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A 10 per cent increase in global land evapotranspiration from 2003 to 2019


Accurate quantification of global land evapotranspiration is necessary for understanding variability in the global water cycle, which is expected to intensify under climate change1,2,3. Current global evapotranspiration products are derived from a variety of sources, including models4,5, remote sensing6,7 and in situ observations8,9,10. However, existing approaches contain extensive uncertainties; for example, relating to model structure or the upscaling of observations to a global level11. As a result, variability and trends in global evapotranspiration remain unclear12. Here we show that global land evapotranspiration increased by 10 ± 2 per cent between 2003 and 2019, and that land precipitation is increasingly partitioned into evapotranspiration rather than runoff. Our results are based on an independent water-balance ensemble time series of global land evapotranspiration and the corresponding uncertainty distribution, using data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) satellites13. Variability in global land evapotranspiration is positively correlated with El Niño–Southern Oscillation. The main driver of the trend, however, is increasing land temperature. Our findings provide an observational constraint on global land evapotranspiration, and are consistent with the hypothesis that global evapotranspiration should increase in a warming climate.

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Fig. 1: Water-balance seasonal cycles.
Fig. 2: Comparison of ET with other products.
Fig. 3: Trends in the water balance.

Data availability

The data that support the findings of this study have been added to the Zenodo repository and can be accessed at data are provided with this paper.

Code availability

The code that produced the findings of this study is available from the corresponding author upon reasonable request.


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The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Government sponsorship acknowledged. This work was supported by funding from NASA’s GRACE-FO Science Team, Principal Investigator J.T.R. We thank A. Bloom for sharing code used to produce the confidence interval plots.

Author information




M.P.-C. conceived, and carried out the research, led the data analysis and wrote the manuscript. J.T.R. conceived the research, designed the analysis and provided comments on the manuscript. H.A.C. produced the global discharge dataset and also provided input on the analysis. M.R. provided comments on the manuscript.

Corresponding author

Correspondence to Madeleine Pascolini-Campbell.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Di Long, Bart Nijssen 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 figures and tables

Extended Data Fig. 1 Input water cycle timeseries.

ac, The raw time series for global land precipitation (GPCPV2.3, MERRA-2, NOAA–NCEP and ERA-5) (a), discharge (JRA-55, and ocean mass-balance estimates EN4–OAFlux–GPCP, EN4–OAFlux–CMAP, EN4–ERA5, EN4–MERRA2) (b) and change in total water storage (dS/dt) from GRACE/GRACE-FO using three different methods to compute the derivate (backward difference with three-month smoothing, centred finite difference and backward difference) (c).

Source data

Extended Data Fig. 2 Error budget of water-balance components.

a, b, Monthly error time series for precipitation (a) and discharge (b) calculated as the standard deviation of input data sets. c, Error in dS/dt calculated from the formal GRACE JPL RL06 mascon error product, and propagated into derivative. Monthly time series of errors plotted for 2003 to 2019 in units of mm yr−1.

Source data

Extended Data Fig. 3 Ratios of water-balance components.

ac, Ratios between components of the water balance for ET/Q (a), ET/Pr (b) and Q/Pr (c). In each case, the ratios are calculated using the time series with the seasonal cycle removed and 15-month smoothing applied. The ensemble mean for each variable (ET, Pr and Q) is used.

Source data

Extended Data Fig. 4 Water-balance relationship with ENSO.

Left: correlation with ERSST version 4 SST against ET (a), Pr (c), Q (e) and dS/dt (g). Right: time series of MEI index against ET (b), Pr (d), Q (f) and dS/dt (h). For each panel, the SST and water-balance variable have the seasonal cycle removed and a 15-month moving average filter applied. The r value of the correlation between the MEI and water-cycle variable are shown in top left corner (right panels). Stippling on the maps (left panels) indicates that the value of the Pearson correlation between the SST and the water-balance variable (ET, Pr, Q and dS/dt) at that grid point is significant (α = 0.05 level). Maps created using MATLAB with the M_Map package (online at

Source data

Extended Data Fig. 5 Effect of ENSO and temperature.

a, Multiple linear regression of global surface temperature (yellow line) and MEI (red line) onto ET (blue line). bd, Multiple regression of MEI (red line) onto Pr (b), Q (c) and dS/dt (d). In each, the input times series data has been filtered using a 15-month moving average. The amount of variability explained is indicated by R2 (top left corner of panels).

Source data

Extended Data Fig. 6 Effect of removing natural climate variability and temperature on ET.

a, ET anomaly time series (solid blue line) and linear trend (dashed blue line), and ET anomaly time series minus the multiple regression model of MEI onto ET (solid red lines) and trend (dashed red line). b, Same as a, but for multiple regression model of surface temperature. The value of the trends in mm yr−1 are indicated in the top left.

Source data

Extended Data Fig. 7 Influence of ENSO on ET products.

Left: correlation with ERSST version 4 SST against different ET products: ET (a), MOD16A2GF (c), FLUXCOM (e), PT-JPL (g) and GLDAS2.2 (i). Right: time series of MEI index against ET (b), MOD16A2GF (d), FLUXCOM (f), PT-JPL (h) and GLDAS2.2 (j). For each panel, the SST and water-balance variable have the seasonal cycle removed and a 15-month moving average filter applied. The r value of the correlation between MEI and ET shown in top left corner (right panels) (r values surrounded by () are not significant at the α = 0.05 level). Stippling on the maps (left panels) indicates that the value of the correlation at that grid point is significant (α = 0.05 level). Maps created using MATLAB with the M_Map package (online at

Source data

Extended Data Fig. 8 Contribution of ice sheets to ET.

ac, Seasonal cycle for ET, Pr, Q and dS/dt calculated without Greenland + Antarctica (a), without Antarctica (b) and with all global land (ET from this study) (c). The shading is the standard deviation among the bias-corrected seasonal cycle of the ET ensemble (red shading), and input datasets used for Pr (four datasets, blue shading), Q (five datasets, black shading) and dS/dt (three methods to calculate derivative from JPL RL06 GRACE TWS, teal shading). df, Ensemble of ET compared with other ET products for ET calculated without Greenland + Antarctica (d), without Antarctica (e) and with all global land (ET from this study) (f). The shading represents the confidence intervals for the ensemble of ET (range shown in the colour bar).

Source data

Extended Data Table 1 ET long-term mean and trends
Extended Data Table 2 ET seasonal cycle and trends

Source data

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Pascolini-Campbell, M., Reager, J.T., Chandanpurkar, H.A. et al. A 10 per cent increase in global land evapotranspiration from 2003 to 2019. Nature 593, 543–547 (2021).

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