Glacies, Volume 2, Issue 4 (December 2025) – 6 articles

Convection associated with the leading mode of subseasonal variability of the tropical atmosphere, the Madden–Julian Oscillation (MJO), can excite Rossby wave trains that extend well into the extratropics and allow the MJO to modulate many components of the Earth system. To improve our understanding of teleconnections between the MJO and Antarctic sea ice, composite anomalies of daily change in sea ice concentration (ΔSIC) from 1989 to 2019 were binned by phase 0–20 days after an active MJO and compared to anomalies of surface air temperature, the meridional component of surface wind, and sea-level pressure. In May, ΔSIC anomalies were strongest in the Indian Ocean (IO) sector, 16 days after phase 8. There, a wavenumber-three pattern in sea-level pressure anomalies associated with the MJO resulted in anomalously poleward winds and warmer temperatures over the central and eastern IO that were collocated with anomalously negative ΔSIC. Furthermore, anomalously equatorward winds and colder temperatures in the western IO were collocated with anomalously positive ΔSIC. In July, ΔSIC anomalies were strongest in the Weddell Sea (WS) sector nine days after an active MJO in phase 2. There, a wavenumber-three pattern in sea-level pressure anomalies resulted in anomalously poleward winds and warmer temperatures over the western and central WS that were collocated with negative ΔSIC anomalies; anomalously equatorward winds and colder temperatures over the eastern WS were collocated with positive ΔSIC anomalies. In September, the largest ΔSIC anomalies were observed in the IO and WS sectors six days after an active MJO in phase 8. No meaningful modulation of sea ice anomalies was found after an active MJO in November or January. These results extend our understanding of teleconnections between the MJO and Antarctic sea ice on the subseasonal time scale. Full article

Accurate assessment of compacted snow strength is critical for ensuring the safety and performance of snow runways in cold environments. The Russian Snow Penetrometer (RSP) is widely used in snow science and engineering due to its simplicity, portability, and capability for rapid field measurements under extreme conditions. Conversely, the California Bearing Ratio (CBR) test remains the benchmark for evaluating the load-bearing capacity of conventional granular materials but is seldom applied to snow because of logistical constraints and the material’s complex mechanical behavior. The relationship between these two pavement evaluation tools remains poorly defined. This work investigates how RSP strength indices relate to CBR measurements to determine whether the RSP can serve as a practical proxy for snow pavement load-bearing capacity. Side-by-side field measurements of snow pavement strength were collected over a 30 h period at two test section locations. Both methods captured temporal strength increases and spatial variability, with consistently higher values at the second site attributed to extended sintering. A moderate linear correlation (R² = 0.44) between RSP and CBR results supports a quantifiable relationship between the two methods. These findings begin to bridge the gap between conventional pavement testing and snow-specific strength evaluation, demonstrating the potential of the RSP for rapid assessment of snow runways. Continued data collection and analysis will refine this relationship and strengthen its applicability for operational use. Full article

Snow construction plays a crucial role in military operations in cold regions, providing tactical fortifications, thermal insulation, and emergency infrastructure in environments where conventional building materials are scarce or require extensive infrastructure for support. Current snow construction methods, including manual piling and compaction, are labor-intensive and inconsistent, limiting their use in large-scale or time-sensitive operations. This study explores the feasibility of adapting a compressed earth block (CEB) machine to produce compressed snow blocks (CSBs) as modular, uniform building units for cold-region applications. Using an AECT Impact 2001A hydraulic press, naturally occurring snow was processed with a snowblower and compacted at maximum operating pressure (i.e., 20,684 kPa) to evaluate block formation, dimensional consistency, and density. The machine successfully produced relatively consistent CSBs, but the initial 3–4 blocks following block height adjustment were generally unsuccessful (e.g., incorrect block height or collapsed/broke) while the machine reached its steady state cyclic condition. These blocks were discarded and excluded from the dataset. The successful CSBs had mean block heights of 7.76 ± 0.56 cm and densities comparable to ice (i.e., 0.83 g/cm³). Variations in block height and mass may be attributed to manual snow loading and minor material impurities. While the dataset is limited, the results warrant further investigation into this technology, particularly regarding CSB strength (i.e., hardness and compressive strength) and performance under variable snow and environmental conditions. Mechanized snow compaction using existing CEB technology is technically feasible and capable of producing uniform, structurally stable CSBs but requires further investigation and modifications to reach its full potential. With design improvements such as automated snow feeding, cold-resistant components, and system winterization, this approach could enable scalable CSB production for rapid, on-site construction of snow-based structures in Arctic environments, supporting the military and civilian needs. Full article

