Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Human influence on joint changes in temperature, rainfall and continental aridity


Despite the pervasive impact of drought on human and natural systems, the large-scale mechanisms conducive to regional drying remain poorly understood. Here we use a multivariate approach1,2 to identify two distinct externally forced fingerprints from multiple ensembles of Earth system model simulations. The leading fingerprint, FM1(x), is characterized by global warming, intensified wet–dry patterns3 and progressive large-scale continental aridification, largely driven by multidecadal increases in greenhouse gas (GHG) emissions. The second fingerprint, FM2(x), captures a pronounced interhemispheric temperature contrast4,5, associated meridional shifts in the intertropical convergence zone6,7,8,9 and correlated anomalies in precipitation and aridity over California10, the Sahel11,12 and India. FM2(x) exhibits nonlinear temporal behaviour: the intertropical convergence zone moves southwards before 1975 in response to increases in hemispherically asymmetric sulfate aerosol emissions, and it shifts northwards after 1975 due to reduced sulfur dioxide emissions and the GHG-induced warming of Northern Hemisphere landmasses. Both fingerprints are statistically identifiable in observations of joint changes in temperature, rainfall and aridity during 1950–2014. We show that the reliable simulation of these changes requires combined forcing by GHGs, direct and indirect effects of aerosols, and large volcanic eruptions. Our results suggest that GHG-induced aridification may be modulated regionally by future reductions in sulfate aerosol emissions.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Temporal and spatial components of the first leading multivariate fingerprint.
Fig. 2: Temporal and spatial components of the second leading multivariate fingerprint.
Fig. 3: Projections of HIST+8.5, NAT, AA and GHG data onto the first and second fingerprints, and associated distributions of regression coefficients.
Fig. 4: Covariability relationships between simulated and observed projection time series used as proxies for the ITCZ location, and their associated CMI anomalies.

Data availability

All model data used in this paper are freely available for download through the Earth System Grid ( The postprocessed data can be obtained from the corresponding author. All reanalyses and observational data are publicly available for download via the following links: CRU data from the University of East Anglia,; NCEP Reanalysis provided by the NOAA/OAR/ESRL PSD,; NOAA-CIRES 20th Century Reanalysis version 2c,; GISTEMP Surface Temperature Analysis v4 data,; GPCP precipitation data provided by the NOAA/OAR/ESRL PSD, Global emissions67 are from the RCP Concentration Calculation and Data Group:

Code availability

All codes used in the analysis are available from the corresponding author on request.


  1. 1.

    Marvel, K. & Bonfils, C. Identifying external influences on global precipitation. Proc. Natl Acad. Sci. USA 110, 19301–19306 (2013).

    CAS  Google Scholar 

  2. 2.

    Santer, B. D. et al. Ocean variability and its influence on the detectability of greenhouse warming signals. J. Geophys. Res. Oceans 100, 10693–10725 (1995).

    Google Scholar 

  3. 3.

    Held, I. & Soden, B. Robust responses of the hydrological cycle to global warming. J. Clim. 19, 5686–5699 (2006).

    Google Scholar 

  4. 4.

    Chiang, J., Chang, C. & Wehner, M. Long-term behavior of the Atlantic interhemispheric SST gradient in the CMIP5 historical simulations. J. Clim. 26, 8628–8640 (2013).

    Google Scholar 

  5. 5.

    Friedman, A., Hwang, Y., Chiang, J. & Frierson, D. Interhemispheric temperature asymmetry over the twentieth century and in future projections. J. Clim. 26, 5419–5433 (2013).

    Google Scholar 

  6. 6.

    Frierson, D. & Hwang, Y. Extratropical influence on ITCZ shifts in slab ocean simulations of global warming. J. Clim. 25, 720–733 (2012).

    Google Scholar 

  7. 7.

    Hwang, Y., Frierson, D. & Kang, S. Anthropogenic sulfate aerosol and the southward shift of tropical precipitation in the late 20th century. Geophys. Res. Lett. 40, 2845–2850 (2013).

    Google Scholar 

  8. 8.

    Chung, E. & Soden, B. Hemispheric climate shifts driven by anthropogenic aerosol–cloud interactions. Nat. Geosci. 10, 566–571 (2017).

    CAS  Google Scholar 

  9. 9.

    Wang, C. Anthropogenic aerosols and the distribution of past large-scale precipitation change. Geophys. Res. Lett. 42, 10876–10884 (2015).

    CAS  Google Scholar 

  10. 10.

