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.

  • Letter
  • Published:

Higher climatological temperature sensitivity of soil carbon in cold than warm climates

Nature Climate Change volume 7, pages 817–822 (2017)Cite this article

Subjects

Abstract

The projected loss of soil carbon to the atmosphere resulting from climate change is a potentially large but highly uncertain feedback to warming. The magnitude of this feedback is poorly constrained by observations and theory, and is disparately represented in Earth system models (ESMs)1,2,3. To assess the climatological temperature sensitivity of soil carbon, we calculate apparent soil carbon turnover times4 that reflect long-term and broad-scale rates of decomposition. Here, we show that the climatological temperature control on carbon turnover in the top metre of global soils is more sensitive in cold climates than in warm climates and argue that it is critical to capture this emergent ecosystem property in global-scale models. We present a simplified model that explains the observed high cold-climate sensitivity using only the physical scaling of soil freeze–thaw state across climate gradients. Current ESMs fail to capture this pattern, except in an ESM that explicitly resolves vertical gradients in soil climate and carbon turnover. An observed weak tropical temperature sensitivity emerges in a different model that explicitly resolves mineralogical control on decomposition. These results support projections of strong carbon–climate feedbacks from northern soils5,6 and demonstrate a method for ESMs to capture this emergent behaviour.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: Global distributions of the inferred apparent turnover time (τ) of global soil organic matter as function of climatological temperature.
Figure 2: Inferred ‘climatological Q10’ as a function of temperature.
Figure 3: A hierarchy of simplified models to explain the cold-climate emergent regime of high climatological temperature sensitivities.
Figure 4: A comparison of relationships between soil turnover times and climate as predicted by a suite of ESMs and offline land models.

Similar content being viewed by others

References

  1. Arora, V. K. et al. Carbon-concentration and carbon–climate feedbacks in CMIP5 Earth System Models. J. Clim. 26, 5289–5314 (2013).

    Article  Google Scholar 

  2. Ciais, P. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 465–570 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  3. Koven, C. D. et al. Controls on terrestrial carbon feedbacks by productivity versus turnover in the CMIP5 Earth System Models. Biogeosciences 12, 5211–5228 (2015).

    Article  Google Scholar 

  4. Carvalhais, N. et al. Global covariation of carbon turnover times with climate in terrestrial ecosystems. Nature 514, 213–217 (2014).

    Article  CAS  Google Scholar 

  5. Ciais, P. et al. Large inert carbon pool in the terrestrial biosphere during the Last Glacial Maximum. Nat. Geosci. 5, 74–79 (2012).

    Article  CAS  Google Scholar 

  6. Koven, C. D. et al. Permafrost carbon-climate feedbacks accelerate global warming. Proc. Natl Acad. Sci. USA 108, 14769–14774 (2011).

    Article  CAS  Google Scholar 

  7. Mahecha, M. D. et al. Global convergence in the temperature sensitivity of respiration at ecosystem level. Science 329, 838–840 (2010).

    Article  CAS  Google Scholar 

  8. Davidson, E. & Janssens, I. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).

    Article  CAS  Google Scholar 

  9. Jenkinson, D., Adams, D. & Wild, A. Model estimates Of CO2 emissions from soil in response to global warming. Nature 351, 304–306 (1991).

    Article  CAS  Google Scholar 

  10. Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).

    Article  CAS  Google Scholar 

  11. Wieder, W. R., Bonan, G. B. & Allison, S. D. Global soil carbon projections are improved by modelling microbial processes. Nat. Clim. Change 3, 909–912 (2013).

    Article  CAS  Google Scholar 

  12. Tang, J. & Riley, W. J. Weaker soil carbon-climate feedbacks resulting from microbial and abiotic interactions. Nat. Clim. Change 5, 56–60 (2015).

    Article  CAS  Google Scholar 

  13. Bradford, M. A. et al. Thermal adaptation of soil microbial respiration to elevated temperature. Ecol. Lett. 11, 1316–1327 (2008).

