Antarctica plays a crucial role in Earth’s radiative energy balance, with the surface albedo of its snow cover being a key parameter. Snow albedo depends strongly on the optical properties of snow grains in the near-surface layers. Other factors including illumination geometry, cloud conditions and impurities influence albedo, though Antarctic snow is generally pristine. Surface roughness (SR), operating at multiple scales, also modulates albedo and directional reflectance but remains challenging to quantify. Advances in laser scanning have enabled high-resolution measurements over larger areas, opening new possibilities for modeling. While roughness–albedo relationships have been explored in alpine and boreal snow, Antarctic snow has not received equivalent attention. We conducted a field campaign in western Queen Maud Land during the austral summer of 2022–23, using unmanned aerial vehicle (UAV)-mounted laser scanning alongside in situ observations to investigate the impact of centimeter-scale SR on snow albedo. We observed SR with mean root-mean-square slopes in the range of 0.2–0.4 rad from the UAV, confirmed with near-surface laser scanning once scale variability was accounted for. The resulting albedo impact was in the range of 0.01–0.06 (mean of ∼0.03), in accordance with prior studies and exhibiting logical relationships with, e.g., local snowfall or high wind events.

Rapid warming of the Arctic has accelerated the mass loss of snow and ice. The glacier equilibrium line altitude (ELA) is a suitable location for monitoring the surface energy balance (SEB) and surface mass balance (SMB). This study presents SEB and SMB variations during 2012–2022 around the ELA on the Qaanaaq Ice Cap in northwest Greenland. The SMB was analyzed using SEB analysis and the surface height change method (SHM) with in situ meteorological data. Results revealed that the most significant surface melt occurred in the summers of 2019 (1058 and 1191 mm w.e. a−1) and 2015 (974 and 731 mm w.e. a−1) for the SEB and SHM, respectively. Additionally, the primary energy contributing to the surface melt in 2014/15 was net shortwave radiation caused by snow/ice albedo feedback, whereas in 2018/19, it was not only shortwave radiation but also increased downward longwave radiation from warm cloud cover. Furthermore, the cumulative SMB over the decade (2012–2022) is slightly positive, with a value of +0.26 and +0.28 m w.e., for the SEB and SHM, respectively. The results on the amplification of the SEB and SMB around the ELA provide valuable insights into monitoring environmental changes in the Arctic.

Glacier energy-balance models offer mechanistic insights into glacier mass balance under a changing climate, yet their considerable data requirements hinder large-scale applications. Here we present the open-source Python Energy Balance model for Snow and Ice (PEBSI), which includes physically based albedo evolution using the Snow, Ice and Aerosol Radiative (SNICAR) model. PEBSI is calibrated and validated using robust in situ data from Gulkana Glacier, Alaska from 2000 to 2024. Simulations forced with original and bias-corrected climate reanalysis data show that statistically downscaling reanalysis data with in situ observations is necessary to reproduce summer mass balance (mean absolute error [MAE] = 0.75 m w.e. vs 0.22 m w.e., respectively). A grid search across two parameters, a precipitation factor and a densification parameter, reveals tradeoffs in performance compared to seasonal mass balance and end-of-winter snow density and depth. No single combination of parameters minimizes all errors, underscoring the inherent overparameterization of energy-balance models and challenges with translating coarse climate data to the glacier scale. The calibrated model successfully simulates the 2024 melt season, agreeing with surface-height change (MAE = 0.48 m) and albedo (MAE = 0.066) observations. Moving forward, PEBSI provides unique opportunities to quantify albedo feedbacks and their impact on present and future glacier mass loss.

A catalogue of seismic events is produced and analysed for Sørsdal Glacier, East Antarctica. Recordings were made using an irregular array of three broadband and eight short-period seismometers, with approximately 3 km aperture, deployed slightly upstream of the expected grounding line during the 2017–18 austral summer. The broadband sensors were used to construct the event catalogue, and the short-period instruments were used to aid constraints on source directionality relative to the array. We observe a diurnal cycle of seismicity, which is characterised by Rayleigh waves with peak activity corresponding to low surface temperature, indicating surface crevassing enhanced by thermal stress as the dominant source mechanism. Event groups were formed using manual analysis, followed by template matching. These groups revealed spatial and temporal clusters with distinct crevassing zones operating in diurnal cycles, and other near-surface sources with weaker periodicity potentially originating from firn or hydrological processes. These cycles and source variability show the evolution of the surface on daily and seasonal timescales, so they may provide useful insights into hydrofracture and ice shelf stability. The analysis techniques and workflows employed are transferable to other polar ice sheet outlet glaciers where seismicity is generated largely outside the aperture of the array.

