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Dramatic Change in the Hydrologic Cycle Anticipated at
Regional Scale
Qiuhong Tanga* **et al.** and Dennis P. Lettenmaier
a
Department of Civil and Environmental Engineering Box 352700,
University of Washington, Seattle, WA 98195
*
Email: [email protected]
Abstract
It is essential to assess the possible future changes in the hydrologic cycle at regional
scale for developing adaptation strategies to anthropogenic climate change. A quantilequantile (Q-Q) relation between observation-based and estimated relative change from
general circulation model (GCM) ensemble was developed in the last half of the
twentieth century for three hydrologic variables, land precipitation, evaporation, and
runoff. The relations were used to scale the GCM predicted changes in the twenty first
century. The scaled relative change has a spatial pattern consistent with the probability of
the change direction with pattern amplitude constrained by recent climate change. The
results show that the land area that will experience large change in the hydrologic
variables increase, indicating more dramatic change in the hydrologic cycle is anticipated
at regional scale in the next couple of decades. The portion of global population that lives
in the area with more than 30% change in runoff increases from one quarter in the
twentieth century to near half in the twenty first century. The land areas with both wet
and dry condition increase and the area with moderate condition decreases. The
fingerprint pattern of climate impacts on the hydrologic cycle is generally consistent with
the distribution of climatic zones with drier arid area and wetter humid area. Most nonpolar arid areas and the Amazonia become drier, likely because of the weakening of the
tropical circulation systems. The northern high latitudes and Asian monsoon regions
become wetter. It suggests the enhanced land-sea thermal contrast and warmer sea
surface may intensify monsoon and enhance atmospheric moisture convergence over land.
1. Introduction
It is essential to assess the impact of climate change and future climate variability on
hydrology at regional scale for determining the adaption water management policy
responses to anthropogenic climate change. Although current general circulation models
(GCMs) are able to project the response of the climate system to anthropogenic changes
into 2100, the uncertainty in forecasts is high (Allen et al. 2000). Ideally, the forecasts of
climate change should be constrained objectively by the observed climate and recent
climate change. Precipitation is the primary forcing of the terrestrial hydrologic cycle and
is a determining parameter of land surface evaporation and runoff. Quantifying the
possible changes in precipitation is fraught with difficulties because the constraints on
future changes are considerably weak (Allen and Ingram 2002).
Runoff, which represents a major portion of freshwater resources to terrestrial inhabitants,
has more significant impacts on a wide range of social and environmental systems. The
forecast of runoff change may be constrained by recent runoff observations and physical
control: precipitation forecast. However, runoff forecast is expected to be more uncertain
than that of global mean temperature and precipitation. Runoff is generally not spatially
observed. The observed runoff is usually constructed from streamflow, a temporally
lagged, spatial integral of runoff over a river basin. Streamflow observations have shorter
record and less global coverage than precipitation and surface temperature observations
(Peel and McMahon 2006; Fekete et al. 2002; Dai et al. 2009). Furthermore, the runoff
observations are of a matter of substantial uncertainty because of human impounding and
impeding the flow of rivers (Postel et al. 1996; Vitousek et al. 1997). For example, only
half of the observed streamflow decrease in Yellow river during the last half of the
twentieth century was caused by climate fluctuation (Tang et al. 2008). The other half
was attributed to the irrigation water withdrawals. Physical constrains of runoff forecast
are weaker than that in global mean temperature and precipitation. Annual runoff is
largely determined by precipitation and annual evaporation in a region. It is anticipated
that runoff increases (decreases) when precipitation increases (decreases). The ratio of
annual runoff to precipitation (runoff coefficient) is about 36% over the global land
(Gleick 1993; Oki and Kanae 2006) although with large spatiotemporal variations
(Agarwal and Singh 2004). The impact of relative small human-induced fluctuation in
global temperature and its consequent fluctuation in precipitation would cause much
larger fluctuation in runoff (Langbein, 1949; Revelle and Waggoner 1983; Karl and
Riebsame 1989).
Another difficulty for assessing the change in the hydrologic cycle is its spatial scale. The
regional hydrologic change is of more interest than the global mean change. Previous
studies have projected the global change patterns of hydrologic variables based on single
climate model (Arnell 1999; Vorosmarty et al. 2000; Arora and Boer 2001). However,
different individual model could project much different spatial pattern of the
hydroclimatic changes (Dessai et al., 2006; Nohara et al. 2006; Cai et al. 2009). It was
suggested that climate change projections from any single model should be treated as
only one of a range of possibilities (Forest et al. 2000). The Program for Climate Model
Diagnosis and Intercomparison (PCMDI) archived coupled climate models results from
many climate-modeling centers for the Fourth Assessment Report (AR4) of the
Intergovernmental Panel on Climate Change (IPCC) (Meehl et al. 2007). The multimodel data are increasingly being used to reduce the single model’s uncertainty and
increase the reliability of the projections (Giorgi and Mearns 2002; Held and Soden 2006;
Zhang et al. 2007). Statistical methods are developed to produce probabilistic projections
of ensembles of GCMs and to evaluate the probability distribution of global mean
temperature change under different forcing scenarios (Allen et al. 2000; Stott and
Kettleborough 2002; Tebaldi et al. 2005; Lopez et al. 2006). A common method, called
multi-model ensemble mean or median (MM), is to calculate the (weighted) mean or
median of the response-patterns of an ensemble of GCM simulations (Allen and Tett
1999). The variance in the response patterns from an ensemble is smaller than the
variance in the observations (if all distributions are Gaussian).The model simulated
trajectory is then needed to be linearly scaled to match the magnitude of the observed
anthropogenic signal for the predictions of anthropogenic warming (Allen and Tett 1999;
Allen et al. 2000). This approach is justified for temporal pattern of global mean results
(see Lambert and Boer 2001; Reichler and Kim 2008). It is therefore increasingly being
used to analyze the spatiotemporal pattern of the hydroclimatic variables at regional scale
(Nohara et al. 2006; Milly et al., 2005; Seager et al. 2007; Barnett et al. 2008). The
problem of using GCM results in regional study is that variability on small spatial scales
is likely to be under-represented in any finite representation of a continuous turbulent
system. Most importantly, the MM method has significantly reduced spatial variability.
