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Solar geoengineering can alleviate climate change pressures on crop yields

Abstract

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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Fig. 1: Methodology and global summary of key model variables.
Fig. 2: Validation of CLM simulated crop production and yields for the recent past with FAO data.
Fig. 3: Partial and total effects of SG or ER on global crop yield.
Fig. 4: Global partial and total effects of SG or ER on yield for each crop type.
Fig. 5: CLM5 simulated changes in global crop yields under SG or ER.

Data availability

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 availability

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.

References

  1. 1.

    Lawrence, M. G. et al. Evaluating climate geoengineering proposals in the context of the Paris Agreement temperature goals. Nat. Commun. 9, 3734 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    MacMartin, D. G., Ricke, K. L. & Keith, D. W. Solar geoengineering as part of an overall strategy for meeting the 1.5 °C Paris target. Phil. Trans. R. Soc. A 376, 20160454 (2018).

    ADS  PubMed  Google Scholar 

  3. 3.

    Crutzen, P. J. Albedo enhancement by stratospheric sulfur injections: a contribution to resolve a policy dilemma? Climatic Change 77, 211–220 (2006).

    ADS  CAS  Google Scholar 

  4. 4.

    Ahlm, L. et al. Marine cloud brightening—as effective without clouds. Atmos. Chem. Phys. 17, 13071–13087 (2017).

    ADS  CAS  Google Scholar 

  5. 5.

    Muri, H. et al. Climate response to aerosol geoengineering: a multimethod comparison. J. Clim. 31, 6319–6340 (2018).

    ADS  Google Scholar 

  6. 6.

    Kravitz, B. et al. The Geoengineering Model Intercomparison Project (GeoMIP). Atmos. Sci. Lett. 12, 162–167 (2011).

    ADS  Google Scholar 

  7. 7.

    Robock, A., Oman, L. & Stenchikov, G. L. Regional climate responses to geoengineering with tropical and Arctic SO2 injections. J. Geophys. Res. Atmos. 113, D16101 (2008).

    ADS  Google Scholar 

  8. 8.

    Tjiputra, J. F., Grini, A. & Lee, H. Impact of idealized future stratospheric aerosol injection on the large-scale ocean and land carbon cycles. J. Geophys. Res. Biogeosci. 121, 2015JG003045 (2016).

    Google Scholar 

  9. 9.

    Russell, L. M. et al. Ecosystem impacts of geoengineering: a review for developing a science plan. Ambio 41, 350–369 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Xia, L. et al. Solar radiation management impacts on agriculture in China: a case study in the Geoengineering Model Intercomparison Project (GeoMIP). J. Geophys. Res. Atmos. 119, 8695–8711 (2014).

    ADS  Google Scholar 

  11. 11.

    Zhan, P., Zhu, W., Zhang, T., Cui, X. & Li, N. Impacts of sulfate geoengineering on rice yield in china: results from a multimodel ensemble. Earth Future 7, 395–410 (2019).

    ADS  Google Scholar 

  12. 12.

    Parkes, B., Challinor, A. & Nicklin, K. Crop failure rates in a geoengineered climate: impact of climate change and marine cloud brightening. Environ. Res. Lett. 10, 084003 (2015).

  13. 13.

    Yang, H. et al. Potential negative consequences of geoengineering on crop production: a study of Indian groundnut. Geophys. Res. Lett. 43, 11786–11795 (2016).

    ADS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Pongratz, J., Lobell, D. B., Cao, L. & Caldeira, K. Crop yields in a geoengineered climate. Nat. Clim. Change 2, 101–105 (2012).

    ADS  CAS  Google Scholar 

  15. 15.

    Proctor, J., Hsiang, S., Burney, J., Burke, M. & Schlenker, W. Estimating global agricultural effects of geoengineering using volcanic eruptions. Nature 560, 480–483 (2018).

    ADS  CAS  PubMed  Google Scholar 

  16. 16.

    Tjiputra, J. F. et al. Evaluation of the carbon cycle components in the Norwegian Earth System Model (NorESM). Geosci. Model Dev. 6, 301–325 (2013).

    ADS  Google Scholar 

  17. 17.

    MacMartin, D. G. & Kravitz, B. Mission-driven research for stratospheric aerosol geoengineering. Proc. Natl Acad. Sci. USA 116, 1089–1094 (2019).

    ADS  CAS  PubMed  Google Scholar 

  18. 18.

    Lombardozzi, D. L. et al. Simulating agriculture in the community land model version 5. J. Geophys. Res. Biogeosci. 125, e2019JG005529 (2020).

