Calving glaciers in Greenland frequently exhibit seasonal cycles of terminus advance and retreat and changes in ice velocity and calving rate associated with changing air temperatures, surface melt rates and local sea ice conditions. Landfast sea ice that forms each winter in Greenland and glacier mélange (sikussaq) are part of a continuum of ice forms in front of marine-terminating outlet glaciers, posited to offer a buttressing effect on marine calving glaciers equivalent to floating ice shelves and potentially thereby helping to reduce the risk of marine ice sheet instability feedbacks. However, the role of mélange in controlling calving rates and front position is controversial, with previous studies showing mixed results and limited effects on terminus ice velocities. Here, we use a comprehensive range of different types of data including a novel in situ dataset of high time resolution GNSS position information, combined with satellite datasets of ice velocity and calving front position for three representative glaciers in north west Greenland. Our study at the Tracy, Melville, and Farquhar glaciers took place during the period from late winter (March) to peak melt season (July) in 2022 and 2023. Seasonal variations in outlet glacier velocity, calving activity and terminus position vary in-step with the seasonal cycle of air temperature and landfast sea ice formation and break-up. Our observations are consistent with previous granular material theoretical frameworks where fast ice acts to delay the removal of mélange. We also observe large calving events at the peak of the fast ice season suggesting that neither landfast ice nor mélange fully suppress calving activity. We find no evidence of tidally driven movements within the mélange zone during the fast ice season. Our observations show that the unbonded mélange at these outlet glaciers has very little influence on glacier velocity or calving rates and it is only when the separate ice blocks in the mélange zone are frozen into a matrix of landfast sea ice, losing the granular material properties that are characteristic of mélange but more associated with rigid multiyear sea ice, that they appear to exert any kind of (limited) influence on the calving rates. Our observations form a comprehensive and useful dataset for evaluating models of mélange interactions and developing insights into the material properties of fast ice and mélange. We conclude that at these representative small and medium-sized Greenland outlet glaciers, factors including surface melt and glacier velocity as well as the presence of landfast sea ice bonding mélange modulate seasonal calving behaviour.
Cold-adapted algae and cyanobacteria are key drivers of snow and ice albedo reduction, yet their dynamics on dust-rich Central Asian glaciers remain poorly understood compared to the well-documented algal blooms in the Arctic. This study investigated the spatio-temporal distribution of phototrophic communities on Urumqi Glacier No. 1, eastern Tien Shan, during a two-month melt season. Our findings reveal a distinct seasonal succession where snow-covered surfaces were dominated by snow algae Chloromonadinia species, whereas the ablation of snow exposed the bare-ice surface, manifesting as a sharp biomass increase dominated by the newly uncovered filamentous cyanobacteria (Oscillatoriaceae). Notably, glacier algae such as Ancylonema spp., which drive darkening on Arctic ice, were entirely absent, suggesting a fundamental ecological divergence. Statistical analyses indicated that cyanobacterial proliferation is closely linked to environmental factors, showing significant positive correlations with mineral-derived ions and negative correlations with inorganic nitrogen. These results, supported by recent evidence that specialized cyanobacterial taxa drive the initiation and structural development of cryoconite granules, suggest that mineral-rich glacier surfaces may provide environmental conditions favorable for the persistence of cyanobacteria-dominated communities. The widespread coverage of bare ice surfaces by dispersed cryoconite composed of filamentous cyanobacteria and mineral particles may contribute to sustained biological darkening throughout the melt season, contrasting with the transient snow algal blooms commonly observed in polar regions. Our study highlights the necessity of integrating region-specific microbial dynamics, which is characterized by the absence of glacier algae and the dominance of mineral-buffered cyanobacterial communities, into glacier mass balance models to improve the accuracy of future projections for Central Asian water resources.