    Cvijanovic, I. et al. Future loss of Arctic sea-ice cover could drive a substantial decrease in California’s rainfall. Nat. Commun. 8, 1947 (2017).

    Google Scholar 

  11. 11.

    Haywood, J., Jones, A., Bellouin, N. & Stephenson, D. Asymmetric forcing from stratospheric aerosols impacts Sahelian rainfall. Nat. Clim. Change 3, 660–665 (2013).

    CAS  Google Scholar 

  12. 12.

    Giannini, A. & Kaplan, A. The role of aerosols and greenhouse gases in Sahel drought and recovery. Climatic Change 152, 449–466 (2019).

    Google Scholar 

  13. 13.

    Wu, P., Christidis, N. & Stott, P. Anthropogenic impact on Earth’s hydrological cycle. Nat. Clim. Change 3, 807–810 (2013).

    Google Scholar 

  14. 14.

    Zhang, X. et al. Detection of human influence on twentieth-century precipitation trends. Nature 448, 461–465 (2007).

    CAS  Google Scholar 

  15. 15.

    Undorf, S. et al. Detectable impact of local and remote anthropogenic aerosols on the 20th century changes of West African and South Asian monsoon precipitation. J. Geophys. Res. Atmos. 123, 4871–4889 (2018).

    CAS  Google Scholar 

  16. 16.

    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).

    Google Scholar 

  17. 17.

    Marvel, K. et al. Twentieth-century hydroclimate changes consistent with human influence. Nature 569, 59–65 (2019).

    CAS  Google Scholar 

  18. 18.

    Allen, M. & Ingram, W. Constraints on future changes in climate and the hydrologic cycle. Nature 419, 228–232 (2002).

    CAS  Google Scholar 

  19. 19.

    Wang, H., Xie, S., Tokinaga, H., Liu, Q. & Kosaka, Y. Detecting cross-equatorial wind change as a fingerprint of climate response to anthropogenic aerosol forcing. Geophys. Res. Lett. 43, 3444–3450 (2016).

    Google Scholar 

  20. 20.

    Rotstayn, L. & Lohmann, U. Tropical rainfall trends and the indirect aerosol effect. J. Clim. 15, 2103–2116 (2002).

    Google Scholar 

  21. 21.

    Bollasina, M., Ming, Y. & Ramaswamy, V. Anthropogenic aerosols and the weakening of the South Asian summer monsoon. Science 334, 502–505 (2011).

    CAS  Google Scholar 

  22. 22.

    Baker, L. et al. Climate responses to anthropogenic emissions of short-lived climate pollutants. Atmos. Chem. Phys. 15, 8201–8216 (2015).

    CAS  Google Scholar 

  23. 23.

    Allen, R., Evan, A. & Booth, B. Interhemispheric aerosol radiative forcing and tropical precipitation shifts during the late twentieth century. J. Clim. 28, 8219–8246 (2015).

    Google Scholar 

  24. 24.

    Iles, C. & Hegerl, G. The global precipitation response to volcanic eruptions in the CMIP5 models. Environ. Res. Lett. 9, 104012 (2014).

    Google Scholar 

  25. 25.

    Colose, C., LeGrande, A. & Vuille, M. Hemispherically asymmetric volcanic forcing of tropical hydroclimate during the last millennium. Earth Syst. Dyn. 7, 681–696 (2016).

    Google Scholar 

  26. 26.

    Andrews, T., Gregory, J., Webb, M. & Taylor, K. Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere–ocean climate models. Geophys. Res. Lett. 39, L09712 (2012).

    Google Scholar 

  27. 27.

    Booth, B. B. B., Dunstone, N. J., Halloran, P. R., Andrews, T. & Bellouin, N. Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature 484, 228–232 (2012).

    CAS  Google Scholar 

  28. 28.

    Bindoff, N. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 867–952 (IPCC, Cambridge Univ. Press, 2013).

  29. 29.

    Schurer, A. et al. Estimating the transient climate response from observed warming. J. Clim. 31, 8645–8663 (2018).

    Google Scholar 

  30. 30.

    Stevenson, S., Otto-Bliesner, B., Fasullo, J. & Brady, E. “El Nino like” hydroclimate responses to last millennium volcanic eruptions. J. Clim. 29, 2907–2921 (2016).

    Google Scholar 

  31. 31.

    Ding, Y. et al. Ocean response to volcanic eruptions in Coupled Model Intercomparison Project 5 simulations. J. Geophys. Res. Oceans 119, 5622–5637 (2014).

    CAS  Google Scholar 

  32. 32.

