Skip to main content

Thank you for visiting nature.com. 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.

An observation-based constraint on permafrost loss as a function of global warming

Nature Climate Change volume 7, pages 340–344 (2017)Cite this article

Subjects

Abstract

Permafrost, which covers 15 million km2 of the land surface, is one of the components of the Earth system that is most sensitive to warming1,2. Loss of permafrost would radically change high-latitude hydrology and biogeochemical cycling, and could therefore provide very significant feedbacks on climate change3,4,5,6,7,8. The latest climate models all predict warming of high-latitude soils and thus thawing of permafrost under future climate change, but with widely varying magnitudes of permafrost thaw9,10. Here we show that in each of the models, their present-day spatial distribution of permafrost and air temperature can be used to infer the sensitivity of permafrost to future global warming. Using the same approach for the observed permafrost distribution and air temperature, we estimate a sensitivity of permafrost area loss to global mean warming at stabilization of million km2 °C−1 (1σ confidence), which is around 20% higher than previous studies9. Our method facilitates an assessment for COP21 climate change targets11: if the climate is stabilized at 2 °C above pre-industrial levels, we estimate that the permafrost area would eventually be reduced by over 40%. Stabilizing at 1.5 °C rather than 2 °C would save approximately 2 million km2 of permafrost.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

Rent or Buy

All prices are NET prices.

Figure 1: Defining the spatial distribution of observed permafrost as a function of observed air temperature.
Figure 2: Comparison of our estimate of global permafrost area with that simulated by the CMIP5 models (stabilization runs at 2300).
Figure 3: Relationship between global warming stabilization scenario and remaining permafrost area using our approach.
Figure 4: Changes in spatial patterns of permafrost under future stabilization scenarios.

References

  1. 1

    Romanovsky, V. et al. in Arctic Report Card 2013 131–136 (NOAA Arctic Program, 2013); ftp://ftp.oar.noaa.gov/arctic/documents/ArcticReportCard_full_report2013.pdf

    Google Scholar 

  2. 2

    Romanovsky, V., Burgess, M., Smith, S., Yoshikawa, K. & Brown, J. Permafrost temperature records: indicators of climate change. Eos 83, 589–594 (2002).

    Article  Google Scholar 

  3. 3

    Grosse, G., Goetz, S., McGuire, A. D., Romanovsky, V. E. & Schuur, E. A. G. Changing permafrost in a warming world and feedbacks to the Earth system. Environ. Res. Lett. 11, 040201 (2016).

    Article  Google Scholar 

  4. 4

    Schaefer, K., Lantuit, H., Romanovsky, V. E., Schuur, E. A. G. & Witt, R. The impact of the permafrost carbon feedback on global climate. Environ. Res. Lett. 9, 085003 (2014).

    Article  Google Scholar 

  5. 5

    Schneider von Deimling, T. et al. Estimating the near-surface permafrost-carbon feedback on global warming. Biogeosciences 9, 649–665 (2012).

    Article  Google Scholar 

  6. 6

    Schuur, E. A. G. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).

    CAS  Article  Google Scholar 

  7. 7

    MacDougall, A. H., Avis, C. A. & Weaver, A. J. Significant contribution to climate warming from the permafrost carbon feedback. Nat. Geosci. 5, 719–721 (2012).

    CAS  Article  Google Scholar 

  8. 8

    Burke, E. J., Hartley, I. P. & Jones, C. D. Uncertainties in the global temperature change caused by carbon release from permafrost thawing. Cryosphere 6, 1063–1076 (2012).

    Article  Google Scholar 

  9. 9

    Koven, C. D., Riley, W. J. & Stern, A. Analysis of permafrost thermal dynamics and response to climate change in the CMIP5 Earth System Models. J. Clim. 26, 1877–1900 (2013).

    Article  Google Scholar 

  10. 10

    Slater, A. G. & Lawrence, D. M. Diagnosing present and future permafrost from climate models. J. Clim. 26, 5608–5623 (2013).

    Article  Google Scholar 

  11. 11

    Adoption of the Paris Agreement FCCC/CP/2015/L.9/Rev.1 (UNFCCC, 2015); http://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf

  12. 12

    Zhang, T., Barry, R. G., Knowles, K., Heginbottom, J. A. & Brown, J. Statistics and characteristics of permafrost and ground-ice distribution in the Northern Hemisphere. Pol. Geogr. 23, 132–154 (1999).

    Article  Google Scholar 

  13. 13

    Schaefer, K., Lantuit, H., Romanovsky, V. & Schuur, E. A. G. Policy Implications of Warming Permafrost (United Nations Environment Programme, 2012); http://wedocs.unep.org/handle/20.500.11822/8533

    Google Scholar 

  14. 14

    Hugelius, G. et al. Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps. Biogeosciences 11, 6573–6593 (2014).

    Article  Google Scholar 

  15. 15

    Anisimov, O. A. & Nelson, F. E. Permafrost zonation and climate change in the Northern Hemisphere: results from transient general circulation models. Climatic Change 35, 241–258 (1997).

    Article  Google Scholar 

  16. 16

    Chadburn, S. E. et al. Impact of model developments on present and future simulations of permafrost in a global land-surface model. Cryosphere 9, 1505–1521 (2015).

    Article  Google Scholar 

  17. 17

    Westermann, S., Østby, T. I., Gisnås, K., Schuler, T. V. & Etzelmüller, B. A ground temperature map of the North Atlantic permafrost region based on remote sensing and reanalysis data. Cryosphere 9, 1303–1319 (2015).

