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

Nature volume 565, pages 476–479 (2019)Cite this article

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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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Fig. 1: Global NBP during the twenty-first century.
Fig. 2: Biosphere photosynthetic activity response curves.
Fig. 3: Correlations of the ratio of autotrophic respiration (Ra) to GPP.
Fig. 4: Regional NBP changes.

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.

References

  1. Le Quéré, C. et al. Global carbon budget 2017. Earth Syst. Sci. Data 10, 405–448 (2018).

    Article  ADS  Google Scholar 

  2. Ballantyne, A. P. et al. Audit of the global carbon budget: estimate errors and their impact on uptake uncertainty. Biogeosciences 12, 2565–2584 (2015).

    Article  ADS  Google Scholar 

  3. Zhao, M. & Running, S. W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329, 940–943 (2010).

    Article  CAS  ADS  Google Scholar 

  4. Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).

    Article  CAS  ADS  Google Scholar 

  5. Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).

    Article  CAS  ADS  Google Scholar 

  6. Seneviratne, S. I. et al. Investigating soil moisture-climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010).

    Article  CAS  ADS  Google Scholar 

  7. Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).

    Article  CAS  ADS  Google Scholar 

  8. Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).

    Article  ADS  Google Scholar 

  9. Seneviratne, S. I. et al. Impact of soil moisture–climate feedbacks on CMIP5 projections: first results from the GLACE-CMIP5 experiment. Geophys. Res. Lett. 40, 5212–5217 (2013).

    Article  ADS  Google Scholar 

  10. 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).

    Article  CAS  ADS  Google Scholar 

  11. Wieder, W. R., Cleveland, C. C., Smith, W. K. & Todd-Brown, K. Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441–444 (2015).

    Article  CAS  ADS  Google Scholar 

  12. McDowell, N. G. & Allen, C. D. Darcy’s law predicts widespread forest mortality under climate warming. Nat. Clim. Change 5, 669–672 (2015).

    Article  ADS  Google Scholar 

  13. Berg, A. et al. Land–atmosphere feedbacks amplify aridity increase over land under global warming. Nat. Clim. Change 6, 869–874 (2016).

    Article  ADS  Google Scholar 

  14. Lorenz, R. et al. Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble. J. Geophys. Res. Atmos. 121, 607–623 (2016).

    Article  ADS  Google Scholar 

  15. Schwalm, C. R. et al. Reduction in carbon uptake during turn of the century drought in western North America. Nat. Geosci. 5, 551–556 (2012).

    Article  CAS  ADS  Google Scholar 

  16. Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).

    Article  CAS  ADS  Google Scholar 

  17. Bateni, S. M. & Entekhabi, D. Relative efficiency of land surface energy balance components. Wat. Resour. Res. 48, 1–8 (2012).

    Article  Google Scholar 

  18. Seneviratne, S. I., Lüthi, D., Litschi, M. & Schär, C. Land-atmosphere coupling and climate change in Europe. Nature 443, 205–209 (2006).

    Article  CAS  ADS  Google Scholar 

  19. Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to drought-fire interactions. Proc. Natl Acad. Sci. USA 111, 6347–6352 (2014).

    Article  CAS  ADS  Google Scholar 

  20. Orlowsky, B. & Seneviratne, S. I. Elusive drought: uncertainty in observed trends and short-and long-term CMIP5 projections. Hydrol. Earth Syst. Sci. 17, 1765–1781 (2013).

    Article  ADS  Google Scholar 

  21. Greve, P., Roderick, M. L. & Seneviratne, S. I. Simulated changes in aridity from the last glacial maximum to 4×CO2. Environ. Res. Lett. 12, 114021 (2017).

    Article  ADS  Google Scholar 

  22. Peñuelas, J. & Filella, I. Responses to a warming world. Science 294, 793–795 (2001).

    Article  Google Scholar 

  23. Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).

    Article  CAS  ADS  Google Scholar 

  24. Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).

    Article  ADS  Google Scholar 

  25. Hovenden, M. & Newton, P. Plant responses to CO2 are a question of time. Science 360, 263–264 (2018).

    Article  CAS  Google Scholar 

  26. Reich, P. B., Hobbie, S. E. & Lee, T. D. Plant growth enhancement by elevated CO2 eliminated by joint water and nitrogen limitation. Nat. Geosci. 7, 920–924 (2014).

    Article  CAS  ADS  Google Scholar 

  27. Egea, G., Verhoef, A. & Vidale, P. L. Towards an improved and more flexible representation of water stress in coupled photosynthesis-stomatal conductance models. Agric. For. Meteorol. 151, 1370–1384 (2011).

    Article  ADS  Google Scholar 

  28. Verhoef, A. & Egea, G. Modeling plant transpiration under limited soil water: comparison of different plant and soil hydraulic parameterizations and preliminary implications for their use in land surface models. Agric. For. Meteorol. 191, 22–32 (2014).

    Article  ADS  Google Scholar 

  29. Franks, P. J., Berry, J. A., Lombardozzi, D. L. & Bonan, G. B. Stomatal function across temporal and spatial scales: deep-time trends, land-atmosphere coupling and global models. Plant Physiol. 174, 583–602 (2017).

    Article  CAS  Google Scholar 

  30. Konings, A. G., Williams, A. P. & Gentine, P. Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation. Nat. Geosci. 10, 284–288 (2017).

