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To examine the influence of global warming including increased heavy snowfall frequency on the potential of natural dry snow avalanche frequency and magnitude, we estimated the frequency of weak layer formation and the associated slab overload above the weak layer over northern Japan. The estimation was numerically performed using climate models' output for 1800 winter simulations in each of the historical (1951–2010) and +4°C experiments by forcing a physical-based snowpack model with the result of the climate models. Here the +4°C experiment was defined as a climate when the global mean air temperature had increased by 4°C from the preindustrial level. The estimation results showed that the probability of weak layer formation, identified by the natural stability index, would decrease all over the area because of the shorter age of the weak layers caused by a warmer climate, indirectly indicating a potential decrease in avalanche frequency. However, because of increased heavy snowfall frequency, slab overload would increase by 10–15% in inland areas for weak layers of decomposing fragments/precipitation particles and the mountainous area facing the Sea of Japan for weak layers of facets/depth hoar, thereby potentially indicating an increased magnitude of avalanches.

To examine the influence of global warming including increased heavy snowfall frequency on the potential of natural dry snow avalanche frequency and magnitude, we estimated the frequency of weak layer formation and the associated slab overload above the weak layer over northern Japan. The estimation was numerically performed using climate models' output for 1800 winter simulations in each of the historical (1951–2010) and +4°C experiments by forcing a physical-based snowpack model with the result of the climate models. Here the +4°C experiment was defined as a climate when the global mean air temperature had increased by 4°C from the preindustrial level. The estimation results showed that the probability of weak layer formation, identified by the natural stability index, would decrease all over the area because of the shorter age of the weak layers caused by a warmer climate, indirectly indicating a potential decrease in avalanche frequency. However, because of increased heavy snowfall frequency, slab overload would increase by 10–15% in inland areas for weak layers of decomposing fragments/precipitation particles and the mountainous area facing the Sea of Japan for weak layers of facets/depth hoar, thereby potentially indicating an increased magnitude of avalanches. [ABSTRACT FROM AUTHOR]

Effective snow grain radius (re) is mapped at high resolution using near-infrared hyperspectral imaging (NIR-HSI). The NIR-HSI method can be used to quantify re spatial variability, change in re due to metamorphism, and visualize water percolation in the snowpack. Results are presented for three different laboratory-prepared snow samples (homogeneous, ice lens, fine grains over coarse grains), the sidewalls of which were imaged before and after melt induced by a solar lamp. The spectral reflectance in each ~3 mm pixel was inverted for re using the scaled band area of the ice absorption feature centered at 1030 nm, producing re maps consisting of 54 740 pixels. All snow samples exhibited grain coarsening post-melt as the result of wet snow metamorphism, which is quantified by the change in re distributions from pre- and post-melt images. The NIR-HSI method was compared to re retrievals from a field spectrometer and X-ray computed microtomography (micro-CT), resulting in the spectrometer having the same mean re and micro-CT having 23.9% higher mean re than the hyperspectral imager. As compact hyperspectral imagers become more widely available, this method may be a valuable tool for assessing re spatial variability and snow metamorphism in field and laboratory settings.

The response of snowpack to a +2°C global warming relative to the present climate was estimated in Hokkaido, Japan, using a physical snowpack model driven by dynamically downscaled (DDS) data, after model evaluation. The evaluation revealed that the snowpack model successfully reproduced the height of snow cover (HS), snow water equivalent (SWE) and snow-covered days (SCDs), but had a moderate bias in the thickness ratios of melt form (MF) and hoar category (HC). The DDS-forced simulation predicted that the seasonal-maximum HS and SWE would decrease by 30–40% in the southwestern and eastern parts of Hokkaido due to a large decrease in snowfall during the accumulation period, and that the HS and SWE in the north would decrease, albeit not significantly due to uncertain atmospheric forcing. The number of SCDs in Hokkaido was predicted to decline by ~30 d. Additionally, ~50% of snowpack thickness during a season would be MF in most areas, whereas HC would be

