Uptake of Hydrogen Peroxide from the Gas Phase to Grain Boundaries: A Source in Snow and Ice.

Hydrogen peroxide is a primary atmospheric oxidant significant in terminating gas-phase chemistry and sulfate formation in the condensed phase. Laboratory experiments have shown an unexpected oxidation acceleration by hydrogen peroxide in grain boundaries. While grain boundaries are frequent in natural snow and ice and are known to host impurities, it remains unclear how and to which extent hydrogen peroxide enters this reservoir. We present the first experimental evidence for the diffusive uptake of hydrogen peroxide into grain boundaries directly from the gas phase. We have machined a novel flow reactor system featuring a drilled ice flow tube that allows us to discern the effect of the ice grain boundary content on the uptake. Further, adsorption to the ice surface for temperatures from 235 to 258 K was quantified. Disentangling the contribution of these two uptake processes shows that the transfer of hydrogen peroxide from the atmosphere to snow at temperatures relevant to polar environments is considerably more pronounced than previously thought. Further, diffusive uptake to grain boundaries appears to be a novel mechanism for non-acidic trace gases to fill the highly reactive impurity reservoirs in snow's grain boundaries.

European In-Situ Snow Measurements: Practices and Purposes.

In-situ snow measurements conducted by European institutions for operational, research, and energy business applications were surveyed in the framework of the European Cooperation in Science and Technology (COST) Action ES1404, called "A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology, and numerical weather prediction". Here we present the results of this survey, which was answered by 125 participants from 99 operational and research institutions, belonging to 38 European countries. The typologies of environments where the snow measurements are performed range from mountain to low elevated plains, including forests, bogs, tundra, urban areas, glaciers, lake ice, and sea ice. Of the respondents, 93% measure snow macrophysical parameters, such as snow presence, snow depth (HS), snow water equivalent (SWE), and snow density. These describe the bulk characteristics of the whole snowpack or of a snow layer, and they are the primary snow properties that are needed for most operational applications (such as hydrological monitoring, avalanche forecast, and weather forecast). In most cases, these measurements are done with manual methods, although for snow presence, HS, and SWE, automatized methods are also applied by some respondents. Parameters characterizing precipitating and suspended snow (such as the height of new snow, precipitation intensity, flux of drifting/blowing snow, and particle size distribution), some of which are crucial for the operational services, are measured by 74% of the respondents. Parameters characterizing the snow microstructural properties (such as the snow grain size and shape, and specific surface area), the snow electromagnetic properties (such as albedo, brightness temperature, and backscatter), and the snow composition (such as impurities and isotopes) are measured by 41%, 26%, and 13% of the respondents, respectively, mostly for research applications. The results of this survey are discussed from the perspective of the need of enhancing the efficiency and coverage of the in-situ observational network applying automatic and cheap measurement methods. Moreover, recommendations for the enhancement and harmonization of the observational network and measurement practices are provided.

Microbial abundance and community structure in a melting alpine snowpack.

Snowmelt is a crucial period for alpine soil ecosystems, as it is related to inputs of nutrients, particulate matter and microorganisms to the underlying soil. Although snow-inhabiting microbial communities represent an important inoculum for soils, they have thus far received little attention. The distribution and structure of these microorganisms in the snowpack may be linked to the physical properties of the snowpack at snowmelt. Snow samples were taken from snow profiles at four sites (1930-2519 m a.s.l.) in the catchment of the Tiefengletscher, Canton Uri, Switzerland. Microbial (Archaea, Bacteria and Fungi) communities were investigated through T-RFLP profiling of the 16S and 18S rRNA genes, respectively. In parallel, we assessed physical and chemical parameters relevant to the understanding of melting processes. Along the snow profiles, density increased with depth due to compaction, while other physico-chemical parameters, such as temperature and concentrations of DOC and soluble ions, remained in the same range (e.g. <2 mg DOC L(-1), 5-30 µg NH4 (+)-N L(-1)) in all samples at all sites. Along the snow profiles, no major change was observed either in cell abundance or in bacterial and fungal diversity. No Archaea could be detected in the snow. Microbial communities, however, differed significantly between sites. Our results show that meltwater rearranges soluble ions and microbial communities in the snowpack.

A Database of Snow on Sea Ice in the Central Arctic Collected during the MOSAiC expedition.

Snow plays an essential role in the Arctic as the interface between the sea ice and the atmosphere. Optical properties, thermal conductivity and mass distribution are critical to understanding the complex Arctic sea ice system's energy balance and mass distribution. By conducting measurements from October 2019 to September 2020 on the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have produced a dataset capturing the year-long evolution of the physical properties of the snow and surface scattering layer, a highly porous surface layer on Arctic sea ice that evolves due to preferential melt at the ice grain boundaries. The dataset includes measurements of snow during MOSAiC. Measurements included profiles of depth, density, temperature, snow water equivalent, penetration resistance, stable water isotope, salinity and microcomputer tomography samples. Most snowpit sites were visited and measured weekly to capture the temporal evolution of the physical properties of snow. The compiled dataset includes 576 snowpits and describes snow conditions during the MOSAiC expedition.

Near-infrared digital photography to estimate snow correlation length for microwave emission modeling.

The study is based on experimental work conducted in alpine snow. We made microwave radiometric and near-infrared reflectance measurements of snow slabs under different experimental conditions. We used an empirical relation to link near-infrared reflectance of snow to the specific surface area (SSA), and converted the SSA into the correlation length. From the measurements of snow radiances at 21 and 35 GHz, we derived the microwave scattering coefficient by inverting two coupled radiative transfer models (the sandwich and six-flux model). The correlation lengths found are in the same range as those determined in the literature using cold laboratory work. The technique shows great potential in the determination of the snow correlation length under field conditions.

Reflectance Modeling for Real Snow Structures Using a Beam Tracing Model.

It is important to understand reflective properties of snow, for example for remote sensing applications and for modeling of energy balances in snow packs. We present a method with which we can compare reflectance measurements and calculations for the same snow sample structures. Therefore, we first tomograph snow samples to acquire snow structure images (6 x 2 mm). Second, we calculated the sample reflectance by modeling the radiative transfer, using a beam tracing model. This model calculates the biconical reflectance (BR) derived from an arbitrary number of incident beams. The incident beams represent a diffuse light source. We applied our method to four different snow samples: Fresh snow, metamorphosed snow, depth hoar, and wet snow. The results show that (i) the calculated and measured reflectances agree well and (ii) the model produces different biconical reflectances for different snow types. The ratio of the structure to the wavelength is large. We estimated that the size parameter is larger than 50 in all cases we analyzed. Specific surface area of the snow samples explains most of the difference in radiance, but not the different biconical reflectance distributions. The presented method overcomes the limitations of common radiative transfer models which use idealized grain shapes such as spheres, plates, needles and hexagonal particles. With this method we could improve our understanding for changes in biconical reflectance distribution associated with changes in specific surface area.

Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts.

Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region.