Ridge B is one of the least studied areas in Antarctica but has been considered to be a potential location for the oldest ice on Earth. Among important parameters for calculating where very old ice may exist, geothermal heat flux (GHF) is critical but poorly understood. Here, GHF is determined by quantifying the transitions between dry and wet basal conditions using a radioglaciological method applied to airborne radio-echo sounding data. GHF is then constrained by a thermodynamic model matched to the transitions. The results show that GHF in Ridge B varies locally and ranges from 48.5 to 65.1 mW m−2, with an average value of 58.0 mW m−2, which is consistent with the current known GHF constrained by subglacial lakes and derived from Vostok ice core temperature measurements. Our work highlights the value of considering local GHF when locating the oldest ice in this potential region or other regions.

The density–depth relationship of the Antarctic ice sheet is important for establishing a high-precision surface mass balance model and predicting future ice-sheet contributions to global sea levels. A new algorithm is used to reconstruct firn density and densification rate by inverting monostatic radio wave echoes from ground-operated frequency-modulated continuous wave radar data collected near four ice cores along the transect from Zhongshan Station to Dome A. The inverted density profile is consistent with the core data within 5.54% root mean square error. Due to snow redistribution, the densification rate within 88 km of ice core DT401 is correlated with the accumulation rate and varies greatly over horizontal distances of <5 km. That is, the depth at which a critical density of 830 kg m−3 is reached decreases and densification rate increases in high-accumulation regions but decreases in low-accumulation regions. This inversion technique can be used to analyse more Antarctic radar data and obtain the density distribution trend, which can improve the accuracy of mass-balance estimations.

During the last few decades, bed-elevation profiles from radar sounders have been used to quantify bed roughness. Various methods have been employed, such as the ‘two-parameter’ technique that considers vertical and slope irregularities in topography, but they struggle to incorporate roughness at multiple spatial scales leading to a breakdown in their depiction of bed roughness where the relief is most complex. In this article, we describe a new algorithm, analogous to wavelet transformations, to quantify the bed roughness at multiple scales. The ‘Self-Adaptive Two-Parameter’ system calculates the roughness of a bed profile using a frequency-domain method, allowing the extraction of three characteristic factors: (1) slope, (2) skewness and (3) coefficient of variation. The multi-scale roughness is derived by weighted-summing of these frequency-related factors. We use idealized bed elevations to initially validate the algorithm, and then actual bed-elevation data are used to compare the new roughness index with other methods. We show the new technique is an effective tool for quantifying bed roughness from radar data, paving the way for improved continental-wide depictions of bed roughness and incorporation of this information into ice flow models.

Using frequency-modulated continuous wave radar data from the 32nd Chinese Antarctic Research Expedition in 2015/16, subsurface profiles were obtained along an East Antarctic inland traverse from Zhongshan station to Dome A, and four distinct regions were selected to analyze the spatiotemporal variability in historical surface mass balance (SMB). Based on depth, density, and age data from ice cores along the traverse, the radar data were calibrated to yield average SMB data. The zone 49–195 km from the coast has the highest SMB (235 kg m−2 a−1). The 780–892 km zone was most affected by the Medieval Warm Period and the Little Ice Age, and the SMB during ad 1454–1836 (71 kg m−2 a−1) was only one-quarter of that in the 20th century. The SMB in the 1080–1157 km zone fluctuates the most, possibly due to erosion or irregular deposition of snow by katabatic winds in low SMB areas with surface elevation fluctuations. Dome A (1157–1236 km) has the lowest SMB (29 kg m−2 a−1) and did not decrease during Little Ice Age. Understanding the spatiotemporal variability of SMB in a larger space can help us understand the complex climate history of Antarctica.

Radio-echo sounding (RES) can be used to understand ice-sheet processes, englacial flow structures and bed properties, making it one of the most popular tools in glaciological exploration. However, RES data are often subject to ‘strip noise’, caused by internal instrument noise and interference, and/or external environmental interference, which can hamper measurement and interpretation. For example, strip noise can result in reduced power from the bed, affecting the quality of ice thickness measurements and the characterization of subglacial conditions. Here, we present a method for removing strip noise based on combined wavelet and two-dimensional (2-D) Fourier filtering. First, we implement discrete wavelet decomposition on RES data to obtain multi-scale wavelet components. Then, 2-D discrete Fourier transform (DFT) spectral analysis is performed on components containing the noise. In the Fourier domain, the 2-D DFT spectrum of strip noise keeps its linear features and can be removed with a ‘targeted masking’ operation. Finally, inverse wavelet transforms are performed on all wavelet components, including strip-removed components, to restore the data with enhanced fidelity. Model tests and field-data processing demonstrate the method removes strip noise well and, incidentally, can remove the strong first reflector from the ice surface, thus improving the general quality of radar data.

Studies of palaeo-accumulation rates at Dome A, East Antarctica, are entirely absent. Here, spatio-temporal variations in ice accumulation rates for the past ~161 ka are calculated from isochronous internal layering, traced from radio-echo sounding (RES) data collected by the Polar Research Institute of China (PRIC) during the 21st CHINARE. The layers are dated by linking them to the site of Vostok ice core along an RES profile, which was flown by the Alfred-Wegener-Institut (AWI) in the Dome Connection East Antarctica (DoCo) project. The Dansgaard-Johnsen model is used to determine the spatial and temporal pattern of ice accumulation in Dome A region. The results show that there is a slight increasing pattern of ice accumulation from south to north along the 216 km radar profile at Dome A. The lowest ice accumulation rates were calculated around the Dome A zone. In the past ~90 ka, there were relatively high accumulation rates during the time period 34–47 ka (Marine Isotope Stage 3) at Dome A.