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  • PANGAEA  (367)
  • Copernicus
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  • 1
    Publication Date: 2017-07-06
    Description: This Article reports results from a field experiment investigating the influence of volcanic tephra coverage on glacier ablation. These influences are known to be significantly different from those of moraine debris on glaciers due to the contrasting grain size distribution and thermal conductivity. Influences of tephra deposits on glacier ablation have hardly been studied so far. For the experiment, artificial plots of two different tephra types from Eyjafjallajökull and Grimsvötn volcanoes were installed on a snow-covered glacier surface of Vatnajökull ice cap, Iceland.Ablation was automatically monitored along with atmospheric variables and ablation on a non-tephra covered reference site over the summer season 2015. For each of the two volcanic tephra types, three plots (~ 1.5 mm, ~ 8.5 mm and ~ 80 mm) were monitored. After limiting the records to a period of reliable measurements, a 50-days dataset of hourly records was obtained, which can be downloaded from the Pangaea data repository (https://www.pangaea.de; https://doi.org/10.1594/PANGAEA.876656). The experiment shows a substantial increase of ablation under the ~ 1.5 mm and ~ 8.5 mm tephra plots when compared to uncovered conditions. Only under the thick tephra cover some insulating effects could be observed. This result is in contrast to other studies which depicted insulating effects for much thinner tephra coverson bare-ice glacier surfaces. Differences between the influences of the two different petrological types of tephra exist but are small.
    Electronic ISSN: 1866-3591
    Topics: Geosciences
    Published by Copernicus
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  • 2
    Publication Date: 2018-01-10
    Description: We report results from a field experiment investigating the influence of volcanic tephra coverage on glacier ablation. These influences are known to be significantly different from those of moraine debris on glaciers due to the contrasting grain size distribution and thermal conductivity. Thus far, the influences of tephra deposits on glacier ablation have rarely been studied. For the experiment, artificial plots of two different tephra types from Eyjafjallajökull and Grímsvötn volcanoes were installed on a snow-covered glacier surface of Vatnajökull ice cap, Iceland. Snow-surface lowering and atmospheric conditions were monitored in summer 2015 and compared to a tephra-free reference site. For each of the two volcanic tephra types, three plots of variable thickness (∼ 1.5, ∼ 8.5 and ∼ 80 mm) were monitored. After limiting the records to a period of reliable measurements, a 50-day data set of hourly records was obtained, which can be downloaded from the Pangaea data repository (https://www.pangaea.de; doi:10.1594/PANGAEA.876656). The experiment shows a substantial increase in snow-surface lowering rates under the ∼ 1.5 and ∼ 8.5 mm tephra plots when compared to uncovered conditions. Under the thick tephra cover some insulating effects could be observed. These results are in contrast to other studies which depicted insulating effects for much thinner tephra coverage on bare-ice glacier surfaces. Differences between the influences of the two different petrological types of tephra exist but are negligible compared to the effect of tephra coverage overall.
    Print ISSN: 1866-3508
    Electronic ISSN: 1866-3516
    Topics: Geosciences
    Published by Copernicus
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  • 3
    Publication Date: 2019-01-02
    Description: Snow depths and bulk densities of the annual snow layer were measured at 69 different locations on glaciers across Nordenskiöldland, Svalbard, during the spring seasons of the period 2014–2016. Sampling locations lie along nine transects extending over 17 individual glaciers. Several of the locations were visited repeatedly, leading to a total of 109 point measurements, on which we report in this study. Snow water equivalents were calculated for each point measurement. In the dataset, snow depth and density measurements are accompanied by appropriate uncertainties which are rigorously transferred to the calculated snow water equivalents using a straightforward Monte Carlo simulation-style procedure. The final dataset can be downloaded from the Pangaea data repository (https://www.pangaea.de; https://doi.org/10.1594/PANGAEA.896581). Snow cover data indicate a general and statistically significant increase of snow depths and water equivalents with terrain elevation. A significant increase of both quantities with decreasing distance towards the east coast of Nordenskiöldland is also evident, but shows distinct interannual variability. Snow density does not show any characteristic spatial pattern.
