PICO
This session focuses on advances in observing and modelling mountain glaciers and ice caps at the regional to global scale. We invite both observation- and modelling-based contributions, which may include, but are not limited to, the following topics:
• Observation and modelling results that reveal previously underappreciated regional differences in glacier changes or in their dynamics.
• Large-scale impact studies, including glacier contribution to sea level change, susceptibility to natural hazards or changes in water availability from glacierised regions.
• Advances in large-scale modelling (reconciling machine learning (ML) with classical approaches, including physical processes, improving/extending strategies for data assimilation/inverse approaches, refining climatic downscaling, increasing representativeness, etc.)
• Advances in large-scale monitoring (ML-boosted monitoring and interpretation, multi-sensor homogenisation, meta-analysis of ground-based data, process inferences, etc.)
• Development and dissemination of regional to global glacier datasets.
The Fluctuations of Glaciers (FoG) database is the outcome of 130 years of coordinated international glacier monitoring. It is curated by the World Glacier Monitoring Service (WGMS) and regularly updated with observations submitted by a worldwide network of scientific collaborators. Glacier mass, elevation, and length change observations (1535–2025) are complemented by an inventory of glacier-related events (e.g., surges, lake outbursts, and ice avalanches). We will explain the data integration process and evaluate the spatial, temporal, and climatic coverage of the observations.
We will also highlight many enhancements to the database, including structured authorship and bibliographic metadata for each observation, a near-complete full-text archive of all cited literature, glacier names in multiple languages and scripts, the outline used to derive each glacier-wide elevation change, and a representative outline for each in-situ mass change series.
Finally, we will present an annual estimate of global glacier mass loss from 1976 through the 2025 hydrological year. Since 2025, the WGMS combines the latest in-situ mass change observations and remotely-sensed elevation change observations in FoG to calculate a mass-change time series for every glacier on Earth (separate from the Greenland and Antarctic ice sheets) and corresponding regional and global totals.
How to cite: Welty, E. and the WGMS Network: Global glacier change observations (1535–2025) and mass loss (1976–2025), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11914, https://doi.org/10.5194/egusphere-egu26-11914, 2026.
With the availability of open-access satellite imagery and derived products from open-source tools, global studies of glaciers have become increasingly manageable. Many studies have used these global datasets to delineate spatial and temporal variations in glacier mass balance and dynamics. This has highlighted the effects of seasonal fluctuations and climatic trends on glaciers and has aided the identification of glacier surges. Here we focus on the often-neglected effects volcanoes may have on glacier mass balance and dynamics. First, we analyse the elevation distribution of the world’s glaciers with respect to their proximity to volcanoes and find that there is measurable evidence of the negative mass-balance effect volcanoes have on nearby glaciers. Second, we use the ITS_LIVE glacier velocity dataset along with the new TICOI regularisation and signal decomposition to identify velocity anomalies on glaciers located around volcanoes. This allows us to discern both short-lived velocity anomalies as well as long-term trends and variations in seasonal patterns. We identify multiple velocity anomalies, many of which correlate with climatic trends or weather events, others with surges, but some stand out as volcanically induced. We demonstrate examples of what effect various volcanic processes such as geothermal activity, mass wasting events, unrest, and pre-eruptive activity can have on glacier velocity.
How to cite: Unnsteinsson, T., Spagnolo, M., Rea, B., Girona, T., Barr, I., and Mullan, D.: Feeling the heat: global detection of volcanic effects on glacier mass balance and dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14408, https://doi.org/10.5194/egusphere-egu26-14408, 2026.
In this contribution, we will demonstrate the first prototype of the Svalbard cryosphere digital twin (SvalbardDT v1; https://svalbarddt.org/), which is a Digital Twin Component of ESAs Digital Twin Earth (DTE). An environmental digital twin observes a physical entity (i.e. the cryosphere), fuses multi-modal observations together, then generates what if scenarios to improve human decision-making. In this way, there are two-way flows of information (i.e. data) between the physical and digital assets. In Svalbard, where rates of warming are six times faster than the global average, digital twins have the potential to be used by researchers to analyse trends in the cryosphere, by local communities to improve navigation, and by policy-makers to improve decision-making. Furthermore, Svalbard is considered a ‘super site’ of in situ observations in a pan-Arctic context owing to the permanent infrastructure and long history of international scientific collaboration on the archipelago, hence it is a prime location to develop Arctic digital twin technology. Here, we will provide a demonstration of the capabilities of SvalbardDT version 1 as a new tool for monitoring Svalbard’s cryosphere in the 21st century (2010-present), the underlying architecture, and the next steps towards full operationalisation.
