Abstract. The recovery of a 1.5 Myr long ice core from Antarctica represents a keystone to our understanding of Quaternary climate, the progression of glaciation over this time period and the role of greenhouse gas cycles in this progression. Here we show that such old ice is most likely to exist in the plateau area of the East Antarctic Ice Sheet (EAIS) without stratigraphic disturbance and should be able to be recovered after careful pre-site selection studies. Based on a simple ice and heat flow model and glaciological observations, we conclude that positions in the vicinity of major domes and saddle positions on the East Antarctic Plateau will most likely have such old ice in store and represent the best study areas for dedicated reconnaissance studies in the near future. In contrast to previous ice core drill site selections, we strongly argue for significantly reduced ice thickness to avoid bottom melting, while at the same time maximizing the resolution and the distance of such old ice to the bedrock. For example for the geothermal heat flux and accumulation conditions at Dome C, an ice thickness lower than 2500 m would be required to find 1.5 Myr old ice. However, the final choice is strongly dependent on the local geothermal heat flux, which is largely unknown for the EAIS and has to be determined beforehand. In addition, the detailed bedrock topography and ice flow history for candidates of an Oldest Ice ice coring site has to be reconstructed. Finally, we argue strongly for rapid access drilling before any full deep ice coring activity commences to bring datable samples to the surface and to allow an age check of the oldest ice.
Abstract The influence of trees on the ground thermal regime is important to the overall winter energy exchange in a snow‐covered, forested watershed. In this work, spatial zones around a single conifer tree were defined and examined for their controls on the snow cover, snow‐ground interface temperatures and frozen ground extent. A large white spruce ( Picea glauca ), approximately 18 m tall with a crown diameter of 7.5 m and located in northern Vermont, was the subject of this study. The tree was instrumented with thermistors to measure the snow‐ground interface temperature between the tree trunk and 6 m from the tree into undisturbed snow. Four distinct zones around the conifer are defined that affect the snow distribution characteristics: adjacent to the trunk; the tree well; the tree crown perimeter; and the unaffected area away from the tree. At the time of peak snow accumulation and during the ablation season, snow depth and density profiles were measured. The area beneath the canopy accumulated 34% of the snow accumulated in the undisturbed zone. By the end of the ablation season, the depth of snow under the canopy had decreased to 18% of the undisturbed snow depth. The tree and branch characteristics of spruce in this temperate climate resulted in a different snow depth profile compared with previous empirical relationships around a single conifer. A new relationship is presented for snow distribution around conifer trees that has the ability to better fit data from a variety of conifer types than previously published relationships. Less snow beneath the canopy led to colder snow‐ground interface temperatures than measured in undisturbed snow. The depth of frozen ground in the different zones was modelled using a simple analytical solution that showed deeper frost penetration in the tree well than beneath the undisturbed snow.
Optical grain size (OGS) and specific surface area (SSA) are key parameters for measuring the atmospheric interactions of snow, as well as tracking metamorphosis and allowing for the ground truthing of remote sensing data. This paper describes a device using a shortwave infrared camera with changeable optical bandpass filters (centered at 1300 nm and 1550 nm) that can be used to quickly measure the spectral albedo over an area of 0.25 m 2 and from it derive SSA. The device and method are compared with measurements taken by a field spectral radiometer. The instrument is designed to be towed and powered by a four wheeled autonomous ground vehicle, and is built to run and collect data for hours or days without human intervention.