The near-surface weathering crust is a thin (<0.5 m), low density ice layer that develops on glacier surfaces during the ablation season and is formed by internal melting driven by the penetration of shortwave radiation (SWR) into polycrystalline glacier ice. This ‘photic zone’ hosts microbial communities, mediates biogeochemical processes and routes meltwater to the channelised supraglacial drainage network. Despite these critical roles, direct field measurements of weathering crust formation and evolution are scarce—rather, current understanding is largely derived from modelling approaches. Here, we present in situ measurements of weathering crust evolution at five sites on the western Greenland ice sheet, each over a 19–25 hour period. Shallow ice cores revealed weathering crust ice densities of 420–910 kg m−3, demonstrating dynamic evolution of the weathering crust linked to diurnal SWR receipt. We compare our empirical data with two existing weathering crust models, neither of which fully reproduce the observed ice density or its temporal variability. Additionally, we reveal that the density of the uppermost 0.1 m of the weathering crust is a key control upon bare-ice albedo. Our findings highlight the need for improved process-level-understanding and parameterisations of weathering crust dynamics in surface energy balance models.

Increased occurrence of high melt summers across Arctic ice caps and the Greenland Ice Sheet creates thicker, more numerous ice layers within the near surface snow and firn of their accumulation zones. Ice layers may reduce vertical percolation of surface meltwater, promote lateral runoff, reduce refreezing at depth in underlying snow and firn promoting a positive feedback towards more negative surface mass balance. Despite their significance for ice sheet mass balance, controls on ice layer permeability are poorly understood. Here, we explore ice layer permeability using cold-laboratory snowpack experiments with predefined thermal regimes and ice layer thicknesses. We found that in a cold thermal regime (−3°C), ice layers (5–20 mm thick) within the snowpack are impermeable. Meltwater runs off laterally or ponds and subsequently refreezes within 3 h. With temperate conditions (0°C), meltwater ponds over ice layers (10–60 mm thick) without freezing. Temperate ice layers are permeable over timescales of ∼4 to 18 h. We propose that permeability of refrozen ice layers is primarily a function of thermal regime and that ice layer thickness is a secondary control. Our findings pave the way for improved snow and firn model parameterizations of ice layer permeability and ice sheet mass loss projections.

Snow cover influences sea ice thermodynamics and mass balance, making its distribution and properties critical to polar research. Grounded icebergs in coastal Antarctica substantially affect surface snow distribution and landfast sea ice patterns, which have received limited scientific attention. To address this gap, this study integrates airborne laser scanning observations with numerical snow transport simulations to investigate snow distribution on landfast ice around icebergs, emphasizing the influence of wind and iceberg size. Observations show that persistent wind directions shape characteristic snow patterns around icebergs, with substantial windward and lateral drifts and an elongated snow-depleted region in the lee. Data further reveal that snowdrift size scales nonlinearly with iceberg size, indicating reduced snow accumulation efficiency for larger icebergs, which simulations partially captured. This study also highlights the key role of wind direction shifts in reproducing measured snow distributions and suggests that the maximum extent of snowdrifts is constrained by peak wind speeds encountered on site. Together, our findings show that iceberg-induced snowdrifts connect ice shelf and fast ice dynamics, reflect local wind conditions and provide key insights into snow mass balance on Antarctic landfast sea ice.

The timing of snowmelt onset (SMO) is a critical climate indicator in the Arctic. However, spaceborne, in-situ measurements, and model simulations yield different estimates for the timing. Understanding these discrepancies is essential for identifying the physical mechanisms driving SMO. In this study, SMO, snow, and sea ice thermodynamics were simulated using a single-column snow/ice model (HIGHTSI) along trajectories of 42 ice mass balance buoys operating in the period of 2010 to 2015. The results were compared with passive microwave remote sensing and ice mass balance observations. The modeled surface-SMO has a high inter-annual correlation (0.94) with the ice mass balance-derived results but occurred on average 5 days earlier than observations. The remote-sensing-derived Early-SMO was 12 days before the ice mass balance-derived surface-SMO, while the Continuous-SMO showed a 5 day lag. The modeled average snow depth, ice thickness, and snow/ice temperature captured the recorded seasonal variations. The modeled snow/ice temperature showed seasonal biases of 0.4°C/0.5°C between May–September, and −2.7°C/−4.6°C between October–April, respectively. The corresponding biases for average snow depth and ice thickness were −0.05 m/−0.15 m and 0.03 m/0.14 m, respectively. Accurate representation of air temperature forcing and solar radiation absorption is crucial for realistic simulation of SMO.

