Letter | Published:

Large influence of soil moisture on long-term terrestrial carbon uptake

Naturevolume 565pages476479 (2019) | Download Citation

Abstract

Although the terrestrial biosphere absorbs about 25 per cent of anthropogenic carbon dioxide (CO2) emissions, the rate of land carbon uptake remains highly uncertain, leading to uncertainties in climate projections1,2. Understanding the factors that limit or drive land carbon storage is therefore important for improving climate predictions. One potential limiting factor for land carbon uptake is soil moisture, which can reduce gross primary production through ecosystem water stress3,4, cause vegetation mortality5 and further exacerbate climate extremes due to land–atmosphere feedbacks6. Previous work has explored the impact of soil-moisture availability on past carbon-flux variability3,7,8. However, the influence of soil-moisture variability and trends on the long-term carbon sink and the mechanisms responsible for associated carbon losses remain uncertain. Here we use the data output from four Earth system models9 from a series of experiments to analyse the responses of terrestrial net biome productivity to soil-moisture changes, and find that soil-moisture variability and trends induce large CO2 fluxes (about two to three gigatons of carbon per year; comparable with the land carbon sink itself1) throughout the twenty-first century. Subseasonal and interannual soil-moisture variability generate CO2 as a result of the nonlinear response of photosynthesis and net ecosystem exchange to soil-water availability and of the increased temperature and vapour pressure deficit caused by land–atmosphere interactions. Soil-moisture variability reduces the present land carbon sink, and its increase and drying trends in several regions are expected to reduce it further. Our results emphasize that the capacity of continents to act as a future carbon sink critically depends on the nonlinear response of carbon fluxes to soil moisture and on land–atmosphere interactions. This suggests that the increasing trend in carbon uptake rate may not be sustained past the middle of the century and could result in accelerated atmospheric CO2 growth.

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Data availability

The GLACE-CMIP5 simulations are available from S.I.S. (sonia.seneviratne@ethz.ch) and the climate modelling groups upon reasonable request. All other data supporting the findings of this study are freely available from the following locations: CMIP5 model data, https://pcmdi.llnl.gov/; GOME-2 SIF data, ftp://ftp.gfz-potsdam.de/home/mefe/GlobFluo/GOME-2/gridded/; GRACE TWS data, https://grace.jpl.nasa.gov/data/get-data/; Atmospheric Infrared Sensor temperature and relative humidity data, https://airs.jpl.nasa.gov/data/get_data.

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Acknowledgements

This research was supported by a NASA Earth and Space Science Fellowship. We acknowledge the World Climate Research Programme (WCRP) Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Extended Data Table 1 and Extended Data Figs. 9, 10) for producing and making available their model output. For CMIP the US Department of Energy's Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led the development of software infrastructure, in partnership with the Global Organization for Earth System Science Portals. The GLACE-CMIP5 project was co-sponsored by WCRP’s Global Energy and Water Exchanges Project (GEWEX) Land–Atmosphere System Study (GLASS) and the International Geosphere–Biosphere Programme (IGBP) Integrated Land–Ecosystem–Atmosphere Processes Study (ILEAPS). S.I.S. acknowledges the European Research Council (ERC) DROUGHT-HEAT project, funded by the European Commission’s Seventh Framework Programme (grant agreement FP7-IDEAS-ERC-617518).

Reviewer information

Nature thanks C. Schwalm, A. Verhoef and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Affiliations

  1. Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA

    • Julia K. Green
    •  & Pierre Gentine
  2. Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland

    • Sonia I. Seneviratne
  3. Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA

    • Alexis M. Berg
  4. Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA

    • Kirsten L. Findell
  5. Institute of Coastal Research, Helmholtz-Zentrum Geesthacht, Geesthacht, Germany

    • Stefan Hagemann
  6. Climate and Global Dynamics Laboratory, Terrestrial Sciences, National Center for Atmospheric Research, Boulder, CO, USA

    • David M. Lawrence
  7. The Earth Institute, Columbia University, New York, NY, USA

    • Pierre Gentine

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Contributions

J.K.G. wrote the main manuscript in collaboration with P.G. J.K.G. performed the data analysis and prepared the figures. J.K.G., P.G. and S.I.S. designed the study. A.M.B., K.L.F., S.H., D.M.L, S.I.S. and P.G. reviewed and edited the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Julia K. Green.

