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Estimating global agricultural effects of geoengineering using volcanic eruptions

Nature volume 560, pages 480–483 (2018)Cite this article

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Abstract

Solar radiation management is increasingly considered to be an option for managing global temperatures1,2, yet the economic effects of ameliorating climatic changes by scattering sunlight back to space remain largely unknown3. Although solar radiation management may increase crop yields by reducing heat stress4, the effects of concomitant changes in available sunlight have never been empirically estimated. Here we use the volcanic eruptions that inspired modern solar radiation management proposals as natural experiments to provide the first estimates, to our knowledge, of how the stratospheric sulfate aerosols created by the eruptions of El Chichón and Mount Pinatubo altered the quantity and quality of global sunlight, and how these changes in sunlight affected global crop yields. We find that the sunlight-mediated effect of stratospheric sulfate aerosols on yields is negative for both C4 (maize) and C3 (soy, rice and wheat) crops. Applying our yield model to a solar radiation management scenario based on stratospheric sulfate aerosols, we find that projected mid-twenty-first century damages due to scattering sunlight caused by solar radiation management are roughly equal in magnitude to benefits from cooling. This suggests that solar radiation management—if deployed using stratospheric sulfate aerosols similar to those emitted by the volcanic eruptions it seeks to mimic—would, on net, attenuate little of the global agricultural damage from climate change. Our approach could be extended to study the effects of solar radiation management on other global systems, such as human health or ecosystem function.

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Fig. 1: Large volcanic eruptions alter the global optical environment.
Fig. 2: Global summary statistics of key model variables.
Fig. 3: Empirical estimates of the insolation effect of SSAs on crop yield.
Fig. 4: Partial and total effects of SRM on yields.

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Acknowledgements

We thank M. Anderson, M. Auffhammer, D. Baldocchi, K. Caldeira, C. Field, A. Goldstein, D. Keith, P. Huybers, R. Kopp, D. Lobell, K. Ricke, J. Sallee and seminar participants at Berkeley, Chicago, Columbia, Cornell, Harvard, Johns Hopkins and Stanford universities, the Massachusetts Institute of Technology and the Allied Social Science Association Annual Meeting for useful comments. We thank I. Bolliger for his contributions to the project and all the members of the Global Policy Laboratory for their valuable feedback. We thank L. Thomason for generously sharing SAOD data used in Fig. 1a–c. This material is based upon work supported by the National Science Foundation Grant No. CNH-L 1715557 and the National Science Foundation Graduate Research Fellowship under Grant No. DGE 1752814.

Reviewer information

Nature thanks L. Gu and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Author notes

  1. These authors contributed equally: Jonathan Proctor, Solomon Hsiang

Authors and Affiliations

  1. Global Policy Laboratory, Goldman School of Public Policy, University of California, Berkeley, Berkeley, CA, USA

    Jonathan Proctor & Solomon Hsiang

  2. Department of Agricultural and Resource Economics, University of California, Berkeley, Berkeley, CA, USA

    Jonathan Proctor

  3. National Bureau of Economic Research, Cambridge, MA, USA

    Solomon Hsiang, Marshall Burke & Wolfram Schlenker

  4. School of Global Policy and Strategy, University of California, San Diego, San Diego, CA, USA

    Jennifer Burney

  5. Department of Earth System Science, Stanford University, Stanford, CA, USA

    Marshall Burke

  6. School of International and Public Affairs and The Earth Institute, Columbia University, New York, NY, USA

    Wolfram Schlenker

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  1. Jonathan Proctor
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Contributions

S.H. conceived the study; J.P., S.H., J.B., M.B. and W.S. designed the study; J.P. collected and analysed the data with contributions from J.B.; J.P., S.H., J.B., M.B. and W.S. interpreted results; J.P. and S.H. wrote the paper.

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Correspondence to Jonathan Proctor.

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Extended data figures and tables

Extended Data Fig. 1 Countries included in the estimation of the insolation-mediated effect of SAOD on crop yield.

