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Soil moisture–atmosphere feedbacks mitigate declining water availability in drylands

A Publisher Correction to this article was published on 20 January 2021

This article has been updated

Abstract

Global warming alters surface water availability (precipitation minus evapotranspiration, P–E) and hence freshwater resources. However, the influence of land–atmosphere feedbacks on future P–E changes and the underlying mechanisms remain unclear. Here we demonstrate that soil moisture (SM) strongly impacts future P–E changes, especially in drylands, by regulating evapotranspiration and atmospheric moisture inflow. Using modelling and empirical approaches, we find a consistent negative SM feedback on P–E, which may offset ~60% of the decline in dryland P–E otherwise expected in the absence of SM feedbacks. The negative feedback is not caused by atmospheric thermodynamic responses to declining SM; rather, reduced SM, in addition to limiting evapotranspiration, regulates atmospheric circulation and vertical ascent to enhance moisture transport into drylands. This SM effect is a large source of uncertainty in projected dryland P–E changes, underscoring the need to better constrain future SM changes and improve the representation of SM–atmosphere processes in models.

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Fig. 1: Multi-model mean annual changes in surface water availability and soil moisture.
Fig. 2: Soil moisture effects on changes in temperature, specific humidity and vertical ascent in GLACE-CMIP5.
Fig. 3: Soil moisture feedbacks on water availability in GLACE-CMIP5 models and reanalysis datasets.
Fig. 4: Soil moisture effects on the three components of mean flow convergence.

Data availability

The GLACE-CMIP5 simulations are available from S.I.S. (sonia.seneviratne@ethz.ch) and the climate modelling groups upon reasonable request. All other data used in this study are available online. The CMIP5 model simulations are from https://esgf-node.llnl.gov/search/cmip5/. The ERA5 reanalysis data are from https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era5. The MERRA-2 reanalysis data are from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/. The source data for the figures are publicly available (https://doi.org/10.6084/m9.figshare.12982880).

Code availability

The code used for modelling and reanalysis data analyses is publicly available (https://doi.org/10.5281/zenodo.4041736).

Change history

  • 20 January 2021

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. 1.

    Oki, T. & Kanae, S. Global hydrological cycles and world water resources. Science 313, 1068–1072 (2006).

    CAS  Google Scholar 

  2. 2.

    Rockström, J. et al. Future water availability for global food production: the potential of green water for increasing resilience to global change. Water Resour. Res. 45, W00A12 (2009).

    Google Scholar 

  3. 3.

    Anderegg, W. R. L. et al. Tree mortality predicted from drought-induced vascular damage. Nat. Geosci. 8, 367–371 (2015).

    CAS  Google Scholar 

  4. 4.

    Ruppert, J. C. et al. Quantifying drylands’ drought resistance and recovery: the importance of drought intensity, dominant life history and grazing regime. Glob. Change Biol. 21, 1258–1270 (2015).

    Google Scholar 

  5. 5.

    Huntington, T. G. Evidence for intensification of the global water cycle: review and synthesis. J. Hydrol. 319, 83–95 (2006).

    Google Scholar 

  6. 6.

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

    Google Scholar 

  7. 7.

    Lorenz, D. J. & DeWeaver, E. T. The response of the extratropical hydrological cycle to global warming. J. Clim. 20, 3470–3484 (2007).

    Google Scholar 

  8. 8.

    Greve, P. & Seneviratne, S. I. Assessment of future changes in water availability and aridity. Geophys. Res. Lett. 42, 5493–5499 (2015).

    CAS  Google Scholar 

  9. 9.

    Byrne, M. P. & O’Gorman, P. A. The response of precipitation minus evapotranspiration to climate warming: why the ‘wet-get-wetter, dry-get-drier’ scaling does not hold over land. J. Clim. 28, 8078–8092 (2015).

    Google Scholar 

  10. 10.

    Chou, C., Neelin, J. D., Chen, C.-A. & Tu, J.-Y. Evaluating the ‘rich-get-richer’ mechanism in tropical precipitation change under global warming. J. Clim. 22, 1982–2005 (2009).

    Google Scholar 

  11. 11.

    Vecchi, G. A. et al. Weakening of tropical Pacific atmospheric circulation due to anthropogenic forcing. Nature 441, 73–76 (2006).

