Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Anthropogenic warming forces extreme annual glacier mass loss


Glaciers are unique indicators of climate change. While recent global-scale glacier decline has been attributed to anthropogenic forcing, direct links between human-induced climate warming and extreme glacier mass-loss years have not been documented. Here we apply event attribution methods to document this at the regional scale, targeting the highest mass-loss years (2011 and 2018) across New Zealand’s Southern Alps. Glacier mass balance is simulated using temperature and precipitation from multiple climate model ensembles. We estimate extreme mass loss was at least six times (2011) and ten times (2018) (>90% confidence) more likely to occur with anthropogenic forcing than without. This increased likelihood is driven by present-day temperatures ~1.0 °C above the pre-industrial average, confirming a connection between anthropogenic emissions and high annual ice loss. These results suggest that as warming and extreme heat events continue and intensify, there will be an increasingly visible human fingerprint on extreme glacier mass-loss years in the coming decades.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Increasing extreme mass-loss measurements in recent decades.
Fig. 2: Annual Brewster and Rolleston Glacier mass-balance and snowline probability distributions.
Fig. 3: Likelihoods of glacier mass loss with natural and anthropogenic forcing.
Fig. 4: Annual snowline probability distributions.

Data availability

Global glacier mass-balance data in Fig. 1 and Extended Data Figure 1 are available from the World Glacier Monitoring Service at See Supplementary Table 4 for 2011 and 2018 mass-balance and snowline measurements. Snowlines through 2015 are available from National Institute of Water and Atmospheric Research (NIWA) at CMIP5 GCM output is available from public repositories, including CESM output is available from the CESM/UCAR repository at VCSN data are available from Raw figures are available from

Code availability

All code is available from (ref. 52).


  1. Fluctuations of Glaciers Database (WGMS, 2018);

  2. Roe, G. H., Baker, M. B. & Herla, F. Centennial glacier retreat as categorical evidence of regional climate change. Nat. Geosci. 10, 95–99 (2017).

    CAS  Google Scholar 

  3. Oerlemans, J. Extracting a climate signal from 169 glacier records. Science 308, 675–677 (2005).

    CAS  Google Scholar 

  4. Oerlemans, J. The Microclimate of Valley Glaciers (IGITUR, 2010).

  5. 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).

    CAS  Google Scholar 

  6. King, A. D. Attributing changing rates of temperature record breaking to anthropogenic influences. Earths Future 5, 1156–1168 (2017).

    Google Scholar 

  7. Lewis, S. C. & Karoly, D. J. Anthropogenic contributions to Australia’s record summer temperatures of 2013. Geophys. Res. Lett. 40, 3705–3709 (2013).

    Google Scholar 

  8. King, A. D., Karoly, D. J. & Henley, B. J. Australian climate extremes at 1.5 °C and 2 °C of global warming. Nat. Clim. Change 7, 412–416 (2017).

    Google Scholar 

  9. Radić, V. & Hock, R. Regionally differentiated contribution of mountain glaciers and ice caps to future sea-level rise. Nat. Geosci. 4, 91–94 (2011).

    Google Scholar 

  10. Marzeion, B., Jarosch, A. & Hofer, M. Past and future sea-level change from the surface mass balance of glaciers. Cryosphere 6, 1295–1322 (2012).

    Google Scholar 

  11. Huss, M. & Hock, R. A new model for global glacier change and sea-level rise. Front. Earth Sci. 3, 1–54 (2015).

    Google Scholar 

  12. Zemp, M. et al. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Nature 568, 382–386 (2019).

    CAS  Google Scholar 

  13. Xu, J. et al. The melting Himalayas: cascading effects of climate change on water, biodiversity, and livelihoods. Conserv. Biol. 23, 520–530 (2009).

    CAS  Google Scholar 

  14. Huss, M. & Hock, R. Global-scale hydrological response to future glacier mass loss. Nat. Clim. Change 8, 135–140 (2018).

    Google Scholar 

  15. Hock, R. et al. In IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) Ch. 2 (IPCC, 2019).

  16. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Google Scholar 

  17. Kay, J. et al. The Community Earth System Model (CESM) Large Ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96, 1333–1349 (2015).

    Google Scholar 

  18. Perkins-Kirkpatrick, S. et al. The role of natural variability and anthropogenic climate change in the 2017/18 Tasman Sea marine heatwave. Bull. Am. Meteorol. Soc. 100, S105–S110 (2019).