SvalbardDT represents a digital twin of Svalbard’s cryosphere covering glaciers, snow and sea ice observed through a combination of Earth Observation data sets and reanalysis data products. These data products are multi-modal i.e. they are collected at different resolutions, scales and time/spatial periods. SvalbardDT fine-tunes a deep learning foundational model to ingest the relevant data products and harmonise them into a 4D data cube describing the data set variable, x-dimension, y-dimension, and its changes over time. This produces weekly to monthly data cubes describing ~21 parameters. SvalbardDT focuses on the application of two case studies which are accessed through an online dashboard: (1) exploring the current state of cryosphere conditions in Svalbard to simulate terrestrial and marine navigation routes across the archipelago; and (2) forecast extreme Rain on Snow and Ice (ROSI) events using an AI foundation model. The associated toolboxes help to improve our understanding of the interconnecting processes shaping glaciers and ice caps across Svalbard.
How to cite: Harcourt, W. D., Fotheringham, M., Leontidis, G., Durrant, A., Morris, A., Malnes, E., Ricker, R., Luckman, A., Van Pelt, W., Pohjola, V., Jakob, L., and Gourmelen, N.: Monitoring and forecasting the state of Svalbard’s cryosphere using a digital twin (SvalbardDT), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5777, https://doi.org/10.5194/egusphere-egu26-5777, 2026.
Glaciers in the southern Andes are retreating rapidly because of ongoing climate warming. This process is reshaping the mountainous landscape and promoting the formation of new glacial lakes. Therefore, quantifying glacier ice thickness and subglacial topography is vitally important for assessing future water resources and glacier-retreat hazard. Of particular interest are bed over-deepening that will fill with water once they become ice-free. Such water bodies are potentially trigger sites for devasting Glacial Lake Outburst Floods (GLOF). However, estimates of ice thickness at the regional scale in the southern Andes remain uncertain due to the scarcity of in situ data on current glacier thickness as well as strong climatic and topographic gradients.
Here, we present a regional reconstruction of glacier ice thickness and subglacial topography for the entire Southern Andes, systematically constrained by the extensive archive of ground-penetrating radar measurements available for the region. Ice thickness is reconstructed, assuming perfect plasticity. Apart from direct measurements, we use glacier retreat and observe elevation changes to constrain the reconstruction. The resultant thickness map shows latitudinal contrasts in glacier geometry, with thin ice bodies in the desert Andes and substantially thicker ice masses in Patagonia.
We estimate a total volume of glacial ice of 5,960.6 km³ in the Southern Andes, equivalent to a global sea-level rise of 15.1 mm, with more than 95% of this volume concentrated in Patagonia, particularly in the two vast icefields there. By removing the modeled ice cover, we determine the subglacial topography and identify more than 6,000 overdeepenings that represent potential sites for future glacial lake formation. For each potential lake, we estimate its area, depth, and volume, yielding a total potential lake volume of approximately 177.6 km³ across the Southern Andes, with most of this volume concentrated in Patagonia (171.18 km³).
Our results provide a new measurement-informed, database for assessing future glacier evolution, freshwater storage, and emerging GLOF hazards in the Andes, providing critical information for climate adaptation and risk management in high-mountain regions
How to cite: Berkhoff, J. A., Farías-Barahona, D., Iribarren-Anacona, P., Sommer, C., Schaefer, M., Rodriguez, J. L., Uribe, J., and Fürst, J. J.: Unveiling the subglacial landscape in the Southern Andes shows an abundance of future glacial lakes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15350, https://doi.org/10.5194/egusphere-egu26-15350, 2026.
The European Alps have experienced massive glacier loss over the last decade, and 50% of all glaciers are expected to disappear even under current climate conditions by 2050. Glacier retreat, rapid meltwater production and damming, debustressing, and accelerated release of sediment massively increase the natural hazards in the region. The decline of glaciated areas in alpine regions directly impacts water reservoirs, tourist infrastructure, and alpine communities; therefore, precise quantification of rates of glacier retreat at a regional scale are paramount for foreseen prevention and mitigation plans.
