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.

The age distribution of global soil carbon inferred from radiocarbon measurements

Nature Geoscience volume 13, pages 555–559 (2020)Cite this article

Subjects

Abstract

Soils contain more carbon than the atmosphere and vegetation combined. An increased flow of carbon from the atmosphere into soil pools could help mitigate anthropogenic emissions of carbon dioxide and climate change. Yet we do not know how quickly soils might respond because the age distribution of soil carbon is uncertain. Here we used 789 radiocarbon (∆14C) profiles, along with other geospatial information, to create globally gridded datasets of mineral soil ∆14C and mean age. We found that soil depth is a primary driver of ∆14C, whereas climate (for example, mean annual temperature) is a major control on the spatial pattern of ∆14C in surface soil. Integrated to a depth of 1 m, global soil carbon has a mean age of 4,830 ± 1,730 yr, with older carbon in deeper layers and permafrost regions. In contrast, vertically resolved land models simulate ∆14C values that imply younger carbon ages and a more rapid carbon turnover. Our data-derived estimates of older mean soil carbon age suggest that soils will accumulate less carbon than predicted by current Earth system models over the twenty-first century. Reconciling these models with the global distribution of soil radiocarbon will require a better representation of the mechanisms that control carbon persistence in soils.

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

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

Fig. 1: Global distribution of soil ∆14C and mean carbon age (MCA).
Fig. 2: Age distribution of global soil carbon.
Fig. 3: Comparison of land surface model predictions of soil ∆14C with the data-derived product developed here for different depths and biomes.

Similar content being viewed by others

Data availability

The gridded maps of soil ∆14C and MCA are archived at Zenodo (https://doi.org/10.5281/zenodo.3823612). Other data that support the findings of this study are publicly available. Soil ∆14C measurements are available at https://zenodo.org/record/2613911#.XsNtQi-z124. Global soil carbon and soil clay content in SoilGrids are available at https://landgis.opengeohub.org. Soil carbon content in HWSD is available at https://go.nature.com/2ASmPC3. Global soil order data are available at https://go.nature.com/3hgdsgb. The climate data used can be downloaded from https://crudata.uea.ac.uk/cru/data/hrg/. The land cover map can be obtained from the MODIS Land cover MCD12Q1 product (https://lpdaac.usgs.gov/products/mcd12q1v006/). The permafrost map was generated by the National Snow and Ice Data Center (https://go.nature.com/2AZbTTe).

Code availability

All code relating to this study is available from the corresponding author upon request.

References

  1. Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).

    Google Scholar 

  2. Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci. USA 112, 436–441 (2015).

    Google Scholar 

  3. Minasny, B. et al. Soil carbon 4 per mille. Geoderma 292, 59–86 (2017).

    Google Scholar 

  4. Todd-Brown, K. E. O. et al. Changes in soil organic carbon storage predicted by Earth system models during the 21st century. Biogeosciences 11, 2341–2356 (2014).

    Google Scholar 

  5. Schlesinger, W. H. Evidence from chronosequence studies for a low carbon-storage potential of soils. Nature 348, 232–234 (1990).

    Google Scholar 

  6. van Groenigen, K. J. et al. Faster turnover of new soil carbon inputs under increased atmospheric CO2. Glob. Change Biol. 23, 4420–4429 (2017).

    Google Scholar 

  7. Richter, D. D., Markewitz, D., Trumbore, S. E. & Wells, C. G. Rapid accumulation and turnover of soil carbon in a re-establishing forest. Nature 400, 56–58 (1999).

    Google Scholar 

  8. Plaza, C. et al. Direct observation of permafrost degradation and rapid soil carbon loss in tundra. Nat. Geosci. 12, 627–631 (2019).

    Google Scholar 

  9. Sanderman, J., Hengl, T. & Fiske, G. J. Soil carbon debt of 12,000 years of human land use. Proc. Natl Acad. Sci. USA 114, 9575–9580 (2017).

    Google Scholar 

  10. Trumbore, S. Radiocarbon and soil carbon dynamics. Annu. Rev. Earth Planet. Sci. 37, 47–66 (2009).

    Google Scholar 

  11. Torn, M. S., Trumbore, S. E., Chadwick, O. A., Vitousek, P. M. & Hendricks, D. M. Mineral control of soil organic carbon storage and turnover. Nature 389, 170–173 (1997).

    Google Scholar 

  12. Lawrence, C. R. et al. An open-source database for the synthesis of soil radiocarbon data: International Soil Radiocarbon Database (ISRaD) version 1.0. Earth Syst. Sci. Data 12, 61–76 (2020).

