporates upper-air relaxation, a revised topography, tuned parameters in the cloud scheme to generate more precipitation towards the AIS interior and modified snow properties reducing drifting snow sublimation and increasing surface snowmelt.

Comparisons of RACMO2 model output with several independent observational data show that the existing biases in AIS temperature, radiative fluxes and SMB components are further reduced with respect to the previous model version. The model-integrated annual average SMB for the ice sheet including ice shelves (minus the Antarctic Peninsula, AP) now amounts to 2229Gty⁻¹, with an interannual variability of 109Gty⁻¹. The largest improvement is found in modelled surface snowmelt, which now compares well with satellite and weather station observations. For the high-resolution (∼5.5km) AP simulation, results remain comparable to earlier studies.

The updated model provides a new, high-resolution data set of the contemporary near-surface climate and SMB of the AIS; this model version will be used for future climate scenario projections in a forthcoming study.