Bulletin of Glaciological Research
Online ISSN : 1884-8044
Print ISSN : 1345-3807
ISSN-L : 1345-3807
Volume 36
Displaying 1-3 of 3 articles from this issue
  • Nuerasimuguli ALIMASI
    2018 Volume 36 Pages 1-13
    Published: 2018
    Released on J-STAGE: April 14, 2018

    The Arctic is experiencing rapid environmental change due to climate warming, resulting in snow condition changes. Passive microwave observation is a useful tool to monitor these changes. However, the ground conditions in boreal regions, comprised of forest, permafrost, and lakes, are complex. The rapid change of season from winter to spring is also important information obtained through snow observations when studying the Arctic climate. This study introduces previous attempts to retrieve Arctic microwave observations, examples of flight observations, and the use of the low-frequency 6GHz band to improve the assessment of snow conditions. Flight observations carried out over a forest, wetland, and lake using an airborne microwave radiometer provides detailed brightness temperature variations of the Arctic and winter - spring changes. Flight and satellite microwave observations were used to monitor warming in spring and indicated the early warming of lowlands and late warming of mountainous areas. The diurnal amplitude variation (DAV) is useful to monitor snowmelt in the Arctic. During the short winter-spring transition in the Arctic, microwave emissions showed local and temporal variations with forest, permafrost, and lake. They are available for further discussion on microwave observation of snow in the Arctic and implementation of changing Arctic cryospheric environment.

    Download PDF (5348K)
  • Sumito MATOBA, Masashi NIWANO, Tomonori TANIKAWA, Yoshinori IIZUKA, Te ...
    2018 Volume 36 Pages 15-22
    Published: 2018
    Released on J-STAGE: May 18, 2018

    During spring 2017, we conducted research expeditions to the SIGMA-A site, which is located on the northwestern Greenland Ice Sheet. We maintained an automated weather station (AWS) to enable continuous meteorological observations. We extended 1.5-m long poles of the AWS and replaced two aerovane sensors, two thermo-hydrometers and an ultrasonic snow gauge. We also drilled an ice core and recovered a core with a total length of 60.06m, conducted stratigraphic observations, and measured the density of the ice core. In addition, we conducted snow-pit observations and snow sampling, measured the specific surface area of snow using near-infrared reflectance, performed sunphotometry observations, and measured the spectral albedo. To schedule research activities in the field camp and helicopter pick-up flights, we received weather forecasts from the Meteorological Research Institute of Japan through the Internet using a satellite phone every day. We took a male dog to the field camp to alert us to approaching animals.

    Download PDF (1377K)
  • Motoshi NISHIMURA, Akihiko SASAKI, Keisuke SUZUKI
    2018 Volume 36 Pages 23-35
    Published: 2018
    Released on J-STAGE: November 29, 2018

    In this study, the characteristics of snowmelt in the Norikura highland were investigated using an energy balance analysis to calculate the amount of snowmelt. Meteorological observations were conducted on the Norikura highland (1590m a.s.l.) and an energy balance analysis was carried out on the snow surface during the snow cover seasons. The result showed that multi-year datasets of meteorological observations revealed characteristics such as low air temperature and vapor pressure, and weak wind speed. Throughout each season of snow cover averaged net radiation, the sensible heat flux and latent heat flux were 88.9%, 16.4% and -6.3% energy ratio to the total snowmelt energy, respectively. Each day, conditions were classified as rainy or non-rainy. The result for rainy conditions showed that net shortwave radiation decreased, while net longwave radiation increased greatly. Latent heat and sensible heat flux also increased. Although there was little precipitation heat flux, larger snowmelt energy was provided when it rained. In the late snowmelt period, the snowmelt rate calculated from the energy balance analysis was compared to the observed value, and the two were almost consistent.

    Download PDF (2084K)