There is a growing collection of ice-penetrating radar systems being used in a variety of glaciological settings to collect a body of data that is expanding in its scale, diversity and information richness. While there are nuanced differences among particular instruments, there are four main classes of radar systems used to observe the subsurface of ice sheets and glaciers: monotone pulse airborne sounders, chirped airborne sounders, ground-traversed impulsive systems and stationary continuous-wave systems. While these classes are often treated as fundamentally different instruments with distinct communities and literature, we seek to reframe them as choices on a continuum of potential waveforms. In particular, we focus on the tradeoff between achieving large coherent processing gains and mitigating interference from the matched-filter response of strong echoes at shorter delays as the key limiting factors for subsurface target detection. We also discuss the glaciological settings in which different waveform choices perform best, as well as potential avenues this raises for new instrument and experiment designs.

The retreat and acceleration of Greenland’s marine-terminating glaciers are linked to enhanced submarine melting driven by ocean warming. Despite the significance of these ice-ocean interactions, observations of ice-front proximal waters remain limited due to logistical challenges. Buoyant subglacial meltwater plumes entrain deep, warm Atlantic waters during their ascent. Using a plume model, we demonstrate that when these plumes reach the surface in deep fjords (>400 m), their surface temperatures can serve as a proxy for subsurface ocean temperature variability. Here, we optimise and validate a Landsat sea surface temperature algorithm for Arctic waters using in situ measurements, with an accuracy of ∼0.4°C for Landsat 5, 7, 8 and 9. Applying this method, we analyse plume surface temperatures at Sermeq Kujalleq (Store Glacier) from 1985 to 2024 and compare Landsat-derived plume surface temperatures with plume model simulations forced by in situ measurements and four ocean reanalysis products. The Landsat-derived plume surface temperatures are of the correct magnitude and reveal trends consistent with expected interannual ocean variability. While large seasonal variability and confounding physical processes still make it difficult to isolate the subsurface signal, our 40 year record suggests that plume surface temperatures could become a useful proxy for subsurface ocean change.

Accurate knowledge of glacier bed geometry and flow parameters is essential for glaciological modeling and can be inferred from surface data and theoretical principles. This study evaluates the performance of the Instructed Glacier Model (IGM) v2.2.1 in estimating glacier ice thickness using the protocol from Phase II of the Ice Thickness Models Intercomparison eXperiment (ITMIX II). Results show that IGM ranks among the best-performing models, producing largely unbiased ice-thickness fields. Compensating errors, where underestimations of ice thickness in some glacier regions are offset by overestimations in others, are generally small, indicating robust overall performance. Extreme deviations from observed thicknesses are less frequent in IGM than in the ITMIX II model ensemble. We also examine the ability of IGM to reproduce glacier dynamics by comparing modeled and observed surface velocities. While IGM is generally capable of reproducing the surface velocities well, our analysis reveals a trade-off between matching ice thickness and surface velocity, possibly due to inconsistencies or biases in the observational datasets. Generally, the overall good performance of IGM in reproducing glacier geometry and dynamics supports its potential for initializing glacier evolution simulations.

Supraglacial lakes on the Greenland Ice Sheet are becoming increasingly abundant, impacting the distribution and routing of surface runoff with implications for drainage system evolution and ice flow. Their dynamics are well understood in summer when optical satellite imagery provides effective monitoring; however, their distribution and impact are less clear in winter when monitoring is restricted by the 24-hour darkness of the polar night. Using Sentinel-1 C-band Synthetic Aperture Radar (SAR) time series from 2018 to 2019, we assess whether an unsupervised time series clustering approach can be used to identify dynamic lake processes in winter. Our results show that: 1) winter lakes can be detected with an 87.6% accuracy; 2) SAR backscatter time series can be used to automatically and accurately (75.0%) classify winter lake behaviour (freeze-through, buried, no winter lake presence) without training data; 3) the temporal backscatter pattern of winter lake behaviour is consistent across regions and years and therefore generalisable; and 4) individual lakes can exhibit multiple behaviours over winter. Our work underscores the need for methods that capture spatio-temporal complexities in SAR backscatter, and provides insights into how C-band backscatter can be used to contribute towards year-round monitoring of lake dynamics across Greenland.