    ADS  Google Scholar 

  19. 19.

    Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Change 42, 331–345 (2017).

    Google Scholar 

  20. 20.

    O’Neill, B. C. et al. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).

    ADS  Google Scholar 

  21. 21.

    IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  22. 22.

    FAOSTAT (FAO, 2019); http://www.fao.org/faostat/en/?#data/QC

  23. 23.

    Tai, A. P. K., Martin, M. V. & Heald, C. L. Threat to future global food security from climate change and ozone air pollution. Nat. Clim. Change 4, 817–821 (2014).

    ADS  CAS  Google Scholar 

  24. 24.

    Hsiao, J., Swann, A. L. S. & Kim, S.-H. Maize yield under a changing climate: the hidden role of vapor pressure deficit. Agric. For. Meteorol. 279, 107692 (2019).

    ADS  Google Scholar 

  25. 25.

    Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. https://doi.org/10.1111/nph.16485 (2020).

  26. 26.

    Rigden, A. J., Mueller, N. D., Holbrook, N. M., Pillai, N. & Huybers, P. Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields. Nat. Food 1, 127–133 (2020).

    Google Scholar 

  27. 27.

    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).

    ADS  CAS  Google Scholar 

  28. 28.

    Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Change 6, 1023–1027 (2016).

    ADS  CAS  Google Scholar 

  29. 29.

    Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2: Tansley review. New Phytol. 165, 351–372 (2004).

    Google Scholar 

  30. 30.

    Bishop, K. A., Leakey, A. D. B. & Ainsworth, E. A. How seasonal temperature or water inputs affect the relative response of C3 crops to elevated CO2: a global analysis of open top chamber and free air CO2 enrichment studies. Food Energy Secur. 3, 33–45 (2014).

    Google Scholar 

  31. 31.

    Ainsworth, E. A. et al. A meta-analysis of elevated CO2 effects on soybean (Glycine max) physiology, growth and yield. Glob. Change Biol. 8, 695–709 (2002).

    ADS  Google Scholar 

  32. 32.

    Leakey, A. D. B. Rising atmospheric carbon dioxide concentration and the future of C4 crops for food and fuel. Proc. R. Soc. B 276, 2333–2343 (2009).

    CAS  PubMed  Google Scholar 

  33. 33.

    Ainsworth, E. A. & Rogers, A. The response of photosynthesis and stomatal conductance to rising CO2: mechanisms and environmental interactions. Plant Cell Environ. 30, 258–270 (2007).

    CAS  PubMed  Google Scholar 

  34. 34.

    National Research Council Climate Intervention: Reflecting Sunlight to Cool Earth (National Academies, 2015); https://doi.org/10.17226/18988

  35. 35.

    Lutsko, N. J., Seeley, J. T. & Keith, D. W. Estimating impacts and trade-offs in solar geoengineering scenarios with a moist energy balance model. Geophys. Res. Lett. 47, e2020GL087290 (2020).

    ADS  Google Scholar 

  36. 36.

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

    ADS  Google Scholar 

  37. 37.

    Tilmes, S. et al. The hydrological impact of geoengineering in the geoengineering model intercomparison project (GeoMIP). J. Geophys. Res. Atmos. 118, 11,036–11,058 (2013).

    Google Scholar 

  38. 38.

    Lawrence, D. M. et al. The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11, 4245–4287 (2019).

    ADS  Google Scholar 

  39. 39.

    Fisher, R. A. et al. Parametric controls on vegetation responses to biogeochemical forcing in the CLM5. J. Adv. Model. Earth Syst. 11, 2879–2895 (2019).

    ADS  Google Scholar 

  40. 40.

    Bonan, G. B. et al. Model structure and climate data uncertainty in historical simulations of the terrestrial carbon cycle (1850–2014). Glob. Biogeochem. Cycles 33, 1310–1326 (2019).

    ADS  CAS  Google Scholar 

  41. 41.

    Osborne, T., Rose, G. & Wheeler, T. Variation in the global-scale impacts of climate change on crop productivity due to climate model uncertainty and adaptation. Agric. For. Meteorol. 170, 183–194 (2013).

    ADS  Google Scholar 

  42. 42.

    Peng, B. et al. Improving maize growth processes in the community land model: implementation and evaluation. Agric. For. Meteorol. 250–251, 64–89 (2018).

    ADS  Google Scholar 

  43. 43.

    Buzan, J. R. & Huber, M. Moist heat stress on a hotter earth. Annu. Rev. Earth Planet. Sci. 48, 623–655 (2020).

