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Observed changes in dry-season water availability attributed to human-induced climate change

Abstract

Human-induced climate change impacts the hydrological cycle and thus the availability of water resources. However, previous assessments of observed warming-induced changes in dryness have not excluded natural climate variability and show conflicting results due to uncertainties in our understanding of the response of evapotranspiration. Here we employ data-driven and land-surface models to produce observation-based global reconstructions of water availability from 1902 to 2014, a period during which our planet experienced a global warming of approximately 1 °C. Our analysis reveals a spatial pattern of changes in average water availability during the driest month of the year over the past three decades compared with the first half of the twentieth century, with some regions experiencing increased and some decreased water availability. The global pattern is consistent with climate model estimates that account for anthropogenic effects, and it is not expected from natural climate variability, supporting human-induced climate change as the cause. There is regional evidence of drier dry seasons predominantly in extratropical latitudes and including Europe, western North America, northern Asia, southern South America, Australia and eastern Africa. We also find that the intensification of the dry season is generally a consequence of increasing evapotranspiration rather than decreasing precipitation.

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Fig. 1: Patterns of change in dry-season water availability from observation-based reconstructions and climate models.
Fig. 2: Pattern correlations of changes in dry-season water availability between simulations with and without anthropogenic climate change and observation-based reconstructions.
Fig. 3: Contribution of changes in precipitation and evapotranspiration to changes in dry-season water availability.
Fig. 4: Summary of average latitudinal changes in dry-season water availability, precipitation and evapotranspiration.

Data availability

Precipitation data from the Global Soil Wetness Project Phase 3 (GSWP-3) are available at https://doi.org/10.20783/DIAS.501. The runoff reconstruction dataset GRUN is available at https://doi.org/10.3929/ethz-b-000324386, and the reconstruction of changes in terrestrial water storage is available at https://doi.org/10.6084/m9.figshare.7670849. The land-surface model reconstructions and CMIP5 climate model data used in this study are available at https://esgf-node.llnl.gov/projects/esgf-llnl/.

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Acknowledgements

R.S.P., L.G. and S.I.S. acknowledge partial support from the European Research Council (ERC) DROUGHT-HEAT project funded by the European Community’s Seventh Framework Programme (grant agreement FP7-IDEAS-ERC-617518) and from the European Union’s Horizon 2020 Research and Innovation Program (grant agreement 821003 (4C)). D.M.L. was supported in part by the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computing Scientific Focus Area (RUBISCO SFA), which is sponsored by the Regional and Global Climate Modeling (RGCM) Program in the US Department of Energy Office of Science. J.M. was also supported by the RUBISCO SFA. Oak Ridge National Laboratory is managed by UT‐BATTELLE for DOE under contract number DE‐AC05‐00OR22725. D.P. acknowledges the European Union’s Horizon 2020 research and innovation program under Grant Agreement 641816 (CRESCENDO) that partially funded the CMCC simulations. H.K. acknowledges Grant-in-Aid for Specially Promoted Research 16H06291 and 18KK0117 from Japan Society for the Promotion of Science. The LS3MIP simulation of the Institut Pierre Simon Laplace (IPSL) was performed at the Très Grand Centre de Calcul (TGCC) under the allocation 2018- R0040110492 (project gencmip6) provided by GENCI (Grand Equipement National de Calcul Intensif). We acknowledge the World Climate Research Program’s Working Group on Coupled Modelling, which is responsible for the Coupled Model Intercomparison Project (CMIP), and we thank the climate modelling groups for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We thank U. Beyerle, J. Sedlacek and L. Brunner for downloading and processing the CMIP5 and LS3MIP data.

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Authors and Affiliations

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Contributions

R.S.P., L.G. and S.I.S. designed the study. R.S.P. performed the analysis and wrote the manuscript. B.D., A.D., D.M.L., J.M. and D.P. contributed to the land-surface model reconstructions. G.K., S.I.S. and H.K. coordinated the land-surface model experiments. H.K. produced the forcing dataset for the data-driven and land-surface model reconstructions. All authors discussed the results and read and reviewed the manuscript.

Corresponding author

Correspondence to Ryan S. Padrón.

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Peer review information Primary Handling Editors: Tamara Goldin; Heike Langenberg.

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Extended data

Extended Data Fig. 1 Agreement between reconstructions from data driven (DDM) and land surface models (LSM).

Mean change in dry season water availability from the DDM and LSM reconstructions (that is mean of Fig. 1a, b of the main article). Shown are only grid cells where both reconstructions agree on the sign of change. Grey lines indicate tropical boundaries at 23.5°S and 23.5°N. Antarctica, Greenland and desert regions with annual P below 100 mm are masked in grey.

Extended Data Fig. 2 Temporal evolution of land area fraction with decrease in dry season water availability.

Δ(P – ET) is obtained as the difference between average P – ET from a 30-year period centered around the indicated year and average P – ET from the reference period 1902–1950. Lines indicate the DDM estimate, as well as the mean of individual LSM reconstructions and the mean of individual climate model simulations. The shaded area indicates the ensemble range of the 6 individual LSM reconstructions. The ensemble range of individual climate model simulations is not shown, but in the most recent period corresponds to 0.42–0.71 for models with full historical forcing (hist) and 0.46–0.54 for models with only natural historical forcing (histNat). Antarctica, Greenland and desert regions with annual P below 100 mm are omitted.

Extended Data Fig. 3 Sensitivity to the definition of the reference time period.

The reference period considered in Fig. 1 of the article is 1902–1950, whereas here we consider 4 alternative options: (a, b) 1902–1930, (c, d) 1911–1940, (e, f) 1921–1950 and (g, h) 1951–1980. Note that during the period 1951–1980 the influence of aerosol emissions was relatively high. Plots from the DDM reconstruction are shown on the left (a, c, e, g) and plots from the LSM reconstruction on the right (b, d, f, h). Grey lines indicate tropical boundaries at 23.5°S and 23.5°N. Antarctica, Greenland and desert regions with annual P below 100 mm are masked in grey.

Extended Data Fig. 4 Contribution of Δ(ΔTWS) to Δ(P – ET) from the DDM reconstruction (Fig. 1a) and associated uncertainty.

a, Δ(ΔTWS) is the difference in average ΔTWS corresponding to the month with minimum P – ET between years in the periods 1985–2014 and 1902–1950. Note that based on the water balance Δ(P – ET) = Δ(ΔTWS) + ΔR. The ΔTWS reconstruction used in the article corresponds to the mean of 100 stochastic realizations, whereas no stochastic realizations of the R reconstruction are available. b, Fraction of stochastic realizations of Δ(ΔTWS) that result in positive Δ(P – ET) at each grid cell. Grey lines indicate tropical boundaries at 23.5°S and 23.5°N. Antarctica, Greenland and desert regions with annual P below 100 mm are masked in grey.

Extended Data Fig. 5 Agreement between reconstructions from six individual land surface models used for the mean LSM reconstruction (Fig. 1b).

Fraction of reconstructions with positive Δ(P – ET) at each grid cell. Grey lines indicate tropical boundaries at 23.5°S and 23.5°N. Antarctica, Greenland and desert regions with annual P below 100 mm are masked in grey.

Extended Data Fig. 6 Sensitivity to the definition of dry season water availability.

Dry season water availability is represented by minimum monthly P – ET in Fig.1 of the article, whereas here we use minimum 3-monthly P – ET. Grey lines indicate tropical boundaries at 23.5°S and 23.5°N. Antarctica, Greenland and desert regions with annual P below 100 mm are masked in grey.

Extended Data Fig. 7 Agreement in the sign of Δ(P– ET) between individual climate model simulations.

Fraction of individual climate model simulations with positive Δ(P – ET) at each grid cell for (a) simulations with full historical forcing and (b) simulations with only natural historical forcing. Grey lines indicate tropical boundaries at 23.5°S and 23.5°N. Antarctica, Greenland and desert regions with annual P below 100 mm are masked in grey.

Extended Data Table 1 List of climate model simulations
Extended Data Table 2 Attribution of changes in dry season water availability to human-induced climate change based on reconstructions from individual land surface models

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Padrón, R.S., Gudmundsson, L., Decharme, B. et al. Observed changes in dry-season water availability attributed to human-induced climate change. Nat. Geosci. 13, 477–481 (2020). https://doi.org/10.1038/s41561-020-0594-1

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