HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-24-2921-2020Comparing Palmer Drought Severity Index drought assessments using the traditional offline approach with direct climate model outputsCoupled versus offline assessments of PDSI drought in climate projectionsYangYutingyuting_yang@tsinghua.edu.cnhttps://orcid.org/0000-0002-4573-1929ZhangShuleiRoderickMichael L.https://orcid.org/0000-0002-3630-7739McVicarTim R.YangDawenLiuWenbinhttps://orcid.org/0000-0002-9569-6762LiXiaoyanhttps://orcid.org/0000-0002-7454-7821State Key Laboratory of Hydroscience and Engineering, Department of
Hydraulic Engineering, Tsinghua University, Beijing, ChinaState Key Laboratory of Earth Surface Process and Resource Ecology,
School of Natural Resources, Faculty of Geographical Science, Beijing Normal
University, Beijing, ChinaResearch School of Earth Sciences, Australian National University,
Canberra, ACT, AustraliaAustralian Research Council Centre of Excellence for Climate Extremes, Canberra, ACT, AustraliaCSIRO Land and Water, Canberra, ACT, AustraliaKey Laboratory of Water Cycle and Related Land Surface Processes,
Institute of Geographic Sciences and Natural Resources Research, Chinese
Academy of Sciences, Beijing, China
Anthropogenic warming has been projected to increase global
drought for the 21st century when calculated using traditional offline
drought indices. However, this contradicts observations of the overall
global greening and little systematic change in runoff over the past few
decades and climate projections of future greening with slight increases in
global runoff for the coming century. This calls into question the drought
projections based on traditional offline drought indices. Here we calculate
a widely used traditional drought index (i.e., the Palmer Drought Severity Index, PDSI) using direct outputs from 16 Coupled Model Intercomparison Project Phase 5 (CMIP5) models (PDSI_CMIP5) such that the hydrologic consistency between PDSI_CMIP5 and CMIP5 models is maintained. We find that the PDSI_CMIP5-depicted drought increases (in terms of drought severity, frequency, and extent) are much smaller than that reported when PDSI is calculated
using the traditional offline approach that has been widely used in previous
drought assessments under climate change. Further analyses indicate that the
overestimation of PDSI drought increases reported previously using the
PDSI is primarily due to ignoring the vegetation response to
elevated atmospheric CO2 concentration ([CO2]) in the traditional offline
calculations. Finally, we show that the overestimation of drought using the
traditional PDSI approach can be minimized by accounting for the effect of
CO2 on evapotranspiration.
Introduction
Drought is an intermittent disturbance of the water cycle that has profound
impacts on regional water resources, agriculture, and other ecosystem services (Sherwood and Fu, 2014). By taking meteorological outputs from
climate model projections as the inputs to offline drought
indices/hydrological impact models, numerous studies have projected
increases in future drought, in terms of severity, frequency, and extent, mainly as a consequence of warming associated with anthropogenic climate
change (Cook et al., 2014, 2015; Dai, 2011, 2012; Dai et al., 2018; Huang et
al., 2016, 2017; Lehner et al., 2017; Liu et al., 2018; Naumann et al.,
2018; Park et al., 2018; Samaniego et al., 2018; Sternberg, 2011; Trenberth
et al., 2014). However, this substantial increase in projected drought
contradicts observations of global vegetation greening and little systematic
change in runoff over the past few decades and climate projections of future
greening with slight increases in global runoff for the coming century
(Alkama et al., 2013; Greve et al., 2017; Labat et al., 2014; Roderick et
al., 2015; Milly and Dunne, 2016; Scheff et al., 2017; Yang et al.,
2018, 2019; Zhu et al., 2016). The scientific basis
underpinning the projected drying trend using traditional offline drought
indices/hydrological impact models is that the calculated increases in
evapotranspiration (E) are larger than the projected increase in
precipitation (P) in many places (Sternberg, 2011), which results in
an increasing water deficit and thus increasing simulated future drought.
However, direct climate model outputs of E exhibit a much smaller increasing
trend (Fig. S1 in the Supplement), and the global land mean P is actually projected to increase more quickly than its E counterpart (Greve et al., 2017;
Milly and Dunne, 2016, 2017; Roderick et al., 2015; Yang et al., 2018), leading to a very different conclusion.