Previous researches had shown that MM method would give more spatially homogeneous
pattern associated with climate change (Giorgi and Mearns 2002; Delworth and Knutson
2000; Milly et al., 2005). Although some studies had evaluated the facility of using the
multi-model simulations to depict regional changes (Dessai et al. 2005; Watterson 2008),
few of them had used objectively information, i.e. the observed recent change, to
constrain the forecast of hydrologic variables. In this study, we present a mathematical
framework that attempts to assess the regional change in hydrologic cycle in a changing
climate. The spatial variance, which is suppressed in the MM method in statistical
inference, is linear scaled to observed spatial variance. We present probabilistic forecasts
of hydrologic changes at regional scale, obtained with multi-model simulations and
constrained by the observed hydroclimatic changes.
2. Data and Method
The PCMDI/AR4 archive of GCM outputs was used. Most of the GCMs performed
historical simulations (20C3M) of climate with prescribed external forcing for the late
nineteenth century and the whole of the twentieth century. Future climate simulations
with an assumed forcing based on the IPCC Special Report on Emissions Scenarios
(SRES) were also performed. The SRES A1B scenario, which represents a very rapid
economic growth with increasing globalization into the future, is chosen for this study.
Three hydrologic variables, precipitation (P), evaporation (E), and runoff (R), of the
20C3M and SRES A1B simulations were analyzed. P, E, and R were obtained from the
monthly mean precipitation flux (pr), surface upward latent heat flux (hfls), and runoff
flux (mrro) data of the IPCC standard output from the PCMDI/AR4. The outputs of the
GCMs were of different atmosphere resolution (Gleckler et al 2008). The resolution is
typically at the order of 4 degree. The GCM outputs were converted to a common
0.5°×0.5° latitude-longitude grid. We consider only one realization (“run1” in the archive
when available) for each variable from each of 22 models (listed in Table 1). There are
21 models for P and 20 models for E and R.
Global P, E, and R data from observations or observation-forced simulation using land
surface model in the period of 1950-99 were collected from various sources. One set of
monthly P observation was obtained from the 5°×5° grid dataset of the Global Historical
Climatology Network (GHCN) (Peterson and Vose 1997).The GHCN dataset comprises
over 30,000 stations with varying temporal coverage. It has been carefully quality
controlled and considered to be sufficient to describe global scale land precipitation
change, particularly after 1950 (New et al. 2001). Another set of monthly P observation
was taken from Dai et al. (2009). The P data, thereafter referred as NCAR P, were
derived by observation-based analyses of monthly precipitation with intramonthly
variations from the National Centers for Environmental Prediction-National Center for
Atmospheric Research (NCEP-NCAR) reanalysis over global land areas (Dai et al. 1997;
Chen et al. 2002; Qian et al. 2006). The NCAR P and other observed atmospheric forcing
were used to force the Community Land Model (CLM; Dickinson et al. 2006) and to
generate global land surface E and R (Qian et al. 2006; Dai et al. 2009). The CLM
simulated monthly E and R, thereafter referred as CLM E and CLM R, and the NCAR P
were collected in the period of 1950-99 at T42 (~2.8°) resolution. The data were
converted to the same 0.5°×0.5° grid as the GCM outputs. A set of historical monthly
streamflow at the farthest downstream stations for the world’s 925 largest ocean-reaching
rivers was gotten from Dai et al. (2009). The network of the stations covers for ~80% of
global ocean-draining areas and accounts for about 73% of global total runoff. The
drainage basin associated with each station was determined using the Simulated
Topological Network (STN-30p) at 0.5°×0.5° spatial resolution (Vörösmarty et al. 2000).
The relative change in each hydrologic variable between the first 20 yr and the last 20 yr
of the last half of the twentieth century was examined. The relative change was computed
as 100 times the difference between 1980-99 and 1950-69, divided by 1950-69 value.
Computing the PCMDI/AR4 ensemble median of the relative changes at each 0.5°×0.5°
grid yielded a spatial pattern of the relative change. The PCMDI/AR4 change was then
compared to the observed changes. The MM is widely applied to seasonal weather
forecasting and climate change projections (see Harrison et al. 1999; Giorgi and Mearns
2002). The forecast skill of MM is usually superior to that of each ensemble member
(Fritsch et al. 2000; Pierce et al. 2009). Pierce et al. (2009) found that the superiority of
MM was largely caused by the cancellation of offsetting errors in the individual global
models. The superiority of MM may be interpreted in statistics. If the GCM ensemble is
assumed to be a sample of the full potential climate model, the ensemble mean or median
is approximately where the peak of the probability density function is (exactly so if the
distribution is Gaussian). Statistically, the ensemble mean is the center of mass and the
median is the halfway point (half the population has a lower value) for the probability
density. The peak of the probability density function is the most likely value (“best
guess”) of the ensemble. Although the mean or median value can depart from the peak
value (for different distribution), previous studies (see Stott and Kettleborough 2002;
Tebaldi et al. 2005; Lopez et al. 2006) have shown that the discrepancy may be small for
GCM ensemble that is most likely normally distributed (exactly be normally distributed if
the number of ensemble member is sufficiently large and the ensemble members have
identical distribution with finite mean and variance). Since the GCM ensemble is usually
very sparse, the ensemble median is used in this study in order to give more robust guess.
Another particular reason for using median not mean is that the median is order statistics,
and this permits a relative direct interpretation of the probability distribution function.
The direction of change, i.e. positive or negative change, is of great interest at regional
scale. The probability of a positive change (Pp) is approximately the ratio of the number
of the ensemble member showing a positive change to the total. The probability of a
negative change (Pn) is 1–Pp. The range of Pp [0, 1] can be linearly transformed to the
range of [-1, 1] using the transfer function f(Pp) = 2 Pp – 1 = Pp – Pn. Therefore Pp – Pn
ranges from -1.0 indicating high probability of negative change, to 1.0 indicating high
probability of positive change. The estimated probability of the direction of change, i.e.
Pp – Pn, was calculated at each 0.5°×0.5° grid for each hydrologic variable.
