JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, D19106, doi:10.1029/2010JD015192, 2011
Changes of climate extremes in a typical arid zone: Observations
and multimodel ensemble projections
Tao Yang,1,2 Xiaoyan Wang,2 Chenyi Zhao,1 Xi Chen,1 Zhongbo Yu,3 Quanxi Shao,4
Chong‐Yu Xu,5 Jun Xia,6 and Weiguang Wang2
Received 13 October 2010; revised 6 July 2011; accepted 13 July 2011; published 7 October 2011.
[1] This article presents an analysis of the spatiotemporal changes (1960–2100) in
temperature and precipitation extremes of a typical arid zone (i.e., the Tarim River Basin)
in Central Asia. The latest observations in the past five decades (1960–2009) and
Coupled General Circulation Model (CGCM) multimodel ensemble projections
(2010–2100) using the Bayesian Model Average (BMA) approach are employed for
analysis in this study. Results indicate: (1) Most warm (cold) extreme temperature
indices have shown significantly positive (negative) trends in the Tarim River Basin
in past five decades, while only slight changes in precipitation extremes can be observed.
From the spatial perspective, more significantly warm (cold) extremes are found in the
desert zones than in upstream mountain zones (i.e., the Tian Shan Mountain and Kunlun
Mountain systems which surround the basin). Whereas, there are no identical spatial
patterns for the change in extreme precipitation; (2) Ensemble of five CGCM models in
Phase 3 of the Coupled Model Intercomparison Project (CMIP3) based on the BMA
method suggests that the increasing consecutive dry days (CDD), together with the
decreasing frost day (FD) and increasing warm nights frequency (TN90) may lead to more
frequent droughts in Tarim in future. Meanwhile, slight increase of annual count of
days with precipitation of more than 10 mm (R10), maximum 5‐day precipitation total
(R5D), simple daily intensity index (SDII), and annual total precipitation with precipitation
>95th percentile (R95) in projections indicate a probability of flood occurrence in
summer together with frequent occurrence of droughts. The results can provide beneficial
reference to water resource and eco‐environment management strategies in arid zones
for associated policymakers and stakeholders.
Citation: Yang, T., X. Wang, C. Zhao, X. Chen, Z. Yu, Q. Shao, C.‐Y. Xu, J. Xia, and W. Wang (2011), Changes of climate
extremes in a typical arid zone: Observations and multimodel ensemble projections, J. Geophys. Res., 116, D19106,
doi:10.1029/2010JD015192.
1. Introduction
[2] A number of studies on changes in climatic extremes,
using both observations and the output from climate models
[e.g., Folland et al., 2001; Alexander et al., 2006; Vincent
and Mekis, 2006], were conducted in past years. Based on
daily station data across the world for the second half of the
20th century, Frich et al. [2002] found coherent patterns of
1
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute
of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China.
2
State Key Laboratory of Hydrology‐Water Resources and Hydraulic
Engineering, Hohai University, Nanjing, China.
3
Department of Geoscience, University of Nevada, Las Vegas, Nevada,
USA.
4
Mathematics, Informatics and Statistics CSIRO, Wembley, Western
Australia, Australia.
5
Department of Geosciences, University of Oslo, Oslo, Norway.
6
Institute of Geographical Sciences and Natural Resources Research,
Chinese Academy of Science, Beijing, China.
Copyright 2011 by the American Geophysical Union.
0148‐0227/11/2010JD015192
statistically significant changes in some indices for temperature extremes, such as an increase in warm summer
nights and a decrease in the annual number of frost days.