Several recent studies have demonstrated that the drying bias in the offline
calculated E trend is primarily due to neglecting the impact of increasing
atmospheric CO2 concentration ([CO2]) (and its resultant vapor
pressure deficit increase) on the water use efficiency of vegetation
(Lemordant et al., 2018; Milly and Dunne, 2016, 2017; Roderick et al., 2015;
Swann et al., 2016; Yang et al., 2019). This vegetation-[CO2] response
only impacts transpiration, not soil evaporation, interception from
vegetation surfaces or sublimation in snow environments; however, it should be noted that transpiration dominates (∼65 %; note that a
transpiration over an evapotranspiration ratio of 0.41±0.11 is estimated by the Coupled Model Intercomparison Project Phase 5 – CMIP5 – models) global terrestrial evapotranspiration (Lian
et al., 2018; Zhang et al., 2016). In existing hydrologic impact
models/drought indices, P and potential evapotranspiration (EP; the rate
of evapotranspiration that would occur with an unlimited supply of water)
are the two key input variables, which, respectively, represent water supply to, and water demand from, the land surface. While P is a direct climate
model output, EP is not produced by climate models. The traditional
approach is to calculate EP offline using the meteorological variables
in the climate model output. The calculated EP together with the climate model projected P are used to force an offline hydrologic impact
model (or hydrologic calculations embedded in drought indices) that
independently calculates E, runoff (Q), and storage change (ΔS), to assess hydrologic changes under future climate scenarios (the right-hand
column shown in Fig. 1). Among various EP models, the
open-water Penman model (Shuttleworth, 1993) and the reference crop Penman–Monteith model (Allen et al., 1998) have been most widely used in existing drought assessment studies, given their sound physical basis and
relatively simple formulations. Nevertheless, both Penman-based models do
not faithfully capture the biological processes embedded in the climate
models. The open-water Penman model was designed for water surfaces, where surface resistance (rs) is, by definition, equal to zero. Allen et al. (1998)'s reference crop Penman–Monteith model prescribed a constant rs at
70 s m-1, which is appropriate for an idealized reference crop in the
current climate but does not account for the fact that rs increases with
elevated [CO2] over vegetated surfaces in climate model projections
(Yang et al., 2019). As a result, existing traditional offline hydrologic
impact models/drought indices calculate estimates of E, Q, and ΔS that are different from those same variables in the original fully coupled climate model output. For that reason, the consequent assessments of drought
changes in existing traditional offline hydrologic impact models/drought
indices do not correctly represent the projections in the underlying
fully coupled climate models. Figure 1 illustrates the inconsistency in the hydrologic predictions (also see Milly and Dunne, 2017) that have resulted in
different trends in projected future drought between climate models and
traditional offline hydrologic impact models/drought indices.
Conceptual plot illustrating the inconsistency in the hydrologic
predictions between climate models and offline hydrologic impact models. The
symbols P, EP, E, Q, and ΔS represent precipitation, potential evapotranspiration, actual evapotranspiration, runoff, and storage change, respectively. The meteorological variables used to calculate EP depend
on the adopted EP model, but mainly include net radiation, near-surface air temperature, vapor pressure, and wind speed. The biological factor here
is the response of surface resistance to elevated [CO2] over vegetated
lands.
Here, we re-assess changes in future global drought using climate model
projections from 16 CMIP5 models under historical (1861–2005) and Representative Concentration Pathway 8.5 (RCP8.5; 2006–2100) experiments (Taylor et al., 2012). These 16 CMIP5
models were selected as they output all variables, including runoff, that
are needed for the analysis performed herein. The Palmer Drought Severity
Index (PDSI; Palmer, 1965) is adopted here to quantify drought as it has
been widely used for operational drought monitoring and is increasingly used
in studies assessing drought under climate change (Cook et al., 2014, 2015;
Dai, 2011, 2012; Dai et al., 2018; Lehner et al., 2017; Liu et al., 2018;
Sheffield et al., 2012; Swann et al., 2016; Trenberth et al., 2014). To
maintain consistency between the calculated PDSI and the CMIP5 models, we
first calculate PDSI using direct hydrologic outputs (i.e., P, E, Q, ΔS) from the CMIP5 models (PDSI_CMIP5; corresponds to the
center column in Fig. 1; also see the Data and methods section). This procedure provides a reference for the PDSI projections. We then replicate the traditional PDSI
calculation by using only meteorological data as inputs to calculate the
reference crop Penman–Monteith EP (PDSI_PM-RC) (the right-hand column shown in Fig. 1). The inference is that this traditional
offline approach that only responds to meteorological forcing will
overestimate drought relative to the direct climate model output because it
does not consider the biological effect of elevated [CO2]. To evaluate
that inference, we again re-calculate the PDSI using an offline formulation
that considers both the same meteorological forcing along with the
biological effects of elevated CO2 (Yang et al., 2019) (the left-hand
column in Fig. 1).