The location and extent of mountain glaciers are conditioned by two independent factors: topography and climatic conditions. In this contribution, we explore the glacier changes over the last decade in two contrasting regions: (i) the drier and colder Ötztal Alps, host of the largest glaciers in Austria, and (ii) the wetter/warmer Zillertal Alps. Our aim is to decipher the key factors controlling glacier retreat at the regional scale. We used photogrammetric reconstruction of high-resolution aerial imagery at a 20cm resolution to (i) manually map the glacier extent in 2009/2010 and 2022 for 87 glaciers in the Zillertal and 48 glaciers in the Ötztal. The manual mapping was supported by stereographic visualizations for a precise photo-interpretation of glacier boundaries, (ii) quantify glacier retreat for each glacier in terms of relative area change (km2) and geodetic mass balance (m w.e.a-1), and (iii) calculate morphometric parameters such as slope and slope orientation. Finally, we contrasted ERA5 precipitation and temperature data with the findings.
This study presents a unique compilation of glacier extents and geodetic mass balance over 202 km² of the Ötztal and 184 km² of the Zillertal Alps from 2009 to 2022. Despite the contrasting morphological and climatic characteristics, glaciers in both mountain ranges retreat at similar rates. These findings demonstrate the dominant climatic control on glacier loss in the Eastern Alps and suggest that, under current climatic conditions, glacier morphology plays a minor role at the regional scale.
How to cite: Barbosa, N., Mayer, C., Jubanski, J., Münzer, U., Siegert, F., and Krautblatter, M.: Alpine glacier response to increasing temperature between 2009 and 2022. Insights from photogrammetric analysis in the Ötztal and Zillertal Alps (AT)., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18021, https://doi.org/10.5194/egusphere-egu26-18021, 2026.
Mountain glaciers affect water resources, natural hazards, sea-level rise, and more. Snowlines and melt extents are surrogate measurements of glacier mass balance that improve our understanding of glacier changes and help constrain models. Sentinel-1 synthetic aperture radar (SAR) provides reliable data every 12 days since ~2016 regardless of cloud coverage or daylight. We present a framework to derive glacier melt extents and transient snowlines from SAR data and demonstrate its utility for 99% of glaciers with an area of at least 2 km2 in Alaska, representing over 85% of the total glaciated area in Alaska. We subsequently leverage these observations as calibration data for the large-scale Python Glacier Evolution Model (PyGEM) and quantify changes in mass and equilibrium line altitude through the end of the 21st century for various emissions scenarios. We highlight stark spatial and temporal patterns in melt across and within various subregions in the observational dataset, as well as long-term trends revealed via modeling. All of our results are produced via an automated workflow that can easily be applied to other regions across the world.
How to cite: Wells, A., Rounce, D., Fahnestock, M., and Tober, B.: Present and future spatiotemporal patterns of glacier change across Alaska, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3601, https://doi.org/10.5194/egusphere-egu26-3601, 2026.
The demise of glaciers in the European Alps appears inevitable. The remaining question is the rate of ice loss and the timing of complete disappearance. This is particularly important for local water resource management and hazard assessment. Answering this requires glacier models that can robustly project glacier evolution over the coming decades. With the Instructed Glacier Model (IGM), an important tool for such projections has been made available, enabling computationally efficient modeling of glaciers as three-dimensional objects.
The remaining task is to accurately calibrate the glacier model. Fortunately, major advances in remote sensing provide an unprecedented amount of satellite observations that can be used for calibration on regional to global scale, even in remote areas without in-situ observations. The Framework for assimilating Remote-sensing Observations for Surface mass balance Tuning (FROST) utilizes a global elevation change product to infer equilibrium line altitudes and surface mass balance gradients. We tested the framework on 409 individual glaciers in the European Alps that were larger than 1 km² at the beginning of the century and evaluated the results against in situ measurements and end-of-summer snow line altitudes. The results show that the calibrated equilibrium line altitudes agree well with the snow line altitudes, while the surface mass balance gradients differ from glaciological measurements. This discrepancy is partially explained by the presence of small glaciers, which challenge gradient-based surface mass balance models, and by uncertainties of observation that lead to inaccurately modelled glacier flow. The first regional application of FROST shows promising results, provides meaningful insights and reveals challenges for glacier projections in central Europe.
