A parameterization of the impact of internal waves on momentum transfer at the sea-ice–ocean interface based on previous work by McPhee has been implemented in a sea-ice model for the first time. The ice–ocean drag from internal waves is relevant for shallow mixed layer depth and the presence of a density jump at the pycnocline and is also a function of the strength of the stratification beneath the ocean mixed layer and geometry of the ice interface. We present results from a coupled sea-ice–ocean model where the parameterization of internal wave drag has been implemented. We conducted simulations spanning the years from 2000 to 2017. We find a deceleration of ice drift by 5–8% in both winter and summer, but with significant spatial and temporal variation reaching seasonal average values of ~10%. The spatial variation of ice transport leads to local impacts on deformed ice of magnitude ~0.05 m (2–5%), and reductions in ocean-to-ice heat fluxes of ~1 W m−2, and a decrease in bottom melt of ~0.02–0.04 cm d−1. There is an increase of up to 15% in thickness and ice concentration in the Canadian Arctic and a 10% overall impact on the total sea-ice volume.

An outdoor sea-ice tank system for measuring the microwave properties of sea ice has been developed. With the natural cooling and the help of a cooling unit in the movable roof attached to the tank, the system can grow sea ice to about 50 cm thickness continuously without the effects of snowfall and/or melting. Portable microwave radiometers are attached beside the tank to measure the brightness temperature of the ice. As sea ice grows, the system can measure microwave brightness temperature, and seawater/ice temperature at each depth and thickness of sea ice. The bulk salinity of sea ice is measured by sampling. The following results were obtained from experiments conducted during two winters. (1) Rapid desalination was observed during the early stages of growth with ice thicknesses of 0–5 cm. (2) A new relational expression between ice thickness and bulk salinity at this thickness was obtained. (3) The rapid rise in microwave brightness temperature associated with the increase in sea-ice thickness during the early stages of sea-ice growth was captured in each frequency band. The microwave measurements from the sea-ice tank system are expected to provide new insights into the microwave properties of sea ice.

This study describes the morphological and dynamic changes of Parkachik Glacier, Suru River valley, Ladakh Himalaya, India. We used medium-resolution satellite images; CORONA KH-4, Landsat and Sentinel-2A from 1971–2021, and field surveys between 2015 and 2021. In addition, we used the laminar flow-based Himalayan Glacier Thickness Mapper and provide results for recent margin fluctuations, surface ice velocity, ice thickness, and identified glacier-bed overdeepenings. The results revealed that overall the glacier retreated by −210.5 ± 80 m with an average rate of 4 ± 1 m a−1 between 1971 and 2021. Whereas a field study suggested that the glacier retreat increased to −123 ± 72 m at an average rate of −20 ± 12 m a−1 between 2015 and 2021. Surface ice velocity was estimated using COSI-Corr on the Landsat data. Surface ice velocity in the lower ablation zone was 45 ± 2 m a−1 in 1999–2000 and 32 ± 1 m a−1 in 2020–2021, thus reduced by 28%. Further, the maximum thickness of the glacier is estimated to be ~441 m in the accumulation zone, while for glacier tongue it is ~44 m. The simulation results suggest that if the glacier continues to retreat at a similar rate, three lakes of different dimensions may form in subglacial overdeepenings.

At a time when nobody has yet landed on the Antarctic continent (1879), this presentation and accompanying paper predicts the morphology, dynamics and thermal regime of the Antarctic ice sheet. Mathematical modelling of the ice sheet is based on the assumptions that the thickness of tabular icebergs reflects the average thickness of the ice at the margin and that the surface gradients are comparable to those of reconstructed former ice sheets in the Northern Hemisphere. The modelling shows that (a) ice is thickest near the centre at the South Pole and thins towards the margin; (b) the thickness at the pole is independent of the amount of snowfall at that place; and (c) the mean velocity at the margin, assuming a mean annual snowfall of two inches per year, is 400–500 feet per year. The thermal regime of the ice sheet is influenced by three heat sources – namely, the bed, the internal friction of ice flow and the atmosphere. The latter is the most significant and, since ice has a downwards as well as horizontal motion, this carries cold ice down into the ice sheet. Since the temperature at which ice melts is lowered by pressure at a rate of 0.0137 °F for every atmosphere of pressure (something known since 1784), much of the ice sheet and its base must be below the freezing point. Estimates of the thickness of ice at the centre depend closely on the surface gradients assumed and range between 3 and 24 miles. Such uncertainty is of concern since both the volume and gravitational attraction of the ice mass have an effect on global sea level. In order to improve our estimate of the volume of ice, we will have to wait 76 years for John Glen to develop a realistic flow law for ice.