    Article  Google Scholar 

  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. Jones, C. et al. Twenty-first-century compatible CO2 emissions and airborne fraction simulated by CMIP5 earth system models under four Representative Concentration Pathways. J. Clim. 26, 4398–4413 (2013).

    Article  Google Scholar 

  16. Bradford, M. A. et al. Managing uncertainty in soil carbon feedbacks to climate change. Nat. Clim. Change 6, 751–758 (2016).

    Article  Google Scholar 

  17. Todd-Brown, K. E. O. et al. Causes of variation in soil carbon simulations from CMIP5 Earth System Models and comparison with observations. Biogeosciences 10, 1717–1736 (2013).

    Article  Google Scholar 

  18. He, Y. et al. Radiocarbon constraints imply reduced carbon uptake by soils during the 21st century. Science 353, 1419–1424 (2016).

    Article  CAS  Google Scholar 

  19. Crowther, T. W. et al. Quantifying global soil carbon losses in response to warming. Nature 540, 104–108 (2016).

    Article  CAS  Google Scholar 

  20. Jenny, H. Factors of Soil Formation: A System of Quantitative Pedology (Courier Corporation, 1941).

    Book  Google Scholar 

  21. Post, W. M., Emanuel, W. R., Zinke, P. J. & Stangenberger, A. G. Soil carbon pools and world life zones. Nature 298, 156–159 (1982).

    Article  CAS  Google Scholar 

  22. Raich, J. W. & Schlesinger, W. H. The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus B 44, 81–99 (1992).

    Article  Google Scholar 

  23. Doetterl, S. et al. Soil carbon storage controlled by interactions between geochemistry and climate. Nat. Geosci. 8, 780–783 (2015).

    Article  CAS  Google Scholar 

  24. Giardina, C. P. & Ryan, M. G. Evidence that decomposition rates of organic carbon in mineral soil do not vary with temperature. Nature 404, 858–861 (2000).

    Article  CAS  Google Scholar 

  25. Lloyd, J. & Taylor, J. On the temperature-dependence of soil respiration. Funct. Ecol. 8, 315–323 (1994).

    Article  Google Scholar 

  26. Harden, J. W. et al. Field information links permafrost carbon to physical vulnerabilities of thawing. Geophys. Res. Lett. 39, L15704 (2012).

    Article  Google Scholar 

  27. Koven, C. D. et al. The effect of vertically-resolved soil biogeochemistry and alternate soil C and N models on C dynamics of CLM4. Biogeosciences 10, 7109–7131 (2013).

    Article  CAS  Google Scholar 

  28. Koven, C. D., Lawrence, D. M. & Riley, W. J. Permafrost carbon-climate feedback is sensitive to deep soil carbon decomposability but not deep soil nitrogen dynamics. Proc. Natl Acad. Sci. USA 112, 3752–3757 (2015).

    CAS  Google Scholar 

  29. Wang, Y. P. et al. Responses of two nonlinear microbial models to warming and increased carbon input. Biogeosciences 13, 887–902 (2016).

    Article  CAS  Google Scholar 

  30. Sistla, S. A. et al. Long-term warming restructures Arctic tundra without changing net soil carbon storage. Nature 497, 615–618 (2013).

    Article  CAS  Google Scholar 

  31. FAO, IIASA, ISRIC, ISSCAS & JRC Harmonized World Soil Database (version 1.2) (FAO and IIASA, 2012).

  32. Hugelius, G. et al. The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions. Earth Syst. Sci. Data 5, 3–13 (2013).

    Article  Google Scholar 

  33. Zhao, M., Heinsch, F., Nemani, R. & Running, S. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens. Environ. 95, 164–176 (2005).

    Article  Google Scholar 

  34. Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).

    Article  Google Scholar 

  35. Schneider, U. et al. GPCC Full Data Reanalysis Version 6.0 at 0.5°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data. (2011); http://doi.org/10.5676/DWD_GPCC/FD_M_V6_050

  36. Mu, Q., Zhao, M. & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800 (2011).

    Article  Google Scholar 

  37. Slater, A. et al. The representation of snow in land surface schemes: results from PILPS 2(d). J. Hydrometeorol. 2, 7–25 (2001).

