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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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.
Analysis code written in Python is available at GitHub (https://github.com/katemarvel/drought-atlas).
Cook, E. R., Seager, R., Cane, M. A. & Stahle, D. W. North American drought: reconstructions, causes, and consequences. Earth Sci. Rev. 81, 93–134 (2007).
Cook, E. R. et al. Megadroughts in North America: placing IPCC projections of hydroclimatic change in a long-term palaeoclimate context. J. Quaternary Sci. 25, 48–61 (2010).
Cook, E. R. et al. Old World megadroughts and pluvials during the Common Era. Sci. Adv. 1, e1500561 (2015).
Stahle, D. W. et al. The Mexican drought atlas: tree-ring reconstructions of the soil moisture balance during the late pre-Hispanic, colonial, and modern eras. Quat. Sci. Rev. 149, 34–60 (2016).
Cook, E. R. et al. Asian monsoon failure and megadrought during the last millennium. Science 328, 486–489 (2010).
Palmer, J. G. et al. Drought variability in the eastern Australia and New Zealand summer drought atlas (ANZDA, ce 1500–2012) modulated by the interdecadal pacific oscillation. Environ. Res. Lett. 10, 124002 (2015).
Cook, B. I., Anchukaitis, K. J., Touchan, R., Meko, D. M. & Cook, E. R. Spatiotemporal drought variability in the Mediterranean over the last 900 years. J. Geophys. Res. 121, 2060–2074 (2016).
Griffin, D. & Anchukaitis, K. J. How unusual is the 2012–2014 California drought? Geophys. Res. Lett. 41, 9017–9023 (2014).
Cook, B. I., Seager, R. & Smerdon, J. E. The worst North American drought year of the last millennium: 1934. Geophys. Res. Lett. 41, 7298–7305 (2014).
Bindoff, N. et al. Detection and Attribution of Climate Change: from Global to Regional Vol. 10, 867–952 (Cambridge Univ. Press, Cambridge, 2013).
Berg, A. & Sheffield, J. Climate change and drought: the soil moisture perspective. Curr. Clim. Change Rep. 4, 180–191 (2018).
Bonfils, C. et al. Competing influences of anthropogenic warming, ENSO, and plant physiology on future terrestrial aridity. J. Clim. 30, 6883–6904 (2017).
Milly, P. C. & Dunne, K. A. Potential evapotranspiration and continental drying. Nat. Clim. Change 6, 946–949 (2016).
Swann, A. L., 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).
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
Martens, B. et al. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10, 1903–1925 (2017).
Reichle, R. H. et al. Assessment of MERRA-2 land surface hydrology estimates. J. Clim. 30, 2937–2960 (2017).
Santer, B. et al. Separating signal and noise in atmospheric temperature changes: the importance of timescale. J. Geophys. Res. 116, D22105 (2011).
Santer, B. D. et al. Ocean variability and its influence on the detectability of greenhouse warming signals. J. Geophys. Res. 100, 10693–10725 (1995).
Santer, B. D. et al. Identifying human influences on atmospheric temperature. Proc. Natl Acad. Sci. USA 110, 26–33 (2013).
Marvel, K. & Bonfils, C. Identifying external influences on global precipitation. Proc. Natl Acad. Sci. USA 110, 19301–19306 (2013).
van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).
Cook, B. I., Smerdon, J. E., Seager, R. & Coats, S. Global warming and 21st century drying. Clim. Dyn. 43, 2607–2627 (2014).
Dai, A. Characteristics and trends in various forms of the Palmer drought severity index during 1900–2008. J. Geophys. Res. 116, D12115 (2011).
van der Schrier, G., Briffa, K., Jones, P. & Osborn, T. Summer moisture variability across Europe. J. Clim. 19, 2818–2834 (2006).
Stott, P. A. & Tett, S. F. Scale-dependent detection of climate change. J. Clim. 11, 3282–3294 (1998).
Marvel, K. et al. External influences on modeled and observed cloud trends. J. Clim. 28, 4820–4840 (2015).
Baek, S. H. et al. Precipitation, temperature, and teleconnection signals across the combined North American, monsoon Asia, and Old World drought atlases. J. Clim. 30, 7141–7155 (2017).
Santer, B. D. et al. Identification of human-induced changes in atmospheric moisture content. Proc. Natl Acad. Sci. USA 104, 15248–15253 (2007).
Santer, B. D. et al. Human and natural influences on the changing thermal structure of the atmosphere. Proc. Natl Acad. Sci. USA 110, 17235–17240 (2013).
Hegerl, G. C. et al. Good Practice Guidance Paper on Detection and Attribution Related to Anthropogenic Climate Change (IPCC, 2010).
Mastrandrea, M. D. et al. Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties (IPCC, 2010).
Dai, A. & Zhao, T. Uncertainties in historical changes and future projections of drought. Part I: estimates of historical drought changes. Clim. Change 144, 519–533 (2017).