Extreme precipitation is a major contributor to the total precipitation over Antarctica and its variability. However, it remains poorly understood whether Antarctic extreme precipitation has undergone recent changes and, if so, whether these changes are anthropogenically driven. Using ERA5 reanalysis for 1979–2023, we identify significant regional trends in total and extreme precipitation across Antarctic drainage basins, including significant increases over the Filchner-Ronne sector, Dronning Maud Land, and Enderby Land in East Antarctica. We then perform a regression-based detection and attribution analysis of these trends using precipitation outputs from CESM1 global climate model large ensembles based on “all-forcing” experiments and “single-forcing” experiments that isolate the effects of greenhouse gases, anthropogenic aerosols, and stratospheric ozone. For five of the six basins exhibiting positive trends in total precipitation (within the Filchner-Ronne sector, Dronning Maud Land, and Enderby Land) and three of the four basins exhibiting positive trends in extreme precipitation (within Dronning Maud Land and Enderby Land), the ERA5 signal was formally detected in the CESM1 all-forcing simulations, indicating that these trends are driven by a combination of anthropogenic and natural forcings. Our analysis further show that for one basin (within Enderby Land) the increases in total and extreme precipitation are robustly attributed to greenhouse gases and stratospheric ozone, while for another basin (within Dronning Maud Land) the increases in total precipitation are attributed to stratospheric ozone only. In contrast, none of the precipitation trends could be attributed to anthropogenic aerosols despite all-forcing and single-forcing simulations of anthropogenic aerosols exhibiting similar trend patterns. Applying the same analysis to CESM2 large ensembles confirmed that the ERA5 precipitation signal was detected in the all-forcing simulations but unlike CESM1 did not provide robust attribution of total or extreme precipitation to any individual forcing. These findings provide evidence that external drivers have already impacted East Antarctic total and extreme precipitation, while demonstrating that uncertainties in attributing individual forcings remain a key limitation in understanding future changes to the Antarctic surface mass balance.
Spatial estimates of snow water equivalent (SWE) often depend at least partially on extrapolation from sparse measurements, but wind drifts are frequently missing from the underlying observational datasets. Despite the increasing availability of airborne lidar surveys, which constrain snow depth variability in drifts, the contribution of wind-packing densification to SWE heterogeneity remains unknown. Here, we leverage extensive snow pit density profiles (78 total, including 14 at least 2 m deep) from the early ablation season across the Wind River Range (Wyoming, USA) to strategically constrain snow density in rarely sampled settings, including deep drifts, steep slopes, avalanche runouts, and high elevations. At the most extreme, complete vertical profiling of a 5.9 m drift in a nivation hollow reveals a bulk density of 585 kg m−3, exceeding the predictions of global empirical models by 14 % to 35 %. In contrast, we contemporaneously observe bulk densities as low as 339 kg m−3 in adjacent forested areas. We scale up our snow density observations to full watersheds at the 3 m grid scale using a Bayesian statistical model weighted by a representativeness metric that is derived from airborne lidar snow depth data across multiple watersheds. The most representative density measurements are associated with alpine snow drifts deeper than 2 m. Like most terrestrial monitoring networks, all 88 of the in-situ daily snow monitoring sites in Wyoming (SNOTEL) are located in relatively low-elevation forested areas, structurally excluding alpine wind drifts. Beyond this systematic underestimation of drift-related snow density, wind drifting also drives depth variations, compounding the underestimation of SWE heterogeneity in extrapolated datasets. Across three large-domain near-real-time gridded SWE datasets, the standard deviation of SWE across the mountain range is underestimated by 33 %–75 % relative to our lidar-based SWE map at the same 500 m grid-scale. These extrapolated datasets underestimate SWE by 65 %–73 % in a 1 km2 glacial cirque basin despite only 2 %–17 % underestimation of the landscape mean. Despite the systematic underrepresentation of drifts in observational datasets, drifts are the most representative snowpack components in windy alpine mountains, so it is essential to account for drift impacts on both snow depth and density to capture the spatial distribution of SWE.
