Significance We show that the water savings that plants experience under high CO 2 conditions compensate for much of the effect of warmer temperatures, keeping the amount of water on land, on average, higher than we would predict with common drought metrics, and with a different spatial pattern.  The implications of plants needing less water under high CO 2 reaches beyond drought prediction to the assessment of climate change impacts on agriculture, water resources, wildfire risk, and vegetation dynamics. 

Abstract Rising atmospheric CO 2 will make Earth warmer, and many studies have inferred that this warming will cause droughts to become more widespread and severe.  However, rising atmospheric CO 2 also modifies stomatal conductance and plant water use, processes that are often are overlooked in impact analysis.  We find that plant physiological responses to CO 2 reduce predictions of future drought stress, and that this reduction is captured by using plant-centric rather than atmosphere-centric metrics from Earth system models (ESMs).  The atmosphere-centric Palmer Drought Severity Index predicts future increases in drought stress for more than 70% of global land area.  This area drops to 37% with the use of precipitation minus evapotranspiration (P-E), a measure that represents the water flux available to downstream ecosystems and humans.  The two metrics yield consistent estimates of increasing stress in regions where precipitation decreases are more robust (southern North America, northeastern South America, and southern Europe).  The metrics produce diverging estimates elsewhere, with P-E predicting decreasing stress across temperate Asia and central Africa.  The differing sensitivity of drought metrics to radiative and physiological aspects of increasing CO 2 partly explains the divergent estimates of future drought reported in recent studies.  Further, use of ESM output in offline models may double-count plant feedbacks on relative humidity and other surface variables, leading to overestimates of future stress.  The use of drought metrics that account for the response of plant transpiration to changing CO 2 , including direct use of P-E and soil moisture from ESMs, is needed to reduce uncertainties in future assessment. 

The demand for water by the atmosphere is widely predicted to increase due to climate change (1).  It is commonly inferred that this will cause droughts to become more widespread and severe (2).  Many recent studies, however, ignore the impact of rising atmospheric CO 2 on plant water use (3⇓⇓⇓⇓⇓⇓⇓–11).  Plants absorb CO 2 through stomates in their leaves, and simultaneously lose water to the atmosphere by means of transpiration through the same pathway.  Higher atmospheric CO 2 concentrations allow plants to reduce water losses per unit of carbon gain (12), in part by reducing stomatal conductance when the gradient of CO 2 between the atmosphere and the leaf interior increases.  If leaf area stays the same, this physiological response has the potential to reduce water losses from the land surface, increase soil moisture, and reduce plant water stress (13)—the opposite effect of an increase in drought stress and aridity as predicted by many drought metrics (3, 14, 15).  A plant-centric view may therefore suggest that ecosystem-level tradeoffs between water loss and photosynthesis under increasing CO 2 are potentially large enough to reduce drought, despite the large projected increases in water demand from a warmer atmosphere. 

Drought indices, river routing schemes, and water balance models frequently use potential evapotranspiration (PET), rather than actual evapotranspiration, to estimate surface fluxes of water to the atmosphere (Tables S1 and S2).  However, even the physically based estimates of this quantity (i.e., the Penman−Monteith equation) do not account for changes in transpiration caused by the physiological response of plants to increasing CO 2 , thereby making the implicit assumption that surface conductance is invariant with changing CO 2 .  Although the climate implications of the physiological effects of CO 2 on plants have been recognized in the literature (16⇓–18), the effects have not been well integrated into studies examining impacts and risks of climate change, including flood risk, water resource stress, predictions of future species distributions, agricultural productivity, and ecosystem processes.  Further, the science community uses many different drought metrics (Table S1), and the relative sensitivity of these metrics to plant physiological responses has not been systematically quantified.  Our current best estimate of the effects of plant physiology on water fluxes are already integrated within the Earth system models (ESMs) used in the Coupled Model Intercomparison Project, phase 5 (CMIP5), whereby changing atmospheric CO 2 influences transpiration and thus soil moisture.  Predictions of available water on land within an ESM are thus disconnected from predictions of drought stress derived from the same model's output using metrics that assume plant and canopy conductance of water remain invariant. 

Table S1.  Drought metrics used to predict future drought (see ref.  54)

Table S2.  Impact assessment that depends on soil water, and thus plant physiology

To quantify the effect of increasing CO 2 concentrations on the prediction of drought, we compare idealized experiments for seven ESMs from the CMIP5 archive originally intended to constrain carbon−climate feedbacks, each with a 1% per year increase (from 284 ppm to 1,140 ppm over 140 y) in CO 2 mole fractions, but with the increasing CO 2 influencing different components of the Earth system.  We use three experiments to separate the physiological and atmospheric radiative forcing contributions to different hydrologically relevant quantities.  One of the three experiments isolates the effect of CO 2 on atmospheric radiative forcing (CO 2 rad), so that increases in CO 2 solely influence atmospheric radiative transfer within the ESM.  The second experiment isolates the effect of CO 2 on plant physiology (CO 2 phys), so that CO 2 directly influences only photosynthetic processes.  A third fully coupled experiment combines both effects (FULL) (Materials and Methods).  We define plant-centric variables or metrics as those that explicitly include the influence of atmospheric CO 2 on plant processes and evapotranspiration.  Variables within this class include precipitation minus evapotranspiration (P-E), runoff, and soil moisture.  Similarly, we define atmosphere-centric variables and metrics as those that do not allow for surface conductance to change in response to increasing CO 2 .  Variables within this class include PET and the Palmer Drought Severity Index (PDSI).  As commonly formulated, PET is calculated with time-invariant surface conductance (19).  Although it is theoretically possible to formulate PET with a sensitivity of conductance to atmospheric CO 2 , in practice, this is rarely done because it requires estimating the influence of CO 2 on stomatal conductance, leaf area, and other ecosystem processes.  In past work, PDSI has been used as a measure of soil water availability (i.e., refs.  3, 4, and 20) and thus representative of hydrologic drought.  We classify it here as atmosphere-centric because PDSI is derived using PET and therefore does not allow for plants to modify surface conductance, yielding a sensitivity to future change driven solely by changing meteorological conditions. 

SI Materials and Methods There is significant uncertainty in the expected magnitude of response of ecosystems to future climate conditions.  This includes quantities relevant to the findings we show here, such as transpiration, GPP, and WUE (WUE = GPP/transpiration).  Existing observations of changes in any of the three variables under changing CO 2 are limited and consist primarily of two types: (i) tree-ring–type analysis of WUE and growth change over past CO 2 increases (34, 35, 49), and (ii) manipulation experiments such as free air CO 2 enrichment (FACE)-type experiments where CO 2 is elevated in an open-air environment for some time (50⇓–52).  Both of these types of experiments show increasing rather than static WUE as CO 2 increases (28).  Tree-ring studies in several cases do not find evidence of increased growth, implying that transpiration has likely decreased to realize increases in WUE (34, 35).  The two forested FACE experiments show decreases (51) or little change (50) in transpiration associated with significant decreases in WUE (28).  Previous studies have found that land surface models tend to underestimate the response of WUE seen in observations (28), while possibly overestimating the response of GPP [i.e., CO 2 fertilization effects too large (53)].  We estimate the behavior of the model simulations presented here against a similar range of conditions from two forested FACE experiments at Duke and Oak Ridge National Laboratory (ORNL).  From the CO 2 phys experiments (Materials and Methods), we select the spatial grid cell and CO 2 range that matches two FACE forest manipulation experiments and analyze the magnitude of response in WUE, GPP, and transpiration (Fig.  S5).  We find a multimodel mean increase in WUE of about 20% (σ across models = 4.4%) at Duke and about 16% (σ = 2.3%) increase at ORNL, compared with observational findings of 29.7 ± 13.8% (range is interannual variability; years = 10) at Duke and 37.6 ± 7.1% (years = 4) increase at ORNL (observations as reported in ref.  28).  For transpiration, the multimodel mean drop of about 5% (σ = 6.3%) at Duke slightly overestimates the observed response of an increase of 2 ± 6%, whereas the 8% (σ = 2.6%) decrease at ORNL underestimates the observed response of and drop of 16 ± 2% (observations as reported in ref.  28).  The percent change in each variable for individual models is presented in Fig.  S5 with observational estimates as reported in figures 7 and 8 from ref.  28.  The larger than observed decrease in transpiration could be due to one of two inconsistencies between the CO 2 phys simulations and the conditions at FACE experiments.  First, changes in surface fluxes from the model simulations are reported only for the grid cell average and not for different plant types.  Duke and ORNL are located spatially close to one another yet have different plant types—hence the similar modeled response to changing CO 2 .  Second, we use the CO 2 phys simulations for this comparison, which isolate the effect of increasing CO 2 on plants in the absence of radiative forcing from that CO 2 , as is done in FACE experiments.  However, the CO 2 phys simulations have preindustrial levels of radiatively active atmospheric CO 2 , and thus may have a cooler climate and lower transpiration rates in the mean state compared with FACE experiments.  Although global models such as the ones presented here do not perfectly replicate the limited available observations, the models are within the correct order of magnitude and, for most factors (WUE, transpiration at ORNL, and GPP), models underestimate rather than overestimate the response of plants to increasing CO 2 observed in the FACE experiments. 

Materials and Methods We use the output from seven ESMs (38⇓⇓⇓⇓⇓–44) from the CMIP5 archive (see Table S4) to (i) quantify the different continental patterns of drought derived from atmospheric centric and plant-centric metrics and (ii) separate the radiative and physiological impacts of increasing CO 2 on different variables and metrics that are widely used in assessments of climate impacts on future drought.  These models have full carbon cycles, which include leaf area on land that varies in response to climate and atmospheric CO 2 mole fraction.  Two single forcing runs and one fully coupled run were analyzed, each with an idealized 1% per year increase in CO 2 emissions up to a quadrupling of preindustrial atmospheric CO 2 mole fractions, with the exception of the GFDL-ESM2M model (see Table S4 for model information), which increased to a doubling of CO 2 and was held fixed for the remainder of the run (45).  In CO 2 phys runs (CMIP5 experiment name: esmFixClim1), plant physiology experiences the increase in atmospheric CO 2 , whereas the radiation code experiences fixed CO 2 .  In CO 2 rad runs (CMIP5 experiment name: esmFdbk1), the radiation code experiences increasing CO 2 whereas plant physiology does not.  The third run analyzed, the FULL run (CMIP5 experiment name: 1pctCO 2 ), is a combination of the two single forcing runs, where the carbon system is fully coupled, incorporating both effects.  Change in a field due to increasing CO 2 is calculated as the difference between the average of the last 20 y with the first 20 y of the simulation.  Spatial averages (Fig.  1, Fig.  S4, and Table S3) are reported for latitudes between 45°S and 45°N unless otherwise noted.  The multimodel mean spatial maps (Fig.  4 and Figs.  S1 and S2) were made by first regridding each model’s fields to a common 1° × 1° grid, then averaging the different models together.  Table S4.  Table of models used in this analysis with references Seven models were included in this analysis (Table S4).  The number of models was limited to those including all variables necessary for the analysis that include near-surface air temperature, near-surface relative humidity, sensible heat flux at the surface, latent heat flux at the surface, precipitation, gross primary production (GPP), and soil moisture.  We additionally used the variables for surface winds and runoff where available.  A few variables were corrected due to errors in the originally reported data: Relative humidity in the CanESM2 FULL run was adjusted by a factor of 100 and runoff in all runs from IPSL-CM5A-LR were adjusted by a factor of 48 to correct errors noted in the CMIP5 errata (cmip-pcmdi.llnl.gov/cmip5/errata/cmip5errata.html) and bring them into agreement with Earth System Grid Federation standard reporting units.  PET was calculated using the Penman−Monteith approach, as the PDSI has been shown to depend on the choice of formulation of PET (46, 47), and, as in ref.  15, using monthly mean surface values of temperature, latent heat flux, sensible heat flux, relative humidity, and winds.  Where wind output was available from the model, it was used; otherwise, winds were held fixed at 1 m/s, as in ref.  3, which found that changes in winds were a minor contributor to future changes in PET.  Models where winds were held constant are HadGEM2-ES and NorESM1-ME.  PET is calculated using time- and space-invariant surface conductance, as is typical for global studies (e.g., refs.  3, 14, 15, and 19).  The PDSI was calculated as in ref.  20 using a MATLAB script from B. Cook but substituting PET that we calculated using a Penman−Monteith algorithm.  Values of PDSI larger than 20 or smaller than −20 were discarded as in ref.  3.  The baseline period for PDSI was set to the first 20 y of the FULL model run for all experiments in a given model (including CO 2 rad and CO 2 phys).  The temperature (T) vs. precipitation plots (Fig.  3 and Fig.  S3) were made by finding all of the grid cells with annual mean values that fall within each bin defined by bounds in temperature and precipitation and taking an area-weighted average.  The multimodel mean of plots in this space was taken by averaging the T vs. P bins together for all models.  T vs. P bins are shown only for values of T and P for which at least six models had a value.  The individual effects of radiation and physiology in the FULL experiment are linearly attributed to each of the single forcing components (Fig.  1A and Table S3) by calculating the fraction of the FULL run explained by each of the single forcing runs (CO 2 phys/FULL, CO 2 rad/FULL) and then normalizing by the sum of the total fraction explained by both (CO 2 phys/FULL + CO 2 rad/FULL).  This is equivalent to calculating CO 2 phys/(CO 2 phys + CO 2 rad) as the attribution fraction of CO 2 phys and CO 2 rad/(CO 2 phys + CO 2 rad) as the attribution fraction of CO 2 rad.  The attribution fraction can be larger than 1 if the two single forcing runs have changes of opposite sign.  Values of the attribution fraction are plotted for some variables in Fig.  1A, and are shown in Table S3. 

Acknowledgments We thank B. Cook for sharing his software for calculating PDSI and M. Mu for help with retrieving data from the Earth System Grid Federation.  A.L.S.S. was supported by National Science Foundation Grants AGS-1321745 and EF-1340649.  F.M.H., C.D.K., and J.T.R. received support from the Regional and Global Climate Modeling Program in the Climate and Environmental Sciences Division of the Biological and Environmental Research Program in the US Department of Energy Office of Science.  We acknowledge the organizations and groups responsible for CMIP, including the World Climate Research Programme, the climate modeling groups (listed in Table S4), and the US Department of Energy's Program for Climate Model Diagnosis and Intercomparison. 

Footnotes Author contributions: A.L.S.S., F.M.H., C.D.K., and J.T.R. designed research; A.L.S.S. performed research; A.L.S.S. and C.D.K. analyzed data; and A.L.S.S. wrote the paper. 

The authors declare no conflict of interest. 

This article is a PNAS Direct Submission. 

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1604581113/-/DCSupplemental.