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

Nature Climate Change volume 10, pages 726–731 (2020)Cite this article

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Abstract

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.

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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.

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Data availability

All model data used in this paper are freely available for download through the Earth System Grid (https://esgf-node.llnl.gov/projects/esgf-llnl/). 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, https://crudata.uea.ac.uk/cru/data/hrg/; NCEP Reanalysis provided by the NOAA/OAR/ESRL PSD, http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.derived.html; NOAA-CIRES 20th Century Reanalysis version 2c, https://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV2c.html; GISTEMP Surface Temperature Analysis v4 data, https://data.giss.nasa.gov/gistemp/; GPCP precipitation data provided by the NOAA/OAR/ESRL PSD, https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html. Global emissions67 are from the RCP Concentration Calculation and Data Group: http://www.iiasa.ac.at/web-apps/tnt/RcpDb.

Code availability

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

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Acknowledgements

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 (http://collaboration.beta.cmc.ec.gc.ca/cmc/cccma/CanSISE/output/CCCma/CanESM2).

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Authors and Affiliations

  1. Program for Climate Model Diagnosis and Intercomparison (PCDMI), Lawrence Livermore National Laboratory, Livermore, CA, USA

    Céline J. W. Bonfils, Benjamin D. Santer & Thomas J. Phillips

  2. Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada

    John C. Fyfe

  3. Department of Applied Mathematics and Applied Physics, Columbia University, New York, NY, USA

    Kate Marvel

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

    Kate Marvel

  5. Center for Accelerator Mass Spectrometry (CAMS), Lawrence Livermore National Laboratory, Livermore, CA, USA

    Susan R. H. Zimmerman

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Contributions

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.

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Correspondence to Céline J. W. Bonfils.

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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.

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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).

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

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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). https://doi.org/10.1038/s41558-020-0821-1

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