    Google Scholar 

  19. Mastrandrea, M. D. et al. Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties (IPCC, 2010).

  20. Willsman, A., Chinn, T. & Macara, G. New Zealand Glacier Monitoring: End of Summer Snowline Survey 2017 (New Zealand Ministry of Business, Innovation and Employment, 2018).

  21. Cullen, N. et al. An 11-year record of mass balance of Brewster Glacier, New Zealand, determined using a geostatistical approach. J. Glaciol. 63, 199–217 (2017).

    Google Scholar 

  22. Purdie, H. et al. The impact of extreme summer melt on net accumulation of an avalanche fed glacier, as determined by ground-penetrating radar. Geogr. Ann. Ser. A 97, 779–791 (2015).

    Google Scholar 

  23. Chinn, T., Fitzharris, B., Willsman, A. & Salinger, M. Annual ice volume changes 1976–2008 for the New Zealand Southern Alps. Glob. Planet. Change 92, 105–118 (2012).

    Google Scholar 

  24. LaChapelle, E. Assessing glacier mass budgets by reconnaissance aerial photography. J. Glaciol. 4, 290–297 (1962).

    Google Scholar 

  25. Mackintosh, A. N. et al. Regional cooling caused recent New Zealand glacier advances in a period of global warming. Nat. Commun. 8, 14202 (2017).

    CAS  Google Scholar 

  26. Mullan, A., Stuart, S., Hadfield, M. & Smith, M. Report on the Review of NIWA’s ‘Seven-Station’ Temperature Series Information Series No. 78 (NIWA, 2010).

  27. Oerlemans, J. Glaciers and Climate Change (CRC Press, 2001).

  28. Summary for Policymakers. In Special Report on Global Warming of 1.5°C (eds Masson-Delmotte, V. et al.) (WMO, 2018).

  29. Vargo, L. J. et al. Using structure from motion photogrammetry to measure past glacier changes from historic aerial photographs. J. Glaciol. 63, 1105–1118 (2017).

    Google Scholar 

  30. Cogley, J. G. et al. Glossary of Glacier Mass Balance and Related Terms IHP-VII Technical Documents in Hydrology No. 86 (UNESCO, 2011).

  31. Pellicciotti, F. et al. An enhanced temperature-index glacier melt model including the shortwave radiation balance: development and testing for Haut Glacier d’Arolla, Switzerland. J. Glaciol. 51, 573–587 (2005).

    Google Scholar 

  32. Oerlemans, J. Climate sensitivity of glaciers in southern Norway: application of an energy-balance model to Nigardsbreen, Hellstugubreen and Alfotbreen. J. Glaciol. 38, 223–232 (1992).

    Google Scholar 

  33. Berger, A. Long-term variations of daily insolation and Quaternary climatic changes. J. Atmos. Sci. 35, 2362–2367 (1978).

    Google Scholar 

  34. Corripio, J. G. Vectorial algebra algorithms for calculating terrain parameters from DEMs and solar radiation modelling in mountainous terrain. Int. J. Geogr. Inf. Sci. 17, 1–23 (2003).

    Google Scholar 

  35. Pellicciotti, F., Raschle, T., Huerlimann, T., Carenzo, M. & Burlando, P. Transmission of solar radiation through clouds on melting glaciers: a comparison of parameterizations and their impact on melt modelling. J. Glaciol. 57, 367–381 (2011).

    Google Scholar 

  36. Tait, A. & Liley, B. Interpolation of daily solar radiation for New Zealand using a satellite data-derived cloud cover surface. Weather Clim. 29, 70–88 (2009).

    Google Scholar 

  37. Cuffey, K. M. & Paterson, W. S. B. The Physics of Glaciers (Academic Press, 2010).

  38. Brock, B. W., Willis, I. C. & Sharp, M. J. Measurement and parameterization of albedo variations at Haut Glacier d’Arolla, Switzerland. J. Glaciol. 46, 675–688 (2000).

    Google Scholar 

  39. Tait, A., Henderson, R., Turner, R. & Zheng, X. Thin plate smoothing spline interpolation of daily rainfall for New Zealand using a climatological rainfall surface. Int. J. Climatol. 26, 2097–2115 (2006).

    Google Scholar 

  40. Tait, A. & Macara, G. Evaluation of interpolated daily temperature data for high elevation areas in New Zealand. Weather Clim. 34, 36–49 (2014).

    Google Scholar 

  41. Anderson, B. et al. Climate sensitivity of a high-precipitation glacier in New Zealand. J. Glaciol. 56, 114–128 (2010).

