Snow avalanches are a major natural hazard, endangering human life and infrastructure in mountainous areas throughout the world. In many countries with seasonally snow‐covered mountains, avalanche‐forecasting services reliably warn the public by issuing occurrence probabilities for a certain region. However, at present, a single avalanche event cannot be predicted in time and space. Much about the release process remains unknown, mainly because of the highly variable, layered character of the snowpack, a highly porous material that exists close to its melting point. The complex interaction between terrain, snowpack, and meteorological conditions leading to avalanche release is commonly described as avalanche formation. It is relevant to hazard mapping and essential to short‐term forecasting, which involves weighting many contributory factors. Alternatively, the release process can be studied and modeled. This approach relies heavily on snow mechanics and snow properties, including texture. While the effect of meteorological conditions or changes on the deformational behavior of snow is known in qualitative or semiquantitative manner, the knowledge of the quantitative relation between snow texture and mechanical properties is limited, but promising developments are under way. Fracture mechanical models have been applied to explain the fracture propagation, and micromechanical models including the two competing processes (damage and sintering) have been applied to explain snow failure. There are knowledge gaps between the sequence of processes that lead to the release of the snow slab: snow deformation and failure, damage accumulation, fracture initiation, and fracture propagation. Simultaneously, the spatial variability that affects damage, fracture initiation, and fracture propagation has to be considered. This review focuses on dry snow slab avalanches and shows that dealing with a highly porous media close to its melting point and processes covering several orders of scale, from the size of a bond between snow grains to the size of a mountain slope, will continue to be very challenging.

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

The mean snow depth, the duration of continuous snow cover and the number of snowfall days in the Swiss Alps all show very similar trends during the observation period 1931–99: a gradual increase until the early 1980s (with insignificant interruptions during the late 1950s and early 1970s) followed by a statistically significant decrease towards the end of the century. Regional and altitudinal variations are large; high altitudes show only slight changes, and the trends become more pronounced at mid and low altitudes. At any particular time the southern part of the Alps often has different conditions than the north. Shorter snow duration is mainly caused by earlier snow melting in spring than by later first snowfalls in autumn.Trends for heavy snowfall events are somewhat different: at elevations above 1300 m a.s.l. a very weak increasing trend towards heavier snowfalls has persisted since the 1960s, and only low altitudes below 650 m a.s.l. show a marked drop since the early 1980s, indicating that heavy winter precipitation to an increasing degree falls in the form of rain instead of snow.A literature review confirms that, throughout the temperate and subpolar Northern Hemisphere, a similar general pattern of temporal snow variations occurred during the 20th century. Copyright © 2003 Royal Meteorological Society

Precise measurements of snow structural parameters are crucial to understand the formation of snowpacks by deposition and metamorphism and to characterize the stratigraphy for many applications and remote sensing in particular. The area‐wide acquisition of structural parameters at high spatial resolution from state‐of‐the‐art methods is, however, still cumbersome, since the time required for a single profile is a serious practical limitation. As a remedy we have developed a statistical model to extract three major snow structural parameters: density, correlation length, and specific surface area (SSA) solely from the SnowMicroPen (SMP), a high‐resolution penetrometer, which allows a meter profile to be measured with millimeter resolution in less than 1 min. The model was calibrated by combining SMP data with 3‐D microstructural data from microcomputed tomography which was used to reconstruct full‐depth snow profiles from different snow climates (Alpine, Arctic, and Antarctic). Density, correlation length, and SSA were derived from the SMP with a mean relative error of 10.6%, 16.4%, and 23.1%, respectively. For validation, we compared the density and SSA derived from the SMP to traditional measurements and near‐infrared profiles. We demonstrate the potential of our method by the retrieval of a two‐dimensional stratigraphy at Kohnen Station, Antarctica, from a 46 m long SMP transect. The result clearly reveals past depositional and metamorphic events, and our findings show that the SMP can be used as an objective, high‐resolution tool to retrieve essential snow structural parameters efficiently in the field.

The specific surface area (SSA) is considered an essential microstructural parameter for the characterization of snow. Photography in the near-infrared (NIR) spectrum is sensitive to the SSA. We calculated the snow reflectance from calibrated NIR images of snow-pit walls and measured the SSA of samples obtained at the same locations. This new method is used to map the snow stratigraphy. The correlation between reflectance and SSA was found to be 90%. Calibrated NIR photography allows quantitative determination of SSA and its spatial variation in a snow profile in two dimensions within an uncertainty of 15%. In an image covering 0.5–1.0 m², even layers of 1mm thickness can be documented and measured. Spatial maps of SSA are an important tool in initializing and validating physical and chemical models of the snowpack.