Data and methodsClimate model projections
We used outputs from 16 climate models participating in Phase 5 of the
Coupled Model Intercomparison Project (CMIP5; Table S1 in the Supplement) under
historical (1861–2005) and RCP8.5 (2006–2100) experiments (Taylor et al.,
2012). We used monthly series of runoff, precipitation, soil moisture, and sensible and latent heat flux at the land surface along with near-surface
air temperature, air pressure, wind speed, and specific humidity. All outputs from the 16 CMIP5 models were resampled to a common 1∘ spatial resolution
by using the first-order conservative remapping scheme (Jones, 1999).
Calculation of PDSI
The PDSI was used to quantify drought (Palmer, 1965). To minimize the impact of initial conditions on PDSI
estimates, the first 40 years (1861–1900) are used for model spin-up with
the analyses focused on the 1901–2100 period. Briefly, the PDSI model
consists of two parts: (i) a two-stage bucket model that calculates the
monthly water balance components (i.e., E, Q, and ΔS) using P and EP as inputs and (ii) a dimensionless index that describes the moisture departure between the actual precipitation and the precipitation needed to
maintain a normal soil moisture level for a given time (i.e., the
climatically appropriate one for existing condition values; these values were calculated for the entire period of 1901–2100). The soil available water
capacity (AWC) needed for PDSI calculation was derived from the Global
Gridded Surfaces of Selected Soil Characteristics
(https://webmap.ornl.gov/ogcdown/dataset.jsp?ds_id=569, last access: 1 June 2020).
While this parameter is inevitably subject to uncertainties, Sheffield et al. (2012) demonstrated that the PDSI calculation is insensitive to AWC inputs.
Detailed descriptions of PDSI can be found in Palmer (1965). A drought event
is identified with negative PDSI values, with a more negative PDSI
indicating a more severe drought, whereas moist events are associated with
positive PDSI values.
We calculated PDSI following Palmer (1965), yet calculated EP using the reference crop Penman–Monteith model (PDSI_PM-RC; the right-hand column in Fig. 1). The Penman–Monteith model explicitly
considers influences from both radiative and aerodynamic components and has
been widely used in previous PDSI calculations (e.g., Dai, 2012; Sheffield et al., 2012). In addition, we also
used a modified Penman–Monteith model (PM[CO2]; detailed later in the Methods section and also see Yang et al., 2019) that accounts for the impact of
elevated [CO2] on rs to calculate EP and then PDSI
(PDSI_PM[CO2]; the left-hand column in Fig. 1).
Flowchart of PDSI calculations. Note that PDSI_PM-RC, PDSI_PM[CO2], and PDSI_CMIP5, respectively, follow the right-hand, left-hand, and center columns in Fig. 1.
Additionally, instead of using hydrological simulations from the simplified
water balance model embedded in the original PDSI model, we also calculated
PDSI by using direct hydrologic outputs E, Q, and ΔS from the 16 CMIP5 models (PDSI_CMIP5; the center column in Fig. 1). This approach ensures that PDSI_CMIP5 faithfully represented the
CMIP5 output. As the original PDSI model depends on a two-stage “bucket”
model of the soil, we correspondingly regarded the moisture in the upper portion of the soil column (integrated over the uppermost 10 cm) from CMIP5 models as the moisture in the first layer and the total soil moisture content as the
available moisture in both layers (so differences between total soil-depth
representation in CMIP5 models may lead to differences in PDSI estimates
from individual models but are unlikely to impact the PDSI changes).