The accumulation area ratio (AAR) of a glacier reflects its current state of equilibrium, or disequilibrium, with climate and its vulnerability to future climate change. Here, we present an inventory of glacier-specific annual accumulation areas and equilibrium line altitudes (ELAs) for over 3000 glaciers in Alaska and northwest Canada (88% of the regional glacier area) from 2018 to 2022 derived from Sentinel-2 imagery. We find that the 5 year average AAR of the entire study area is 0.41, with an inter-annual range of 0.25–0.49. More than 1000 glaciers, representing 8% of the investigated glacier area, were found to have effectively no accumulation area. Summer temperature and winter precipitation from ERA5-Land explained nearly 50% of the inter-annual ELA variability across the entire study region (${R}^2=0.47$). An analysis of future climate scenarios (SSP2-4.5) projects that ELAs will rise by ∼170 m on average by the end of the 21st century. Such changes would result in a loss of 25% of the modern accumulation area, leaving a total of 1900 glaciers (22% of the investigated area) with no accumulation area. These results highlight the current state of glacier disequilibrium with modern climate, as well as glacier vulnerability to projected future warming.

This study investigates black carbon (BC) concentrations in the seasonal snowpack on the Godwin-Austen Glacier and in surface snow at K2 Camps 1 and 2 (Karakoram Range), assessing their impact on snowmelt during the 2019 ablation season. Potential BC and moisture sources were identified through back-trajectory analysis and atmospheric reanalyses. Variations in water stable isotopes (δ1⁸O and δ2H) in the snowpack were analysed to confirm its representativeness as a climatic record for the 2018–19 accumulation season. The average BC concentration in the snow pits (12 ng g−1) generated 66 mm w.e. (or 53 mm w.e. excluding the basal zone) of meltwater. Surface snow at K2 Camp 1 showed BC concentrations of 7 ng g−1, consistent with those on the snowpack surface, suggesting it may reflect local BC levels in late February 2019. In contrast, higher concentrations at K2 Camp 2 (26 ng g−1) were potentially linked to expedition activities.

Knowledge of snow and firn-density change is needed to use elevation-change measurements to estimate glacier mass change. Additionally, firn-density evolution on glaciers is closely connected to meltwater percolation, refreezing and runoff, which are key processes for glacier mass balance and hydrology. Since 2016, the U.S. Geological Survey Benchmark Glacier Project has recovered firn cores from a site on Wolverine Glacier in Alaska's Kenai Mountains. We use annual horizons in repeat cores to track firn densification and meltwater retention over seasonal and interannual timescales, and we use density measurements to quantify how the firn air content (FAC) changes through time. The results suggest the firn is densifying due primarily to compaction rather than refreezing. Liquid-water retention in the firn is transient, likely due to gravity-fed drainage and irreducible-water-content decreases that accompany decreasing porosity. We show that the uncertainty (±60 kg m−3) in the commonly used volume-to-mass conversion factor of 850 kg m−3 is an underestimation when glacier-wide FAC variability exceeds 12% of the glacier-averaged height change. Our results demonstrate how direct measurements of firn properties on mountain glaciers can be used to better quantify the uncertainty in geodetic volume-to-mass conversions.