How to cite: Herrmann, O., Prasad, V., Zöller, A., Groos, A. R., Cook, S., Sommer, C., and Fürst, J. J.: FROST in the European Alps – Implementing a data assimilation framework for calibration of surface mass balance models., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5383, https://doi.org/10.5194/egusphere-egu26-5383, 2026.
The accelerating loss of glacier mass is disrupting local ecosystems, reshaping hydrological regimes, increasing the likelihood of glacier-related hazards, and undermining the resilience of dependent communities. Quantifying annual glacier mass balance remains challenging because existing estimates rely on either sparse in situ and remote sensing observations or process-based models, each with inherent limitations. Observational approaches, while consistent at global scales, exhibit large regional variations. In contrast, modelling frameworks provide complete spatiotemporal fields but are typically deterministic, sensitive to calibration data, and constrained by assumptions that may overlook key energy balance drivers. Through our approach, we combine the strengths of models and various observational methods in a Bayesian neural network framework. Specifically, we use a Bayesian Neural Field architecture that is first pre-trained on fixed-geometry annual mass balance outputs from the Open Global Glacier Model (OGGM) and then finetuned using multimodal observations available at different spatial and temporal scales. The model uses static glacier characteristics and near-surface climate variables as predictors. We demonstrate through a blocked testing strategy that our framework can fill gaps for glaciers with missing or highly uncertain mass balance records as well as reconstruct meaningful long-term time series. In summary, this approach provides a scalable, uncertainty-aware method for generating spatially and temporally complete annual glacier mass balance estimates.
How to cite: Anilkumar, R., Bamber, J., Maussion, F., and Zemp, M.: Reconstructing Annual Global Glacier Mass Balance using Bayesian Neural Fields, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13047, https://doi.org/10.5194/egusphere-egu26-13047, 2026.
Glacier surface mass balance observations are highly heterogeneous, with point glaciological measurements spanning decadal timescales being limited to a few well-monitored regions, while geodetic observations are available globally with a multi-annual baseline and almost full glacier coverage. Existing surface mass balance modelling approaches can only calibrate their parameters for individual glaciers or regions with available observations. This often implies that a big part of the available observations per glacier (generally glaciological data) cannot be exploited for calibration since they play the role of independent data for validation. There is a need to move towards flexible surface mass balance models, capable of leveraging in a coherent fashion both glaciological and remote sensing data.
To address this challenge, we introduce a new version of MassBalanceMachine, a neural network-based model that predicts monthly surface mass balance using topographical features and monthly climate forcing as inputs. The pointwise nature of the model, combined with the global availability of input features thanks to OGGM and ERA5, enables the implementation of custom loss functions to train the model. This loss function allows the model to align with both glaciological measurements and geodetic mass balance observations over multiple decades.
By leveraging the interannual variability and point-wise nature from glaciological data and correcting long term biases with geodetic data, MassBalanceMachine can generate reliable high-resolution monthly mass balance predictions for unmonitored glaciers, and correct existing predictions where previous training strategies are known to fail, e.g. in the Alps. This approach exploits data from data-rich sparse regions to make predictions for other unmonitored regions or glaciers, offering a scalable solution for global glacier mass balance estimation.
How to cite: Gossard, A., Bolibar, J., van der Meer, M., and Hauknes Sjursen, K.: One mass balance model to rule them all: joint assimilation of remote sensing and glaciological data into MassBalanceMachine, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11039, https://doi.org/10.5194/egusphere-egu26-11039, 2026.
