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.

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Despite in-situ observations of perennial firn aquifers (PFAs) at specific locations of the Antarctic ice sheet, a comprehensive continent-wide mapping of PFA distribution is currently lacking. We present an estimate of their distribution across Antarctica in the form of a probability assessment using a Monte Carlo technique. Our approach involves a novel methodology that combines observations from Sentinel-1 and Advanced SCATterometer (ASCAT) with output from a regional climate model. To evaluate our method, we conduct an extensive comparison with Operation Ice Bridge observations from the Greenland Ice Sheet. Application to Antarctica reveals high PFA probabilities in the Antarctic Peninsula (AP), particularly along its northern, northwestern, and western coastlines, as well as on the Wilkins, Müller, and George VI ice shelves. Outside the AP, PFA probability is low, except for some locations with marginally higher probabilities, such as on the Abbot, Totten, and Shackleton ice shelves.

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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.

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Recently, climate extremes have been grabbing attention as important drivers of environmental change. Here, we assemble an observational inventory of energy and mass fluxes to quantify the ice loss from glaciers on the Russian High Arctic archipelago of Novaya Zemlya. Satellite altimetry reveals that 70 ± 19% of the 149 ± 29 Gt mass loss between 2011 and 2022 occurred in just four high-melt years. We find that 71 ± 3% of the melt, including the top melt cases, are driven by extreme energy imports from atmospheric rivers. The majority of ice loss occurs on leeward slopes due to foehn winds. 45 of the 54 high-melt days (>1 Gt d−1) in 1990 to 2022 show a combination of atmospheric rivers and foehn winds. Therefore, the frequency and intensity of atmospheric rivers demand accurate representation for reliable future glacier melt projections for the Russian High Arctic.@en

The utility of historical image repositories is often limited due to the lack of geo-referencing. A good example is the TriMetrogon Aerial (TMA) archive, a collection of historical aerial images of Antarctica between 1940 and 2000. These images are difficult to use, as their geolocation is only approximately, with location errors in the order of tens of km. This study addresses this challenge by developing an automated geo-referencing workflow that leverages recent advancements in machine-learning-based tie-point matching. We use the algorithm LightGlue, to establish tie-points between geo-referenced Sentinel-2 satellite imagery and historical non-geo-referenced aerial images. To aid the process, we use already known approximate positions of the historical images. Due to the sub-optimal and inhomogeneous quality of the aerial images, only a portion of the images can be geo-referenced directly by matching. For the remaining images, we employed alternative means of geo-referencing, again based on tie-point matching. Out of a subset of 4,459 images located inside the research area, 3,393 images could be geo-referenced, a percentage of 76%. Reasons for the geo-referencing failing are insufficient contrast or an approximate position too far away from the real position. The workflow can easily be applied to historical images from other archives, to enhance the usability of historical image repositories for scientific research.

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Correction to: Nature Reviews Earth & Environment https://doi.org/10.1038/s43017-023-00507-9, published online 23 January 2024.

In the version of the article initially published, in Fig. 5, under “Radar altimeter”, “O(16–160 m)” previously read “O(16–160 km)”. This has now been corrected in the HTML and PDF versions of the article.

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Most of the Greenland and Antarctic ice sheets are covered with firn — the transitional material between snow and glacial ice. Firn is vital for understanding ice-sheet mass balance and hydrology, and palaeoclimate. In this Review, we synthesize knowledge of firn, including its formation, observation, modelling and relevance to ice sheets. The refreezing of meltwater in the pore space of firn currently prevents 50% of meltwater in Greenland from running off into the ocean and protects Antarctic ice shelves from catastrophic collapse. Continued atmospheric warming could inhibit future protection against mass loss. For example, warming in Greenland has already contributed to a 5% reduction in firn pore space since 1980. All projections of future firn change suggest that surface meltwater will have an increasing impact on firn, with melt occurring tens to hundreds of kilometres further inland in Greenland, and more extensively on Antarctic ice shelves. Although progress in observation and modelling techniques has led to a well-established understanding of firn, the large uncertainties associated with meltwater percolation processes (refreezing, ice-layer formation and storage) must be reduced further. A tighter integration of modelling components (firn, atmosphere and ice-sheet models) will also be needed to better simulate ice-sheet responses to anthropogenic warming and to quantify future sea-level rise.