The spatial pattern of the ensemble median provides the most likely pattern with
amplitude of hydroclimatic change predicted by the GCM ensemble. The variance in the
spatial pattern from an M-member ensemble is 1/M times the variance in the spatial
pattern of the ensemble member if the ensemble members have the same normal
distribution. This will significantly underestimate the pattern-amplitudes. Denote the
variable value at time t by xt, the relative change between time n and time 1 can be
expressed as 100 times change ratio minus 100, where the change ratio is defined as the
X= xn/x1. The change ratio between time n and time 1 can be expressed as:
X=xn/x1=xn/xn-1 xn-1/xn-2 … x2/x1=(1+ε1) (1+ε2)… (1+εn-1)
where εt denotes the relative change divided by 100. By assuming the increments εt to be
independent random variables, the change ratio can be thought of as the multiplicative
product of many independent random variables each of which is positive. It may be
modeled as lognormal distribution. A Q-Q plot (quantile-quantile plot) was used to
compare the observed and estimated probability distributions of the logarithm of the
change ratios. The linear regression of the Q-Q relationship for the logarithm of the
change ratio of each hydrologic variable between the observation and estimation was
obtained based on the data in the historical period of 1950-1999. If the logarithm of the
GCM ensemble change ratio X20 is normally distributed with mean μ20 and standard
deviation σ20, the scaled change ratio X20s=X20/a –b/a using the Q-Q relationship is also
normally distributed with mean μ20–b/a and standard deviation σ20/a, where a is the slope
and b is the intercept of the linear regression of the Q-Q relationship. The scaled change
ratio has the same standard deviation (i.e. pattern-amplitude) as the observation. It
indicates the observation can be approximated by scaling the standard deviation and
offsetting the GCM ensemble mean with –b/a. The Q-Q relationship was used to
constrain the GCM ensemble estimate in future time period. Denote the logarithm of the
GCM ensemble change ratio in a future period by X21, the mean of X21 by μ21 and the
standard deviation by σ21. Assuming standard deviation difference between the scaled X21
and scaled X20 is the same as that between X21 and X20, the scaled X21 can be expressed as
X21s=X21[σ20/a+(σ21– σ20)]/σ21 –b/a. The scaled X21 was used to approximate the change
ratio in the future period.
The GCM ensemble relative change in each hydrologic variable between the first 20 yr
and the last 20 yr of the twenty first century was computed using the ensemble median of
the available GCMs. The GCM ensemble relative change was then scaled using the
relationship developed in the historical period. The scaled relative change in the twenty
first century was compared to that in the twentieth century. The impact of climate change
on hydrology and its potential consequences on human society were discussed.
3. Results and Analysis
Figure 1 shows the last half of the twentieth century change in precipitation estimated
using the GHCN P data, the NCAR P data, and the PCMDI/AR4 GCM ensemble, and the
probability of the direction of change estimated by the GCM ensemble. The estimated
spatial patterns based on observations (i.e. GHCN P and NCAR P) bear a considerable
resemblance to each other with positive change over the United States, southeastern
South America and Western Australia and negative change over large part of Siberia and
Africa. There are large regional differences, however, between the observations and
GCM ensemble in the western United States, Siberia, and middle Africa. The probability
of the direction of change shows a pattern generally corresponding to that of the change
from GCM ensemble. The amplitude of the change estimated by the GCM ensemble is
much smaller than those of the observations (as expected from ensemble averaging).
Figure 2 shows the last half of the twentieth century change in evaporation estimated
using the CLM E data and the PCMDI/AR4 GCM ensemble, and the probability of the
direction of change estimated by the GCM ensemble. The direction of change in CLM E
is consistent with that in NCAR P (see Figure 1) except for relative large regional
differences in Canada, eastern Siberia and South Asia where NCAR P shows negative
change while CLM E shows positive change. The direction of change in evaporation
estimated by GCM ensemble corresponds to that in GCM ensemble precipitation with
relative large regional differences in northwestern United States where precipitation
decreased while evaporation increased. Both the observation-based and GCM ensemble
estimates show areas of increased evaporating grow. The change amplitude of the
observation-based estimates is greater than that of the GCM ensemble as expected.
Figure 3 shows the last half of the twentieth century change in runoff estimated using the
observed R data, the CLM R data, and the PCMDI/AR4 GCM ensemble, and the
probability of the direction of change estimated by the GCM ensemble. The number of
the runs showing valid data is also shown. Some of the GCMs give invalid runoff (but
not for precipitation and evaporation) output (e.g. negative runoff or constant value). The
invalid values were removed from the ensemble. Most of the invalid values appear in
hyper-arid area in Sahara, Middle East and Central Asia, and the Greenland. The
Greenland was masked out in the runoff analysis later because the ice sheet runoff is not
well presented in the models and the runoff observation is absent. The hyper-arid area
with less valid runs was not masked out since the regions are of interest. The small
number of the runs may decrease the reliability of the ensemble estimates over these
regions. The observed R change was reconstructed from the CLM R data and streamflow
observations. A scaling factor of each river basin was computed as the observed
streamflow change divided by the CLM estimated change of the accumulated runoff over
the corresponding drainage area. The scaling factor was extended to the surround grid if
the grid was in a basin that was not covered by the streamflow observations. The
observed R change was computed as the difference between the scaled runoff in 1980-99
(using the scaling factor times the CLM estimated runoff) and the CLM estimate runoff
in 1950-69, divided by the 1950-69 value. Large regional difference between the
observed R and the CLM R is found in Siberia where the observed R increased but the
CLM R decreased. The change in CLM R shows a spatial pattern generally consistent
with that in NCAR P, although amplified. The runoff change estimated by GCM
ensemble coincides with the precipitation change estimated by GCM ensemble over most
part of the land, while the region with decreased precipitation in western United Station
and Europe expanded to a greater area with decreased runoff. Comparing with the
observed R, the GCM ensemble R reproduces the positive changes in Siberia, eastern
United States and Western Australia, and the negative changes in Northwestern and
Southwestern United States, western Africa, and eastern Australia. The amplitude of the
GCM ensemble change is much smaller than that of the observations. The GCM
ensemble approach, which gives change in R (increase or decrease) less than 20% over
most of the land girds, apparently underestimates the pattern amplitude of the change at
regional scale.
