Knowledge of frontal ablation from marine-terminating glaciers (i.e., mass lost at the calving face) is critical for constraining glacier mass balance, improving projections of mass change, and identifying the processes that govern frontal mass loss. Here, we discuss the challenges involved in computing frontal ablation and the unique issues pertaining to both glaciers and ice sheets. Frontal ablation estimates require numerous datasets, including glacier terminus area change, thickness, surface velocity, density, and climatic mass balance. Observations and models of these variables have improved over the past decade, but significant gaps and regional discrepancies remain, and better quantification of temporal variability in frontal ablation is needed. Despite major advances in satellite-derived large-scale datasets, large uncertainties remain with respect to ice thickness, depth-averaged velocities, and the bulk density of glacier ice close to calving termini or grounding lines. We suggest ways in which we can move toward globally complete frontal ablation estimates, highlighting areas where we need improved datasets and increased collaboration.

Increases in ocean temperatures in the Filchner Ronne region of Antarctica are likely to result in increased ice mass loss and sea level rise. We constrain projections of the 21st century sea level contribution of this region using process-based ice-sheet modeling, with model parameters controlling ice dynamics calibrated using observed surface speeds and Markov-chain Monte Carlo sampling. We use climate forcing from Representative Concentration Pathway (RCP) scenarios as well as a set of hypothetical scenarios of deep ocean warming to evaluate the sensitivity of this region to ocean temperatures. Projected changes in regional ice mass correspond to a decrease in global mean sea level of 24±7 mm over 2015–2100 under RCP 2.6 and 28±9 mm under RCP 8.5. Increased regional inland surface accumulation related to higher warming levels in RCP 8.5 leads to more ice above flotation, offsetting increased ice shelf basal melt. The tests involving step changes in ocean temperatures with constant surface forcing show that one degree of ocean warming from present results in an additional +11 mm contribution to sea level by 2100 and 1% of the ice-covered area in the domain becomes ungrounded (23 200 km2). The rate of mass loss with temperature increases at higher temperatures.

A recent study (Millan and others, 2022a, Nature Geoscience 15(2), 124–129) claims that ice volume contained in all glaciers outside the ice sheets and its potential contribution to sea level is 20% less than previously estimated. However, the apparent decrease is largely due to differences in choice of domain, as the study excludes 80% of the glacier area in the Antarctic periphery that was included in previous global glacier volume estimates. The issue highlights the difficulty in separating glaciers from the ice-sheet proper, especially in Antarctica, and the need for both the glacier and ice-sheet communities to develop standards and protocols to avoid double-counting in global ice volume and mass-change assessments and projections. Process-based inversion models have replaced earlier scaling methods, but large uncertainties in global glacier volume estimation remain due to the ill-posed nature of the inversion problem and poorly constrained parameters emphasizing the need for more direct ice thickness observations.

Glacier monitoring has been internationally coordinated for more than 125 years. Despite this long history, there is no authoritative answer to the popular question: ‘Which glaciers are the largest in the world?’ Here, we present the first systematic assessment of this question and identify the largest glaciers in the world – distinct from the two ice sheets in Greenland and Antarctica but including the glaciers on the Antarctic Peninsula. We identify the largest glaciers in two domains: on each of the seven geographical continents and in the 19 first-order glacier regions defined by the Global Terrestrial Network for Glaciers. Ranking glaciers by area is non-trivial. It depends on how a glacier is defined and mapped and also requires differentiating between a glacier and a glacier complex, i.e. glaciers that meet at ice divides such as ice caps and icefields. It also depends on the availability of a homogenized global glacier inventory. Using separate rankings for glaciers and glacier complexes, we find that the largest glacier complexes have areas on the order of tens of thousands of square kilometers whereas the largest glaciers are several thousands of square kilometers. The world's largest glaciers and glacier complexes are located in the Antarctic, Arctic and Patagonia.

Increased surface melt in the percolation zone of the Greenland ice sheet causes significant changes in the firn structure, directly affecting the amount and timing of meltwater runoff. Here we force an energy-balance model with automatic weather stations data at two sites in the percolation zone of southwest Greenland ($2040$ and 2360 m a.s.l.) between spring $2017$ and fall $2019$. Extensive model validation and sensitivity analysis reveal that the skin layer formulation used to compute the surface temperature by closing the energy balance leads to a consistent overestimation of melt by more than a factor of two or three depending on the site. In contrast, model results match the observations well when the model is forced by observed surface temperatures; however, unexplained residuals in the energy balance occur. The sensible and ground heat flux differ markedly in the two simulations accounting largely for the difference in modeled melt amounts. This indicates that the energy available for melt is highly sensitive to small changes in surface temperature. Thus, regional climate models that also use the skin layer formulation may have a bias in surface temperature and melt energy in the percolation zone of the ice sheet.