Full snow-pit observations were performed on a monthly basis over ten winter seasons from 1995 to 2004, at 15 study plots spaced at 100 m elevation intervals (1300–2700 m a.s.l.) in the mountainous forest of the Japanese Central Alps. We observed 514 pits with an average depth of 1.12 m. Density measurements were taken in 2610 snow layers in total. Monthly trends indicate that snow depth has a strong linear correlation with elevation and that the mean density of snow cover has a moderate linear correlation with elevation in midwinter. Snow water equivalent can increase as a quadratic function of elevation in January and February. For this reason, the influence of overburden load and wind packing is elevation-dependent from January to February, a period when a facet-prominent snowpack existed on account of low snow and air temperatures. The density of depth hoar is greater at higher elevations than it is for rounded grains in midwinter due to densification. On forested slopes, with increasing elevation, snowfall frequency and the impact of wind upon snow increases while air temperature decreases, causing elevational variance in grain shapes.

Effective snow grain radius (re) is mapped at high resolution using near-infrared hyperspectral imaging (NIR-HSI). The NIR-HSI method can be used to quantify re spatial variability, change in re due to metamorphism, and visualize water percolation in the snowpack. Results are presented for three different laboratory-prepared snow samples (homogeneous, ice lens, fine grains over coarse grains), the sidewalls of which were imaged before and after melt induced by a solar lamp. The spectral reflectance in each ~3 mm pixel was inverted for re using the scaled band area of the ice absorption feature centered at 1030 nm, producing re maps consisting of 54 740 pixels. All snow samples exhibited grain coarsening post-melt as the result of wet snow metamorphism, which is quantified by the change in re distributions from pre- and post-melt images. The NIR-HSI method was compared to re retrievals from a field spectrometer and X-ray computed microtomography (micro-CT), resulting in the spectrometer having the same mean re and micro-CT having 23.9% higher mean re than the hyperspectral imager. As compact hyperspectral imagers become more widely available, this method may be a valuable tool for assessing re spatial variability and snow metamorphism in field and laboratory settings. [ABSTRACT FROM AUTHOR]

The response of snowpack to a +2°C global warming relative to the present climate was estimated in Hokkaido, Japan, using a physical snowpack model driven by dynamically downscaled (DDS) data, after model evaluation. The evaluation revealed that the snowpack model successfully reproduced the height of snow cover (HS), snow water equivalent (SWE) and snow-covered days (SCDs), but had a moderate bias in the thickness ratios of melt form (MF) and hoar category (HC). The DDS-forced simulation predicted that the seasonal-maximum HS and SWE would decrease by 30–40% in the southwestern and eastern parts of Hokkaido due to a large decrease in snowfall during the accumulation period, and that the HS and SWE in the north would decrease, albeit not significantly due to uncertain atmospheric forcing. The number of SCDs in Hokkaido was predicted to decline by ~30 d. Additionally, ~50% of snowpack thickness during a season would be MF in most areas, whereas HC would be <50% all over Hokkaido. [ABSTRACT FROM AUTHOR]

The albedo of seasonal snow cover plays an important role in the global climate system due to its influence on Earth's radiation budget and energy balance. Volunteer Community Collaborative Rain, Hail, and Snow-Albedo (CoCoRaHS-Albedo) observers collected 3249 individual daily albedo, snow depth and density measurements using standardized techniques at dozens of sites across New Hampshire, USA over four winter seasons. The data show that albedo increases rapidly with snow depth up to ~0.14 m. Multiple linear regression models using snowpack age, snow depth or density, and air temperature provide reasonable approximations of surface snow albedo during times of albedo decay. However, the linear models also reveal systematic biases that highlight an important non-linearity in snow albedo decay. Modeled albedo values are reasonably accurate within the range of 0.6–0.9, but exhibit a tendency to overestimate lower albedo values and underestimate higher albedo values. We hypothesize that rapid reduction in high albedo fresh snow results from a decrease in snow specific surface area, while during melt-events the presence of liquid water in the snowpack accelerates metamorphism and grain growth. We conclude that the CoCoRaHS-Albedo volunteer observer network provides useful snow albedo, depth and density measurements and serves as an effective model for future measurement campaigns.

