Shifts in Arctic vegetation and associated feedbacks under climate change

Journal name:
Nature Climate Change
Volume:
3,
Pages:
673–677
Year published:
DOI:
doi:10.1038/nclimate1858
Received
Accepted
Published online

Climate warming has led to changes in the composition, density and distribution of Arctic vegetation in recent decades1, 2, 3, 4. These changes cause multiple opposing feedbacks between the biosphere and atmosphere5, 6, 7, 8, 9, the relative magnitudes of which will have globally significant consequences but are unknown at a pan-Arctic scale10. The precise nature of Arctic vegetation change under future warming will strongly influence climate feedbacks, yet Earth system modelling studies have so far assumed arbitrary increases in shrubs (for example, +20%; refs 6, 11), highlighting the need for predictions of future vegetation distribution shifts. Here we show, using climate scenarios for the 2050s and models that utilize statistical associations between vegetation and climate, the potential for extremely widespread redistribution of vegetation across the Arctic. We predict that at least half of vegetated areas will shift to a different physiognomic class, and woody cover will increase by as much as 52%. By incorporating observed relationships between vegetation and albedo, evapotranspiration and biomass, we show that vegetation distribution shifts will result in an overall positive feedback to climate that is likely to cause greater warming than has previously been predicted. Such extensive changes to Arctic vegetation will have implications for climate, wildlife and ecosystem services.

At a glance

Figures

  1. Observed and predicted distributions of vegetation.
    Figure 1: Observed and predicted distributions of vegetation.

    Observed distributions of vegetation classes (left) and predicted distributions for the 2050s based on an equilibrium dispersal scenario (unrestricted colonization of trees), Random Forest model, HadCM3 AOGCM, and A2a emissions scenario (right). a, Siberia. b, Alaska. c, Western Canada. Modelled vegetation classes are overlaid on a physical terrain map (US National Park Service). Projection: Lambert azimuthal equal area.

  2. Predicted changes in area by vegetation class for the 2050s.
    Figure 2: Predicted changes in area by vegetation class for the 2050s.

    a, Restricted tree dispersal scenarios. b, Equilibrium scenario (unrestricted colonization of trees). Grey bars show the range of predictions due to alternative machine-learning models, AOGCMs, emissions scenarios and dispersal scenarios. Tree classes are excluded from this figure because they have no present-day coverage within the study region, so relative changes in area cannot be calculated.

  3. Predicted monthly changes in surface net short-wave radiation for the 2050s.
    Figure 3: Predicted monthly changes in surface net short-wave radiation for the 2050s.

    Red shading shows the range of predicted changes in monthly SN across machine-learning models, AOGCMs and emissions scenarios under equilibrium (unrestricted tree dispersal scenario). Blue shading shows the range of predicted changes in monthly SN across machine-learning models, AOGCMs and emissions scenarios under restricted dispersal. Note that changes in SN are driven solely by albedo as incident short-wave radiation is held constant.