    Article  CAS  ADS  Google Scholar 

  31. Anderegg, W. R. L. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).

    Article  CAS  ADS  Google Scholar 

  32. Green, J. K. et al. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nat. Geosci. 10, 410–414 (2017).

    Article  CAS  ADS  Google Scholar 

  33. Koster, R. D. et al. Regions of strong coupling between soil moisture and precipitation. Science 305, 1138–1140 (2004).

    Article  CAS  ADS  Google Scholar 

  34. Oleson, K. W. et al. Technical description of version 4.0 of the Community Land Model (CLM). NCAR Technical Note NCAR/TN-503+STR (2013); http://dx.doi.org/10.5065/D6RR1W7M.

  35. Friedlingstein, P. et al. Climate–carbon cycle feedback analysis: results from the C4MIP model intercomparison. J. Clim. 19, 3337–3353 (2006).

    Article  ADS  Google Scholar 

  36. Koster, R. D. et al. On the nature of soil moisture in land surface models. J. Clim. 22, 4322–4335 (2009).

    Article  ADS  Google Scholar 

  37. Köhler, P., Guanter, L. & Joiner, J. A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data. Atmos. Meas. Tech. 8, 2589–2608 (2015).

    Article  Google Scholar 

  38. Watkins, M. M., Wiese, D. N., Yuan, D., Boening, C. & Landerer, F. W. Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons. J. Geophys. Res. Solid Earth 120, 2648–2671 (2015).

    Article  ADS  Google Scholar 

  39. Porcar-Castell, A. et al. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J. Exp. Bot. 65, 4065–4095 (2014).

    Article  CAS  Google Scholar 

  40. Guanter, L. et al. Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sens. Environ. 121, 236–251 (2012).

    Article  ADS  Google Scholar 

  41. Frankenberg, C. et al. New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett. 38, L17706 (2011).

    Article  ADS  Google Scholar 

  42. Joiner, J. et al. Global monitoring of terrestrial chlorophyll fluorescence from moderate spectral resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos. Meas. Tech. 6, 3883–3930 (2013).

    Article  Google Scholar 

  43. Sun, Y. et al. OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence. Science 358, eaam5747 (2017).

    Article  Google Scholar 

  44. Jiang, W. et al. Annual variations of monsoon and drought detected by GPS: a case study in Yunnan, China. Sci. Rep. 7, 1–10 (2017).

    Article  ADS  Google Scholar 

  45. Yang, Y. et al. GRACE satellite observed hydrological controls on interannual and seasonal variability in surface greenness over mainland Australia. J. Geophys. Res. Biogeosci. 119, 2245–2260 (2014).

    Article  ADS  Google Scholar 

  46. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  ADS  Google Scholar 

  47. Neale, R. B. et al. The mean climate of the Community Atmosphere Model (CAM4) in forced SST and fully coupled experiments. J. Clim. 26, 5150–5168 (2013).

    Article  ADS  Google Scholar 

  48. Lawrence, D. M. et al. Parameterization improvements and functional and structural advances in version 4 of the Community Land Model. J. Adv. Model. Earth Syst. 3, 1–27 (2011).

    Google Scholar 

  49. Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon Earth system models. Part I: physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).

    Article  ADS  Google Scholar 

  50. Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon Earth system models. Part II: carbon system formulation and baseline simulation characteristics. J. Clim. 26, 2247–2267 (2013).

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  52. Dufresne, J.-L. et al. Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165 (2013).

    Article  Google Scholar 

  53. Hourdin, F. et al. Impact of the LMDZ atmospheric grid configuration on the climate and sensitivity of the IPSL-CM5A coupled model. Clim. Dyn. 40, 2167–2192 (2013).

    Article  Google Scholar 

  54. Chéruy, F. et al. Combined influence of atmospheric physics and soil hydrology on the simulated meteorology at the SIRTA atmospheric observatory. Clim. Dyn. 40, 2251–2269 (2013).

    Article  Google Scholar 

  55. Stevens, B. et al. Atmospheric component of the MPI-M Earth System Model: ECHAM6. J. Adv. Model. Earth Syst. 5, 146–172 (2013).

    Article  ADS  Google Scholar 

  56. Hagemann, S., Loew, A. & Andersson, A. Combined evaluation of MPI-ESM land surface water and energy fluxes. J. Adv. Model. Earth Syst. 5, 259–286 (2013).

    ADS  Google Scholar 

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

    Article  Google Scholar 

  58. Brovkin, V., Raddatz, T., Reick, C. H., Claussen, M. & Gayler, V. Global biogeophysical interactions between forest and climate. Geophys. Res. Lett. 36, L07405 (2009).

    Article  ADS  Google Scholar 

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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.

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Authors and 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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  1. Julia K. Green
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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.

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Correspondence to Julia K. Green.

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Extended data figures and tables

Extended Data Fig. 1 Regional GPP changes.

a–d, 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.

Extended Data Fig. 2 Regional temperature changes.

a–d, 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.

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.

Extended Data Fig. 4 Regional autotrophic respiration changes.

a–d, 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.

Extended Data Fig. 5 Autotrophic respiration response curves.

a–h, 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.

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.

Extended Data Fig. 7 Water-use efficiency changes.

a–d, 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.

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.

Extended Data Fig. 9 CO2 fertilization effects on NBP.

a–c, 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).

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.

Extended Data Table 1 GLACE-CMIP5 model information
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Green, J.K., Seneviratne, S.I., Berg, A.M. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019). https://doi.org/10.1038/s41586-018-0848-x

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