    CAS  Google Scholar 

  12. 12.

    Chadwick, R., Boutle, I. & Martin, G. Spatial patterns of precipitation change in CMIP5: why the rich do not get richer in the tropics. J. Clim. 26, 3803–3822 (2012).

    Google Scholar 

  13. 13.

    Guillod, B. P., Orlowsky, B., Miralles, D. G., Teuling, A. J. & Seneviratne, S. I. Reconciling spatial and temporal soil moisture effects on afternoon rainfall. Nat. Commun. 6, 6443 (2015).

    CAS  Google Scholar 

  14. 14.

    Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010).

    CAS  Google Scholar 

  15. 15.

    Taylor, C. M., Parker, D. J. & Harris, P. P. An observational case study of mesoscale atmospheric circulations induced by soil moisture. Geophys. Res. Lett. 34, L15801 (2007).

    Google Scholar 

  16. 16.

    Ookouchi, Y., Segal, M., Kessler, R. C. & Pielke, R. A. Evaluation of soil moisture effects on the generation and modification of mesoscale circulations. Mon. Weather Rev. 112, 2281–2292 (1984).

    Google Scholar 

  17. 17.

    Segal, M. & Arritt, R. W. Nonclassical mesoscale circulations caused by surface sensible heat-flux gradients. Bull. Am. Meteor. Soc. 73, 1593–1604 (1992).

    Google Scholar 

  18. 18.

    Taylor, C. M., de Jeu, R. A. M., Guichard, F., Harris, P. P. & Dorigo, W. A. Afternoon rain more likely over drier soils. Nature 489, 423–426 (2012).

    CAS  Google Scholar 

  19. 19.

    Hsu, H., Lo, M.-H., Guillod, B. P., Miralles, D. G. & Kumar, S. Relation between precipitation location and antecedent/subsequent soil moisture spatial patterns: precipitation–soil moisture coupling. J. Geophys. Res. Atmos. 122, 6319–6328 (2017).

    Google Scholar 

  20. 20.

    Froidevaux, P., Schlemmer, L., Schmidli, J., Langhans, W. & Schär, C. Influence of the background wind on the local soil moisture–precipitation feedback. J. Atmos. Sci. 71, 782–799 (2013).

    Google Scholar 

  21. 21.

    Seneviratne, S. I. et al. Impact of soil moisture–climate feedbacks on CMIP5 projections: first results from the GLACE-CMIP5 experiment. Geophys. Res. Lett. 40, 5212–5217 (2013).

    Google Scholar 

  22. 22.

    Byrne, M. P. & O’Gorman, P. A. Land–ocean warming contrast over a wide range of climates: convective quasi-equilibrium theory and idealized simulations. J. Clim. 26, 4000–4016 (2012).

    Google Scholar 

  23. 23.

    Joshi, M. M., Gregory, J. M., Webb, M. J., Sexton, D. M. H. & Johns, T. C. Mechanisms for the land/sea warming contrast exhibited by simulations of climate change. Clim. Dyn. 30, 455–465 (2008).

    Google Scholar 

  24. 24.

    Fasullo, J. T. Robust land–ocean contrasts in energy and water cycle feedbacks. J. Clim. 23, 4677–4693 (2010).

    Google Scholar 

  25. 25.

    Tokinaga, H., Xie, S.-P., Deser, C., Kosaka, Y. & Okumura, Y. M. Slowdown of the Walker circulation driven by tropical Indo-Pacific warming. Nature 491, 439–443 (2012).

    CAS  Google Scholar 

  26. 26.

    Lu, J., Vecchi, G. A. & Reichler, T. Expansion of the Hadley cell under global warming. Geophys. Res. Lett. 34, L06805 (2007).

    Google Scholar 

  27. 27.

    Karnauskas, K. B. & Ummenhofer, C. C. On the dynamics of the Hadley circulation and subtropical drying. Clim. Dyn. 42, 2259–2269 (2014).

    Google Scholar 

  28. 28.

    Lau, W. K. M. & Kim, K.-M. Robust Hadley circulation changes and increasing global dryness due to CO2 warming from CMIP5 model projections. Proc. Natl Acad. Sci. USA 112, 3630–3635 (2015).

