Climate and meteorological processes of the East Antarctic ice sheet between Zhongshan and Dome-A

The 1228 km over-snow traverse route between the Chinese Zhongshan Station, on the coast of Prydz Bay, and Dome-A, at 4091 m elevation the highest point of the East Antarctic ice sheet, has been the focus of CHINARE surface meteorological and climate studies since 2002. A network of seven Automatic Weather Stations has been deployed along this section, including at Dome-A itself, and some of these have now provided nearly-hourly data for over a decade. Atmospheric boundary layer turbulence and radiation observations have been made over the near-coastal ice sheet inland of Zhongshan and surface turbulence measurements using an ultrasonic anemometer system have also been made in the deep interior of the ice sheet. Summer GPS radiosonde soundings of the atmospheric boundary layer have been made at Kunlun Station, near Dome-A. In this paper these observations are combined to provide a comprehensive overview of the meteorological regime of this region of the ice sheet, its climate variability, and as a reference for future study of climate change. This includes investigation of the variation of surface climate features with elevation and distance from the coast, the height and structure of the boundary layer over the ice sheet, and seasonal and regional changes in ice/snow–air interactions, including turbulent and radiative energy fluxes. The air temperature and snow temperature between the coastal Zhongshan and Dome-A on the inland plateau have not changed significantly in the past decade compared with the inter-annual variability

Surface energy balance on the Antarctic plateau as measured with an automatic weather station during 2014

AWS data during 2014 collected at PANDA-N station, on the East Antarctica Plateau, are analysed. Net Short Wave Radiation (QSWR), net Long Wave Radiation (QLWR), sensible (QH), latent (QL) and subsurface or ground (QG) heat fluxes are computed. Annual averages for QSWR, QLWR, QH, QL and QG of 19.65, −49.16, 26.40, −0.77 and 3.86 W·m−2 were derived based on an albedo value of 0.8. QSWR and QH are the major sources of heat gain to the surface and QLWR is the major component of heat loss from the surface. An iterative method is used to estimate surface temperature in this paper; surface temperature of snow/ice is gradually increased or decreased, thereby changing longwave radiation, sensible, latent and subsurface heat fluxes, so that the net energy balance becomes zero. Mass loss due to sublimation at PANDA-N station for 2014 is estimated to be 12.18 mm w.e.·a−1; and mass gain due to water vapour deposition is estimated to be 3.58 mm w.e.·a−1. Thus the net mass loss due to sublimation/deposition is 8.6 mm w.e.·a−1. This study computes surface energy fluxes using a model, instead of direct measurements. Also there are missing data especially for wind speed, though 2 m air temperature data is almost continuously available throughout the year. The uncertainties of albedo, wind speed and turbulent fluxes cause the most probable error in monthly values of QLWR, QH, QL, QG and surface temperature of about ±4%, ±20%, ±50%, ±11% and ±0.74 K respectively

Dome Argus: Ideal site for deep ice drilling

Located on the centre of ice drainage range, the highest Dome Argus (Dome A) of East Antarctic Ice Sheet (EAIS), could be represented as an ideal site for deep ice cores drilling containing oldest paleo-climate records. To select a suitable drilling site for deep ice core, it needs gather all information pertaining to the local meteorology, ice sheet landforms, ice thickness, subglacial topography of bed rocks, ice velocity, internal structures of ice sheet, etc. Based on the International Partnerships in Ice Core Sciences (IPICS), we present recent achievement of glaciological research and its perspective at Dome A in this paper. We systematically discussed the merits and possible ventures of potential drilling sites around Dome A. Among all the candidates, we find that the Chinese Antarctic Kunlun Station is the best site for carrying out the first deep ice core drilling campaign. We emphasize and assess further the possibility to obtain a replicate core for studying dynamics and evolution of climate change

Recovery of the three-dimensional wind and sonic temperature data from a physically deformed sonic anemometer

A sonic anemometer reports three-dimensional (3-D) wind and sonic temperature (Ts) by measuring the time of ultrasonic signals transmitting along each of its three sonic paths, whose geometry of lengths and angles in the anemometer coordinate system was precisely determined through production calibrations and the geometry data were embedded into the sonic anemometer operating system (OS) for internal computations. If this geometry is deformed, although correctly measuring the time, the sonic anemometer continues to use its embedded geometry data for internal computations, resulting in incorrect output of 3-D wind and Ts data. However, if the geometry is remeasured (i.e., recalibrated) and to update the OS, the sonic anemometer can resume outputting correct data. In some cases, where immediate recalibration is not possible, a deformed sonic anemometer can be used because the ultrasonic signal-transmitting time is still correctly measured and the correct time can be used to recover the data through post processing. For example, in 2015, a sonic anemometer was geometrically deformed during transportation to Antarctica. Immediate deployment was critical, so the deformed sonic anemometer was used until a replacement arrived in 2016. Equations and algorithms were developed and implemented into the post-processing software to recover wind data with and without transducer-shadow correction and Ts data with crosswind correction. Post-processing used two geometric datasets, production calibration and recalibration, to recover the wind and Ts data from May 2015 to January 2016. The recovery reduced the difference of 9.60 to 8.93&thinsp;°C between measured and calculated Ts to 0.81 to −0.45&thinsp;°C, which is within the expected range, due to normal measurement errors. The recovered data were further processed to derive fluxes. As data reacquisition is time-consuming and expensive, this data-recovery approach is a cost-effective and time-saving option for similar cases. The equation development can be a reference for related topics.</p