Glaciers distinct from the Greenland and Antarctic ice sheets are shrinking rapidly, altering regional hydrology1, raising global sea level2 and elevating natural hazards3. Yet, owing to the scarcity of constrained mass loss observations, glacier evolution during the satellite era is known only partially, as a geographic and temporal patchwork4,5. Here we reveal the accelerated, albeit contrasting, patterns of glacier mass loss during the early twenty-first century. Using largely untapped satellite archives, we chart surface elevation changes at a high spatiotemporal resolution over all of Earth’s glaciers. We extensively validate our estimates against independent, high-precision measurements and present a globally complete and consistent estimate of glacier mass change. We show that during 2000–2019, glaciers lost a mass of 267 ± 16 gigatonnes per year, equivalent to 21 ± 3 per cent of the observed sea-level rise6. We identify a mass loss acceleration of 48 ± 16 gigatonnes per year per decade, explaining 6 to 19 per cent of the observed acceleration of sea-level rise. Particularly, thinning rates of glaciers outside ice sheet peripheries doubled over the past two decades. Glaciers currently lose more mass, and at similar or larger acceleration rates, than the Greenland or Antarctic ice sheets taken separately7,8,9. By uncovering the patterns of mass change in many regions, we find contrasting glacier fluctuations that agree with the decadal variability in precipitation and temperature. These include a North Atlantic anomaly of decelerated mass loss, a strongly accelerated loss from northwestern American glaciers, and the apparent end of the Karakoram anomaly of mass gain10. We anticipate our highly resolved estimates to advance the understanding of drivers that govern the distribution of glacier change, and to extend our capabilities of predicting these changes at all scales. Predictions robustly benchmarked against observations are critically needed to design adaptive policies for the local- and regional-scale management of water resources and cryospheric risks, as well as for the global-scale mitigation of sea-level rise.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Global, regional, tile and per-glacier elevation and mass change time series, elevation change maps for 5-, 10- and 20-year periods at 100 m resolution, and tables in this article are publicly available at https://doi.org/10.6096/13. Source data are provided with this paper.
The code developed for the global processing and analysis of all data, and to generate figures and tables in this article, is publicly available at https://github.com/rhugonnet/ww_tvol_study. Code concomitantly developed for processing ASTER data is available as the Python package pymmaster at https://github.com/luc-girod/MMASTER-workflows (with supporting documentation at https://mmaster-workflows.readthedocs.io) and for processing DEM time series as the Python package pyddem at https://github.com/iamdonovan/pyddem (with supporting documentation at https://pyddem.readthedocs.io).
Pritchard, H. D. Asia’s shrinking glaciers protect large populations from drought stress. Nature 569, 649–654 (2019).
WCRP Global Sea Level Budget Group. Global sea-level budget 1993–present. Earth Syst. Sci. Data 10, 1551–1590 (2018).
Stoffel, M. & Huggel, C. Effects of climate change on mass movements in mountain environments. Prog. Phys. Geogr. 36, 421–439 (2012).
IPCC. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H. O. et al.) (IPCC, 2019).
Gardner, A. et al. A reconciled estimate of glacier contributions to sea level rise: 2003 to 2009. Science 340, 852–857 (2013).
Nerem, R. S. et al. Climate-change-driven accelerated sea-level rise detected in the altimeter era. Proc. Natl Acad. Sci. USA 115, 2022–2025 (2018).
IMBIE Team. Mass balance of the Greenland Ice Sheet from 1992 to 2018. Nature 579, 233–239 (2020).
IMBIE team. Mass balance of the Antarctic Ice Sheet from 1992 to 2017. Nature 558, 219–222 (2018).
Smith, B. et al. Pervasive ice sheet mass loss reflects competing ocean and atmosphere processes. Science 368, 1239–1242 (2020).
Kääb, A., Berthier, E., Nuth, C., Gardelle, J. & Arnaud, Y. Contrasting patterns of early twenty-first-century glacier mass change in the Himalayas. Nature 488, 495–498 (2012).
Kulp, S. A. & Strauss, B. H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat. Commun. 10, 4844 (2019); author correction 10, 5752 (2019).
Immerzeel, W. W. et al. Importance and vulnerability of the world’s water towers. Nature 577, 364–369 (2020).
