Article | Published:

Twentieth-century hydroclimate changes consistent with human influence

Naturevolume 569pages5965 (2019) | Download Citation

Abstract

Although anthropogenic climate change is expected to have caused large shifts in temperature and rainfall, the detection of human influence on global drought has been complicated by large internal variability and the brevity of observational records. Here we address these challenges using reconstructions of the Palmer drought severity index obtained with data from tree rings that span the past millennium. We show that three distinct periods are identifiable in climate models, observations and reconstructions during the twentieth century. In recent decades (1981 to present), the signal of greenhouse gas forcing is present but not yet detectable at high confidence. Observations and reconstructions differ significantly from an expected pattern of greenhouse gas forcing around mid-century (1950–1975), coinciding with a global increase in aerosol forcing. In the first half of the century (1900–1949), however, a signal of greenhouse-gas-forced change is robustly detectable. Multiple observational datasets and reconstructions using data from tree rings confirm that human activities were probably affecting the worldwide risk of droughts as early as the beginning of the twentieth century.

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Data availability

All model data used in this paper are available through the Earth System Grid (see https://esgf-node.llnl.gov/projects/esgf-llnl/) and freely available for download. All observational and reconstructed PDSI and soil moisture data are freely available for download from the indicated links. Data for NADA, MXDA, OWDA, http://drought.memphis.edu; ANZDA, https://www.dropbox.com/s/nrizk1a1a289awh/anzdaV2.nc; MADA, https://www.dropbox.com/s/n2lo99h9qn17prg/madaV2.nc; CRU, https://crudata.uea.ac.uk/cru/data/drought/; DAI, https://rda.ucar.edu/datasets/ds299.0/; MERRA-2, https://goldsmr4.gesdisc.eosdis.nasa.gov/data/MERRA2_MONTHLY/; GLEAM, https://www.gleam.eu.

Code availability

Analysis code written in Python is available at GitHub (https://github.com/katemarvel/drought-atlas).

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Supplementary Table 3) for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led development of the software infrastructure in partnership with the Global Organization for Earth System Science Portals. We thank K. Taylor, G. Schmidt and R. Pincus for discussions. K.M. and C.J.W.B. were supported by the US Department of Energy Biological and Environmental Research Grant DE-SC0014423. K.M., B.I.C. and A.P.W. were supported for this work by the NASA Modeling, Analysis, and Prediction program (NASA 80NSSC17K0265). J.E.S. was supported in part by US National Science Foundation (NSF) grants AGS-1243204 and AGS-1602581; J.E.S. and A.P.W. were further supported by NSF grant OISE-1743738 and A.P.W. was supported by NSF grant AGS-1703029. P.J.D. was supported through PCDMI SFA funding from the DOE Regional and Global Model Analysis Program. Work at LLNL was performed under the auspices of the US Department of Energy under contract DE-AC52-07NA27344.

Reviewer information

Nature thanks Hans Linderholm and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. These authors contributed equally: Kate Marvel, Benjamin I. Cook

Affiliations

  1. NASA Goddard Institute for Space Studies, New York, NY, USA

    • Kate Marvel
    •  & Benjamin I. Cook
  2. Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, USA

    • Kate Marvel
  3. Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA, USA

    • Céline J. W. Bonfils
    •  & Paul J. Durack
  4. Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA

    • Jason E. Smerdon
    •  & A. Park Williams

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Contributions

K.M. and B.I.C. designed the study. K.M. performed the analyses with contributions from B.I.C. C.J.W.B. contributed to developing and applying the detection and attribution methodology. A.P.W. provided the code to calculate PSDI from CMIP5 model data. J.E.S. and A.P.W provided guidance on interpretation of the drought atlas data. P.J.D. provided the code to download and access CMIP5 data. K.M. and B.I.C. wrote the manuscript with contributions from all authors.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Kate Marvel.

Extended data figures and tables

  1. Extended Data Fig. 1 Noise estimates.

    Distributions of overlapping (all possible) 50-year trends in the projection of preindustrial reconstructions (1400–1850) of the drought atlas onto the fingerprints shown in Fig. 1a–e. Best-fit Gaussian distributions are overlaid for visual clarity.

  2. Extended Data Fig. 2 Trends in the GDA.

    Linear trends in the multi-model mean CMIP5 historical simulations extended to 2100 with RCP8.5. Trends are calculated for each grid cell using ordinary least-squares regression. The pattern is extremely similar to the fingerprint shown in Fig. 2a, with the pattern correlation exceeding 99%.

  3. Extended Data Figure 3 Models with and without aerosol indirect effects.

    a, b, The approximated aerosol fingerprint for models with (a) and without (b) aerosol indirect effects, defined as the leading EOF of the multi-model average historical simulations over the years 1950–1975. Models are grouped according to the previously reported classifications39. c, d, Associated principal components for the fingerprints shown in a and b.

  4. Extended Data Figure 4 Noise time dependence.

    The standard deviation of all 50-year trends in projections of the drought atlas for 1400–1850 onto the fingerprints in Fig. 1a–e were calculated from years early (x axis) and late (y axis) in the preindustrial record. There is no evidence for a systematic difference in noise estimates across drought atlas regions.

Supplementary information

  1. Supplementary Table 1

    S/N ratios and percentiles relative to tree ring noise and forced (H85) simulations for individual drought atlases and combinations of drought atlases

  2. Supplementary Table 2

    For the CMIP5 models, the number of soil layers and approximate depths used for the calculation of surface and root zone soil moisture indices from variable mrlsl

  3. Supplementary Table 3

    Modeling group information for the CMIP5 models used in this study

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https://doi.org/10.1038/s41586-019-1149-8

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