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Because Antarctic surface melt is mostly driven by local processes, its simulation necessitates high‐resolution regional climate models (RCMs). However, the current horizontal resolution of RCMs (≈25–30 km) is inadequate for capturing small‐scale melt processes. To address this limitation, we present SUPREME (SUPer‐REsolution‐based Melt Estimation over Antarctica), a deep learning method to downscale surface melt to 5.5 km resolution using a physically‐informed super‐resolution model. The physical information integrated into the model originates from observations tied to surface melt, specifically remote sensing‐derived albedo and elevation. These remote sensing data, in addition to a Regional Atmospheric Climate Model (RACMO) run at 27 km resolution, account for the diverse drivers of surface melt across Antarctica, facilitating effective generalization beyond the training region of the Antarctic Peninsula. A comparison of SUPREME with a dynamically downscaled RACMO run at 5.5 km over the Antarctic Peninsula shows high accuracy, with average yearly RMSE and bias of 5.5 mm w.e. yr−1 and 4.5 mm w.e. yr−1, respectively. Validation at five automatic weather stations reveals SUPREME's marked improvement with substantially lower average RMSE (81 mm w.e.) compared to RACMO 27 km (129 mm w.e.). Beyond the training region, SUPREME aligns more closely with remote sensing products associated with surface melt than super‐resolution models lacking physical constraints. While further validation of SUPREME is needed, our study highlights the potential of super‐resolution techniques with physical constraints for high‐resolution surface melt monitoring in Antarctica, providing insights into the impacts of localized melting on processes affecting ice shelf integrity such as hydrofracturing. Plain Language Summary: To improve surface melt monitoring in Antarctica, high‐resolution climate models are essential. Existing models, like the Regional Atmospheric Climate Model (RACMO), do not have a fine enough spatial resolution to capture small‐scale melt processes. To overcome this, we introduce SUPREME (SUPer‐REsolution‐based Melt Estimation over Antarctica), a method that refines surface melt data to a higher resolution of 5.5 km using an advanced super‐resolution model. We enhance this model with physical information derived from observations directly related to surface melt, specifically using albedo and elevation data from remote sensing. By incorporating these observations, along with RACMO data at 27 km resolution, we account for the various triggers of surface melt across Antarctica. SUPREME accurately predicts high‐resolution surface melt beyond the training region of the Antarctic Peninsula, showing promising results compared to existing melt observations. Further validation is needed, but this approach, combining super‐resolution techniques and remote sensing data, holds potential for accurate surface melt monitoring in Antarctica. This may advance our understanding of the impacts of localized features on processes affecting ice shelf integrity such as meltwater‐induced hydrofracturing. Key Points: Our method downscales Antarctic surface melt from a regional climate model, employing a physically‐informed super‐resolution architectureThe super‐resolution model relies on physical information derived from remote sensing data, specifically surface albedo and elevationIncorporating physical information boosts model generalization, enabling accurate high‐resolution surface melt beyond the training region [ABSTRACT FROM AUTHOR]

The intrusion of Circumpolar Deep Water in the Amundsen and Bellingshausen Sea embayments of Antarctica causes ice shelves in the region to melt from below, potentially putting their stability at risk. Earlier studies have shown how digital elevation models can be used to obtain ice shelf basal melt rates at a high spatial resolution. However, there has been limited availability of high-resolution elevation data, a gap the Reference Elevation Model of Antarctica (REMA) has filled. In this study we use a novel combination of REMA and CryoSat-2 elevation data to obtain high-resolution basal melt rates of the Dotson Ice Shelf in a Lagrangian framework, at a 50 m spatial posting on a 3-yearly temporal resolution. We present a novel method: Basal melt rates Using REMA and Google Earth Engine (BURGEE). The high resolution of BURGEE is supported through a sensitivity study of the Lagrangian displacement. The high-resolution basal melt rates show a good agreement with an earlier basal melt product based on CryoSat-2. Both products show a wide melt channel extending from the grounding line to the ice front, but our high-resolution product indicates that the pathway and spatial variability of this channel is influenced by a pinning point on the ice shelf. This result emphasizes the importance of high-resolution basal melt rates to expand our understanding of channel formation and melt patterns. BURGEE can be expanded to a pan-Antarctic study of high-resolution basal melt rates. This will provide a better picture of the (in)stability of Antarctic ice shelves. [ABSTRACT FROM AUTHOR]