    Google Scholar 

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

    Google Scholar 

  14. Stuiver, M. & Polach, H. A. Discussion reporting of 14C data. Radiocarbon 19, 355–363 (1977).

    Google Scholar 

  15. Hemingway, J. D. et al. Mineral protection regulates long-term global preservation of natural organic carbon. Nature 570, 228–231 (2019).

    Google Scholar 

  16. Kramer, M. G. & Chadwick, O. A. Climate-driven thresholds in reactive mineral retention of soil carbon at the global scale. Nat. Clim. Change 8, 1104–1108 (2018).

    Google Scholar 

  17. Xu, X. et al. Soil properties control decomposition of soil organic carbon: results from data-assimilation analysis. Geoderma 262, 235–242 (2016).

    Google Scholar 

  18. Rasmussen, C. et al. Beyond clay: towards an improved set of variables for predicting soil organic matter content. Biogeochemistry 137, 297–306 (2018).

    Google Scholar 

  19. Lalonde, K., Mucci, A., Ouellet, A. & Gélinas, Y. Preservation of organic matter in sediments promoted by iron. Nature 483, 198–200 (2012).

    Google Scholar 

  20. Gentsch, N. et al. Temperature response of permafrost soil carbon is attenuated by mineral protection. Glob. Change Biol. 24, 3401–3415 (2018).

    Google Scholar 

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

    Google Scholar 

  22. Fan, N. et al. Apparent ecosystem carbon turnover time: uncertainties and robust features. Earth Syst. Sci. Data Discuss. https://doi.org/10.5194/essd-2019-235 (2020).

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

    Google Scholar 

  24. Reimer, P. J. et al. IntCal13 and Marine13 radiocarbon age calibration curves 0–50,000 years cal BP. Radiocarbon 55, 1869–1887 (2013).

    Google Scholar 

  25. Balesdent, J. et al. Atmosphere–soil carbon transfer as a function of soil depth. Nature 559, 599–602 (2018).

    Google Scholar 

  26. Lawrence, D. M. et al. The Community Land Model Version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11, 4245–4287 (2019).

    Google Scholar 

  27. Zhu, Q. et al. Representing nitrogen, phosphorus, and carbon interactions in the E3SM Land Model: development and global benchmarking. J. Adv. Model. Earth Syst. 11, 2238–2258 (2019).

    Google Scholar 

  28. Chen, J. et al. Comparison with global soil radiocarbon observations indicates needed carbon cycle improvements in the E3SM Land Model. J. Geophys. Res. Biogeosci. 124, 1098–1114 (2019).

    Google Scholar 

  29. 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).

    Google Scholar 

  30. Koven, C. D., Hugelius, G., Lawrence, D. M. & Wieder, W. R. Higher climatological temperature sensitivity of soil carbon in cold than warm climates. Nat. Clim. Change 7, 817–822 (2017).

    Google Scholar 

  31. Sierra, C. A., Hoyt, A. M., He, Y. & Trumbore, S. E. Soil organic matter persistence as a stochastic process: age and transit time distributions of carbon in soils. Glob. Biogeochem. Cycles 32, 1574–1588 (2018).

    Google Scholar 

  32. Hobley, E., Baldock, J., Hua, Q. & Wilson, B. Land-use contrasts reveal instability of subsoil organic carbon. Glob. Change Biol. 23, 955–965 (2017).

    Google Scholar 

  33. Pellegrini, A. F. A. et al. Fire frequency drives decadal changes in soil carbon and nitrogen and ecosystem productivity. Nature 553, 194–198 (2018).

    Google Scholar 

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

    Google Scholar 

  35. Parton, W. J., Stewart, J. W. B. & Cole, C. V. Dynamics of C, N, P and S in grassland soils: a model. Biogeochemistry 5, 109–131 (1988).

    Google Scholar 

  36. Bond-Lamberty, B., Bailey, V. L., Chen, M., Gough, C. M. & Vargas, R. Globally rising soil heterotrophic respiration over recent decades. Nature 560, 80–83 (2018).

    Google Scholar 

  37. 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).

    Google Scholar 

  38. Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).

    Google Scholar 

  39. Soil Survey Staff Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Surveys 2nd edn (USDA, 1999).

  40. Levin, I. & Kromer, B. Twenty years of atmospheric 14CO2 observations at Schauinsland station, Germany. Radiocarbon 39, 205–218 (1997).

    Google Scholar 

  41. Levin, I., Kromer, B. & Hammer, S. Atmospheric Δ14CO2 trend in Western European background air from 2000 to 2012. Tellus B 65, 20092 (2013).

    Google Scholar 

  42. Hua, Q., Barbetti, M. & Rakowski, A. Z. Atmospheric radiocarbon for the period 1950–2010. Radiocarbon 55, 2059–2072 (2013).