    Google Scholar 

  42. Rye, C. J., Willis, I. C., Arnold, N. S. & Kohler, J. On the need for automated multiobjective optimization and uncertainty estimation of glacier mass balance models. J. Geophys. Res. Earth Surf. 117, F02005 (2012).

    Google Scholar 

  43. Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change 109, 213 (2011).

    CAS  Google Scholar 

  44. Le Quéré, C. et al. Global carbon budget 2018. Earth Syst. Sci. Data 10, 2141–2194 (2018).

    Google Scholar 

  45. Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1108 (2009).

    Google Scholar 

  46. Hay, L. E., Wilby, R. L. & Leavesley, G. H. A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States. J. Am. Water Resour. Assoc. 36, 387–397 (2000).

    Google Scholar 

  47. Clarke, G. K., Jarosch, A. H., Anslow, F. S., Radić, V. & Menounos, B. Projected deglaciation of western Canada in the twenty-first century. Nat. Geosci. 8, 372–377 (2015).

    CAS  Google Scholar 

  48. Zekollari, H., Huss, M. & Farinotti, D. Modelling the future evolution of glaciers in the European Alps under the EURO-CORDEX RCM ensemble. Cryosphere 13, 1125–1146 (2019).

    Google Scholar 

  49. Hock, R. Temperature index melt modelling in mountain areas. J. Hydrol. 282, 104–115 (2003).

    Google Scholar 

  50. Farinotti, D. On the effect of short-term climate variability on mountain glaciers: insights from a case study. J. Glaciol. 59, 992–1006 (2013).

    Google Scholar 

  51. Naughten, K. A. et al. Future projections of Antarctic ice shelf melting based on CMIP5 scenarios. J. Clim. 31, 5243–5261 (2018).

    Google Scholar 

  52. Vargo, L. lvargo13/glacier_attribution: First release of glacier mass loss attribution code. Zenodo (2020).

  53. RGI Consortium Randolph Glacier Inventory—A Dataset of Global Glacier Outlines: Version 6.0 Technical Report, Global Land Ice Measurements from Space, Colorado USA (Digital Media, 2017);

Download references


This work was supported by NIWA Strategic Science Internal Funding of the ‘Climate Present and Past’ project CAOA1901, a subcontract to Victoria University of Wellington from NIWA for ‘Structure from Motion of Southern Alps glaciers’ and a Victoria University of Wellington Doctoral Scholarship. A.D.K. received support from the Australian Research Council (DE180100638). We thank N. Cullen and P. Sirguey for sharing Brewster mass-balance data, and T. Kerr and H. Purdie for sharing Rolleston mass-balance data. We acknowledge the climate modelling groups that contributed model output to CMIP5 and the groups measuring glacier mass balance that makes up the data in Fig. 1. We thank R. Hock, S. Eaves and C. Lukens for their input, T. Chinn, A. Willsman and A. Woods for their work on the snowline survey.

Author information

Authors and Affiliations



L.J.V., B.M.A., H.J.H. and R.D. developed the glacier model. L.J.V. performed the analysis and led the writing. All authors contributed to the design of the study, discussed the results and contributed to writing of the manuscript.

Corresponding author

Correspondence to Lauren J. Vargo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Extended data

Extended Data Fig. 1 Global glaciers, increasing extreme mass-loss years in recent decades, and study area.

Full map: All global glaciers53 (black), including all glaciers with >30 years of mass-balance measurements1 (open black circles). Each colored square represents one extreme mass-loss year, with the color showing the timing, by decade. Extreme mass-loss years are defined as the 90th percentile of negative mass balances for each individual glacier. Inset: Glaciers in the Southern Alps of New Zealand53 (black), including the subset of glaciers used in this study (blue).

Extended Data Fig. 2 Changes in GCM temperature and precipitation output between natural and present climates.

Temperature (top; C) and precipitation (bottom; %) changes for present-world scenarios compared with natural-world scenarios. For each model or CESM ensemble member, values are averaged for all ten glaciers. Present world is defined as RCP8.5 (April 2006 – March 2026) for both CMIP5 and CESM. The natural world is defined as HistoricalNat (April 1901 – March 2005) for CMIP5 and the CESM LE control run (April year 1 – March year 1800) for CESM.

Supplementary information

Supplementary Information

Supplementary Figs. 1–4, Tables 1–4, discussions and references.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vargo, L.J., Anderson, B.M., Dadić, R. et al. Anthropogenic warming forces extreme annual glacier mass loss. Nat. Clim. Chang. 10, 856–861 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing