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Simulating US agriculture in a modern Dust Bowl drought

Nature Plants volume 3, Article number: 16193 (2017) Cite this article

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Abstract

Drought-induced agricultural loss is one of the most costly impacts of extreme weather1–3, and without mitigation, climate change is likely to increase the severity and frequency of future droughts4,5. The Dust Bowl of the 1930s was the driest and hottest for agriculture in modern US history. Improvements in farming practices have increased productivity, but yields today are still tightly linked to climate variation6 and the impacts of a 1930s-type drought on current and future agricultural systems remain unclear. Simulations of biophysical process and empirical models suggest that Dust-Bowl-type droughts today would have unprecedented consequences, with yield losses ∼50% larger than the severe drought of 2012. Damages at these extremes are highly sensitive to temperature, worsening by ∼25% with each degree centigrade of warming. We find that high temperatures can be more damaging than rainfall deficit, and, without adaptation, warmer mid-century temperatures with even average precipitation could lead to maize losses equivalent to the Dust Bowl drought. Warmer temperatures alongside consecutive droughts could make up to 85% of rain-fed maize at risk of changes that may persist for decades. Understanding the interactions of weather extremes and a changing agricultural system is therefore critical to effectively respond to, and minimize, the impacts of the next extreme drought event.

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Figure 1: Histogram of historical JJA (June, July, August) average PGF (Princeton University Global Meteorological Forcing Dataset, Phase 2; ref. 41) precipitation weighted by present-day US maize production.
Figure 2: Box plots of US modelled maize (left), soy (centre) and wheat (right) yields under 2012 (blue) and 2035 (red) technology (tech.) and CO2 concentrations.
Figure 3: National maize yield response surface to changes in temperature and precipitation.

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Acknowledgements

This research was performed as part of the Center for Robust Decision-making on Climate and Energy Policy (RDCEP) at the University of Chicago. RDCEP is funded by a grant from NSF (no. SES-0951576) through the Decision Making Under Uncertainty program. M.G. acknowledges support of an NSF Graduate Fellowship (no. DGE-1144082). We thank C. Müller, A. Ruane and J. Winter—as well as the AgMIP (Agricultural Model Intercomparison and Improvement Project) community—for valuable insight in formulating the ideas for this research. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, 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 software infrastructure development in partnership with the Global Organization for Earth System Science Portals. Computing for this project was facilitated using the Swift parallel scripting language (NSF grant OCI-1148443), and completed in part with resources provided by the University of Chicago Research Computing Center.

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Author notes

  1. Michael Glotter and Joshua Elliott: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of the Geophysical Sciences, University of Chicago, 5734 S Ellis Avenue, Chicago, 60637, Illinois, USA

    Michael Glotter

  2. NASA Goddard Institute for Space Studies, 2880 Broadway, New York, 10025, New York, USA

    Joshua Elliott

  3. Computation Institute, University of Chicago, 5735 S Ellis Avenue, Chicago, 60637, Illinois, USA

    Joshua Elliott

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  1. Michael Glotter
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M.G. and J.E. contributed equally to this work. Both authors designed and performed the experiments, analysed the data, discussed the results, and wrote the paper.

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Correspondence to Michael Glotter or Joshua Elliott.

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The authors declare no competing financial interests.

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Glotter, M., Elliott, J. Simulating US agriculture in a modern Dust Bowl drought. Nature Plants 3, 16193 (2017). https://doi.org/10.1038/nplants.2016.193

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