Marzeion, B., Cogley, J. G., Richter, K. & Parkes, D. Attribution of global glacier mass loss to anthropogenic and natural causes. Science 345, 919–921 (2014).
Huss, M. & Hock, R. Global-scale hydrological response to future glacier mass loss. Nat. Clim. Chang. 8, 135–140 (2018).
IPCC. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects (Cambridge University Press, 2014).
Cauvy-Fraunié, S. & Dangles, O. A global synthesis of biodiversity responses to glacier retreat. Nat. Ecol. Evol. 3, 1675–1685 (2019).
World Glacier Monitoring Service (WGMS). Fluctuations of Glaciers Database https://wgms.ch/data_databaseversions/ (2019).
Bamber, J. L., Westaway, R. M., Marzeion, B. & Wouters, B. The land ice contribution to sea level during the satellite era. Environ. Res. Lett. 13, 063008 (2018); corrigendum 13, 099502 (2018).
Wouters, B., Gardner, A. S. & Moholdt, G. Global glacier mass loss during the GRACE satellite mission (2002–2016). Front. Earth Sci. 7, 96 (2019).
Ciracì, E., Velicogna, I. & Swenson, S. Continuity of the mass loss of the world’s glaciers and ice caps from the GRACE and GRACE Follow-On missions. Geophys. Res. Lett. 47, 226 (2020).
Zemp, M. et al. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Nature 568, 382–386 (2019).
RGI Consortium. Randolph Glacier Inventory – A Dataset of Global Glacier Outlines. Technical Report https://www.glims.org/RGI/00_rgi60_TechnicalNote.pdf (Global Land Ice Measurements from Space, 2017).
Huss, M. Density assumptions for converting geodetic glacier volume change to mass change. Cryosphere 7, 877–887 (2013).
Ablain, M. et al. Uncertainty in satellite estimates of global mean sea-level changes, trend and acceleration. Earth Syst. Sci. Data 11, 1189–1202 (2019).
Velicogna, I. et al. Continuity of ice sheet mass loss in Greenland and Antarctica from the GRACE and GRACE Follow-On missions. Geophys. Res. Lett. 47, L11501 (2020).
Larsen, C. F. et al. Surface melt dominates Alaska glacier mass balance. Geophys. Res. Lett. 42, 5902–5908 (2015).
Blazquez, A. et al. Exploring the uncertainty in GRACE estimates of the mass redistributions at the Earth surface: implications for the global water and sea level budgets. Geophys. J. Int. 215, 415–430 (2018).
Shean, D. E. et al. A systematic, regional assessment of High Mountain Asia glacier mass balance. Front. Earth Sci. 7, 363 (2020).
Braun, M. H. et al. Constraining glacier elevation and mass changes in South America. Nat. Clim. Chang. (2019).
Dehecq, A. et al. Elevation changes inferred from TanDEM-X data over the Mont-Blanc area: impact of the X-band interferometric bias. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9, 3870–3882 (2016).
Sandberg Sørensen, L. et al. 25 years of elevation changes of the Greenland Ice Sheet from ERS, Envisat, and CryoSat-2 radar altimetry. Earth Planet. Sci. Lett. 495, 234–241 (2018).
Bevis, M. et al. Accelerating changes in ice mass within Greenland, and the ice sheet’s sensitivity to atmospheric forcing. Proc. Natl Acad. Sci. USA 116, 1934–1939 (2019).
Garreaud, R. D. et al. The Central Chile Mega Drought (2010–2018): a climate dynamics perspective. Int. J. Climatol. 40, 421–439 (2020).
Raper, S. C. B. & Braithwaite, R. J. Low sea level rise projections from mountain glaciers and icecaps under global warming. Nature 439, 311–313 (2006).
Parkes, D. & Marzeion, B. Twentieth-century contribution to sea-level rise from uncharted glaciers. Nature 563, 551–554 (2018).
Becker, J. J. et al. Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. Mar. Geod. 32, 355–371 (2009).
Tielidze, L. G. & Wheate, R. D. The Greater Caucasus glacier inventory (Russia, Georgia and Azerbaijan). Cryosphere 12, 81–94 (2018).