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The TriMetrogon Aerial (TMA) archive is an archive of historical images of Antarctica taken by the US Navy between 1940 and 2000 with analogue cameras. The analysis of such historic data can give a view of Antarctica's glaciers predating modern satellite imagery and provide unique insights into the long-term impact of changing climate conditions with essential validation data for climate modelling. However, the lack of semantic information for these images presents a challenge for large-scale computer-driven analysis. Such information can be added to the data using semantic segmentation, but traditional algorithms fail on these scanned historical grayscale images, due to varying image quality, lack of colour information and artefacts in the images. To address this, we present a deep-learning-based U-net workflow. Our approach includes creating training data by pre-processing and labelling the raw images. Furthermore, different versions of the U-net are trained to optimize its hyperparameters and augmentation methods. With the optimal hyper-parameters and augmentation methods, a final model has been trained for a use-case to segment 118 images covering Adelaide Island. We tested our approach by segmenting challenging historical images using a U-net model with just 80 training images, achieving an accuracy of 73% for 20 validation images. While no test data is available for our use case, a visual examination of the segmented images shows that our method performs effectively. The comparison of the hyper-parameters and augmentation methods provides directions for training other U-net-based models so that the presented workflow can be used to segment other archives with historical imagery. Additionally, the labelled training data and the segmented images of the test are publicly available at https://github.com/fdahle/antarctic_segmentation.

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Ice shelves play a pivotal role in stabilizing the Antarctic ice sheet by providing crucial buttressing support. However, their vulnerability to basal melting poses significant concerns for ice sheet and shelf stability. Our study focuses on assessing basal melt rates at a 50 m posting of 12 ice shelves where earlier studies have identified high melt rates. We make use of the Reference Elevation Model of Antarctica (REMA) strips to generate surface elevation- and melt rates using the Basal melt rates Using Rema and Google Earth Engine (BURGEE) methodology. BURGEE reveals higher melt rates in areas with thinner ice than existing remote sensing basal melt products. This is for instance the case for basal channels on both Dotson, Totten and Pine Island ice shelves. Modelling studies have already shown that remote sensing inferred basal melt rates are underestimated at the thinnest part of basal channels, and that this underestimation scales with resolution coarsening. Since the thinner parts of an ice shelf also represent its weakest part, it is crucial that we capture its melting well to fully grasp the vulnerability of the ice shelf. Our work, therefore, represents a crucial step in uncovering the vulnerability of Antarctic ice shelves. By exposing detailed melting patterns, particularly in areas like basal channels, we highlight not just extensive melting but also potential weak points, significantly contributing to our understanding of ice shelf stability. These findings bear substantial importance in comprehending the broader implications of ongoing climate changes on Antarctica's ice sheet integrity and, consequently, global sea levels.@en