    Toohey, M. & Sigl, M. Volcanic stratospheric sulfur injections and aerosol optical depth from 500 bce to 1900 ce. Earth Syst. Sci. Data 9, 809–831 (2017).

    Google Scholar 

  33. 33.

    Wang, H., Xie, S. & Liu, Q. Comparison of climate response to anthropogenic aerosol versus greenhouse gas forcing: distinct patterns. J. Clim. 29, 5175–5188 (2016).

    Google Scholar 

  34. 34.

    Bandoro, J., Solomon, S., Santer, B. D., Kinnison, D. & Mills, M. Detectability of the impacts of ozone-depleting substances and greenhouse gases upon stratospheric ozone accounting for nonlinearities in historical forcings. Atmos. Chem. Phys. 18, 143–166 (2018).

    CAS  Google Scholar 

  35. 35.

    Rotstayn, L., Collier, M. & Luo, J. Effects of declining aerosols on projections of zonally averaged tropical precipitation. Environ. Res. Lett. 10, 044018 (2015).

    Google Scholar 

  36. 36.

    Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–471 (1996).

    Google Scholar 

  37. 37.

    Sandeep, S., Stordal, F., Sardeshmukh, P. & Compo, G. Pacific Walker Circulation variability in coupled and uncoupled climate models. Clim. Dyn. 43, 103–117 (2014).

    Google Scholar 

  38. 38.

    Willmott, C. J. & Feddema, J. J. A more rational Climatic Moisture Index*. Prof. Geogr. 44, 84–88 (1992).

    Google Scholar 

  39. 39.

    Allen, R., Pereira, L., Raes, D. & Smith, M. Crop Evapotranspiration—Guidelines for Computing Crop Water Requirements Irrigation and Drainage Paper No. 56 (FAO, 1998);

  40. 40.

    Harris, I., Jones, P., Osborn, T. & Lister, D. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).

    Google Scholar 

  41. 41.

    Lee, D. & Biasutti, M. Climatology and variability of precipitation in the Twentieth-Century Reanalysis. J. Clim. 27, 5964–5981 (2014).

    Google Scholar 

  42. 42.

    Slivinski, L. et al. Towards a more reliable historical reanalysis: improvements for version 3 of the Twentieth Century Reanalysis system. Q. J. R. Meteorol. Soc. 145, 2876–2908 (2019).

    Google Scholar 

  43. 43.

    Lenssen, N. et al. Improvements in the GISTEMP Uncertainty Model. J. Geophys. Res. Atmos. 124, 6307–6326 (2019).

    Google Scholar 

  44. 44.

    GISTEMP Team GISS Surface Temperature Analysis (GISTEMP) Version 4 (NASA Goddard Institute for Space Studies, 2019);

  45. 45.

    Adler, R. et al. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeorol. 4, 1147–1167 (2003).

    Google Scholar 

  46. 46.

    Taylor, K., Stouffer, R. & Meehl, G. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Google Scholar 

  47. 47.

    Santer, B. D. et al. Human influence on the seasonal cycle of tropospheric temperature. Science 361, eaas8806 (2018).

    Google Scholar 

  48. 48.

    Gagne, M., Fyfe, J., Gillett, N., Polyakov, I. & Flato, G. Aerosol-driven increase in Arctic sea ice over the middle of the twentieth century. Geophys. Res. Lett. 44, 7338–7346 (2017).

    Google Scholar 

  49. 49.

    Deser, C. et al. Insights from Earth system model initial-condition large ensembles and future prospects. Nat. Clim. Change 10, 277–286 (2020).

    Google Scholar 

  50. 50.

    Gagne, M., Kirchmeier-Young, M., Gillett, N. & Fyfe, J. Arctic sea ice response to the eruptions of Agung, El Chichon, and Pinatubo. J. Geophys. Res. Atmos. 122, 8071–8078 (2017).

    Google Scholar 

  51. 51.

    Zwiers, F. W. et al. in Climate Science for Serving Society: Research, Modelling and Prediction Priorities 339–389 (eds Asrar, G. & Hurrell, J.) (2013).

  52. 52.

    Williams, A. et al. Contribution of anthropogenic warming to California drought during 2012–2014. Geophys. Res. Lett. 42, 6819–6828 (2015).

    Google Scholar 

  53. 53.

    Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3, 52–58 (2013).

    Google Scholar 

  54. 54.

    Douville, H., Ribes, A., Decharme, B., Alkama, R. & Sheffield, J. Anthropogenic influence on multidecadal changes in reconstructed global evapotranspiration. Nat. Clim. Change 3, 59–62 (2013).

