[1] In mountain regions wind is known to cause snow redistribution. While physically based models of snow redistribution have been developed for flat to gently rolling terrain, extension of these findings to steep terrain has been limited by the complexity of wind fields in such areas. In this study, we applied a nonhydrostatic and compressible atmospheric prediction model to steep alpine topography and compared the results to a fully distributed data set of snow depth estimations. The results show reduced horizontal wind velocity as well as an increasing downward vertical wind velocity over areas with the largest winter accumulation, which are mostly glacierized. We show that the wind velocity normal to the local surface, which should be zero in a nondivergent flow field and is a direct measure of increased or decreased local deposition, is a function of small-scale features of local topography. The correlation between wind fields, snow accumulation, and glacierization suggests that accurate modeling of wind fields over glacierized areas in complex terrain is a key factor for understanding the mass balance distribution of glaciers.Citation: Dadic, R., R. Mott, M. Lehning, and P. Burlando (2010), Wind influence on snow depth distribution and accumulation over glaciers,

Abstract. The spatial distribution of alpine snow covers is characterised by large variability. Taking this variability into account is important for many tasks including hydrology, glaciology, ecology or natural hazards. Statistical modelling is frequently applied to assess the spatial variability of the snow cover. For this study, we assembled seven data sets of high-resolution snow-depth measurements from different mountain regions around the world. All data were obtained from airborne laser scanning near the time of maximum seasonal snow accumulation. Topographic parameters were used to model the snow depth distribution on the catchment-scale by applying multiple linear regressions. We found that by averaging out the substantial spatial heterogeneity at the metre scales, i.e. individual drifts and aggregating snow accumulation at the landscape or hydrological response unit scale (cell size 400 m), that 30 to 91 % of the snow depth variability can be explained by models that are calibrated to local conditions at the single study areas. As all sites were sparsely vegetated, only a few topographic variables were included as explanatory variables, including elevation, slope, the deviation of the aspect from north (northing), and a wind sheltering parameter. In most cases, elevation, slope and northing are very good predictors of snow distribution. A comparison of the models showed that importance of parameters and their coefficients differed among the catchments. A "global" model, combining all the data from all areas investigated, could only explain 23 % of the variability. It appears that local statistical models cannot be transferred to different regions. However, models developed on one peak snow season are good predictors for other peak snow seasons.

Spectral albedo was measured along a 6 km transect near the Allan Hills in East Antarctica. The transect traversed the sequence from new snow through old snow, firn, and white ice, to blue ice, showing a systematic progression of decreasing albedo at all wavelengths, as well as decreasing specific surface area (SSA) and increasing density. Broadband albedos under clear‐sky range from 0.80 for snow to 0.57 for blue ice, and from 0.87 to 0.65 under cloud. Both air bubbles and cracks scatter sunlight; their contributions to SSA were determined by microcomputed tomography on core samples of the ice. Although albedo is governed primarily by the SSA (and secondarily by the shape) of bubbles or snow grains, albedo also correlates highly with porosity, which, as a proxy variable, would be easier for ice sheet models to predict than bubble sizes. Albedo parameterizations are therefore developed as a function of density for three broad wavelength bands commonly used in general circulation models: visible, near‐infrared, and total solar. Relevance to Snowball Earth events derives from the likelihood that sublimation of equatorward‐flowing sea glaciers during those events progressively exposed the same sequence of surface materials that we measured at Allan Hills, with our short 6 km transect representing a transect across many degrees of latitude on the Snowball ocean. At the equator of Snowball Earth, climate models predict thick ice, or thin ice, or open water, depending largely on their albedo parameterizations; our measured albedos appear to be within the range that favors ice hundreds of meters thick.

The albedo and transmittance of melting, first‐year Arctic sea ice were measured during two cruises of the Impacts of Climate on the Eco‐Systems and Chemistry of the Arctic Pacific Environment (ICESCAPE) project during the summers of 2010 and 2011. Spectral measurements were made for both bare and ponded ice types at a total of 19 ice stations in the Chukchi and Beaufort Seas. These data, along with irradiance profiles taken within boreholes, laboratory measurements of the optical properties of core samples, ice physical property observations, and radiative transfer model simulations are employed to describe representative optical properties for melting first‐year Arctic sea ice. Ponded ice was found to transmit roughly 4.4 times more total energy into the ocean, relative to nearby bare ice. The ubiquitous surface‐scattering layer and drained layer present on bare, melting sea ice are responsible for its relatively high albedo and relatively low transmittance. Light transmittance through ponded ice depends on the physical thickness of the ice and the magnitude of the scattering coefficient in the ice interior. Bare ice reflects nearly three‐quarters of the incident sunlight, enhancing its resiliency to absorption by solar insolation. In contrast, ponded ice absorbs or transmits to the ocean more than three‐quarters of the incident sunlight. Characterization of the heat balance of a summertime ice cover is largely dictated by its pond coverage, and light transmittance through ponded ice shows strong contrast between first‐year and multiyear Arctic ice covers.

ABSTRACT. Recognising the scarcity of glacier mass-balance data in the Southern Hemisphere, a massbalance measurement programme was started at Brewster Glacier in the Southern Alps of New Zealand in 2004. Evolution of the measurement regime over the 11 years of data recorded means there are differences in the spatial density of data obtained. To ensure the temporal integrity of the dataset a new geostatistical approach is developed to calculate mass balance. Spatial co-variance between elevation and snow depth allows a digital elevation model to be used in a co-kriging approach to develop a snow depth index (SDI). By capturing the observed spatial variability in snow depth, the SDI is a more reliable predictor than elevation and is used to adjust each year of measurements consistently despite variability in sampling spatial density. The SDI also resolves the spatial structure of summer balance better than elevation. Co-kriging is used again to spatially interpolate a derived mean summer balance index using SDI as a co-variate, which yields a spatial predictor for summer balance. The average glacier-wide surface winter, summer and annual balances over the period 2005-15 are 2484, −2586 and −102 mm w.e., respectively, with changes in summer balance explaining most of the variability in annual balance.

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.

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Abstract. High-resolution, well-dated climate archives provide an opportunity to investigate the dynamic interactions of climate patterns relevant for future projections.