Despite advances in global glacier modeling, notable uncertainties remain in part due to model overparameterization. We develop a novel Bayesian inference framework to calibrate the Python Glacier Evolution Model (PyGEM) against spatially distributed surface elevation change data. The utility of this framework is demonstrated using a 1994–2021 airborne laser altimetry record for 185 glaciers in Alaska, which altogether represent over half of the region’s glacier area. Calibration against these data reduces parameter uncertainty by 5–18% and lowers the mean absolute error in hindcast surface elevation change by 0.18 m a−1 (15% compared to prior models). Model projections for these 185 glaciers estimate mass loss ranging from 43 $ \pm $ 17% to 67 $ \pm $ 27% depending on the emissions scenario. The projections highlight considerable subregional variability ranging from a median glacier mass loss of 57–99% by the end of the century for Lake Clark, Denali, Kenai Fjords, and Glacier Bay national parks, compared to 28–51% lost for Wrangell–St. Elias National Park and Preserve. As additional spatially distributed elevation change datasets become available in Alaska and elsewhere, the framework presented here is well suited to further refine model calibration and improve projections, while remaining flexible to incorporate additional glacier change observations as they become available.

If crevasse fields deliver meltwater to the bed of the Greenland ice sheet, it would affect seasonal ice flow speeds and total mass balance. The best current automated tool to map crevasse fields extends only a few dozen kilometers inland. To address this gap, we develop MimiNet, a neural-network-based tool that identifies surface crevasse fields. We train MimiNet on Sentinel-1 scenes across a 629 km2 area in Pâkitsoq, central-western Greenland, and use it to locate crevasse fields annually over 2015–24. We find that the crevassed area varied from a minimum of 106 ± 5 km2 in 2019 to a maximum of 144 ± 6 km2 in 2016, with no overall trend over the 10 year study period. We find some evidence that seasonal ice velocity anomalies in crevasse fields are higher than those in moulin-drained areas in the late melt season. This may suggest that the subglacial drainage system under crevasse fields remains inefficient all summer, and thus that at least some Pâkitsoq crevasse fields deliver meltwater to the bed. Interannual variability in ice dynamics may drive the observed variability in crevassed areas; we expect crevasse extent to become more variable in time as ice flow speeds and their variations, amplify under climate change.

The firn layer in the upper part of the Antarctic ice sheet represents a crucial transitional zone during the transformation from snow to glacial ice, and its structural characteristics play an essential role in evaluating the surface mass balance of the ice sheet. Surface-wave inversion offers strong constraints on shallow subsurface stratification, and compared with conventional ambient noise, traffic-induced seismic vibrations provide higher-frequency signals that can significantly enhance near-surface imaging resolution. In this study, we identified traffic noise signatures within ambient seismic data recorded during the 40th Chinese National Antarctic Research Expedition in the Larsemann Hills, East Antarctica. Using these data, we performed high-resolution shear-wave velocity imaging of the firn layer through surface-wave inversion based on seismic interferometry and one-dimensional Monte Carlo inversion. The retrieved virtual shot gathers revealed clear surface-wave energy, and the inverted shear-wave velocity profiles were in good agreement with borehole data obtained at the center of the survey line. Vibrations generated by vehicles such as PB300 can serve as an effective, low-cost and environmentally friendly seismic source. This traffic-source-based approach provides a practical pathway for high-resolution firn imaging and offers valuable potential for future large-scale, long-term monitoring of ice-sheet structure and mass balance.

Observing glacier mass changes is essential for understanding and projecting the impacts of climate change on sea-level rise, water resources and natural hazards, as well as providing data for developing, calibrating and validating glacier evolution models. The principal methods used to measure glacier mass changes — glaciological, geodetic (surface elevation differencing) and gravimetric — differ in the spatial and temporal scales at which they are most effectively applied. Here, we review these methods in the context of challenges that arise when comparing published mass-balance estimates. Compatibility can be hampered by (1) inconsistent reporting and lack of relevant information; (2) discrepancies in which mass-balance components are included; (3) differences in the time span analyzed; and (4) variations in the spatial domain of the reported mass balance. We provide recommendations for more rigorous and comprehensive reporting of mass-balance estimates to improve comparability and synthesis of reported glacier mass changes, and we emphasize open data and code sharing to enable full reproducibility and future reinterpretation. Our recommendations apply equally to both glacier and ice-sheet mass-balance reporting, and they are generally valid for mass balances simulated by numerical models.

