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.

Global soil carbon projections are improved by modelling microbial processes

Nature Climate Change volume 3, pages 909–912 (2013)Cite this article

Subjects

Abstract

Society relies on Earth system models (ESMs) to project future climate and carbon (C) cycle feedbacks. However, the soil C response to climate change is highly uncertain in these models1,2 and they omit key biogeochemical mechanisms3,4,5. Specifically, the traditional approach in ESMs lacks direct microbial control over soil C dynamics6,7,8. Thus, we tested a new model that explicitly represents microbial mechanisms of soil C cycling on the global scale. Compared with traditional models, the microbial model simulates soil C pools that more closely match contemporary observations. It also projects a much wider range of soil C responses to climate change over the twenty-first century. Global soils accumulate C if microbial growth efficiency declines with warming in the microbial model. If growth efficiency adapts to warming, the microbial model projects large soil C losses. By comparison, traditional models project modest soil C losses with global warming. Microbes also change the soil response to increased C inputs, as might occur with CO2 or nutrient fertilization. In the microbial model, microbes consume these additional inputs; whereas in traditional models, additional inputs lead to C storage. Our results indicate that ESMs should simulate microbial physiology to more accurately project climate change feedbacks.

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

from$1.95

to$39.95

Learn more

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

Figure 1: Diagram of the CLM microbial model.
Figure 2: Global distribution of soil C pools (0–100 cm) from observations19 and models.
Figure 3: Divergent model responses of global soil C pools in global change simulations.

References

  1. Friedlingstein, P. et al. Climate-carbon cycle feedback analysis: Results from the C4MIP Model intercomparison. J. Clim. 19, 3337–3353 (2006).

    Article  Google Scholar 

  2. Todd-Brown, K. E. O. et al. Causes of variation in soil carbon predictions from CMIP5 Earth system models and comparison with observations. Biogeosciences 10, 1717–1736 (2013).

    Article  Google Scholar 

  3. Conant, R. T. et al. Temperature and soil organic matter decomposition rates—synthesis of current knowledge and a way forward. Glob. Change Biol. 17, 3392–3404 (2011).

    Article  Google Scholar 

  4. Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The microbial efficiency-matrix stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: Do labile plant inputs form stable soil organic matter? Glob. Change Biol. 19, 988–995 (2013).

    Article  Google Scholar 

  5. Schmidt, M. W. I. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  7. Six, J., Frey, S. D., Thiet, R. K. & Batten, K. M. Bacterial and fungal contributions to carbon sequestration in agroecosystems. Soil Sci. Soc. Am. J. 70, 555–569 (2006).

    Article  CAS  Google Scholar 

  8. Treseder, K. et al. Integrating microbial ecology into ecosystem models: Challenges and priorities. Biogeochemistry 109, 7–18 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  10. Parton, W. J., Schimel, D. S., Cole, C. V. & Ojima, D. S. in Quantitative Modeling of Soil Forming Processes (eds Bryant, R. B. & Arnold, R. W.) 147–167 (SSSA Special Publication, ASA, CSSA and SSA, 1994).

    Google Scholar 

  11. Ise, T. & Moorcroft, P. R. The global-scale temperature and moisture dependencies of soil organic carbon decomposition: An analysis using a mechanistic decomposition model. Biogeochemistry 80, 217–231 (2006).

    Article  CAS  Google Scholar 

  12. Manzoni, S. & Porporato, A. Soil carbon and nitrogen mineralization: Theory and models across scales. Soil Biol. Biochem. 41, 1355–1379 (2009).

    Article  CAS  Google Scholar 

  13. Lawrence, C. R., Neff, J. C. & Schimel, J. P. Does adding microbial mechanisms of decomposition improve soil organic matter models? A comparison of four models using data from a pulsed rewetting experiment. Soil Biol. Biochem. 41, 1923–1934 (2009).

    Article  CAS  Google Scholar 

  14. Tucker, C. L., Bell, J., Pendall, E. & Ogle, K. Does declining carbon-use efficiency explain thermal acclimation of soil respiration with warming? Glob. Change Biol. 19, 252–263 (2013).

    Article  Google Scholar 

  15. German, D. P., Marcelo, K. R. B., Stone, M. M. & Allison, S. D. The Michaelis–Menten kinetics of soil extracellular enzymes in response to temperature: A cross-latitudinal study. Glob. Change Biol. 18, 1468–1479 (2012).