    Electronic ISSN: 1866-3591
    Topics: Geosciences
    Published by Copernicus
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  • 4
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    PANGAEA
    In:  Supplement to: Möller, Marco; Möller, Rebecca (2019): Snow cover variability across glaciers in Nordenskiöldland (Svalbard) from point measurements in 2014–2016. Earth System Science Data Discussions, https://doi.org/10.5194/essd-2018-158
    Publication Date: 2023-01-13
    Description: Snow depths and bulk densities of the annual snow layer were measured at 69 different locations on glaciers across Nordenskiöldland, Svalbard, during the spring seasons of the period 2014–2016. Sampling locations lie along nine transects extending over 17 individual glaciers. Several of the locations were visited repeatedly, leading to a total of 109 point measurements, on which we report in this study. Snow water equivalents were calculated for each point measurement. In the dataset, snow depth and density measurements are accompanied by appropriate uncertainties which are rigorously transferred to the calculated snow water equivalents using a straightforward Monte Carlo simulation-style procedure. The final dataset can be downloaded from the Pangaea data repository (https://www.pangaea.de; https://doi.org/10.1594/PANGAEA.896581). Snow cover data indicate a general and statistically significant increase of snow depths and water equivalents with terrain elevation. A significant increase of both quantities with decreasing distance towards the east coast of Nordenskiöldland is also evident, but shows distinct interannual variability. Snow density does not show any characteristic spatial pattern.
    Keywords: A-01; A-02; A-03; A-04; A-05; A-06; B-01; B-02; B-03; B-04; B-05; B-06; B-07; B-08; B-09; B-10; B-11; C-01; C-02; C-03; C-04; C-05; D-01; D-02; D-03; D-04; D-05; D-06; D-07; DATE/TIME; Density, snow; Density, snow, uncertainty; E-01; E-02; E-03; E-04; E-05; Elevation of event; Event label; F-01; F-02; F-03; F-04; F-05; F-06; F-07; F-08; F-09; F-10; F-11; F-12; Flag; G-01; G-02; G-03; G-04; H-01; H-02; H-03; H-04; H-05; H-06; H-07; H-08; H-09; I-01; I-02; I-03; I-04; I-05; I-06; I-07; I-08; I-09; I-10; ICEM; Ice measurement; Latitude of event; Longitude of event; Number; Randolph Glacier Inventory 6.0, glacier ID; Snow thickness; Snow thickness, uncertainty; Snow water equivalent; Snow water equivalent, uncertainty; Svalbard; Svalbard_A-01; Svalbard_A-02; Svalbard_A-03; Svalbard_A-04; Svalbard_A-05; Svalbard_A-06; Svalbard_B-01; Svalbard_B-02; Svalbard_B-03; Svalbard_B-04; Svalbard_B-05; Svalbard_B-06; Svalbard_B-07; Svalbard_B-08; Svalbard_B-09; Svalbard_B-10; Svalbard_B-11; Svalbard_C-01; Svalbard_C-02; Svalbard_C-03; Svalbard_C-04; Svalbard_C-05; Svalbard_D-01; Svalbard_D-02; Svalbard_D-03; Svalbard_D-04; Svalbard_D-05; Svalbard_D-06; Svalbard_D-07; Svalbard_E-01; Svalbard_E-02; Svalbard_E-03; Svalbard_E-04; Svalbard_E-05; Svalbard_F-01; Svalbard_F-02; Svalbard_F-03; Svalbard_F-04; Svalbard_F-05; Svalbard_F-06; Svalbard_F-07; Svalbard_F-08; Svalbard_F-09; Svalbard_F-10; Svalbard_F-11; Svalbard_F-12; Svalbard_G-01; Svalbard_G-02; Svalbard_G-03; Svalbard_G-04; Svalbard_H-01; Svalbard_H-02; Svalbard_H-03; Svalbard_H-04; Svalbard_H-05; Svalbard_H-06; Svalbard_H-07; Svalbard_H-08; Svalbard_H-09; Svalbard_I-01; Svalbard_I-02; Svalbard_I-03; Svalbard_I-04; Svalbard_I-05; Svalbard_I-06; Svalbard_I-07; Svalbard_I-08; Svalbard_I-09; Svalbard_I-10
    Type: Dataset
    Format: text/tab-separated-values, 981 data points
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  • 5
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    PANGAEA
    In:  Supplement to: Möller, Marco; Möller, Rebecca (2017): Modeling glacier-surface albedo across Svalbard for the 1979-2015 period: The HiRSvaC500-alpha data set. Journal of Advances in Modeling Earth Systems, 9, 19 pp, https://doi.org/10.1002/2016MS000752
    Publication Date: 2023-01-13
    Description: Albedo is an important quantity for determining the energy balance of snow and ice surfaces and thus also for the mass balance of glaciers. It is especially important in polar regions where shortwave radiation fluxes typically provide most of the energy input to a glacier. In order to use albedo data in any spatially distributed glaciological modeling, it is vital that the albedo fields are not only of high accuracy, but also available on sufficiently high spatial resolution and in a manner that is consistent over time. This article presents the newly developed data set HiRSvaC500-alpha which provides daily updated, gapless albedo fields for all glacierized areas of the Arctic archipelago Svalbard on a 500 m resolution over the period 1979–2015. Albedo modeling for creation of the data set is done using a multi-step geostatistical approach on the basis of remotely-sensed Moderate Resolution Imaging Spectroradiometer (MODIS) albedo data and gridded ERA-Interim climate data. Validation of the modeled HiRSvaC500-alpha albedo fields against in situ albedo measurements at automatic weather stations operated on two different glaciers suggests that the accuracy of the newly developed data set lies close to that of remotely-sensed MODIS albedo data. An analysis of the HiRSvaC500-alpha albedo data set yields a mean annual-average albedo of 0.754 across all glaciers of Svalbard over 1979–2015. A decrease of albedo with time is found, following a highly significant (95% level) trend of -0.010 per decade. For certain subregions, this trend even reaches up to -0.014 per decade.
    Keywords: Date/time end; Date/time start; File format; File name; File size; MULT; Multiple investigations; Svalbard; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 216 data points
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  • 6
    Publication Date: 2023-01-13
    Keywords: Ablation; Automatic weather station; AWS; Comment; DATE/TIME; HEIGHT above ground; Humidity, relative; Iceland_AWS; Precipitation; Short-wave downward (GLOBAL) radiation; Short-wave upward (REFLEX) radiation; Surface temperature; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 43854 data points
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  • 7
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    PANGAEA
    In:  Supplement to: Möller, Marco; Möller, Rebecca; Beaudon, Emilie; Mattila, Olli-Pekka; Finkelnburg, Roman; Braun, Matthias Holger; Grabiec, Mariusz; Jonsell, Ulf; Luks, Bartłomiej; Puczko, Dariusz; Scherer, Dieter; Schneider, Christoph (2011): Snowpack characteristics of Vestfonna and De Geerfonna (Nordaustlandet, Svalbard) - a spatiotemporal analysis based on multiyear snow-pit data. Geografiska Annaler Series A-Physical Geography, 93(4), 273-285, https://doi.org/10.1111/j.1468-0459.2011.00440.x
    Publication Date: 2023-12-13
    Description: Extensive glaciological field measurements were carried out on the ice cap Vestfonna as well as on the minor ice body De Geerfonna (Nordaustlandet, Svalbard) within the framework of IPY Kinnvika. Field campaigns were conducted during the period 2007-2010 in spring (April/May) and summer (August). In this study we compile and present snow cover information obtained from 22 snow pits that were dug on Vestfonna during this period. Locations are along two transects on the northwestern, land terminating slope of the ice cap, on its central summit, Ahlmann Summit, and at a set of several other locations in the eastern and northern part of the ice cap. Snow-cover information acquired from four snow pits on adjacent De Geerfonna is also incorporated in this study. Field data are analysed regarding snow stratigraphy, snow density, snow hardness and snow temperature. Results reveal mean snow densities of around 400 kg/m**3 for the snowpack of Vestfonna with no apparent spatial or interannual variability. A distinctly higher value of more than 450 kg/m**3 was obtained for De Geerfonna. A spatial comparison of snow water equivalents above the previous end-of-summer surface serves for obtaining insights into the spatial distribution of snow accumulation across Vestfonna. Altitude was found to be the only significant spatial parameter for controlling snow accumulation across the ice cap.
    Keywords: DATE/TIME; DeGeerfonna_DG; Density, snow; Density, standard deviation; Depth, relative; Depth water equivalent; ELEVATION; Event label; Glacier; Hardness description; Identification; International Polar Year (2007-2008); International Polar Year 2007-2008; IPY; IPY-4; Latitude of event; Longitude of event; Nordaustlandet, Svalbard; SNOWPIT; Snow pit; Standard deviation; Temperature, air; Temperature, ice/snow; Temperature, ice/snow, maximum; Temperature, ice/snow, minimum; Temperature, ice/snow, standard deviation; UTM Easting, Universal Transverse Mercator; UTM Northing, Universal Transverse Mercator; UTM Zone, Universal Transverse Mercator; Vestfonna_V1; Vestfonna_V10; Vestfonna_V11; Vestfonna_V12; Vestfonna_V13; Vestfonna_V14; Vestfonna_V15; Vestfonna_V16; Vestfonna_V17; Vestfonna_V18; Vestfonna_V2; Vestfonna_V3; Vestfonna_V4; Vestfonna_V5; Vestfonna_V6; Vestfonna_V7; Vestfonna_V8; Vestfonna_V9
    Type: Dataset
    Format: text/tab-separated-values, 454 data points
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  • 8
    Publication Date: 2020-06-12
    Description: Many practical applications of statistical post-processing methods for ensemble weather forecasts require accurate modeling of spatial, temporal, and inter-variable dependencies. Over the past years, a variety of approaches has been proposed to address this need. We provide a comprehensive review and comparison of state-of-the-art methods for multivariate ensemble post-processing. We focus on generally applicable two-step approaches where ensemble predictions are first post-processed separately in each margin and multivariate dependencies are restored via copula functions in a second step. The comparisons are based on simulation studies tailored to mimic challenges occurring in practical applications and allow ready interpretation of the effects of different types of misspecifications in the mean, variance, and covariance structure of the ensemble forecasts on the performance of the post-processing methods. Overall, we find that the Schaake shuffle provides a compelling benchmark that is difficult to outperform, whereas the forecast quality of parametric copula approaches and variants of ensemble copula coupling strongly depend on the misspecifications at hand.