Passive microwave satellite observations are used to identify the presence of surface meltwater across Antarctica at daily intervals from July 1979 to June 2020, with a focus on ice shelves. Antarctic Peninsula ice shelves have the highest number of annual days of melt, with a maximum of 89 days. Over the entire time period, there are few significant linear trends in days of melt per year. High melt years can be split into two distinct categories, those with high melt days in Dronning Maud Land and Wilkes Land, and those with high melt days in the Antarctic Peninsula and the Bellingshausen Sea sector of West Antarctica. The first pattern coincides with significant negative correlations between melt days and spring and summer Southern Annular Mode. Both patterns also form the primary modes of spatial and annual variability in the dataset observed by Principal Component Analysis. Areas experiencing extended melt for the first time in years tend to show large decreases in subsequent winter microwave emissions due to structural changes in the firn. We use this to identify the impact of novel melt events, particularly over the austral summers of 1991/92 and 2015/16 on the Ross Ice Shelf.

Refreezing of meltwater in firn is a major component of Greenland ice-sheet's mass budget, but in situ observations are rare. Here, we compare the firn density and total ice layer thickness in the upper 15 m of 19 new and 27 previously published firn cores drilled at 15 locations in southwest Greenland (1850–2360 m a.s.l.) between 1989 and 2019. At all sites, ice layer thickness covaries with density over time and space. At the two sites with the earliest observations (1989 and 1998), bulk density increased by 15–18%, in the top 15 m over 28 and 21 years, respectively. However, following the extreme melt in 2012, elevation-detrended density using 30 cores from all sites decreased by 15 kg m−3 a−1 in the top 3.75 m between 2013 and 2019. In contrast, the lowest elevation site's density shows no trend. Thus, temporary build-up in firn pore space and meltwater infiltration capacity is possible despite the long-term increase in Greenland ice-sheet melting.

Alaska's largest city, Anchorage, depends on Eklutna Glacier meltwater for drinking water and hydropower generation; however, the 29 km2 glacier is rapidly retreating. We used a temperature-index model forced with local weather station data to reconstruct the glacier's mass balance for the period 1985–2019 and quantify the impacts of glacier change on discharge. Model calibration involved a novel combination of in situ, geodetic mass-balance measurements and observed snowlines from satellite imagery. A resulting ensemble of 250 best-fitting model parameters was used to model mass balance and discharge. Eklutna Glacier experienced a significant negative trend (−0.31 m w.e. decade−1) in annual mean surface mass balance (mean: −0.62 ± 0.06 m w.e.). The day of the year when 95% of annual melt occurs was five days later in 2011–19 than in 1985–93, demonstrating a prolongation of melt season (May–September). Modeled mean specific discharge increased at 0.14 m decade−1, indicating peak water, the year when annual discharge reaches a maximum due to glacier retreat, has not been reached. Four of the five highest discharge years occurred since 2000. Increases in discharge quantity and melt season length require water resource managers consider future decreased discharge as the glacier continues to shrink.

Glacier mass loss in Alaska has implications for global sea level rise, fresh water input into the Gulf of Alaska and terrestrial fresh water resources. We map all glaciers (>4000 km2) on the Kenai Peninsula, south central Alaska, for the years 1986, 1995, 2005 and 2016, using satellite images. Changes in surface elevation and volume are determined by differencing a digital elevation model (DEM) derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer stereo images in 2005 from the Interferometric Synthetic Aperture Radar DEM of 2014. The glacier area shrunk by 543 ± 123 km2 (12 ± 3%) between 1986 and 2016. The region-wide mass-balance rate between 2005 and 2014 was −0.94 ± 0.12 m w.e. a−1 (−3.84 ± 0.50 Gt a−1), which is almost twice as negative than found for earlier periods in previous studies indicating an acceleration in glacier mass loss in this region. Area-averaged mass changes were most negative for lake-terminating glaciers (−1.37 ± 0.13 m w.e. a−1), followed by land-terminating glaciers (−1.02 ± 0.13 m w.e. a−1) and tidewater glaciers (−0.45 ± 0.14 m w.e. a−1). Unambiguous attribution of the observed acceleration in mass loss over the last decades is hampered by the scarcity of observational data, especially at high elevation, and by large interannual variability.

The response of glaciers to climate change has major implications for sea-level change and water resources around the globe. Large-scale glacier evolution models are used to project glacier runoff and mass loss, but are constrained by limited observations, which result in models being over-parameterized. Recent systematic geodetic mass-balance observations provide an opportunity to improve the calibration of glacier evolution models. In this study, we develop a calibration scheme for a glacier evolution model using a Bayesian inverse model and geodetic mass-balance observations, which enable us to quantify model parameter uncertainty. The Bayesian model is applied to each glacier in High Mountain Asia using Markov chain Monte Carlo methods. After 10,000 steps, the chains generate a sufficient number of independent samples to estimate the properties of the model parameters from the joint posterior distribution. Their spatial distribution shows a clear orographic effect indicating the resolution of climate data is too coarse to resolve temperature and precipitation at high altitudes. Given the glacier evolution model is over-parameterized, particular attention is given to identifiability and the need for future work to integrate additional observations in order to better constrain the plausible sets of model parameters.

Global-scale 21st-century glacier mass change projections from six published global glacier models are systematically compared as part of the Glacier Model Intercomparison Project. In total 214 projections of annual glacier mass and area forced by 25 General Circulation Models (GCMs) and four Representative Concentration Pathways (RCP) emission scenarios and aggregated into 19 glacier regions are considered. Global mass loss of all glaciers (outside the Antarctic and Greenland ice sheets) by 2100 relative to 2015 averaged over all model runs varies from 18 ± 7% (RCP2.6) to 36 ± 11% (RCP8.5) corresponding to 94 ± 25 and 200 ± 44 mm sea-level equivalent (SLE), respectively. Regional relative mass changes by 2100 correlate linearly with relative area changes. For RCP8.5 three models project global rates of mass loss (multi-GCM means) of >3 mm SLE per year towards the end of the century. Projections vary considerably between regions, and also among the glacier models. Global glacier mass changes per degree global air temperature rise tend to increase with more pronounced warming indicating that mass-balance sensitivities to temperature change are not constant. Differences in glacier mass projections among the models are attributed to differences in model physics, calibration and downscaling procedures, initial ice volumes and varying ensembles of forcing GCMs.

Glaciers spanning large altitudinal ranges often experience different climatic regimes with elevation, creating challenges in acquiring mass-balance and climate observations that represent the entire glacier. We use mixed methods to reconstruct the 1991–2014 mass balance of the Kahiltna Glacier in Alaska, a large (503 km2) glacier with one of the greatest elevation ranges globally (264–6108 m a.s.l.). We calibrate an enhanced temperature index model to glacier-wide mass balances from repeat laser altimetry and point observations, finding a mean net mass-balance rate of −0.74 mw.e. a−1( ± σ = 0.04, std dev. of the best-performing model simulations). Results are validated against mass changes from NASA's Gravity Recovery and Climate Experiment (GRACE) satellites, a novel approach at the individual glacier scale. Correlation is strong between the detrended model- and GRACE-derived mass change time series (R2 = 0.58 and p ≪ 0.001), and between summer (R2 = 0.69 and p = 0.003) and annual (R2 = 0.63 and p = 0.006) balances, lending greater confidence to our modeling results. We find poor correlation, however, between modeled glacier-wide balances and recent single-stake monitoring. Finally, we make recommendations for monitoring glaciers with extreme altitudinal ranges, including characterizing precipitation via snow radar profiling.

We study the evolution of the Juneau Icefield, one of the largest icefields in North America (>3700 km2), using the Parallel Ice Sheet Model (PISM). We test two climate datasets: 20 km Weather Research and Forecasting Model (WRF) output, and data from the Scenarios Network for Alaska Planning (SNAP), derived from spatial interpolation of observations. Good agreement between simulated and observed surface mass balance was achieved only after substantially adjusting WRF precipitation to account for unresolved orographic effects, while SNAP's climate pattern is incompatible with observations of surface mass balance. Using the WRF data forced with the RCP6.0 emission scenario, the model projects a decrease in ice volume by 58–68% and a 57–63% area loss by 2099 compared with 2010. If the modeled 2070–99 climate is held constant beyond 2099, the icefield is eliminated by 2200. With constant 1971–2010 climate, the icefield stabilizes at 86% of its present-day volume. Experiments started from an ice-free state indicate that steady-state volumes are largely independent of the initial ice volume when forced by identical scenarios of climate stabilization. Despite large projected volume losses, the complex high-mountain topography makes the Juneau Icefield less susceptible to climate warming than low-lying Alaskan icefields.

We present a detailed, complete glacier inventory for Alaska and neighboring Canada using multi-sensor satellite data from 2000 to 2011. For each glacier, we derive outlines and 51 variables, including center-line lengths, outline types and debris cover. We find 86 723 km2 of glacier area (27 109 glaciers >0.025 km2), ∼12% of the global glacierized area outside ice sheets. Of this area 12.0% is drained by 39 marine-terminating glaciers (74 km of tidewater margin), and 19.3% by 148 lake- and river-terminating glaciers (420 km of lake-/river margin). The overall debris cover is 11%, with considerable differences among regions, ranging from 1.4% in the Kenai Mountains to 28% in the Central Alaska Range. Comparison of outlines from different sources on >2500 km2 of glacierized area yields a total area difference of ∼10%, emphasizing the difficulties in accurately delineating debris-covered glaciers. Assuming fully correlated (systematic) errors, uncertainties in area reach 6% for all Alaska glaciers, but further analysis is needed to explore adequate error correlation scales. Preliminary analysis of the glacier database yields a new set of well-constrained area/length scaling parameters and shows good agreement between our area–altitude distributions and previously established synthetic hypsometries. The new glacier database will be valuable to further explore relations between glacier variables and glacier behavior.

Lake-calving Yakutat Glacier in southeast Alaska, USA, is undergoing rapid thinning and terminus retreat. We use a simplified glacier model to evaluate its future mass loss. In a first step we compute glacier-wide mass change with a surface mass-balance model, and add a mass loss component due to ice flux through the calving front. We then use an empirical elevation change curve to adjust for surface elevation change of the glacier and finally use a flotation criterion to account for terminus retreat due to frontal ablation. Surface mass balance is computed on a daily timescale; elevation change and retreat is adjusted on a decadal scale. We use two scenarios to simulate future mass change: (1) keeping the current (2000–10) climate and (2) forcing the model with a projected warming climate. We find that the glacier will disappear in the decade before 2110 or 2070 under constant or warming climates, respectively. For the first few decades, the glacier can maintain its current thinning rates by retreating and associated loss of high-ablating, low-elevation areas. However, once higher elevations have thinned substantially, the glacier can no longer counteract accelerated thinning by retreat and mass loss accelerates, even under constant climate conditions. We find that it would take a substantial cooling of 1.5°C to reverse the ongoing retreat. It is therefore likely that Yakutat Glacier will continue its retreat at an accelerating rate and disappear entirely.

Alaskan glaciers are among the largest regional contributors to sea-level rise in the latter half of the 20th century. Earlier studies have documented extensive and accelerated ice wastage in most regions of Alaska. Here we study five decades of mass loss on high-elevation, land-terminating glaciers of the Wrangell Mountains (~ 4900 km2) in central Alaska based on airborne center-line laser altimetry data from 2000 and 2007, a digital elevation model (DEM) from ASTER and SPOT5, and US Geological Survey topographic maps from 1957. The regional mass-balance estimates derived from center-line laser altimetry profiles using two regional extrapolation techniques agree well with that from DEM differencing. Repeat altimetry measurements reveal accelerated mass loss over the Wrangell Mountains, with the regional mass-balance rate evolving from –0.07 ± 0.19 m w.e. a–1 during 1957–2000 to –0.24 ± 0.16 m w.e. a–1 during 2000–07. Nabesna, the largest glacier in this region (˜1056 km2), lost mass four times faster during 2000–07 than during 1957–2000. Although accelerated, the mass change over this region is slower than in other glacierized regions of Alaska, particularly those with tidewater glaciers. Together, our laser altimetry and satellite DEM analyses demonstrate increased wastage of these glaciers during the last 50 years.

The Randolph Glacier Inventory (RGI) is a globally complete collection of digital outlines of glaciers, excluding the ice sheets, developed to meet the needs of the Fifth Assessment of the Intergovernmental Panel on Climate Change for estimates of past and future mass balance. The RGI was created with limited resources in a short period. Priority was given to completeness of coverage, but a limited, uniform set of attributes is attached to each of the ~198 000 glaciers in its latest version, 3.2. Satellite imagery from 1999–2010 provided most of the outlines. Their total extent is estimated as 726 800 ± 34 000 km2. The uncertainty, about ±5%, is derived from careful single-glacier and basin-scale uncertainty estimates and comparisons with inventories that were not sources for the RGI. The main contributors to uncertainty are probably misinterpretation of seasonal snow cover and debris cover. These errors appear not to be normally distributed, and quantifying them reliably is an unsolved problem. Combined with digital elevation models, the RGI glacier outlines yield hypsometries that can be combined with atmospheric data or model outputs for analysis of the impacts of climatic change on glaciers. The RGI has already proved its value in the generation of significantly improved aggregate estimates of glacier mass changes and total volume, and thus actual and potential contributions to sea-level rise.

Many glaciological and hydrological studies require outlines of individual glaciers rather than total ice cover. Here we develop a new semiautomatic algorithm that uses a digital elevation model (DEM) and outlines of glacier complexes to calculate the extents of individual glaciers. The algorithm first applies hydrological modeling tools to a modified DEM to calculate flowsheds. It then merges flowsheds that belong to individual glaciers using a distance-based approach, whose required empirical parameters are derived from the Juneau Icefield area in Alaska. In this region, 2% of ~1300 glaciers were misclassified. The algorithm was validated on >25 000 km2 of ice in other regions in Alaska and on >40 000 km2 of ice in Arctic Canada, resulting in ~2% and ~3% misclassified glaciers, respectively. Results indicate that the algorithm is robust provided the DEM and the outlines are of good quality.