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 RETRACTED ARTICLE: A constraint on historic growth in global photosynthesis due to increasing CO2

Nature volume 600, pages 253–258 (2021)Cite this article

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This article was retracted on 30 May 2022

This article has been updated

Abstract

The global terrestrial carbon sink is increasing1,2,3, offsetting roughly a third of anthropogenic CO2 released into the atmosphere each decade1, and thus serving to slow4 the growth of atmospheric CO2. It has been suggested that a CO2-induced long-term increase in global photosynthesis, a process known as CO2 fertilization, is responsible for a large proportion of the current terrestrial carbon sink4,5,6,7. The estimated magnitude of the historic increase in photosynthesis as result of increasing atmospheric CO2 concentrations, however, differs by an order of magnitude between long-term proxies and terrestrial biosphere models7,8,9,10,11,12,13. Here we quantify the historic effect of CO2 on global photosynthesis by identifying an emergent constraint14,15,16 that combines terrestrial biosphere models with global carbon budget estimates. Our analysis suggests that CO2 fertilization increased global annual photosynthesis by 11.85 ± 1.4%, or 13.98 ± 1.63 petagrams carbon (mean ± 95% confidence interval) between 1981 and 2020. Our results help resolve conflicting estimates of the historic sensitivity of global photosynthesis to CO2, and highlight the large impact anthropogenic emissions have had on ecosystems worldwide.

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Fig. 1: A constraint on the sensitivity of global photosynthesis to CO2.
Fig. 2: Long-term changes in global annual photosynthesis from TBMs and multiple satellite observations.
Fig. 3: Spatial differences in the estimated long-term changes in global photosynthesis from LUE theory, TBMs and satellite observations combined with theory.

Data availability

All data used to support the findings of this study are publicly available. TRENDY model simulations are available on request from TRENDY coordinator S. Sitch (s.a.sitch@exeter.ac.uk; https://blogs.exeter.ac.uk/trendy/). The Multivariate ENSO Index is available from https://psl.noaa.gov/enso/mei/. The GIMMS fAPAR data are available on request from R. Myneni (rmyneni@bu.edu). Climate forcings used are available from the Climate Research Unit at East Anglia University (https://crudata.uea.ac.uk/cru/data/hrg/). Upscaled GPP data are available from the Max Planck Institute for Biogeochemistry (https://www.bgc-jena.mpg.de/geodb/projects/Home.php). Locations for FLUXNET tower sites are available at www.fluxnet.org.

Code availability

Code used to support the findings of this study is publicly available at www.github.com/trevorkeenan/gpp-co2.

Change history

  • 16 March 2022

    Editor's Note: Readers are alerted that the uncertainties reported in this manuscript are currently in question. Appropriate editorial action will be taken once this matter is resolved.

  • 30 May 2022

    This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1038/s41586-022-04869-w

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Acknowledgements

T.F.K., X.L. and Y.Z. acknowledge primary support from the NASA IDS Award NNH17AE86I. T.F.K. acknowledges additional support from NASA award 80NSSC21K1705 and by the Director, Office of Science, Office of Biological and Environmental Research of the US Department of Energy (DOE) under Contract DE-AC02-05CH11231 as part of the RUBISCO SFA and a DOE ECRP Award DE-SC0021023. M.G.D.K. acknowledges support from the Australian Research Council (ARC) Centre of Excellence for Climate Extremes (CE170100023), the ARC Discovery Grant (DP190101823) and the NSW Research Attraction and Acceleration Program. I.C.P. acknowledges the Imperial College initiative on Grand Challenges in Ecosystems and the Environment and the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no. 787203 REALM). N.G.S. acknowledges support from NSF DEB-2045968 and Texas Tech University. B.D.S. was funded by the Swiss National Science Foundation grant no. PCEFP2_181115. C.T. was supported by a Lawrence Fellow award through Lawrence Livermore National Laboratory (LLNL), the DOE LLNL contract DE-AC52-07NA27344, and the LLNL-LDRD Program project 20-ERD-055. We thank R. Myneni and Z. Zhu for the provision of the fAPAR dataset, the Max Planck Institute for Biogeochemistry Department of Biogeochemical Integration for the provision of the upscaled GPP data. We thank the TRENDY team for the provision of the DGVM simulations, and the researchers of the Global Carbon Project for making their data publicly available. We thank A. Walker for useful discussions on interpreting the deuterium isotopomer results, and acknowledge the stimulating discussions during the Integrating CO2 Fertilization Evidence Streams and Theory (ICOFEST) meeting September 2018, part of the FACE model Data-Synthesis project funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research.

