Quantifying snow water equivalent (SWE) with ground-penetrating radar (GPR) in a warming climate is complicated by the incidence and variability of liquid water in snow. Snow surveys conducted during the melt season serve as a valuable analog to conditions under future warming. Here, we determine the variability of wet snowpack properties (relative permittivity and density) to quantify their impact on SWE estimates using GPR. We collected spatially continuous snowpack measurements with 400 MHz GPR in 2012 and 2021 across repeat transects (∼150 km each year) along with spring and summer snow depth and density measurements from snow pits and snow cores. Snow relative permittivity values ranged from 2.06 to 2.62 in 2012 and 2.11 to 5.11 in 2021, resulting in calculated volumetric liquid water content (LWC) between 1.7% and 5.7% in 2012 and 2.1% and 16% in 2021. This variability in snow relative permittivity results in SWE uncertainties of 8% —33%, with more extreme cases reaching 13%—45%. We attribute this uncertainty to spatial and temporal variability in LWC when using GPR to estimate SWE. As snowpacks become wetter with rising atmospheric temperatures, GPR surveys should include in situ relative permittivity measurements to reduce depth and SWE interpretation uncertainties.

Subglacial drainage models, often motivated by the relationship between hydrology and ice flow, sensitively depend on numerous unconstrained parameters. We explore using borehole water-pressure time series to calibrate the uncertain parameters of a popular subglacial drainage model, taking a Bayesian perspective to quantify the uncertainty in parameter estimates and in the calibrated model predictions. To reduce the computation time associated with Markov Chain Monte Carlo sampling, we construct a fast Gaussian process emulator to stand in for the subglacial drainage model. We first carry out a calibration experiment using synthetic observations consisting of model simulations with hidden parameter values as a demonstration of the method. Using real borehole water pressures measured in western Greenland, we find meaningful constraints on four of the eight model parameters and a factor-of-three reduction in uncertainty of the calibrated model predictions. These experiments illustrate Gaussian process-based Bayesian inference as a useful tool for calibration and uncertainty quantification of complex glaciological models using field data. However, significant differences between the calibrated model and the borehole data suggest that structural limitations of the model, rather than poorly constrained parameters or computational cost, remain the most important constraint on subglacial drainage modelling.

This study introduces a custom implementation of the Ensemble Kalman Filter (EnKF) for calibrating a three-dimensional glacier evolution model. The EnKF can assimilate observations as they become available and provides uncertainty measures for the initial state after calibration. We calibrate an elevation-dependent surface mass balance (SMB) model using elevation change observations and test the EnKF’s performance in a Twin Experiment by varying internal and external hyperparameters. The best-performing configuration is applied to the Rhône Glacier in a Real-World Experiment. Using satellite-based elevation change fields for calibration, the EnKF estimates an average equilibrium line altitude of $2920 \pm 37$ m for the period 2000–19. A comparison of the results with glaciological measurements demonstrates the capabilities of the EnKF to simultaneously calibrate multiple SMB parameters. With this proof of concept, we expect that our methodology is readily extendable to other map or point observations and their combination, as well as to other calibration parameters.

Quantitative results from regional climate models (RCMs) run over ice sheets are frequently used to make projections of surface melt, ice-shelf stability, and subsequently sea-level rise. However, modelled relevant mass fluxes need to be evaluated first before using future output data for projections. This study makes the case for a two-step framework when evaluating RCMs. Firstly, the reliability of the RCM when forced with reanalysis data must be assessed through comparison with historical observations. Secondly, the accuracy of using a non-observationally constrained Earth System Model as forcing must be assessed through comparison with the reanalysis forced run during the same historical period. Simulating surface melt in Antarctica with the RCM RACMO2.3p2 is given as an example. Applying this two-step procedure we show that RACMO2.3p2 respectively forced with ERA5 and CESM2 is robust for modelling contemporary and future surface melt in Antarctica. Building on this conclusion, we briefly discuss an application, i.e. three future SSP realizations of melt-over-accumulation across the Antarctic ice sheet until 2100 are presented, providing insights into the future sensitivity to meltwater ponding of major Antarctic ice shelves.

We present analyses of bubble number-density (BND) data from the South Pole Ice Core (SPC14) showing warming of ∼7.5°C from the Late Glacial (∼19.5 ka), then relatively stable temperatures during the Holocene (<0.5°C warming), in close agreement with results of independent paleothermometers. The BND data span from ∼160 m just below pore close-off, to ∼1200 m, where bubble loss by clathrate formation is significant. Measurements were made with standard bubble ‘thick’-section techniques and a new application of three-dimensional micro-computed tomography (CT) imagery; the nearly identical results recommend the faster, nondestructive micro-CT. The very high BND at South Pole, typically 800 and 900 bubbles cm−3, reflects the joint effects of the relatively low mean-annual temperature (−49°C) and high accumulation rate (∼7.5 cm w.e. a−1). High BND is physically linked to small grain sizes at pore close-off, which in turn helps explain the near-absence of brittle-ice behavior at the site, contributing to the high quality of the recovered core with implications for siting of future ice cores. The accumulation history, derived from δ15N-N2 firn-column thickness estimates, correlates with the temperature history but varies somewhat more than saturation vapor pressure, suggesting dynamic controls including upstream slope variability.

Pizolgletscher, Swiss Alps, was already a very small glacier when the monitoring of length change was initiated 130 years ago. In situ mass balance measurements at seasonal resolution began in 2006. During the last 18 years, the glacier has lost 98% of its volume and is considered extinct since 2022. However, a tiny remnant of ice of a few thousand square metres is preserved under rockfall debris. The case of Pizolgletscher allows tracking the extinction of a glacier with a comprehensive long-term observational series. Furthermore, the vanished glacier has a touristic and cultural significance, as exemplified by a commemoration ceremony held in 2019. Here, detailed monitoring data sets (mass balance, area, volume, length) are presented that shed light on the processes of glacier disintegration before ultimate disappearance. Comparison to regional mass balance variations indicates that the signal from very small glaciers can remain representative at larger scales even during the final phase of a glacier’s lifecycle.