Snow metamorphism and settlement change the microstructure of a snowpack simultaneously. Past experiments investigated snow deformation under isothermal conditions. In nature, temperature gradient metamorphism and settlement often occur together. We investigated snow settlement in the first days after the onset of temperature-gradient metamorphism in laboratory experiments by means of in-situ time-lapse micro-computed tomography. We imposed temperature gradients of up to 95 K m−1 on samples of rounded snow with a density of ~230 kg m−3 and induced settlement by applying 1.7 kPa stress with a passive load on the samples simultaneously. We found that snow settled about half as fast when a temperature gradient was present, compared with isothermal conditions. The change in specific surface area after 4 days caused by temperature-gradient metamorphism was only a few percent. The viscosity evolution correlated with the amount of the temperature gradient. Finite element simulations of the snow samples revealed that stress-bearing chains had developed in the snow structure, causing the large increase in viscosity. We could show that a small change in microstructure caused a large change in the mechanical properties. This explains the difficulty of predicting snow mechanical properties in applications such as firn compaction or snow avalanche formation.

Full snow-pit observations were performed on a monthly basis over ten winter seasons from 1995 to 2004, at 15 study plots spaced at 100 m elevation intervals (1300–2700 m a.s.l.) in the mountainous forest of the Japanese Central Alps. We observed 514 pits with an average depth of 1.12 m. Density measurements were taken in 2610 snow layers in total. Monthly trends indicate that snow depth has a strong linear correlation with elevation and that the mean density of snow cover has a moderate linear correlation with elevation in midwinter. Snow water equivalent can increase as a quadratic function of elevation in January and February. For this reason, the influence of overburden load and wind packing is elevation-dependent from January to February, a period when a facet-prominent snowpack existed on account of low snow and air temperatures. The density of depth hoar is greater at higher elevations than it is for rounded grains in midwinter due to densification. On forested slopes, with increasing elevation, snowfall frequency and the impact of wind upon snow increases while air temperature decreases, causing elevational variance in grain shapes. [ABSTRACT FROM AUTHOR]

The albedo of seasonal snow cover plays an important role in the global climate system due to its influence on Earth's radiation budget and energy balance. Volunteer Community Collaborative Rain, Hail, and Snow-Albedo (CoCoRaHS-Albedo) observers collected 3249 individual daily albedo, snow depth and density measurements using standardized techniques at dozens of sites across New Hampshire, USA over four winter seasons. The data show that albedo increases rapidly with snow depth up to ~0.14 m. Multiple linear regression models using snowpack age, snow depth or density, and air temperature provide reasonable approximations of surface snow albedo during times of albedo decay. However, the linear models also reveal systematic biases that highlight an important non-linearity in snow albedo decay. Modeled albedo values are reasonably accurate within the range of 0.6–0.9, but exhibit a tendency to overestimate lower albedo values and underestimate higher albedo values. We hypothesize that rapid reduction in high albedo fresh snow results from a decrease in snow specific surface area, while during melt-events the presence of liquid water in the snowpack accelerates metamorphism and grain growth. We conclude that the CoCoRaHS-Albedo volunteer observer network provides useful snow albedo, depth and density measurements and serves as an effective model for future measurement campaigns. [ABSTRACT FROM AUTHOR]