To assess the amount of ice volume stored in glaciers or ice caps, a method to estimate ice thickness distribution is required for glaciers where no direct observations are available. In this study, we use an existing inverse method to estimate the bedrock topography and ice thickness of the Renland Ice Cap, East Greenland, using satellite-based observations of the surface topography. The inverse approach involves a procedure in which an ice dynamical model is used to build-up an ice cap in steady state with climate forcing from a regional climate model, and the bedrock is iteratively adjusted until the modelled and observed surface topography match. We validate our model results against information from airborne radar data and satellite observed surface velocity, and we find that the inferred ice thickness and thereby the stored total volume of the ice cap is sensitive to the assumed ice softness and basal slipperiness. The best basal model parameters for the Renland Ice Cap are determined and the best estimated total ice volume of 384 km3 is found. The Renland Ice Cap is particularly interesting because of its location at a high elevation plateau and hence assumed low sensitivity to climate change.

Twin 5-month seasonal forecast experiments are performed to predict the September 2018 mean and minimum ice extent using the fully coupled Navy Earth System Prediction Capability (ESPC). In the control run, ensemble forecasts are initialized from the operational US Navy Global Ocean Forecasting System (GOFS) 3.1 but do not assimilate ice thickness data. Another set of forecasts are initialized from the same GOFS 3.1 fields but with sea ice thickness derived from CryoSat-2 (CS2). The Navy ESPC ensemble mean September 2018 minimum sea ice extent initialized with GOFS 3.1 ice thickness was over-predicted by 0.68 M km2 (5.27 M km2) vs the ensemble forecasts initialized with CS2 ice thickness that had an error of 0.40 M km2 (4.99 M km2), a 43% reduction in error. The September mean integrated ice edge error shows a 18% improvement for the Pan-Arctic with the CS2 data vs the control forecasts. Comparison against upward looking sonar ice thickness in the Beaufort Sea reveals a lower bias and RMSE with the CS2 forecasts at all three moorings. Ice concentration at these locations is also improved, but neither set of forecasts show ice free conditions as observed at moorings A and D.

The impact of April sea-ice thickness (SIT) initialization on the predictability of September sea-ice extent (SIE) is investigated based on a series of perfect model ensemble experiments using the MIROC5.2 climate model. Ensembles with April SIT initialization can accurately predict the September SIE for greater lead times than in cases without the initialization – up to 2 years ahead. The persistence of SIT correctly initialized in April contributes to the skilful prediction of SIE in the first September. On the other hand, errors in the initialization of SIT in April cause errors in the predicted sea-ice concentration and thickness in the Pacific sector from July to September and consequently influence the predictive skill with respect to SIE in September. The present study suggests that initialization of the April SIT in the Pacific sector significantly improves the accuracy of the September SIE forecasts by decreasing the errors in sea-ice fields from July to September.

In this paper we explore simulated responses of electromagnetic (EM) signals relative to in situ field surveys and quantify the effects that different values of conductivity in sea ice have on the EM fields. We compute EM responses of ice types with a three-dimensional (3-D) finite-volume discretization of Maxwell’s equations and present 2-D sliced visualizations of their associated EM fields at discrete frequencies. Several interesting observations result: First, since the simulator computes the fields everywhere, each gridcell acts as a receiver within the model volume, and captures the complete, coupled interactions between air, snow, sea ice and sea water as a function of their conductivity; second, visualizations demonstrate how 1-D approximations near deformed ice features are violated. But the most important new finding is that changes in conductivity affect EM field response by modifying the magnitude and spatial patterns (i.e. footprint size and shape) of current density and magnetic fields. These effects are demonstrated through a visual feature we define as ‘null lines’. Null line shape is affected by changes in conductivity near material boundaries as well as transmitter location. Our results encourage the use of null lines as a planning tool for better ground-truth field measurements near deformed ice types.