Dunse, T. et al. Glacier-surge mechanisms promoted by a hydro-thermodynamic feedback to summer melt. Cryosphere 9, 197–215 (2015).
McMillan, M. et al. Rapid dynamic activation of a marine-based Arctic ice cap: ice cap dynamic activation. Geophys. Res. Lett. 41, 8902–8909 (2014).
Nuth, C. et al. Dynamic vulnerability revealed in the collapse of an Arctic tidewater glacier. Sci. Rep. 9, 5541 (2019).
Howat, I. M., Negrete, A. & Smith, B. E. The Greenland Ice Mapping Project (GIMP) land classification and surface elevation data sets. Cryosphere 8, 1509–1518 (2014).
Fretwell, P. et al. Bedmap2: improved ice bed, surface and thickness datasets for Antarctica. Cryosphere 7, 375–393 (2013).
NASA/METI/AIST/Japan Spacesystems & U.S./Japan ASTER Science Team. ASTER Level 1A Data Set – Reconstructed, Unprocessed Instrument Data. 2001, NASA EOSDIS Land Processes DAAC, 2001); https://doi.org/10.5067/ASTER/AST_L1A.003.
Porter, C. et al. ArcticDEM (Harvard Dataverse, 2018); https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OHHUKH.
Howat, I. M., Porter, C., Smith, B. E., Noh, M.-J. & Morin, P. The reference elevation model of Antarctica. Cryosphere 13, 665–674 (2019).
Rizzoli, P. et al. Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS J. Photogramm. Remote Sens. 132, 119–139 (2017).
Vassilaki, D. I. & Stamos, A. A. TanDEM-X DEM: comparative performance review employing LIDAR data and DSMs. ISPRS J. Photogramm. Remote Sens. 160, 33–50 (2020).
Nuth, C. & Kääb, A. Co-registration and bias corrections of satellite elevation data sets for quantifying glacier thickness change. Cryosphere 5, 271–290 (2011).
Rupnik, E., Daakir, M. & Pierrot Deseilligny, M. MicMac – a free, open-source solution for photogrammetry. Open Geospat. Data Softw. Stand. 2, 14 (2017).
Girod, L., Nuth, C., Kääb, A., McNabb, R. & Galland, O. MMASTER: improved ASTER DEMs for elevation change monitoring. Remote Sens. 9, 704 (2017).
Wales, D. J. & Doye, J. P. K. Global optimization by basin-hopping and the lowest energy structures of Lennard–Jones clusters containing up to 110 atoms. J. Phys. Chem. A 101, 5111–5116 (1997).
Noh, M.-J. & Howat, I. M. The surface extraction from TIN based Search-space Minimization (SETSM) algorithm. ISPRS J. Photogramm. Remote Sens. 129, 55–76 (2017).
Dussaillant, I. et al. Two decades of glacier mass loss along the Andes. Nat. Geosci. 12, 802–808 (2019); author correction 13, 711 (2020).
Brun, F., Berthier, E., Wagnon, P., Kääb, A. & Treichler, D. A spatially resolved estimate of High Mountain Asia glacier mass balances, 2000–2016. Nat. Geosci. 10, 668–673 (2017); author correction 11, 543 (2018).
Toutin, T. Three-dimensional topographic mapping with ASTER stereo data in rugged topography. IEEE Trans. Geosci. Remote Sens. 40, 2241–2247 (2002).
Lacroix, P. Landslides triggered by the Gorkha earthquake in the Langtang valley, volumes and initiation processes. Earth Planets Space 68, 1–10 (2016).
Shean, D. E. et al. An automated, open-source pipeline for mass production of digital elevation models (DEMs) from very-high-resolution commercial stereo satellite imagery. ISPRS J. Photogramm. Remote Sens. 116, 101–117 (2016).
Höhle, J. & Höhle, M. Accuracy assessment of digital elevation models by means of robust statistical methods. ISPRS J. Photogramm. Remote Sens. 64, 398–406 (2009).
Williams, C. K. I. & Rasmussen, C. E. Gaussian Processes for Machine Learning Vol. 2 (MIT Press, 2006).
Schiefer, E., Menounos, B. & Wheate, R. Recent volume loss of British Columbian glaciers, Canada. Geophys. Res. Lett. (2007).