    CAS  Google Scholar 

  29. 29.

    Seager, R. et al. Model projections of an imminent transition to a more arid climate in Southwestern North America. Science 316, 1181–1184 (2007).

    CAS  Google Scholar 

  30. 30.

    Seager, R., Naik, N. & Vecchi, G. A. Thermodynamic and dynamic mechanisms for large-scale changes in the hydrological cycle in response to global warming. J. Clim. 23, 4651–4668 (2010).

    Google Scholar 

  31. 31.

    O’Gorman, P. A. & Schneider, T. Stochastic models for the kinematics of moisture transport and condensation in homogeneous turbulent flows. J. Atmos. Sci. 63, 2992–3005 (2006).

    Google Scholar 

  32. 32.

    He, J. & Soden, B. J. A re-examination of the projected subtropical precipitation decline. Nat. Clim. Change 7, 53–57 (2017).

    Google Scholar 

  33. 33.

    Chadwick, R., Ackerley, D., Ogura, T. & Dommenget, D. Separating the influences of land warming, the direct CO2 effect, the plant physiological effect, and SST warming on regional precipitation changes. J. Geophys. Res. Atmos. 124, 624–640 (2019).

    CAS  Google Scholar 

  34. 34.

    Findell, K. L. et al. Rising temperatures increase importance of oceanic evaporation as a source for continental precipitation. J. Clim. 32, 7713–7726 (2019).

    Google Scholar 

  35. 35.

    Krakauer, N., Book, B. I. & Puma, M. J. Contribution of soil moisture feedback to hydroclimatic variability. Hydrol. Earth Syst. Sci. 16, 505–520 (2010).

    Google Scholar 

  36. 36.

    Roudier, P. et al. Projections of future floods and hydrological droughts in Europe under a +2°C global warming. Climatic Change 135, 341–355 (2016).

    Google Scholar 

  37. 37.

    Zhou, S., Zhang, Y., Williams, A. P. & Gentine, P. Projected increases in intensity, frequency, and terrestrial carbon costs of compound drought and aridity events. Sci. Adv. 5, eaau5740 (2019).

    Google Scholar 

  38. 38.

    Lorenz, R. et al. Influence of land–atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble. J. Geophys. Res. Atmos. 121, 607–623 (2016).

    Google Scholar 

  39. 39.

    Berg, A. et al. Land–atmosphere feedbacks amplify aridity increase over land under global warming. Nat. Clim. Change 6, 869–874 (2016).

    Google Scholar 

  40. 40.

    Zhou, S. et al. Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity. Proc. Natl Acad. Sci. USA 116, 18848–18853 (2019).

    CAS  Google Scholar 

  41. 41.

    Gelaro, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).

    Google Scholar 

  42. 42.

    Green, J. K. et al. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nat. Geosci. 10, 410–414 (2017).

    CAS  Google Scholar 

  43. 43.

    Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–171 (2016).

    Google Scholar 

  44. 44.

    Milly, P. C. D. & Dunne, K. A. Potential evapotranspiration and continental drying. Nat. Clim. Change 6, 946–949 (2016).

    Google Scholar 

  45. 45.

    Zhou, S., Yu, B., Huang, Y. & Wang, G. The complementary relationship and generation of the Budyko functions. Geophys. Res. Lett. 42, 1781–1790 (2015).

    Google Scholar 

  46. 46.

    Choudhury, B. J. Evaluation of an empirical equation for annual evaporation using field observations and results from a biophysical model. J. Hydrol. 216, 99–110 (1999).

    Google Scholar 

  47. 47.

    Wei, J., Dickinson, R. E. & Chen, H. A negative soil moisture–precipitation relationship and its causes. J. Hydrometeorol. 9, 1364–1376 (2008).

    Google Scholar 

  48. 48.

    Zhang, J., Wang, W.-C. & Wei, J. Assessing land–atmosphere coupling using soil moisture from the Global Land Data Assimilation System and observational precipitation. J. Geophys. Res. 113, D17119 (2008).

    Google Scholar 

  49. 49.

    Seneviratne, S. I. et al. Soil moisture memory in AGCM simulations: analysis of Global Land–Atmosphere Coupling Experiment (GLACE) data. J. Hydrometeorol. 7, 1090–1112 (2006).

