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• Lovell, Harold; Benn, Douglas I.; Jiskoot, Hester; Stokes, Chris R.; Flowers, Gwenn E. & Guillet, Gregoire [Vis alle 15 forfattere av denne artikkelen] (2026). Glacier surging and surge-related hazards in a changing climate. Nature Reviews Earth & Environment. 7, s. 162–180. doi: 10.1038/s43017-025-00757-9. Fulltekst i vitenarkiv Vis sammendrag

  Glacier surges are ice flow instabilities characterized by periods of acceleration, during which mass is rapidly transferred from high to low elevations and the glacier front often advances. In this Review, we outline global trends in glacier surging and the influence of climate on the distribution and behaviour of surge-type glaciers and surge-related hazards. Glaciers exhibit diverse surging behaviours and typically recur at intervals that range from 5 years to over 100 years, with all surges largely driven by a reduction in basal friction. Most of the ~3,100 surge-type glaciers identified globally are clustered in the Arctic and Subarctic (48.3%) and High Mountain Asia (50.5%), where climate conditions are conducive to the development of surge instabilities, and there is emerging evidence that climate warming is changing surge behaviour. At least 81 surge-type glaciers globally have caused hazards such as ice-dammed glacial lake outburst floods, which can lead to infrastructure damage and loss of life. Future research should acquire spatiotemporally high-resolution remote-sensing data and direct observations of basal processes during all stages of surges, develop numerical models to better capture surge mechanisms and diversity, and project the impact of future climate warming on surge-type glacier behaviour and distribution.

• Chevrollier, Lou-Anne; Wehrlé, Adrien; Cook, Joseph M.; Guillet, Gregoire; Benning, Liane G. & Anesio, Alexandre M. [Vis alle 7 forfattere av denne artikkelen] (2025). Mapping red algal blooms and their albedo-reducing effect on seasonal snowfields at Hardangervidda, Southern Norway. Frontiers in Earth Science. 13. doi: 10.3389/feart.2025.1508719. Fulltekst i vitenarkiv Vis sammendrag

  Red snow algae bloom at the surface of snowfields worldwide, and their detection is relevant for ecological, biogeochemical and mass balance studies. In this study, we co-located RGB imagery acquired with a light-weight Uncrewed Aerial Vehicle (UAV) to 129 hyperspectral reflectance spectra from which the snow surface properties were retrieved, thereby enabling high-resolution aerial mapping of algal properties. We present maps of red snow algae abundance and albedo reducing effect over ∼ 9700 m2 of seasonal snowfields across Hardangervidda, Southern Norway, in July and August 2023. The average albedo reducing effect of the algae over the entire area was 0.012 ± 0.005, and attained 0.028 ± 0.004 on a snowfield of ∼ 710m2 Across snow surfaces with visible blooms only, the algal albedo reducing effect was 0.045 ± 0.003, equivalent to an additional ∼ 3 mm of daily melting under local illumination conditions, and aggregating to 5,500 ± 2,300 kg of daily snowmelt. The intensity and spatial coverage of surface algal blooms were very variable between and within the individual snowfields. Analysis of the UAV imagery suggests that multiple small and distributed samples are at least twice more likely to yield representative estimates of the average snow algal concentration of a snowfield compared to fewer, larger samples. Our study demonstrates the potential of low-cost and easy to deploy UAVs for red snow algal monitoring at the cm to sub-cm scale, which can be used to better understand their spatial ecology and role in albedo reduction.

• Piermattei, Livia; Zemp, Michael; Sommer, Christian; Brun, Fanny; Braun, Matthias H. & Andreassen, Liss M. [Vis alle 35 forfattere av denne artikkelen] (2024). Observing glacier elevation changes from spaceborne optical and radar sensors - an inter-comparison experiment using ASTER and TanDEM-X data. The Cryosphere. ISSN 1994-0416. 18(7), s. 3195–3230. doi: 10.5194/tc-18-3195-2024. Fulltekst i vitenarkiv Vis sammendrag

  Observations of glacier mass changes are key to understanding the response of glaciers to climate change and related impacts, such as regional runoff, ecosystem changes, and global sea level rise. Spaceborne optical and radar sensors make it possible to quantify glacier elevation changes, and thus multi-annual mass changes, on a regional and global scale. However, estimates from a growing number of studies show a wide range of results with differences often beyond uncertainty bounds. Here, we present the outcome of a community-based inter-comparison experiment using spaceborne optical stereo (ASTER) and synthetic aperture radar interferometry (TanDEM-X) data to estimate elevation changes for defined glaciers and target periods that pose different assessment challenges. Using provided or self-processed digital elevation models (DEMs) for five test sites, 12 research groups provided a total of 97 spaceborne elevation-change datasets using various processing approaches. Validation with airborne data showed that using an ensemble estimate is promising to reduce random errors from different instruments and processing methods but still requires a more comprehensive investigation and correction of systematic errors. We found that scene selection, DEM processing, and co-registration have the biggest impact on the results. Other processing steps, such as treating spatial data voids, differences in survey periods, or radar penetration, can still be important for individual cases. Future research should focus on testing different implementations of individual processing steps (e.g. co-registration) and addressing issues related to temporal corrections, radar penetration, glacier area changes, and density conversion. Finally, there is a clear need for our community to develop best practices, use open, reproducible software, and assess overall uncertainty to enhance inter-comparison and empower physical process insights across glacier elevation-change studies.

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• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Mazzolini, Marco; Guillet, Gregoire; Yang, Ruitang & Hock, Regine (2025). Data assimilation for snow and glacier modeling in climate applications. Fulltekst i vitenarkiv
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Guillet, Gregoire; Rounce, David; Tober, Brandon & Yang, Ruitang [Vis alle 7 forfattere av denne artikkelen] (2025). Calibrating future glacier projections using data assimilation. doi: https:/imc2025.info/imc25/abstract/calibrating-future-glacier-projections-using-data-assimilation/. Fulltekst i vitenarkiv Vis sammendrag

  Global glacier mass is changing rapidly, and projections of global glacier mass balance under changing climatic conditions are crucial for informed decision-making. Existing glacier projections use relatively simple glacier models constrained by sparse observations. In these projections, model calibration plays a key role in constraining uncertainty. The use of Bayesian data assimilation methods for calibration by integrating multiple emerging observational data sets (in-situ mass balance measurements, climate reanalyses, and satellite remote sensing) to constrain model parameters and their dynamics remains relatively unexplored. Such a probabilistic calibration strategy could enable us to quantify and disentangle uncertainties related to the glacier model, the selected climate model forcing, and internal climate variability. The Python Glacier Evolution Model (PyGEM) is one of a handful of global glacier models that allow us to simulate the evolution of the mass balance of all glaciers in the world. In this work, we adopt ensemble-based data assimilation methods to calibrate PyGEM model parameters and thus constrain future projections of glacier mass balance across Scandinavia. We compare our results with traditional glacier model calibration algorithms and the Bayesian gold standard Markov Chain Monte Carlo (MCMC) method in PyGEM for glacio-hydrological indicators (surface mass balance and runoff projections) between 2015 and 2100 with four SSP scenarios. This work serves as the kernel for a scalable glacier data assimilation framework to produce policy relevant global glacier projections and scenarios within the recently funded ERC-AdG GLACMASS project. The probabilistic calibration framework developed in this study can in principle be adapted for a wide range of cryospheric applications.

• Aalstad, Kristoffer; Guillet, Gregoire; Yang, Ruitang; Yilmaz, Yeliz & Hock, Regine (2025). Glacier reanalysis using hierarchical data assimilation. Fulltekst i vitenarkiv
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Guillet, Gregoire & Hock, Regine (2025). Strategies for Future Projections with PyGEM. Fulltekst i vitenarkiv
• Aalstad, Kristoffer; Alonso-Gonzalez, Esteban; Fiddes, Joel; Guillet, Gregoire; Kääb, Andreas Max & Mazzolini, Marco [Vis alle 11 forfattere av denne artikkelen] (2025). Ensemble-based cryospheric reanalysis to infer global snow mass. Fulltekst i vitenarkiv Vis sammendrag

  Snow, glaciers, and permafrost are essential climate variables (ECV) that regulate the global cycles of energy, carbon, and water. At the same time, these cryospheric ECVs are only partially observable by satellites due to gaps, noise, and indirect retrieval algorithms. Data assimilation (DA), namely the Bayesian fusion of uncertain models and noisy observations, presents a natural solution to the problem of partial observability. Nonetheless, unlike in numerical weather prediction, DA has received relatively little attention from the Earth observation (EO) community despite its potential as a generalized retrieval framework that adds value by filling gaps and inferring latent ECVs with uncertainty quantification. This contribution presents active research on applying ensemble-based DA techniques to carry out cryospheric reanalysis constrained by satellite data. Our future goals with these efforts are to generate consistent global high resolution reanalyses of seasonal snow mass (also known as snow water equivalent or simply SWE), glacier mass balance, and the thermal state of permafrost. In doing so, we seek to leverage a myriad of data streams including Earth observing satellites, global atmospheric reanalyses, airborne retrievals, and in-situ observations. Here we focus on seasonal snow since accurate global snow mass estimation, particularly in mountainous terrain, remains a major unsolved problem in snow hydrology. To help tackle this problem, we are developing a global scale ensemble-based snow reanalysis product by assimilating ESA Snow_cci fractional snow-covered area retrievals. Using a simple snow model we are able to devote considerable computational resources to generate a global snow reanalysis at daily temporal resolution and kilometric spatial resolution while still being able to afford using promising iterative ensemble-based DA schemes. For the latter, we explore recent developments in hybridizing iterative ensemble Kalman and particle methods to provide robust Bayesian posterior inference. In particular, we demonstrate how these schemes can be used as nested smoothers to hierarchically infer prior hyperparameters related to snow climatology. The new reanalysis approach is evaluated using independent spaceborne, airborne, and in-situ validation data by comparing its performance to state-of-the-art regional snow reanalyses and existing global snow mass products in the form of ERA5-Land reanalysis data and ESA Snow_cci SWE retrievals. Unlike existing global snow products, this new snow reanalysis product is uncertainty-aware, assimilates snow satellite data, and specifically targets a key knowledge gap concerning mountain snow mass. Concurrent efforts to adapt this DA framework towards the generation of global ensemble-based reanalyses for glaciers and permafrost will also be showcased to emphasize synergies across these terrestrial cryospheric reanalysis efforts. We highlight that, by combining EO with cryospheric models, ensemble-based DA can transform largely untapped climate data into actionable climate information on cryospheric ECVs. The baked-in uncertainty quantification in this probabilistic climate information empowers us to make decisions in response to climate change and its perilous impacts on the mountain cryosphere.

• Moudgil, Pragay Shourya; Vaate, Inger Bij de; Hock, Regine & Guillet, Gregoire (2025). Mass balance modeling of glaciers in Switzerland using machine learning. Fulltekst i vitenarkiv

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