Nuimura, T., Fujita, K., Yamaguchi, S. & Sharma, R. R. Elevation changes of glaciers revealed by multitemporal digital elevation models calibrated by GPS survey in the Khumbu region, Nepal Himalaya, 1992–2008. J. Glaciol. 58, 648–656 (2012).
Willis, M. J., Melkonian, A. K., Pritchard, M. E. & Rivera, A. Ice loss from the Southern Patagonian Ice Field, South America, between 2000 and 2012. Geophys. Res. Lett. 39, L17501 (2012).
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Zwally, H. J., Schutz, R., Hancock, D. & Dimarzio, J. GLAS/ICESat L2 Global Land Surface Altimetry Data (HDF5), Version 34 (NASA Snow and Ice Data Center, 2014); https://nsidc.org/data/GLAH14.
Alexandrov, O., McMichael, S. & Beyer., R. A. IceBridge DMS L3 Ames Stereo Pipeline Photogrammetric DEM, Version 1 (accessed 1 June 2019); https://nsidc.org/data/IODEM3/versions/1.
Larsen, C. IceBridge UAF Lidar Scanner L1B Geolocated Surface Elevation Triplets, Version 1 (accessed 20 February 2020); https://nsidc.org/data/ILAKS1B/versions/1.
Beyer, R. A., Alexandrov, O. & McMichael, S. The Ames Stereo Pipeline: NASA’s open source software for deriving and processing terrain data. Earth Space Sci. 5, 537–548 (2018).
Harding, D. J. ICESat waveform measurements of within-footprint topographic relief and vegetation vertical structure. Geophys. Res. Lett. 32, L21S10 (2005).
Gardelle, J., Berthier, E. & Arnaud, Y. Impact of resolution and radar penetration on glacier elevation changes computed from DEM differencing. J. Glaciol. 58, 419–422 (2012).
McNabb, R., Nuth, C., Kääb, A. & Girod, L. Sensitivity of glacier volume change estimation to DEM void interpolation. Cryosphere 13, 895–910 (2019).
Cressie, N. A. C. Statistics for Spatial Data Vol. 4, 613–617 (Wiley, 1993).
Rolstad, C., Haug, T. & Denby, B. Spatially integrated geodetic glacier mass balance and its uncertainty based on geostatistical analysis: application to the western Svartisen ice cap, Norway. J. Glaciol. 55, 666–680 (2009).
Dehecq, A. et al. Automated processing of declassified KH-9 Hexagon satellite images for global elevation change analysis since the 1970s. Front. Earth Sci. 8, 566802 (2020).
Menounos, B. et al. Heterogeneous changes in western North American glaciers linked to decadal variability in zonal wind strength. Geophys. Res. Lett. 46, 200–209 (2018).
Howat, I. M., Smith, B. E., Joughin, I. & Scambos, T. A. Rates of southeast Greenland ice volume loss from combined ICESat and ASTER observations. Geophys. Res. Lett. 35, L17505 (2008).
Wang, D. & Kääb, A. Modeling glacier elevation change from DEM time series. Remote Sens. 7, 10117–10142 (2015).
Cogley, J. G. & Adams, W. P. Mass balance of glaciers other than the ice sheets. J. Glaciol. 44, 315–325 (1998).
Journel, A. G. & Huijbregts, C. J. Mining Geostatistics Vol. 600 (Academic Press, 1978).
Webster, R. & Oliver, M. A. Geostatistics for Environmental Scientists (John Wiley & Sons, 2007).
Gräler, B., Pebesma, E. & Heuvelink, G. Spatio-temporal interpolation using gstat. R J. 8, 204 (2016).
Mälicke, M. & Schneider, H. D. Scikit-GStat 0.2.6: A Scipy Flavored Geostatistical Analysis Toolbox Written in Python (2019); https://zenodo.org/record/3531816#.YFsJ737Le00.
Dussaillant, I., Berthier, E. & Brun, F. Geodetic mass balance of the Northern Patagonian Icefield from 2000 to 2012 using two independent methods. Front. Earth Sci. 6, 8 (2018).
Berthier, E., Scambos, T. A. & Shuman, C. A. Mass loss of Larsen B tributary glaciers (Antarctic Peninsula) unabated since 2002. Geophys. Res. Lett. 39, L13501 (2012).