Glacial lakes in the Upper Indus Basin (UIB) are rapidly evolving due to accelerated glacier retreat driven by climate change. Here we present a comprehensive inventory of glacial lakes using Sentinel-1 SAR data with adaptive backscatter thresholding, enabling consistent detection under challenging conditions and improving delineation accuracy. In August 2023, we identified 6019 glacial lakes at scales from 0.001 to 5.80 km², covering a cumulative area of 266 km² (~0.06% of the basin). Although more than 90% of the lakes are smaller than 0.1 km², large lakes (>0.1 km²) account for over 57% of the total lake area. Most lakes are concentrated between 4000 and 4600 m, coinciding with the main glacierized zone. Regional patterns reveal that the Hindu Kush and Himalayas are dominated by glacier erosion lakes (GELs) and moraine-dammed lakes (MDLs), reflecting widespread glacier retreat, whereas the Karakoram is characterized by numerous supraglacial lakes (SGLs) associated with extensive debris-covered glaciers. Compared to previous optical-based inventories, our SAR-based approach captures more lakes and better represents small and transient features such as SGLs. These findings provide a more accurate baseline for assessing cryospheric change and glacial lake hazards in one of the world’s most heavily glacierized basins. Full article

With ongoing climate transformations, reliable Arctic sea ice forecasts are essential for understanding impacts on shipping, ecosystems, and climate teleconnections. This research examines physics-free neural architectures versus physics-informed statistical models for long-term Arctic projections by implementing Fourier Neural Operator (FNO) and Convolutional Neural Network (CNN) alongside a seasonal SARIMAX time series model incorporating physical predictors including temperature anomalies and ice thickness. We test whether neural models trained on historical ice data can match physics-informed SARIMAX reliability, and whether approaches exhibit systematic biases toward specific emission pathways. Using data from January 1979 to December 2024, we conducted forecasts through 2100, with SARIMAX driven by CMIP6 sea ice thickness under SSP2-4.5 and SSP5-8.5 scenarios. Results decisively reject the first hypothesis: both neural models projected ice free Arctic summer by September 2089 regardless of emission scenario, while SARIMAX maintained physically plausible seasonal coverage throughout the century under both pathways. Neural approaches demonstrated systematic bias toward extreme warming exceeding even high-emission projections, revealing fundamental limitations in physics-free deep learning for climate forecasting where physical constraints are paramount. Full article

A previous analysis of Antarctic acoustic data relevant to quantifying frazil contributions to sea ice accretion is reconsidered to address inconsistencies with river frazil results acquired with similar instrumentation but augmented to suppress instrument icing. It was found that sound attenuation by consequent icing limited credible Antarctic acoustic frazil measurements to afternoon and early evening periods, which are shown to encompass daily minimums in frazil production. This reality was masked by use of an unvalidated liquid oblate spheroidal frazil characterization model, which greatly overestimated frazil concentrations. Much lower frazil contents were derived for these periods using a robust 2-frequency characterization algorithm, which incorporated a validated, alternative theory of scattering by elastic solid spheres. Physical arguments based on these results and instrument depth data were strongly suggestive of maximal but, currently, unquantified frazil presences during unanalyzed heavily iced late evening and morning time periods. Full article