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The age distribution of global soil carbon inferred from radiocarbon measurements

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

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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.

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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.

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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.

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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.).

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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

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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.

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Correspondence to Zheng Shi.

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

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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

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