Damage features, such as rifts and crevasses, are the first signs of a weakened ice shelf and the precursor for retreat. Yet, damage changes are not widely quantified on Antarctic ice shelves, leaving future ice shelf weakening poorly understood. Here we use satellite imagery to detect both long-term (24-year) and short-term (annual, 2015–2021) Antarctic-wide damage changes, revealing a multiyear damage development cycle strongly correlated to ice shelf area changes, and a net decline in damaged area from 1997 to 2021. We establish a data-driven link between damage and ice flow characteristics, which shows that ice flow acceleration, strain rate increases and thinning lead to more damage development, in particular under high-emission climate scenarios. This sensitivity to warming suggests that without quantification of damage impacts by detailed physical models the (timing of) ice shelf retreat and Antarctic mass loss may currently be underestimated.

Ice shelves restrain grounded ice discharge into the ocean, and their break-up contributes significantly to Antarctica’s sea level rise. Using aerial imagery from the 1960s and modern satellite data, we construct a long-term record of Wordie Ice Shelf’s disintegration and its effects on tributary glaciers. Early changes in pinning points and ocean warming in Marguerite Bay since the 1960s strongly suggest increasing basal melt as the primary driver of the ice shelf disintegration. Some glaciers responded immediately to the ice shelf break-up, with surface velocities tripling, thinning up to 160 m, and grounding line retreat of 7.5 km, while others reacted decades later due to buttressing from remnant parts of the ice shelf. Our findings emphasize the importance of long-term observations to understand ice shelf disintegration and its impacts, offering crucial insights for assessments of future ice loss from the Antarctic Ice Sheet.

To investigate the drivers of lake drainages in Antarctica, we analyzed optical remote sensing data from the Shackleton Ice Shelf in East Antarctica over seven melt seasons, 2016 to 2023. Our study identified seven drainage event in 2016-2017, one in 2018-2019, fifteen in 2019-2020, and two in 2020-2021. All identified drainages occurred in regions with relatively medium to high levels of satellite-derived ice shelf damage and, except one, all with active damage development. Additionally, 17 out of 25 drainages coincided with increases in tidal heights. These findings provide insights into the factors influencing current lake drainages in Antarctica in both timing and distribution.

Assessing the Surface Mass Balance (SMB) of the Antarctic Ice Sheet is crucial for understanding its response to climate change. Synthetic Aperture Radar observations from Sentinel-1 provide the potential to monitor the variability of SMB processes through changes in the scattering response of near-surface and internal snow layers. However, the interplay between several factors, such as accumulation, wind erosion, deposition, and melt, complicates the interpretation of scattering changes of the microwave signal. Additionally, lack of reliable ground truth measurements of the snow surface limits our capability to associate the SMB processes with dominant scattering mechanism. In this study, we aim to quantify the dominant scattering in Sentinel-1 signal and evaluate the scattering changes in drifting snow-dominated regions of East Antarctica. We introduce a scattering indicator, alpha -{text{scat},varepsilon }, derived from scattering-type and entropy descriptors, providing a measure between volume and pure scattering. By relating the field measurements to alpha -{text{scat},varepsilon }, we establish that the evolution of dominant scattering in the presence of snowdrift is complex. First, alpha -{text{scat},varepsilon } strongly correlates with surface roughness (R^{2}=0.92, RMSE =2^circ). Spatially variable erosion patterns significantly increase the roughness and result in a strong affinity towards pure scattering despite net accumulation. Second, high surface densities also tend to influence pure scattering; however, the effect is dependent on the accumulation rate. With more accumulation, we observe an increasing dominance of volume scattering from internal snow layers. Long-term trends in alpha -{text{scat},varepsilon } (2017/2023) further suggest that it is challenging to address the causes behind the scattering source based on a single snow surface process. We thus demonstrate the potential and limitations of alpha -{text{scat},varepsilon } to infer the variability in dominant scattering from changes in surface processes.

In some areas of Antarctica, blue-colored bare ice is exposed at the surface. These blue ice areas (BIAs) can trap meteorites or old ice and are vital for understanding the climatic history. By combining multi-sensor remote sensing data (MODIS, RADARSAT-2, and TanDEM-X) in a deep learning framework, we map blue ice across the continent at 200-m resolution. We use a novel methodology for image segmentation with “noisy” labels to learn an underlying “clean” pattern with a neural network. In total, BIAs cover ca. 140,000 km² (∼1%) of Antarctica, of which nearly 50% located within 20 km of the grounding line. There, the low albedo of blue ice enhances melt-water production and its mapping is crucial for mass balance studies that determine the stability of the ice sheet. Moreover, the map provides input for fieldwork missions and can act as constraint for other geophysical mapping efforts.

