Aðalgeirsdóttir, G., Magnússon, E., Pálsson, F., Thorsteinsson, T., Belart, J. M. C., Jóhannesson, T., Hannesdóttir, H., Sigurðsson, O., Gunnarsson, A., Einarsson, B., Berthier, E., Schmidt, L. S., Haraldsson, H. H., and Björnsson, H.: Glacier Changes in Iceland From ∼1890 to 2019, Front. Earth Sci., 8, https://doi.org/10.3389/feart.2020.523646, 2020. a

Andreassen, L. M., Elvehøy, H., Kjøllmoen, B., and Belart, J. M. C.: Glacier change in Norway since the 1960s – an overview of mass balance, area, length and surface elevation changes, J. Glaciol., 66, 313–328, https://doi.org/10.1017/jog.2020.10, 2020. a

Anilkumar, R., Bharti, R., Chutia, D., and Aggarwal, S. P.: Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques, The Cryosphere, 17, 2811–2828, https://doi.org/10.5194/tc-17-2811-2023, 2023. a

Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., and Gentine, P.: Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems, Phys. Rev. Lett., 126, 098302, https://doi.org/10.1103/PhysRevLett.126.098302, 2021. a

Bisset, R. R., Dehecq, A., Goldberg, D. N., Huss, M., Bingham, R. G., and Gourmelen, N.: Reversed Surface-Mass-Balance Gradients on Himalayan Debris-Covered Glaciers Inferred from Remote Sensing, Remote Sens., 12, https://doi.org/10.3390/rs12101563, 2020. a

Bolibar, J., Rabatel, A., Gouttevin, I., Galiez, C., Condom, T., and Sauquet, E.: Deep learning applied to glacier evolution modelling, The Cryosphere, 14, 565–584, https://doi.org/10.5194/tc-14-565-2020, 2020. a, b

Bolibar, J., Sapienza, F., Maussion, F., Lguensat, R., Wouters, B., and Pérez, F.: Universal differential equations for glacier ice flow modelling, Geosci. Model Dev., 16, 6671–6687, https://doi.org/10.5194/gmd-16-6671-2023, 2023. a, b, c

Braithwaite, R. and Olesen, O.: Calculation of glacier ablation from air temperature, West Greenland, 219–233, Kluwer Academic Publishers, the Netherlands, j. oerlemans edn., 1989. a, b

Burgess, E. W., Larsen, C. F., and Forster, R. R.: Summer melt regulates winter glacier flow speeds throughout Alaska, Geophys. Res. Lett., 40, 6160–6164, https://doi.org/10.1002/2013GL058228, 2013. a

Cao, W., Aalstad, K., Schmidt, L. S., Westermann, S., and Schuler, T. V.: Glacier data assimilation on an Arctic glacier: Learning from large ensemble twin experiments, arxiv [preprint], https://arxiv.org/abs/2502.09314 (last access: 27 January 2026), 2025. a

Chen, W., Yao, T., Zhang, G., Li, F., Zheng, G., Zhou, Y., and Xu, F.: Towards ice-thickness inversion: an evaluation of global digital elevation models (DEMs) in the glacierized Tibetan Plateau, The Cryosphere, 16, 197–218, https://doi.org/10.5194/tc-16-197-2022, 2022. a

Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y.: Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), edited by: Moschitti, A., Pang, B., and Daelemans, W., 1724–1734, Association for Computational Linguistics, Doha, Qatar, https://doi.org/10.3115/v1/D14-1179, 2014. a

Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., and Jones, P. D.: An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets, J. Geophys. Res.-Atmos., 123, 9391–9409, https://doi.org/10.1029/2017JD028200, 2018. a

Dalla Torre, D., Di Marco, N., Menapace, A., Avesani, D., Righetti, M., and Majone, B.: Suitability of ERA5-Land reanalysis dataset for hydrological modelling in the Alpine region, J. Hydrol.: Reg. Stud. 52, 101718, https://doi.org/10.1016/j.ejrh.2024.101718, 2024. a