Author information

Authors and Affiliations

  1. Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA, USA

    T. F. Keenan, X. Luo, Y. Zhang & S. Zhou

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

    T. F. Keenan, X. Luo, Y. Zhang & S. Zhou

  3. Department of Geography, National University of, Singapore, Singapore

    X. Luo

  4. ARC Centre of Excellence for Climate Extremes, Sydney, New South Wales, Australia

    M. G. De Kauwe

  5. Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

    M. G. De Kauwe

  6. School of Biological Sciences, University of Bristol, Bristol, UK

    M. G. De Kauwe

  7. Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia

    B. E. Medlyn

  8. Department of Life Sciences, Imperial College London, Ascot, UK

    I. C. Prentice

  9. Department of Biological Sciences, Macquarie University, North Ryde, New South Wales, Australia

    I. C. Prentice

  10. Department of Earth System Science, Tsinghua University, Haidian, Beijing, China

    I. C. Prentice & H. Wang

  11. Department of Environmental Systems Science, ETH, Zurich, Switzerland

    B. D. Stocker

  12. Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland

    B. D. Stocker

  13. Department of Biological Sciences, Texas Tech University, Lubbock, TX, USA

    N. G. Smith

  14. Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA

    C. Terrer

  15. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Boston, MA, USA

    C. Terrer

  16. Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Y. Zhang

  17. Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA

    S. Zhou

  18. Earth Institute, Columbia University, New York, NY, USA

    S. Zhou

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

    S. Zhou

  20. State Key Laboratory of Earth Surface Processes and Resources Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    S. Zhou

Authors
  1. T. F. Keenan
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  2. X. Luo
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  3. M. G. De Kauwe
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  4. B. E. Medlyn
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  5. I. C. Prentice
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  6. B. D. Stocker
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  7. N. G. Smith
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  8. C. Terrer
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  9. H. Wang
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  10. Y. Zhang
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  11. S. Zhou
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Contributions

T.F.K. designed the study, performed the analysis and wrote the manuscript. X.L. aided in the regridding of the TRENDY model data. M.G.D.K., B.D.S., I.C.P., H.W., N.G.S., B.E.M., X.L. and S.Z. provided feedback on the remote sensing implementation. S.Z. and Y.Z. provided feedback on the emergent constraint implementation. B.S. provided feedback on the TRENDY model data interpretation. All authors discussed and commented on the results and the manuscript.

Corresponding author

Correspondence to T. F. Keenan.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Peter Cox, Alexander Winkler and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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

This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1038/s41586-022-04869-w

Extended data figures and tables

Extended Data Fig. 1 The relationship between the sensitivity of global primary photosynthesis (GPP) to CO2 \(({{\boldsymbol{\beta }}}_{{\bf{R}}}^{{\bf{GPP}}})\) and the terrestrial carbon sink (SLAND, PgC y−1).

The emergent constraint on \({\beta }_{{\rm{R}}}^{{\rm{GPP}}}\) is comparable to that derived using the normalized SLAND, though the associated uncertainty is considerably higher due to the unexplained variance in the \({\beta }_{{\rm{R}}}^{{\rm{GPP}}}\)~SLAND relationship. The red line and shaded area show the best linear fit across models, and the associated 95% prediction intervals.

Extended Data Fig. 2 A multiple linear model of the terrestrial biosphere model predictions of the global carbon sink.

a, The terrestrial biosphere model (TBM) predictions of the global carbon sink are predicted as a function of the modeled sensitivity of photosynthesis to CO2\(({\beta }_{{\rm{R}}}^{{\rm{GPP}}})\), the modeled sensitivity of respiration to CO2\(({\beta }_{{\rm{R}}}^{{\rm{Reco}}})\) and the magnitude of the modeled non-respired carbon flux (\({\rm{\gamma }}\)) (Extended Data Table 2). The red line and shaded area show the best linear fit across models, and the associated 95% prediction intervals. b, the effect size of each of the terms included in the model (mean, 95% CI), which estimates main effect on the response from changing each predictor value, averaging out the effects of the other predictors. TBM names and details are provided in Extended Data Table 1. Details of the linear model used are provided in Extended Data Table 2.