    Google Scholar 

  55. 55.

    Hegerl, G. et al. Challenges in quantifying changes in the global water cycle. Bull. Am. Meteorol. Soc. 96, 1097–1115 (2015).

    Google Scholar 

  56. 56.

    Stott, P. A. et al. Future challenges in event attribution methodologies. Bull. Am. Meteorol. Soc. 99, S155–S157 (2018).

    Google Scholar 

  57. 57.

    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).

    CAS  Google Scholar 

  58. 58.

    Bonfils, C. et al. Detection and attribution of temperature changes in the mountainous western United States. J. Clim. 21, 6404–6424 (2008).

    Google Scholar 

  59. 59.

    Santer, B. D. et al. Identification of human-induced changes in atmospheric moisture content. Proc. Natl Acad. Sci. USA 104, 15248–15253 (2007).

    CAS  Google Scholar 

  60. 60.

    Pierce, D. W. et al. Attribution of declining western US snowpack to human effects. J. Clim. 21, 6425–6444 (2008).

    Google Scholar 

  61. 61.

    Barnett, T. P. et al. Human-induced changes in the hydrology of the western United States. Science 319, 1080–1083 (2008).

    CAS  Google Scholar 

  62. 62.

    Fyfe, J et al. Large near-term projected snowpack loss over the western United States. Nat. Commun. 8, 14996 (2017).

    CAS  Google Scholar 

  63. 63.

    Hasselmann, K. in Meteorology of Tropical Oceans (Ed. Shaw, D. B.) 251–259 (Royal Meteorological Society, 1979).

  64. 64.

    Thomson, R. & Emery, W. Data Analysis Methods in Physical Oceanography 3rd edn (Elsevier Science, 2014).

  65. 65.

    Monahan, A., Fyfe, J., Ambaum, M., Stephenson, D. & North, G. Empirical orthogonal functions: the medium is the message. J. Clim. 22, 6501–6514 (2009).

    Google Scholar 

  66. 66.

    Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).

    Google Scholar 

  67. 67.

    Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change 109, 213–241 (2011).

    CAS  Google Scholar 

Download references


This work was performed under the auspices of the US Department of Energy (DOE) by Lawrence Livermore National Laboratory under contract no. DE-AC52–07NA27344. We thank K. Taylor for discussions. C.J.W.B., B.D.S. and S.R.H.Z. received LLNL 17-ERD-052 LDRD funding; C.J.W.B. was also partially supported by the DOE-BER Early Career Research Program award. C.J.W.B. and B.D.S. received further funding from the DOE Regional and Global Model Analysis Program under the PCMDI SFA. K.M. was supported by the US DOE-BER grant no. DE-SC0014423. 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 for producing and making available their model output. For CMIP, the US DOE’s PCMDI provides coordinating support and leads the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We acknowledge Environment and Climate Change Canada’s Canadian Centre for Climate Modelling and Analysis for the CanESM2-LE simulations (

Author information




C.J.W.B. designed the study, led the research activity and performed the analyses. B.D.S. and J.C.F. discussed the results and detection methodology and helped write the manuscript and design the figures. J.C.F. provided simulation output from the CanESM2-LEs (and interpretation of the CanESM2 results). K.M. contributed discussions on the role of human activities in aridity changes. S.R.H.Z. contributed discussions on drought information from paleoclimate data. All authors contributed to the writing and review of the manuscript.

Corresponding author

Correspondence to Céline J. W. Bonfils.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Sabine Radanovics, Chien Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Spatial components of the multivariate fingerprints FM1(x) and FM2(x).

Results are calculated using the CMIP5 multi-model average over the 1861–2019 period (black line) and the CanESM2-LE average over the 1950–2019 period (blue line) for FM1(x) (a–c) and FM2(x) (d-f). Calculations rely on the HIST + 8.5 normalized zonal-mean anomalies in T (°C), P (mm/day) and CMI (dimensionless). Zonal-mean patterns of FM1(x) (a–c) and FM2(x) (d–f) are displayed as EOF loadings (multiplied by 100 for visualization purposes). The corresponding Hövmoller figures show, as a function of latitude and time, the reconstructions of the T, P, and CMI anomalies onto their respective EOFs (multiplied by 2.5 for FM2(x) for visualization purposes). Latitudinally coherent signals of major volcanic eruptions are visually obvious.

Extended Data Fig. 2 Signal-to-noise (S/N) ratios as a function of timescale L for the fingerprints FM1(x) and FM2(x).

