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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The GLACE-CMIP5 simulations are available from S.I.S. (email@example.com) 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).
The code used for modelling and reanalysis data analyses is publicly available (https://doi.org/10.5281/zenodo.4041736).
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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.
The authors declare no competing interests.
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 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.
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.
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.
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.
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.
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.
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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