The joint ESA/NASA Mass-change And Geosciences International Constellation (MAGIC) has the objective to extend time-series from previous gravity missions, including an improvement of accuracy and spatio-temporal resolution. The long-term monitoring of Earth’s gravity field carries information on mass change induced by water cycle, climate change and mass transport processes between atmosphere, cryosphere, oceans and solid Earth. MAGIC will be composed of two satellite pairs flying in different orbit planes. The NASA/DLR-led first pair (P1) is expected to be in a near-polar orbit around 500 km of altitude; while the second ESA-led pair (P2) is expected to be in an inclined orbit of 65^◦–70^◦ at approximately 400 km altitude. The ESA-led pair P2 Next Generation Gravity Mission shall be launched after P1 in a staggered manner to form the MAGIC constellation. The addition of an inclined pair shall lead to reduction of temporal aliasing effects and consequently of reliance on de-aliasing models and post-processing. The main novelty of the MAGIC constellation is the delivery of mass-change products at higher spatial resolution, temporal (i.e. subweekly) resolution, shorter latency and higher accuracy than the Gravity Recovery and Climate Experiment (GRACE) and Gravity Recovery and Climate Experiment Follow-On (GRACE-FO). This will pave the way to new science applications and operational services. In this paper, an overview of various fields of science and service applications for hydrology, cryosphere, oceanography, solid Earth, climate change and geodesy is provided. These thematic fields and newly enabled applications and services were analysed in the frame of the initial ESA Science Support activities for MAGIC. The analyses of MAGIC scenarios for different application areas in the field of geosciences confirmed that the double-pair configuration will significantly enlarge the number of observable mass-change phenomena by resolving smaller spatial scales with an uncertainty that satisfies evolved user requirements expressed by international bodies such as IUGG. The required uncertainty levels of dedicated thematic fields met by MAGIC unfiltered Level-2 products will benefit hydrological applications by recovering more than 90 per cent of the major river basins worldwide at 260 km spatial resolution, cryosphere applications by enabling mass change signal separation in the interior of Greenland from those in the coastal zones and by resolving small-scale mass variability in challenging regions such as the Antarctic Peninsula, oceanography applications by monitoring meridional overturning circulation changes on timescales of years and decades, climate applications by detecting amplitude and phase changes of Terrestrial Water Storage after 30 yr in 64 and 56 per cent of the global land areas and solid Earth applications by lowering the Earthquake detection threshold from magnitude 8.8 to magnitude 7.4 with spatial resolution increased to 333 km.

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Ice losses from the Greenland and Antarctic ice sheets have accelerated since the 1990s, accounting for a significant increase in the global mean sea level. Here, we present a new 29-year record of ice sheet mass balance from 1992 to 2020 from the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE). We compare and combine 50 independent estimates of ice sheet mass balance derived from satellite observations of temporal changes in ice sheet flow, in ice sheet volume, and in Earth's gravity field. Between 1992 and 2020, the ice sheets contributed 21.0±1.9g€¯mm to global mean sea level, with the rate of mass loss rising from 105g€¯Gtg€¯yr-1 between 1992 and 1996 to 372g€¯Gtg€¯yr-1 between 2016 and 2020. In Greenland, the rate of mass loss is 169±9g€¯Gtg€¯yr-1 between 1992 and 2020, but there are large inter-annual variations in mass balance, with mass loss ranging from 86g€¯Gtg€¯yr-1 in 2017 to 444g€¯Gtg€¯yr-1 in 2019 due to large variability in surface mass balance. In Antarctica, ice losses continue to be dominated by mass loss from West Antarctica (82±9g€¯Gtg€¯yr-1) and, to a lesser extent, from the Antarctic Peninsula (13±5g€¯Gtg€¯yr-1). East Antarctica remains close to a state of balance, with a small gain of 3±15g€¯Gtg€¯yr-1, but is the most uncertain component of Antarctica's mass balance. The dataset is publicly available at 10.5285/77B64C55-7166-4A06-9DEF-2E400398E452 (IMBIE Team, 2021).

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Our awareness of ice caps' and mountain glaciers' sensitivity to climate change has driven major advances in the application of remote sensing techniques during the past decade. Regarding ESA's SARIn altimeter CryoSat-2, processing the full waveform to generate swaths of elevation estimates has become standard practice in regions of complex topographies. This technique provides information on areas where we would be blind otherwise. In this article, we discuss systematic errors and analyze their impact on surface elevation measurements and change rates of two test areas. In particular, we focus on periodically occurring errors in elevation swaths, caused by the superposition of coherent signals from range-ambiguous surfaces. They can lead to measurement errors in excess of 10 m, affect most measurements in mountainous regions, are difficult to exclude with established post-processing techniques, and occur repeatedly for satellite revisits introducing a 369-day periodicity-difficult to distinguish from the annual cycle. We show a correlation between derived elevation swaths and the sensor view angle and explore the influence of common data exclusion choices on higher level products. Our results indicate that these systematic errors hold a substantial share of the error budget and that the choice of thresholds impacts higher level products. We conclude that error correlations need to be considered to characterize the data accuracy. With the established data editing strategies, systematic errors prevent resolving seasonal mass changes of single mountain glacier basins and impact aggregates over larger areas or longer periods.

