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Recent European drought extremes beyond Common Era background variability

Nature Geoscience volume 14, pages 190–196 (2021)Cite this article

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Abstract

Europe’s recent summer droughts have had devastating ecological and economic consequences, but the severity and cause of these extremes remain unclear. Here we present 27,080 annually resolved and absolutely dated measurements of tree-ring stable carbon and oxygen (δ13C and δ18O) isotopes from 21 living and 126 relict oaks (Quercus spp.) used to reconstruct central European summer hydroclimate from 75 bce to 2018 ce. We find that the combined inverse δ13C and δ18O values correlate with the June–August Palmer Drought Severity Index from 1901–2018 at 0.73 (P < 0.001). Pluvials around 200, 720 and 1100 ce, and droughts around 40, 590, 950 and 1510 ce and in the twenty-first century, are superimposed on a multi-millennial drying trend. Our reconstruction demonstrates that the sequence of recent European summer droughts since 2015 ce is unprecedented in the past 2,110 years. This hydroclimatic anomaly is probably caused by anthropogenic warming and associated changes in the position of the summer jet stream.

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Fig. 1: Growth characteristics and temporal coverage of the central European oak stable isotope dataset.
Fig. 2: Temporal changes in the relation between oak stable isotopes and central European drought.
Fig. 3: Temporal and spatial agreement between the oak stable isotopes and European summer drought.
Fig. 4: Reconstructed central European summer variability over the past 2,110 years.

Data availability statement

The raw tree-ring stable isotope measurements (Supplementary Data 1) and the final drought reconstruction (Supplementary Data 2) are freely available from the NOAA National Centers for Environmental Information (NCEI) at https://www.ncdc.noaa.gov/paleo/study/32292.

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Acknowledgements

The work was supported by the Czech Republic Grant Agency (grant numbers 17-22102S and 18-11004S), and the SustES project—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797). U.B. and J.E. received funding from the ERC project Monostar (grant number AdG 882727) and W.T. acknowledges the German Research Foundation (grant number TE 613/3-2). Relict oak samples from Bavaria were collected and cross-dated by F. Herzig, and I. Roshka and N. Pernicová contributed to sample preparation.

Author information

Authors and Affiliations

  1. Department of Geography, University of Cambridge, Cambridge, UK

    Ulf Büntgen & Paul J. Krusic

  2. Global Change Research Institute of the Czech Academy of Sciences (CzechGlobe), Brno, Czech Republic

    Ulf Büntgen, Otmar Urban, Michal Rybníček, Tomáš Kolář, Alexander Ač, Josef Čáslavský, Jan Esper, Petr Dobrovolný & Miroslav Trnka

  3. Department of Geography, Faculty of Science, Masaryk University, Brno, Czech Republic

    Ulf Büntgen & Petr Dobrovolný

  4. Swiss Federal Research Institute (WSL), Birmensdorf, Switzerland

    Ulf Büntgen, Matthias Saurer & Paolo Cherubini

  5. Department of Physical Geography, Stockholm University, Stockholm, Sweden

    Paul J. Krusic

  6. Department of Wood Science and Technology, Faculty of Forestry and Wood Technology, Mendel University in Brno, Brno, Czech Republic

    Michal Rybníček, Tomáš Kolář & Eva Koňasová

  7. Moravian Dendro-Labor, Brno, Czech Republic

    Tomáš Kyncl

  8. Department of Geography, Johannes Gutenberg University, Mainz, Germany

    Jan Esper & Frederick Reinig

  9. Helmholtz-Centre Geesthacht, Centre for Material and Costal Research, Geesthacht, Germany

    Sebastian Wagner

  10. Chair of Forest Growth and Dendroecology, University of Freiburg, Freiburg, Germany

    Willy Tegel

  11. Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, Canada

    Paolo Cherubini

  12. Department of Agrosystems and Bioclimatology, Faculty of Agronomy, Mendel University in Brno, Brno, Czech Republic

    Miroslav Trnka

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  1. Ulf Büntgen
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Contributions

U.B. and M.T. designed the study. U.B. and P.J.K. performed the analyses and wrote the manuscript. M.R., T. Kolář, T. Kyncl and E.K. developed the Czech oak tree-ring dataset and prepared samples for isotopic analyses. O.U., A.A. and J.Č. processed and measured the stable isotopes. P.J.K. helped to develop the hydroclimatic reconstruction and S.W. provided model data and interpretation. J.E., M.S., W.T., P.D., P.C., F.R. and M.T. helped to place the results in a wider physiological, climatological and historical context. All authors provided critical discussion, helped to write and revise the manuscript and approved its submission.

Corresponding author

Correspondence to Ulf Büntgen.

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Peer review information Nature Geoscience thanks Cody Routson 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 Oak network.

Spatial distribution of 147 living, historical, archaeological and subfossil oaks between 91 BCE and 2018 CE, for which TRW, δ18O and δ13C were measured at annual resolution. While the vast majority of samples originates from the Czech Republic, a few archaeological samples come from Bavaria in south-eastern Germany.

Extended Data Fig. 2 Dendro inventory.

Number of individual tree-ring samples (series), the total chronology length and its start and end year (year, start, end), the mean series length (msl), the minimum, mean and maximum raw measurement values (min, mean, max), as well as the standard deviation, mean sensitivity and first-order autocorrelation coefficient (stdev, sens, ac1), of the four dendro parameters: δ13C, δ13C corrected, δ18O and tree-ring width (13C, 13Ccorr, 18O, TRW). The compound TRSI data are z-scores (mean of zero and standard deviation of one). Carbon and oxygen isotope ratios are reported in per mil (‰) using the usual delta (δ) notation relative to the VPDB (δ13C) and VSMOW (δ18O) standards73.

