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An end-to-end assessment of extreme weather impacts on food security

Nature Climate Change volume 5, pages 997–1001 (2015)Cite this article

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Abstract

Both governments and the private sector urgently require better estimates of the likely incidence of extreme weather events1, their impacts on food crop production and the potential consequent social and economic losses2. Current assessments of climate change impacts on agriculture mostly focus on average crop yield vulnerability3 to climate and adaptation scenarios4,5. Also, although new-generation climate models have improved and there has been an exponential increase in available data6, the uncertainties in their projections over years and decades, and at regional and local scale, have not decreased7,8. We need to understand and quantify the non-stationary, annual and decadal climate impacts using simple and communicable risk metrics9 that will help public and private stakeholders manage the hazards to food security. Here we present an ‘end-to-end’ methodological construct based on weather indices and machine learning that integrates current understanding of the various interacting systems of climate, crops and the economy to determine short- to long-term risk estimates of crop production loss, in different climate and adaptation scenarios. For provinces north and south of the Yangtze River in China, we have found that risk profiles for crop yields that translate climate into economic variability follow marked regional patterns, shaped by drivers of continental-scale climate. We conclude that to be cost-effective, region-specific policies have to be tailored to optimally combine different categories of risk management instruments.

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Figure 1: Schematic diagram of the end-to-end methodology for deriving crop production and economic-risk profiles.
Figure 2: Results of weather index-based modelling of maize yield in Shandong province.
Figure 3: Risk profiles of province-level physical production and aggregate economic loss in China’s northeast Shandong province.

References

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

    Google Scholar 

  2. Conway, G. One Billion Hungry: Can We Feed the World? (Cornell Univ. Press, 2012).

    Google Scholar 

  3. Asseng, S. et al. Uncertainty in simulating wheat yields under climate change. Nature Clim. Change 3, 827–832 (2013).

    Article  CAS  Google Scholar 

  4. Challinor, A. J. et al. A meta-analysis of crop yield under climate change and adaptation. Nature Clim. Change 4, 287–291 (2014).

    Article  Google Scholar 

  5. Moore, F. C. & Lobell, D. B. Adaptation potential of European agriculture in response to climate change. Nature Clim. Change 4, 610–614 (2014).

    Article  Google Scholar 

  6. Overpeck, J. T., Meehl, G. A., Bony, S. & Easterling, D. R. Climate data challenges in the 21st century. Science 331, 700–702 (2011).

    Article  CAS  Google Scholar 

  7. Knutti, R. & Sedláèk, J. Robustness and uncertainties in the new CMIP5 climate model projections. Nature Clim. Change 3, 369–373 (2013).

    Article  Google Scholar 

  8. Hannart, A., Ghil, M., Dufresne, J.-L. & Naveau, P. Disconcerting learning on climate sensitivity and the uncertain future of uncertainty. Climatic Change 119, 585–601 (2013).

    Article  Google Scholar 

  9. Kunreuther, H. et al. Risk management and climate change. Nature Clim. Change 3, 447–450 (2013).

    Article  Google Scholar 

  10. Martinson, D. G. et al. (eds) Natural Climate Variability on Decade-to-Century Time Scales 630 (National Academy Press, 1995).

  11. Ghil, M. et al. Extreme events: Dynamics, statistics and prediction. Nonlin. Process. Geophys. 18, 295–350 (2011).

    Article  Google Scholar 

  12. Ghil, M. & Robertson, A. W. “Waves” vs. “particules” in the atmosphere’s phase space: A pathway to long-range forecasting? Proc. Natl Acad. Sci. USA 99, 2493–2500 (2002).

    Article  Google Scholar 

  13. Chang, C. P., Ghil, M., Latif, M. & Wallace, J. M. (eds) Climate Change: Multidecadal and Beyond (World Scientific/Imperial College Press, in the press)

  14. Rosenzweig, M. R. & Binswanger, H. P. Wealth, Weather and the Composition and Profitability of Agricultural Investment 42 (The World Bank Policy Research Working Paper, WPS 1055, 1992).

    Google Scholar 

  15. Stern, N. The Stern Review on the Economics on Climate Change (Cambridge Univ. Press, 2007).

    Book  Google Scholar 

  16. Nordhaus, W. D. & Boyer, J. Warming the World: Economic Models of Global Warming (MIT Press, 2000).

    Book  Google Scholar 

  17. Deloncle, A., Berk, R., D’Andrea, F. & Ghil, M. Weather regime prediction using statistical learning. J. Atmos. Sci. 64, 1619–1635 (2007).

    Article  Google Scholar 

  18. Ihler, A. T., Kirshner, S., Ghil, M., Robertson, A. W. & Smyth, P. Graphical models for statistical inference and data assimilation. Physica D 230, 72–87 (2007).