The amplitudes of the GCM ensemble changes in the hydrologic variables are smaller
than those of the observation-based changes. The Q-Q plot of the logarithm of the
observation-based change ratios versus GCM change ratios and the cumulative
probabilities of the relative changes over the global land are shown in Fig. 4. Comparing
the cumulative probabilities of the changes in the hydrologic variables (Fig. 4b, 4d, and
4f) among GCM ensembles or CLM estimates, it is shown that the dispersion from zero
change is largest in R and smallest in E. One hypothesis is that both P and E increase
(decrease), but the former increases (decreases) by a larger amount, introducing a large
change in (P-E) which equals to R for a long period mean. The hypothesis was supported
by previous studies (see Seager et al. 2007; Chou and Chen 2010). The Q-Q plots of the
logarithm of the GHCN P change ratio versus GCM ensemble P change ratio and NCAR
P change ratio follow linear lines, although that of the GCM ensemble change is flatter
than that of the NCAR P change and the 1:1 line. This indicates the distribution of the
logarithm of the GCM ensemble P change ratio can agree with that of observation after
linearly transforming the values. And the distribution of the observed P change ratio is
more dispersed than that of the GCM ensemble. With the assumption that the model
simulation of internal variability is correct and each member of the M-member ensemble
has the same distribution, the dispersion of the logarithmic ensemble change ratio, which
is measured by the slope the linear regression, would be 1 M (i.e. 0.22 when M is 21)
in the observations. The regression of slope of 0.17 shows the strict conditions in the
above assumption do not hold. The empirical Q-Q relationship was used to scale the
GCM ensemble distribution to the observation. The dispersion of the logarithmic NCAR
P change ratio is slightly smaller than that of the GHCN observation. It is clear in Fig 4b
which gives the cumulative probabilities of P relative changes. The GCM ensemble
approach shows the P relative change at almost all the land girds is in the range of -10 to
10% while the GHCN observation shows P has decreases 10% at about 20% land grids
and increases 10% at about 10% land girds. If the regression relationship was used to
constrain the GCM ensemble estimates, the shifted and scaled probability distribution
agrees fairly with the observations (Fig. 4b). The Q-Q plots of the logarithm of the
GHCN P change ratio versus P change ratio predicted by individual GCM are often arced
with outliers, indicating that the P change distribution of individual GCM is often more
skewed than the observed distribution. Most of the individual GCM underestimate P
change amplitude. Although the amplitude of the GCM ensemble change is even smaller
than that of individual GCM, the probability plot correlation coefficient of GCM
ensemble is higher than that of individual GCM. The distribution of GCM ensemble
should agree better with the observed distribution than the distribution of individual
GCM if both are scaled to fit the observation. Both GHCN and NCAR P observations
show precipitation decreased over half of the land grids, but the large decrease area was
not detected by the GCM ensemble.
The Q-Q plot of the logarithm of the CLM E change ratio versus GCM ensemble E
change ratio follows linear line. The Q-Q plot of the logarithmic CLM E change ratio
versus logarithmic individual GCM E change ratio is often curved with outliers. It
confirms that the GCM ensemble is more closely related to observation than individual
GCM. Again, most of the individual GCM underestimate E change amplitude. Using the
regression relationship between the GCM ensemble and the observation, the scaled
probability distribution from GCM ensemble agrees well with the CLM estimate (Fig.
4d). Both the scaled GCM ensemble and CLM E show a slightly higher probability of
increase in evaporation.
The Q-Q plot of the logarithm of R change ratios (Fig. 4e) shows strongly nonlinear
patterns for the distributions predicted by individual GCM, suggesting the R change from
individual GCM does not appear to have a common distribution with the observation.
Meanwhile the linearity of the GCM ensemble change ratio suggests a common
distribution to the observed distribution if the quantiles are shifted and scaled. Figure 4f
shows that the GCM ensemble R change is generally between -20 and 20% at global land
girds while the observed R change is smaller than -20% at 20% land girds and greater
than 20% at 15% land grids. Most of the individual GCM underestimate R change
amplitude. The GCM ensemble has also significantly underestimated the amplitude of R
change as expected. The dispersion of the CLM R change is smaller than that of the
observation. It should be noted that the observed runoff has taken into account of direct
human interruption of streamflow (such as irrigation water withdrawal and dam operation)
while the models have not. Using the regression relationship between the GCM ensemble
and the observation, the scaled probability distribution from GCM ensemble is closer to
the observations than the unscaled one. The CLM R shows a higher probability of
decrease in runoff (i.e. the potential area extent with decreasing runoff is larger than that
with increasing runoff) but the observed R and the scaled GCM ensemble do not show
the same change.
Figure 5 shows the GCM ensemble and the scaled relative change in P between the first
20 yr and the last 20 yr of the first half of the twenty first century. Both the GCM
ensemble and the scaled relative change show a pattern generally consistent with that of
the probability of the direction of change. Comparing with the probability of the direction
of change in the last half of the twentieth century (Fig. 1d), the direction of P change in
the first half of the twenty first century tends to have higher probability to be positive in
high latitudes, Far East, South Asia, and equatorial regions, and higher probability to be
negative in southern Europe, Middle East, North and South Africa, Australia, eastern
equatorial South America, and southwestern North America. The scaled pattern
amplitude, which is much greater than that from the GCM ensemble, was constrained by
the observation in the last half of the twentieth century. The scaled relative change should
be a better guess of the GCM ensemble in the twenty first century. The results show that
P increases more than 10% in the high latitudes of North America and Eurasia and
decreases more than 10% in southern Europe, Middle East, southwestern North America,
North and South Africa, and Australia. The (scaled) P change pattern amplitude in the
first half of the twenty first century is larger than (scaled) that in the last half of the
twentieth century (Fig. 5d), indicating more dramatic change in the hydrologic cycle is
anticipated at regional scale. The area extent with increasing precipitation becomes larger
because of the positive P change in high latitudes.
