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.
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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).
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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.
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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’).
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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
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