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.

Influenced by the Indian monsoon, the Kangri Karpo Mountains (KKM) of the southeastern Tibetan Plateau is the most humid part of the plateau, and one of the most important regions with numerous monsoon temperate glaciers. Glacier mass balance estimates have been strongly negative in the KKM over recent decades, but the spatiotemporal characteristics of surface velocity are poorly understood. Using phase-correlation feature tracking on Landsat images, this study estimates spatiotemporal variabilities of monsoon temperate glaciers for the period of 1988–2019. Results show that a significant slowdown was observed below an elevation of 4900 m, while an accelerated ice flow was found at an elevation of 4900–5800 m over the past 30 years. The trend of slowdown was −0.1 m a−1 dec−1 during 1988–2000, and then it increased to −0.5 m a−1 dec−1 during 2001–2019.

Atmospheric ice accretion results from the exposure of technical equipment or facilities to cold and humid environments. Supercooled droplets in a cloud can impact an airplane's surface and quickly form an ice layer. The presence of air pockets in such a layer is well known and explains the white appearance of some of the accretions. However, estimation of its porosity values and studies on the pore formation mechanics remain limited. In this study, we performed tests in an icing wind tunnel and scans with micro-computed tomography to address these issues. Here, we show that the accretion has closed porosity below 1%, which is mostly produced by the interaction between a spray-like impact on the water surface. The insights we provide here are important to improve ice accretion modelling techniques and establish a different approach to address the interaction between the cloud and the surfaces exposed to atmospheric icing.

To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

Greenland glaciers exhibit variable seasonal velocity signals that may reflect differences in subglacial hydrology. Here, we conduct a first GrIS-wide glacier classification based on seasonal velocity patterns derived from 2017 Sentinel-1 radar data. Our classification focuses on two distinct seasonal ice velocity patterns, with the first (type-2 from Moon and others, 2014) showing periods of both speedup and slowdown during the melt season, and the second (type-3) instead showing a longer period of slowdown from elevated velocities in the winter and spring. We analyze 221 glaciers in 2017 and show that 48 exhibit type-2 behavior, and 72 exhibit type-3 behavior. We extend the classification to 2018 and 2019 and find that while the glaciers meeting each criterion vary year to year, type-2 is consistently more common in the northern regions and type-3 is more common in the south. Our results highlight the varied impact of meltwater on subglacial drainage systems and glacier flow in Greenland.