The glacier mass balance is driven by primary processes such as snowfall and surface melt. Secondary processes, like firn pack warming, percolation, and refreezing, are becoming more important even in high-elevation basins, which are triggered by warming Alpine regions. All these processes are functionally related to temporal changes in the firn pack, predominantly its density structure. Here, we provide a detailed assessment of annual changes in firn density, stratigraphy, and compaction rate at different locations of the glacier accumulation area, using multi-year common-midpoint (CMP) radar measurements, representing the first such analysis for a glacier in the European Alps. To achieve this, we combined repeat geophysical observations, predominantly ground-penetrating radar (GPR)-based common-midpoint (CMP) surveys, with direct firn-core investigations from the accumulation area of the Grosser Aletschgletscher, Switzerland. We estimated temporal changes in firn density and compaction rates within the identified 8–9 annual layers using internal reflection horizons (IRHs) from repeat CMP measurements. In addition, we analysed relationships between firn-core-derived chemical impurities and stable isotopes. Our results suggest that the annual changes in firn density decrease with depth and age, with the largest change (∼ 130 kg m−3 yr−1) occurring at the near-surface annual layers (∼ 7–8 m depth) at a low-lying accumulation area where the summer surface melt is more significant than at the higher elevations. Similarly, the estimated compaction rate (maximum ∼ 0.3 m yr−1 at ∼ 7–8 m depth) decreases with depth and age. The CMP-derived density–depth profile agrees with the firn-core results, demonstrating that CMP measurements are a valuable alternative for increasing the spatial distribution of observations and complementing invasive, labour-intensive glaciological measurements. We also estimated spatial changes in firn density and accumulation along a GPR transect and traced the spatial extent of the firn body. The secondary results, obtained by comparing GPR observations from winter 2024 and 2025, suggest that glacier dynamics may influence firn stratigraphy that requires further investigation in future studies. Our results demonstrate that the combination of multi-year GPR profiles, CMP analyses, and firn-core observations can quantify temporal changes in firn density, stratigraphy, and compaction rate, thereby contributing to the future calibration of firn-densification models and improving glacier mass-balance estimates.
Glacier and valley winds are characteristic features of the microclimate of glacierised valleys. The speed of such winds influences the turbulent heat flux, which contributes to ice melt significantly. Sparse in-situ meteorological measurements and the inability of large-scale climate data products to capture such local winds introduce uncertainty into glacier- to global-scale mass-balance calculations. Here, we propose an empirical model having three parameters, namely, the mean wind speed, the sensitivity of the diurnal winds to temperature, and a time-scale parameter, to predict the mean summertime diurnal wind speed on valley glaciers, based only on reanalysis temperature. Utilising data from 28 weather stations on 18 valley glaciers across the globe, we show that the model, driven solely by reanalysis temperature, reproduces the observed mean summertime diurnal wind speed with a mean RMSE of 0.24 m s−1 across stations, which is 8.8 % of the mean observed wind speed. Interestingly, the three model parameters can be estimated even on an ungauged glacier based on the mean temperature and regional slope. A leave-one-out analysis of the stations suggests a root-mean-squared error of 0.85 m s−1 on average, which is a ∼200 % improvement over a standard reanalysis product. The performance of the model is largely independent of the number of stations available for calibration, as long as it is 21 or more. The presented empirical parametrisation can improve wind speed estimates on ungauged glaciers, leading to better glacier mass-balance calculations.
Information about snowpack at high spatio-temporal resolution is important for the timely identification of conditions favouring snow avalanche release. However, such information is often available only at specific instrumented locations. Spaceborne synthetic aperture radar (SAR) sensors can facilitate the acquisition of such information over large areas and in remote and challenging terrain. In this work, we evaluate the use of European Space Agency's (ESA) Copernicus Sentinel-1 (S1) SAR multi-track composites to monitor snowpack wetness evolution. We focus on a study area of 400 km2 around Davos, Switzerland, where comprehensive in-situ information are available for validation of remotely sensed snowpack conditions. We found statistically relevant anticorrelation between S1 SAR backscatter decrease in both polarisations and increase in modelled liquid water content (VV: −0.42, VH: −0.38) and modelled runoff (VV: −0.44, VH: −0.49). We calculate a wet snow ratio (0 referring to dry, 1 to fully wet snowpack conditions) relying on dual-polarisation S1 backscatter time series. By comparing our indicator against the SAR Wet Snow (SWS) products, openly available from the Copernicus Land Monitoring Service, we found clear benefit in terms of spatial performance. We also compare our wet snow ratio time series to a unique catalogue of snow avalanche, aiming to identify conditions that may precede an increase in wet snow avalanche activity. We found a clear transition from dry snow avalanche dominated to wet snow avalanche dominated conditions when the S1 derived wet snow ratio reaches values of 0.17, while at 0.35 only wet snow avalanche were reported. Our results suggest that, despite current limitations in spatial and temporal resolution, S1 multi-track composites may assist wide area evaluation of snowpack wetness conditions, and provide additional indicatots on the initiation of wet snow avalanche release. The continued operation of the S1 mission, together with the growing availability of additional spaceborne SAR platforms, will enable increasingly accurate characterisation of snowpack conditions related to snow avalanche release. As climate warming drives a projected shift towards a higher proportion of wet relative to dry snow avalanche activity, such capabilities will become increasingly critical for operational hazard assessment in alpine environments.
Changes in sea ice concentration (SIC) and derived sea ice extent have been monitored using microwave radiometers since the late 1970s, providing information about the polar response to climate change, making SIC an invaluable variable for numerical models. Antarctic sea ice has experienced an unprecedented decline in the past decade (2016–2025). In the highly dynamic Marginal Ice Zone (MIZ), the region in between the pack ice and the open ocean, physical properties undergo intense variability, which may impact the accuracy of the SIC products retrieved from brightness temperature measurements. For the purpose of this study, the MIZ is defined as the area with SIC between 15 % and 80 %. We simulate the variations of brightness temperature due to changes in the physical parameters describing the sea ice, the snow, and the ocean with the Snow Microwave Radiative Transfer Model (SMRT) and the Passive and Active Reference Microwave to Infrared Ocean model (PARMIO) for a range of prescribed SIC. We then apply the core of the Bootstrap SIC algorithm on the simulated brightness temperatures and compare the retrieved SIC with the prescribed true SIC, yielding the SIC retrieval uncertainty. This allows us to assess the impact of changes on the SIC retrieval by means of numerical radiative transfer simulations. The work identifies the key parameters leading to high uncertainty in the retrieval. In the snowpack, the liquid water fraction, snow grain size, thickness, and snow–ice interface temperature each cause SIC uncertainties within the 5 % range, with some parameters reaching up to 10 % depending on the season. However, the most dominant uncertainty in the cold season comes from the presence of thin ice types like dark nilas and grease, characterised by high salinity or liquid water fraction, which induce uncertainties of up to 70 %. This uncertainty is comparable to that caused by slush, which can be found in the MIZ all year round. Ocean surface impacted by the high-wind conditions affects both warm and cold seasons and gives rise to uncertainties of up to 10 % on the lower SIC MIZ boundary. However, other parameters that were expected to modify the SIC results, such as the temperature and salinity in the snowpack overlying the first-year ice, showed a negligible impact in the tested range. We found that the core of the Bootstrap algorithm is largely robust to the variations in the snowpack properties. In contrast, the presence of thin ice types and slush and ocean surface affected by high wind speeds in the grid cell are the variables leading to the greatest uncertainties, suggesting they are the primary targets to achieve more accurate SIC retrievals in the MIZ.
