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ENSO-driven coupled megadroughts in North and South America over the last millennium

Abstract

Geological evidence from the last millennium indicates that multidecadal megadroughts may have occurred simultaneously in California and Patagonia at least once. However, it is unclear whether or not megadroughts were common in South America, whether or not simultaneous megadroughts in North and South America occurred repeatedly, and what would cause their simultaneous occurrence. Here we use a data-assimilation-based global hydroclimate reconstruction, which integrates palaeoclimate records with constraints from a climate model, to show that there were about a dozen megadroughts in the South American Southwest over the last millennium. Using dynamical variables from the hydroclimate reconstruction, we show that these megadroughts were driven by the El Niño/Southern Oscillation (ENSO). We also find that North American Southwest and South American Southwest megadroughts have occurred simultaneously more often than expected by chance. These coincident megadroughts were driven by an increased frequency of cold ENSO states relative to the last millennium-average frequency. Our results establish the substantial risk that exists for ENSO-driven, coupled megadroughts in two critical agricultural regions.

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Fig. 1: Megadrought in the NASW and the SASW.
Fig. 2: Mean climate states during megadrought and non-megadrought years.
Fig. 3: Frequency changes in tropical SST conditions.
Fig. 4: Hydroclimate dynamics and forcing analysis.

Data availability

All data needed to evaluate the conclusions in the paper are present in public repositories. PHYDA and the palaeoclimate records used in its creation are publicly available in Zenodo data repositories at https://doi.org/10.5281/zenodo.1154913 (ref. 48) and https://doi.org/10.5281/zenodo.1189006 (ref. 50).

Code availability

The code for computing the SOMs is publicly available from https://github.com/ilarinieminen/SOM-Toolbox, while the code for computing PHYDA is publicly available from https://github.com/njsteiger/PHYDA. All additional code related to this paper may be requested from the authors.

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Acknowledgements

We thank E. Tejedor for helpful discussions about historical droughts in Chile. This work was supported in part by the National Science Foundation (NSF) under grants NSF OCE 1657209, OISE-1743738, AGS-1602581, AGS-1703029, AGS-1602920 and AGS-1805490. This work was also supported in part by the National Oceanic and Atmospheric Administration NA20OAR4310379 and in part by the Israel Science Foundation grant 2654/20.

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Contributions

N.J.S. designed the experiments with J.E.S., A.P.W. and R.S. The analysis code was developed by N.J.S., who also ran the experiments, performed the statistical analyses of the experiments and produced the figures. N.J.S. and J.E.S. prepared the manuscript with assistance from A.P.W., R.S. and A.V.-C.

Corresponding author

Correspondence to Nathan J. Steiger.

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The authors declare no competing interests.

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Peer review informationNature Geoscience thanks Roberto Rondanelli, Matthieu Lengaigne and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: James Super.

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Extended data

Extended Data Fig. 1 South American analysis region.

(a) The fraction of annual precipitation that falls within the JJA season, based on a climatology of CRUTS3.23 from 1950 to the present54. The SASW region analyzed here is the land area enclosed within the red bounding box. In this and all panels, the approximately 2 degree grid of PHYDA is overlaid on South America. (b) The fraction of annual precipitation that falls within the DJF season, similar to (a). (c) The maximum r2 correlation of observations-based PDSI62 at the South American moisture-sensitive proxy locations used in PHYDA (n = 60). This is constructed by first computing the r2 value between each proxy location and all other South American locations, using only the observations-based PDSI data. The maximum value of all of these point correlation maps is displayed here. The moisture-sensitive proxy locations are those locations associated with proxy time series that were modeled with PDSI (and not temperature) in the data assimilation framework of PHYDA. (d) Same as in (c), but using an observations-based temperature product63 and the South American temperature-sensitive proxy locations used in PHYDA (n = 116). The correlations were computed using annual mean data, defined as April to the next calendar year March (A2M).

Extended Data Fig. 2 Verification of the SASW reconstructions.

Verification of PHYDA’s reconstruction of the SASW along with a reconstruction of this same region using only the underlying proxy data (PaiCo; using a method of ‘Pairwise Comparisons’60, see Methods and Materials) and the DJF PDSI over this region from the ‘van der Schrier’ (VDS) PDSI product62. PHYDA and PaiCo are shown with error bounds extending to ± 2σ of their respective reconstruction ensembles. All PDSI time series show a DJF average. The PDSI baseline and period of comparison is 1903–1995, bounded on the oldest date by the availability of observations-based PDSI from Ref. 62 and on the newest date by the availability of proxy data for the PaiCo reconstruction. Verification metrics for PHYDA against VDS over this time period are: r = 0.60, mean absolute error = 1.14, continuous ranked probability score = 0.87. Corresponding verification metrics for PaiCo are: r = 0.29, mean absolute error = 1.46, continuous ranked probability score = 1.20. Note that for mean absolute error and continuous ranked probability score64, lower values are better.

Extended Data Fig. 3 Alternative reconstructions of the SASW from PHYDA.

Reconstruction of the SASW region from the published PHYDA along with alternative PHYDA-based reconstructions that were made with no proxies in all of North America (SASW No-NA) and no proxies in all of the North American Southwest (SASW No-NASW). The reconstructions are displayed with an 11-year smoothing (same as in Fig. 1) for clarity of comparison at the decadal-scale. Note that the No-NA and No-NASW reconstructions reflect not only the lack of proxies in NA and the NASW, but also the increased influence of other non-local proxies in the absence of NA and NASW proxies. The PDSI series are shown relative to the mean of the SASW time series over the analysis period (1000-1925).

Extended Data Fig. 4 Comparison of SASW reconstructions.

Comparison of PHYDA’s reconstruction of the SASW (the same as presented in Fig. 1) with a reconstruction of this same region using only the underlying proxy data (SASW PaiCo). SASW PaiCo is a reconstruction based on a method of ‘Pairwise Comparisons’ (PaiCo)60 and all of the moisture-sensitive tree ring time series (n = 47) within the SASW region (Fig. S1) used in PHYDA, see Methods. The PDSI series are shown relative to the analysis period mean (1000-1925).

Extended Data Fig. 5 SASW time series and proxy data analysis.

(a) Comparison of PHYDA and PaiCo reconstructions for the SASW, as in Fig. S4 but for the time series over the years 1400–1800, a probable relative dry period (c.f. Fig. 1). For clarity, each annual time series and uncertainties have been smoothed using a 5-year LOWESS filter. Uncertainties extend to the 5th and 95th percentiles of the probabilistic ensemble generated by each reconstruction method. This panel illustrates that a reconstruction method based on the local proxy data alone, PaiCo, agrees with the PHYDA reconstruction in the result of a generally drier period from approximately 1400–1800; though this period is also marked by high interannual and multi-year variance, with many wet years interspersed within the dry period. The PDSI anomalies are relative to the mean over 1000-1925 (c.f. Fig. 1). (b) Trend in the standardized moisture sensitive proxy data from the years 1800–2000 in the SASW. The sign of the proxy time series have been aligned so as to correlate positively with PDSI for this particular calculation. This panel shows that a majority of proxy data have a wetting trend from 1800–2000. (c) Box plot summaries of the distribution of 100 year means of each proxy time series in the SASW [proxy data were aligned as in panel (b)]. For reference, the century of the 1900s is highlighted in blue and horizontal lines across the panel indicate the top (black), middle (red), and bottom (black) box edges of the distribution for this century. The central box mark of the distributions indicates the median, the bottom and top edges of the box indicate the 25th and 75th percentiles, the whiskers extend to the most extreme data points not considered outliers (approximately ± 2.7σ), with outliers indicated by circles. This panel illustrates that the distribution of proxy values are generally shifted towards drier conditions for all centuries during the 1400–1800s, with the exception of the 1500s; this relatively wetter century is also visible in panel (a).

Extended Data Fig. 6 Alternative SST and PDSI composite analysis.

Composites of the original PHYDA DJF SST and DJF PDSI, as in Fig. 2, except that the SASW megadrought years are calculated according to the alternative PaiCO reconstruction of SASW DJF PDSI (which is based solely on the underlying proxy data, see Methods) shown in Figs. S2 and S4. The years of NASW megadrought are determined through the original PHYDA, thus the top left ‘NASW megadrought’ panel in this figure and Fig. 2 are identical. The years of SASW megadrought use the alternative PaiCo reconstruction of SASW PDSI, thus the remaining panels differ in their constituent years from Fig. 2. Temperature and PDSI data are anomalies with respect to the analysis period 1000–1925.

Extended Data Fig. 7 SOM analysis using six nodes.

This is the same analysis as shown in Fig. 3 but for six SOM nodes instead of eight. The SOM patterns are based on detrended, standardized DJF SST data from PHYDA for the years 1000–1925 (see Methods). Boxes in the lower left corner of each panel show the percent change in the frequency of occurrence of that pattern during simultaneous megadrought years relative to the average frequency over 1000–1925; gray colored frequency changes indicate those percent changes that fall within the 2.5th and 97.5th percentiles of a Markov chain Monte Carlo null distribution for each pattern (see Methods).

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Steiger, N.J., Smerdon, J.E., Seager, R. et al. ENSO-driven coupled megadroughts in North and South America over the last millennium. Nat. Geosci. 14, 739–744 (2021). https://doi.org/10.1038/s41561-021-00819-9

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