In Antarctica, Global Positioning System (GPS) vertical time series exhibit non‐linear signals over a wide range of temporal scales. To explain these non‐linearities, a number of hypotheses have been proposed, among them the short‐term rapid solid Earth response to contemporaneous ice mass change. Here we use GPS vertical time series to reveal the solid Earth response to variations in surface mass balance (SMB) in the Southern Antarctic Peninsula (SAP). At four locations in the SAP we show that interannual variations of SMB anomalies cause measurable elastic deformation. We use regional climate model SMB products to calculate the induced displacement assuming a perfectly elastic Earth. Our results show a reduction of the misfit when fitting a linear trend to GPS time series corrected for the elastic response to SMB variations. Our results imply that, for a better understanding of the glacial isostatic adjustment signal in Antarctica, SMB variability must be considered. Plain Language Summary: The Global Positioning System (GPS) allows us to measure the changing shape of the Earth's surface with high accuracy. These changes reflect multiple processes operating both within the Earth and at the surface. Using continuously recording GPS instruments in the Antarctic Peninsula, we study the response of the solid Earth to seasonal and annual variations in snow and ice accumulation over the past decade. We show that such loading triggers a measurable response that varies between years, complicating efforts to calculate the long‐term Earth response to past ice sheet change. We discuss the importance of our findings for understanding feedbacks between the solid Earth and the Antarctic ice sheet. Key Points: Global Positioning System time series of vertical deformation in the Southern Antarctic Peninsula show transient signalsModeled elastic deformation due to surface mass balance (SMB) variation can largely explain these signalsConsidering SMB elastic deformation improves estimates of linear vertical velocities [ABSTRACT FROM AUTHOR]

Surface melting occurs during summer on the Antarctic and Greenland ice sheets, but the volume of stored surface meltwater has been difficult to quantify due to a lack of accurate depth estimates. NASA's ICESat‐2 laser altimeter brings a new capability: photons penetrate water and are reflected from both the water and the underlying ice; the difference provides a depth estimate. ICESat‐2 sampled Amery Ice Shelf on January 2, 2019 and showed double returns from surface depressions, indicating meltwater. For four melt features, we compared depth estimates from eight algorithms: six based on ICESat‐2 and two from coincident Landsat‐8 and Sentinel‐2 imagery. All algorithms successfully identified surface water at the same locations. Algorithms based on ICESat‐2 produced the most accurate depths; the image‐based algorithms underestimated depths (by 30%–70%). This implies that ICESat‐2 depths can be used to tune image‐based algorithms, moving us closer to quantifying stored meltwater volumes across Antarctica and Greenland. Plain Language Summary: Summer surface melting on Antarctica's ice shelves is a small component of overall ice sheet mass loss but can be important for individual ice shelves and may increase as the climate warms. However, the volume of meltwater has been difficult to monitor because depth estimates are challenging. NASA's ICESat‐2 laser altimetry mission brings a new capability to this problem. ICESat‐2 532 nm photons (green light) are able to pass through water and reflect from both the water surface and the underlying ice surface; the difference in elevation provides meltwater depth estimates. In this pilot study, we compared depths from eight algorithms (six ICESat‐2 and two image based) over four Amery Ice Shelf meltwater lakes for an ICESat‐2 pass in early January 2019. The ICESat‐2 algorithms all produced more reliable depth estimates, and the image‐based algorithms underestimated the depths. This implies that ICESat‐2 water depths can be used to tune image‐based depth retrieval algorithms, enabling improved performance and allowing us to estimate more accurately how much surface melt is stored in melt ponds on the ice sheets each summer. Key Points: ICESat‐2 photons penetrate surface melt lakes and reflect from both the water surface and the underlying ice, providing depth estimatesWe compared depths from eight algorithms (six ICESat‐2 and two image‐based) for four lakes present on Amery Ice Shelf in January 2019Depths from ICESat‐2 were more accurate than from imagery (30%–70% too low); merging these data will improve estimates ice‐sheet wide [ABSTRACT FROM AUTHOR]

Pine Island Glacier and Thwaites Glacier in the Amundsen Sea Embayment are among the fastest changing outlet glaciers in West Antarctica with large consequences for global sea level. Yet, assessing how much and how fast both glaciers will weaken if these changes continue remains a major uncertainty as many of the processes that control their ice shelf weakening and grounding line retreat are not well understood. Here, we combine multisource satellite imagery with modeling to uncover the rapid development of damage areas in the shear zones of Pine Island and Thwaites ice shelves. These damage areas consist of highly crevassed areas and open fractures and are first signs that the shear zones of both ice shelves have structurally weakened over the past decade. Idealized model results reveal moreover that the damage initiates a feedback process where initial ice shelf weakening triggers the development of damage in their shear zones, which results in further speedup, shearing, and weakening, hence promoting additional damage development. This damage feedback potentially preconditions these ice shelves for disintegration and enhances grounding line retreat. The results of this study suggest that damage feedback processes are key to future ice shelf stability, grounding line retreat, and sea level contributions from Antarctica. Moreover, they underline the need for incorporating these feedback processes, which are currently not accounted for in most ice sheet models, to improve sea level rise projections. [ABSTRACT FROM AUTHOR]