Extended data figures and tables

  1. Extended Data Fig. 1 Regional GPP changes.

    ad, Per cent changes in GPP due to soil-moisture variability and trend during the baseline (1971–2000; a, b) and a future period (2056–2085; c, d). Stippling highlights regions where the three models agree on the sign of the change. The latitudinal GPP subplots on the right show how these changes translate to an overall GPP magnitude across latitudes. The thick line in each subplot represents the model mean and the shaded areas show the model spread.

  2. Extended Data Fig. 2 Regional temperature changes.

    ad, Temperature changes (in kelvins) due to soil-moisture variability and trend during the baseline (1971–2000; a, b) and a future modelled period (2056–2085; c, d). Stippling represents regions where at least three of the four models agree on the sign of the change. The latitudinal temperature subplots on the right show how these regional changes translate to a temperature change across latitudes. The thick lines in each subplot represent the model mean and the shaded areas show the model spread.

  3. Extended Data Fig. 3 Correlations between soil-water availability and VPD.

    a, b, Mean correlations between soil moisture and VPD during the baseline period (1971–2000; a) and in the future (2056–2085; b) from multi-model GLACE-CMIP5 simulations for the CTL run. c, Correlation between monthly TWS GRACE data and VPD data from the Atmospheric Infrared Sensor for the period 2007–2016. Monthly growing-season data are used, obtained from SIF observations or GPP simulations with values greater than half of the maximum climatology per pixel, and seasonal cycles were removed before determining the correlations.

  4. Extended Data Fig. 4 Regional autotrophic respiration changes.

    ad, Per cent changes in autotrophic respiration due to soil-moisture variability and trend during the baseline (1971–2000; a, b) and a future modelled period (2056–2085; c, d). Stippling represents regions where the three models agree on the sign of the change. The latitudinal respiration subplots on the right show how these changes translate to an overall respiration magnitude across latitudes. The thick line in each subplot represents the model mean and the shaded areas show the model spread.

  5. Extended Data Fig. 5 Autotrophic respiration response curves.

    ah, Normalized growing-season autotrophic respiration versus standardized soil moisture for the baseline (1971–2000; a–d) and a future period (2056–2085; e–h) in the GLACE-CMIP5 reference scenario. Details of the normalization and standardizations can be found in Methods. The probability density functions of the soil-moisture data are plotted at the top.

  6. Extended Data Fig. 6 GLACE-CMIP5 soil-moisture data.

    a, Monthly soil-moisture data from the GLACE-CMIP5 experiment for a pixel in Central Mexico, obtained using the IPSL model over the twenty-first century. CTL represents the RCP8.5 soil moisture, whereas ExpA uses the mean climatology of soil moisture from 1971–2000 and ExpB assumes soil moisture to be the 30-year running mean through the twenty-first century. b, Per cent change in mean soil moisture between the future and baseline periods in CTL, averaged across the four GLACE-CMIP5 models. c, Per cent change in soil-moisture variability between the future and baseline periods in CTL, averaged across the four GLACE-CMIP5 models.

  7. Extended Data Fig. 7 Water-use efficiency changes.

    ad, Per cent change in water-use efficiency (WUE) between the future (2056–2085) and baseline (1971–2000) periods for the CTL run, obtained using the CESM (a), GFDL (b), echam6 (c) and the IPSL (d) models. The WUE is calculated from GPP and evapotranspiration data. The IPSL GPP data are obtained using the RCP8.5 scenario, on which the CTL run is based.

  8. Extended Data Fig. 8 Change in land-cover types.

    a, b, Multi-model mean per cent change between the future (2056–2085) and baseline (1971–2000) periods for grassland (a) and forested land (b). No data were available for the CESM model in this analysis.

  9. Extended Data Fig. 9 CO2 fertilization effects on NBP.

    ac, Regional and latitudinal changes in NBP during the baseline (1971–2000; a) and a future period (2056–2085; b) due to the effects of CO2 fertilization. The maps are based on the results of seven CMIP5 models for the ESMFixClim1 scenario (c).

  10. Extended Data Fig. 10 GLACE-CMIP5 predictions for CTL NBP.

    a, NBP through the twenty-first century for the CTL runs, as predicted by the GLACE-CMIP5 models listed in Extended Data Table 1. The multi-model mean value of the GLACE-CMIP5 runs, and the multi-model mean of 17 CMIP5 models from RCP8.5 are also displayed. b, Details of the modelling centre, institute and model used for each of the 17 CMIP5 models used to calculate the RCP8.5 mean.

  11. Extended Data Table 1 GLACE-CMIP5 model information

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