(a–c) FM1(x) results, (d–f) FM2(x) results. The signal S is either derived from the regression between the PCF(t) and the observed ZO(t) time series, or between PCF(t) and the individual HHIST+8.5(t) projection time series for the models. The noise σN(L) is the standard deviation of the null distribution of the unforced regressions between PCF(t) and L-year overlapping segments of the control run-derived CF(t) time series. The S/N ratio is simply SN(L)/σN(L) – that is, signal and noise are always estimated for the same timescale L. The 5% significance threshold (S/N = 1.96, assuming a two-tailed test) is displayed as the red dotted line. The start date for signal calculations is 1950 and the minimum length of record L for characterizing the signal is 10 years. Results are plotted on the final year of SN(L). The upper, middle and lower panels show the results for models incorporating two, one, or no aerosol indirect effects (AIE), respectively. Signal detection times are indicated in brackets (for the first and second fingerprints, respectively).

Extended Data Fig. 3 Individual HHIST+8.5(t) time series of projections onto FM1(x) and FM2(x).

(a–c) FM1(x) results in rows 1, 2 and 3 are for models incorporating two, one, or no aerosol indirect effects (AIE), respectively. (d–f) Same as (a–c) for FM2(x). results. For FM2(x), models including the 2 AIE effects (d) clearly provide a better representation of the nonlinear behavior that is seen in the observations over the 20th century (see Fig. 2a in main text, red line).

Extended Data Fig. 4 Explained variance as a function of EOF number for the CMIP5 analysis.

The variance explained shows a much smoother transition in the “mean-removed” case (cyan line) than in the “mean-included” case (orange line). (See Supplementary Discussion 4).

Extended Data Fig. 5 As for Fig. 1 in the main text, but for T, P and CMI data from which the global mean has been removed at each time step before performing the EOF analysis.

The anomalies were calculated by first removing the climatological means, and then by removing the weighted global mean at every time step prior to computing zonal means. These steps were performed before the normalization process. (See Supplementary Discussion 4).

Extended Data Fig. 6 As for Fig. 2 in the main text, but for T, P and CMI data from which the global mean has been removed at each time step prior to performing the EOF analysis.

(See Supplementary Discussion 4).

Extended Data Fig. 7 Comparison of multivariate and univariate principal component time series.

Results are for the projection of zonal-mean CMIP5 HIST+8.5 anomalies of T, P, and CMI onto multivariate and univariate versions of FM1(x) (a) and FM2(x) (b). The correlation between the resulting multivariate and univariate principal component time series is shown in brackets, together with the variance explained by the fingerprint. All calculations are performed over the 1861–2019 period. Dates of major volcanic eruptions are indicated as described in Fig. 1a of the main text.

Extended Data Fig. 8 Temporal correlation between the time series for PCF2(t) and the multi-model average CMI over the 1861–2019 period within four regions.

Only the grid cells satisfying \(\left( {\left| {r\left( j \right)} \right|} \right)/\sigma \left( j \right)\) > 1 are displayed, where r(j) is the model-averaged correlation at grid-point jand σ(j) is the between-model standard deviation of the correlation at j (see Methods).

Extended Data Fig. 9 Influence of different analysis periods on maps of local anomaly time series of T, P, and CMI regressed onto PCF1(t) and PCF1,Can(t).

Fingerprints and regressions were computed using CMIP5 data over the 1861–2019 period (column a) and the 1950–2019 period (column b) and using PCF1,Can(t) and CanESM2 T, P, and CMI data over the 1950–2019 period (column c). For each variable, the spatial correlations calculated between the two CMIP5 regression maps over the 1861–2019 and the 1950–2019 periods are indicated in panel b. The spatial correlations calculated between the regression maps using CanEMS2 data over the 1950–2019 period, and the regression maps using CMIP5 data over either the 1861–2019 period or the 1950–2019 period are indicated in panel c. (see Supplementary Discussion 5).

Extended Data Fig. 10 Influence of different analysis periods on maps of local anomaly time series of T, P, and CMI regressed onto PCF2(t) and PCF2,Can(t).

(see Supplementary Discussion 5).

Supplementary information

Supplementary Information

Supplementary Figs. 1–4, Tables 1 and 2, and Discussions 1–8.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bonfils, C.J.W., Santer, B.D., Fyfe, J.C. et al. Human influence on joint changes in temperature, rainfall and continental aridity. Nat. Clim. Chang. 10, 726–731 (2020).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing