Letter | Published:

Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016

Naturevolume 568pages382386 (2019) | Download Citation

Abstract

Glaciers distinct from the Greenland and Antarctic ice sheets cover an area of approximately 706,000 square kilometres globally1, with an estimated total volume of 170,000 cubic kilometres, or 0.4 metres of potential sea-level-rise equivalent2. Retreating and thinning glaciers are icons of climate change3 and affect regional runoff4 as well as global sea level5,6. In past reports from the Intergovernmental Panel on Climate Change, estimates of changes in glacier mass were based on the multiplication of averaged or interpolated results from available observations of a few hundred glaciers by defined regional glacier areas7,8,9,10. For data-scarce regions, these results had to be complemented with estimates based on satellite altimetry and gravimetry11. These past approaches were challenged by the small number and heterogeneous spatiotemporal distribution of in situ measurement series and their often unknown ability to represent their respective mountain ranges, as well as by the spatial limitations of satellite altimetry (for which only point data are available) and gravimetry (with its coarse resolution). Here we use an extrapolation of glaciological and geodetic observations to show that glaciers contributed 27 ± 22 millimetres to global mean sea-level rise from 1961 to 2016. Regional specific-mass-change rates for 2006–2016 range from −0.1 metres to −1.2 metres of water equivalent per year, resulting in a global sea-level contribution of 335 ± 144 gigatonnes, or 0.92 ± 0.39 millimetres, per year. Although statistical uncertainty ranges overlap, our conclusions suggest that glacier mass loss may be larger than previously reported11. The present glacier mass loss is equivalent to the sea-level contribution of the Greenland Ice Sheet12, clearly exceeds the loss from the Antarctic Ice Sheet13, and accounts for 25 to 30 per cent of the total observed sea-level rise14. Present mass-loss rates indicate that glaciers could almost disappear in some mountain ranges in this century, while heavily glacierized regions will continue to contribute to sea-level rise beyond 2100.

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Data availability

The temporal variabilities for the glaciological clusters as well as the regional and global mass-change results have been deposited in the Zenodo repository (https://doi.org/10.5281/zenodo.1492141). The full sample of glaciological and geodetic observations for individual glaciers is publicly available from the WGMS (https://doi.org/10.5904/wgms-fog-2018-11).

Code availability

The analytical scripts are available from the authors on request.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

We thank the national correspondents and principal investigators of the WGMS network as well as the Global Land Ice Measurements from Space (GLIMS) and RGI communities for free and open access to their data sets. We thank B. Armstrong for polishing the language. Arctic digital elevation model (DEM) strips were provided by the Polar Geospatial Center under National Science Foundation (NSF) Office of Polar Programs (OPP) awards 1043681, 1559691 and 1542736. This study was enabled by support from the Federal Office of Meteorology and Climatology MeteoSwiss within the framework of the Global Climate Observing System (GCOS) Switzerland, the Cryospheric Commission of the Swiss Academy of Science, the Copernicus Climate Change Service (C3S) implemented by the European Centre for Medium-range Weather Forecasts (ECMWF) on behalf of the European Commission, the European Space Agency (ESA) projects Glaciers_cci (4000109873/14/I-NB) and Sea-Level Budget Closure CCI (4000119910/17/I-NB), and Irstea Grenoble as part of LabEx OSUG@2020.

Reviewer information

Nature thanks A. Rowan and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. Deceased: J. G. Cogley

Affiliations

  1. Department of Geography, University of Zurich, Zurich, Switzerland

    • M. Zemp
    • , J. Huber
    • , H. Machguth
    • , S. U. Nussbaumer
    • , I. Gärtner-Roer
    •  & F. Paul
  2. Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland

    • M. Huss
  3. Department of Geosciences, University of Fribourg, Fribourg, Switzerland

    • M. Huss
    • , M. Barandun
    • , H. Machguth
    •  & S. U. Nussbaumer
  4. Université Grenoble Alpes, Irstea, UR ETGR, Grenoble, France

    • E. Thibert
    •  & N. Eckert
  5. Department of Geosciences, University of Oslo, Oslo, Norway

    • R. McNabb
  6. Department of Geography and Planning, Queen’s University, Kingston, Ontario, Canada

    • L. Thomson
  7. Department of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria

    • F. Maussion
  8. Department of Glaciology, Institute of Geography, Russian Academy of Sciences, Moscow, Russia

    • S. Kutuzov
  9. Department of Geography, Trent University, Peterborough, Ontario, Canada

    • J. G. Cogley

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Contributions

M.Z. initiated and coordinated the study and wrote the manuscript; the basic concept was jointly developed during a workshop in the Swiss pre-Alps. J.G.C. compiled extensive data from the research community and the literature, and was a dedicated glaciologist and pioneer in glacier mass-balance studies. M.Z., S.U.N., H.M., I.G.-R., J.H., F.P., L.T. and S.K. compiled data from the research community and the literature. R.M., J.H. and M.B. computed additional geodetic results. M.H., M.B. and F.M. defined clusters and regions used in the analysis. E.T. and N.E. ran the variance decomposition model. M.H. performed the calibration of the glaciological signal to the geodetic series and the extrapolation to regional changes. M.Z., I.G.-R., S.U.N., E.T. and J.H. produced the figures. All authors commented on the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to M. Zemp.