    ADS  CAS  Google Scholar 

  44. 44.

    Wieder, W. R. et al. Beyond static benchmarking: using experimental manipulations to evaluate land model assumptions. Glob. Biogeochem. Cycles 33, 1289–1309 (2019).

    ADS  CAS  Google Scholar 

  45. 45.

    Mercado, L. M. et al. Impact of changes in diffuse radiation on the global land carbon sink. Nature 458, 1014–1017 (2009).

    ADS  CAS  PubMed  Google Scholar 

  46. 46.

    Cheng, S. J. et al. Variations in the influence of diffuse light on gross primary productivity in temperate ecosystems. Agric. For. Meteorol. 201, 98–110 (2015).

    ADS  Google Scholar 

  47. 47.

    Shao, L. et al. The fertilization effect of global dimming on crop yields is not attributed to an improved light interception. Glob. Change Biol. 26, 1697–1713 (2020).

    ADS  Google Scholar 

  48. 48.

    Vattioni, S. et al. Exploring accumulation-mode H2SO4 versus SO2 stratospheric sulfate geoengineering in a sectional aerosol–chemistry–climate model. Atmos. Chem. Phys. 19, 4877–4897 (2019).

    ADS  CAS  Google Scholar 

  49. 49.

    Levis, S., Badger, A., Drewniak, B., Nevison, C. & Ren, X. CLMcrop yields and water requirements: avoided impacts by choosing RCP 4.5 over 8.5. Climatic Change 146, 501–515 (2018).

    ADS  Google Scholar 

  50. 50.

    Fricko, O. et al. The marker quantification of the Shared Socioeconomic Pathway 2: a middle-of-the-road scenario for the 21st century. Glob. Environ. Change 42, 251–267 (2017).

    Google Scholar 

  51. 51.

    Lauvset, S. K., Tjiputra, J. & Muri, H. Climate engineering and the ocean: effects on biogeochemistry and primary production. Biogeosciences 14, 5675–5691 (2017).

    ADS  Google Scholar 

  52. 52.

    Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change 109, 117–161 (2011).

    ADS  Google Scholar 

  53. 53.

    West, T. O. et al. Cropland carbon fluxes in the United States: increasing geospatial resolution of inventory-based carbon accounting. Ecol. Appl. 20, 1074–1086 (2010).

    PubMed  Google Scholar 

  54. 54.

    Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).

    ADS  CAS  PubMed  Google Scholar 

  55. 55.

    Farquhar, G., von Caemmerer, Svon & Berry, J. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).

    CAS  Google Scholar 

  56. 56.

    Collatz, G. J., Ribas-Carbo, M. & Berry, J. A. Coupled photosynthesis-stomatal conductance model for leaves of C4 plants. Funct. Plant Biol. 19, 519–538 (1992).

    Google Scholar 

  57. 57.

    Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).

    ADS  Google Scholar 

  58. 58.

    Long, S. P., Ainsworth, E. A., Leakey, A. D. B., Nösberger, J. & Ort, D. R. Food for thought: lower-than-expected crop yield stimulation with rising CO2 concentrations. Science 312, 1918–1921 (2006).

    ADS  CAS  PubMed  Google Scholar 

  59. 59.

    The NCAR Command Language (NCL, Version 6.5.0) (UCAR, NCAR, CISL, TDD, 2018); https://doi.org/10.5065/D6WD3XH5

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Acknowledgements

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.

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Contributions

Y.F. and J.T. designed the study. Y.F. conducted the simulations and analysed the data with contributions from J.T., H.M. and D.L. H.M. provided the original MSB and CCT experimental settings. Y.F., J.T., H.M., D.L., C.-E.P., S.W. and D.K. interpreted the data and results. Y.F. wrote the first draft and all authors contributed to editing and revising the manuscript.

Corresponding author

Correspondence to Yuanchao Fan.

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The authors declare no competing interests.

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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.

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

Extended data

Extended Data Fig. 1 Regional effects of SG or ER on selected main crops per region.

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.

Extended Data Fig. 2 Regional effects of SG or ER on other minor crops.

a-d, Partial and total effects under SAI (a), MSB (b), CCT (c) and ER (d) as in Extended Data Fig. 1 but showing other minor crops existing in each of the six regions. Regional total effects of all six crops (black lines and asterisks) are identical to Extended Data Fig. 1.

Extended Data Fig. 3 Response of yield to changes in CO2 concentration for each crop type.

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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Supplementary Sections 1 and 2, Tables 1 and 2 and Figs. 1–11.

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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

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