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It has been argued that the −5 °C annual mean 2 m air temperature isotherm defines a limit of ice shelf viability on the Antarctic Peninsula as melt ponding increases at higher temperatures. It is, however, presently unknown whether this threshold can also be applied to other Antarctic ice shelves. Here we use two present-day and three future high-resolution Antarctic climate simulations to predict warming thresholds for Antarctic ice shelf melt pond formation on the basis of the melt-over-accumulation ratio. The associated warming thresholds match well with observed melt pond volumes and are found to be spatially highly variable and controlled by snow accumulation. For relatively wet ice shelves, the −5 °C temperature threshold was confirmed; but cold and dry ice shelves such as Amery, Ross and Filchner-Ronne are more vulnerable than previously thought, with threshold temperatures well below −15 °C. Coupled Model Intercomparison Project Phase 6 models predict that towards the end of this century these thresholds can be reached on many ice shelves, even on cold ice shelves and under moderate warming scenarios.

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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.@en

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.

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Antarctic ice sheet (AIS) mass loss is predominantly driven by increased solid ice discharge, but its variability is governed by surface processes. Snowfall fluctuations control the surface mass balance (SMB) of the grounded AIS, while meltwater ponding can trigger ice shelf collapse potentially accelerating discharge. Surface processes are essential to quantify AIS mass change, but remain poorly represented in climate models typically running at 25-100 km resolution. Here we present SMB and surface melt products statistically downscaled to 2 km resolution for the contemporary climate (1979-2021) and low, moderate and high-end warming scenarios until 2100. We show that statistical downscaling modestly enhances contemporary SMB (3%), which is sufficient to reconcile modelled and satellite mass change. Furthermore, melt strongly increases (46%), notably near the grounding line, in better agreement with in-situ and satellite records. The melt increase persists by 2100 in all warming scenarios, revealing higher surface melt rates than previously estimated.

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Geoscientific models are facing increasing challenges to exploit growing datasets coming from remote sensing. Universal differential equations (UDEs), aided by differentiable programming, provide a new scientific modelling paradigm enabling both complex functional inversions to potentially discover new physical laws and data assimilation from heterogeneous and sparse observations. We demonstrate an application of UDEs as a proof of concept to learn the creep component of ice flow, i.e. a nonlinear diffusivity differential equation, of a glacier evolution model. By combining a mechanistic model based on a two-dimensional shallow-ice approximation partial differential equation with an embedded neural network, i.e. a UDE, we can learn parts of an equation as nonlinear functions that then can be translated into mathematical expressions. We implemented this modelling framework as ODINN.jl, a package in the Julia programming language, providing high performance, source-to-source automatic differentiation (AD) and seamless integration with tools and global datasets from the Open Global Glacier Model in Python. We demonstrate this concept for 17 different glaciers around the world, for which we successfully recover a prescribed artificial law describing ice creep variability by solving ∼ 500 000 ordinary differential equations in parallel. Furthermore, we investigate which are the best tools in the scientific machine learning ecosystem in Julia to differentiate and optimize large nonlinear diffusivity UDEs. This study represents a proof of concept for a new modelling framework aiming at discovering empirical laws for large-scale glacier processes, such as the variability in ice creep and basal sliding for ice flow, and new hybrid surface mass balance models.@en