In many sea ice models, a single-category, zero layer thermodynamic scheme is employed, in which sea ice albedo is prescribed based on surface types depending on snow cover, surface temperature, or sea ice thickness. The Parkinson and Washington parametrisation (PW79) is a commonly used one, which assigns four constant albedo values corresponding to distinct surface types. This parametrisation is too simple to capture the spatiotemporal variability of observed sea ice albedo. Here, we aim for an improved parametrisation by discovering an interpretable, physically consistent equation for sea ice albedo using symbolic regression, an interpretable machine learning technique, combined with physical constraints. Leveraging daily pan-Arctic satellite and reanalyses data from 2013–2020 – dominated by conditions representative of the Central Arctic – we apply sequential feature selection which identifies snow depth, surface temperature, sea ice thickness and 2 m air temperature as the most informative features for sea ice albedo. As a function of these features, our data-driven equation identifies two critical mechanisms for determining sea ice albedo: the high sensitivity of sea ice albedo to small changes in thin snow and a weighted difference of the sea ice surface and 2 m air temperature, serving as a seasonal proxy that indicates the transition between melting and freezing conditions. To understand how additional model complexity reduces errors, we evaluate our discovered equation against baseline models with different complexities, such as multilayer perceptron neural networks (NNs) and polynomials on an error-complexity plane, showing that the equation excels in balancing error and complexity and reduces the mean squared error by about 51 % compared to PW79. Unlike NNs, our discovered equation allows for further regional and seasonal analyses due to its inherent interpretability. When fine-tuning its coefficients offline on regional or seasonal subsets, we uncover differences in physical conditions that drive sea ice albedo. As a use case, we further assess the Barents Sea as a contrasting sea ice regime compared to the Central Arctic, showing that the functional form of the equation remains transferable across different sea ice regimes. This study demonstrates that learning an equation from observational data can deepen the process-level understanding of the Arctic Ocean’s surface radiative budget and improve climate projections.
Atmospheric rivers (ARs) transport concentrated fluxes of heat and moisture poleward, driving temperature and precipitation extremes. Yet, their vertical structure in the High Arctic – where small thermodynamic perturbations govern rain-snow partitioning and cryospheric response – remains poorly constrained. Here we present atmospheric vapour isotope, radiosonde, and meteorological observations from Svalbard during a record-setting AR in March 2022. The AR developed in the northwest Atlantic when a deep “bomb” cyclone established a sustained conduit of poleward heat/moisture. Integrated vapour transport exceeded 450 kg m−1 s−1, with warming and enhanced moisture emerging ∼ 2–6 km aloft before deepening through the lower-troposphere, tripling near-surface humidity. On 15 March, air temperatures rose to 5.6 °C accompanied by 43.9 mm rainfall – the highest daily March total on record. Concurrently, vapour δ18O (d-excess) attained its seasonal maximum (minimum) and marine aerosol (Na+) concentrations spiked, constraining the geochemical signature of Atlantic moisture advection. The two-day AR event delivered ∼ 0.5 Gt snowfall across Svalbard, locally equivalent to over 8 % of net 2022 glacier accumulation and offsetting surface mass loss by ∼ 7 %. Although rainfall comprised less than one-third of the total precipitation, it impacted 60 % of the glacierised terrain, driving winter rain-on-snow melt and densification across lower-elevation areas and altering snowpack structure. Our study underscores the vulnerability of Svalbard and other glacierised Arctic archipelagos to intensifying poleward moisture and heat transport by ARs, with substantial but nuanced impacts on glacier surface energy budget and mass balance through the delivery of anomalous winter rainfall, snowfall, and latent heat.
Monitoring surface elevation change of the Greenland Ice Sheet at monthly resolution is important for resolving both seasonal variability and long-term trends, yet irregular sampling and data gaps in satellite altimetry make the reconstruction of consistent spatio-temporal records challenging. We present a data-driven State Space Model (SSM) framework, the Three-Dimensional Elevation Change Model (3D-ECM), for deriving monthly surface elevation changes of the Greenland Ice Sheet from CryoSat-2 radar altimetry data. Unlike approaches with pre-defined seasonal models, seasonality here emerges directly from the data, enabling the detection of seasonal cycles and long-term trends. The method combines a Gaussian Markov Random Field for spatial dependence with an autoregressive process for temporal correlations, allowing robust signal extraction even in regions with sparse or irregular sampling. With this framework, we derive monthly surface elevation changes for the Greenland Ice Sheet in 5 km resolution over the period 2011–2025. Validation against independent datasets shows strong agreement. At three selected Automatic Weather Station (AWS) sites representing different climatic regimes of the Greenland Ice Sheet, Pearson correlation coefficients between our surface elevation change (dSEC) product and AWS-derived surface elevation records range from 0.58 to 0.7, while comparisons with time series derived from a fusion between laser-altimetry observations and a firn densification model yield correlation coefficients of up to 0.76. Additional comparisons with ICESat-2 and NASA Operation IceBridge airborne data confirm that the applied spatio-temporal post-processing of the dSEC fields reduces the standard deviation by approximately 40 %–45 % while maintaining minimal bias. Furthermore, intercomparison with two published Greenland-wide SEC products derived from satellite altimetry demonstrates that dSEC achieves lower misfit relative to ICESat-2 ATL15, indicating improved agreement with independent laser altimetry. The resulting monthly surface elevation change data set captures both seasonal variability and long-term trends across the Greenland Ice Sheet. The flexible and fully data-driven 3D-ECM framework is directly transferable to other altimetry missions and multi-sensor records, offering a pathway toward continuous, long-term monitoring of ice-sheet elevation change across satellite generations.
