Surface wind trends and variability over Antarctica and the Southern Ocean and their implications to wind energy in the region are analyzed using the gridded ERA-Interim reanalysis data between 1979 and 2017 and the Self-Organizing Map (SOM) technique. In general, surface winds are stronger over the coastal regions of East Antarctica and the Transantarctic Mountains and weaker over the Ross and Ronne ice shelves and the Antarctic Peninsula; and stronger in winter and weaker in summer. Winds in the southern Indian and Pacific Oceans and along coastal regions exhibit a strong interannual variability that appears to be correlated to the Antarctic Oscillation (AAO) index. A significantly positive trend in surface wind speeds is found across most regions and about 20% and 17% of the austral autumn and summer wind trends, respectively, and less than 1% of the winter and spring wind trends may be explained by the trends in the AAO index. Except for the Antarctic Peninsula, Ronne and Ross ice shelves, and small areas in the interior East Antarctica, most of the continent is found to be suitable for the development of wind power.

The Antarctic ice sheet and the continent both play critical roles in global sea level rise and climate change but they remain poorly understood because data collection is greatly limited by the remote location and hostile conditions there. Airborne platforms have been extensively used in Antarctica due to their capabilities and flexibility and have contributed a great deal of knowledge to both the ice sheet and the continent. The Snow Eagle 601 fixed-wing airborne platform has been deployed by China for Antarctic expeditions since 2015. Scientific instruments on the airplane include an ice-penetrating radar, a gravimeter, a magnetometer, a laser altimeter, a camera and a Global Navigation Satellite System (GNSS). In the past five austral seasons, the airborne platform has been used to survey Princess Elizabeth Land, the largest data gap in Antarctica, as well as other critical areas. This paper reviews the scientific operations of Snow Eagle 601 including airborne and ground-based scientific instrumentation, aviation logistics, field data acquisition and processing and data quality control. We summarize the progress of airborne surveys to date, focusing on scientific motivations, data coverage and national and international collaborations. Finally, we discuss potential regions for applications of the airborne platform in Antarctica and developments of the airborne scientific system for future work.

The internal layers of ice sheets from ice-penetrating radar (IPR) investigation preserve critical information about the ice-flow field and englacial conditions. This paper presents a new detailed analysis of spatial distribution characteristics of internal layers and subglacial topography of the East Antarctic ice sheet (EAIS) from Zhongshan Station to Dome A. The radar data of 1244 km along a traverse between Zhongshan Station and Dome A of EAIS were collected during the 29th Chinese National Antarctic Research Expedition (CHINARE 29, 2012/2013). In this study, the Internal Layering Continuity Index (ILCI) and basal roughness were taken as indicators to provide an opportunity to evaluate the past internal environment and dynamics of the ice sheet. Except for the upstream of Lambert Glacier, the fold patterns of internal layers are basically similar to that of the bed topography. The relatively flat basal topography and the decrease of ILCI with increasing depth provide evidence for identifying previous rapid ice flow areas that are unavailable to satellites, especially in the upstream of Lambert Glacier. Continuous internal layers of Dome A, recording the spatial change of past ice accumulation and ice-flow history over 160 ka, almost extend to the bed, with high ILCI and high basal roughness of the corresponding bed topography. There are three kinds of basal roughness patterns along the traverse, that is, “low ξ_t low η”, “low ξ_t high η”, and “high ξ_t high η”, where ξ_t represents the amplitude of the undulations, and quantifies the vertical variation of the bedrock, and η measures the frequency variation of fluctuations and the horizontal irregularity of the profile. The characteristics of internal layers and basal topography of the traverse between Zhongshan Station and Dome A provide new information for understanding the ancient ice-flow activity and the historical evolution of EAIS.

The airborne ice-penetrating radar (IPR) is an effective method used for ice sheet exploration and is widely applied for detecting the internal structures of ice sheets and for understanding the mechanism of ice flow and the characteristics of the bottom of ice sheets. However, because of the ambient influence and the limitations of the instruments, IPR data are frequently overlaid with noise and interference, which further impedes the extraction of layer features and the interpretation of the physical characteristics of the ice sheet. In this paper, we first applied conventional filtering methods to remove the feature noise and interference in IPR data. Furthermore, machine learning methods were introduced in IPR data processing for noise removal and feature extraction. Inspired by a comparison of the filtering methods and machine learning methods, we propose a fusion method combining both filtering methods and machine-learning-based methods to optimize the feature extraction in IPR data. Field data tests indicated that, under different conditions of IPR data, the application of different methods and strategies can improve the layer feature extraction.

Understanding englacial and subglacial structures is a fundamental method of inferring ice sheets’ historical evolution and surface mass balance. The internal layer continuity index and the basal roughness are key parameters and indicators for the speculation of the relationship between the ice sheet’s internal structure or bottom and ice flow. Several methods have been proposed in the past two decades to quantitatively calculate the continuity index of ice layer geometry and the roughness of the ice–bedrock interface based on radar echo signals. These methods are mainly based on the average of the absolute value of the vertical gradient of the echo signal amplitude and the standard deviation of the horizontal fluctuation of the bedrock interface. However, these methods are limited by the amount and quality of unprocessed radar datasets and have not been widely used, which also hinders further research, such as the analysis of the englacial reflectivity, the subglacial conditions, and the history of the ice sheets. In this paper, based on geophysical processing methods for radar image denoising and deep learning for ice layer and bedrock interface extraction, we propose a new method for calculating the layer continuity index and basal roughness. Using this method, we demonstrate the ice-penetrating radar data processing and compare the imaging and calculation of the radar profiles from Dome A to Zhongshan Station, East Antarctica. We removed the noise from the processed radar data, extracted ice layer continuity features, and used other techniques to verify the calculation. The potential application of this method in the future is illustrated by several examples. We believe that this method can become an effective approach for future Antarctic geophysical and glaciological research and for obtaining more information about the history and dynamics of ice sheets from their radar-extracted internal structure.

Antarctic geothermal heat flux is a basic input variable for ice sheet dynamics simulation. It greatly affects the temperature and mechanical properties at the bottom of the ice sheet, influencing sliding, melting, and internal deformation. Due to the fact that the Antarctica is covered by a thick ice sheet, direct measurements of heat flux are very limited. This study was carried out to estimate the regional heat flux in the Antarctic continent through geophysical inversion. Princess Elizabeth Land, East Antarctica is one of the areas in which we have a weak understanding of geothermal heat flux. Through the latest airborne geomagnetic data, we inverted the Curie depth, obtaining the heat flux of bedrock based on the one-dimensional steady-state heat conduction equation. The results indicated that the Curie depth of the Princess Elizabeth Land is shallower than previously estimated, and the heat flux is consequently higher. Thus, the contribution of subglacial heat flux to the melting at the bottom of the ice sheet is likely greater than previously expected in this region. It further provides research clues for the formation of the developed subglacial water system in Princess Elizabeth Land.