Glacier surges are dynamic instabilities that dramatically alter glacier flow and geometry. Their triggers remain poorly understood, and improved methods of monitoring to further constrain the phenomenon are therefore important. We present a novel method for detecting glacier surges automatically using surface elevation data from NASA’s ICESat-2 laser altimetry satellite. Elevation changes from 2018 to 2023 were computed relative to a high-resolution reference digital elevation model and analyzed using a hypsometric binning approach. We trained a Random Forest classifier on known surge events in Svalbard to identify spatial elevation change patterns indicative of surging. Our model detected 110 surges, of which 48 were false positives, 20 uncertain cases that may or may not be surges and 42 certain surges confirmed by external validation. Two of these are currently not part of any surge inventory. The classifier achieved an accuracy of 88.4% and highlighted features in the lower glacier region as most predictive. This study demonstrates that sparse altimetry data such as from ICESat-2 can effectively detect glacier surges and offers a promising, scalable approach to monitoring dynamic glacier instabilities.

Snow grain size is a primary control on albedo and snowmelt. Compact imaging spectrometers and lidars mounted on uncrewed aerial systems (UAS) now offer flexible, high-resolution observations, but their relative performance for optical snow property retrievals has not been assessed. We compared grain size retrievals from two UAS-mounted imaging spectrometers (925–1700 nm and 900–2500 nm) and two lidars (905 nm and 1550 nm) against field spectroscopy over snow covered terrain (Mores Creek Summit, ID in March 2024). The compact imagers and 905 nm lidar retrieved grain sizes within ∼10% of field reference values, with little sensitivity to flight altitude (40–120 m). The 1550 nm lidar showed large negative bias (>90% error) due to strong shortwave-infrared absorption by ice. Results demonstrate that affordable, lightweight UAS sensors can accurately map grain size, with 905 nm lidar offering flexibility, notably in conditions unsuitable for passive optical measurements.

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.

Quantifying micromechanical and microstructural properties of snow is crucial, as they control bulk thermal, electrical and mechanical properties. Snow density can provide an estimate of mechanical properties, while direct observation of snow microstructure is necessary to determine mechanical properties. We utilized a novel non-contacting laser ultrasound system, providing high-frequency acoustic waveform measurements, to observe mechanical properties at the microscale. We investigated the temporal relationship between p-wave velocity, snow crystal type, p-wave modulus, stiffness and specific surface area (SSA). We created homogeneous snow samples, each composed of a single type of precipitation particle, compacted to a density of 250 kg m-3. We measured wave propagation through these snow samples and observed changes in p-wave speed during equilibrium metamorphism. We also measured SSA with the InfraSnow Sensor, as well as micromechanical properties with the SnowMicroPenetrometer. With these data, we observed changes in mechanical properties, with up to a factor of 2 increase in elastic modulus depending on crystal type during a period of 72 hours. Estimated elastic moduli increased over time, as expected and in agreement with previous work. Needles and columns had faster p-wave velocities, implying larger elastic modulus, compared to plates and dendrites, and had a higher rate of change within the first hour.

Meltwater drainage through glaciers strongly influences ice dynamics and related hazards, yet detailed observations of active englacial and subglacial networks remain scarce due to challenges in direct observation. We present a novel high-density, UAV-based 3D ground-penetrating radar (GPR) survey of Switzerland’s Otemma Glacier, integrated with dye tracing experiments, photogrammetry and hydraulic potential modeling, to map internal water pathways with unprecedented spatial resolution and coverage. Advanced 3D imaging techniques, adapted from seismic diffraction processing, enhance the detection of small-scale conduits within the ice. We identify two primary subglacial channels having distinct hydraulic efficiencies and transport behaviors. Englacial drainage is found to be structurally complex, comprising lateral conduits, branching networks and localized pooling zones. These observations provide critical constraints on the geometry and dynamics of glacier drainage, supporting and extending existing models, and are essential for forecasting glacier behavior under future warming. UAV-based 3D GPR, in combination with novel processing strategies, emerges as an innovative approach for large-scale, repeatable surveys of glacier hydrology.