Countries in light green are included in the estimation of the insolation-mediated effect of SSAs on yields for both C3 (soy, rice and wheat) and C4 (maize) crops. Countries in dark green are included only in estimation of the insolation effect for C3 crops, and countries in red are included only in estimation of the insolation effect for maize. Countries in grey are not included in the analysis owing to missing data.

Extended Data Fig. 2 Estimated response of yields to changes in growing-season average temperature (orange), precipitation (blue) and cloud fraction (grey).

Temperature, precipitation and cloud fraction axes show growing-season means. The y axes show partial effects on yield relative to a value of zero for each climatological variable (fT(Tit), fP(Pit) and fC(Cit) in equation (16) in Supplementary Information). Vertical dotted lines show the placement of the knots for the restricted cubic splines specification. Dashed lines show the 95% confidence intervals. n = 2,501, 1,256, 1,562 and 2,010 country-years for maize, soy, rice and wheat, respectively.

Extended Data Fig. 3 Flexible (blue) and linear (red) estimation of the insolation-mediated effect of SSAs on crop yields.

The SAOD axes show growing-season means. Each point on a curve gives the optical effect of SAOD, relative to a value of zero (the slope of the red lines is β in equation (16) in Supplementary Information). Vertical dotted lines show the placement of the knots for the restricted cubic splines specification. Dashed lines show the 95% confidence intervals.

Extended Data Fig. 4 Effect of SRM on climatological determinants of yield.

SRM-induced changes in maize growing-season average SAOD, temperature, precipitation and cloud fraction, relative to the climate-change-only scenario. Changes in uncropped land have been masked out by setting the values to zero.

Extended Data Fig. 5 Total effect of SRM on maize, soy, rice and wheat yields.

Total effects are constructed by summing the partial effects from insolation, temperature, precipitation and clouds. Effects are relative to the climate-change-only scenario. Changes in uncropped land have been masked out by setting the values to zero. Statistically insignificant effects (P > 0.05) are hatched. We calculate P values using a two-sided t-test comparing the estimated effect of SRM to a null hypothesis of zero effect. When calculating the distribution of the estimated SRM effect, we consider only statistical uncertainty. This uncertainty is shown in Extended Data Table 2, Extended Data Fig. 2, and the calculations are described in Supplementary Information, section IV.4.

Extended Data Fig. 6 The finding that SRM mitigates little of the damages of climate change is consistent across three ensemble runs.

Bar graphs show the total effect of SRM on global yields (cropped-fraction weighted average), relative to the climate change control, for each of the three Earth system model runs. Results are similar across ensemble member runs. Maps on the right show the total effect of SSAs on maize yields for each of the ensemble runs. Error bars in the bar graphs show 95% confidence intervals for estimated mean effects for each crop. Statistically insignificant effects (P > 0.05) are hatched in the maps. Changes in uncropped land have been masked out by setting the values to zero. We calculate P values using a two-sided t-test comparing the estimated effects to a null hypothesis of zero effect. Within each ensemble member, we calculate the distributions of the estimated effects considering only statistical uncertainty. This uncertainty is shown in Extended Data Table 2, Extended Data Fig. 2, and the calculations are described in Supplementary Information, section IV.4.

Extended Data Fig. 7 Effects of climate change and SRM relative to an historical scenario.

a, As in Fig. 4e, but comparing a climate change scenario (RCP 4.5) to an historical scenario (Supplementary Information, section IV.3). b, As in Fig. 4e, but comparing a climate-change-with-SRM scenario to an historical scenario. Note that these calculations consider only climatological and sunlight-mediated effects; changes in yields owing to carbon fertilization, or other factors that may differ between scenarios, are not included. Error bars show 95% confidence intervals around the estimated mean effect.

Extended Data Table 1 Effect of SSAs on total, direct and diffuse insolation
Full size table
Extended Data Table 2 Robustness of the insolation effect of SSAs on yields to changes in model specification, data sample and data source
Full size table
Extended Data Table 3 Effect of SSAs on atmospheric forward scattering
Full size table

Supplementary information

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Proctor, J., Hsiang, S., Burney, J. et al. Estimating global agricultural effects of geoengineering using volcanic eruptions. Nature 560, 480–483 (2018). https://doi.org/10.1038/s41586-018-0417-3

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