Uncertainties in the response of vegetation to rising atmospheric CO2 concentrations1,2 contribute to the large spread in projections of future climate change3,4. Climate–carbon cycle models generally agree that elevated atmospheric CO2 concentrations will enhance terrestrial gross primary productivity (GPP). However, the magnitude of this CO2 fertilization effect varies from a 20 per cent to a 60 per cent increase in GPP for a doubling of atmospheric CO2 concentrations in model studies5,6,7. Here we demonstrate emergent constraints8,9,10,11 on large-scale CO2 fertilization using observed changes in the amplitude of the atmospheric CO2 seasonal cycle that are thought to be the result of increasing terrestrial GPP12,13,14. Our comparison of atmospheric CO2 measurements from Point Barrow in Alaska and Cape Kumukahi in Hawaii with historical simulations of the latest climate–carbon cycle models demonstrates that the increase in the amplitude of the CO2 seasonal cycle at both measurement sites is consistent with increasing annual mean GPP, driven in part by climate warming, but with differences in CO2 fertilization controlling the spread among the model trends. As a result, the relationship between the amplitude of the CO2 seasonal cycle and the magnitude of CO2 fertilization of GPP is almost linear across the entire ensemble of models. When combined with the observed trends in the seasonal CO2 amplitude, these relationships lead to consistent emergent constraints on the CO2 fertilization of GPP. Overall, we estimate a GPP increase of 37 ± 9 per cent for high-latitude ecosystems and 32 ± 9 per cent for extratropical ecosystems under a doubling of atmospheric CO2 concentrations on the basis of the Point Barrow and Cape Kumukahi records, respectively.
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This work was funded by the Horizon 2020 European Union’s Framework Programme for Research and Innovation under Grant Agreement No. 641816, the Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach (CRESCENDO) project and the DLR Klimarelevanz von atmosphärischen Spurengasen, Aerosolen und Wolken: Auf dem Weg zu EarthCARE und MERLIN (KliSAW) project. We acknowledge the World Climate Research Programme (WCRP) Working Group on Coupled Modelling (WGCM), which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. For CMIP the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We thank ETH Zurich for help in accessing data from the ESGF archive.
The authors declare no competing financial interests.
Nature thanks V. Brovkin and the other anonymous reviewer(s) for their contribution to the peer review of this work.
Extended data figures and tables
a, b, Time series of monthly mean atmospheric CO2 between 1860 and 2005 at BRW (a) and KMK (b) at surface level, as simulated by the CMIP5 models in the historical simulations and observed (black lines) at each measuring site.
Extended Data Figure 2 Annual mean high-latitude GPP (60° N–90° N) against the amplitude of the CO2 seasonal cycle at BRW for each of the CMIP5 ESMs.
The markers show the values for the individual years between 1850 and 2005 for the CMIP5 historical simulations and lines show the linear best fit for each model. The black line indicates the multi-model mean of the CMIP5 models.
a, The correlation for both the historical (asterisks) and the 1%BGC (circles) CMIP5 model simulations. The markers show the values for the individual years and lines show the linear best fit for each model. b, The comparison of the gradients in a for each model. The red solid line shows the gradient of the correlation and the black dashed line indicates a 1:1 correlation.
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Wenzel, S., Cox, P., Eyring, V. et al. Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2. Nature 538, 499–501 (2016). https://doi.org/10.1038/nature19772
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