While the influence of surface melt on Antarctic ice shelf stability can be large, the duration and affected area of melt events are often small. Therefore, melt events are difficult to capture with remote sensing, as satellite sensors always face the trade-off between spatial and temporal resolution. To overcome this limitation, we developed UMelt: a surface melt record for all Antarctic ice shelves with a high spatial (500 m) and high temporal (12 h) resolution for the period 2016–2021. Our approach is based on a deep learning model, specifically a U-Net, which was developed in Google Earth Engine. The U-Net combines microwave remote sensing observations from three sources: Sentinel-1, Special Sensor Microwave Imager/Sounder (SSMIS), and Advanced Scatterometer (ASCAT). The U-Net was trained on the Shackleton Ice Shelf for melt seasons 2017–2021, using the fine-scale melt patterns of Sentinel-1 as reference data and SSMIS, ASCAT, a digital elevation model, and multi-year Sentinel-1 melt fraction as predictors. The trained U-Net performed well on the Shackelton Ice Shelf for test melt season 2016–2017 (accuracy: 91.3%; F1-score: 86.9%), and the Larsen C Ice Shelf, which was not considered during training (accuracy: 91.0%; F1-score: 89.3%). Using the trained U-Net model, we have successfully developed the UMelt record. UMelt allows Antarctic-wide surface melt to be detected at a small scale while preserving a high temporal resolution, which could lead to new insights into the response of ice shelves to a changing atmospheric forcing.

Accurately estimating the surface melt volume of the Antarctic Ice Sheet is challenging and has hitherto relied on climate modeling or observations from satellite remote sensing. Each of these methods has its limitations, especially in regions with high surface melt. This study aims to demonstrate the potential of improving surface melt simulations with a regional climate model by deploying a deep learning model. A deep-learning-based framework has been developed to correct surface melt from the regional atmospheric climate model version 2.3p2 (RACMO2), using meteorological observations from automatic weather stations (AWSs) and surface albedo from satellite imagery. The framework includes three steps: (1) training a deep multilayer perceptron (MLP) model using AWS observations, (2) correcting Moderate Resolution Imaging Spectroradiometer (MODIS) albedo observations, and (3) using these two to correct the RACMO2 surface melt simulations. Using observations from three AWSs at the Larsen B and C ice shelves, Antarctica, cross-validation shows a high accuracy (root-mean-square error of 0.95ĝ€¯mmĝ€¯w.e.ĝ€¯d-1, mean absolute error of 0.42ĝ€¯mmĝ€¯w.e.ĝ€¯d-1, and a coefficient of determination of 0.95). Moreover, the deep MLP model outperforms conventional machine learning models and a shallow MLP model. When applying the trained deep MLP model over the entire Larsen Ice Shelf, the resulting corrected RACMO2 surface melt shows a better correlation with the AWS observations for two out of three AWSs. However, for one location (AWS 18), the deep MLP model does not show improved agreement with AWS observations; this is likely because surface melt is largely driven by factors (e.g., air temperature, topography, katabatic wind) other than albedo within the corresponding coarse-resolution model pixels. Our study demonstrates the opportunity to improve surface melt simulations using deep learning combined with satellite albedo observations. However, more work is required to refine the method, especially for complicated and heterogeneous terrains.

To better understand and quantify the impact of clouds on the Greenland Ice Sheet surface mass balance (SMB), we study the spatiotemporal variability of the cloud radiative effect (CRE). The total CRE is separated in short-term and long-term impacts by performing multiple simulations with the SNOWPACK model for 2001-+2010. The annual total CRE is 16.8 ± 4.5 W m⁻², reducing the SMB with −157 ± 3.8 Gt yr⁻¹. Summer cloud radiative cooling is −6.4 ± 5.7 W m⁻² in the ablation area, increasing the SMB with 121 ± 2.2 Gt yr⁻¹. The annual integrated impact is cloud-reduced SMB of −36 Gt yr⁻¹. The short-term effect dominates the opposing long-term effects through the albedo-melt feedback. A long-term warming effect decreases the albedo and so preconditions the surface for enhanced (summer) melt. The impact of the CRE, determined by spatial, temporal and initial conditions, explains existing conflicted views on the role of cloud radiation and emphasizes the need for accurate cloud and albedo representations in future studies.