Glacier mass balance is a key indicator of climate change and a central driver of glacier evolution, yet most glaciers worldwide lack long-term in-situ measurements. For estimating glacier mass balance, data-driven models offer a complementary pathway to traditional numerical approaches by learning empirical relationships between climate forcing, topography, and mass balance directly from observations. Here, we develop a recurrent neural network based on a Long Short-Term Memory (LSTM) architecture within the Mass Balance Machine (MBM) framework and evaluate its ability to predict seasonal and annual point surface mass balance across the Swiss Alps. MBM is trained on 30'000 point observations from 30 glaciers and tested on eight glaciers excluded from training to assess spatial generalization. MBM predicts both winter and annual mass balance with high accuracy on unseen glaciers, and its recurrent structure provides a clear advantage by allowing the model to learn temporal dependencies, which improves the representation of seasons with strong accumulation or ablation. Beyond point predictions, MBM produces spatially distributed mass balance maps that closely resemble reference products directly derived from in-situ data, capture elevation-dependent gradients, and yield glacier-wide mass changes consistent with the differencing of repeated terrain models. Monthly outputs further show that the model captures the seasonal transition from winter accumulation to summer ablation and its dependence on elevation with realistic timing and magnitude. These results indicate that a recurrent neural network approach can recover key characteristics of glacier mass balance dynamics from sparse in-situ observations and that the learned relationships are transferable across glaciers with distinct meteorological and topographic settings. The demonstrated generalization skill and suitability for transfer learning highlight the potential of MBM for predicting glacier mass balance in regions with limited or no direct measurements.
How to cite: van der Meer, M., Zekollari, H., Gossard, A., Hauknes Sjursen, K., Bolibar, J., Huss, M., and Farinotti, D.: Glacier mass balance modeling using a Long Short-Term Memory network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20051, https://doi.org/10.5194/egusphere-egu26-20051, 2026.
Warm atmospheric conditions promote the rise of local microclimates over mountain glaciers and the generation of cool katabatic winds. These “Glacier winds” act to reinforce a shallow boundary layer above melting snow and ice in the summer months, mediating their response to temperature fluctuations in the wider mountain domain. Recent observational studies have highlighted the magnitude to which these temperature fluctuations over glaciers are decoupled from broader temperature changes, promoting cooling that can slow down melting. Nevertheless, they have largely drawn upon individual glacier case studies with little broader quantification of its scale and relevance for glaciers response to climate change.
We compiled meteorological observations from > 350 on-glacier automatic weather station records from >60 glaciers around the world over the past three decades. We contrast the air temperature conditions on-glacier with local, off-glacier weather station records to find that above-ice air temperatures warms ~0.73°C on average for every 1°C change in the surrounding mountain atmosphere. Using a combination of available meteorological and topographical data for each glacier in our dataset, we build a statistical model to estimate the magnitude of cooling over mountain glaciers globally. A global estimate of near-surface cooling over mountain glaciers based upon ERA5-Land climatologies for 2000-2022 reveals stark regional variability in how glaciers ‘feel’ temperature warming in the mountains. We demonstrate that larger glaciers in warmer and more humid environments maintain stronger and more persistent glacier winds that promote greater localised cooling. In contrast, on small, fragmenting glaciers in drier environments, especially those where exposure to synoptic winds or increased debris cover is common, glacier winds and localised cooling are limited.
We leverage ensemble climate estimates from socio-economic pathway SSP2-4.5 and SSP5-8.5 scenarios of sixth phase of the Coupled Model Intercomparison Project (CMIP6) as well as published estimates of glacier volume loss until 2100 to provide a first estimate of the expected future glacier cooling under a warmer climate. While glacier winds are expected to increase near-surface cooling under a warmer climate of the 2030’s and 2040’s in several glacier regions of the world, widespread glacier retreat will limit the development of these glacier winds and, ‘recouple’ temperature variability over glaciers to their surroundings and thus enhance the sensitivity of glaciers to broader climatic warming in the latter half of the 21st century. In the European Alps, it is estimated that the period of maximised glacier katabatic wind development has already passed, signalling the expected demise of cooler meteorological conditions there.
How to cite: Shaw, T., Miles, E., McCarthy, M., Buri, P., Guyennon, N., Salerno, F., Carturan, L., Brock, B., and Pellicciotti, F.: Cool no More! Mountain Glaciers Recouple to Atmospheric Warming over the Twenty-First Century, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11494, https://doi.org/10.5194/egusphere-egu26-11494, 2026.