References

  1. Tape, K., Sturm, M. & Racine, C. The evidence for shrub expansion in Northern Alaska and the Pan-Arctic. Glob. Change Biol. 12, 686702 (2006).
  2. Goetz, S. J. et al. in Eurasian Arctic Land Cover and Land Use in a Changing Climate (eds Gutman, G. & Reissell, A.) 936 (Springer, 2011).
  3. Elmendorf, S. C. et al. Plot-scale evidence of tundra vegetation change and links to recent summer warming. Nature Clim. Change 2, 453457 (2012).
  4. Macias-Fauria, M., Forbes, B. C., Zetterberg, P. & Kumpula, T. Eurasian Arctic greening reveals teleconnections and the potential for structurally novel ecosystems. Nature Clim. Change 2, 613618 (2012).
  5. Chapin, F. S. et al. Role of land-surface changes in Arctic summer warming. Science 310, 657660 (2005).
  6. Lawrence, D. M. & Swenson, S. C. Permafrost response to increasing Arctic shrub abundance depends on the relative influence of shrubs on local soil cooling versus large-scale climate warming. Environ. Res. Lett. 6, 045504 (2011).
  7. Blok, D. et al. Shrub expansion may reduce summer permafrost thaw in Siberian tundra. Glob. Change Biol. 16, 12961305 (2010).
  8. Swann, A. L., Fung, I. Y., Levis, S., Bonan, G. B. & Doney, S. C. Changes in Arctic vegetation amplify high-latitude warming through the greenhouse effect. Proc. Natl Acad. Sci. USA 107, 12951300 (2010).
  9. Schuur, E. A. G. et al. The effect of permafrost thaw on old carbon release and net carbon exchange from tundra. Nature 459, 556559 (2009).
  10. Loranty, M. M. & Goetz, S. J. Shrub expansion and climate feedbacks in Arctic tundra. Environ. Res. Lett. 7, 011005 (2012).
  11. Bonfils, C. J. W. et al. On the influence of shrub height and expansion on northern high latitude climate. Environ. Res. Lett. 7, 015503 (2012).
  12. IPCC Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) (Cambridge Univ. Press, 2007).
  13. Peterson, A. T. et al. Ecological Niches and Geographic Distributions (Princeton Univ. Press, 2011).
  14. Chapin, F. S.III, Bret-Harte, M. S., Hobbie, S. E. & Zhong, H. Plant functional types as predictors of transient responses of arctic vegetation to global change. J. Veg. Sci. 7, 347358 (1996).
  15. Euskirchen, E. S., McGuire, A. D., Chapin, F. S., Yi, S. & Thompson, C. C. Changes in vegetation in Northern Alaska under scenarios of climate Change, 2003–2100: Implications for climate feedbacks. Ecol. Appl. 19, 10221043 (2009).
  16. Wolf, A., Callaghan, T. & Larson, K. Future changes in vegetation and ecosystem function of the Barents Region. Climatic Change 87, 5173 (2008).
  17. Epstein, H. E., Walker, M. D., Chapin, F. S. & Starfield, A. M. A transient, nutrient-based model of arctic plant community response to climatic warming. Ecol. Appl. 10, 824841 (2000).
  18. Friedlingstein, P. et al. Climate–carbon cycle feedback analysis: Results from the C4MIP model intercomparison. J. Clim. 19, 33373353 (2006).
  19. McGuire, A. D. et al. Sensitivity of the carbon cycle in the Arctic to climate change. Ecol. Monogr. 79, 523555 (2009).
  20. Alsos, I. G. et al. Frequent long-distance plant colonization in the changing arctic. Science 316, 16061609 (2007).
  21. Pearson, R. G. Climate change and the migration capacity of species. Trends Ecol. Evol. 21, 111113 (2006).
  22. Racine, C., Jandt, R., Meyers, C. & Dennis, J. Tundra fire and vegetation change along a hillslope on the Seward Peninsula, Alaska, USA. Arct. Antarct. Alp. Res. 36, 110 (2004).
  23. Schuur, E. A. G., Crummer, K. G., Vogel, J. G. & Mack, M. C. Plant species composition and productivity following permafrost thaw and thermokarst in Alaskan tundra. Ecosystems 10, 280292 (2007).
  24. Forchhammer, M. C., Post, E., Stenseth, N. C. & Boertmann, D. M. Long-term responses in arctic ungulate dynamics to changes in climatic and trophic processes. Popul. Ecol. 44, 113120 (2002).
  25. Post, E. et al. Ecological dynamics across the arctic associated with recent climate change. Science 325, 13551358 (2009).
  26. Zöckler, C. Migratory bird species as indicators for the state of the environment. Biodiversity 6, 713 (2005).
  27. Sturm, M. et al. Winter biological processes could help convert Arctic tundra to shrubland. BioScience 55, 17 (2005).
  28. Fletcher, C. G., Zhao, H., Kushner, P. J. & Fernandes, R. Using models and satellite observations to evaluate the strength of snow albedo feedback. J. Geophys. Res. 117, D11117 (2012).
  29. Zhang, K., Kimball, J. S., Kim, Y. & McDonald, K. C. Changing freeze-thaw seasons in northern high latitudes and associated influences on evapotranspiration. Hydrol. Process. 25, 41424151 (2011).
  30. Walker, D. A. et al. The Circumpolar Arctic vegetation map. J. Veg. Sci. 16, 267282 (2005).

Download references

Author information

Affiliations

  1. Center for Biodiversity and Conservation, American Museum of Natural History, New York, New York 10024, USA

    • Richard G. Pearson &
    • Sarah J. Knight
  2. AT&T Labs-Research, 180 Park Avenue, Florham Park, New Jersey 07932, USA

    • Steven J. Phillips
  3. Woods Hole Research Center, 149 Woods Hole Road, Falmouth, Massachusetts 02540, USA

    • Michael M. Loranty,
    • Pieter S. A. Beck &
    • Scott J. Goetz
  4. Department of Geography, Colgate University, Hamilton, New York 13346, USA

    • Michael M. Loranty
  5. Department of Computer Science, Cornell University, Ithaca, New York 14853, USA

    • Theodoros Damoulas
  6. Department of Biology, University of York, York YO10 5DD, UK

    • Sarah J. Knight

Contributions

R.G.P. and S.J.G. conceived the study; R.G.P. analysed data; S.J.P. analysed data and ran Random Forests models; M.M.L. led albedo and evapotranspiration analyses; P.S.A.B. led biomass and SN analyses; T.D. ran multi-kernel Relevance Vector Machines models; S.J.K. ran preliminary analyses; R.G.P., M.M.L. and P.S.A.B. wrote the paper with contributions from all authors.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Author details

Supplementary information

Additional data