    Google Scholar 

  50. 50.

    Geladi, P. & Kowalski, B. R. Partial least-squares regression: a tutorial. Anal. Chim. Acta 185, 1–17 (1986).

    CAS  Google Scholar 

  51. 51.

    Zhou, S. et al. Sources of uncertainty in modeled land carbon storage within and across three MIPs: diagnosis with three new techniques. J. Clim. 31, 2833–2851 (2018).

    Google Scholar 

  52. 52.

    Zhou, S. et al. Response of water use efficiency to global environmental change based on output from terrestrial biosphere models: drivers of WUE variability. Glob. Biogeochem. Cycles 31, 1639–1655 (2017).

    CAS  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and thank the climate modelling groups (listed in Supplementary Table 1) for producing and making available their model output. For CMIP the U.S. 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. S.Z. acknowledges support from the Lamont–Doherty Postdoctoral Fellowship and the Earth Institute Postdoctoral Fellowship. P.G. acknowledges support from NASA ROSES Terrestrial hydrology (NNH17ZDA00IN-THP) and NOAA MAPP NA17OAR4310127. A.P.W. and B.I.C. acknowledge support from the NASA Modeling, Analysis, and Prediction (MAP) program (NASA 80NSSC17K0265). T.F.K. acknowledges support from the RUBISCO SFA, which is sponsored by the Regional and Global Model Analysis (RGMA) Program in the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research (BER) in the U.S. Department of Energy Office of Science, and additional support from DOE Early Career Research Program award #DE-SC0021023. We also acknowledge Richard Seager and Jason Smerdon from Lamont–Doherty Earth Observatory (LDEO) of Columbia University for insightful discussion and techincal assistance with and interpretation of the moisture convergence decomposition. LDEO contribution no. 8453.

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Contributions

S.Z. conceived and designed the study. S.Z. processed model simulations and reanalysis data. S.Z., A.P.W., B.R.L., A.M.B., Y.Z., T.F.K., B.I.C., S.H., S.I.S. and P.G. contributed to data analysis and interpretation. S.Z. drafted the manuscript. All authors edited the manuscript.

Corresponding author

Correspondence to Sha Zhou.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks William Lau 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 Illustration of total column monthly soil moisture (SM) in the three simulations in GLACE-CMIP5.

SM data shown in the figure are obtained from a grid cell in the GFDL model.

Extended Data Fig. 2 Global distribution of dry and wet regions and assessment of the ‘dry-get-drier, and wet-get-wetter’ paradigm.

a-d, Global distribution of dry and wet regions in CMIP5 models (a-b), and GLACE-CMIP5 models (c-d). e-h, Percentages of the dry and wet regions that show significant P-E changes in CMIP5 and GLACE-CMIP5 in Fig. 1. DD (WW) represents the percentage of dry (wet) regions that show significant P-E decreases (increases). DW (WD) represents the percentage of dry (wet) regions that show significant P-E increases (decreases). DDWW (DWWD) represents the percentage of land or ocean regions with DD and WW (DW and WD). Antarctica is excluded from the land regions.

Extended Data Fig. 3 Future SM changes and associated P-E changes in the four GLACE-CMIP5 models.

a-d, Percent changes in SM between historical (1971–2000) and future (2071–2100) periods. e-h, Future changes in P-E induced by SM changes. i-l, Mean changes in SM and P-E for drylands and non-drylands. The spatial correlation coefficients (r) between changes in SM and P-E over drylands (left) and non-drylands (right) are also shown. All the correlation coefficients are statistically significant at the 0.001(*) level following the Student’s t-test.

Extended Data Fig. 4 Future SM changes and associated P-E changes for each season in GLACE-CMIP5.

a-d, Multi-model mean percent changes in SM between historical (1971–2000) and future (2071–2100) periods in the four seasons. e-h, Multi-model mean changes in P-E induced by SM changes. i-l, Mean changes in SM and P-E for drylands and non-drylands. The spatial correlation coefficients (r) between changes in SM and P-E over drylands (left) and non-drylands (right) are also shown. All the correlation coefficients are statistically significant at the 0.001(*) level following the Student’s t-test.