Davaze, L., Rabatel, A., Dufour, A., Hugonnet, R., and Arnaud, Y.: Region-Wide Annual Glacier Surface Mass Balance for the European Alps From 2000 to 2016, Front. Earth Sci., 8, https://doi.org/10.3389/feart.2020.00149, 2020. a

Daw, A., Karpatne, A., Watkins, W., Read, J., and Kumar, V.: Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling, arxiv [preprint], https://arxiv.org/abs/1710.11431 (last access: 27 January 2026), 2021. a, b

Delhasse, A., Kittel, C., Amory, C., Hofer, S., van As, D., S. Fausto, R., and Fettweis, X.: Brief communication: Evaluation of the near-surface climate in ERA5 over the Greenland Ice Sheet, The Cryosphere, 14, 957–965, https://doi.org/10.5194/tc-14-957-2020, 2020. a

Drolon, V., Maisongrande, P., Berthier, E., Swinnen, E., and Huss, M.: Monitoring of seasonal glacier mass balance over the European Alps using low-resolution optical satellite images, J. Glaciol., 62, 912–927, https://doi.org/10.1017/JOG.2016.78, 2016. a

Elvehøy, H., Kjøllmoen, B., Andreassen, L. M., and Sjursen, K. H.: Glaciological point mass balance measurements for Norway 1962–2021, Norwegian Water Resources and Energy Directorate (NVE) [data set], https://doi.org/10.58059/sjse-6w92, 2025. a

Fischer, A.: Comparison of direct and geodetic mass balances on a multi-annual time scale, The Cryosphere, 5, 107–124, https://doi.org/10.5194/tc-5-107-2011, 2011. a

Flowers, G. E., Jarosch, A. H., Belliveau, P. T. A. P., and Fuhrman, L. A.: Short-term velocity variations and sliding sensitivity of a slowly surging glacier, Ann. Glaciol., 57, 71–83, https://doi.org/10.1017/aog.2016.7, 2016. a

Fox, E., Schwartz-Marin, E., Rangecroft, S., Palmer, S., and Harrison, S.: ‘Water resource’ framing for the value and governance of glacier water availability in the semi-arid Chilean Andes, Front. Water, 6, https://doi.org/10.3389/frwa.2024.1367889, 2024. a

GCOS: The 2022 GCOS ECVs Requirements (GCOS 245), https://library.wmo.int/idurl/4/58111 (last access: 27 January 2026), 2022. a, b

Geissler, J., Mayer, C., Jubanski, J., Münzer, U., and Siegert, F.: Analyzing glacier retreat and mass balances using aerial and UAV photogrammetry in the Ötztal Alps, Austria, The Cryosphere, 15, 3699–3717, https://doi.org/10.5194/tc-15-3699-2021, 2021. a

GLACIOCLIM: French glacier mass balance observations, Observatoire GLACIOCLIM, France, point mass balance measurements for Argentière, Gébroulaz and Saint-Sorlin glaciers, https://glacioclim.osug.fr/, last access: 23 September 2026. a

GLAMOS: Swiss Glacier Mass Balance, release 2024, Glacier Monitoring Switzerland, GLAMOS Data [data set], https://doi.org/10.18750/massbalance.2024.r2024, 2024. a

GLIMS Consortium: GLIMS Glacier Database, Version 1, Boulder Colorado, USA. NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.7265/N5V98602, 2015. a

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a

Hock, R.: Temperature index melt modelling in mountain areas, J. Hydrol., 282, 104–115, https://doi.org/10.1016/S0022-1694(03)00257-9, 2003. a, b, c, d, e, f

Hock, R.: Glacier melt: a review of processes and their modelling, Prog. Phys. Geogr.: Earth and Environment, 29, 362–391, https://doi.org/10.1191/0309133305pp453ra, 2005. a