    Print ISSN: 1023-5809
    Electronic ISSN: 1607-7946
    Topics: Geosciences , Physics
    Published by Copernicus on behalf of European Geosciences Union.
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  • 9
    Publication Date: 2017-06-12
    Description: An extensive validation of line-of-sight tropospheric slant total delays (STD) from Global Navigation Satellite Systems (GNSS), ray tracing in numerical weather prediction model (NWM) fields and microwave water vapour radiometer (WVR) is presented. Ten GNSS reference stations, including collocated sites, and almost 2 months of data from 2013, including severe weather events were used for comparison. Seven institutions delivered their STDs based on GNSS observations processed using 5 software programs and 11 strategies enabling to compare rather different solutions and to assess the impact of several aspects of the processing strategy. STDs from NWM ray tracing came from three institutions using three different NWMs and ray-tracing software. Inter-techniques evaluations demonstrated a good mutual agreement of various GNSS STD solutions compared to NWM and WVR STDs. The mean bias among GNSS solutions not considering post-fit residuals in STDs was −0.6 mm for STDs scaled in the zenith direction and the mean standard deviation was 3.7 mm. Standard deviations of comparisons between GNSS and NWM ray-tracing solutions were typically 10 mm ± 2 mm (scaled in the zenith direction), depending on the NWM model and the GNSS station. Comparing GNSS versus WVR STDs reached standard deviations of 12 mm ± 2 mm also scaled in the zenith direction. Impacts of raw GNSS post-fit residuals and cleaned residuals on optimal reconstructing of GNSS STDs were evaluated at inter-technique comparison and for GNSS at collocated sites. The use of raw post-fit residuals is not generally recommended as they might contain strong systematic effects, as demonstrated in the case of station LDB0. Simplified STDs reconstructed only from estimated GNSS tropospheric parameters, i.e. without applying post-fit residuals, performed the best in all the comparisons; however, it obviously missed part of tropospheric signals due to non-linear temporal and spatial variations in the troposphere. Although the post-fit residuals cleaned of visible systematic errors generally showed a slightly worse performance, they contained significant tropospheric signal on top of the simplified model. They are thus recommended for the reconstruction of STDs, particularly during high variability in the troposphere. Cleaned residuals also showed a stable performance during ordinary days while containing promising information about the troposphere at low-elevation angles.
    Print ISSN: 1867-1381
    Electronic ISSN: 1867-8548
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 10
    Publication Date: 2017-03-21
    Description: Measuring temperature and salinity profiles in the world's oceans is crucial to understanding ocean dynamics and its influence on the heat budget, the water cycle, the marine environment and on our climate. Since 1983 the German research vessel and icebreaker Polarstern has been the platform of numerous CTD (conductivity, temperature, depth instrument) deployments in the Arctic and the Antarctic. We report on a unique data collection spanning 33 years of polar CTD data. In total 131 data sets (1 data set per cruise leg) containing data from 10 063 CTD casts are now freely available at doi:10.1594/PANGAEA.860066. During this long period five CTD types with different characteristics and accuracies have been used. Therefore the instruments and processing procedures (sensor calibration, data validation, etc.) are described in detail. This compilation is special not only with regard to the quantity but also the quality of the data – the latter indicated for each data set using defined quality codes. The complete data collection includes a number of repeated sections for which the quality code can be used to investigate and evaluate long-term changes. Beginning with 2010, the salinity measurements presented here are of the highest quality possible in this field owing to the introduction of the OPTIMARE Precision Salinometer.
    Print ISSN: 1866-3508
    Electronic ISSN: 1866-3516
    Topics: Geosciences
    Published by Copernicus
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