Precipitation indices showed more mixed patterns of
change, but significant increases were detected in the totals
derived from wet spells in some regions. In another global‐
scale investigation by analyzing gridded annual and seasonal mean data, Horton et al. [2001] reported an increase in
warm extremes and a decrease in cold extremes in ocean
surface temperatures since the late 19th century. Regional
studies on climatic extremes have also been conducted in
many parts of the world. A variety of those reports were
found in Southeast Asia and the South Pacific [Griffiths
et al., 2005; Manton et al., 2001], the Caribbean region
[Peterson et al., 2002], southern and west Africa [New et al.,
2006], South America [Haylock et al., 2006; Vincent et al.,
2005], Middle East [Zhang et al., 2005], Central America
and northern South America [Aguilar et al., 2005], Central
and south Asia [Klein Tank et al., 2006], Asia‐Pacific Network region [Choi et al., 2009], China [You et al., 2010; Zhai
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Figure 1. Map of the Tarim River Basin, in which, names
for the mainstream and three major tributary rivers are set in
bold.
and Pan, 2003; Zhai et al., 1999, 2005; Xu et al., 2009],
Western central Africa [Aguilar et al., 2009] and North
America [Peterson et al., 2008]. Results obtained from
above studies indicated that there is remarkable consistency
in temperature extremes, but less spatial coherence in precipitation extremes.
[3] Substantial progress in both global and regional
modeling at medium to high resolution allowed for an
increasing number of studies on modeling climate changes.
Recent modeling efforts have enabled us to characterize
changes in climate extremes with closely relevance to
impacts than the traditional climate model outputs of mean
temperature and precipitation [e.g., Meehl et al., 2005;
Intergovernmental Panel on Climate Change (IPCC),
2007]. In support of the IPCC Fourth Assessment Report
[IPCC, 2007], over 20 modeling groups around the world
conducted climate change simulation by different Coupled
General Circulation Models (CGCM) [IPCC, 2007]. This
constitutes Phase 3 of the Coupled Model Intercomparison
Project (CMIP3) [Meehl et al., 2007] ensemble of simulations. These models provided extreme indices/indicators for
the present and future climates, offering opportunities to
conduct the multimodel ensemble analysis of the simulation
and projection of climate extremes. For example, Tebaldi
et al. [2006] analyzed historical and future simulations of
these indicators derived from an ensemble of nine CMIP3‐
CGCM models under a range of emission scenarios, and
found that on global and continental scales, the simulated
historical trends generally agree with previous observational
studies, providing a sense of reliability for the simulations.
[4] Meanwhile, the Bayesian model averaging (BMA)
approach is introduced to model evaluation and multimodel
averaging with a systematic consideration of modern
uncertainty in climate impact research [Min et al., 2004,
2005; Min and Hense, 2006], and its application to global
mean surface air temperature (SAT) changes is shown
from multi Atmosphere‐Ocean General Circulation Model
(AOGCM) [IPCC, 2007] ensembles of IPCC AR4 simulations. BMA provides a way to combine different models and
is a rather promising method for calibrating ensemble in
modeling and forecasts. BMA is also a method of combining forecasts from different sources into a consensus probability distribution function (PDF), an ensemble analog to
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consensus forecasting methods applied to deterministic
forecasts from different sources [Krishnamurti et al., 1999].
BMA naturally applies ensemble systems to make a set of
discrete models (such as the Canadian ensemble system). In
BMA, the overall forecast PDF is a weighted average of
individual forecast PDFs. The weights are the estimated
posterior model probabilities and reflect the forecast skill of
individual models in the training period. The weights can
also provide a basis for selecting ensemble members: there
is only little loss by removing the ensemble member with
small weights [Raftery et al., 2005; Wilson et al., 2007].
This can be a useful strategy, given that the computational
cost of running ensembles is more affordable nowadays.
Due to pronounced advantages, increasing studies using
various BMA methods for climate change detection and
attribution [Min et al., 2004, 2005] as well as for future
projections of climate changes [Tebaldi et al., 2006] were
reported.
[5] However, even in the available literatures on climate
impact research over the arid zones [e.g., Zhang et al., 2005;
Klein Tank et al., 2006], in‐depth studies regarding changes
of climate extremes are still inadequate to understand the
unique change of climate extremes in arid zones under the
global warming conditions. Most of aforementioned efforts
focused only on the detection of variability and trends in
climate extremes. To the best of our knowledge, reports in
constructing reliable scenarios of future climate extremes
in arid zones are very limited so far, motivating our research
conducted in this study. Our work strives to offer a comprehensive analysis of changes in temperature and precipitation extremes in a typical arid zone using the latest
observations (1960–2009) and CMIP3‐CGCM multimodel
ensemble projections (2010–2100) through the Bayesian
Model Averaging (BMA) approach. This article seeks to: (1)
identify spatial and inter‐annual changes in temperature and
precipitation extremes in the Tarim River Basin (1960–
2009) using the latest observations in the past five decades;
and (2) construct scenarios of climate extremes using multimodel ensemble projections (2010–2100) in the basin
provided by CMIP3 based on the BMA method.
