Observations show that glaciers around the world are in retreat and losing mass. Internationally coordinated for over a century, glacier monitoring activities provide an unprecedented dataset of glacier observations from ground, air and space. Glacier studies generally select specific parts of these datasets to obtain optimal assessments of the mass-balance data relating to the impact that glaciers exercise on global sea-level fluctuations or on regional runoff. In this study we provide an overview and analysis of the main observational datasets compiled by the World Glacier Monitoring Service (WGMS). The dataset on glacier front variations (∼42 000 since 1600) delivers clear evidence that centennial glacier retreat is a global phenomenon. Intermittent readvance periods at regional and decadal scale are normally restricted to a subsample of glaciers and have not come close to achieving the maximum positions of the Little Ice Age (or Holocene). Glaciological and geodetic observations (∼5200 since 1850) show that the rates of early 21st-century mass loss are without precedent on a global scale, at least for the time period observed and probably also for recorded history, as indicated also in reconstructions from written and illustrated documents. This strong imbalance implies that glaciers in many regions will very likely suffer further ice loss, even if climate remains stable.

This study presents a reanalysis of the glaciologically obtained annual glacier mass balances at Hintereisferner, Ötztal Alps, Austria, for the period 2001–2011. The reanalysis is accomplished through a comparison with geodetically derived mass changes, using annual high-resolution airborne laser scanning (ALS). The grid-based adjustments for the method-inherent differences are discussed along with associated uncertainties and discrepancies of the two methods of mass balance measurements. A statistical comparison of the two datasets shows no significant difference for seven annual, as well as the cumulative, mass changes over the 10-year record. Yet, the statistical view hides significant differences in the mass balance years 2002/03 (glaciological minus geodetic records = +0.92 m w.e.), 2005/06 (+0.60 m w.e.), and 2006/07 (−0.45 m w.e.). We conclude that exceptional meteorological conditions can render the usual glaciological observational network inadequate. Furthermore, we consider that ALS data reliably reproduce the annual mass balance and can be seen as validation or calibration tools for the glaciological method.

Mapping snow cover (SC) on glaciers at the end of the ablation period provides a possibility to rapidly obtain a proxy for their equilibrium line altitude (ELA) which in turn is a metric for the mass balance. Satellite determination of glacier snow cover, derived over large regions, can reveal its spatial variability and temporal trends. Accordingly, snow mapping on glaciers has been widely applied using several satellite sensors. However, as glacier ice originates from compressed snow, both have very similar spectral properties and standard methods to map snow struggle to distinguish snow on glaciers. Hence, most studies applied manual delineation of snow extent on glaciers. Here we present an automated tool, named ‘ASMAG’ (automated snow mapping on glaciers), to map SC on glaciers and derive the related snow line altitude (SLA) for individual glaciers using multi-temporal Landsat satellite imagery and a digital elevation model (DEM). The method has been developed using the example of the Ötztal Alps, where an evaluation of the method is possible using field-based observations of the annual equilibrium line altitude (ELA) and the accumulation area ratio (AAR) measured for three glaciers for more than 30 years. The tool automatically selects a threshold to map snow on glaciers and robustly calculates the SLA based on the frequency distribution of elevation bins with more than 50% SC. The accuracy of the SC mapping was about 90% and the SLA was determined successfully in 80% of all cases with a mean uncertainty of ±19 m. When cloud-free scenes close to the date of the highest snowline are available, a good to very good agreement of SC ratios (SCR)/SLA with field data of AAR/ELA are obtained, otherwise values are systematically higher/lower as useful images were often acquired too early in the summer season. However, glacier specific differences are still well captured. Snow mapping on glaciers is impeded by clouds and their shadows or when fresh snow is covering the glaciers, so that more frequent image acquisitions (as now provided by Sentinel-2) would improve results.

In the hydrological year 2021/2022, Alpine glaciers showed unprecedented mass loss. On Hintereisferner (Ötztal Alps, Austria), the glacier-wide mass balance was −3319 kg m⁻². Near-daily observations of the surface elevation changes from a permanent terrestrial laser scanning set-up allowed the determination of the day when the mass balance of Hintereisferner started to become negative. This Glacier Loss Day (GLD) was already reached on 23 June in 2022 and gave way to a long ice ablation period. In 2021/2022, this and the high cumulative positive degree days explain the record-breaking mass loss. By comparing the GLDs of 2019/2020–2021/2022, we found a gross yet expressive indicator of the glacier's imbalance with the persistently warming climate.

The Lewis Glacier on Mt Kenya is one of the best-studied tropical glaciers, but full understanding of the interaction of the glacier mass balance and its climatic drivers has been hampered by a lack of long-term meteorological data. Here we present 2.5 yr of meteorological data collected from the glacier surface from October 2009 to February 2012. The location of measurements is in the upper portion of Lewis Glacier, but this location experiences negative annual mass balance, and the conditions are comparable to those experienced in the lower ablation zones of South American glaciers in the inner tropics. In the context of other glaciated mountains of equatorial East Africa, the summit zone of Mt Kenya shows strong diurnal cycles of convective cloud development as opposed to the Rwenzoris, where cloud cover persists throughout the diurnal cycle, and Kilimanjaro, where clear skies prevail. Surface energy fluxes were calculated for the meteorological station site using a physical mass- and energy-balance model driven by measured meteorological data and additional input parameters that were determined by Monte Carlo optimization. Sublimation rate was lower than those reported on other tropical glaciers, and melt rate was high throughout the year, with the glacier surface reaching the melting point on an almost daily basis. Surface mass balance is influenced by both solid precipitation and air temperature, with radiation providing the greatest net source of energy to the surface. Cloud cover typically reduces the net radiation balance compared to clear-sky conditions, and thus the frequent formation of convective clouds over the summit of Mt Kenya and the associated higher rate of snow accumulation are important in limiting the rate of mass loss from the glacier surface. The analyses shown here form the basis for future glacier-wide mass and energy balance modeling to determine the climate proxy offered by the glaciers of Mt Kenya.

Wind-driven snow redistribution affects the glacier mass balance by eroding or depositing mass from or to different parts of the glacier’s surface. High-resolution observations are used to test the ability of large-eddy simulations as a tool for distributed mass balance modeling. We present a case study of observed and simulated snow redistribution over Hintereisferner glacier (Ötztal Alps, Austria) between 6 and 9 February 2021. Observations consist of three high-resolution digital elevation models (Δx=1 m) derived from terrestrial laser scans taken shortly before, directly after, and 15 h after snowfall. The scans are complemented by datasets from three on-site weather stations. After the snowfall event, we observed a snowpack decrease of 0.08 m on average over the glacier. The decrease in the snow depth can be attributed to post-snowfall compaction and the wind-driven redistribution of snow. Simulations were performed with the Weather Research and Forecasting (WRF) model at Δx=48 m with a newly implemented snow drift module. The spatial patterns of the simulated snow redistribution agree well with the observed generalized patterns. Snow redistribution contributed −0.026 m to the surface elevation decrease over the glacier surface on 8 February, resulting in a mass loss of −3.9 kg m⁻², which is on the same order of magnitude as the observations. With the single case study we cannot yet extrapolate the impact of post-snowfall events on the seasonal glacier mass balance, but the study shows that the snow drift module in WRF is a powerful tool to improve knowledge on wind-driven snow redistribution patterns over glaciers.