The ice–bedrock interfaces at the bottoms of ice sheets and the internal ice layers record the historical evolution of the ice sheets and are important indicators for inferring glacier dynamics and explaining subglacial topographies. Radar-detected bedrock and internal ice layers can be used to observe conditions in the interiors and bottoms of ice sheets. The low contrast of the internal layers’ feature in radar images, as well as the effects of ice flow, makes it challenging to automatically extract and trace the layers and interfaces of ice sheets. Manual or semiautomatic methods are extensively applied in bedrock and internal ice layers’ extractions. However, the conventional methods require large amounts of time. Especially when processing a large number of radar observation data, the accuracy and efficiency of conventional methods are untoward. Therefore, we propose EisNet, a fusion system consisting of deep neural networks, to extract the multiple types of internal layers. Because of the rareness of high-precision extraction of ice layers, it is virtually impossible to train EisNet in observational data with the corresponded label of ice layers. To train EisNet, we design a novel synthetic method for radar images based on artifact noise and characteristic textures that are similar to those in the observation data. The validation in synthetic data proves EisNet’s capacity for layer extraction and noise removal. The field data application shows the generalization and adaptation abilities of EisNet and indicates that EisNet can significantly improve the efficiency of the automatic processing of ice sheet radiostratigraphy.

The bathymetry under the Amery Ice Shelf steers the flow of ocean currents transporting ocean heat, and thus is a prerequisite for precise modeling of ice‐ocean interactions. However, hampered by thick ice, direct observations of sub‐ice‐shelf bathymetry are rare, limiting our ability to quantify the evolution of this sector and its future contribution to global mean sea level rise. We estimated the bathymetry of this region from airborne gravity anomaly using simulated annealing. Unlike the current model which shows a comparatively flat seafloor beneath the calving front, our estimation results reveal a 255‐m‐deep shoal at the western side and a 1,050‐m‐deep trough at the eastern side, which are important topographic features controlling the ocean heat transport into the sub‐ice cavity. The new model also reveals previously unknown depressions and sills that are critical to an improved modeling of the sub‐ice‐shelf ocean circulation and induced basal melting.

Ice fabric influences the rheology of ice, and hence the age–depth profile at ice core drilling sites. To investigate the age–depth profile to be expected of the ongoing deep ice coring at Kunlun station, Dome A, we use the depth-varying anisotropic fabric suggested by the recent polarimetric measurements around Dome A along with prescribed fabrics ranging from isotropic through girdle to single maximum in a three-dimensional, thermo-mechanically coupled full-Stokes model of a 70 × 70 km ² domain around Kunlun station. This model allows for the simulation of the near basal ice temperature and age, and ice flow around the location of the Chinese deep ice coring site. Ice fabrics and geothermal heat flux strongly affect the vertical advection and basal temperature which consequently control the age profile. Constraining modeled age–depth profiles with dated radar isochrones to 2∕3 ice depth, the surface vertical velocity, and also the spatial variability of a radar isochrones dated to 153.3 ka BP, limits the age of the deep ice at Kunlun to between 649 and 831 ka, a much smaller range than previously inferred. The simple interpretation of the polarimetric radar fabric data that we use produces best fits with a geothermal heat flux of 55 mW m ⁻² . A heat flux of 50 mW m ⁻² is too low to fit the deeper radar layers, and 60 mW m ⁻² leads to unrealistic surface velocities. The modeled basal temperature at Kunlun reaches the pressure melting point with a basal melting rate of 2.2–2.7 mm a ⁻¹ . Using the spatial distribution of basal temperatures and the best fit fabric suggests that within 400 m of Kunlun station, 1-million-year-old ice may be found 200 m above the bed, and that there are large regions where even older ice is well above the bedrock within 5–6 km of the Kunlun station.

Mascon products derived from Gravity Recovery and Climate Experiment satellite gravimetry data are widely used to study the Greenland ice sheet mass balance. However, the products released by different research groups—JPL, CSR, and GSFC—show noticeable discrepancies. To understand them, we compare those mascon products with mascon solutions computed in‐house using a varying regularization parameter. We show that the observed discrepancies are likely dominated by differences in the applied regularization. Furthermore, we present a numerical study aimed at an in‐depth analysis of regularization‐driven biases in the solutions. We demonstrate the ability of our simulations to reproduce 60%–80% of biases observed in real data, which proves that our simulations are sufficiently realistic. After that, we demonstrate that the quality of mascon‐based estimates can be increased by a proper modification of the applied regularization: no correlation between mascons is assumed when they belong to different drainage systems. Using both simulations and real data analysis, we show that the improved regularization mitigates signal leakage between drainage systems by 11%–56%. Finally, we validate various mascon solutions over the SW drainage system, using trends from (i) the GOCO‐06S model and (ii) the Input‐Output Method as control data. In general, the in‐house computed trend estimates are consistent with the trends from CSR and JPL solutions and the trends from the control data.

Radio-echo sounding (RES) has revealed an internal architecture within both the West and East Antarctic ice sheets that records their depositional, deformational and melting histories. Crucially, RES-imaged internal-reflecting horizons, tied to ice-core age–depth profiles, can be treated as isochrones that record the age–depth structure across the Antarctic ice sheets. These enable the reconstruction of past climate and ice dynamical processes on large scales, which are complementary to but more spatially extensive than commonly used proxy records (e.g. former ice limits constrained by cosmogenic dating or offshore sediment sequences) around Antarctica. We review the progress towards building a pan-Antarctic age–depth model from these data by first introducing the relevant RES datasets that have been acquired across Antarctica over the last 6 decades (focussing specifically on those that detected internal-reflecting horizons) and outlining the processing steps typically undertaken to visualise, trace and date (by intersection with ice cores or modelling) the RES-imaged isochrones. We summarise the scientific applications for which Antarctica's internal architecture has been used to date and present a pathway to expanding Antarctic radiostratigraphy across the continent to provide a benchmark for a wider range of investigations: (1) identification of optimal sites for retrieving new ice-core palaeoclimate records targeting different periods; (2) reconstruction of surface mass balance on millennial or historical timescales; (3) estimation of basal melting and geothermal heat flux from radiostratigraphy and comprehensive mapping of basal-ice units to complement inferences from other geophysical and geological methods; (4) advancement of the knowledge of volcanic activity and fallout across Antarctica; and (5) refinement of numerical models that leverage radiostratigraphy to tune time-varying accumulation, basal melting and ice flow, firstly to reconstruct past behaviour and then to reduce uncertainties in projecting future ice-sheet behaviour.