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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Transforming the global energy sector from fossil-fuel based to renewable energy sources is crucial to limiting global warming and achieving climate neutrality. The decentralized nature of the renewable energy system allows private households to deploy photovoltaic systems on their rooftops. However, inconsistent data on installed photovoltaic (PV) systems complicate planning for an efficient grid expansion. To address this issue, deep-learning techniques, can support collecting data about PV systems from aerial and satellite imagery. Previous research, however, lacks the consideration for ground truth data-specific characteristics of PV panels. This study aims to implement a semantic segmentation model that detects PV systems in aerial imagery to explore the impact of area-specific characteristics in the training data and CNN hyperparameters on the performance of a CNN. Hence, a U-Net architecture is employed to analyze land use types, rooftop colors, and lower-resolution images. Additionally, the impact of near-infrared data on the detection rate of PV panels is analyzed. The results indicate that a U-Net is suitable for classifying PV panels in high-resolution aerial imagery (10 cm) by reaching F1 scores of up to 91.75% while demonstrating the importance of adapting the training data to area-specific ground truth data concerning urban and architectural properties.

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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.

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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

More than 60% of meteorite finds on Earth originate from Antarctica. Using a data-driven analysis that identifies meteorite-rich sites in Antarctica, we show climate warming causes many extraterrestrial rocks to be lost from the surface by melting into the ice sheet. At present, approximately 5,000 meteorites become inaccessible per year (versus ~1,000 finds per year) and, independent of the emissions scenario, ~24% will be lost by 2050, potentially rising to ∼76% by 2100 under a high-emissions scenario.

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This paper presents an overview of the Sub-daily Land Atmosphere INTEractions (SLAINTE) mission. SLAINTE comprises a constellation of identical synthetic aperture radars (SAR) with interferometric capability. It aims to bridge a critical observation gap, by providing sub-daily, ≤1 km scale observations related to ecosystem water status, including vegetation water content and surface soil moisture over key regions of scientific, ecological, societal and economic interest. These data will provide unprecedented insight into vegetation water, carbon and health improving our ability to study, understand and model the response of ecosystems to climate change and human impact. This mission concept has been submitted in response to ESA's call for proposals for Earth Explorer 12.

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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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Digital twins of the Earth are digital representations of the Earth system, spanning scales and domains. Their purpose is to monitor, forecast and assess the Earth system and the consequences of human interventions on the Earth system. Providing users with the capability to interact with and interrogate the system, digital twins of the Earth are decision support systems for addressing environmental challenges. By informing humans of their impact on the Earth system, digital twins aspire to promote new pathways moving forward. By answering causal queries through intervention analysis, they can enhance evidence-based policy making. Existing digital twins of the Earth are primarily technological information systems that represent the physical world. However, as the social and physical worlds are intrinsically interconnected, we argue that humans must be accounted for both within and outside digital twins of the Earth: Within twins to represent human impacts and responses that are integral to the Earth system; and outside twins to govern access and development and to guide responsible use of information acquired from twins. Incorporating human interactions in digital twins of the Earth represents a transformative frontier, promising unparalleled insights into Earth system dynamics and empower humans for action.@en

Imagery acquired by the Moderate-resolution Imaging Spectroradiometer (MODIS) provides a global archive of dailyNormalized Difference Snow Index (NDSI) at 500 m nominal resolution since the year 2000. While Sentinel-2 (S2) NDSI provides an increased spatial resolution of 20 m since the year 2015, the temporal resolution amounts to only 5 days and thus lacks the high temporal resolution of MODIS. Efforts to combine NDSI datasets for an increased temporal and spatial resolution have so far focused on the deriving binary snow cover maps or combining data from other sensors. In contrast, we produce fine scale (20 m) fractional snow cover (FSC) by downscaling MODIS NDSI to S2 resolution. Random forest regression predicts S2 NDSI based on dynamic features (MODIS NDSI, day-of-year) and static, topographic features for an alpine study site. Subsequently, FSC is derived from S2 NDSI. Cross-validation results in R² of 0.795 and RMSE of 0.155 for FSC and outperforms common resampling methods. Multi-annual S2 NDSI metrics are able to slightly improve model accuracy. Our results suggest that combining topographical data and low-resolution NDSI allows to produce daily, high-resolution S2 NDSI and FSC and improve fine scale characterization of snow cover dynamics in mountain landscapes.