Granshaw, F. D. & Fountain, A. G. Glacier change (1958–1998) in the North Cascades National Park Complex, Washington, USA. J. Glaciol. 52, 251–256 (2006).
Pfeffer, W. et al. The Randolph Glacier Inventory: a globally complete inventory of glaciers. J. Glaciol. 60, 537–552 (2014).
Rastner, P. et al. The first complete inventory of the local glaciers and ice caps on Greenland. Cryosphere 6, 1483–1495 (2012).
Bolch, T., Menounos, B. & Wheate, R. Landsat-based inventory of glaciers in western Canada, 1985–2005. Remote Sens. Environ. 114, 127–137 (2010).
Pelto, B. M., Menounos, B. & Marshall, S. J. Multi-year evaluation of airborne geodetic surveys to estimate seasonal mass balance, Columbia and Rocky Mountains, Canada. Cryosphere 13, 1709–1727 (2019).
Wagnon, P. et al. Seasonal and annual mass balances of Mera and Pokalde glaciers (Nepal Himalaya) since 2007. Cryosphere 7, 1769–1786 (2013).
Berthier, E., Schiefer, E., Clarke, G. K. C., Menounos, B. & Rémy, F. Contribution of Alaskan glaciers to sea-level rise derived from satellite imagery. Nat. Geosci. 3, 92–95 (2010).
Berthier, E., Cabot, V., Vincent, C. & Six, D. Decadal region-wide and glacier-wide mass balances derived from multi-temporal ASTER Satellite Digital Elevation Models. Validation over the Mont-Blanc area. Front. Earth Sci. 4, 63 (2016).
Glacier Monitoring Switzerland. Swiss Glacier Volume Change, Release 2018 (2018); https://doi.glamos.ch/data/volumechange/volumechange_2018_r2018.html.
Bauder, A., Funk, M. & Huss, M. Ice-volume changes of selected glaciers in the Swiss Alps since the end of the 19th century. Ann. Glaciol. 46, 145–149 (2007).
Davaze, L., Rabatel, A., Dufour, A., Hugonnet, R. & Arnaud, Y. Region-wide annual glacier surface mass balance for the European Alps from 2000 to 2016. Front. Earth Sci. 8, 149 (2020).
Schuler, T. V. et al. Reconciling Svalbard Glacier mass balance. Front. Earth Sci. 8, 523646 (2020).
Aðalgeirsdóttir, G. et al. Glacier Changes in Iceland From ~1890 to 2019. Front. Earth Sci. 8, 520 (2020).
Hersbach, H. & Dee, D. ERA5 reanalysis is in production. ECMWF Newsl. 147, 5–6 (2016).
Skliris, N., Zika, J. D., Nurser, G., Josey, S. A. & Marsh, R. Global water cycle amplifying at less than the Clausius–Clapeyron rate. Sci. Rep. 6, 38752 (2016).
Sakakibara, D., Sugiyama, S., Sawagaki, T., Marinsek, S. & Skvarca, P. Rapid retreat, acceleration and thinning of Glaciar Upsala, Southern Patagonia Icefield, initiated in 2008. Ann. Glaciol. 54, 131–138 (2013).
Farr, T. G. et al. The Shuttle Radar Topography Mission. Rev. Geophys. 45, RG2004 (2007).
We thank C. Porter for discussions on ArcticDEM and REMA DEMs, B. Meyssignac for comments on sea-level rise and A. Dehecq for input on the presentation of the manuscript. The GLIMS initiative (in particular J. Kargel and B. Raup) allowed the population of a vast archive of ASTER stereo images over glaciers. Hakai Institute and the University of Northern British Columbia provided computational resources for processing ASTER stereo imagery. SPOT6/7 data were obtained from GEOSUD (ANR-10-EQPX-20, programme ‘Investissements d’Avenir’). ArcticDEM DEMs were provided by the Polar Geospatial Center under NSF-OPP awards 1043681, 1559691 and 1542736 and REMA DEMs were provided by the Byrd Polar and Climate Research Center and the Polar Geospatial Center under NSF-OPP awards 1543501, 1810976, 1542736, 1559691, 1043681, 1541332, 0753663, 1548562, 1238993 and NASA award NNX10AN61G. Computer time was provided through a Blue Waters Innovation Initiative. DEMs were produced using data from DigitalGlobe, Inc. R.H. acknowledges a fellowship from the University of Toulouse. E.B. acknowledges support from the French Space Agency (CNES) through ISIS and TOSCA programmes. R.M., C.N., L.G. and A.K. acknowledge support by ESA through Glaciers_cci and EE10 (4000109873/14/I-NB, 4000127593/19/I-NS, 4000127656/19/NL/FF/gp), and by the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC grant agreement number 320816. B.M. acknowledges funding from the National Sciences and Engineering Research Council of Canada, the Canada Research Chairs Program, the Tula Foundation and Global Water Futures. R.H., D.F. and M.H. acknowledge funding from the Swiss National Science Foundation, grant number 184634.