Extended Data Fig. 5 SM impacts on precipitation and evapotranspiration changes in the four GLACE-CMIP5 models.

a-b, SM induced changes (Δ) in precipitation (a) and evapotranspiration (b) between historical (1971-2000) and future (2071-2100) periods. c-f, The same as a-b, but for the effects of SM variability (c-d) and SM trends (e-f). g-h, Contributions of total SM changes, SM variability (SM_v), and SM trends (SM_t) to precipitation and evapotranspiration changes across drylands (g) and non-drylands (h) in the four models. Stippling denotes regions where the changes in precipitation and evapotranspiration are significant at the 95% level (Student’s t-test) and the sign of the change is consistent with the sign of multi-model means (as shown in the figures) in at least three of the four models.

Extended Data Fig. 6 Soil moisture effects on vertical ascent in the IPSL model.

a, Percent changes of SM in expB (SM trends) between historical (1971–2000) and future (2071–2100) periods. b, Future changes in P-E induced by SM trends (expB-expA). c-f, Changes in the spatial pattern of negative pressure velocity (-Δω, expB-expA) at different pressure levels of the troposphere. The spatial correlation coefficients (r) between changes in P-E and negative pressure velocity over land (drylands in parentheses) are also shown in c-f. All the correlation coefficients are statistically significant at the 0.001(*) level following the Student’s t-test.

Extended Data Fig. 7 Soil moisture effects on vertical ascent for each season in the IPSL model.

a-h, Spatial patterns of future changes in negative pressure velocity (−Δω, 525 hPa, a-d) and P-E (e-h) between historical (1971–2000) and future (2071–2100) periods due to SM trends (expB-expA) in the four seasons. i-l, Spatial correlation coefficients (r) between future changes in P-E and negative pressure velocity over land and drylands. All the correlation coefficients are statistically significant at the 0.001(*) level following the Student’s t-test.

Extended Data Fig. 8 Contributions of each component to moisture convergence variations.

a,b, Contributions of the mean flow convergence to moisture convergence variations (R(MC,MFC)) in MERRA-2 (1980–2018) and ERA5 (1979-2018). c-j, The same as a,b, but for contributions of the transient eddy convergence (R(MC,TEC)) (c,d), the mean circulation dynamic component (R(MC,MCD)) (e,f), the thermodynamic component (R(MC,TH)) (g,h), and the covariation component (R(MC,COV)) (i,j).

Extended Data Fig. 9 Multi-model mean differences in monthly P-E extremes between expA and REF in GLACE-CMIP5.

a-b, Differences in 95th percentile P-E (a), and 5th percentile P-E (b) between expA and REF over the period of 1950-2100. c-d, Ratio of the frequency of extreme high P-E (above 95th percentile P-E in REF) (c) and extreme low P-E (below 5th percentile P-E in REF) (d) between expA and REF. The inset barplots show area-weighted means for the four models (EC-EARTH, ECHAM6, GFDL, IPSL) in GLACE-CMIP5.

Extended Data Fig. 10 Soil moisture feedbacks on water availability in GLACE-CMIP5 models.

Mean sensitivity coefficients for soil moisture (SM)→precipitation minus evapotranspiration (P-E), SM→evapotranspiration (E) and SM→precipitation (P) identified based on REF of the four GLACE-CMIP5 models during 1979–2018 (a-c), 2061–2100 (d-f) and 1971–2100 (g-i). The sensitivity coefficient for X→Y denotes the partial derivative of standardized Y to standardized X in the previous month, where the seasonal cycles and long-term trends in X and Y are removed (a-f). In g-i, the seasonal cycles of X and Y are removed but the trends in X and Y are retained. Stippling denotes regions where the sensitivity coefficient is significant at the 95% level according to a bootstrap test and the sign of the sensitivity coefficient is consistent with the sign of multi-model means (as shown in the figure) in at least three of the four GLACE-CMIP5 models.

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Zhou, S., Williams, A.P., Lintner, B.R. et al. Soil moisture–atmosphere feedbacks mitigate declining water availability in drylands. Nat. Clim. Chang. 11, 38–44 (2021). https://doi.org/10.1038/s41558-020-00945-z

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