Glaciers distinct from the Greenland and Antarctic ice sheets are currently losing mass rapidly with direct and severe impacts on the habitability of some regions on Earth as glacier meltwater contributes to sea-level rise and alters regional water resources in arid regions. In this review, we present the different techniques developed during the last two decades to measure glacier mass change from space: digital elevation model (DEM) differencing from stereo-imagery and synthetic aperture radar interferometry, laser and radar altimetry and space gravimetry. We illustrate their respective strengths and weaknesses to survey the mass change of a large Arctic ice body, the Vatnajökull Ice Cap (Iceland) and for the steep glaciers of the Everest area (Himalaya). For entire regions, mass change estimates sometimes disagree when a similar technique is applied by different research groups. At global scale, these discrepancies result in mass change estimates varying by 20%-30%. Our review confirms the need for more thorough inter-comparison studies to understand the origin of these differences and to better constrain regional to global glacier mass changes and, ultimately, past and future glacier contribution to sea-level rise.

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In recent years a vast amount of glacier surface velocity data from satellite imagery has emerged based on correlation between repeat images. Thereby, much emphasis has been put on the fast processing of large data volumes and products with complete spatial coverage. The metadata of such measurements are often highly simplified when the measurement precision is lumped into a single number for the whole dataset, although the error budget of image matching is in reality neither isotropic nor constant over the whole velocity field. The spread of the correlation peak of individual image offset measurements is dependent on the image structure and the non-uniform flow of the ice and is used here to extract a proxy for measurement uncertainty. A quantification of estimation error or dispersion for each individual velocity measurement can be important for the inversion of, for instance, rheology, ice thickness and/or bedrock friction. Errors in the velocity data can propagate into derived results in a complex and exaggerating way, making the outcomes very sensitive to velocity noise and outliers. Here, we present a computationally fast method to estimate the matching precision of individual displacement measurements from repeat imaging data, focusing on satellite data. The approach is based upon Gaussian fitting directly on the correlation peak and is formulated as a linear least-squares estimation, making its implementation into current pipelines straightforward. The methodology is demonstrated for Sermeq Kujalleq (Jakobshavn Isbræ), Greenland, a glacier with regions of strong shear flow and with clearly oriented crevasses, and Malaspina Glacier, Alaska. Directionality within an image seems to be the dominant factor influencing the correlation dispersion. In our cases these are crevasses and moraine bands, while a relation to differential flow, such as shear, is less pronounced on the correlation spread.

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Inversion methods play an important role in glacier models, both to calibrate and estimate parameters of interest (e.g. Glen's coefficients). However, inversions are usually made for each glacier individually, without using any global information, i.e. without deriving general laws governing the spatiotemporal variability of those parameters. The reason behind this limitation is twofold: the statistical challenge of making constrained inferences with multiple glaciers, and the computational limitation of processing massive glacier datasets. Machine learning powered with differential programming is a tool that can address both limitations. We introduce a statistical framework for functional inversion of physical processes governing global-scale glacier changes. We apply this framework to invert a prescribed function describing the spatial variability of Glen’s coefficient (A). Instead of estimating a single parameter per glacier, we learn the parameters of a regressor (i.e. a neural network) that encodes information related to each glacier (i.e. long-term air temperature) to the parameter of interest. The inversion is done by embedding a neural network inside the Shallow Ice Approximation PDE - resulting in a Universal Differential Equation - with the goal of minimizing the error on the simulated ice surface velocities. We previously had shown that this hybrid model training is possible thanks to the use of differential programming, enabling differentiation of a PDE, a numerical solver and a neural network simultaneously. In this work we upscale this approach to include larger datasets and with the goal of learning real empirical laws from observations. This framework is built inside ODINN.jl, an open-source package in the Julia programming language for global glacier evolution modelling using Universal Differential Equations. ODINN exploits the latest generation of ice surface velocities and geodetic mass balance remote sensing products, as well as many preprocessing tools from the Open Global Glacier Model (OGGM).@en