Glacier mass-balance modelling relies on parameterizations to distribute meteorological variables such as precipitation and air temperature over glacier surfaces. However, meteorological observations at the highest altitudes are sparse, particularly outside of Europe, which presents challenges for glacier modelling in high-altitude regions such as the central Himalaya. This study utilizes a dense network of weather stations in the Khumbu Valley, Nepal, to derive parameterizations for distributing air temperature and precipitation over the Khumbu Glacier. These parameterizations are then compared to those of the GlacierMIP project. This study finds a seasonally varying temperature gradient less negative than those employed by most models, a precipitation gradient which follows an exponential decay and a modelled annual Khumbu Glacier accumulation of 575±24 mm water equivalent, lower than any of the models surveyed, which overestimate accumulation by between 7% and 41%. Physical, process-based interpretations of the parameterizations suggest that these points of difference with GlacierMIP models are likely common across the central Himalaya. Errors in these parameterizations will lead to errors in modelled glacier responses to climate change.

Calving from tidewater glaciers and ice shelves is an important component of global mass balance and may contribute significantly to future sea-level rise. Current prognostic ice-sheet models cannot predict future calving losses because they lack a robust calving law. We argue that the key to finding a general calving law is to recognise that calving glaciers are stochastic dynamic systems that exhibit self-organisation. Collectively, calving events have statistical properties that reflect underlying fragmentation processes. These reflect distinct styles of calving and give rise to persistent patterns of advance and retreat, including fluctuations around pinning points and periods of instability and transition. These patterns motivate a stochastic calving function scaled to the stress within the ice, which we demonstrate in a set of model experiments with Elmer/Ice, for synthetic geometries representative of a Greenland outlet glacier and an Antarctic ice shelf. Self-organising behaviour emerges spontaneously from the model, including expected calving-size distributions and system convergence on quasi-stable states. The model simulates calving behaviour over a wide range of spatial and temporal scales and produces short calving cycles for a Greenland-type geometry and long cycles for an Antarctic shelf-type geometry. The long-standing calving law problem may yield to this kind of approach.

Attenuation rates inferred from radar sounding offer one of the few ways to observationally constrain the large-scale temperature structure of ice sheets. However, existing methods struggle in regions with near-uniform ice thickness or disrupted radiostratigraphy—common across much of Antarctica and Greenland—where direct temperature estimates are most needed. We adapt the spectral ratio method, originally developed for seismic data, to estimate englacial radar attenuation rates, focusing on regions where traditional methods fail. By analyzing the relative amplitude change of surface and bed reflections across the radar bandwidth, we produce full-column attenuation estimates independent of internal layer continuity or significant variability in bed topography. We apply this method to radar surveys in interior Antarctica and Greenland. Our results agree with attenuation rates derived from borehole temperature profiles and alternative radar-based methods, where comparisons are possible. The spectral ratio method is broadly applicable to any radar dataset that preserves the original amplitude spectra. By expanding the spatial coverage of reliable attenuation estimates, our approach enables continental scale mapping of ice sheet temperature.

Deep ice cores from polar ice sheets enable reconstructions of Earth’s past climate. Ice core records are therefore crucial for projecting future climate change, but our ability to interpret them relies on our understanding of polycrystalline-ice microstructures and mechanics. In turn, these microstructures enable modeling of ice flow and large-scale effects of ice sheet evolution. Since drilling began in the 1950s, the ice textures and climate proxies developed to decipher ice core records have been analyzed in one-dimensional (1D) or two-dimensional (2D) spaces, necessitated by the analytical instruments of core-processing lines and laboratories. Here, we develop a three-dimensional (3D), non-destructive approach to textural analysis that preserves the natural context of ice and complements standard methods. Our method combines lab-based absorption and diffraction contrast tomography to simultaneously visualize, measure, and spatially correlate ice grains and air bubbles from volumetric and 3D crystallographic perspectives, both lost during traditional sample preparations. We evaluate the representation of 3D versus 2D data and discuss how access to both c- and a-axis directions of grains may help constrain micromechanical models. We also built a specially designed cooling device for the laboratory X-ray system to extend observational volumes by several orders of magnitude over previous synchrotron-based measurements.