At high elevations on the Greenland ice sheet meltwater percolates and refreezes in place, and hence does not contribute to mass loss. However, meltwater generation and associated surface runoff is occurring from increasingly higher altitudes, causing changes in firn stratigraphy that have led to the presence of near-surface ice slabs. These ice slabs force meltwater to flow laterally instead of percolating downwards. Here we present a simple, physics-based quasi-2-D model to simulate lateral meltwater runoff and superimposed ice (SI) formation on top of ice slabs. Using an Eulerian Darcy flow scheme, the model calculates how far meltwater can travel within a melt season and when it appears at the snow surface. Results show that lateral flow is a highly efficient runoff mechanism, as lateral outflow exceeds locally generated meltwater in all model gridcells, with total meltwater discharge sometimes reaching more than 30 times the average amount of in situ generated melt. SI formation, an important process in the formation and thickening of the ice slabs, can retain up to 40% of the available meltwater, and generally delays the appearance of visible runoff. Validating the model against field- or remote-sensing data remains challenging, but the results presented here are a first step towards a more comprehensive understanding and description of the hydrological system in the accumulation zone of the southwestern Greenland ice sheet.

We conducted a series of experiments that revealed the formation of mm-scale penitente structures in ice illuminated by broadband light under moderate vacuum conditions between 50 and 2000 Pa. The experimental apparatus consists of a 0.3 m diameter cylindrical vacuum chamber with a cooling jacket surrounding the outer radius and bottom surface. Light shines in through an optical window at the top to illuminate most of the ice surface. We observe penitente-like structures at temperatures between −15$^\circ$C and $-2^\circ$C and pressures close to the equilibrium vapor pressure at the ice surface temperature. The formation of these structures is very sensitive to slight changes in background pressure, and the structures tend to vanish with significant deviations away from the equilibrium curve, resulting in a smooth sublimated crater formation instead of penitentes. Application of the physical model by Claudin and others (2015, doi: 10.1103/PhysRevE.92.033015) at experimental conditions generally agrees with observations for penitente spacing.

Accurately quantifying all the components of the surface energy balance (SEB) is a prerequisite for the reliable estimation of surface melt and the surface mass balance over ice and snow. This study quantifies the SEB closure by comparing the energy available for surface melt, determined from continuous measurements of radiative fluxes and turbulent heat fluxes, to the surface ablation measured on the Greenland ice sheet between 2003 and 2023. We find that the measured daily energy available for surface melt exceeds the observed surface melt by on average 18 ± 30 W m−2 for snow and 12 ± 54 W m−2 for ice conditions (mean ± SD), which corresponds to 46 and 10% of the average energy available for surface melt, respectively. When the surface is not melting, the daily SEB is on average closed within 5 W m−2. Based on the inter-comparison of different ablation sensors and radiometers installed on different stations, and on the evaluation of modelled turbulent heat fluxes, we conclude that measurement uncertainties prevent a better daily to sub-daily SEB closure. These results highlight the need and challenges in obtaining accurate long-term in situ SEB observations for the proper evaluation of climate models and for the validation of remote sensing products.

Avalanche modeling is an essential tool to assess snow avalanche hazard. Today, most popular numerical approaches adopt depth-averaged equations. These methods are computationally efficient but limited in capturing processes occurring in the flow depth direction, e.g. erosion or deposition, which are often considered using ad hoc parameterizations or neglected completely. However, processes such as snow erosion, can crucially influence flow dynamics and run-out and are often not negligible. We address these issues by using a new three-dimensional (3-D) model, based on the material point method and finite strain elastoplasticity. To assess the possibilities and challenges associated with these highly detailed but computationally expensive calculations, we simulated the ‘Salezer’ snow avalanche that released in Davos, Switzerland in 2019. To reproduce the event in our simulations, we use the release areas mapped in a photogrammetric drone survey and estimate the snow conditions on the day of the event. We compare macroscopic features, such as flow outline and snow deposition of the simulated avalanche to field observations. An in-depth analysis of transient 3-D flow structures at the avalanche head not only demonstrates the degree of physical detail in the model, but also highlights challenges which still need to be addressed.

We developed a multi-frequency, multi-Global Navigation Satellite System (GNSS) positioning instrument optimized for autonomous applications in the cryosphere. At lower power requirements and a fraction of the cost and weight compared to commercially available options, this instrument simplifies field usage and associated logistics. In this paper, we assess several baseline aspects of performance in a polar environment relative to geodetic receivers commonly used for glaciological applications. Evaluations of precision and relative accuracy of positioning show millimeter to centimeter-level (‘geodetic-grade’) quality of this instrument, making it a competitive alternative for GNSS glaciological and geophysical applications such as monitoring surface elevation change and ice flow. An array of these instruments, tested in the field on the Greenland Ice Sheet, also demonstrated robustness throughout the polar winter and met power and reliability requirements.