Moreover, since the estimation of the weighting factor that converts
moisture anomalies into the PDSI index also requires knowledge of EP, we
used the EP computed from a modified Penman–Monteith equation that explicitly considers the biological effect of elevated [CO2] (i.e.,
PM[CO2]) (Yang et al., 2019). To comprehensively document how the
different PDSIs were calculated, we illustrate the calculation procedures of
the different PDSIs in Fig. 2. Additionally, Matlab codes with worked
examples of the different PDSIs can be accessed through https://github.com/zslthu/Calculate-PDSI-in-Matlab (last access: 1 June 2020). The PDSIs
were calculated using outputs of each CMIP5 model in turn, and the ensemble
PDSIs (averaging PDSIs over the 16 CMIP5 models) were used in the following
analyses.
Calculation of potential evapotranspiration
Two potential evapotranspiration formulations were used to calculate
EP. The first is the reference crop Penman–Monteith EP model, which computes EP (mm d-1) as (Allen et al., 1998)
EP=0.408ΔRn∗+γ900T+273uDΔ+γ(1+0.34u),
where Δ (Pa K-1) is the gradient of the saturation vapor pressure with respect to temperature, γ (Pa K-1) is the
psychrometric constant, Rn∗ (MJ m-2 d-1) is the
surface available radiation (i.e., net radiation minus ground heat flux),
D (Pa) is the vapor pressure deficit of the air at 2 m height, and u (m s-1) is the wind speed at 2 m height. In the reference crop Penman–Monteith model, rs is prescribed as 70 s m-1, and this parameter value is embedded
in the equation.
In addition, we used a modified reference crop Penman–Monteith EP model (i.e., PM[CO2]) that accounts for the impact of rising [CO2]
(expressed in ppm units) on rs, as derived in Yang et al. (2019). The
PM[CO2] model calculatesEP as
EP=0.408ΔRn∗+γ900T+273uDΔ+γ1+u0.34+2.4×10-4[CO2]-300.
Determining the timing of global warming target
To demonstrate the impact of warming on drought changes, we assessed changes
in PDSI_CMIP5 under two future warming targets: 1.5
and 2 ∘C warming above the pre-industrial level. The 1.5 and 2 ∘C warming levels have been extensively discussed (Huang et al., 2017;
Lehner et al., 2017; Liu et al., 2018; Park et al., 2018; Samaniego et al.,
2018), as they are the two key warming targets set in the Paris Agreement on
climate change (UNFCCC, 2015). The timing when the global warming targets
(i.e., t1.5 and t2) are reached in each of the 16 CMIP5 models was computed based on the model output of the near-surface air temperature
(Ta). We first selected 1986–2005 as the baseline period, which is a
widely used reference period for climate impact assessment (Lehner et al.,
2017; Liu et al., 2018; Park et al., 2018). Then, we applied a 20-year
moving average filter to the global mean annual Ta time series to
remove the interannual fluctuations in annual Ta (Liu et al., 2018;
Park et al., 2018). Each 20-year moving average is indexed to its final year
(for example, the 20-year running mean Ta for 2080 is an average of
Ta for 2061–2080). Finally, t1.5 and t2 are, respectively, determined at the times when global mean Ta reached 0.9
and 1.4 ∘C above the 1986–2005 baseline, as this period was at
least 0.6 ∘C warmer than the pre-industrial level (Hawkins et
al., 2017; Schleussner et al., 2016).
ResultsPredicted drought changes
Figure 3 shows the global patterns of PDSI trends for the three PDSIs.
Evident drought increases are depicted by PDSI_PM-RC across
much of the North America, South America, central to southern Europe, Congo basin, southern Africa, southeastern China, and southern coastal areas of Australia (Fig. 3a), as widely reported previously (Dai, 2011, 2012; Dai
et al., 2018; Cook et al., 2014; Lehner et al., 2018; Liu et al., 2018).
However, those broad-scale trends are not identified by either PDSI_CMIP5 (Fig. 3b) or PDSI_PM[CO2]
(Fig. 3c). Compared with PDSI-PM-RC, both PDSI_CMIP5 and
PDSI_PM[CO2] show much smaller changes. This result
clearly indicates an inconsistency between the PDSI_PM-RC
that has been widely used in traditional offline calculations for drought
assessment studies and the underlying CMIP5 models, as the
PDSI_CMIP5 as used here is based on the direct hydrologic
outputs (E, Q, and ΔS) from CMIP5 models.
Global spatial pattern of PDSI trends. Spatial distribution of PDSI trends during 1901–2100 for (a) PDSI_PM-RC, (b) PDSI_CMIP5, and (c) PDSI_PM[CO2], respectively. Black dots represent locations where the same sign of the PDSI
trend is identified in at least 13 out of the 16 CMIP5 models (i.e.,
>80 % of models).