    Article  Google Scholar 

  18. 18

    Jorgenson, M. et al. Resilience and vulnerability of permafrost to climate change. Can. J. For. Res. 40, 1219–1236 (2010).

    Article  Google Scholar 

  19. 19

    Gruber, S. Derivation and analysis of a high-resolution estimate of global permafrost zonation. Cryosphere 6, 221–233 (2012).

    Article  Google Scholar 

  20. 20

    Brown, J., Ferrians, O. J. Jr, Heginbottom, J. & Melnikov, E. Circum-Arctic Map of Permafrost and Ground Ice Conditions (National Snow and Ice Data Center, 1998); http://nsidc.org/data/docs/fgdc/ggd318_map_circumarctic

    Google Scholar 

  21. 21

    Weedon, G. P. et al. The Watch Forcing Data 1958–2001: A Meteorological Forcing Dataset for Land Surface- and Hydrological-Models WATCH Technical Report 22 (WATCH, 2010); http://www.eu-watch.org/publications

  22. 22

    Weedon, G. P. et al. The WFDEI meteorological forcing data set: WATCH forcing data methodology applied to ERA-interim reanalysis data. Wat. Resour. Res. 50, 7505–7514 (2014).

    Article  Google Scholar 

  23. 23

    Goodrich, L. The influence of snow cover on the ground thermal regime. Can. Geotech. J. 19, 421–432 (1982).

    Article  Google Scholar 

  24. 24

    Nelson, F. E. Permafrost distribution in central Canada: applications of a climate-based predictive model. Ann. Assoc. Am. Geogr. 76, 550–569 (1986).

    Article  Google Scholar 

  25. 25

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. A Summary of the CMIP5 Experiment Design PCMDI Tech. Rep. (WCRP, 2009); http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf

  26. 26

    IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. et al.) (Cambridge Univ. Press, 2013).

  27. 27

    Xie, Y., Liu, Y. & Huang, J. Overestimated Arctic warming and underestimated Eurasia mid-latitude warming in CMIP5 simulations. Int. J. Climatol. 36, 4475–4487 (2016).

    Article  Google Scholar 

  28. 28

    Center for International Earth Science Information Network, Columbia University, International Food Policy Research Institute, The World Bank, & Centro Internacional de Agricultura Tropical Global Rural-Urban Mapping Project, Version 1 (grumpv1): Population Density Grid (NASA Socioeconomic Data and Applications Center, 2011); http://dx.doi.org/10.7927/H4R20Z93

  29. 29

    Balk, D. et al. Determining global population distribution: methods, applications and data. Adv. Parasitol. 62, 119–156 (2006).

    CAS  Article  Google Scholar 

  30. 30

    Shindell, D. & Faluvegi, G. Climate response to regional radiative forcing during the twentieth century. Nat. Geosci. 2, 294–300 (2009).

    CAS  Article  Google Scholar 

  31. 31

    Miller, G. H. et al. Arctic amplification: can the past constrain the future? Quat. Sci. Rev. 29, 1779–1790 (2010); Special theme: Arctic palaeoclimate synthesis (1674–1790).

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge funding and support from the Permafrost in the Arctic and Global Effects in the 21st century (PAGE21) Framework 7 project GA282700. S.E.C., G.H. and S.W. were funded under the Joint Partnership Initiative (JPI) project COnstraining Uncertainties in the Permafrost-climate feedback (COUP) (S.E.C.: National Environment Research Council grant NE/M01990X/1; G.H.: Swedish Research Council grant no. E0689701; S.W.: Research Council of Norway project no. 244903/E10 with additional funding for S.W. through SatPerm and Permanor (Research Council of Norway project no. 239918 and 255331/E10)). E.J.B. was supported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). P.M.C. and P.F. acknowledge funding from CRESCENDO (EU project 641816). S.E.C. is grateful to the University of Exeter for access to facilities. Thanks to D. Pearson for helpful discussions, and A. Lebéhot for comments on the manuscript.

Author information

Affiliations

  1. University of Leeds, School of Earth and Environment, Leeds LS2 9JT, UK

    S. E. Chadburn

  2. University of Exeter, College of Engineering, Mathematics and Physical Sciences, Exeter EX4 4QF, UK

    S. E. Chadburn, P. M. Cox & P. Friedlingstein

  3. Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK

    E. J. Burke

  4. Department of Physical Geography, Stockholm University, 106 91 Stockholm, Sweden

    G. Hugelius

  5. Department of Geosciences, University of Oslo, PO Box 1047 Blindern, NO-0316 Oslo, Norway

    S. Westermann

Authors
  1. S. E. Chadburn
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. J. Burke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. M. Cox
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. P. Friedlingstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. G. Hugelius
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. S. Westermann
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.E.C. developed the techniques, made the calculations for future projections of permafrost, and produced the plots and manuscript. S.W. and G.H. provided and analysed data for evaluation, along with advice and comments. E.J.B. extracted CMIP5 model data. P.M.C. came up with the original idea to address this question. P.M.C., E.J.B. and P.F. provided advice, ideas and discussion throughout the process. All authors contributed towards writing the manuscript.

Corresponding author

Correspondence to S. E. Chadburn.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Information (PDF 1300 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chadburn, S., Burke, E., Cox, P. et al. An observation-based constraint on permafrost loss as a function of global warming. Nature Clim Change 7, 340–344 (2017). https://doi.org/10.1038/nclimate3262

Download citation

Further reading

Search

Advanced search

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