Hock, R., Kootstra, D.-S., and Reijmer, C.: Deriving glacier mass balance from accumulation area ratio on Storglaciären, Sweden, in: 7th Scientific Assembly of the International Association of Hydrological Science, IAHS – Workshop on Andean Glaciology and Symposium on the Contribution from Glaciers and Snow Cover to Runoff from Mountains in Different Climates, 318, 163–170, https://www.scopus.com/inward/record.uri?eid=2-s2.0-38549156622&partnerID=40&md5=abe3ce04fbedff2850dd16badab023de (last access: 27 January 2026), 2007. a

Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., and Kääb, A.: Accelerated global glacier mass loss in the early twenty-first century, Nature, 592, 726–731, https://doi.org/10.1038/S41586-021-03436-Z, 2021. a, b, c, d, e, f, g, h

Huss, M.: Density assumptions for converting geodetic glacier volume change to mass change, The Cryosphere, 7, 877–887, https://doi.org/10.5194/tc-7-877-2013, 2013. a

Huss, M. and Hock, R.: A new model for global glacier change and sea-level rise, Front. Earth Sci., 3, https://doi.org/10.3389/feart.2015.00054, 2015. a, b, c, d, e, f, g, h

Icelandic Glaciers: Iceland Glacier Web Portal, includes glacier mass balance and observational data, https://www.icelandicglaciers.is/ (last access: 3 January 2026), 2025. a

Jennings, K. S., Winchell, T. S., Livneh, B., and Molotch, N. P.: Spatial variation of the rain–snow temperature threshold across the Northern Hemisphere, Nat. Commun., 9, 1148, https://doi.org/10.1038/s41467-018-03629-7, 2018. a

Jia, X., Willard, J., Karpatne, A., Read, J. S., Zwart, J. A., Steinbach, M., and Kumar, V.: Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles, ACM/IMS Trans. Data Sci., 2, https://doi.org/10.1145/3447814, 2021. a, b

Jouvet, G.: Inversion of a Stokes glacier flow model emulated by deep learning, J. Glaciol., 69, 13–26, https://doi.org/10.1017/jog.2022.41, 2023. a

Jouvet, G. and Cordonnier, G.: Ice-flow model emulator based on physics-informed deep learning, J. Glaciol., 69, 1941–1955, https://doi.org/10.1017/jog.2023.73, 2023. a

Jouvet, G., Cordonnier, G., Kim, B., Lüthi, M., Vieli, A., and Aschwanden, A.: Deep learning speeds up ice flow modelling by several orders of magnitude, J. Glaciol., 68, 651–664, https://doi.org/10.1017/jog.2021.120, 2022. a

Kingma, D. P. and Ba, J. L.: Adam: A Method for Stochastic Optimization, in: 3rd International Conference on Learning Representations, ICLR 2015 – Conference Track Proceedings, International Conference on Learning Representations, ICLR, arxiv, https://doi.org/10.48550/arxiv.1412.6980, 2014. a

Li, B., Sun, T., Tian, F., and Ni, G.: Enhancing process-based hydrological models with embedded neural networks: A hybrid approach, J. Hydrol., 625, 130107, https://doi.org/10.1016/j.jhydrol.2023.130107, 2023. a, b

Litt, M., Shea, J., Wagnon, P., Steiner, J., Koch, I., Stigter, E., and Immerzeel, W.: Glacier ablation and temperature indexed melt models in the Nepalese Himalaya, Sci. Rep., 9, 5264, https://doi.org/10.1038/s41598-019-41657-5, 2019. a

Marzeion, B., Jarosch, A. H., and Hofer, M.: Past and future sea-level change from the surface mass balance of glaciers, The Cryosphere, 6, 1295–1322, https://doi.org/10.5194/tc-6-1295-2012, 2012. a, b

Maslov, K. A., Persello, C., Schellenberger, T., and Stein, A.: Globally scalable glacier mapping by deep learning matches expert delineation accuracy, Nat. Commun., 16, 43, https://doi.org/10.1038/s41467-024-54956-x, 2025a. a

Maslov, K. A., Schellenberger, T., Persello, C., and Stein, A.: Three times accelerated glacier area loss in Svalbard revealed by deep learning, EarthArXiv [preprint], https://doi.org/10.31223/X5472T (last access: 3 January 2026), 2025b. a

Maslov, K. A., Hugonnet, R., Tolle, F., Bernard, E., Friedt, J.-M., Griselin, M., Małecki, J., Schellenberger, T., Persello, C., and Stein, A.: GlaUnTI: Glacier surface mass balance dataset, Version 1, 4TU.ResearchData [data set], https://doi.org/10.4121/5ea53bc3-2c85-42bb-89d1-606c8ed1d80a, 2026a. a

Maslov, K. A., Schellenberger, T., Pandit, P., Persello, C., and Stein, A.: Automating glacier facies classification: Benchmark dataset and deep learning baseline from pan-European sample, Science of Remote Sensing, 14, 100506, https://doi.org/10.1016/j.srs.2026.100506, 2026b. a

Maslov, K. A., Schellenberger, T., Persello, C., and Stein, A.: GlaUnTI: GLAcier-UNiversal Temperature Index model v1.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.23418693, 2026c. a

Maslov, K. A., Schellenberger, T., Persello, C., and Stein, A.: DEM Imputation v1.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.23418711, 2026d. a

Maussion, F., Butenko, A., Champollion, N., Dusch, M., Eis, J., Fourteau, K., Gregor, P., Jarosch, A. H., Landmann, J., Oesterle, F., Recinos, B., Rothenpieler, T., Vlug, A., Wild, C. T., and Marzeion, B.: The Open Global Glacier Model (OGGM) v1.1, Geosci. Model Dev., 12, 909–931, https://doi.org/10.5194/gmd-12-909-2019, 2019. a

Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a

Ohmura, A.: Physical Basis for the Temperature-Based Melt-Index Method, J. Appl. Meteorol., 40, 753–761, https://doi.org/10.1175/1520-0450(2001)040<0753:PBFTTB>2.0.CO;2, 2001. a

Pellicciotti, F., Buergi, C., Immerzeel, W. W., Konz, M., and Shrestha, A. B.: Challenges and Uncertainties in Hydrological Modeling of Remote Hindu Kush–Karakoram–Himalayan (HKH) Basins: Suggestions for Calibration Strategies, Mt. Res. Dev., 32, 39–50, https://doi.org/10.1659/MRD-JOURNAL-D-11-00092.1, 2012. a

Podgórski, J., Kinnard, C., Pętlicki, M., and Urrutia, R.: Performance Assessment of TanDEM-X DEM for Mountain Glacier Elevation Change Detection, Remote Sens., 11, https://doi.org/10.3390/rs11020187, 2019. a

Prein, A. F. and Gobiet, A.: Impacts of uncertainties in European gridded precipitation observations on regional climate analysis, Int. J. Climatol., 37, 305–327, https://doi.org/10.1002/joc.4706, 2017. a

Rabatel, A., Dedieu, J. P., and Vincent, C.: Using remote-sensing data to determine equilibrium-line altitude and mass-balance time series: validation on three French glaciers, 1994–2002, J. Glaciol., 51, 539–546, https://doi.org/10.3189/172756505781829106, 2005. a

Rabatel, A., Sirguey, P., Drolon, V., Maisongrande, P., Arnaud, Y., Berthier, E., Davaze, L., Dedieu, J. P., and Dumont, M.: Annual and seasonal glacier-wide surface mass balance quantified from changes in glacier surface state: A review on existing methods using optical satellite imagery, Remote Sens., 9, https://doi.org/10.3390/rs9050507, 2017. a

Rackauckas, C., Ma, Y., Martensen, J., Warner, C., Zubov, K., Supekar, R., Skinner, D., Ramadhan, A., and Edelman, A.: Universal Differential Equations for Scientific Machine Learning, arxiv, https://arxiv.org/abs/2001.04385 (last access: 27 January 2026), 2021. a

Rahimi, A. and Recht, B.: Weighted sums of random kitchen sinks: replacing minimization with randomization in learning, in: Proceedings of the 22nd International Conference on Neural Information Processing Systems, NIPS'08, 1313–1320, Curran Associates Inc., Red Hook, NY, USA, ISBN 9781605609492, 2008. a

Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019. a, b

RGI Consortium: Randolph Glacier Inventory – A Dataset of Global Glacier Outlines, Version 7, Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center [data set], https://doi.org/10.5067/f6jmovy5navz, 2023. a, b, c

Rounce, D. R., Khurana, T., Short, M. B., Hock, R., Shean, D. E., and Brinkerhoff, D. J.: Quantifying parameter uncertainty in a large-scale glacier evolution model using Bayesian inference: application to High Mountain Asia, J. Glaciol., 66, 175–187, https://doi.org/10.1017/jog.2019.91, 2020. a

Rounce, D. R., Hock, R., Maussion, F., Hugonnet, R., Kochtitzky, W., Huss, M., Berthier, E., Brinkerhoff, D., Compagno, L., Copland, L., Farinotti, D., B., and McNabb, R. W.: Global glacier change in the 21st century: Every increase in temperature matters, Science, 379, 78–83, https://doi.org/10.1126/science.abo1324, 2023. a, b, c, d, e, f, g

Scher, C., Steiner, N. C., and McDonald, K. C.: Mapping seasonal glacier melt across the Hindu Kush Himalaya with time series synthetic aperture radar (SAR), The Cryosphere, 15, 4465–4482, https://doi.org/10.5194/tc-15-4465-2021, 2021. a

Schmidt, L. S., Schuler, T. V., Thomas, E. E., and Westermann, S.: Meltwater runoff and glacier mass balance in the high Arctic: 1991–2022 simulations for Svalbard, The Cryosphere, 17, 2941–2963, https://doi.org/10.5194/tc-17-2941-2023, 2023. a

Schuler, T. V., Kohler, J., Elagina, N., Hagen, J. O. M., Hodson, A. J., Jania, J. A., Kääb, A. M., Luks, B., Małecki, J., Moholdt, G., Pohjola, V. A., Sobota, I., and Van Pelt, W. J. J.: Reconciling Svalbard Glacier Mass Balance, Front. Earth Sci., 8, https://doi.org/10.3389/feart.2020.00156, 2020. a

Sjursen, K. H., Dunse, T., Tambue, A., Schuler, T. V., and Andreassen, L. M.: Bayesian parameter estimation in glacier mass-balance modelling using observations with distinct temporal resolutions and uncertainties, J. Glaciol., 69, 1803–1822, https://doi.org/10.1017/jog.2023.62, 2023. a

Sjursen, K. H., Bolibar, J., van der Meer, M., Andreassen, L. M., Biesheuvel, J. P., Dunse, T., Huss, M., Maussion, F., Rounce, D. R., and Tober, B.: Machine learning improves seasonal mass balance prediction for unmonitored glaciers, The Cryosphere, 19, 5801–5826, https://doi.org/10.5194/tc-19-5801-2025, 2025. a, b, c

Sundararajan, M., Taly, A., and Yan, Q.: Axiomatic Attribution for Deep Networks, in: Proceedings of the 34th International Conference on Machine Learning, edited by: Precup, D. and Teh, Y. W., vol. 70 of Proceedings of Machine Learning Research, 3319–3328, PMLR, https://proceedings.mlr.press/v70/sundararajan17a.html (last access: 27 January 2026), 2017. a, b