Figure 6 shows the GCM ensemble and the scaled relative change in E between the first
20 yr and the last 20 yr of the first half of the twenty first century. The scaled relative
change shows that E increases more than 10% in the high latitudes of North America and
Eurasia and decreases more than 10% in the Middle East, North Africa, and southern
Australia. The amplitude of E change in the first half of the twenty first century is larger
than that in the last half of the twentieth century (Fig. 6d), showing more dramatic change
is anticipated in E at regional scale. The spatial pattern of the relative change in E is
generally consistent with that of the change in P although the pattern amplitude is smaller
and the area with negative change shrinks.
Figure 7 shows the GCM ensemble and the scaled relative change in R between the first
20 yr and the last 20 yr of the first half of the twenty first century. The scaled relative
change shows that R increases more than 20% in the high latitudes of North America and
Eurasia, South Asia, and the La Plata basin of South America and decreases more than
20% in southern Europe, Middle East, southwestern North America, North and South
Africa, and Australia. The spatial pattern of the relative change in R corresponds with the
spatial pattern of the change in P although the pattern amplitude in R is greater. The
spatial pattern is generally consistent with the finding in Milly et al. (2005) while their
study shows a wetter Australia and North Africa. The pattern amplitude of R change of
Milly et al. (2005) is slightly greater than that of the GCM ensemble. The relative change
in R in Milly et al. (2005) was calculated as the difference between period 2041-60 and
1900-70. The size of the ensemble (i.e. the number of the members, 20) in this study is
larger than the size of the ensemble (12) in Milly et al. (2005). It is expected that the
estimated amplitude from a larger member ensemble is smaller. The scaled pattern
amplitude of R change, which was constrained by the observation in the last half of the
twentieth century, is greater than that from the GCM ensemble and that in Milly et al.
(2005). The scaled GCM ensemble approach shows R will change (increase or decrease)
more than 20% at about half land grids and will change more than 50% at 14% land grids.
It delineates a vision of future hydrologic cycle with more dramatic change at regional
scale than we expected before.
The scaled relative change in P, E and R between the first 20 yr and the last 20 yr of the
twentieth and twenty first centuries are shown in Fig 8. For either the twentieth or twenty
first century, the spatial patterns of the change in E and R are generally consistent with
that in P because precipitation is the primary forcing variable of the land surface
hydrology. The spatial patterns of the changes between the first 20 yr and the last 20 yr of
in the twenty first century are similar with those in the first half of the twenty first
century (Fig. 5, 6 and 7), but with large pattern amplitude. The pattern amplitude of
change in E is the smallest and that in R is the greatest. Significant differences are found
in the pattern amplitudes between the twentieth century and twenty first century. The
large relative change (decrease or increase more than 10%) in P occurs at 43 and 70%
land grids in the twentieth century and the twenty first century, respectively (Table 2).
The relative change (decrease or increase) is greater than 10% at 17% land grids in the
twentieth century, but at 58% land girds in the twenty first century. These indicate more
dramatic regional change in hydrologic cycle is anticipated in the twenty first century.
The portion of global land girds (i.e. potential area) with large decrease change in P
slightly increases (by 2%) in the twenty first century, comparing with the portion in the
twentieth century. Comparing with the twentieth century, the potential area with large
increase change in P in the twenty first century enlarges by 30, 27, and 16% of land for
an increase magnitude more than 10, 20, and 30%, respective. It indicates the climate is
globally turning wetter but dry area may be drier at regional scale. It implies the area with
high risk of both flooding and drought may increase in the twenty first century. The
potential area with large decrease change in E is similar in the twentieth and twenty first
centuries. However, the land area with large increase change in E is predicted to increase
in the twenty first century. E will increase more than 10% over about half of the land area.
The increase in E over large land area is likely due to surface warming. The land girds
with large R relative change (decrease or increase more than 30%) in the twenty first
century is about twice than that in the twentieth century. The potential area with
deceasing R increases. The potential area with >30% decrease in R increases from 13%
land area in the twentieth century to 18% land area in the twenty first century. The
expansion of the area with decreasing R is likely caused by the expansion of the area with
increasing E. Dai et al. (2004) found a subsequent expansion of global very dry area since
1970s based on the ground observations from 1870 to 2002. Our results suggest that
expansion of dry area will continue in the twenty first century. The potential area with
increasing R also increases. The potential area where R increases more than 30%
increases from 13% land area in the twentieth century to 31% land area in the twenty first
century. It indicates the global land areas in very wet condition will increase. Dai et al.
(2004) showed that the global very wet land areas declined by 5% from the early 1980s to
early 1990s. The GCM projection suggests the global very wet land area may expand in
the twenty first century, reversing the trend in the late twentieth century.
On the basis of this analysis, large hydroclimatic changes are anticipated to occur in the
next couple of decades. The hydroclimatic changes would directly influence human
society. Domestic and industrial water demand was determined by population and
socioeconomic factor such as gross domestic product (GDP). The geography of
contemporary population was obtained from the Gridded Population of the World,
version 3 (GPWv3) data at 0.5° resolution (CIESIN 2005a). The global GDP distribution
was gotten from the Global Gridded Gross Domestic Product for 1990 dataset at 0.5°
resolution (CIESIN 2005b). Comparing with the twentieth century, the area with large
(increase or decrease) change in R is anticipated to cover more people in the twenty first
century (Table 2). About one quarter of global population are in the area with >30%
change in R in the twentieth century. The portion will increase to near half if the
geography distribution of population remains. It shows more people are heading to a
wetter climate with large increase in R because the nations with large population size (i.e.
China and India) are anticipated to receive more precipitation. Coinciding with the
increase of population portion to a wetter climate, the population portion to a drier
climate decreases. However, the population portion that will live in a much drier climate
(with >30% decrease in R) does not change, indicating the globally wetter climate may
not relieve drought at regional scale. The portion of global GDP at area with large change
(increase or decrease) in R will increase, suggesting that more socioeconomic essential
infrastructure will be exposed to the dramatic change. The portion of GDP at area with
large increase in R increases because climate is becoming wetter over large portion of the
land area. The portion of GDP at area with large decrease in R also increases. It is
attributed to the anticipated drier conditions in the region with high GDP such as southern
Europe, Middle East and southwestern North America.
Figure 9 shows the spatial pattern of global aridity map. The climatic aridity index (AI) is
defined as the ratio of annual precipitation to potential evapotranspiration (UNEP 1997).
The aridity index data was obtained from the Global Aridity Index dataset (Trabucco and
Zomer, 2009). The global land is classified into five zones of aridity: hyper-arid zone (AI
< 0.03), arid zone (0.03<AI<0.2), semi-arid zone (0.2<AI<0.5), sub-humid zone
(0.5<AI<0.65), and humid zone (AI>0.65). Comparing the spatial pattern of the changes
in hydroclimatic variable (Fig. 8) with the global aridity map, it is found that most arid
area (AI<0.2) tends to become drier and most humid area (AI>0.65) tends to be wetter in
the twenty first century. A few arid and humid areas will change to a more moderate
climate. These areas include Amazon and southern Europe where the humid condition is
predicted to become drier and a line across southeastern Sahara, Middle East to Central
Asia where the arid condition is predicted to become wetter (Fig. 9a). Intense drought in
the Amazon was reported in 2005 and the causes and the implications of Amazonian
drought for future climate change were widely discussed (Malhi et al. 2007; Marengo et
al. 2008; Cox et al. 2008; Phillips et al. 2009). Figure 9a shows the risk of Amazonia
drought increases in the twenty first century. The drying of Amazonia may be associated
with a weakening of tropical circulation systems (Cook and Vizy 2008). Large part of the
area with moderate climate condition (0.5<AI<0.65) is predicted to turn to dry or wet
condition (Fig. 9b). The area where the climate is predicted to depart from moderate
condition is larger than the area approaching moderate condition, indicating the marginal
areas between arid and humid zones will shrink under climate change. On the base of this
analysis, the semi-arid zone in cold area will shrink as the high latitude of North
Hemisphere is moisturized. In arid and semi-arid regions, water is often a limiting factor
for crop growth. The crop water requirement must be by irrigation if the precipitation in
the growing season is inadequate for the plant’s needs. Irrigation is usually taken from
the renewable water resources which can be estimated by R. The semi-arid regions in
northern China, western India, Ethiopia, and north and central Argentina are becoming
more humid (increase in R is more than 20%) and more favorable to crop growth. These
regions are the important centers of food production and productive agricultural areas.
This suggests climate change is expected to benefit the agriculture in the above
developing nations. On the other hand, the semi-arid regions in the Great Plains area of
the United States, northern Mexico, southern Europe, Turkey, Afghanistan, South Africa,
and east coast of Australia will be adversely affected. These regions are susceptible to
desertification, land degradation and drought. The drying climate is expected to make the
agriculture and ecosystems more vulnerable in these regions. As a result of shrinking of
the transition zone between arid and humid area, most non-polar desert will expand. The
Sahara will extend to its north and southwest. The Syrian Desert and Arabian Desert will
grow to the Black Sea. The Kalahari Desert (Africa) may expand to the India Ocean. The
deserts in Australia are growing to the Pacific coast. The area of the Great Basin Desert,
Sonoran Desert and Chihuahuan Desert will expand in the northeast to the Great Plains
and in the south to the coastal plains of the northern Mexico. One exception is the Gobi
Desert (Asia) which tends to diminish. The weakening of the tropical circulation may
explain the imminent transition to a more arid climate in the subtropics (Vecchi et al
2006; Seager et al. 2007; Chou and Chen 2010). The location of the Gobi Desert is quite
far north for a non-polar desert. It t is a continental interior desert which is separated from
ocean moisture by topographic barriers and large distances (Broccoli and Manabe 1992).
Atmospheric flow may transport more moisture into this region. Most of the cold semiarid area will become more moisturized, expanding the global humid area. The
moistening is due to the apparently robust increase in high latitude precipitation (Allen
and Ingram 2002; Zhang et al. 2007). The Asian monsoon regions such as North China
and India become wetter. A possible explanation is the enhanced land-sea thermal
contrast will intensify the Asian monsoon and the warmer sea surface will supply an
enhanced moisture source to fuel stronger monsoon rainfall. The model studies that found
increased monsoon rainfall relative to control simulations (Kitoh et al 1997; Douville et
al 2000; Hu et al 2000; Meehl and Arblaster 2003) support this possibility. The areas with
humid condition at high latitude tend to merge with the east coast humid area of the
Eurasian continent, form two humid belts along the north and east coast of the North
American and the Eurasian continents. The tropic humid belt may slightly shift to south
in South America and to north in Eastern Africa. These changes indicate climate change
is shaping the global pattern of arid and humid area. More concentrated arid or humid
belts and narrower margin area between the belts are expected.
4. Discussion and Conclusions
This study used the hydroclimatic observations in the late half of the twentieth century to
constrain the ensemble predictions of the PCMDI/AR4 GCM models. Assuming GCMs
are designed to reproduce the observed climate, the robust prediction in future climate
should be as model independent as possible and be constrained by the only objective
information. The ensemble median of the available GCMs provides a spatial pattern of
the change which is less dependent to individual model but with smaller pattern
amplitude than individual model and observations. A Q-Q plot was used to compare the
observed and estimated probability distributions of the logarithm of the change ratios.
The Q-Q relationships between the observations and GCM ensemble estimates were
developed for P, E and R. Assuming the discrepancies in the spatial variance do not
change, the relationships were used to scale the GCM ensemble estimates of the relative
changes in the hydroclimatic variables in the twenty first century. The scaled estimate of
the relative change has a spatial pattern corresponding to the probability of the change
direction with pattern amplitude constrained by the observed climate and recent climate
change. This approach used historical observations to constrain the GCM ensemble
estimates and to provide the “best guess” of the future climate projection at regional scale.
It differs from the Bayesian type of approach which is usually used to quantify the
uncertainty in projections of regional climate change (Giorgi and Mearns 2002; Tebaldi
et al. 2005). A basic assumption in the Bayesian type of approach is that the ability of a
GCM to reproduce current mean climate (and future weighted ensemble mean)
constitutes a measure of its reliability. However, this study assumes the ensemble median
method gives the “best guess” of the spatial pattern although the pattern amplitude should
be constrained using historical climate observations.