Skeie, R. et al. Anthropogenic radiative forcing time series from pre-industrial times until 2010. Atmos. Chem. Phys. 11, 11827–11857 (2011).
Miller, R. L. et al. CMIP5 historical simulations (1850–2012) with GISS ModelE2. J. Adv. Model. Earth Syst. 6, 441–478 (2014).
Biasutti, M. & Giannini, A. Robust Sahel drying in response to late 20th century forcings. Geophys. Res. Lett. 33, L11706 (2006).
Polson, D., Bollasina, M., Hegerl, G. & Wilcox, L. Decreased monsoon precipitation in the Northern Hemisphere due to anthropogenic aerosols. Geophys. Res. Lett. 41, 6023–6029 (2014).
Bollasina, M. A., Ming, Y. & Ramaswamy, V. Anthropogenic aerosols and the weakening of the South Asian summer monsoon. Science 334, 502–505 (2011).
Wilcox, L. J., Highwood, E. J. & Dunstone, N. J. The influence of anthropogenic aerosol on multi-decadal variations of historical global climate. Environ. Res. Lett. 8, 024033 (2013).
Pincus, R., Forster, P. M. & Stevens, B. The radiative forcing model intercomparison project (RFMIP): experimental protocol for CMIP6. Geosci. Model Dev. 9, 3447–3460 (2016).
Zelinka, M. D., Andrews, T., Forster, P. M. & Taylor, K. E. Quantifying components of aerosol–cloud–radiation interactions in climate models. J. Geophys. Res. 119, 7599–7615 (2014).
Ekman, A. M. Do sophisticated parameterizations of aerosol-cloud interactions in CMIP5 models improve the representation of recent observed temperature trends? J. Geophys. Res. 119, 817–832 (2014).
Gelaro, R. et al. The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).
Medhaug, I., Stolpe, M. B., Fischer, E. M. & Knutti, R. Reconciling controversies about the ‘global warming hiatus’. Nature 545, 41–47 (2017).
Kosaka, Y. & Xie, S.-P. Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature 501, 403–407 (2013).
England, M. H. et al. Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nat. Clim. Change 4, 222–227 (2014).
Schmidt, G. A., Shindell, D. T. & Tsigaridis, K. Reconciling warming trends. Nat. Geosci. 7, 158–160 (2014).
Johansson, D. J. A., O’Neill, B. C., Tebaldi, C. & Häggström, O. Equilibrium climate sensitivity in light of observations over the warming hiatus. Nat. Clim. Change 5, 449–453 (2015).
Risbey, J. S. et al. A fluctuation in surface temperature in historical context: reassessment and retrospective on the evidence. Environ. Res. Lett. 13, 123008 (2018).
Trenberth, K. E., Fasullo, J. T., Branstator, G. & Phillips, A. S. Seasonal aspects of the recent pause in surface warming. Nat. Clim. Change 4, 911–916 (2014).
Palmer, W. C. Meteorological Drought. Research Paper No. 45 (US Department of Commerce, 1965).
Guttman, N. B. Comparing the Palmer drought index and the standardized precipitation index. J. Am. Water Resour. Assoc. 34, 113–121 (1998).
Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).
Trenberth, K. E. et al. Global warming and changes in drought. Nat. Clim. Change 4, 17–22 (2014).
Williams, A. P. et al. Contribution of anthropogenic warming to California drought during 2012–2014. Geophys. Res. Lett. 42, 6819–6828 (2015).
Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010).
Yin, D., Roderick, M. L., Leech, G., Sun, F. & Huang, Y. The contribution of reduction in evaporative cooling to higher surface air temperatures during drought. Geophys. Res. Lett. 41, 7891–7897 (2014).
Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3, 52–58 (2013).
Feng, S., Trnka, M., Hayes, M. & Zhang, Y. Why do different drought indices show distinct future drought risk outcomes in the US great plains? J. Clim. 30, 265–278 (2017).
Dai, A., Trenberth, K. E. & Qian, T. A global dataset of Palmer drought severity index for 1870–2002: relationship with soil moisture and effects of surface warming. J. Hydrometeorol. 5, 1117–1130 (2004).
Hasselmann, K. Optimal fingerprints for the detection of time-dependent climate change. J. Clim. 6, 1957–1971 (1993).
Smith, S. J. et al. Anthropogenic sulfur dioxide emissions: 1850–2005. Atmos. Chem. Phys. 11, 1101–1116 (2011).
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.
Nature thanks Hans Linderholm and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
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.
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%.
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.
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.
S/N ratios and percentiles relative to tree ring noise and forced (H85) simulations for individual drought atlases and combinations of drought atlases
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
Modeling group information for the CMIP5 models used in this study
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Marvel, K., Cook, B.I., Bonfils, C. et al. Twentieth-century hydroclimate changes consistent with human influence. Nature 569, 59–65 (2019). https://doi.org/10.1038/s41586-019-1149-8
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