Abstract:Maintaining long time series of observations of the Cryosphere is a key issue in climate research. Long observational time series involve problems due to change in methodology or observers. In order to extend time series and introduce new methods, careful comparisons must be made to ensure homogeneity in the observational data. We have compared an established method for snow grain-size observations used by the Abisko Scientific Research Station (ASRS) in northern Sweden, based on visual interpretation, with a … Show more
“… Fig. 6. Physical parameters retrieved from the snow pit in 2010: (a) temperature sampled by PT100; (b) density sampled manually by weighing samples with 3 cm increments (solid line) and using the snow fork with 3 cm resolution (dots); (c) electrical conductivity measurements using the snow fork with 3 cm resolution; (d) stratigraphy based on manual observation and (e) the particle sizes determined using the DSPP method (Ingvander and others, 2012); and (f) EC sampled with 8cm increments and analyzed in the OCEC instrument, and the concentrations calculated based on the sample volume.…”
ABSTRACT. We studied the variability of elemental carbon (EC) over 3 years in the winter snowpack of Storglaciären, Sweden. The goal of this study was to relate the seasonal variation in EC to specific snow accumulation events in order to improve understanding of how different atmospheric circulation patterns control the deposition of EC. Specifically, we related meteorological parameters (e.g. wind direction, precipitation) to snow physical properties, EC content, stable-isotope d d
18O ratios and anion concentrations in the snowpack. The distribution of EC in the snowpack varied between years. Low EC contents corresponded to a predominance of weather systems originating in the northwest, i.e. North Atlantic. Analysis of single layers within the snowpacks showed that snow layers enriched in heavy isotopes coincided predominantly with low EC contents but high chloride and sulfate concentration. Based on this isotopic and geochemical evidence, snow deposited during these events had a strong oceanic, i.e. North Atlantic, imprint. In contrast, snow layers with high EC content coincided with snow layers depleted in heavy isotopes but high anion concentrations, indicating a more continental source of air masses and origin of EC from industrial emissions.
“… Fig. 6. Physical parameters retrieved from the snow pit in 2010: (a) temperature sampled by PT100; (b) density sampled manually by weighing samples with 3 cm increments (solid line) and using the snow fork with 3 cm resolution (dots); (c) electrical conductivity measurements using the snow fork with 3 cm resolution; (d) stratigraphy based on manual observation and (e) the particle sizes determined using the DSPP method (Ingvander and others, 2012); and (f) EC sampled with 8cm increments and analyzed in the OCEC instrument, and the concentrations calculated based on the sample volume.…”
ABSTRACT. We studied the variability of elemental carbon (EC) over 3 years in the winter snowpack of Storglaciären, Sweden. The goal of this study was to relate the seasonal variation in EC to specific snow accumulation events in order to improve understanding of how different atmospheric circulation patterns control the deposition of EC. Specifically, we related meteorological parameters (e.g. wind direction, precipitation) to snow physical properties, EC content, stable-isotope d d
18O ratios and anion concentrations in the snowpack. The distribution of EC in the snowpack varied between years. Low EC contents corresponded to a predominance of weather systems originating in the northwest, i.e. North Atlantic. Analysis of single layers within the snowpacks showed that snow layers enriched in heavy isotopes coincided predominantly with low EC contents but high chloride and sulfate concentration. Based on this isotopic and geochemical evidence, snow deposited during these events had a strong oceanic, i.e. North Atlantic, imprint. In contrast, snow layers with high EC content coincided with snow layers depleted in heavy isotopes but high anion concentrations, indicating a more continental source of air masses and origin of EC from industrial emissions.
“…1b), and density and conductivity with 3 cm resolution (Sugiyama et al 2012). Snow particle size was analysed using the DSPP method (Ingvander et al 2012(Ingvander et al , 2013. The method is based on digital camera images (Canon EOS 350D) and object-oriented image analysis (Definiens Developer 7.0 software; Definiens 2008).…”
In this study, snow particle size variability was investigated along a transect in Dronning Maud Land from the coast to the polar plateau. The aim of the study was to better understand the spatial and temporal variations in surface snow properties. Samples were collected twice daily during a traverse in 2007-08 to capture regional variability. Local variability was assessed by sampling in 10 × 10 m grids (5 m spacing) at selected locations. The particle size and shape distributions for each site were analysed through digital image analysis. Snow particle size variability is complex at different scales, and shows an internal variability of 0.18-3.31 mm depending on the sample type (surface, grid or pit). Relationships were verified between particle size and both elevation and distance to the coast (moisture source). Regional seasonal changes were also identified, particularly on the lower elevations of the polar plateau. This dataset may be used to quantitatively analyse the optical properties of surface snow for remote sensing. The details of the spatial and temporal variations observed in our data provide a basis for further studies of the complex and coupled processes affecting snow particle size and the interpretation of remote sensing of snow covered areas.
“…The ASRS grain size observations are based on reference objects (flour, semolina, rice, peas, and nuts), or can be classified as flakes. In Ingvander et al (2011), a quantitative value for each class is given. Here, the original classification will be used (Table 1).…”
Section: Study Area and Methodsmentioning
confidence: 99%
“…The same was found for grain compactness category ''ice hard'' (C6, Table 1). In the following analysis R5 will denote observations of both very hard snow layers and ice layers, and The size estimates given for the ASRS grain size are described in Ingvander et al (2011). The snow layer hardness observations follow the a hand test described in Colbeck et al (1990) and Fierz et al (2009) similarly C6 will also include observations of ice.…”
A unique long term, 49-year record (divided into three time periods 1961-1976, 1977-1992, and 1993-2009) of snow profile stratigraphy from the Swedish sub Arctic, was analyzed with a focus on changes in snow characteristics. The data set contained grain size, snow layer hardness, grain compactness, and snow layer dryness, observed every second week during the winter season. The results showed an increase in very hard snow layers, with harder snow in early winter and more moist snow during spring. There was a striking increase in the number of observations with very hard snow at ground level over time. More than twice as many occasions with hard snow at ground level were observed between 1993 and 2009 compared to previous years, which may have a significant effect on plants and animals. The changes in snow characteristics are most likely a result of the increasing temperatures during the start and the end of the snow season.
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