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Enhanced risk of concurrent regional droughts with increased ENSO variability and warming

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  • 1.

    Zscheischler, J. et al. A typology of compound weather and climate events. Nat. Rev. Earth Environ. 1, 333–347 (2020).

    Google Scholar 

  • 2.

    AghaKouchak, A. et al. Climate extremes and compound hazards in a warming world. Annu. Rev. Earth Planet. Sci. 48, 519–548 (2020).

    CAS 

    Google Scholar 

  • 3.

    Zscheischler, J. et al. Future climate risk from compound events. Nat. Clim. Change 8, 469–477 (2018).

    Google Scholar 

  • 4.

    Leonard, M. et al. A compound event framework for understanding extreme impacts. WIREs Clim. Change 5, 113–128 (2014).

    Google Scholar 

  • 5.

    Raymond, C. et al. Understanding and managing connected extreme events. Nat. Clim. Change 10, 611–621 (2020).

    Google Scholar 

  • 6.

    Sarhadi, A., Ausín, M. C., Wiper, M. P., Touma, D. & Diffenbaugh, N. S. Multidimensional risk in a nonstationary climate: joint probability of increasingly severe warm and dry conditions. Sci. Adv. 4, aau3487 (2018).

    Google Scholar 

  • 7.

    Mishra, V., Thirumalai, K., Singh, D. & Aadhar, S. Future exacerbation of hot and dry summer monsoon extremes in India. npj Clim. Atmos. Sci. 3, 1–9 (2020).

    CAS 

    Google Scholar 

  • 8.

    Raveh-Rubin, S. & Wernli, H. Large-scale wind and precipitation extremes in the Mediterranean: a climatological analysis for 1979–2012. Q. J. R. Meteorol. Soc. 141, 2404–2417 (2015).

    Google Scholar 

  • 9.

    Kornhuber, K. et al. Amplified Rossby waves enhance risk of concurrent heatwaves in major breadbasket regions. Nat. Clim. Change 10, 48–53 (2020).

    Google Scholar 

  • 10.

    Heslin, A. et al. Simulating the cascading effects of an extreme agricultural production shock: global implications of a contemporary US dust bowl event. Front. Sustain. Food Syst. 4, 1–12 (2020).

    Google Scholar 

  • 11.

    Anderson, W., Seager, R., Baethgen, W. & Cane, M. Trans-Pacific ENSO teleconnections pose a correlated risk to agriculture. Agric. Meteorol. 262, 298–309 (2018).

    Google Scholar 

  • 12.

    Anderson, W. B., Seager, R., Baethgen, W., Cane, M. & You, L. Synchronous crop failures and climate-forced production variability. Sci. Adv. 5, 1–10 (2019).

    Google Scholar 

  • 13.

    Gaupp, F., Hall, J., Hochrainer-stigler, S. & Dadson, S. Changing risks of simultaneous global breadbasket failure. Nat. Clim. Change 10, 54–57 (2019).

    Google Scholar 

  • 14.

    Tigchelaar, M., Battisti, D. S., Naylor, R. L. & Ray, D. K. Future warming increases probability of globally synchronized maize production shocks. Proc. Natl Acad. Sci. USA 115, 6644–6649 (2018).

    Google Scholar 

  • 15.

    Singh, D. et al. Climate and the global famine of 1876–78. J. Clim. 31, 9445–9467 (2018).

    Google Scholar 

  • 16.

    Mehrabi, Z. & Ramankutty, N. Synchronized failure of global crop production. Nat. Ecol. Evol. 3, 780–786 (2019).

    Google Scholar 

  • 17.

    Mills, E. Insurance in a climate of change. Science 309, 1040–1044 (2005).

    CAS 

    Google Scholar 

  • 18.

    Levermann, A. Climate economics: make supply chains climate-smart. Nature 506, 27–29 (2014).

    CAS 

    Google Scholar 

  • 19.

    Singh, J., Moetasim, A., Skinner, C. B., Anderson, W. B. & Deepti, S. Amplified risk of spatially compounding droughts during co-occurrences of modes of natural ocean variability. npj Clim. Atmos. Sci. 4, 1–14 (2021).

    Google Scholar 

  • 20.

    Rogers, C. D. W. et al. Recent increases in exposure to extreme humid-heat events disproportionately affect populated regions. Geophys. Res. Lett. 48, 1–13 (2021).

    Google Scholar 

  • 21.

    Anderson, W. B., Seager, R., Baethgen, W., Cane, M. & You, L. Synchronous crop failures and climate-forced production variability. Sci. Adv. 5, 1–10 (2019).

    Google Scholar 

  • 22.

    IPCC Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) (Cambridge Univ. Press, 2012).

  • 23.