    Article  Google Scholar 

  38. Slater, A. G., Lawrence, D. M. & Koven, C. D. Process-level model evaluation: a snow and heat transfer metric. Cryosphere Discuss. 2016, 1–16 (2016).

    Article  Google Scholar 

  39. Oleson, K. W. et al. Technical Description of version 4.0 of the Community Land Model (CLM) (National Center for Atmospheric Research, 2010).

    Google Scholar 

  40. Raddatz, T. J. et al. Will the tropical land biosphere dominate the climate-carbon cycle feedback during the twenty-first century? Clim. Dynam. 29, 565–574 (2007).

    Article  Google Scholar 

  41. Jones, C. D. et al. The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model Dev. 4, 543–570 (2011).

    Article  Google Scholar 

  42. Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cycles 19, GB1015 (2005).

    Article  Google Scholar 

  43. Milly, P. C. D. et al. An enhanced model of land water and energy for global hydrologic and earth-system studies. J. Hydrometeorol. 15, 1739–1761 (2014).

    Article  Google Scholar 

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

    Article  Google Scholar 

  45. 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 (2012).

    Article  Google Scholar 

  46. Sato, H., Itoh, A. & Kohyama, T. SEIB–DGVM: a new Dynamic Global Vegetation Model using a spatially explicit individual-based approach. Ecol. Model. 200, 279–307 (2007).

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the Director, Office of Science, Office of Biological and Environmental Research of the US Department of Energy (DOE) under Contract DE-AC02-05CH11231 as part of their Regional and Global Climate Modeling (BGC-Feedbacks SFA), and Terrestrial Ecosystem Science Programs (NGEE-Arctic and NGEE-Tropics), and used resources of the National Energy Research Scientific Computing Center, also supported by the Office of Science of the US Department of Energy, under Contract DE-AC02-05CH11231. National Center for Atmospheric Research (NCAR) is sponsored by the National Science Foundation (NSF). The CESM project is supported by the NSF and the Office of Science (BER) of the US Department of Energy. Computing resources were provided by the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory, sponsored by NSF and other agencies. G.H. acknowledges funding from the Swedish Research Council (grant numbers E0689701 and E0641701), the EU JPI-climate COUP project and Marie Sklodowska Curie Actions, Cofund, Project INCA 600398. D.M.L. is supported by funding from the US Department of Energy BER, as part of its Climate Change Prediction Program, Cooperative Agreement DE-FC03-97ER62402/ A010 and NSF Grants AGS-1048996 and ARC-1048987. W.R.W. is supported by funding from the US Department of Agriculture NIFA 2015-67003-23485 and US Department of Energy TES DE-SC0014374. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Supplementary Table 2 of this paper) for producing and making available their model output.

Author information

Authors and Affiliations

  1. Earth and Environmental Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA

    Charles D. Koven

  2. Department of Physical Geography & Bolin Centre of Climate Research, Stockholm University, Stockholm, SE-10691, Sweden

    Gustaf Hugelius

  3. School of Earth, Energy, and Environmental Sciences, Stanford University, Stanford, California, 94305, USA

    Gustaf Hugelius

  4. Climate & Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado, 80305, USA

    David M. Lawrence & William R. Wieder

  5. Institute of Arctic & Alpine Research, University of Colorado, Boulder, Colorado, 80309, USA

    William R. Wieder

Authors
  1. Charles D. Koven
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gustaf Hugelius
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David M. Lawrence
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. William R. Wieder
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.D.K. designed the study and performed analyses, based on ideas developed through discussions with G.H., D.M.L. and W.R.W. W.R.W. contributed MIMICS results, C.D.K. and D.M.L. contributed CLM4.5 results, and G.H. contributed NCSCD data. All authors wrote the manuscript.

Corresponding author

Correspondence to Charles D. Koven.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Information (PDF 6553 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Koven, C., Hugelius, G., Lawrence, D. et al. Higher climatological temperature sensitivity of soil carbon in cold than warm climates. Nature Clim Change 7, 817–822 (2017). https://doi.org/10.1038/nclimate3421

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nclimate3421

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

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