Extended data figures and tables

  1. Extended Data Fig. 1 Regional glacier hypsometry and observational coverage.

    as, For each of the 19 first-order regions, glacier hypsometry from RGI 6.0 (blue)1 is overlaid with glacier hypsometry of both the geodetic (grey) and the glaciological (black) samples used here. Values for the total number (N) and total area (S) of glaciers are given for each region, together with the relative coverage of both the glaciological and the geodetic samples. Plots are ordered according to the region numbers in RGI 6.0 (see Table 1); m a.s.l., metres above sea level.

  2. Extended Data Fig. 2 Cumulative regional glacier changes since the 1960s.

    a, b, Cumulative mass changes in m w.e. (a) and Gt (b) are shown for the 19 regions. Specific mass changes (a) indicate the observed glacier thickness changes. Total glacier mass changes (b, left vertical axis) correspond to the regional contributions to global mean sea-level rise (b, right vertical axis). As an example, cumulative specific mass changes were most negative in the Southern Andes with an average regional glacier thickness change of approximately −40 m w.e. (a), resulting in a cumulative mass change of −1,200 Gt (b). Glaciers in Alaska experience less negative specific mass changes (a) but contribute much more to global sea-level rise (b) because of the larger regional glacier area.

  3. Extended Data Fig. 3 Relative annual ice loss for the period from 2006 to 2016.

    Annual mass change rates (see Fig. 3b) relative to estimated total ice volumes2 are plotted as vertical bars (% yr−1).

  4. Extended Data Fig. 4 Relative error contributions for the period 2006–2016.

    Shown are relative contributions (%) of the different sources to the overall regional error bars (Fig. 3b). Taking Alaska as an example, the overall error estimate is dominated by the glaciological and the geodetic errors with contributions of 47% and 37%, respectively, whereas the errors for extrapolation (10%), glacier area (5%), and second-order crossed uncertainties (less than 1%) are of less importance. A special case is Central Europe: the large number of high-quality observations from airborne surveys comes with reported geodetic uncertainties that are one order of magnitude smaller than the spaceborne estimates in other regions. As a result, the overall error bars are much smaller (Fig. 3) and the relative contributions from other error sources become larger. In the Southern Andes, the relative contribution of the geodetic error is reduced by the large sample size, while glaciological and interpolation errors feature large absolute values.

  5. Extended Data Fig. 5 Comparison of regional mass changes with results from IPCC AR5.

    a, b, Annual specific mass-change rates in m w.e. yr−1 (a) and in Gt yr−1 (b), as shown in Fig. 3 but for the period 2003–2009. The estimates and related error bars (corresponding to 95% confidence intervals) found here are shown in blue. The results from IPCC AR511,24 are shown in red, differentiating between those based on glaciological and geodetic observations (crosses) and those based on ICESat and/or the Gravity Recovery and Climate Experiment (GRACE; diamonds). Global mass change rates are −260 ± 28 Gt yr−1 and −307 ± 148 Gt yr−1, as estimated by IPCC AR511,24 and this study, respectively.

  6. Extended Data Fig. 6 Temporal variability in the glaciological mass balance for Alaska and British Columbia, 1961–2016.

    a, b, Annual (a; m w.e. yr−1) and cumulative (b; m w.e.) values for the cluster’s smooth trend (g(t); blue lines) and annual deviations (g(t) + z(t); orange lines), as reconstructed from the variance decomposition (see Methods, equations (1) and (2)) on the basis of glaciological measurements from 19 glaciers (Extended Data Table 2, cluster C01).

  7. Extended Data Fig. 7 Calibration of temporal variability from glaciological sample to geodetic values of individual glaciers.

    Schematic representation of the approach to calibrate the cumulative temporal variability (black line; m w.e.), as derived from the variance decomposition (see Extended Data Fig. 6), to geodetic values of individual glaciers (blue and purple lines; m w.e.). For Glacier 1 and Glacier 2, the mean annual deviations between the glaciological balance of the cluster and the glacier-individual geodetic balances were 0.1 m w.e. yr−1 and −0.2 m w.e. yr−1, respectively, over corresponding survey periods between t0 and t1 (see  Methods, equation (3)).

  8. Extended Data Table 1 Overview of new geodetic volume changes
  9. Extended Data Table 2 Spatial clusters used to analyse temporal variability from glaciological samples

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