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Over the last decade, the Southern Ocean has experienced episodes of severe sea ice area decline. Abrupt events of sea ice loss are challenging to predict, in part due to incomplete understanding of processes occurring at the scale of individual ice floes. Here, we use high-resolution altimetry (ICESat-2) to quantify the seasonal life cycle of floes in the perennial sea ice pack of the Weddell Sea. The evolution of the floe chord distribution (FCD) shows an increase in the proportion of smaller floes between November and February, which coincides with the asymmetric melt–freeze cycle of the pack. The freeboard ice thickness distribution (fITD) suggests mirrored seasonality between the western and southern sections of the Weddell Sea ice cover, with an increasing proportion of thicker floes between October and March in the south and the opposite in the west. Throughout the seasonal cycle, there is a positive correlation between the mean chord length of floes and their average freeboard thickness. Composited floe profiles reveal that smaller floes are more vertically round than larger floes and that the mean roundness of floes increases during the melt season. These results show that regional differences in ice concentration and type at larger scales occur in conjunction with different behaviors at the small scale. We therefore suggest that floe-derived metrics obtained from altimetry could provide useful diagnostics for floe-aware models and improve our understanding of sea ice processes across scales.

Abstract Basal melting of ice shelves is fundamental to Antarctic ice sheet mass loss, yet direct observations remain sparse. We present the first year-round melt record (2017-2021) from a phase-sensitive radar on Fimbulisen, one of the fastest flowing ice shelves in Dronning Maud Land, East Antarctica. The observed long-term mean ablation rate at 350 m depth below the central ice shelf was 1.0 ± 0.5 m yr?1, marked by substantial sub-weekly variability ranging from 0.4 to 3.5 m yr?1. 36-h filtered basal melt rate fluctuations closely align with ocean velocity. On seasonal time scales, melt rates peak during austral spring to autumn (September-March), driven by both elevated ocean velocities and thermal driving near the base. The combined effect of thermal driving and current speed explains the majority of the melt rate variability (r = 0.84), highlighting the dominant role of shear-driven turbulence. This relationship enables parameterization of melt rates for the decade-long ocean record (2010?2021), although deviations appear under low and high forcing conditions. Both observed and parameterized melt rates show similar yearly mean magnitudes compared to satellite-derived melt rates but with a tenfold lower seasonal amplitude and a 3-month delay in seasonality. These detailed concurrent ice?ocean observations provide essential validation data for remote sensing and numerical models that aim to quantify and project ice-shelf response to a change in ocean forcing. In situ measurements and continued monitoring are crucial for accurately assessing and modeling future basal melt rates, and for understanding the complex dynamics driving ice-shelf stability and sea-level change.

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.

Glaciers are indicators of ongoing anthropogenic climate change1. Their melting leads to increased local geohazards2, and impacts marine3 and terrestrial4,5 ecosystems, regional freshwater resources6, and both global water and energy cycles7,8. Together with the Greenland and Antarctic ice sheets, glaciers are essential drivers of present9,10 and future11–13 sea-level rise. Previous assessments of global glacier mass changes have been hampered by spatial and temporal limitations and the heterogeneity of existing data series14–16. Here we show in an intercomparison exercise that glaciers worldwide lost 273 ± 16 gigatonnes in mass annually from 2000 to 2023, with an increase of 36 ± 10% from the first (2000–2011) to the second (2012–2023) half of the period. Since 2000, glaciers have lost between 2% and 39% of their ice regionally and about 5% globally. Glacier mass loss is about 18% larger than the loss from the Greenland Ice Sheet and more than twice that from the Antarctic Ice Sheet17. Our results arise from a scientific community effort to collect, homogenize, combine and analyse glacier mass changes from in situ and remote-sensing observations. Although our estimates are in agreement with findings from previous assessments14–16 at a global scale, we found some large regional deviations owing to systematic differences among observation methods. Our results provide a refined baseline for better understanding observational differences and for calibrating model ensembles12,16,18, which will help to narrow projection uncertainty for the twenty-first century11,12,18.

Supraglacial lakes on Antarctic ice shelves can have far-reaching implications for ice-sheet stability, highlighting the need to understand their dynamics, controls and role in the ice-sheet mass budget. We combine a detailed satellite-based record of seasonal lake evolution in Dronning Maud Land with a high-resolution simulation from the regional climate model Modèle Atmosphérique Régional to identify drivers of lake variability between 2014 and 2021. Correlations between summer lake extents and climate parameters reveal complex relationships that vary both in space and time. Shortwave radiation contributes positively to the energy budget during summer melt seasons, but summers with enhanced longwave radiation are more prone to surface melting and ponding, which is further enhanced by advected heat from summer precipitation. In contrast, previous winter precipitation has a negative effect on summer lake extents, presumably by increasing albedo and pore space, delaying the accumulation of meltwater. Downslope katabatic or föhn winds promote ponding around the grounding zones of some ice shelves. At a larger scale, we find that summers during periods of negative southern annular mode are associated with increased ponding in Dronning Maud Land. The high variability in seasonal lake extents indicates that these ice shelves are highly sensitive to future warming or intensified extreme events.

Satellite ocean color observations are extensively utilized in global carbon sink evaluation. However, the valid coverage of chlorophyll-a concentration (Chla, mg m−3) measurements from these observations is severely limited during autumn and winter in high latitude oceans. The high solar zenith angle (SZA) stands as one of the primary contributors to the reduced quality of Chla products in the high-latitude Southern Ocean during these seasons. This study addresses this challenge by employing a random forest-based regression ensemble (RFRE) method to enhance the quality of Moderate Resolution Imaging Spectroradiometer (MODIS) Chla products affected by high SZA conditions. The RFRE model incorporates the color index (CI), band-ratio index (R), SZA, sensor zenith angle (senz), and Rayleigh-corrected reflectance at 869 nm (Rrc(869)) as predictors. The results indicate that the RFRE model significantly increased the MODIS observed Chla coverage (1.03 to 3.24 times) in high-latitude Southern Ocean regions to the quality of standard Chla products. By applying the recovered Chla to re-evaluate the carbon sink in South Ocean, results showed that the Southern Ocean’s ability to absorb carbon dioxide (CO2) in winter has been underestimated (5.9–18.6 Tg C year−1) in previous assessments. This study underscores the significance of improving the Chla products for a more accurate estimation of winter carbon sink in the Southern Ocean.