Changes in active layer thickness (ALT) are used as an indicator of permafrost degradation. Increases in ALT can lead to increased greenhouse gas emissions, altered hydrology and ecology, ground instability, and a positive climate feedback. Quantifying ALT spatial heterogeneity remains challenging due to the influence of localized variations in terrain, microclimate, snow/soil properties, vegetation cover, and surface disturbances. It is also unclear how local ALT patterns and mechanisms (e.g., sub-meter to 10 m) scale up to broader landscape footprints (e.g., 10 to 1000 m) represented from global satellite observations and Earth system models. We assessed ALT spatial heterogeneity in the Arctic-foothills tundra within the North Slope of Alaska through intensive field sampling over four 90 m × 90 m plots, combined with multi-source remote sensing and machine learning (ML). Analysis using field observations and ML revealed that vegetation, surface wetness, subsurface rocks, and micro-topography exert strong influence on 5 m ALT variations, whereas terrain controls dominate (∼65 % contribution) at coarser 10 m spatial resolution. By leveraging centimeter-level optical-infrared drone imagery, we further generated 0.1 m ALT maps over a larger 5 km × 5 km region and examined ALT scaling effects. Our analysis showed a quadratic relationship in resolution-dependent uncertainties, characterized by a rapid increase in uncertainties at the sub-meter level (e.g., RMSE normalized by the standard deviation of 0.1 m ALT climbed by ∼10 %), followed by another 10 % increase from 1 to 30 m resolution, and a more conservative error increase (∼5 %) from 30 to 1000 m resolution. Our study allows for improved interpretation of remote sensing and process-based ALT simulations for the changing Arctic by clarifying resolution-dependent uncertainties and underlying mechanisms.
Glacier-permafrost interactions significantly influenced geomorphological processes in several glacier forefields in the European Alps during the Little Ice Age glacier advances. The resulting landforms, thrust moraine complexes, and glacier-forefield-connected rock glaciers, no longer show any active glacier-permafrost interaction today, but their internal structure – e.g., incorporated sedimentary ice – and recent morphodynamics are still influenced by the former interaction. Since these landforms are highly sensitive to changes in external climatic conditions due to their high ice content, it is essential to understand the relationships between underground structures and surface morphodynamics in order to assess landscape development under the influence of climate change. This study investigates the internal structure (e.g., ground ice distribution and characteristics) and surface morphodynamics (kinematic behavior) of both landform types aiming to determine the relationship between both. We combined electrical resistivity tomography (ERT) for assessing subsurface resistivity with Differential Interferometric Synthetic Aperture Radar (DInSAR) to derive surface displacement patterns. The study focuses on three glacier forefields in two valleys in the Valais region (Swiss Alps), analyzing spatial movement patterns and their correlation with subsurface properties through regression analysis. ERT revealed distinct differences between the ice-rich thrust moraine complexes and the more heterogeneous internal structure of the investigated rock glaciers. DInSAR-derived displacement patterns showed that the investigated moraine complexes exhibit predominantly vertical subsidence with high seasonal variability, while the rock glaciers display more consistent horizontal movement. Regression analysis confirmed strong correlations between high-resistivity zones and surface movement rates in thrust moraine complexes, with the maximum electrical resistivity of the subsurface correlating with absolute horizontal displacement (R2=0.75) and elevation change (R2=0.76). Instead, rock glaciers exhibited weaker correlations (R2≤0.3), likely due to heterogeneous internal structures and more complex creep processes, which differ from the subsidence-dominated movements in the ice-rich moraines. These findings underscore the importance of distinguishing between thrust moraines and rock glaciers in permafrost studies and climate change assessments, since the different landform types might react morphologically differently to changing climate conditions.