    Google Scholar 

  43. Friedl, M. A. et al. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).

    Google Scholar 

  44. Sierra, C. A. et al. Predicting decadal trends and transient responses of radiocarbon storage and fluxes in a temperate forest soil. Biogeosciences 9, 3013–3028 (2012).

    Google Scholar 

  45. Schrumpf, M. et al. Storage and stability of organic carbon in soils as related to depth, occlusion within aggregates, and attachment to minerals. Biogeosciences 10, 1675–1691 (2013).

    Google Scholar 

  46. Gaudinski, J. B., Trumbore, S. E., Davidson, E. A. & Zheng, S. H. Soil carbon cycling in a temperate forest: radiocarbon-based estimates of residence times, sequestration rates and partitioning of fluxes. Biogeochemistry 51, 33–69 (2000).

    Google Scholar 

  47. Thompson, M. V. & Randerson, J. T. Impulse response functions of terrestrial carbon cycle models: methods and application. Glob. Change Biol. 5, 371–394 (1999).

    Google Scholar 

  48. Sierra, C. A., Müller, M., Metzler, H., Manzoni, S. & Trumbore, S. E. The muddle of ages, turnover, transit, and residence times in the carbon cycle. Glob. Change Biol. 23, 1763–1773 (2017).

    Google Scholar 

  49. Harmonized World Soil Database Version 1.2 (FAO, 2012).

  50. Brown, J., Ferrians O. Jr, Heginbottom, J. & Melnikov, E. Circum-Arctic Map of Permafrost and Ground-Ice Conditions (US Geological Survey, 1997).

  51. Collier, N. et al. The International Land Model Benchmarking (ILAMB) system: design, theory, and implementation. J. Adv. Model. Earth Syst. 10, 2731–2754 (2018).

    Google Scholar 

  52. Bonan, G. B. et al. Model structure and climate data uncertainty in historical simulations of the terrestrial carbon cycle (1850–2014). Glob. Biogeochem. Cycles 33, 1310–1326 (2019).

    Google Scholar 

  53. Lawrence, D. M. et al. The land use model intercomparison project (LUMIP) contribution to CMIP6: rationale and experimental design. Geosci. Model Dev. 9, 2973–2998 (2016).

    Google Scholar 

  54. Brunke, M. A. et al. Implementing and evaluating variable soil thickness in the Community Land Model, Version 4.5 (CLM4.5). J. Clim. 29, 3441–3461 (2016).

    Google Scholar 

Download references

Acknowledgements

This work was supported by the European Research Council (Horizon 2020 Research and Innovation Programme, grant agreement 695101, to S.T. and J.T.R.), by the US DOE Office of Science Biological and Environmental Research RUBISCO Science Focus Area (to J.T.R. and Q.Z.) and award DE-SC0014374 (to S.D.A. and J.T.R.) and by a NASA Earth and Space Science Fellowship (to P.A.L.).

Author information

Authors and Affiliations

  1. Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA, USA

    Zheng Shi & Steven D. Allison

  2. Department of Earth System Science, University of California Irvine, Irvine, CA, USA

    Zheng Shi, Steven D. Allison, Yujie He, Paul A. Levine & James T. Randerson

  3. Department of Biogeochemical Processes, Max-Planck-Institute for Biogeochemistry, Jena, Germany

    Alison M. Hoyt, Jeffrey Beem-Miller & Susan Trumbore

  4. Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Qing Zhu

  5. Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA

    William R. Wieder

Authors
  1. Zheng Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Steven D. Allison
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yujie He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paul A. Levine
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alison M. Hoyt
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jeffrey Beem-Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qing Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. William R. Wieder
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Susan Trumbore
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. James T. Randerson
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Z.S., Y.H., S.D.A., S.T. and J.T.R. designed the study; Z.S. and Y.H. analysed the data using machine learning and other approaches; P.A.L., W.R.W. and Q.Z. provided analysis of the land surface models; J.B.-M., A.M.H., P.A.L. and S.T. contributed to the development of the version of the ISRaD dataset used here; Z.S., S.D.A. and J.T.R. wrote the paper with substantial contributions from all of the authors.

Corresponding author

Correspondence to Zheng Shi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Primary Handling Editor: Rebecca Neely.

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

Supplementary information

Supplementary Information

Supplementary Figs. 1–17 and Tables 1–5.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, Z., Allison, S.D., He, Y. et al. The age distribution of global soil carbon inferred from radiocarbon measurements. Nat. Geosci. 13, 555–559 (2020). https://doi.org/10.1038/s41561-020-0596-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41561-020-0596-z

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