To examine changes in drought frequency and extent, changes in months under
drought within each year and changes in land area subject to dry and moist
extremes are, respectively, shown in Figs. 4 and 5. In applications, a PDSI <-3.0 is considered to be severe drought conditions, while a PDSI >3.0 is considered exceptionally moist (e.g., Palmer, 1965; Liu
et al., 2018). We find that months with PDSI_PM-RC <-3.0 increase substantially over areas where PDSI_PM-RC
evidently decreases, suggesting an increased drought frequency in these
regions (Fig. 4a). However, when assessed with PDSI_CMIP5
and PDSI_PM[CO2], these drought frequency increases
largely diminish (Fig. 4b and c). Yet, moving to PDSI_CMIP5 and PDSI_PM[CO2] apparently does not reduce the widespread distribution of drought frequency increase compared to drought
frequency decrease (Fig. 4b and c; i.e., there are more land areas with increasing drought frequency than with decreasing drought frequency).
Similar results are found for drought extent changes as severe drought
during the 21st century increases by 0.2393±0.0942 % yr-1
(p<0.01) for PDSI_PM-RC but only increases by 0.1099±0.0228 % yr-1 (p<0.01) for PDSI_CMIP5
and 0.1178±0.0308 % yr-1 (p<0.01) for
PDSI_PM[CO2], respectively (Fig. 5a–c). By contrast,
moist areas (i.e., PDSI >3.0) are less divergent among the three
different PDSIs, although the PDSI_PM-RC still shows the
least wetting lands compared to the other two PDSIs (Fig. 5a–c).
Interestingly, both PDSI_CMIP5 and PDSI_PM[CO2] depict the increase in drought area as essentially equivalent to the increase in moist area (Fig. 5a–c), which may suggest
an overall unchanged PDSI_CMIP5 (PDSI_PM[CO2]) series at the global scale (Fig. S2). The
above results are largely retained when assessing changes at different
thresholds (i.e., mild drought/moist events with PDSI <-1.0 and
PDSI >1.0 and moderate drought/moist events with PDSI <-2.0 and PDSI >2.0; Figs. 4d–i and 5d–i). The fact that the results based on PDSI_PM[CO2] closely follow that of
PDSI_CMIP5 highlights the importance of vegetation response
to elevated [CO2] in the control of future surface hydrological
changes. This demonstrates that the inconsistency between the PDSI_PM-RC and CMIP5 models is largely caused by ignoring the vegetation response to elevated [CO2] in the PDSI_PM-RC calculations.
Global spatial pattern of drought trends. (a–c) Spatial
distribution of trends in the number of months under severe drought (PDSI <-3.0) during 1901–2100 for (a) PDSI_PM-RC, (b) PDSI_CMIP5, and (c) PDSI_PM[CO2], respectively. (d–f) Spatial distribution of trends in the number of months under moderate drought (PDSI <-2.0) during 1901–2100 for (d) PDSI_PM-RC, (e) PDSI_CMIP5, and (f) PDSI_PM[CO2], respectively. (g–i) Spatial distribution of trends in the number of months under mild drought (PDSI <-1.0) during 1901–2100 for (g) PDSI_PM-RC, (h) PDSI_CMIP5, and (i) PDSI_PM[CO2], respectively.
Time series of the global average fractional land area
experiencing drought/moist conditions. (a–c) Global average time series of
land area experiencing severe drought (PDSI <-3.0, red) and
exceptionally moist (PDSI >3.0, blue) conditions for (a) PDSI_PM-RC, (b) PDSI_CMIP5, and (c) PDSI_PM[CO2], respectively. (d–f) Global average time series of land area experiencing moderate drought (PDSI <-2.0, red) and moist (PDSI >2.0, blue) conditions for (d) PDSI_PM-RC, (e) PDSI_CMIP5, and (f) PDSI_PM[CO2], respectively. (g–i) Global average time series of land area experiencing mild drought (PDSI <-1.0, red) and moist (PDSI >1.0, blue) conditions for (g) PDSI_PM-RC, (h) PDSI_CMIP5, and (i) PDSI_PM[CO2], respectively. The solid curves represent the ensemble mean of
16 CMIP5 models and the shading represents the range by individual models.
The time series are averaged over global land areas excluding Greenland and
Antarctica.
The effect of warming on drought changes
Warming has been identified as the key driver of the overall future drought
increase in numerous previous studies (Cook et al., 2014, 2015; Dai, 2011,
2012; Dai et al., 2018; Huang et al., 2016, 2017; Lehner et al., 2017; Liu
et al., 2018). To further understand the impact of warming on drought
changes, we assessed changes in PDSI_CMIP5 at 1.5 ∘C and
2 ∘C warming above the pre-industrial level. The PDSI_PM-RC is also presented for comparison. Any substantial increase in drought
is identified when PDSI for a future warming target decreased by 1.0
compared to PDSI during the 1986–2005 baseline (i.e., ΔPDSI <-1). Additionally, only places where the ΔPDSI <-1.0 threshold is reached in at least 8 CMIP5 models (out of the 16 CMIP5 models, so 50 % and more) are considered to be robust projections and thus
used herein. Based on the PDSI_CMIP5, our results show that
almost nowhere on earth (only 0.06 % of the global land area) is projected
to have a substantial drought increase at the 1.5 ∘C warming target,
and this number only slightly increases to 0.77 % at the 2 ∘C warming
target (Fig. 6a and b). In comparison, substantial increase in drought is
identified at 5.10 % and 13.41 % of the global land area at the two
warming targets, respectively, when PDSI_PM-RC is used
(Fig. 6a and c). More places are projected to have a substantial drought
increase under future warming if we relaxed the threshold of PDSI change to
-0.5 (i.e., ΔPDSI <-0.5) (Fig. 6d–f). Nevertheless, the
PDSI_CMIP5 still shows a considerable smaller percentage of
drying lands (6.2 % and 10.0 %) than the PDSI_PM-RC
(26.32 % and 34.77 %) under the two warming targets, respectively,
particularly over North America, much of Amazonia, Europe, the Congo basin, and southeastern China.
Areas with substantial drought increase under future warming. (a) Relative land area with substantial drought increase (ΔPDSI <-1.0) under 1.5 and 2 ∘C warming based on
PDSI_CMIP5 and PDSI_PM-RC. (b, c) Spatial
pattern of substantial drought increase (ΔPDSI <-1.0) under
1.5 and 2 ∘C warming based on (b) PDSI_CMIP5 and
(c) PDSI_PM-RC. (d–f) Similar to panels (a)–(c) but for ΔPDSI <-0.5.
Discussion and concluding remarks
The above results clearly demonstrate an overestimation of drought severity,
frequency, and extent using PDSI in many previous assessments of future drought (e.g., Cook et al., 2014, 2015; Dai, 2011, 2012; Dai et al., 2018;
Lehner et al., 2017; Liu et al., 2018). The overestimation is primarily
caused by neglecting the impact of elevated [CO2] on rs and
consequently on EP in the traditional offline calculation. As EP is
neither used nor produced by climate models, an offline intermediate
EP model is required to estimate EP based on climate model outputs.
However, conventional EP models, such as the open-water Penman model and
the reference crop Penman–Monteith model, involve an important assumption that rs remains constant over time (Allen et al., 1998; Shuttleworth,
1993). This assumption is in general valid for water surfaces and/or wet
bare soils but is not valid over vegetated surfaces. Over vegetated
surfaces, on the one hand, elevated [CO2] leads to a partial stomatal closure that increases rs (e.g., Ainsworth and Rogers, 2007), yet on
the other hand, elevated [CO2] has “fertilized” vegetation resulting
in an increased foliage cover (e.g., Donohue et al., 2013; Zhu et al.,
2016), which effectively suggests a reduction in rs. In addition,
elevated [CO2] serves as the ultimate driver of climate warming in the
CMIP5 models and consequently leads to an increase in atmospheric vapor
pressure deficit, which also tends to increase rs (Lin et al., 2018;
Novick et al., 2016).
While the net effect of elevated [CO2] on rs is still uncertain in
the real world, a recent study clearly showed that in CMIP5 models, elevated
[CO2] increases rs, which, with all else equal, results in a
decrease in EP and thus E (Yang et al., 2019). Yang et al. (2019) also showed that over vegetated surfaces, an increase in EP caused by
warming-induced vapor pressure deficit increase is almost entirely offset by
a decrease in EP caused by the increase in rs driven by elevated
[CO2] in CMIP5 models. This suggests that climate change does not
necessarily lead to a higher EP over vegetated surfaces and hence
increased drought under [CO2] enrichment, which is consistent with
CMIP5 model projections yet contradicts the perception that “warming leads
to drying” presented in many previous studies (Cook et al., 2014, 2015;
Dai, 2011, 2012; Dai et al., 2018; Huang et al., 2016, 2017; Lehner et al.,
2017; Liu et al., 2018; Park et al., 2018; Samaniego et al., 2018;
Sternberg, 2011; Trenberth et al., 2014). Additionally, it is worthwhile
mentioning that the CMIP5 models do project topsoil moisture (within the top
10 cm) declines with a very similar spatial pattern to changes in
PDSI_PM-RC (Dai, 2012; Dai et al., 2018), which might be
important for wildfire risk and various biological processes that take place
close to the surface. However, since no systematic decline in runoff or in
relevant vegetation parameters (e.g., leaf area index and gross/net primary
production) seems to result from it (Greve et al., 2017; Milly and Dunne,
2016, 2017; Roderick et al., 2015; Swann et al., 2016; Yang et al., 2019),
this decline in topsoil moisture in the CMIP projections seems to have
little influence from the vegetation and hydrological perspectives. This is
likely as root-zone or deeper soil moisture that is of more
agricultural/ecological and/or hydrological significance is projected to remain more or less unchanged (Berg et al., 2017; Greve et al., 2017),
consistent with PDSI_CMIP5 and PDSI_PM[CO2] (Fig. 3).
Here, we use PDSI as an illustrating case, but note that similar results were also found in another commonly used drought index (i.e., the
Standardized Precipitation-Evapotranspiration Index, or SPEI;
Vicente-Serrano et al., 2010) (Fig. S3). Nevertheless, both PDSI
and SPEI, as well as other drought/aridity metrics, are secondary offline
impact models. Since climate models are fully coupled land (and ocean)–atmosphere models that are an internally consistent representation of the
climate system (Milly and Dunne, 2016), a scientific prior of applying any
offline hydrological impact models is that the adopted offline model must be
able to recover the hydrological simulations generated by the climate models
(Roderick et al., 2015; Milly and Dunne, 2017; Yang et al., 2019).
Otherwise, any inconsistency in hydrological predictions between offline
impact models and climate models themselves would lead to inconsistent
predictions in other components of the climate system. Unfortunately, this
important scientific prior has been largely ignored in many previous drought
assessment studies, leading to biased drought predictions that are actually
inconsistent with the climate model outputs.
In summary, we have shown that climate model projections of the global
drought area under future climate change have been largely overestimated. Our results suggest that the “warming leads to drying” perception may be
fundamentally flawed, primarily due to ignoring the vegetation response to
elevated [CO2] (also see Yang et al., 2019). However, despite a small
overall trend globally, we find that both drying and wetting areas are
simulated to increase towards the end of this century (Figs. 5 and
S4), suggesting an increased variability in surface
hydrological conditions that will likely be associated with increased
droughts and/or floods and reduced reliability of available water at
local/regional scales (e.g., Kumar et al., 2014). In this light, attention
should be paid to regions where droughts and/or floods are projected to most
likely increase (e.g., Mediterranean Europe and central America), and more efforts may be needed to mitigate the consequent impact there under climate
change.
Code availability
Matlab codes with worked examples of the different PDSIs can be accessed through 10.5281/zenodo.3871420 (zslthu, 2020).
Data availability
The data that support the findings of this study are openly available at https://esgf-node.llnl.gov/search/cmip5/ (Lawrence Livermore National Laboratory, 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/hess-24-2921-2020-supplement.
Author contributions
YY and MLR designed the study. SZ and
YY performed the calculation. YY drafted the manuscript. TRM, DY, WL, and XL
contributed to results discussion and modified the text.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank the HESS editor and three reviewers for constructive comments that improved the study.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant no. 41890821), the Qinghai Department of Science and Technology (grant no. 2019-SF-A4), the Ministry of Science and Technology of China (grant
no. 2019YFC1510604), the Australian Research Council (grant no. CE170100023), and CSIRO Land and Water.
Review statement
This paper was edited by Ryan Teuling and reviewed by three anonymous referees.
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