Extended Data Fig. 3 Temperature sensitivity.

Pearson’s correlation coefficients between the non-standardized δ18O (blue dots) and δ13C (red dots) records (using the median of the individual measurements), as well as their simple average (green circles), and monthly (from previous year January to current year December) and seasonal (all possible 28 monthly pairings between March and October of the growing season: Mar-Apr, Mar-May, Mar-Jun, Mar-Jul, Mar-Aug, Mar-Sep, Mar-Oct, Apr-May, Apr-Jun, Apr-Jul, Apr-Aug, Apr-Sep, Apr-Oct, May-Jun, May-Jul, May-Aug, May-Sep, May-Oct, Jun-Jul, Jun-Aug, Jun-Sep, Jun-Oct, Jul-Aug, Jul-Sep, Jul-Oct, Aug-Sep, Aug-Oct, Sep-Oct) temperature averages over 49–50°N and 15–18°E. Correlations are calculated over the early, late and full period of proxy-target overlap (from left to right).

Extended Data Fig. 4 Isotopic behaviour.

(a) Comparison of the non-standardized, inverse δ18O record (blue) against the non-standardized, inverse and corrected δ13C records (red) using the median of the individual measurements. (b) Difference between the annual δ18O and δ13C values, with the straight line referring to their long-term trend (equation in brackets). (c) Simple average and long-term trend of the annual δ18O and δ13C data. All timeseries cover the period 75 BCE to 2018 CE, during which at least ten samples are included each year. The smoothed curves in (a) and (c) are 50-year low-pass filters.

Extended Data Fig. 5 Calibration-verification statistics.

Statistical information of the full (1901–2018) calibration model, as well as using two equally-long early/late (1901–1959 and 1960–2018) split period calibration windows, for which the corresponding verification results are provided as well. Each column represents a different measure of interaction between the climate target and proxy variable along with, where appropriate, the probability (Pct) of obtaining that value by chance alone, the exceptions being RE (Reduction of Error), and CE (Coefficient of Efficiency). The four measures are, the Pearson, Robust Pearson, and Spearman correlations, and the statistical significance of the Cross Product (Xprod) between X and Y (Corr = correlation, Med = Median, tstat = t-statistic).

Extended Data Fig. 6 Reconstructed hydroclimatic extremes.

The 20 highest (that is, wettest) and lowest (that is, driest) annual JJA scPDSI values between 75 BCE and 2018 CE (including year zero). The two wettest and driest 4-year and 5-year periods of consecutive JJA scPDSI values (for example, 2018 refers to 2015–2018 and 2014–2018 for the four- and five-year periods, respectively).

Extended Data Fig. 7 Reconstruction uncertainty.

(a) Temporal evolution of the reconstruction’s annual error range that combines measurement (Standard Error) and calibration (Root Mean Squared Error) uncertainties. Note that the error range is consistently decreasing towards present, that is, uncertainty was generally lager in the first half of the Common Era (y = −0.0001x + 3.6204, R² = 0.0551). (b) Expressed Population Signal (EPS) of the combined δ18O and δ13C dataset (compound TRSI), and calculated over 50-year windows, lagged by 25 years. (c) Sample size of all TRSI ranges between 10 and 42 series per year.

Extended Data Fig. 8 Trend behaviour.

(a) Linear regression fitted to the JJA scPDSI reconstruction from 75 BCE to 2018 CE (with 2094 degrees of freedom). The Root Mean Squared Error (RMSE) is 1.93, the R-squared value is 0.112, the adjusted R-Squared is 0.112, and the F-statistic versus constant model is 266 (p-value = 1.78e-56). (b) Liner trends of the full the JJA scPDSI reconstruction and three pre-industrial periods (orange), as well as three industrial periods (red). Results from the Mann-Kendall test74, modified to account for autocorrelation on the timeseries, reveal there is a significant (p < 0.01) negative trend in the reconstructed values.

Extended Data Fig. 9 Common Era climate history.

(a) This study compared against (b) central European JJA scPDSI from the OWDA (ref. 12) centred over 49.5°N and 16.5°E, and (c) European JJA temperature anomalies46. Thick curves are 50-year cubic smoothing splines and dashed lines long-term trends.

Extended Data Fig. 10 Volcanic forcing.

(a) Reconstructed JJA scPDSI during five periods of strong volcanism. (b) Superposed composites of the JJA scPDSI reconstruction aligned over the 12 (17) strongest individual volcanic forcing events before (after) 1200 CE, as well as using 12 known Icelandic eruptions between 1200 and 1900 CE and a subset of 24 of the strongest non-Icelandic eruptions54. Peak volcanic forcing either appears in year zero or year one following the volcanic eruption depending on the latitude and season. Forcing and response are calculated relative to a pre-event 5-year background period presumably undisturbed by volcanic forcing (for example, 1804–1808 for the 1809 and 1815 volcanic eruptions, respectively). Data after secondary eruptions (for example, data from lag +6 years following the 1809 eruption) are removed prior to data aggregation.

Supplementary information

Supplementary Information

Supplementary Figs. 1–5.

Supplementary Data 1

TRSI data. Includes annually resolved oxygen and carbon isotope composition records.

Supplementary Data 2

Data and summary plot of the reconstructed (recon) summer (JJA) scPDSI. The data are also fitted with a 50-yr average spline and accompanied by the final error ranges. These data are also plotted in Fig. 4.

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Büntgen, U., Urban, O., Krusic, P.J. et al. Recent European drought extremes beyond Common Era background variability. Nat. Geosci. 14, 190–196 (2021). https://doi.org/10.1038/s41561-021-00698-0

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  • DOI: https://doi.org/10.1038/s41561-021-00698-0

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