The authors declare no competing interests.
Peer review information Nature thanks Beata Csatho, Thomas Frederikse, Michael Willis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Flow diagram describing the processing steps from satellite imagery to global glacier mass change time series. Processing steps correspond to sections in Methods.
a–c, Spatial distribution of DEMs as a strip count for ArcticDEM strips above 50° N (a), ASTER DEM strips (b) and REMA strips below 50° S (c), shown on top of a world hillshade36. 67,986 ArcticDEM and 9,369 REMA strips are counted before co-registration to TanDEM-X. This later reduces their number to 40,391 and 3,456, respectively, owing to the limited stable terrain in polar regions. d, Temporal distribution of the strip count as a bi-mensual histogram from January 2000 to December 2019. We note that ArcticDEM and REMA strip footprints (15 km × 50 km) are generally much smaller than ASTER DEM strip footprints (180 km × 60 km).
a–e, Empirical and modelled elevation measurement error (a) and temporal covariance of glacier elevation (b) estimated globally. These are used to condition the filtering (c, d) and elevation time series estimation (e) of elevation observations, illustrated here for a 100 m × 100 m pixel on the ablation area of Upsala, where a strong nonlinear elevation loss occurred99. a, Squared measurement error, estimated by the squared NMAD of elevation differences to TanDEM-X on stable terrain as a function of terrain slope and of quality of stereo-correlation. We express the quality of stereo-correlation as a percentage ranging from 0% for poor correlations to 100% for good correlations. b, Variance between pairwise glacier elevations in time, or temporal variogram. The empirical temporal variogram is derived from the aggregated median of variances binned by time lags of 0.25 yr. Here, pixels were selected on glacierized terrain showing a linear trend of elevation change (estimated from weighted least squares) between −1.5 and −1.0 m yr−1. The median of the linear trend at these locations (−1.2 m yr−1) was directly used to derive the linear model (orange), which has a quadratic variance. The other models are calibrated so that their sum (dashed black line) matches the empirical variogram. c, Spatial and temporal filtering by conditioning a maximum linear elevation change rate from the neighbouring TanDEM-X elevations (see Supplementary Information for further details). d, Filtering by successive GP regression fits for credible intervals of size 20σ, 12σ, 9σ, 6σ and 4σ. e, Elevation time series of final GP regression after the removal of outliers.
a–d, ICESat64 and IceBridge65,66 measurements compared to our surface elevation time series over glacierized terrain in the Saint-Elias Mountains, Alaska (a–c) and at the global scale (d). b, Absolute z-scores (white to purple) are shown on top of the 2000–2019 surface elevation change. z-scores correspond to elevation differences to ICESat (dashed outlines) or IceBridge (solid outlines), standardized by our time series uncertainty. c, Time series for a 100 m × 100 m pixel extracted on the tongue of Agassiz Glacier with neighbouring ICESat and IceBridge elevation differences for demonstration purposes. d, Summary of global validation statistics for categories of time, season, region, elevation, observation time lag and total elevation change, with density distributions of measurements for ICESat (light grey) and IceBridge (dark grey). Mean elevation differences, subject to snow-cover biases, are shown only by region (summer mean) and by two-month seasonal component (difference to the annual mean) for each hemisphere.