Meltwater from High Mountain Asia (HMA) provides water resources to large downstream populations of central, southern and eastern Asia. Recent decades have seen critical changes in the health of the high mountain cryosphere of the region, which poses risks to those dependent upon its water supply. Glaciers of HMA have been observed to change mass at varying rates regionally, likely caused by a combination of heterogeneous climate change and region-specific glacier sensitivities. Regional studies on glacier sensitivities have been limited by computational power to rely on degree day models with higher dependence on calibration and equifinality issues. The same computational limits and data availability have limited energy balance studies to short periods, localised domains or coarsely resolved glacier regions. Explicit representation of glacier specific hypsometry and surface characteristics allows the inclusion of non-linear sensitivity of mass balance with elevation, as well as the inclusion of effects of debris cover. Here we provide a new regional picture of HMA glacier sensitivity to climate using a physically-based high resolution energy and mass balance model (Tethys & Chloris).
We run the model for 50 glaciers in each glacier subregion of HMA, chosen to be representative in terms of glacier area, median elevation, debris area and mean debris thickness. Each glacier is modelled using a clustering of points to discretise its surface, where the number of points is optimised depending on glacier size. We first build a baseline of glacier response to present climate (2000-2010) by forcing the model with bias-corrected hourly ERA5-Land data. For the bias correction, a precipitation factor and temperature bias is optimised for each glacier so that model results match against remotely sensed observations of glacier mass balance, snow line altitude and surface albedo.
To determine glacier sensitivities and their drivers, we perturb temperature and precipitation annually to quantify the mass balance changes at the glacier and regional scale. Further experiments perturbing seasonal climatic signals reveal the relevance and relative impacts of temperature and precipitation changes for regions previously identified as experiencing anomalous mass balance behaviour, such as the Pamir-Karakoram. We analyse the contribution of the main energy fluxes and melt components to the glacier-wide mass balance and quantify the relative influence of precipitation solid fraction and sublimation under these different climate perturbation scenarios. We study the sensitivity of the accumulation area ratio and the glacier equilibrium line altitude as indicators of the regional patterns in glacier health, its year-to-year variability and susceptibility to abrupt shifts under climatic extremes. With this, we identify and explain which regions may be most susceptible to future climate change signals, with an unprecedented attribution to the underlying glacier characteristics and changes in physical processes.
How to cite: Tumarkin, E., Shaw, T., Jouberton, A., Ren, S., and Pellicciotti, F.: Energy and mass balance sensitivities of glaciers to climate across High Mountain Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12522, https://doi.org/10.5194/egusphere-egu26-12522, 2026.
Glaciers are currently losing mass at unprecedented rates. These losses are often attributed to past climate forcing. However, a substantial share of glacier volume loss reflects the delayed adjustment of glacier geometry to past climate conditions. This lag arises because glacier geometry can evolve only as fast as ice can flow and surface mass balance can add or remove ice, making glacier change a continual race to adapt to changing climatic conditions.
In this study, we globally quantify the fraction of glacier volume loss that is committed to past climate forcing, relative to losses driven by concurrent climate conditions. Using the Open Global Glacier Model (OGGM), we decompose volume loss over the preceding decade for reference years. Each period is simulated twice: once forced by the climate of the reference period and once with the climate from a preceding period. This allows us to isolate the fraction of volume loss attributable to committed adjustment. Preliminary results indicate that of the 4.5% global glacier volume loss between 2000 and 2019, approximately 90% would also have occurred under the climate conditions of 2000–2010 alone, and nearly half can be attributed to climate conditions from 1990–2000.
Our results provide a new perspective on the evolving balance between glacier geometry and climate forcing, enabling a clearer interpretation of recent glacier change and its underlying drivers.
How to cite: Ponds, M., Aguayo, R., and Zekollari, H.: Lagged glacier response to past climate forcing and its role in recent volume loss, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7409, https://doi.org/10.5194/egusphere-egu26-7409, 2026.
Glacier thermal structure exerts a strong control on ice dynamics, yet it remains poorly characterized at the global scale. Despite its influence on glacier flow and, consequently, on melt and sea-level projections, thermal structure is typically neglected in large-scale glacier models. This omission stems from the fact that current methods of determining thermal structure require extensive field observations or computationally expensive modelling, resulting in only dozens of glaciers having well-defined thermal structures. In this work, we developed and applied a proxy-based approach to estimate the major heat sources (meltwater refreezing and strain heating) and transport mechanisms (advection and diffusion) in the heat transfer equation, which shapes glacier thermal structure. Proxies derived from publicly available observations and model output are calculated for glaciers in RGI-01 (Alaska) and RGI-07 (Svalbard and Jan Mayen). This framework enables a rapid regional assessment of thermal characteristics without requiring coupled thermomechanical modelling.