This paper is the second (Paper II) in a set of studies concerning the errors involved in the estimate of ice thickness and ice volume. Here we present a detailed analysis of the errors involved in the generation of ice-thickness DEMs constructed, most often, from GPR data, complemented by boundary data and sometimes, additional synthetic data arising from estimates based on theoretical considerations supported by independent data. We describe a complete methodology of error analysis that, starting from the errors in the data, propagates them to the grid nodes. In turn, the interpolation error at the grid nodes is calculated using a novel procedure that also provides an estimate of the bias introduced by the interpolation process. Finally, both errors are combined at the grid nodes to produce a gridpoint-dependent error estimate, which is complemented by an overall error estimate providing an assessment of the quality of the DEM. This methodology is illustrated with the case study of Werenskioldbreen, a land-terminating polythermal glacier in Svalbard.

This is the first (Paper I) of three companion papers focused respectively, on the estimates of the errors in ice thickness retrieved from pulsed ground-penetrating radar (GPR) data, on how to estimate the errors at the grid points of an ice-thickness DEM, and on how the latter errors, plus the boundary delineation errors, affect the ice-volume estimates. We here present a comprehensive analysis of the various errors involved in the computation of ice thickness from pulsed GPR data, assuming they have been properly migrated. We split the ice-thickness error into independent components that can be estimated separately. We consider, among others, the effects of the errors in radio-wave velocity and timing. A novel aspect is the estimate of the error in thickness due to the uncertainty in horizontal positioning of the GPR measurements, based on the local thickness gradient. Another novel contribution is the estimate of the horizontal positioning error of the GPR measurements due to the velocity of the GPR system while profiling, and the periods of GPS refreshing and GPR triggering. Their effects are particularly important for airborne profiling. We illustrate our methodology through a case study of Werenskioldbreen, Svalbard.

Empirical data on the volume/area scaling of glaciers and ice caps are compared with theoretical models of longitudinal glacier profiles. Ice caps are described well by theoretical solutions of the differential equations modeling longitudinal profiles, but glaciers for which the surface slope is approximated by the ice thickness divided by the length are not. A power-law profile recently derived analytically, reproduces exactly the thickness-length scaling, first predicted by scaling analysis for ice caps.

The clay mineralogical composition of a 552 cm long sediment core from Lake Terrasovoje in Amery Oasis, East Antarctica, was analysed and compared with that in surface sediments from other locations in the vicinity. The lower part of the sediment core is formed by sub- and proglacial sediments with a dominance of smectite and illite, and lower amounts of kaolinite and chlorite. The upper part of the core is deposited after 12 500 cal yr bp and mainly composed of illite and kaolinite, with low amounts of smectite and chlorite, such as found in samples from rock outcrops and covering sediments throughout Amery Oasis. The clay composition in the lower section of core Lz1005 suggest that the basin of Lake Terrasovoje was filled by a 150–200 m thickened Nemesis Glacier prior to 12 500 cal yr bp rather than by local ice caps.

A study of early winter first-year sea ice conditions and development in the western Ross Sea in May and June 1995 included measurements of snow and ice thickness, freeboard, ice core structure and stable isotopic composition. These variables showed strong spatial variability between the Ross Ice Shelf and the ice edge 1400 km to the north, and indicate that the development of the Ross Sea pack ice is quite different from that observed in other Antarctic sea ice zones. The thinnest snow and ice occurred in a 200 km wide coastal zone. The thickest snow and ice were observed in a continental shelf zone 200–600 km from the coast where the average ice thickness (0.8 m) determined by drilling is as thick as first-year sea ice later in winter elsewhere in Antarctica. A zone of moderate snow and ice thickness occurred on the deep ocean from 600 km to the ice edge at 1400 km. Thermodynamic thickening of the ice in the inner pack ice, <800 km from the coast, was dominated by congelation ice growth, which occurred in a greater amount (65%) and in thicker layers (mean: 20 cm) than was observed in the outer pack ice >800 km from the coast (amount: 22%; mean layer thickness: 12 cm) and elsewhere in the Antarctic pack ice. The preponderance of congelation ice in the inner pack ice might be due to a low oceanic heat flux on the Ross Sea continental shelf, and a colder, less stormy environment which favours the more frequent and prolonged calm conditions necessary for significant congelation ice growth. In the outer pack ice, thermodynamic thickening occurred mainly by snow ice formation (mean layer thickness: 20 cm) while dynamic processes, i.e., rafting and ridging, caused the thickening of frazil ice and columnar ice (mean layer thickness: 14 cm and 12 cm respectively). A greater amount of snow ice (37%) occurred in the outer pack ice than in the inner pack ice (15%), and both values indicate that in the Ross Sea, unlike other Antarctic sea ice zones, there can be significant seawater flooding of the snow/ice interface and snow ice formation before midwinter.