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 (1979RACMO2 ( -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 2229 Gt y −1 , with an interannual variability of 109 Gt y −1 . The largest improvement is found in modelled surface snowmelt, which now compares well with satellite and weather station observations. For the high-resolution (∼ 5.5 km) 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.

Abstract. Melt ponds on sea ice strongly reduce the surface albedo and accelerate the decay of Arctic sea ice. Due to different spectral properties of snow, ice, and water, the fractional coverage of these distinct surface types can be derived from multispectral sensors like the Moderate Resolution Image Spectroradiometer (MODIS) using a spectral unmixing algorithm. The unmixing was implemented using a multilayer perceptron to reduce computational costs.Arctic-wide melt pond fractions and sea ice concentrations are derived from the level 3 MODIS surface reflectance product. The validation of the MODIS melt pond data set was conducted with aerial photos from the MELTEX campaign 2008 in the Beaufort Sea, data sets from the National Snow and Ice Data Center (NSIDC) for 2000 and 2001 from four sites spread over the entire Arctic, and with ship observations from the trans-Arctic HOTRAX cruise in 2005. The root-mean-square errors range from 3.8 % for the comparison with HOTRAX data, over 10.7 % for the comparison with NSIDC data, to 10.3 % and 11.4 % for the comparison with MELTEX data, with coefficient of determination ranging from R 2 = 0.28 to R 2 = 0.45. The mean annual cycle of the melt pond fraction per grid cell for the entire Arctic shows a strong increase in June, reaching a maximum of 15 % by the end of June. The zonal mean of melt pond fractions indicates a dependence of the temporal development of melt ponds on the geographical latitude, and has its maximum in mid-July at latitudes between 80 • and 88 • N.Furthermore, the MODIS results are used to estimate the influence of melt ponds on retrievals of sea ice concentrations from passive microwave data. Results from a case study comparing sea ice concentrations from ARTIST Sea Ice-, NASA Team 2-, and Bootstrap-algorithms with MODIS sea ice concentrations indicate an underestimation of around 40 % for sea ice concentrations retrieved with microwave algorithms.

[1] The maximum effect of open leads within sea ice on the near-surface atmospheric temperature is estimated using a 1D atmospheric model coupled with a thermodynamic snow/sea ice model. The study is restricted to clear-sky conditions during polar night. The model is initialized with a typical wintertime atmospheric temperature profile. Results are analyzed at different integration times corresponding to different fetches over the fractured sea ice as a function of wind speed and sea ice concentration A. The results demonstrate that for A > 90% small changes in the sea ice fraction have a strong effect on the near-surface temperature. A change by 1% causes a temperature signal of up to 3.5 K. A threshold value of about 4 m s À1 for the 10-m wind speed divides the air-ice interaction process into a weak-wind and strong-wind regime. Citation: Lüpkes, C., T. Vihma, G. Birnbaum, and U. Wacker (2008), Influence of leads in sea ice on the temperature of the atmospheric boundary layer during polar night, Geophys. Res. Lett., 35, L03805,

[1] High-resolution Antarctic Mesoscale Prediction System archive data were used to investigate high-precipitation events at the deep ice core drilling site Kohnen Station, Dronning Maud Land, Antarctica, during the period [2001][2002][2003][2004][2005][2006]. The precipitation is found to be highly episodic, with, on average, approximately eight high-precipitation events per year that can bring more than half of the total annual accumulation. The duration of the events varies between 1 day and about 1 week. On most days in the remaining time of the year, however, daily precipitation sums are about one order of magnitude smaller than that for the high-precipitation events. Synoptic weather patterns causing these events were directly connected to frontal systems of cyclones in only 20% of the 51 investigated cases. The majority of the events occurred in connection with (blocking) anticyclones and correspondingly amplified Rossby waves, which lead to advection of warm, moist air from relatively low latitudes. Possible changes in the seasonality and frequency of these events in a different climate can lead to a bias in ice core properties and might also strongly influence the mass balance of the Antarctic continent and thus global sea level change.

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