Swenson, S. C. and Lawrence, D. M.: A new fractional snow-covered area parameterization for the Community Land Model and its effect on the surface energy balance, J. Geophys. Res.-Atmos., 117, https://doi.org/10.1029/2012JD018178, 2012. a

van der Meer, M., Zekollari, H., Huss, M., Bolibar, J., Sjursen, K. H., and Farinotti, D.: A minimal machine-learning glacier mass balance model, The Cryosphere, 19, 805–826, https://doi.org/10.5194/tc-19-805-2025, 2025. a, b, c, d

Van Tricht, L., Paice, C. M., Rybak, O., Popovnin, V., Satylkanov, R., and Huybrechts, P.: Monitoring the annual geodetic mass balance of Bordu and Sary-Tor glaciers using UAV data, Ann. Glaciol., 64, 320–329, https://doi.org/10.1017/aog.2023.71, 2023. a

Vidaller, I., Revuelto, J., Izagirre, E., Rojas-Heredia, F., Alonso-González, E., Gascoin, S., René, P., Berthier, E., Rico, I., Moreno, A., Serrano, E., Serreta, A., and López-Moreno, J. I.: Toward an Ice-Free Mountain Range: Demise of Pyrenean Glaciers During 2011–2020, Geophys. Res. Lett., 48, e2021GL094339, https://doi.org/10.1029/2021GL094339, 2021. a

WGMS: Fluctuations of Glaciers Database, World Glacier Monitoring Service, Zurich, Switzerland [data set], https://doi.org/10.5904/wgms-fog-2021-05, 2021. a, b

Zekollari, H., Huss, M., and Farinotti, D.: Modelling the future evolution of glaciers in the European Alps under the EURO-CORDEX RCM ensemble, The Cryosphere, 13, 1125–1146, https://doi.org/10.5194/tc-13-1125-2019, 2019. a

Zekollari, H., Huss, M., Schuster, L., Maussion, F., Rounce, D. R., Aguayo, R., Champollion, N., Compagno, L., Hugonnet, R., Marzeion, B., Mojtabavi, S., and Farinotti, D.: Twenty-first century global glacier evolution under CMIP6 scenarios and the role of glacier-specific observations, The Cryosphere, 18, 5045–5066, https://doi.org/10.5194/tc-18-5045-2024, 2024. a, b, c

Zemp, M., Huss, M., Thibert, E., Eckert, N., McNabb, R., Huber, J., Barandun, M., Machguth, H., Nussbaumer, S. U., Gärtner-Roer, I., Thomson, L., Paul, F., Maussion, F., Kutuzov, S., and Cogley, J. G.: Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016, Nature, 568, 382–386, https://doi.org/10.1038/S41586-019-1071-0, 2019. a

Zemp, M., Jakob, L., Dussaillant, I., Nussbaumer, S. U., Gourmelen, N., Dubber, S., A, G., Abdullahi, S., Andreassen, L. M., Berthier, E., Bhattacharya, A., Blazquez, A., Boehm Vock, L. F., Bolch, T., Box, J., Braun, M. H., Brun, F., Cicero, E., Colgan, W., Eckert, N., Farinotti, D., Florentine, C., Floricioiu, D., Gardner, A., Harig, C., Hassan, J., Hugonnet, R., Huss, M., Jóhannesson, T., Liang, C.-C. A., Ke, C.-Q., Khan, S. A., King, O., Kneib, M., Krieger, L., Maussion, F., Mattea, E., McNabb, R., Menounos, B., Miles, E., Moholdt, G., Nilsson, J., Pálsson, F., Pfeffer, J., Piermattei, L., Plummer, S., Richter, A., Sasgen, I., Schuster, L., Seehaus, T., Shen, X., Sommer, C., Sutterley, T., Treichler, D., Velicogna, I., Wouters, B., Zekollari, H., Zheng, W., and Team, T.: Community estimate of global glacier mass changes from 2000 to 2023, Nature, 639, 382–388, https://doi.org/10.1038/s41586-024-08545-z, 2025. a, b