Solar geoengineering (SG) and CO2 emissions reduction can each alleviate anthropogenic climate change, but their impacts on food security are not yet fully understood. Using an advanced crop model within an Earth system model, we analysed the yield responses of six major crops to three SG technologies (SGs) and emissions reduction when they provide roughly the same reduction in radiative forcing and assume the same land use. We found sharply distinct yield responses to changes in radiation, moisture and CO2, but comparable significant cooling benefits for crop yields by all four methods. Overall, global yields increase ~10% under the three SGs and decrease 5% under emissions reduction, the latter primarily due to reduced CO2 fertilization, relative to business as usual by the late twenty-first century. Relative humidity dominates the hydrological effect on yields of rainfed crops, with little contribution from precipitation. The net insolation effect is negligible across all SGs, contrary to previous findings.
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The intermediate data that support the findings of this study are available at https://doi.org/10.7910/DVN/Y1UHID. Source model data are available upon request from the corresponding author.
Code for replicating the figures and analysis was written in R (version 3.6.2) or NCAR Command Language Version 6.5.0 and has been deposited in the Harvard Dataverse at https://doi.org/10.7910/DVN/Y1UHID. NorESM1-ME is available at https://github.com/NorESMhub/NorESM. CLM5 is available at https://github.com/ESCOMP/CTSM.
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This study was supported by the Bjerknes Centre for Climate Research SKD-Fast Track Initiatives project (grant number 808011) and by Harvard University’s Solar Geoengineering Research Program fellowship. J.T. was supported by the Research Council of Norway funded projects INES (grant number 270061) and COLUMBIA (grant number 275268). Y.F. and J.T. acknowledge funding from the European Commission, H2020 framework programme (CRESCENDO, grant number 641816). C.-E.P. was supported by Brain Pool Programs through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (grant number 2019H1D3A1A01071022). The simulations were performed on resources provided by UNINETT Sigma2—the National Infrastructure for High Performance Computing and Data Storage in Norway, account numbers NS2345K and NS9033K. A. Grini provided original the SAI experimental settings in NorESM1-ME and P. Lawrence provided surface input data for CLM5. We also thank P. Irvine, J. Proctor, K. McColl and A. Berg for their helpful comments and communication on this work.
The authors declare no competing interests.
Peer review information Nature Food thanks Thomas Leirvik, Peter Irvine and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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a-d, Effects of temperature, radiation (direct and diffuse effects combined) and moisture (precipitation and relative humidity effects combined) on regional crop yield (crop-area weighted average; rainfed and irrigated proportions merged per crop) under SAI (a), MSB (b), CCT (c) and ER (with extra effect of CO2; d) relative to RCP8.5. The black line depicts regional total effect on all six crops in each region, with the asterisks each representing one of the six regions defined in Fig.5. The colour lines represent the partial climate and CO2 effects, with each point indicating one of three representative crops with largest cultivation areas in each region. Other minor crops for each region are shown in Extended Data Fig. 2. All values are averaged over the period of 2075–2099.
Points are prognostic yield difference against CO2 difference between the experiments RCP8.5 and RCP8.5(45CO2) during 2006–2099. Lines are the predicted CO2 effect using linear and quadratic coefficients from regression (see Methods). Red points and line indicate the modified yield change for soy when its CO2 coefficients from regression are reduced by a factor of 2.
Extended Data Fig. 4 Sensitivity of crop yields to unit changes in different climate variables under SAI estimated using the per-grid-cell MLR method.
T, RD, RI, P and RH stand for temperature, direct radiation, diffuse radiation, precipitation and relative humidity, respectively. T*RH and RH*P indicate the interactions between T and RH and between RH and P, respectively. The regressions are refitted with log change in RD, RI and P so that their coefficients can be easily converted and applied to percentage changes in these variables and because these variables show log linear relationship with yield. a, Global average responses to unit changes applied uniformly to all grid cells (% indicates the native unit (percent) for relative humidity, but relative changes in the radiation terms). b, Global average response to a standard deviation (sd) of a variable for each crop grid cell under the scenario SAI – RCP8.5 during 2020–2099 (that is, sd is the local variability of climate change induced by SAI). Error bars indicate the 2.5th to 97.5th percentile confidence interval of the global average response from Bootstrap resampling and spatial aggregation.
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Fan, Y., Tjiputra, J., Muri, H. et al. Solar geoengineering can alleviate climate change pressures on crop yields. Nat Food 2, 373–381 (2021). https://doi.org/10.1038/s43016-021-00278-w
Nature Food (2021)