    Article  Google Scholar 

  19. Läderach, P. et al. Mesoamerican Coffee Building a Climate Change Adaptation Strategy (International Center for Tropical Agriculture Policy Brief No. 2, 2013).

    Google Scholar 

  20. Joshi, M. M., Turner, A. G. & Hope, C. The use of land-sea warming contrast under climate change to improve impact metrics. Climatic Change 117, 951–960 (2013).

    Article  Google Scholar 

  21. Sanchez, B., Rasmussen, A. & Porter, J. Temperatures and the growth and development of maize and rice: A review. Glob. Change Biol. 20, 408–417 (2014).

    Article  Google Scholar 

  22. Lobell, D. B. & Burke, M. B. Why are agricultural impacts of climate so uncertain? The importance of temperature relative to precipitation. Environ. Res. Lett. 3, 034007 (2008).

    Article  Google Scholar 

  23. Lobell, D. B., Bänzinger, M., Mogorokosho, C. & Bindiganavile, V. Nonlinear heat effects on African maize as evidenced by historical yield trials. Nature Clim. Change 1, 42–45 (2011).

    Article  Google Scholar 

  24. Cheng, W., Sakai, H., Yagi, K. & Hasegawa, T. Combined effects of elevated [CO2] and high night temperature on carbon assimilation, nitrogen absorption, and the allocations of C and N by rice (Oryza sativa L.). Agric. Forest Meteorol. 150, 1174–1181 (2010).

    Article  Google Scholar 

  25. Mohammed, A. R. & Tarpley, L. High night time temperatures affect rice productivity through altered pollen germination and spikelet fertility. Agric. Forest Meteorol. 149, 999–1008 (2010).

    Article  Google Scholar 

  26. Sheehy, J. E., Mitchell, P. L. & Ferrer, A. B. Decline in rice grain yields with temperature: Models and correlations can give different estimates. Field Crop. Res. 98, 151–156 (2006).

    Article  Google Scholar 

  27. in East Asian Monsoon (ed Chang, C.-P.) (World Scientific, 2004).

  28. Ding, Y. & Chan, J. C. L. The East Asian summer monsoon: An overview. Meteorol. Atmos. Phys. 89, 117–142 (2005).

    Article  Google Scholar 

  29. NOAA Historical El Niño/La Niña episodes (1950–present) ; http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml

  30. Michel-Kerjan, E. et al. Catastrophe risk models for evaluating disaster risk reduction in investments in developing countries. Risk Anal. 33, 984–998 (2013).

    Article  CAS  Google Scholar 

  31. Mechler, R. & Islam, N. in The Economic Impacts of Natural Disasters (eds Guha-Sapir, D., Santos, I. & Borde, A.) 80–106 (Oxford Univ. Press, 2013).

    Chapter  Google Scholar 

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Acknowledgements

T. Jiang and M. Gemmer of the National Climate Centre of the China Meteorological Administration shared the gridded weather data, C. Yang of the Chinese Academy of Mathematics and System Sciences communicated the provincial input–output tables, and simulated crop data was shared by W. Xiong of the Chinese Academy of Agricultural Sciences. It is a pleasure to thank M. Vrac and P. Naveau of the Laboratoire des Sciences du Climat et de l’Environnement, as well as Maarten Speekenbrink of University College London for many helpful discussions. The work of E.C. was supported by the CONACyT, Mexico, and by the Grantham Institute for Climate Change, Imperial College London. M.G. acknowledges support from the US Department of Energy, grant DE-SC0006694, and from the US National Science Foundation, grant OCE-1243175.

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Authors and Affiliations

  1. Imperial College London, Centre for Environmental Policy, London SW7 1NA, UK

    Erik Chavez & Gordon Conway

  2. Finance Department, Imperial College London, Imperial College Business School, London SW7 2AZ, UK

    Erik Chavez

  3. Geosciences Department and Environmental Research and Teaching Institute, Ecole Normale Supérieure, Paris, F-75230 Paris Cedex 05, France

    Michael Ghil

  4. Department of Atmospheric and Oceanic Sciences and Institute of Geophysics and Planetary Physics, University of California, Los Angeles, Los Angeles, California 90095-1565, USA

    Michael Ghil

  5. Agriculture and Environmental Services Department, The World Bank, Risk and Markets Practice, Washington DC 20433, USA

    Marc Sadler

Authors
  1. Erik Chavez
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  2. Gordon Conway
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  3. Michael Ghil
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  4. Marc Sadler
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Contributions

E.C., G.C. and M.G. designed the study. E.C. obtained the data and carried out the calculations. M.S. provided further insights into the application of risk profiles to market practice. All four authors contributed to the writing.

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Correspondence to Erik Chavez.

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Chavez, E., Conway, G., Ghil, M. et al. An end-to-end assessment of extreme weather impacts on food security. Nature Clim Change 5, 997–1001 (2015). https://doi.org/10.1038/nclimate2747

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