Surface mass balance (SMB) provides mass input to the surface of the Antarctic and Greenland Ice Sheets and therefore comprises an important control on ice sheet mass balance and resulting contribution to global sea level change. As ice sheet SMB varies highly across multiple scales of space (meters to hundreds of kilometers) and time (hourly to decadal), it is notoriously challenging to observe and represent in models. In addition, SMB consists of multiple components, all of which depend on complex interactions between the atmosphere and the snow/ice surface, large‐scale atmospheric circulation and ocean conditions, and ice sheet topography. In this review, we present the state‐of‐the‐art knowledge and recent advances in ice sheet SMB observations and models, highlight current shortcomings, and propose future directions. Novel observational methods allow mapping SMB across larger areas, longer time periods, and/or at very high (subdaily) temporal frequency. As a recent observational breakthrough, cosmic ray counters provide direct estimates of SMB, circumventing the need for accurate snow density observations upon which many other techniques rely. Regional atmospheric climate models have drastically improved their simulation of ice sheet SMB in the last decade, thanks to the inclusion or improved representation of essential processes (e.g., clouds, blowing snow, and snow albedo), and by enhancing horizontal resolution (5–30 km). Future modeling efforts are required in improving Earth system models to match regional atmospheric climate model performance in simulating ice sheet SMB, and in reinforcing the efforts in developing statistical and dynamic downscaling to represent smaller‐scale SMB processes. Plain Language Summary: Ice sheets, the largest class of glaciers, contain the majority of ice on Earth. The amount of ice contained in ice sheets changes constantly with the addition of new snow and ice, and melting taking place at the surface, base, and terminus of ice sheets. The balance between these inputs and outputs is known as the "mass balance." Processes affecting the addition and removal of snow on top of the ice sheet are termed the "surface mass balance" and include rainfall, moisture evaporation, snow‐transporting winds, and melting due to temperature changes. Scientists can now monitor these processes with tools on‐site, such as automated weather stations, Global Positioning Systems, and sensors that record high‐energy radiation (cosmic rays) originating outside the Earth's atmosphere. Several methods are also available where Earth‐orbiting satellites measure how ice is changing. Data collected in these ways have revealed how the surface mass balance varies over time and space. A better understanding of these processes is critical to predicting future behavior of ice sheets and their effect on sea level. Improvements to regional‐scale models in the past decade have allowed good simulations of surface mass balance, and the next step is to build models that work at a global scale. Key Points: Emerging (remote) observational techniques provide enhanced insights in spatial and temporal variability of ice sheet surface mass balance (SMB)Regional climate models can be used to assess ice sheet SMB, although deficiencies remain in representing subgrid processesIn the near future, Earth System Models can be used to assess internal variability, forced change, and positive feedbacks on ice sheet SMB [ABSTRACT FROM AUTHOR]

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. • Satellite resolution trade-offs hamper accurate surface melt detection in Antarctica. • We present UMelt: An Antarctic-wide, high-resolution surface melt record. • UMelt was developed by merging multi-source remote sensing data using deep learning. • UMelt detects intricate surface melt events that were difficult to capture before. [ABSTRACT FROM AUTHOR]

Pine lsland Glacier (PIG) and Thwaites Glacier (THW) in the Amundsen Sea Embayment are among the fastest changing outlet glaciers in West Antarctica. Over the last decades both glaciers have accelerated, retreated and thinned rapidly and if these changes continue, a sufficient weakening or loss of their ice shelves may result in marine ice sheet instability with substantial grounding line retreat and large consequences for global sea level. Yet, assessing how much, how fast the ice in the Amundsen Sea Embayment will change, remains a major uncertainty.In this study, we use time series of multi-source satellite imagery (i.e. Landsat, Sentinel-1, Sentinel-2, Aster, Cryosat) to show the rapid development of damage areas on the PIG and THW ice shelves that appear crucial to the future of both glaciers, but are not included in current models of their retreat. These damage areas consist of highly crevassed areas near the grounding line and of open fractures that contain dense ice melange within the shear zones of both ice shelves. The observed damage compromises the integrity of both PIG and THW ice shelves, similar to the preconditioning of the shear zones of Larsen B prior to its collapse. To assess the importance of the damage in the shear zones, a continuum damage model (CDM) was coupled to the BISICLES ice sheet model. The model was applied on an idealized marine ice sheet geometry following the MISMIP+ setup which has strong similarities to PIG conditions. Several time-dependent simulations were carried using the CDM-BISICLES model with different ocean-induced melting and damage parameterizations. In this framework, several damage scenarios were implemented by locally enhancing damage in the shear zone at the grounding line. The model results highlight the importance of damage in the shear zones as driver for ice shelf instability as they precondition the ice shelves with structural weak zones. The model results also indicate that damage enhances thinning at the grounding line, in the shear zones and of grounded ice upstream. Finally, the model shows a speedup of the glacier tongue as a result of the damage, which results in an increase in strain rate and in velocity gradients across the shear zone similar to the increasing velocity gradients across PIG and THW observed by satellites since 1992.The damage also has an important impact on the modeled grounding line retreat as the enhanced damage scenarios in the model initiate a grounding line retreat equivalent to quadrupling the oceanic melting near the grounding line from 25 to 100 m/year. This illustrates that weakening these glaciers at their most vulnerable locations is a very effective way of introducing grounding line retreat, increased ice flux and hence mass loss. The combination of these satellite observations and model results suggest that damage processes are key to ice shelf stability, grounding line retreat, and future sea level contributions from Antarctica. Moreover, they underline the need for incorporating damage processes in models to improve sea level rise projections. [ABSTRACT FROM AUTHOR]