Across both Alaska and Svalbard, meltwater refreezing is the dominant heat source for 73% of glaciers, while diffusion dominates heat transport for 75% of glaciers. Note that this model only considers refreezing in the accumulation area, and 21% of these glaciers do not have an accumulation area. The majority (76%) of glaciers exhibit warmer accumulation-area ice transported toward cooler ablation areas. When examined by proxy rank, the most common pattern (47% of glaciers) is characterized by refreezing as the dominant heat source over strain heating, and vertical diffusion over horizontal advection as the primary transport mechanism. This pattern represents 25% of glacierized area, corresponding primarily to smaller glaciers (<10 km) scattered across Alaska, but found across sizes in Svalbard. Large Alaskan glaciers (39% of glacierized area), have a proxy pattern where refreezing dominates strong strain heating, while horizontal advection exceeds vertical diffusion. In Svalbard, this pattern is almost absent (<1% of glaciers). Comparing the proxy rank patterns of all glaciers in Alaska and Svalbard with those of glaciers with known thermal structures provides a correlation-based interpretation of the proxy results. For example, we found that the calculated proxy pattern for known temperate glaciers is characterized by dominant refreezing and advection, whereas known cold glaciers exhibit the pattern characterized by dominant refreezing over strain heating and diffusion over advection. Furthermore, a number of large glaciers on the continental side of the St. Elias Mountains in Alaska exhibit a proxy pattern distinct from those associated with well-established thermal structures in the region.
This work presents a way to calculate heat-source and transport-mechanism proxies as a practical means of estimating glacier thermal characteristics for any RGI region with sufficient input data. The resulting proxy distributions provide new insight into spatial patterns of heat generation and transfer in glaciers at the regional scale and establish a baseline for evaluating thermal responses to climate change. Moreover, these results can serve as comparative datasets for emerging emulator-based, regional-scale thermal modelling.
How to cite: Martinez Gracey, D., Flowers, G., and Jacquemart, M.: A proxy-based method of estimating glacier heat sources and transport mechanisms to map thermal processes at a regional scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14679, https://doi.org/10.5194/egusphere-egu26-14679, 2026.
Recognising the global importance and vulnerability of mountain glaciers, the United Nations General Assembly declared 2025 the International Year of Glaciers’ Preservation, highlighting the critical role of glaciers in the hydrological cycle and the growing societal risks associated with their rapid decline. These concerns are well founded: glaciers worldwide are retreating rapidly, yet projections have traditionally focused on changes in mass and area rather than on the fate of individual glaciers.
Here, we quantify the future evolution of the global number of glaciers and introduce the concept of peak glacier extinction: the year in which the largest number of individual glaciers is projected to disappear. Using three global glacier models and the Randolph Glacier Inventory v6.0, we project the fate of more than 200,000 glaciers worldwide under four policy-relevant global warming scenarios by 2100 (+1.5 °C, +2.0 °C, +2.7 °C and +4.0 °C relative to pre-industrial levels). A glacier is classified as extinct when its area falls below 0.01 km² or its remaining volume declines to less than 1% of its initial value.
Across all scenarios, we identify a pronounced mid-century peak in glacier extinction. Under a +1.5 °C pathway, this peak occurs around 2041, with approximately 2,000 glaciers disappearing per year, whereas under a +4.0 °C scenario it shifts to the mid-2050s and intensifies to nearly 4,000 glacier extinctions annually. Regional differences reflect contrasts in glacier size distributions and climatic conditions. Our results highlight the urgency of ambitious climate action. Whether the world faces the loss of 2,000 or 4,000 glaciers per year by mid-century, and whether roughly 20,000 or 100,000 glaciers remain by the end of the century, will be determined by near-term policy and societal choices made today.
How to cite: Van Tricht, L., Zekollari, H., Huss, M., Rounce, D., Schuster, L., Aguayo, R., Schmitt, P., Maussion, F., Tober, B., and Farinotti, D.: Peak glacier extinction in the mid-twenty-first century, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2716, https://doi.org/10.5194/egusphere-egu26-2716, 2026.