Englacial layers are a product of historic accumulation and are reshaped by ice deformation. Hence, radio-echo sounding (RES), which can resolve englacial layering, has been adopted as an observational tool to infer ice age and ice dynamics from ice stratigraphy. However, the commonly applied synthetic aperture radar focusing algorithms, used to improve image resolution, are either i) incoherent or ii) optimized for the ice-bed interface. Dipping specular reflectors, such as englacial layers, are then lost during focusing. Instead, we focus the RES measurements using subapertures, synthetically squinting the radar beam toward orthogonal incidence for every dipping layer. We then either recombine all subapertures or reject those with low signal to generate an image that resolves all englacial targets together. We apply these methods to both along- and across-flow RES images at Academy Glacier, East Antarctica, which has significant englacial layer relief, especially perpendicular to the ice-flow direction. Our method significantly elevates signal power for dipping englacial layers ($ \gt $15 dB), and quantifiably improves layer continuity compared to other processed data products. This squinted focusing approach enables novel studies of ice deformation (as recorded in englacial layering) in the presence of complex basal topography and heterogeneous substrate properties.

We present an initial assessment of all-season Arctic sea ice thickness estimates from ICESat-2 by combining freeboard retrievals with all-season SnowModel-LG snow loading. ICESat-2 captures the key regional and seasonal patterns of Arctic sea ice variability and shows good agreement with CryoSat-2 all-season estimates, including regional patterns of inter-annual variability in summer ice thickness. ICESat-2 shows consistently thicker ice compared to CryoSat-2 across the western coastal Arctic, while CryoSat-2 shows some periods of thicker ice across the Central Arctic, largely consistent with winter thickness biases. Validation against upward-looking sonar moorings, IceBird-2019 airborne observations and MOSAiC buoy data highlights generally strong performance across a range of conditions, although seasonal biases linked to snow loading, freeboard differences and ice density assumptions persist. The SnowModel-LG and NESOSIM snow accumulation models perform well across the validation datasets, but do not consistently add skill beyond the modified Warren climatology. Experimental ICESat-2/CryoSat-2 dual altimetry winter snow depths show strong performance relative to existing products and future work should extend these into summer for further assessments. Overall, our analysis supports the viability of an all-season ICESat-2-derived thickness record.

Polarimetric multi-offset radio-echo sounding offers improved constraints on englacial thermal conditions, basal properties and ice crystal orientation compared to standard monostatic observations. Nevertheless, such surveys are uncommon in glaciology and are typically limited in offset due to cable losses. In the 2023–24 austral summer, we deployed two radar systems on Eastwind Glacier and the McMurdo Ice Shelf in Antarctica, collecting five polarimetric common-midpoint (CMP) surveys. Using an Autonomous phase-sensitive Radio-Echo Sounder (ApRES), modified with off-the-shelf radio frequency-over-fiber (RFoF) hardware and a low-loss fiber optic link, we detect bed reflections at offsets up to the equivalent of four ice thicknesses, well beyond the theoretical point of total internal reflection. A second, cable-less system built around a software-defined radio (SDR) was deployed simultaneously as an unsynchronized receiver recording the same ApRES transmitter. These co-located datasets demonstrate the potential for cabled radar systems with integrated RFoF technology for extending maximum offsets by overcoming attenuation losses inherent to coaxial cables. Furthermore, we perform polarimetric amplitude-vs-offset analysis to probe glacier dielectric structure. Finally, we present data from deployment of the fiber optic system on Thwaites Glacier, where we detect bed reflections at an offset of 4 km, demonstrating operation on thick ice (~2.2 km).