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Dynamic ecosystems, such as the Amazon forest, are expected to show critical slowing down behavior, or slower recovery from recurrent small perturbations, as they approach an ecological threshold to a different ecosystem state. Drought occurrences are becoming more prevalent across the Amazon, with known negative effects on forest health and functioning, but their actual role in the critical slowing down patterns still remains elusive. In this study, we evaluate the effect of trends in extreme drought occurrences on temporal autocorrelation (TAC) patterns of satellite-derived indices of vegetation activity, an indicator of slowing down, between 2001 and 2019. Differentiating between extreme drought frequency, intensity, and duration, we investigate their respective effects on the slowing down response. Our results indicate that the intensity of extreme droughts is a more important driver of slowing down than their duration, although their impacts vary across the different Amazon regions. In addition, areas with more variable precipitation are already less ecologically stable and need fewer droughts to induce slowing down. We present findings indicating that most of the Amazon region does not show an increasing trend in TAC. However, the predicted increase in extreme drought intensity and frequency could potentially transition significant portions of this ecosystem into a state with altered functionality.

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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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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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Pine Island Glacier (PIG) has recently experienced increased ice loss that has mostly been attributed to basal melt and ocean ice dynamics. However, atmospheric forcing also plays a role in the ice mass budget, as besides lower-latitude warm air intrusions, the steeply sloping terrain that surrounds the glacier promotes frequent Foehn winds. An investigation of 41 years of reanalysis data reveals that Foehn occurs more frequently from June to October, with Foehn episodes typically lasting about 5 to 9 h. An analysis of the surface mass balance indicated that their largest impact is on the surface sublimation, which is increased by about 1.43 mm water equivalent (w.e.) per day with respect to no-Foehn events. Blowing snow makes roughly the same contribution as snowfall, around 0.34–0.36 mm w.e. d−1, but with the opposite sign. The melting rate is 3 orders of magnitude smaller than the surface sublimation rate. The negative phase of the Antarctic oscillation and the positive phase of the Southern Annular Mode promote the occurrence of Foehn at PIG. A particularly strong event took place on 9–11 November 2011, when 10 m winds speeds in excess of 20 m s−1 led to downward sensible heat fluxes higher than 75 W m−2 as they descended the mountainous terrain. Surface sublimation and blowing-snow sublimation dominated the surface mass balance, with magnitudes of up to 0.13 mm w.e. h−1. Satellite data indicated an hourly surface melting area exceeding 100 km2. Our results stress the importance of the atmospheric forcing on the ice mass balance at PIG.@en

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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Muddy coasts provide ecological habitats, supply food and form a natural coastal defence. Relative sea level rise, changing wave energy and human interventions will increase the pressure on muddy coastal zones. For sustainable coastal management it is key to obtain information on the geomorphology of and historical changes along muddy areas. So far, little is known about the distribution and behaviour of muddy coasts at a global scale. In this study we present a global scale assessment of the occurrence of muddy coasts and rates of coastline change therein. We combine publicly available satellite imagery and coastal geospatial datasets, to train an automated classification method to identify muddy coasts. We find that 14% of the world’s ice-free coastline is muddy, of which 60% is located in the tropics. Furthermore, the majority of the world’s muddy coasts are eroding at rates exceeding 1 m/yr over the last three decades.

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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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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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Icelandic glaciers have been losing mass since the Little Ice Age in the mid-to-late 1800s, with higher mass loss rates in the early 21st century, followed by a slowdown since 2011. As of yet, it remains unclear whether this mass loss slowdown will persist in the future. By reconstructing the contemporary (1958–2019) surface mass balance of Icelandic glaciers, we show that the post-2011 mass loss slowdown coincides with the development of the Blue Blob, an area of regional cooling in the North Atlantic Ocean to the south of Greenland. This regional cooling signal mitigates atmospheric warming in Iceland since 2011, in turn decreasing glacier mass loss through reduced meltwater runoff. In a future high-end warming scenario, North Atlantic cooling is projected to mitigate mass loss of Icelandic glaciers until the mid-2050s. High mass loss rates resume thereafter as the regional cooling signal weakens.

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Glaciers play a crucial role in the Earth System: they are important water suppliers to lower-lying areas during hot and dry periods, and they are major contributors to the observed present-day sea-level rise. Glaciers can also act as a source of natural hazards and have a major touristic value. Given their societal importance, there is large scientific interest in better understanding and accurately simulating the temporal evolution of glaciers, both in the past and in the future. Here, we give an overview of the state of the art of simulating the evolution of individual glaciers over decadal to centennial time scales with ice-dynamical models. We hereby highlight recent advances in the field and emphasize how these go hand-in-hand with an increasing availability of on-site and remotely sensed observations. We also focus on the gap between simplified studies that use parameterizations, typically used for regional and global projections, and detailed assessments for individual glaciers, and explain how recent advances now allow including ice dynamics when modeling glaciers at larger spatial scales. Finally, we provide concrete recommendations concerning the steps and factors to be considered when modeling the evolution of glaciers. We suggest paying particular attention to the model initialization, analyzing how related uncertainties in model input influence the modeled glacier evolution and strongly recommend evaluating the simulated glacier evolution against independent data.

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