2. Study Region
[6] The Tarim River Basin in Central Asia, one of the
world’s foremost endorheic drainage systems and the most
densely populated and dominated by an arid inland climate
[Chen et al., 2006], is selected in this study to demonstrate
the regional response to global climate change in arid zones.
The basin is well‐known internationally for its unique and
world’s largest “Pulpous Euphratica” gene library in an
extremely arid zone [Ministry of Water Resources (MWR),
1997–2000].
[7] Situated in the southern Xinjiang Autonomous Region
of Northwest China, the Tarim River is 1,321 km long with
a drainage area of 557,000 km2 (34°∼43°N, 73°∼93°E,
Figure 1). Generally, the drainage system is composed of
one mainstream (the Tarim River) and three major tributary
rivers (i.e., the Aksu River, Yarkant River, and Khotan
River) originated from the Tian Shan Mountain and Kunlun
Mountain systems (the northern edge of the Tibetan Plateau). Among these tributary streams, the Aksu River is the
most important tributary, accounting for 70–80 percents of
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Table 1. List of 20 Meteorological Gauges (1960–2009) in the
Tarim River Basina
Site Name
Site Number
Longitude
Latitude
Altitude (m asl)
Aksu
Baicheng
Luntai
Kuche
Kuerle
Wuqia
Kashi
Aheqi
Bachu
Keping
Tazhong
Tieganlike
Roqiang
Tashikuergan
Shache
Pishan
Hetian
Minfeng
Qiemo
Yutian
51628
51633
51642
51644
51656
51705
51709
51711
51716
51720
51747
51765
51777
51804
51811
51818
51828
51839
51855
51931
80°14′E
81°54′E
84°15′E
82°58′E
86°08′E
75°15′E
75°59′E
78°27′E
78°34′E
79°03′E
83°40′E
87°42′E
88°10′E
75°08′E
77°16′E
78°17′E
79°56′E
82°43′E
85°33′E
81°39′E
41°10′N
41°47′N
41°47′N
41°43′N
41°45′N
39°43′N
39°28′N
40°56′N
39°48′N
40°30′N
39°00′N
40°38′N
39°02′N
37°28′N
38°26′N
37°37′N
37°08′N
37°04′N
38°09′N
36°51′N
1,104
1,230
976
1,081
931
2,175
1,289
1,984
1,117
1,161
1,099
846
887
887
1,231
1,375
1,375
1,409
1,248
1,422
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with different interests, particularly irrigation and ecological
needs. With the negative impacts of global warming and
rapidly growing water consumption by human society in the
past, the oasis spreading along the downstream has been
gradually degrading. These intensified extreme droughts
have seriously threatened the sustainable socio‐economic
developmental efforts and triggered a series of serious issues
of desertification in the lower part of Tarim River. Hence,
the detection of historical changes and robust projection of
future spatiotemporal changes in climate extremes over the
basin is beneficial for formulating a sustainable regional
water resources management strategy in the arid region.
3. Data and Methods
a
Source of data: National Center of Climate, China.
total water volume. The basin is the driest region in Eurasia.
Its predominant part is occupied by the Taklamakan Desert,
whose sand area exceeds 270,000 km2. The basin is a relatively flat desert region with average annual precipitation
of less than 50 mm and the total annual runoff of about
280 × 108 m3. The runoff principally comes from high
mountain precipitation, and seasonal snow and glacier
melting water [MWR, 1997–2000].