The results show that the pattern amplitude of relative change in the twenty first century
is larger than that in the twenty first century for P, E, and R, indicating more dramatic
change in the hydrologic cycle is anticipated at regional scale in the next couple of
decades. It is expected that both the land areas with wet condition and dry condition
increase. More people (about one quarter of global population) are heading to a wetter
climate with over 30% increase in R because the nations with large population size (i.e.
China and India) are anticipated to receive more precipitation. However, the population
portion that will live in a much drier climate does not decrease. The portion of global
GDP at area with either large increase or large decrease in R becomes large, suggesting
that climate change will increase existing risks of natural disasters such as flooding and
drought. Globally, the GCM projection shows arid area becomes drier and humid area
becomes wetter. The global large hot deserts will expand except for the Gobi Desert
which tends to diminish. The areas with humid condition tend to merge together and form
two humid belts along the northern and eastern coast of the North American and the
Eurasian continents. These changes are shaping more concentrated arid or humid belts
and narrower margin area between the belts. It suggests the mechanisms of atmospheric
circulation that contribute to the dryness to arid area or wetness to humid area are
generally strengthening. The change of the mechanisms may be different for different
region. Most non-polar arid areas and the Amazonia become drier, likely because of the
change of the tropical circulation systems. The Asian monsoon regions may become
wetter because the intensified monsoon. It is expected the land warming faster than the
ocean responding to increasing greenhouse gas concentrations in the atmosphere (Barnett
et al. 2000; Brohan et al. 2006; Hansen et al. 2006). The enhanced land-sea thermal
contrast may intensify monsoon and enhance atmospheric moisture convergence over
land. The warmer sea surface might supply an enhanced moisture source to fuel
continental interior. The change in the atmospheric moisture convergence might moisten
the Gobi Desert and the northern high latitudes. One caveat should be noted in the results
from the low resolution PCMDI/AR4 GCMs. High spatial resolution climate model may
give results with important regional scale differences to low resolution GCMs, and
sometimes reverse the direction of the change in hydroclimatic variables at regional scale
(Duffy et al. 2003; Govindasamy et al. 2003; Ashfaq et al. 2009). Despite they may be
challenged by fine scale climate process at regional scale, the PCMDI/AR4 GCMs can
capture large scale climate feature and can be used to assess the relative impacts of
climate change on different regions of the globe.
Given that higher latitudes have warmed more than the lower latitudes in the past half
century (Hansen and Lebedeff 1987; IPCC 2007), the temperature-related change, or
“fingerprint”, in natural systems such as terrestrial organisms, climate and hydrology was
thought to be generally in parallel with latitude (Myneni et al. 1997; Parmesan and Yohe,
2003; Root et al 2003; Reich and Oleksyn, 2004; Zhang et al. 2007; Deutsch et al. 2008).
However, both model and observations showed large heterogeneity across latitude in the
fingerprint of climate impacts on hydrologic cycle (see Dai and Trenberth; 2002; Nohara
et al., 2006). Our results suggest that the fingerprint pattern of climate impacts on the
hydrologic cycle is generally consistent with the distribution of climatic zones with drier
arid area and wetter humid area. Besides local non-climatic influences, the fingerprint
pattern of hydrology may complicate the latitudinal patterns of climate change impacts on
the water-related natural or human systems. It is evident that the hydrologic fingerprint
has potentially important effects on the global pattern of the biodiversity change
(Bonebrake and Mastrandrea 2010), food supply (Rosenzweig and Parry 1994; Parry et al.
2004), and climate-related migration (Feng et al. 2010).
Acknowledgements
The work described in this paper was supported by **xxxx Projects**. We thank Dr
Aiguo Dai of the National Center for Atmospheric Research (NCAR) for providing the
global observed precipitation, and Community Land Model (CLM) simulated evaporation
and runoff data.
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Table 1. A list of PCMDI/AR4 model simulations used in the analysis of the 20C3M and
SRES A1B scenarios. “v” denotes the variable of surface temperature (T), precipitation
(P), evaporation (E), or runoff (R) is available.
Model
Modeling center
T P E R
BCCR BCM2
Bjerknes Center for Climate Research
v v v v
Canadian Centre for Climate Modelling and
CCCMA CGCM3.1 Analysis
v v v v
CCCMA CGCM3.1 Canadian Centre for Climate Modelling and
T63
Analysis
v v v v
CNRM CM3
Center National de Recherches Meteorologiques
v v v v
Commonwealth Scientific and Industrial Research
CSIRO MK3.0
Organisation (CSIRO) Atmospheric Research
v v v v
Commonwealth Scientific and Industrial Research
CSIRO MK3.5
Organisation (CSIRO) Atmospheric Research
v v v v
GFDL CM2.0
Geophysical Fluid Dynamics Laboratory
v v v v
GFDL CM2.1
Geophysical Fluid Dynamics Laboratory
v v
v
GISS AOM
Goddard Institute for Space Studies
v v v v
GISS EH
Goddard Institute for Space Studies
v v v v
GISS ER
Goddard Institute for Space Studies
v
v v
IAP FGOALS1
Institute for Atmospheric Physics
v v v v
INGV ECHAM4
Istituto Nazionale di Geofisica e Vulcanologia
v v v
INM CM3
Institute for Numerical Mathematics
v v v v
IPSL CM4
Institut Pierre Simon Laplace
v v v
MIROC(hires)
Center for Climate System Research
v v v v
MIROC(medres)
Center for Climate System Research
v v
v
MIUB ECHO
Meteorological Institute University of Bonn
v v v v
MPI ECHAM5
Max Planck Institute for Meteorology
v v v v
MRI CGCM2
Meteorological Research Institute
v v v v
NCAR CCSM3
National Center for Atmospheric Research
v v v v
UKMO HadCM3
Met Office’s Hadley Centre for Climate Prediction v v v v
Table 2. Statistics of the scaled relative change in precipitation (P), evaporation (E) and
runoff (R) for the twentieth and twenty first centuries (see Fig. 8). The area (percentage
of global land grids) where the relative change (decrease or increase), decrease, and
increase change magnitudes in P, E, and R are greater than 10, 20, and 30% are given.