Extended Data Fig. 5 Uncertainty analysis of volume changes and validation using high-resolution DEMs.
a–h, Spatial correlation of elevations between the GP time series and ICESat with the time lag to the closest ASTER, ArcticDEM or REMA observation (a, b), propagation of correlations into specific-volume change uncertainties (c), validation of volume change estimates and uncertainties to high-resolution volume changes extracted over the same 588 glaciers and periods (d–f) and contribution from all uncertainty sources to the 2000–2019 specific-mass change estimates (g, h). a, An empirical spatial variogram is shown and fitted with a sum of spherical models at correlation lengths of 0.15, 2, 5, 20, 50, 200 and 500 km for elevation differences sampled at 720 days (2 years) from the closest observation. b, Spatially correlated variances as a function of the time lag to the closest observation. The model for the variance used during uncertainty propagation is shown in plain lines (sum of quadratic and squared sinusoidal functions optimized by least squares). c, Propagation of elevation change uncertainties to volume change uncertainties with varying glacier area. As this computation is specific to the time lag of each pixel to the closest observation, for each glacier, at each time step, c refers to an example. The spatial correlations are computed for a time lag to the closest observation, representing the average of our study, of 0–1 yr for 50% of observations, 1–2 yr for 20% of observations, 2–3 yr for 20% of observations and 3–4 yr for 10% of observations. We assume a mean pixel-wise uncertainty of 10 m and simplify by considering only the first step of integration over a continuous glacierized area (equation (5)). This assumption leads to slightly larger contributions from short-range correlations than with further propagation to the second propagation step between discontinuous glaciers (equation (6)). Uncertainties are largely dominated by short- to long-range spatial correlations. d, Comparison of specific-volume changes per glacier with 1σ uncertainties. The mean of differences in estimates over all glaciers does not statistically differ from zero. e, f, Theoretical and empirical 1σ uncertainties, and their evolution with glacier size. The theoretical uncertainty is the mean of per-glacier uncertainties derived from spatially integrated variograms and the empirical uncertainty is the NMAD of the difference between high-resolution and GP estimates. g, h, Propagation of uncertainty sources to specific-mass changes for each RGI 6.0 region, and all glaciers with and without the Greenland Periphery and the Antarctic and Subantarctic, which are magnified in h. Uncertainties are largely dominated by the volume-to-mass conversion uncertainties globally, and by uncertainties in glacier outlines for regions with a relevant share of small glaciers.
a–h, Elevation change of glaciers between 2000 and 2019 in Coropuna, Peru (a), Pamir Mountains (b), Iceland (c), Karakoram Mountains (d), European Alps (e), Southern Alps, New Zealand (f), West Greenland (note the rotated orientation of map) (g) and Svalbard (h). Except for Svalbard, glacier outlines displayed are from the RGI 6.0. In the background is shown a hillshade derived from several sources36,46,100. In Svalbard, outlines have been updated to include the massive surges of Austfonna Basin 338,39 in the northeast and Nathorstbreen in the southwest40, indicated by blue arrows.
a–d, Mean elevation change rates aggregated by tiles of 1° × 1° for the periods 2000–2004 (a), 2005–2009 (b), 2010–2014 (c) and 2015–2019 (d). The tile area is inversely scaled to the squared 95% confidence interval of the mean elevation change in the tile, and tiles are coloured with mean elevation change rates, on top of a world hillshade36. The minimum tile area is 10% for a 95% confidence interval larger than 2 m yr−1 and tiles are displayed at full size for a 95% confidence interval smaller than 0.5 m yr−1. Region labelling refers to that of Fig. 2. The acceleration of thinning brings the Karakoram anomaly to its apparent end.
This file contains the Supplementary Methods, Supplementary Discussion, Supplementary Figures 1–9 and Supplementary Tables 1–3.
Comparison to IPCC SROCC Table 2A.1 with estimates from this study for periods 2006–2015 and 2000–2019 and recent regional studies (blue). An additional decimal is shown for mass balance rates in Gt yr−1 and mm SLE yr−1. Regions are shown in SROCC ordering. SROCC estimates were combined using the most suitable studies (methods ‘xxx’ and ‘x’).
About this article
Cite this article
Hugonnet, R., McNabb, R., Berthier, E. et al. Accelerated global glacier mass loss in the early twenty-first century. Nature 592, 726–731 (2021). https://doi.org/10.1038/s41586-021-03436-z