[8] Currently, water resources in the basin have been well
developed and heavily committed for a variety of demands
in drinking water supply, irrigation, flood control, and
suppression of salinity intrusion for the past 30 years [MWR,
1997–2000]. The limited water resources severely conflict
3.1. Data Sources of Observation
[9] Daily precipitation, maximum and minimum temperatures during 1960–2009 are provided by the National
Climate Center, China Meteorological Administration. The
density of distribution and the quality of observational data
in China meet the World Meteorological Organization’s
standards at a total of 20 stations (Table 1) in the data sets.
Most stations were established in the 1950s, but any data
before 1960 was excluded due to quality reason.
3.2. Definition of Extreme Indices
[10] A suite of climate change indices on extremes were
developed by the joint CCl/CLIVAR/JCOMM Expert Team
(ET) on Climate Change Detection and Indices (ETCCDI,
http://ccma/seos.uvic.ca/ETCCDMI). In this effort, 27
indices based on daily temperature and precipitation data
were defined and software packages were developed for
end‐users. In this study, 12 temperature and 6 precipitation
indices (18 indices in total) are selected, many of which are
commonly used to validate climate model simulations
[Peterson et al., 2008]. Detailed descriptions are provided in
Table 2.
Table 2. List of the 18 Climate Indices for the Tarim River Basina
Index
Name
Definition
Units
Max Tmax
Max Tmin
Min Tmax
Min Tmin
Cold nights frequency
Cold days frequency
Warm nights frequency
Warm days frequency
Diurnal temperature range
Temperature
Annual count when TN(daily minimum) < 0°C
Annual count when TX(daily maximum) > 25°C
Annual count between first span of at least 6 days with TG > 5°C and first
span after July 1 of 6 days with TG < 5°C
Annual maximum value of daily maximum temperature
Annual maximum value of daily minimum temperature
Annual minimum value of daily maximum temperature
Annual minimum value of daily minimum temperature
Percentage of nights when TN < 10th percentile
Percentage of days when TX < 10th percentile
Percentage of nights when TN > 90th percentile
Percentage of days when TX > 90th percentile
Annual mean difference between TX and TN
°C
°C
°C
°C
%
%
%
%
°C
Max 1‐day precipitation
Max 5‐day precipitation
Simple daily precipitation intensity index
Consecutive dry days
Very wet day precipitation
Wet‐day precipitation
Precipitation
Annual maximum 1‐day precipitation
Annual maximum consecutive 5‐day precipitation
Average precipitation on wet days
Maximum number of consecutive days with RR < 1 mm
Annual total precipitation when RR > 95th percentile
Annual total precipitation in wet days (PR ≥ 1 mm)
mm
mm
mm/day
days
mm
mm
FD
SU
GSL
Frost day
Summer
Growing season length
TXx
TNx
TXn
TNn
TN10
TX10
TN90
TX90
DTR
RX1day
Rx5day
SDII
CDD
R95
PRCPTOT
days
days
days
a
All the indices are calculated by RClimDEX. Abbreviations are as follows: TX, daily maximum temperature; TN, daily minimum temperature; TG,
daily mean temperature; RR, daily precipitation. A wet day is defined when RR ≥ 1 mm, and a dry day is defined when RR < 1 mm.
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Table 3. List of Six IPCC AR4 Global Coupled Climate Models Providing Temperature and Precipitation Extremesa
Model
Country
Institution
CNRM‐CM3
France
MIROC3.2 medres
Japan
GFDL‐CM2.0
NCAR‐PCM
MRI‐CGCM2.3.2
USA
USA
Japan
IPSL_CM4
France
Center National Weather Research,
CNRM, METEO‐FRANCE
Center for Climate System Research
(The University of Tokyo),
National Institute for Environmental Studies,
and Frontier Research Center for Global Change
(JAMSTEC)
Geophysical Fluid Dynamics Laboratory, NOAA
The National Center for Atmospheric Research, NCAR
Meteorological Research Institute,
Japan Meteorological Agency, Japan
Institut Pierre Simon Laplace
Correlation Coefficient for
Daily Temperature
Correlation Coefficient for
Daily Precipitation
0.98
0.75
0.95
0.58
0.86
−0.25
0.74
0.17
−0.47
−0.50
0.36
−0.11
a
Three models set in bold (i.e., GFDL‐CM2.0, CNRM‐CM3 and MIROC3.2‐medres) are recommended and used for their relatively reasonable
performances in simulating the temperature and precipitation extremes over Tarim.