The percentage of global population (Pop) and gross domestic product (GDP) in the area
where the relative change, decrease, and increase change magnitudes in R are greater
than 10, 20, and 30% are given.
Area (%) in P Area (%) in E Area (%) in R Pop (%) in R GDP (%) in R
Century
20th
21st
20th
21st
20th
21st
20th
21st
20th
21st
Change >10%
43
70
17
58
70
83
70
83
64
77
Change >20%
15
43
7
20
44
65
44
62
41
51
Change >30%
6
24
2
6
27
50
24
45
24
32
Decrease >10%
24
22
11
11
29
31
40
28
33
38
Decrease >20%
11
13
5
6
20
24
27
21
23
28
Decrease >30%
5
7
1
3
13
18
15
15
15
19
Increase >10%
19
48
7
46
41
51
29
55
31
39
Increase >20%
4
31
2
15
24
41
17
41
18
22
Increase >30%
1
17
1
3
13
31
9
30
10
13
Figure 1. Relative change in precipitation (P) between the first 20 yr and the last 20 yr of
the last half of the twentieth century estimated using the GHCN P data (a), the NCAR P
data (b) and the ensemble median of the PCMDI/AR4 GCM outputs (c), and the
probability of the direction of change (Pp – Pn) (d).
Figure 2. Relative change in evaporation (E) between the first 20 yr and the last 20 yr of
the last half of the twentieth century estimated using the CLM E data (a) and the
ensemble median of the PCMDI/AR4 GCM outputs (b), and the probability of the
direction of change (Pp – Pn) (c).
Figure 3. Relative change in runoff (R) between the first 20 yr and the last 20 yr of the
last half of the twentieth century estimated using the observed R data (a), the CLM R data
(b) and the ensemble median of the PCMDI/AR4 GCM outputs (c), the probability of the
direction of change (Pp – Pn) (d), and the number of runs showing valid value (e).
Figure 4. Q-Q plot of the logarithm of GHCN P change ratio versus that of GCM
ensemble P change ratio, P change ratio predicted by individual GCM, and NCAR P
change ratio (a), and cumulative probabilities of the P relative changes (b) over the global
land (masked by the GHCN P coverage, see Figure 1a). Q-Q plot of the logarithm of
CLM E change ratio versus that of GCM ensemble E change ratio, and E change ratio
predicted by individual GCM (c), and cumulative probabilities of the E relative changes
(d) over the global land. Q-Q plot of the logarithm of the observed R change ratio versus
that of GCM ensemble R change ratio, R change ratio predicted by individual GCM and
CLM R change ratio (c), and cumulative probabilities of the R relative changes (d) over
the global land (excluding the Greenland). The change ratio is the proportionate rate and
the change is the relative change (percentage) between the first 20 yr and the last 20 yr of
the last half of the twentieth century. The twentieth quantiles of changes (the circles), the
linear regressions of the twentieth quantiles of the GCM ensemble and CLM change
ratios and the linear regression parameters (slope, intercept and correlation coefficient)
are shown in the left panels. The relative changes which are shifted and scaled using the
regression relationships are compared to the observed changes in the right panel.
Figure 5. Relative change in P between the first 20 yr and the last 20 yr of the first half of
the twenty first century estimated using the GCM ensemble (a) and the scaled GCM
ensemble (c), and the probability of the direction of change (Pp – Pn) (b), and cumulative
probabilities of the GCM ensemble and scaled P relative changes (d) over the global land.
The cumulative probabilities of the GCM ensemble (P_20c) and scaled P (P_20c_scaled)
relative changes between the first 20 yr and the last 20 yr of the last half of the twentieth
century and the GCM ensemble (P_21c) and scaled P (P_21c_scaled) relative changes
between the first 20 yr and the last 20 yr of the last half of the twenty first century are
shown in Fig. 5d.
Figure 6. Relative change in E between the first 20 yr and the last 20 yr of the first half of
the twenty first century estimated using the GCM ensemble (a) and the scaled GCM
ensemble (c), and the probability of the direction of change (Pp – Pn) (b), and cumulative
probabilities of the GCM ensemble and scaled E relative changes (d) over the global land.
The cumulative probabilities of the GCM ensemble (E_20c) and scaled E (E_20c_scaled)
relative changes between the first 20 yr and the last 20 yr of the last half of the twentieth
century and the GCM ensemble (E_21c) and scaled E (E_21c_scaled) relative changes
between the first 20 yr and the last 20 yr of the last half of the twenty first century are
shown in Fig. 6d.
Figure 7. Relative change in R between the first 20 yr and the last 20 yr of the first half of
the twenty first century estimated using the GCM ensemble (a) and the scaled GCM
ensemble (c), and the probability of the direction of change (Pp – Pn) (b), and cumulative
probabilities of the GCM ensemble and scaled R relative changes (d) over the global land.
The cumulative probabilities of the GCM ensemble (R_20c) and scaled R (R_20c_scaled)
relative changes between the first 20 yr and the last 20 yr of the last half of the twentieth
century and the GCM ensemble (R_21c) and scaled R (R_21c_scaled) relative changes
between the first 20 yr and the last 20 yr of the last half of the twenty first century are
shown in Fig. 7d.
Figure 8. Scaled relative change in P between the first 20 yr and the last 20 yr of the
twentieth century (a) and the twenty first century (b); scaled relative change in E between
the first 20 yr and the last 20 yr of the twentieth century (c) and the twenty first century
(d); and scaled relative change in R between the first 20 yr and the last 20 yr of the
twentieth century (e) and the twenty first century (f).
Figure 9. Arid (AI < 0.2) or humid area (AI > 0.65) where the climate tends to become
moderate in the twenty first century (a). The black arrow shows the arid area where R is
predicted to increase more than 20% and the red arrow shows the humid area where R is
predicted to decrease more than 20%. Area with moderate climate (0.2 < AI < 0.65)
where the climate tends to become dry or wet in the twenty first century (b). The black
and red arrows show the moderate area (0.2 < AI < 0.65) where R is predicted to increase
or decrease more than 20%, respectively.