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Flash droughts present a new challenge for subseasonal-to-seasonal prediction

  • 1.

    Pulwarty, R. S. & Sivakumar, M. V. K. Information systems in a changing climate: early warnings and drought risk management. Weather Clim. Extrem. 3, 14–21 (2014).

    • Google Scholar
  • 2.

    Global Assessment Report on Disaster Risk Reduction (UNDRR, 2019).

  • 3.

    Wilhite, D. A. & Pulwarty, R. S. in Drought and Water Crises: Integrating Science, Management, and Policy (eds Wilhite, D. & Pulwarty, R. S.) Ch. 25 (CRC, 2017).

  • 4.

    Christensen, J. et al. in Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) Ch. 11 (IPCC, Cambridge Univ. Press, 2007).

  • 5.

    Seneviratne, S. I. et al. in Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) 109–230 (IPCC, Cambridge Univ. Press, 2012).

  • 6.

    Wilhite, D. A., Sivakumar, M. V. K. & Pulwarty, R. Managing drought risk in a changing climate: the role of national drought policy. Weather Clim. Extrem. 3, 4–13 (2014).

    • Google Scholar
  • 7.

    Svoboda, M. et al. The Drought Monitor. Bull. Am. Meteorol. Soc. 83, 1181–1190 (2002).

    • Google Scholar
  • 8.

    Otkin, J. A. et al. Flash droughts: a review and assessment of the challenges imposed by rapid-onset droughts in the United States. Bull. Am. Meteorol. Soc. 99, 911–919 (2018).

    • Google Scholar
  • 9.

    Robertson, A. W. et al. Improving and promoting subseasonal to seasonal prediction. Bull. Am. Meteorol. Soc. 96, ES49–ES53 (2015).

    • Google Scholar
  • 10.

    Hoerling, M. P. et al. Is a transition to semipermanent drought conditions imminent in the U.S. Great Plains? J. Clim. 25, 8380–8386 (2012).

    • Google Scholar
  • 11.

    Namias, J. Anatomy of Great Plains protracted heat waves (especially the 1980 U.S. summer drought). Mon. Weather Rev. 110, 824–838 (1982).

    • Google Scholar
  • 12.

    Yuan, X., Wang, L. & Wood, E. F. Anthropogenic intensification of southern African flash droughts as exemplified by the 2015/16 season. Bull. Am. Meteorol. Soc. 99, S86–S90 (2018).

    • Google Scholar
  • 13.

    Yuan, X., Ma, Z., Pan, M. & Shi, C. Microwave remote sensing of short‐term droughts during crop growing seasons. Geophys. Res. Lett. 42, 4394–4401 (2015).

    • Google Scholar
  • 14.

    Li, Y. et al. Mechanisms and early warning of drought disasters: experimental drought meteorology research over China. Bull. Am. Meteorol. Soc. 100, 673–687 (2019).

    • Google Scholar
  • 15.

    Nguyen, H. et al. Using evaporative stress index to monitor flash drought in Australia. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/ab2103 (2019).

  • 16.

    Ford, T. W. & Labosier, C. F. Meteorological conditions associated with the onset of flash drought in the eastern United States. Agric. Meteorol. 247, 414–423 (2017).

    • Google Scholar
  • 17.

    Hobbins, M. T., Ramírez, J. A. & Brown, T. C. Trends in pan evaporation and actual evapotranspiration across the conterminous U.S.: paradoxical or complementary? Geophys. Res. Lett. 31, https://doi.org/10.1029/2004GL019846 (2004).

  • 18.

    Ramírez, J. A., Hobbins, M. T. & Brown, T. C. Observational evidence of the complementary relationship in regional evaporation lends strong support for Bouchet’s hypothesis. Geophys. Res. Lett. 32, L15401 (2005).

    • Google Scholar
  • 19.

    Koster, R. D. et al. Flash drought as captured by reanalysis data: disentangling the contributions of precipitation deficit and excess evapotranspiration. J. Hydrometeorol. https://doi.org/10.1175/JHM-D-18-0242.1 (2019).

  • 20.

    Seneviratne, S. I., Lüthi, D., Litschi, M. & Schär, C. Land–atmosphere coupling and climate change in Europe. Nature 443, 205–209 (2006).

    • Google Scholar
  • 21.

    Fischer, E. M., Seneviratne, S. I., Lüthi, D. & Schär, C. Contribution of land–atmosphere coupling to recent European summer heat waves. Geophys. Res. Lett. 34, L06707 (2007).

    • Google Scholar
  • 22.

    Su, H., Yang, Z.-L., Dickinson, R. E. & Wei, J. Spring soil moisture–precipitation feedback in the Southern Great Plains: how is it related to large-scale atmospheric conditions? Geophys. Res. Lett. 41, 1283–1289 (2014).

    • Google Scholar
  • 23.

    Hoerling, M. et al. Causes and predictability of the 2012 Great Plains drought. Bull. Am. Meteorol. Soc. 95, 269–282 (2014).

    • Google Scholar
  • 24.

    Mo, K. C. & Lettenmaier, D. P. Precipitation deficit flash droughts over the United States. J. Hydrometeorol. 17, 1169–1184 (2016).

    • Google Scholar
  • 25.

    Chiang, F., Mazdiyasni, O. & AghaKouchak, A. Amplified warming of droughts in southern United States in observations and model simulations. Sci. Adv. 4, eaat2380 (2018).

    • Google Scholar
  • 26.

    Pegion, K. et al. The Subseasonal Experiment (SubX): a multi-model subseasonal prediction experiment. Bull. Am. Meteorol. Soc. https://doi.org/10.1175/BAMS-D-18-0270.1 (2019).

  • 27.

    Chen, L. G. et al. Flash drought characteristics based on U.S. Drought Monitor. Atmosphere (Basel) 10, 498 (2019).

    • Google Scholar
  • 28.

    Dirmeyer, P. A., Gentine, P., Ek, M. B. & Balsamo, G. Sub-Seasonal to Seasonal Prediction: The Gap Between Weather and Climate Forecasting (Robertson, A. W. & Vitart, F.) 165–181 (Elsevier, 2019).

  • 29.

    Waliser, D. E. et al. Potential predictability of the Madden–Julian oscillation. Bull. Am. Meteorol. Soc. 84, 33–50 (2003).

    • Google Scholar
  • 30.

    Hendon, H. H. et al. Australian rainfall and surface temperature variations associated with the Southern Hemisphere annular mode. J. Clim. 20, 2452–2467 (2007).

    • Google Scholar
  • 31.

    Zhao, M. & Hendon, H. H. Representation and prediction of the Indian Ocean dipole in the POAMA seasonal forecast model. Q. J. R. Meteorol. Soc. 135, 337–352 (2009).

    • Google Scholar
  • 32.

    Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts (National Academies Press, 2016).

  • 33.

    Zhu, H. et al. Seamless precipitation prediction skill in the tropics and extratropics from a global model. Mon. Weather Rev. 142, 1556–1569 (2014).

    • Google Scholar
  • 34.

    Wheeler, M. C., Zhu, H., Sobel, A. H., Hudson, D. & Vitart, F. Seamless precipitation prediction skill comparison between two global models. Q. J. R. Meteorol. Soc. 143, 374–383 (2017).

    • Google Scholar
  • 35.

    Wang, L. & Robertson, A. W. Week 3–4 predictability over the United States assessed from two operational ensemble prediction systems. Clim. Dyn. 52, 5861–5875 (2019).

    • Google Scholar
  • 36.

    Hudson, D. et al. Forewarned is forearmed: extended-range forecast guidance of recent extreme heat events in Australia. Weather Forecast. 31, 697–711 (2016).

    • Google Scholar
  • 37.

    Vitart, F. & Robertson, A. W. The sub-seasonal to seasonal prediction project (S2S) and the prediction of extreme events. npj Clim. Atmos. Sci. 1, 3 (2018).

    • Google Scholar
  • 38.

    Lehner, F. et al. Mitigating the impacts of climate nonstationarity on seasonal streamflow predictability in the U.S. Southwest. Geophys. Res. Lett. 44, 12208–12217 (2017).

    • Google Scholar
  • 39.

    McEvoy, D. J. et al. The Evaporative Demand Drought Index. Part II: CONUS-wide assessment against common drought indicators. J. Hydrometeorol. 17, 1763–1779 (2016).

    • Google Scholar
  • 40.

    Shukla, S. et al. Examining the value of global seasonal reference evapotranspiration forecasts to support FEWS NET’s food insecurity outlooks. J. Appl. Meteorol. Climatol. 56, 2941–2949 (2017).

    • Google Scholar
  • 41.

    Zhang, C. et al. CAUSES: diagnosis of the summertime warm bias in CMIP5 climate models at the ARM southern Great Plains site. J. Geophys. Res. Atmos. 123, 2968–2992 (2018).

    • Google Scholar
  • 42.

    Vitart, F. Madden–Julian Oscillation prediction and teleconnections in the S2S database. Q. J. R. Meteorol. Soc. 143, 2210–2220 (2017).

    • Google Scholar
  • 43.

    Ukkola, A. M. et al. Land surface models systematically overestimate the intensity, duration and magnitude of seasonal-scale evaporative droughts. Environ. Res. Lett. 11, 104012 (2016).

    • Google Scholar
  • 44.

    Vitart, F. et al. The Subseasonal to Seasonal (S2S) Prediction Project Database. Bull. Am. Meteorol. Soc. 98, 163–173 (2017).

    • Google Scholar
  • 45.

    Koster, R. D. et al. Contribution of land surface initialization to subseasonal forecast skill: first results from a multi-model experiment. Geophys. Res. Lett. 37, https://doi.org/10.1029/2009GL041677 (2010).

  • 46.

    Fisher, R. A. et al. Vegetation demographics in Earth System Models: a review of progress and priorities. Glob. Change Biol. 24, 35–54 (2018).

    • Google Scholar
  • 47.

    Mo, K. C. & Lettenmaier, D. P. Heat wave flash droughts in decline. Geophys. Res. Lett. 42, 2823–2829 (2015).

    • Google Scholar
  • 48.

    Wang, L., Yuan, X., Xie, Z., Wu, P. & Li, Y. Increasing flash droughts over China during the recent global warming hiatus. Sci. Rep. 6, 30571 (2016).

    • Google Scholar
  • 49.

    Zhang, Y., You, Q., Chen, C. & Li, X. Flash droughts in a typical humid and subtropical basin: a case study in the Gan River Basin, China. J. Hydrol. 551, 162–176 (2017).

    • Google Scholar
  • 50.

    Barnett, T. P. et al. Human-induced changes in the hydrology of the Western United States. Science 319, 1080–1083 (2008).

    • Google Scholar
  • 51.

    Marvel, K. et al. Twentieth-century hydroclimate changes consistent with human influence. Nature 569, 59–65 (2019).

    • Google Scholar
  • 52.

    Cook, B. I., Mankin, J. S. & Anchukaitis, K. J. Climate change and drought: from past to future. Curr. Clim. Change Rep. 4, 164–179 (2018).

    • Google Scholar
  • 53.

    Seager, R. et al. Model projections of an imminent transition to a more arid climate in southwestern North America. Science 316, 1181–1184 (2007).

    • Google Scholar
  • 54.

    Zhou, S. et al. Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity. Proc. Natl Acad. Sci. USA 116, 18848–18853 (2019).

    • Google Scholar
  • 55.

    Roderick, M. L., Greve, P. & Farquhar, G. D. On the assessment of aridity with changes in atmospheric CO2. Water Resour. Res. 51, 5450–5463 (2015).

    • Google Scholar
  • 56.

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

    • Google Scholar
  • 57.

    Feng, S. et al. Why do different drought indices show distinct future drought risk outcomes in the U.S. Great Plains? J. Clim. 30, 265–278 (2017).

    • Google Scholar
  • 58.

    Lehner, F. et al. Projected drought risk in 1.5 °C and 2 °C warmer climates. Geophys. Res. Lett. 44, 7419–7428 (2017).

    • Google Scholar
  • 59.

    Swann, A. L. S., Hoffman, F. M., Koven, C. D. & Randerson, J. T. Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proc. Natl Acad. Sci. USA 113, 10019–10024 (2016).

    • Google Scholar
  • 60.

    Bonfils, C. et al. Competing influences of anthropogenic warming, ENSO, and plant physiology on future terrestrial aridity. J. Clim. 30, 6883–6904 (2017).

    • 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.

    Mankin, J. S., Seager, R., Smerdon, J. E., Cook, B. I. & Williams, A. P. Mid-latitude freshwater availability reduced by projected vegetation responses to climate change. Nat. Geosci. https://doi.org/10.1038/s41561-019-0480-x (2019).

  • 63.

    Dirmeyer, P. A. et al. Evidence for enhanced land–atmosphere feedback in a warming climate. J. Hydrometeorol. 13, 981–995 (2012).

    • Google Scholar
  • 64.

    Otkin, J. A. et al. Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought. Agric. Meteorol. 218–219, 230–242 (2016).

    • Google Scholar
  • 65.

    Meko, D. M. et al. Medieval drought in the upper Colorado River Basin. Geophys. Res. Lett. 34, L10705 (2007).

    • Google Scholar
  • 66.

    Woodhouse, C. A., Meko, D. M., MacDonald, G. M., Stahle, D. W. & Cook, E. R. A 1,200-year perspective of 21st century drought in southwestern North America. Proc. Natl Acad. Sci. USA 107, 21283–21288 (2010).

    • Google Scholar
  • 67.

    Woodhouse, C., Stahle, D. & Villanueva Díaz, J. Rio Grande and Rio Conchos water supply variability over the past 500 years. Clim. Res. 51, 147–158 (2012).