[11] The 18 indices were used primarily for the assessment of the many aspects of a changing global climate
covering changes in intensity, frequency and duration of
temperature and precipitation events [Alexander et al., 2006;
You et al., 2010]. According to Alexander et al. [2006], the
indices are grouped into five different categories: (1) percentile‐based indices, such as occurrence of cold nights
(TN10), (2) absolute indices represent maximum or minimum values within a season or a year, such as maximum
daily maximum temperature (TXx) and maximum 1‐day
precipitation amount (RX1day), (3) threshold indices
defined as the number of days on which a temperature or
precipitation value falls above or below a fixed threshold,
such as the number of frost days (FD), (4) duration indices
which define periods of excessive warmth, cold, wetness or
dryness (or in the case of growing season length, periods of
mildness), such as consecutive dry days (CDD), and (5)
other indices, such as diurnal temperature range (DTR).
Most indices have the same name and definition in previous
studies [Aguilar et al., 2005; Alexander et al., 2006; Klein
Tank et al., 2006; New et al., 2006; You et al., 2010],
although their exact definitions may vary slightly.
3.3. Ensemble Projection of Future Climate Extreme
[12] Table 3 lists the six IPCC AR4 global coupled climate
models providing temperature and precipitation extremes.
Three IPCC AR4 global coupled climate models were used
in this study, including GFDL‐CM2.0, CNRM‐CM3 and
MIROC3.2 (medres). These three models were selected
because they showed relatively reasonable performances
in simulating the temperature and precipitation extremes
over Tarim.
[13] This set of scenarios spans almost the entire IPCC
scenario range, with B1 being close to the low end of the
range (CO2 concentration of about 550 ppm by 2100), A2
to the high end of the range (CO2 concentration of about
850 ppm by 2100) and A1B to the middle of the range (CO2
concentration of about 700 ppm by 2100). The models
have different horizontal resolutions in the corresponding
atmospheric components as shown in Table 3. To obtain the
ensemble results of different models, the data were interpolated onto a common 1° × 1°grid. The data set was
obtained from the PCMDI web site (www.pcmdi‐llnl.gov)
and more information about the participating models and
data set can be found on this web site.
[14] Seven key indices (Table 4) suggested by Frich et al.
[2002] are chosen in constructing scenarios of future climate
extremes in the basin. FD and TN90 represent the extreme
temperature and the key indices for extreme precipitation are
CDD, R10, R5D, SDII and R95. For the latter indices,
higher values indicate more extreme precipitation. The CDD
is the length of dry spell whereas R10, R5D, SDII, and R95
stand for the intensity or frequency of precipitation. All
indices mentioned in this paper were calculated on an annual
basis under three emission scenarios and 5 models ensemble
means of the 20th century and the scenario simulations of
21st century, respectively.
3.4. Trend Free Pre‐whitening (TFPW) Approach
[15] Results of partial auto‐correlation tests indicated the
first‐order auto‐correlation exist in the climate series of
some stations in Tarim. Hence, the station‐based data were
corrected to reduce the effects of serial correlation through
the Trend Free Pre‐Whitening (TFPW) approach developed
Table 4. Seven Indices of Climate Extremes as Described by Frich et al. [2002]a
Index
Definitions
Units
FD
TN90
R10
CDD
R5D
SDII
Annual count when TN(daily minimum) < 0°C
Percentage of days when Tmin > 90th percentile
Annual count of days when RR > =10 mm
Maximum number of consecutive dry days with RR < 1 mm
Maximum 5‐day precipitation total
Simple daily intensity index: annual total precipitation divided by the number
of wet days (defined as RR > =1.0 mm) in the year
Annual total precipitation when RR > 95th percentile
days
days
days
days
mm
mm/day
R95
a
Acronyms are same as in Table 2.