    Kitoh, A. et al. Monsoons in a changing world: a regional perspective in a global context. J. Geophys. Res. Atmos. 118, 3053–3065 (2013).

    Google Scholar 

  • 24.

    Byers, E. et al. Global exposure and vulnerability to multi-sector development and climate change hotspots. Environ. Res. Lett. 13, 055012 (2018).

    Google Scholar 

  • 25.

    Han, T., Wang, H. & Sun, J. Strengthened relationship between eastern ENSO and summer precipitation over northeastern China. J. Clim. 30, 4497–4512 (2017).

    Google Scholar 

  • 26.

    Kumar, K. K., Rajagopalan, B. & Cane, M. A. On the weakening relationship between the Indian monsoon and ENSO. Science 284, 2156–2159 (1999).

    CAS 

    Google Scholar 

  • 27.

    Silvestri, G. E. El Niño signal variability in the precipitation over southeastern South America during austral summer. Geophys. Res. Lett. 31, 1–5 (2004).

    Google Scholar 

  • 28.

    Wang, B., Liu, J., Kim, H. J., Webster, P. J. & Yim, S. Y. Recent change of the global monsoon precipitation (1979–2008). Clim. Dyn. 39, 1123–1135 (2012).

    Google Scholar 

  • 29.

    Cook, B. I. et al. Twenty-first century drought projections in the CMIP6 forcing scenarios. Earth’s Future 8, 1–20 (2020).

    Google Scholar 

  • 30.

    Rowell, D. P., Booth, B. B. B., Nicholson, S. E. & Good, P. Reconciling past and future rainfall trends over East Africa. J. Clim. 28, 9768–9788 (2015).

    Google Scholar 

  • 31.

    Jones, B. & O’Neill, B. C. Spatially explicit global population scenarios consistent with the shared socioeconomic pathways. Environ. Res. Lett. 11, 084003 (2016).

    Google Scholar 

  • 32.

    Cai, W. et al. Increased frequency of extreme La Niña events under greenhouse warming. Nat. Clim. Change 5, 132–137 (2015).

    Google Scholar 

  • 33.

    Cai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Clim. Change 4, 111–116 (2014).

    Google Scholar 

  • 34.

    Herrera-Estrada, J. E. et al. Reduced moisture transport linked to drought propagation across North America. Geophys. Res. Lett. 46, 5243–5253 (2019).

    Google Scholar 

  • 35.

    Miralles, D. G., Gentine, P., Seneviratne, S. I. & Teuling, A. J. Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges. Ann. NY Acad. Sci. 1436, 19–35 (2019).

    Google Scholar 

  • 36.

    Pascale, S. et al. Weakening of the North American monsoon with global warming. Nat. Clim. Change 7, 806–812 (2017).

    Google Scholar 

  • 37.

    Lee, J. Y. & Wang, B. Future change of global monsoon in the CMIP5. Clim. Dyn. 42, 101–119 (2014).

    Google Scholar 

  • 38.

    Wang, B., Jin, C. & Liu, J. Understanding future change of global monsoons projected by CMIP6 models. J. Clim. 33, 6471–6489 (2020).

    Google Scholar 

  • 39.

    Takahashi, H. G. et al. Response of the asian summer monsoon precipitation to global warming in a high-resolution global nonhydrostatic model. J. Clim. 33, 8147–8164 (2020).

    Google Scholar 

  • 40.

    Chiodi, A. M. & Harrison, D. E. Global seasonal precipitation anomalies robustly associated with El Niño and La Niña events—an OLR perspective. J. Clim. 28, 6133–6159 (2015).

    Google Scholar 

  • 41.

    Deser, C. et al. Insights from Earth system model initial-condition large ensembles and future prospects. Nat. Clim. Change 10, 277–286 (2020).

    Google Scholar 

  • 42.

    Fasullo, J. T., Otto-Bliesner, B. L. & Stevenson, S. ENSO’s changing influence on temperature, precipitation, and wildfire in a warming climate. Geophys. Res. Lett. 45, 9216–9225 (2018).

    Google Scholar 

  • 43.

    Yun, K.-S. et al. Increasing ENSO–rainfall variability due to changes in future tropical temperature–rainfall relationship. Commun. Earth Environ. 2, 4–10 (2021).

    Google Scholar 

  • 44.

    Fredriksen, H. B., Berner, J., Subramanian, A. C. & Capotondi, A. How does El Niño–Southern Oscillation change under global warming—a first look at CMIP6. Geophys. Res. Lett. 47, GL090640 (2020).

    Google Scholar 

  • 45.

    Cai, W. et al. Changing El Niño–Southern Oscillation in a warming climate. Nat. Rev. Earth Environ. 2, 628–644 (2021).

    Google Scholar 

  • 46.

    Lengaigne, M. & Vecchi, G. A. Contrasting the termination of moderate and extreme El Niño events in coupled general circulation models. Clim. Dyn. 35, 299–313 (2010).

    Google Scholar 

  • 47.

    Detailed Trade Matrix. Food and Agriculture Organization of the United Nations (FAOSTAT, accessed 1 March 2021); http://www.fao.org/faostat/en/#data/

  • 48.

    Dalin, C., Wada, Y., Kastner, T. & Puma, M. J. Groundwater depletion embedded in international food trade. Nature 543, 700–704 (2017).

    CAS 

    Google Scholar 

  • 49.

    D’Odorico, P. et al. Global virtual water trade and the hydrological cycle: patterns, drivers, and socio-environmental impacts. Environ. Res. Lett. 14, 053001 (2019).

    CAS 

    Google Scholar 

  • 50.

    Graham, N. T. et al. Future changes in the trading of virtual water. Nat. Commun. 11, 1–7 (2020).

    Google Scholar 

  • 51.

    Barnston, A. G., Tippett, M. K., L’Heureux, M. L., Li, S. & Dewitt, D. G. Skill of real-time seasonal ENSO model predictions during 2002–11: Is our capability increasing? Bull. Am. Meteorol. Soc. 93, 631–651 (2012).

    Google Scholar 

  • 52.

    Kay, J. E. et al. The community earth system model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96, 1333–1349 (2015).

    Google Scholar 

  • 53.

    Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 1–21 (2015).

    Google Scholar 

  • 54.

    Shannon, C. A mathematical theory of communication. Bell Syst. Tech. J. 27, 623–656 (1948).

    Google Scholar 

  • 55.

    Gao, J. Global 1-km Downscaled Population Base Year and Projection Grids Based on the Shared Socioeconomic Pathways, Revision 01 (NASA, 2020).

  • 56.

    Ramankutty, N., Evan, A. T., Monfreda, C. & Foley, J. A. Global Agricultural Lands: Croplands, 2000 (NASA, 2010).

  • 57.

    Mishra, A. K., Özger, M. & Singh, V. P. An entropy-based investigation into the variability of precipitation. J. Hydrol. 370, 139–154 (2009).

    Google Scholar 

  • 58.

    Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).

    Google Scholar 

  • 59.

    Beguería, S., Vicente-Serrano, S. M., Reig, F. & Latorre, B. Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34, 3001–3023 (2014).

    Google Scholar 

  • 60.

    Milly, P. C. D. & Dunne, K. A. Potential evapotranspiration and continental drying. Nat. Clim. Change 6, 946–949 (2016).

    Google Scholar 

  • 61.

    Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R. & Donohue, R. J. Hydrologic implications of vegetation response to elevated CO2 in climate projections. Nat. Clim. Change 9, 44–48 (2019).

    Google Scholar 

  • 62.

    Berg, A. & McColl, K. A. No projected global drylands expansion under greenhouse warming. Nat. Clim. Change 11, 331–337 (2021).

    Google Scholar 

  • 63.

    Kooperman, G. J. et al. Plant physiological responses to rising CO2 modify simulated daily runoff intensity with implications for global-scale flood risk assessment. Geophys. Res. Lett. 45, 12457–12466 (2018).

    Google Scholar 

  • 64.

    Mankin, J. S. et al. Influence of internal variability on population exposure to hydroclimatic changes. Environ. Res. Lett. 12, 044007 (2017).

    Google Scholar 

  • 65.

    McKee, B. T., Nolan, D. J. & John, K. The relationship of drought frequency and duration to time scales. In 8th Conference on Applied Climatology 179–184 (1993).

  • 66.

    Mishra, A. K. & Singh, V. P. A review of drought concepts. J. Hydrol. 391, 202–216 (2010).

    Google Scholar 

  • 67.

    Batibeniz, F. et al. Doubling of U.S. population exposure to climate extremes by 2050. Earth’s Future 8, 1–14 (2020).

    Google Scholar 

  • 68.

    O’Neill, B. C. et al. Achievements and needs for the climate change scenario framework. Nat. Clim. Change 10, 1074–1084 (2020).

    Google Scholar 

  • 69.

    Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. D https://doi.org/10.1029/2002JD002670 (2003).

  • 70.

    Maher, N., Matei, D., Milinski, S. & Marotzke, J. ENSO change in climate projections: forced response or internal variability? Geophys. Res. Lett. 45, 11,390–11,398 (2018).

    Google Scholar 

  • 71.

    Good, P. I. Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses (Springer, 1994).

  • 72.

    Singh, J. et al. Enhanced Risk of Concurrent Regional Droughts with Increased ENSO Variability and Warming (Zenodo, 2021); https://doi.org/10.5281/zenodo.5759291


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