    Article  Google Scholar 

  16. Manzoni, S., Taylor, P., Richter, A., Porporato, A. & Ågren, G. I. Environmental and stoichiometric controls on microbial carbon-use efficiency in soils. New Phytol. 196, 79–91 (2012).

    Article  CAS  Google Scholar 

  17. Frey, S. D., Lee, J., Melillo, J. M. & Six, J. The temperature response of soil microbial efficiency and its feedback to climate. Nature Clim. Change 3, 395–398 (2013).

    Article  CAS  Google Scholar 

  18. Lawrence, D. et al. Parameterization improvements and functional and structural advances in version 4 of the Community Land Model. J. Adv. Modeling Earth Syst. 3, M03001 (2011).

    Google Scholar 

  19. Harmonized World Soil Database Version 1.2. Accessed July 23; 2012 (FAO, 2012).

  20. 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 

  21. Nadelhoffer, K. J. et al. in Forests in Time: The Environmental Consequences of 1000 Years of Change in New England (eds Foster, D. & Aber, J.) Ch. 15, 300–315 (Yale Univ. Press, 2004).

    Google Scholar 

  22. Sayer, E. J., Heard, M. S., Grant, H. K., Marthews, T. R. & Tanner, E. V. J. Soil carbon release enhanced by increased tropical forest litterfall. Nature Clim. Change 1, 304–307 (2011).

    Article  CAS  Google Scholar 

  23. Hungate, B. A. et al. Assessing the effect of elevated carbon dioxide on soil carbon: A comparison of four meta-analyses. Glob. Change Biol. 15, 2020–2034 (2009).

    Article  Google Scholar 

  24. Leff, J. W. et al. Experimental litterfall manipulation drives large and rapid changes in soil carbon cycling in a wet tropical forest. Glob. Change Biol. 18, 2969–2979 (2012).

    Article  Google Scholar 

  25. Kuzyakov, Y. Priming effects: Interactions between living and dead organic matter. Soil Biol. Biochem. 42, 1363–1371 (2010).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  27. Stone, M. M. et al. Temperature sensitivity of soil enzyme kinetics under N-fertilization in two temperate forests. Glob. Change Biol. 18, 1173–1184 (2012).

    Article  Google Scholar 

  28. Hartley, I. P., Hopkins, D. W., Garnett, M. H., Sommerkorn, M. & Wookey, P. A. Soil microbial respiration in arctic soil does not acclimate to temperature. Ecol. Lett. 11, 1092–1100 (2008).

    Article  Google Scholar 

  29. Dungait, J. A. J., Hopkins, D. W., Gregory, A. S. & Whitmore, A. P. Soil organic matter turnover is governed by accessibility not recalcitrance. Glob. Change Biol. 18, 1781–1796 (2012).

    Article  Google Scholar 

  30. Xia, J. Y., Luo, Y. Q., Wang, Y. P., Weng, E. S. & Hararuk, O. A semi-analytical solution to accelerate spin-up of a coupled carbon and nitrogen land model to steady state. Geosci. Model Dev. 5, 1259–1271 (2012).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The National Center for Atmospheric Research is sponsored by the National Science Foundation. This work was supported by National Science Foundation grant AGS-1020767, the NSF Advancing Theory in Biology Program and the Office of Science (BER), US Department of Energy.

Author information

Authors and Affiliations

  1. National Center for Atmospheric Research, Boulder, Colorado 80307, USA

    William R. Wieder & Gordon B. Bonan

  2. Department of Ecology and Evolutionary Biology and Department of Earth System Science, University of California, Irvine, California 92697, USA

    Steven D. Allison

Authors
  1. William R. Wieder
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gordon B. Bonan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Steven D. Allison
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

W.R.W. and S.D.A. conceived the project and built the model. W.R.W. and G.B.B. assembled input and model evaluation data sets. W.R.W. conducted model runs. All authors contributed to writing the paper.

Corresponding author

Correspondence to William R. Wieder.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Wieder, W., Bonan, G. & Allison, S. Global soil carbon projections are improved by modelling microbial processes. Nature Clim Change 3, 909–912 (2013). https://doi.org/10.1038/nclimate1951

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nclimate1951

This article is cited by

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