Alpine glaciers are retreating rapidly and have a potential for near complete ice loss at the end of the 21st century thus accurate glacier evolution models are crucial for predicting the magnitude and rate of future glacier changes. Without reliable ice thickness assessments, such models lack credibility and cannot be validated, thus here we evaluate several ice thickness models and present new ground-penetrating radar (GPR) ice-thickness measurements of the Hintereisferner – a temperate glacier located in the Ötztal Alps, Austria, which despite being one of 60 WGMS reference glaciers lacks up-to date measured ice thickness data. The GPR data is characterized by strong signal scattering, typical for temperate ice with high water content, however the glacier bed is detectable in most profiles. GPR measurements reveal a maximum ice thickness of ∼162 m along the central flowline and a mean thickness of 81.1 ± 37.4 m (±1 standard deviation) across the surveyed area. We further select three widely used, open-source ice-thickness models, GlabTop2, OGGM, and Millan et al. (2022), and compare their output to the GPR-derived ice thickness. All models systematically overestimate ice thickness across the surveyed area, with mean positive biases (±1σ) of 41 ± 36 m for GlabTop2, 49 ± 38 m for OGGM, and 46 ± 57 m for Millan et al. (2022), while only minor and localized underestimation occurs along the central flowline. These results highlight the limitations of predominantly geometry-based and velocity-informed modelling approaches when applied to small, temperate valley glaciers, where ice rheology and basal conditions may have greater influence on the resulting thickness than these algorithms allow.
To improve the accuracy and timeliness of glacier surface-velocity retrieval in complex mountain terrain, we develop a high-spatial-resolution fusion method combining Landsat, Sentinel-1/2, and UAV (Unmanned Aerial Vehicle) data, and produce monthly velocity products for the Kangri Karpo region for 2015-2024. Compared with existing large-area public datasets, the products offer markedly higher spatial resolution and better detection of small mountain glaciers; relative to single-sensor inputs prior to fusion, the valid-pixel ratio increases by similar to 50 %, the average number of valid months per pixel over the decade rises by similar to 50, and spatial smoothness improves - demonstrating the method's suitability for rugged terrain. Spatially, velocities follow the canonical "fast center, slow margins" pattern, with multi-year maxima >700 myr(-1) and values in lower reaches and most tributaries generally <100 myr(-1). Attribute analysis indicates significant correlations between velocity and area, slope, and aspect: larger glaciers flow faster overall; within individual glaciers, velocity responds more strongly to slope; and, with similar area and slope, south-facing glaciers are slightly faster than north-facing ones. Temporally, the intra-annual series shows clear seasonality, with peaks at the beginning and end of the melt season and sustained high speeds throughout. At the interannual scale, most pixelwise decadal trends lie within -36.5 to +36.5 myr(-1) per decade (overall subdued change), and the median trend is slightly positive, indicating weak regional acceleration; similar to 38.3 % of glaciers accelerate significantly, 25.5 % decelerate significantly, and 36.2 % show no significant trend (p >= 0.05). By aspect, significant acceleration is concentrated on south- and west-facing glaciers, whereas significant deceleration occurs mainly on east- and north-facing glaciers. Month-resolved trends indicate acceleration primarily in April-May (similar to 54.7-73.0 myr(-1) per decade), likely linked to enhanced meltwater input from an advanced melt season, and deceleration concentrated in July-August (<=-54.7 myr(-1) per decade), plausibly associated with intensified mass deficit.
An accurate representation of the land surface is essential for simulating the exchange of energy, water and carbon between the land and the atmosphere. This study evaluates the impact of land cover (LC) representation on snow simulations in the Interactions Between Soil, Biosphere and Atmosphere (ISBA) land surface model in Europe between 2010 and 2022. The study employs the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 atmospheric forcing dataset. Offline simulation experiments were conducted using two different versions of the model to prescribe LC. The most recent version uses the latest LC data from the European Space Agency's (ESA) Climate Change Initiative (CCI). The model's ability to reproduce snow dynamics was evaluated through a comparison of the simulations with ESA CCI satellite snow water equivalent (SWE) and land surface temperature (LST) retrievals and ERA5 snow analyses. The ERA5 analysis shows the highest level of agreement with satellite-derived SWE at the domain scale. On average, both the ERA5 and ISBA simulations tend to overestimate SWE compared to the CCI SWE. However, it is also possible that the CCI SWE product underestimates the actual SWE. This bias is particularly large during the warm winter of 2020, while the scaled SWE anomalies are comparable to those observed by ESA CCI and ERA5. Using ESA CCI LC data reduces the ISBA SWE bias by around 23 %, with this reduction being observed over most of the domain. Further analysis of LC transitions shows that the reduction in SWE bias is driven primarily by a few vegetation changes, particularly the transition from grasslands to forests. Changes in SWE are also found in areas where the dominant LC remains unchanged. This indicates that modifications in sub-grid vegetation fractions can affect snow-vegetation interactions and SWE bias. The updated LC has a very limited impact on the model's performance for land surface temperature, indicating that the impact of LC updates is more noticeable for SWE than for LST. These findings emphasise the importance of accurate land cover data for improving snow representation in land surface models and highlight the need for updated vegetation information in future snow-related applications.
We report a two-year record (March 2024–February 2026) of Micro Rain Radar (MRR) observations at Great Wall Station in the Antarctic Peninsula region. After quality control, near-surface precipitation and snowfall were identified during 32 % and 21 % of the observation time, respectively. Median snowfall radar reflectivity is approximately 9 dBZ and exhibits minimal variation with height, likely due to frequent shallow snowfall. Strong winds are associated with reduced low-level median radar reflectivity below approximately 1 km. We further developed a localized Ze (equivalent radar reflectivity) – S (snowfall rate) parameterization and identified systematic underestimation of cumulative snowfall profiles in ERA5 products.
Using drone and GNSS surveys, we updated the geodetic mass balance of Glacier AX010. This glacier has the oldest observational record in the Nepal Himalayas, showing mass loss rates of −1.2 mw.e.a-1 over the last 15 years (2008–2023). We reconstructed 80 years of annual mass balance using a mass-balance model forced by calibrated reanalysis data. While rising temperatures drive shrinkage, changes in precipitation have neither accelerated nor mitigated mass loss. The glacier began losing mass in the early 1970s, accelerated in the early 2000s, and is projected to disappear within one to two decades.
The future behavior of the Antarctic Ice Sheet is considered to be one of the largest uncertainties in global climate projections, with its stability fundamentally governed by grounding-zone processes, bed geometry, and sensitivity to oceanic forcing. However, observational records only reflect a short moment when considering the length of a full cycle of ice sheet expansion and retreat. Therefore, paleo-data present a valuable extension to the observational period. East Antarctica's deglaciation history remains largely understudied compared to the West Antarctic margin. This emphasizes the urgent need for reliable long-term spatiotemporal data on ice sheet change, particularly for sectors that play key roles in supplying the world's oceans with dense bottom water. In this study, we performed a multi-proxy analysis on geophysical and geological data recovered from two prominent glacial cross-shelf troughs on the Mac. Robertson continental shelf. We classified submarine glacial landforms on the continental shelf along both troughs from combined multibeam swath bathymetry and sub-bottom profiler data to infer past grounding line extent and the pattern of subsequent grounding line retreat. Additionally, combined sedimentological, sediment-physical, and geochemical analyses, including foraminifer radiocarbon dating, reveal the style and timing of this retreat across the shelf. Our study concludes that grounded ice reached the Mac. Robertson continental shelf break until just before similar to 12.7 cal. ka BP, hence preventing the formation of Dense Shelf Water (DSW) in its current form. We therefore infer a different formation mechanism for DSW as an important precursor of Antarctic Bottom Water under such full glacial conditions before continued grounding line retreat exposed the middle continental shelf by similar to 10.8 cal. ka BP and set the stage for more modern-like conditions.
