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During the Quaternary, ice sheets experienced several retreat–advance cycles, strongly influencing climate patterns. In order to properly simulate these phenomena, it is preferable to use physics-based models instead of parameterizations to estimate the surface mass balance (SMB), which strongly influences the evolution of the ice sheet. To further investigate the potential of these SMB models, this work evaluates the BErgen Snow SImulator (BESSI), a multi-layer snow model with high computational efficiency, as an alternative to providing the SMB for the Earth system model iLOVECLIM for multi-millennial simulations as in paleostudies. We compare the behaviors of BESSI and insolation temperature melt (ITM), an existing SMB scheme of iLOVECLIM during the Last Interglacial (LIG). Firstly, we validate the two SMB models using the regional climate model Mod- èle Atmosphérique Régional (MAR) as forcing and reference for the present-day climate over the Greenland and Antarctic ice sheets. The evolution of the SMB over the LIG (130–116 ka) is computed by forcing BESSI and ITM with transient climate forcing obtained from iLOVECLIM for both ice sheets. For present-day climate conditions, both BESSI and ITM exhibit good performance compared to MAR despite a much simpler model setup. While BESSI performs well for both Antarctica and Greenland for the same set of parame- ters, the ITM parameters need to be adapted specifically for each ice sheet. This suggests that the physics embedded in BESSI allows better capture of SMB changes across varying climate conditions, while ITM displays a much stronger sen- sitivity to its tunable parameters. The findings suggest that BESSI can provide more reliable SMB estimations for the iLOVECLIM framework to improve the model simulations of the ice sheet evolution and interactions with climate for multi-millennial simulations.

The unique challenges of polar ecosystems, coupled with the necessity for high-precision data, make Unmanned Aerial Vehicles (UAVs) an ideal tool for vegetation monitoring and conservation studies in Antarctica. This review draws on existing studies on Antarctic UAV vegetation mapping, focusing on their methodologies, including surveyed locations, flight guidelines, UAV specifications, sensor technologies, data processing techniques, and the use of vegetation indices. Despite the potential of established Machine-Learning (ML) classifiers such as Random Forest, K Nearest Neighbour, and Support Vector Machine, and gradient boosting in the semantic segmentation of UAV-captured images, there is a notable scarcity of research employing Deep Learning (DL) models in these extreme environments. While initial studies suggest that DL models could match or surpass the performance of established classifiers, even on small datasets, the integration of these advanced models into real-time navigation systems on UAVs remains underexplored. This paper evaluates the feasibility of deploying UAVs equipped with adaptive path-planning and real-time semantic segmentation capabilities, which could significantly enhance the efficiency and safety of mapping missions in Antarctica. This review discusses the technological and logistical constraints observed in previous studies and proposes directions for future research to optimise autonomous drone operations in harsh polar conditions.

This study compares CL51 ceilometer observations made at Scott Base, Antarctica, with statistics from the ERA5, JRA55, and MERRA2 reanalyses. To enhance the comparison we use a lidar instrument simulator to derive cloud statistics from the reanalyses which account for instrumental factors. The cloud occurrence in the three reanalyses is slightly overestimated above 3 km, but displays a larger underestimation below 3 km relative to observations. Unlike previous studies, we see no relationship between relative humidity and cloud occurrence biases, suggesting that the cloud biases do not result from the representation of moisture. We also show that the seasonal variation of cloud occurrence and cloud fraction, defined as the vertically integrated cloud occurrence, are small in both the observations and the reanalyses. We also examine the quality of the cloud representation for a set of weather states derived from ERA5 surface winds. The variability associated with grouping cloud occurrence based on weather state is much larger than the seasonal variation, highlighting weather state is a strong control of cloud occurrence. All the reanalyses continue to display underestimates below 3 km and overestimates above 3 km for each weather state. But the variability in ERA5 statistics matches the changes in the observations better than the other reanalyses. We also use a machine learning scheme to estimate the quantity of supercooled liquid water cloud from the ceilometer observations. Ceilometer low-level supercooled liquid water cloud occurrences are considerably larger than values derived from the reanalyses, further highlighting the poor representation of low-level clouds in the reanalyses.

Water stable isotope records in polar ice cores have been largely used to reconstruct past local temperatures and other climatic information such as evaporative source region conditions of the precipitation reaching the ice core sites. However, recent studies have identified post-depositional processes taking place at the ice sheet's surface, modifying the original precipitation signal and challenging the traditional interpretation of ice core isotopic records. In this study, we use a combination of existing and new datasets of precipitation, snow surface, and subsurface isotopic compositions (δ18O and deuterium excess (d-excess)); meteorological parameters; ERA5 reanalyses; outputs from the isotope-enabled climate model ECHAM6-wiso; and a simple modelling approach to investigate the transfer function of water stable isotopes from precipitation to the snow surface and subsurface at Dome C in East Antarctica. We first show that water vapour fluxes at the surface of the ice sheet result in a net annual sublimation of snow, from 3.1 to 3.7 mm w.e. yr−1 (water equivalent) between 2018 and 2020, corresponding to 12 % to 15 % of the annual surface mass balance. We find that the precipitation isotopic signal cannot fully explain the mean, nor the variability in the isotopic composition observed in the snow, from annual to intra-monthly timescales. We observe that the mean effect of post-depositional processes over the study period enriches the snow surface in δ18O by 3.0 ‰ to 3.3 ‰ and lowers the snow surface d-excess by 3.4 ‰ to 3.5 ‰ compared to the incoming precipitation isotopic signal. We also show that the mean isotopic composition of the snow subsurface is not statistically different from that of the snow surface, indicating the preservation of the mean isotopic composition of the snow surface in the top centimetres of the snowpack. This study confirms previous findings about the complex interpretation of the water stable isotopic signal in the snow and provides the first quantitative estimation of the impact of post-depositional processes on the snow isotopic composition at Dome C, a crucial step for the accurate interpretation of isotopic records from ice cores.