Extended Data Fig. 3 An emergent constraint on the sensitivity of global photosynthesis to CO2.

a, The relationship between the sensitivity of global primary photosynthesis (GPP) to CO2 and the modeled terrestrial carbon sink (PgC y-1), in relative terms (∆GPP (%)). The vertical gray shading shows the range of the observed terrestrial residual carbon sink over the period of 1982 to 2012, as estimated by the Global Carbon Project. The red line and shaded area show the best linear fit across models, and the associated 95% prediction intervals, and the horizontal dashed line shows the implied emergent constraint on the sensitivity of GPP to CO2. This figure reproduces Fig. 1a, but includes model names, which correspond to labels given in Extended Data Table 1. See Extended Data Fig. 1 for the underlying relationship between the sensitivity of GPP to CO2 and the terrestrial carbon sink. b, Uncertainty contributions to the constrained sensitivity of global photosynthesis to CO2. The unconstrained probability density function (PDF) distribution of \({\beta }_{{\rm{R}}}^{{\rm{GPP}}}\) across models (black line, gray bars), which assumes that all of the TRENDY models are equally likely conditional to be correct and that they come from a Gaussian distribution. The orange area represents the probability distribution derived by applying the constraint from (a) to the across model relationship, with dashed and dotted lines in the orange area indicating the relative contribution of different sources of uncertainty (see methods).

Extended Data Fig. 4 Assessment of the effect of choice of period on the sensitivity of global primary photosynthesis (GPP) to CO2 \(({{\boldsymbol{\beta }}}_{{\bf{R}}}^{{\bf{GPP}}})\).

Estimates of the residual terrestrial sink (SLAND) from the Global Carbon Project (GCP) used in this study were split into two 15-year periods (1982-1997 (a, b) and 1998-2012 (c, d)) and the emergent constraint approach (see methods) was applied to each independently, using GCP estimates of the land sink for those periods to estimate a constrained value of \({\beta }_{{\rm{R}}}^{{\rm{GPP}}}\) from the TRENDY dynamic global vegetation models (Extended Data Table 1). Estimated SLAND in panel a and c is SLAND ~ 1 + \({\beta }_{{\rm{R}}}^{{\rm{GPP}}}\) + \({\beta }_{{\rm{R}}}^{{\rm{Reco}}}\) + \({\beta }_{{\rm{R}}}^{{\rm{Reco}}}\):\({\gamma }\). The vertical dashed lines in a and c indicate the GCP estimate of the mean residual sink for that period. The red lines and shaded areas in a and c show the best linear fit across models, and the associated 95% prediction intervals.

Extended Data Fig. 5 Long-term changes in annual gross primary production (GPP) of global tropical forests.

GPP estimated by terrestrial biosphere models (TBMs) in the TRENDY model ensemble considers either temporally dynamic CO2 and fixed climate and land use (orange, experiment S1), temporally dynamic CO2 and climate, and fixed land use (red, experiment S2), or temporally dynamic CO2, climate, and land use (purple, experiment S3). Shaded areas represent the mean and standard error of the annual estimate across the TRENDY ensemble. Remote sensing (RS) GPP considers temporally dynamic climate and land use, and either fixed (blue) or varying (red) CO2. Tropical forests represent the Evergreen Broadleaf Forest classification within tropical latitudes (23.5°N: 23.5°S).

Extended Data Fig. 6 Assessment of the effect of CO2 on global primary photosynthesis (GPP) at sites included in the FLUXNET 2015 dataset.

(a) The distribution of the length of the observational record at each of the 206 sites in the FLUXNET 2015 open access database. The vertical red line indicates the median site record length (5 years). (b) The expected effect of CO2 on GPP at all sites, demonstrated by comparing the GPP predicted by the original (x-axis) and updated (y-axis) remote sensing-based methods for all site months of observations in the FLUXNET 2015 database96. The mean expected difference across sites is 2.39%.

Extended Data Fig. 7 Global and high latitude changes in the terrestrial carbon cycle.

Both the global (a, b, c) and northern land (high latitude, > 45°N) (d, e, f) contribution of CO2 (orange shaded area, derived from TRENDYv6 CO2-only simulations (S1)) and climate (red shaded area, derived from the difference between TRENDYv6 CO2-only simulations and CO2 + Climate simulations (S2-S1)), to long term (1900-2016) changes in annual net ecosystem productivity (NEP), gross primary production (GPP) and ecosystem respiration (RECO). The shaded areas represent the annual mean and standard error across the TRENDY model ensemble. The impact of climate change is large in high latitude ecosystems, increasing both GPP (e) and RECO (f). This does not however translate to a large impact on the global carbon cycle (a–c).

Extended Data Table 1 The terrestrial biosphere models (TBMs) used
Full size table
Extended Data Table 2 Linear models of the land sink as estimated from terrestrial biosphere models
Full size table
Extended Data Table 3 Calculation of \({\beta }_{{\rm{R}}}^{{\rm{GPP}}}\)from existing proxies
Full size table

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Keenan, T.F., Luo, X., De Kauwe, M.G. et al.  RETRACTED ARTICLE: A constraint on historic growth in global photosynthesis due to increasing CO2. Nature 600, 253–258 (2021). https://doi.org/10.1038/s41586-021-04096-9

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