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

Nature Climate Change volume 12, pages 163–170 (2022)Cite this article

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Abstract

Spatially compounding extremes pose substantial threats to globally interconnected socio-economic systems. Here we use multiple large ensemble simulations of the high-emissions scenario to show increased risk of compound droughts during the boreal summer over ten global regions. Relative to the late twentieth century, the probability of compound droughts increases by ~40% and ~60% by the middle and late twenty-first century, respectively, with a disproportionate increase in risk across North America and the Amazon. These changes contribute to an approximately ninefold increase in agricultural area and population exposure to severe compound droughts with continued fossil-fuel dependence. ENSO is the predominant large-scale driver of compound droughts with 68% of historical events occurring during El Niño or La Niña conditions. With ENSO teleconnections remaining largely stationary in the future, a ~22% increase in frequency of ENSO events combined with projected warming drives the elevated risk of compound droughts.

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Fig. 1: Historical and future characteristics of compound droughts in CESM.
Fig. 2: Crop, pasture lands and population exposure to severe compound droughts.
Fig. 3: Projected changes in the frequency of ENSO events and compound droughts.
Fig. 4: Simulated composites of detrended SPEI and detrended SST anomalies during compound droughts.
Fig. 5: Influence of ENSO and non-ENSO drivers on compound drought characteristics.
Fig. 6: Changes in the ENSO teleconnections with SPEI over land in future climate.

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Data availability

All datasets used in the manuscript are publicly available. CESM1 LENS are publicly available through the Cheyenne cluster at /glade/collections/cdg/data/CLIVAR_LE. Observed CHIRPS precipitation data are publicly available at https://www.chc.ucsb.edu/data/chirps. Population and agricultural land datasets are publicly available at NASA Socioeconomic Data and Applications Center (https://sedac.ciesin.columbia.edu).

Code availability

The scripts developed to analyse these datasets are available from the corresponding author on reasonable request, and are also available in ref. 72.

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Acknowledgements

We would like to thank the National Center for Atmospheric Research (NCAR), Climate Hazards Center UC Santa Barbara and US CLIVAR Working Group on Large Ensembles for archiving and enabling public access to their data. We thank Washington State University for the startup funding that has supported J.S. and D.S. W.B.A. acknowledges funding from Earth Institute Postdoctoral Fellowship. M.A. was supported by the US Air Force Numerical Weather Modeling Program and the National Climate‐Computing Research Center, which is located within the National Center for Computational Sciences at the ORNL and supported under a Strategic Partnership Project (no. 2316‐T849‐08) between DOE and NOAA. This manuscript has been co-authored by employees of Oak Ridge National Laboratory, managed by UT Battelle, LLC, under contract no. DE-AC05-00OR22725 with the US Department of Energy (DOE). The publisher, by accepting the article for publication, acknowledges that the US Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US Government purposes. The US DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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Authors and Affiliations

  1. School of the Environment, Washington State University, Vancouver, WA, USA

    Jitendra Singh & Deepti Singh

  2. Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Moetasim Ashfaq

  3. Department of Environmental, Earth and Atmospheric Sciences, University of Massachusetts Lowell, Lowell, MA, USA

    Christopher B. Skinner

  4. International Research Institute for Climate and Society, Columbia University, Palisades, NY, USA

    Weston B. Anderson

  5. Civil Engineering, Indian Institute of Technology (IIT) Gandhinagar, Gandhinagar, India

    Vimal Mishra

  6. Earth Sciences, Indian Institute of Technology (IIT) Gandhinagar, Gandhinagar, India

    Vimal Mishra

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Contributions

J.S., M.A., C.B.S., W.B.A., V.M. and D.S. conceived and designed the study. J.S. collected the data and performed the analyses. All authors were involved in discussions of the results. J.S. and D.S. wrote the manuscript with feedback from all authors.

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Correspondence to Jitendra Singh.

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Extended data

Extended Data Fig. 1 Observed CHIRPS (1981-2018) summer (June-September) precipitation characteristics over SREX regions considered in this study.

(a) Map shows the fraction of annual precipitation that falls in summer season. (b) Map shows the summer precipitation entropy value over land. (c) Significant (P-value < 0.05) linear regression coefficients between ENSO and JJAS precipitation. (d) Map showing the 10 SREX regions (maroon boxes) considered in this study. Maroon text indicates the fraction of each SREX region with high entropy values [entropy > 4.86, which is the median entropy value across 10 SREX regions] (teal) estimated from observed CHIRPS precipitation data (1981-2018) at 0.250 resolution. The regions shown in teal are used to estimate compound drought characteristics.

Extended Data Fig. 2 Agricultural land and population exposure to moderate compound droughts.

Same as Fig. 2a, but for moderate compound droughts.

Extended Data Fig. 3 Climatology of precipitation and evapotranspiration.

Seasonal climatology of precipitation and evapotranspiration and their projected changes in late-21st century (2071–2100) relative to historical (1971-2100) climate.

Extended Data Fig. 4 Regional exposures to severe compound droughts.

Regional (a) cropland and (b) pastureland exposure risk to severe compound droughts in the historical (1971-2000) and late-21st century (2071–2100) climate. Error bars indicate uncertainties (ensemble spread; ±1σ) in the drought likelihood and exposures.

Extended Data Fig. 5 El Niño and La Niña characteristics.

Relationship between meridional sea surface temperature (SST) gradient and Niño3 rainfall in historical (1971-2000; top row) and late-21st century (2071-2100; bottom row) climate. Dark red, orange, navy blue, and sky-blue dots indicate extreme El Niño (defined as events with Niño3 JJAS rainfall exceeds 4 mm/day), moderate El Niño (defined as events with detrended Niño3 SST anomalies are > 0.5σ and that are not extreme El Niño events), extreme La Niña (defined as events with detrended Niño4 SST anomalies < -1.75σ), and moderate La Niña (defined as events with detrended Niño4 SST anomalies < -0.5σ and that are not extreme La Niña events) events, respectively. Text in each panel indicates the number of extreme El Niño, moderate El Niño, extreme La Niña, and moderate La Niña events.

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Singh, J., Ashfaq, M., Skinner, C.B. et al. Enhanced risk of concurrent regional droughts with increased ENSO variability and warming. Nat. Clim. Chang. 12, 163–170 (2022). https://doi.org/10.1038/s41558-021-01276-3

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