We developed a Handheld Integrating Sphere Snow Grain Sizer (HISSGraS) for field use to measure the specific surface area (SSA) of snow. In addition to snow samples, HISSGraS can directly measure snow surfaces and snow pit walls. The basic measurement principle is the same as that of the IceCube SSA instrument. The retrieval algorithm for SSA from reflectance employs two conversion equations formulated using spherical and nonspherical grain shape models. We observed SSAs using HISSGraS, IceCube and the gas adsorption method in a snowfield in Hokkaido, Japan. Intercomparison of the results confirmed that with HISSGraS direct measurement, SSA profile observations can be completed in just ~1/10 the time required for measurement of snow samples. Our results also suggest that HISSGraS and IceCube have similar accuracy when the same snow samples are measured using the same grain shape model. However, SSAs of near-surface snow layers measured using the three techniques exhibited some biases, possibly due to rapid snow metamorphism or melting during measurement and some technical issues with optical techniques. When excluding SSA data for the surface layer, which metamorphosed remarkably during measurement, IceCube- and HISSGraS-derived SSAs correlated strongly with those obtained by gas adsorption and HISSGraS accuracy is 21–34%.

Converting measurements of ice-sheet surface elevation change to mass change requires measurements of accumulation and knowledge of the evolution of the density profile in the firn. Most firn-densification models are tuned using measured depth–density profiles, a method which is based on an assumption that the density profile in the firn is invariant through time. Here we present continuous measurements of firn-compaction rates in 12 boreholes near the South Pole over a 2 year period. To our knowledge, these are the first continuous measurements of firn compaction on the Antarctic plateau. We use the data to derive a new firn-densification algorithm framed as a constitutive relationship. We also compare our measurements to compaction rates predicted by several existing firn-densification models. Results indicate that an activation energy of 60 kJ mol−1, a value within the range used by current models, best predicts the seasonal cycle in compaction rates on the Antarctic plateau. Our results suggest models can predict firn-compaction rates with at best 7% uncertainty and cumulative firn compaction on a 2 year timescale with at best 8% uncertainty.

Over half of the recent mass loss from the Greenland ice sheet, and its associated contribution to global sea level rise, can be attributed to increased surface meltwater runoff, with the remainder a result of dynamical processes such as calving and ice discharge. It is therefore important to quantify the distribution of melting on the ice sheet if we are to adequately understand past ice sheet change and make predictions for the future. In this article, we present a novel semi-empirical approach for characterising ice sheet surface conditions using high-resolution synthetic aperture radar (SAR) backscatter data from the Sentinel-1 satellite. We apply a state-space model to nine sites within North-East Greenland to identify changes in SAR backscatter, and we attribute these to different surface types with reference to optical satellite imagery and meteorological data. A set of decision-making rules for labelling ice sheet melting states are determined based on this analysis and subsequently applied to previously unseen sites. We show that our method performs well in (1) recognising some of the ice sheet surface types such as snow and dark ice and (2) determining whether the surface is melting or not melting. Sentinel-1 SAR data are of high spatial resolution; thus, in developing a method to identify the state of the surface from these data, we improve our capability to understand the variation of ice sheet melting across time and space.

Snow ice (SI) forms from freezing wet snow, known as slush, and contributes to the thickness of level and brash ice. However, the mechanism of snow-slush-snow ice transformation has not been extensively investigated to date, despite the difference in the freezing rate of slush in comparison with water is important for estimating the ice thickness. In this study, we examined the growth of initial congelation ice (CI) and snow ice (SI) in a fresh water tank exposed to outdoor weather conditions in Luleå, northern Sweden. The tank of size 1.8 × 0.65 × 1.2 m in length, width and height was divided into two compartments to facilitate the simultaneous growth of CI and SI. A total of 12 experiments were conducted in the years 2021 and 2022. The transformation from slush to snow ice was achieved by submerging various amounts of snow in the compartments. It was observed that approximately 35% of the initial snow transformed into SI. Snow ice grew 4 mm°C−0.5 d−0.5 faster than congelation ice. The CI growth under SI was 1 mm°C−0.5 d−0.5 slower than the CI growth under CI. This study provides valuable insights for modelling snow-slush-snow ice transformation and designing future laboratory-scale experiments.