Snow metamorphism and settlement change the microstructure of a snowpack simultaneously. Past experiments investigated snow deformation under isothermal conditions. In nature, temperature gradient metamorphism and settlement often occur together. We investigated snow settlement in the first days after the onset of temperature-gradient metamorphism in laboratory experiments by means of in-situ time-lapse micro-computed tomography. We imposed temperature gradients of up to 95 K m−1 on samples of rounded snow with a density of ~230 kg m−3 and induced settlement by applying 1.7 kPa stress with a passive load on the samples simultaneously. We found that snow settled about half as fast when a temperature gradient was present, compared with isothermal conditions. The change in specific surface area after 4 days caused by temperature-gradient metamorphism was only a few percent. The viscosity evolution correlated with the amount of the temperature gradient. Finite element simulations of the snow samples revealed that stress-bearing chains had developed in the snow structure, causing the large increase in viscosity. We could show that a small change in microstructure caused a large change in the mechanical properties. This explains the difficulty of predicting snow mechanical properties in applications such as firn compaction or snow avalanche formation. [ABSTRACT FROM PUBLISHER]

Understanding physical processes in near-zero accumulation areas can help us to better understand polar ice-core records, particularly during periods when accumulation rates were lower than today. We report measurements from a 5m firn core from the Allan Hills, Antarctica, which include physical properties using computer tomography, stable isotope ratios δD and δ18O, and 210Pb activity. The core shows a highly metamorphosed firn with homogeneous and stable structure, but with discrete layers near the surface. The observed firn structure is caused by a combination of unique depositional and post-depositional processes. The irregular dD and d18O signal does not follow the stratigraphic sequence and implies post-depositional modification caused by microscopic pressure gradients in the firn that can result from either forced ventilation over rough surfaces in the presence of wind or alternating temperature-gradients between the firn and the atmosphere. Our results also indicate impact snow deposition under high winds and with a high initial density and air exchange between the atmosphere and the snowpack. 210Pb activity below 0.3m falls below the detection limit, implying that most of the core is more than 100 years old. We conclude that the Allan Hills record provides a unique opportunity to investigate important processes that would have affected ice-core records from glacial periods. [ABSTRACT FROM AUTHOR]

Up to now, snow cover on Antarctic sea ice and its impact on radar backscatter, particularly after the onset of freeze/thaw processes, are not well understood. Here we present a combined analysis of in situ observations of snow properties from the landfast sea ice in Atka Bay, Antarctica, and high-resolution TerraSAR-X backscatter data, for the transition from austral spring (November 2012) to summer (January 2013). The physical changes in the seasonal snow cover during that time are reflected in the evolution of TerraSAR-X backscatter. We are able to explain 76-93% of the spatio-temporal variability of the TerraSAR-X backscatter signal with up to four snowpack parameters with a root-mean-squared error of 0.87-1.62 dB, using a simple multiple linear model. Over the complete study, and especially after the onset of early-melt processes and freeze/thaw cycles, the majority of variability in the backscatter is influenced by changes in snow/ice interface temperature, snow depth and top-layer grain size. This suggests it may be possible to retrieve snow physical properties over Antarctic sea ice from X-band SAR backscatter. [ABSTRACT FROM AUTHOR]

We developed a Handheld Integrating Sphere Snow Grain Sizer (HISSGraS) for field use to measure the specific surface area (SSA) of snow. In addition to snow samples, HISSGraS can directly measure snow surfaces and snow pit walls. The basic measurement principle is the same as that of the IceCube SSA instrument. The retrieval algorithm for SSA from reflectance employs two conversion equations formulated using spherical and nonspherical grain shape models. We observed SSAs using HISSGraS, IceCube and the gas adsorption method in a snowfield in Hokkaido, Japan. Intercomparison of the results confirmed that with HISSGraS direct measurement, SSA profile observations can be completed in just ~1/10 the time required for measurement of snow samples. Our results also suggest that HISSGraS and IceCube have similar accuracy when the same snow samples are measured using the same grain shape model. However, SSAs of near-surface snow layers measured using the three techniques exhibited some biases, possibly due to rapid snow metamorphism or melting during measurement and some technical issues with optical techniques. When excluding SSA data for the surface layer, which metamorphosed remarkably during measurement, IceCube- and HISSGraS-derived SSAs correlated strongly with those obtained by gas adsorption and HISSGraS accuracy is 21–34%.