    • Google Scholar
  • 68.

    Woodhouse, C. A. & Pederson, G. T. Investigating runoff efficiency in Upper Colorado River streamflow over past centuries. Water Resour. Res. 54, 286–300 (2018).

    • Google Scholar
  • 69.

    Lehner, F., Wahl, E. R., Wood, A. W., Blatchford, D. B. & Llewellyn, D. Assessing recent declines in Upper Rio Grande runoff efficiency from a paleoclimate perspective. Geophys. Res. Lett. 44, 4124–4133 (2017).

    • Google Scholar
  • 70.

    Zhao, M. et al. Weakened eastern Pacific El Niño predictability in the early twenty-first century. J. Clim. 29, 6805–6822 (2016).

    • Google Scholar
  • 71.

    Huning, L. S. & AghaKouchak, A. Mountain snowpack response to different levels of warming. Proc. Natl Acad. Sci. USA 115, 10932–10937 (2018).

    • Google Scholar
  • 72.

    Harpold, A., Dettinger, M. & Rajagopal, S. Defining snow drought and why it matters. Eos https://doi.org/10.1029/2017EO068775 (2017).

  • 73.

    Hoell, A., Perlwitz, J. & Eischeid, J. Drought Assessment Report: The Causes, Predictability, and Historical Context of the 2017 US Northern Great Plains Drought (NOAA/NIDIS/CIRES, 2019).

  • 74.

    Pulwarty, R. S. & Verdin, J. P. in Measuring Vulnerability to Natural Hazards: Towards Disaster Resilient Societies 2nd edn (ed. Birkmann, J.) 124–147 (United Nations Univ. Press, 2013).

  • 75.

    Cutter, S. et al. in Special Report on Managing the Risks of Extremes and Disaster to Advance Climate Change Adaptation (eds Field, C. B. et al.) 291–338 (IPCC, Cambridge Univ. Press, 2012).

  • 76.

    Shrader-Frechette, K. S. Environmental Justice: Creating Equality, Reclaiming Democracy. Environmental Ethics and Science Policy (Oxford Univ. Press, 2002).

  • 77.

    Jamieson, D. Ethics and the Environment: An Introduction (Cambridge Univ. Press, 2008).

  • 78.

    Pulwarty, R. S. et al. in Mapping Vulnerability: Disasters, Development and People (eds Bankoff, G. & Frerks, G.) Ch. 6 (Routledge, 2004).

  • 79.

    Allis, E. et al. The future of climate services. World Meteorol. Organ. Bull. 68, https://public.wmo.int/en/resources/bulletin/future-of-climate-services (2019).

  • 80.

    Gay-Antaki, M. & Liverman, D. Climate for women in climate science: women scientists and the Intergovernmental Panel on Climate Change. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1710271115 (2018).

  • 81.

    Kirtman, B. P. et al. The North American Multimodel Ensemble: Phase-1 seasonal-to-interannual prediction; Phase-2 toward developing intraseasonal prediction. Bull. Am. Meteorol. Soc. 95, 585–601 (2014).

    • Google Scholar
  • 82.

    Alfieri, L. et al. GloFAS—global ensemble streamflow forecasting and flood early warning. Hydrol. Earth Syst. Sci. 17, 1161–1175 (2013).

    • Google Scholar
  • 83.

    Arheimer, B. et al. Global catchment modelling using World-Wide HYPE (WWH), open data and stepwise parameter estimation. Hydrol. Earth Syst. Sci. Discuss. https://doi.org/10.5194/hess-2019-111 (2019).

  • 84.

    Yuan, X. et al. Anthropogenic shift towards higher risk of flash drought over China. Nat. Commun. 10, 4661 (2019).

    • Google Scholar
  • 85.

    Hobbins, M. T., McEvoy, D. J. & Hain, C. R. in Drought and Water Crises: Integrating Science, Management, and Policy (eds Wilhite, D. A. & Pulwarty, R. S.) Ch. 11 (CRC, 2017).

  • 86.

    Liang, X., Lettenmaier, D. P., Wood, E. F. & Burges, S. J. A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res. 99, 14415 (1994).

    • Google Scholar
  • 87.

    Livneh, B. et al. A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: update and extensions. J. Clim. 26, 9384–9392 (2013).

    • Google Scholar
  • 88.

    Livneh, B. et al. A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950-2013. Sci. Data 2, 150042 (2015).

    • Google Scholar
  • 89.

    Lukas, J., Hobbins, M. T., Rangwala, I. & EDDI Team. The EDDI User Guide (NOAA, 2017); https://www.esrl.noaa.gov/psd/eddi/pdf/EDDI_UserGuide_v1.0.pdf


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