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by Yue et al. [2002, 2003]. The TFPW involves estimating a
monotonic trend for the series, removing this trend prior to
pre‐whitening the series and finally adding the monotonic
trend calculated in the first step to the pre‐whitened data
series. In essence, the TFPW approach attempts to separate
the serial correlation that arises from a (linear) trend from
the remaining serial correlation and then only removes the
latter portion of the serial correlation. The TFPW‐MK
procedure of Yue et al. [2002] is applied in the following
manner to detect a significant trend in a serially correlated
time series:
[16] 1. The slope (b) of a trend in sample data is estimated
using the approach proposed by Sen [1968]. The original
sample data Xt were unitized by dividing each of their values
with the sample mean E(Xt) prior to conducting the trend
analysis [Yue et al., 2002]. By this treatment, the mean of
each data set is equal to one and the properties of the
original sample data remain unchanged. The trend is
assumed to be linear, and the sample data are detrended by:
Yt ¼ Xt Tt ¼ Xt ¼ t
ð1Þ
[17] 2. The lag‐1 serial correlation coefficient (r1) of the
detrended series Yt is computed. If r1 is not significantly
different from zero, the sample data are considered to be
serially independent and the MK test is directly applied to
the original sample data. Otherwise, it is considered to be
serially correlated and pre‐whitening is used to remove the
AR(1) process from the detrended series as follows:
Yt′ ¼ Yt r1 Yt1
K
X
p yjyT ¼
p yjMk ; yT p Mk jyT
ð3Þ
[20] The blended series (Y″t ) just includes a trend and a
noise and is no longer influenced by serial correlation. Then
the MK test is applied to the blended series to assess the
significance of the trend.
3.5. Mann‐Kendall Trend Analysis
[21] The Mann–Kendall test method [Mann, 1945;
Kendall, 1975; Yang et al., 2009, 2010] was used to detect
trends in regional series of annual climate extremes in this
study, because serial correlation of regional series is not
statistically significant (at the 5% level of significance)
according to the results of partial auto‐correlation test.
Meanwhile, the Sen’s slope method [Sen, 1968] was used to
estimate the regression coefficients or trend magnitudes
(slopes) based on Kendall’s tau.
3.6. Bayesian Moving Average (BMA)
3.6.1. General Formulation
[22] Bayesian model averaging (BMA) has recently been
proposed as a way of correcting under dispersion in
ð4Þ
k¼1
where p(y|Mk, yT) is the forecast pdf based on model Mk
alone, estimated from the training data; k is the number of
models being combined. p(Mk|yT) is the posterior probability
of model Mk being correct given the training data. This term
is computed with the aid of Bayes’ theory:
pðyT jMk ÞpðMk Þ
p Mk jyT ¼ k
P
pðyT jMl ÞpðMl Þ
ð5Þ
l¼1
[23] Considering the application of BMA to bias‐corrected
forecasts from the k models, equation (4) can be rewritten as:
k
X
p yjf1 . . . . . . f k ; yT ¼
!k pk yjfk ; yT
ð2Þ
[18] The residual series after applying the TFPW procedure should be an independent series.
[19] 3. The identified trend (Tt) and the residual Y′t are
combined as:
Yt′′ ¼ Yt′ þ Tt
ensemble forecasts [Raftery et al., 2005; Min and Hense,
2006]. BMA is a standard statistical procedure for combining predictive distributions from different sources and
provides a way of combining statistical models and at the
same time calibrating them using a training data set. The
output of BMA is a probability density function (pdf), which
is a weighted average of pdfs centered on the bias‐corrected
forecasts. The BMA weights reflect the relative contributions of the component models to the predictive skill over
a training sample. The combined forecast pdf of a variable
y is:
ð6Þ
k¼1
where wk = p(Mk|yT) is the BMA weight for model k computed from the training data set and reflects the relative
performance of models k on the training period. The weights
wk add up to 1, the conditional probabilities pk[y|(fk, yT)]
may be interpreted as the conditional pdf of y given fk (i.e.,
model k has been chosen) and training data yT. These
conditional pdfs are assumed to be normally distributed as:
yj fk ; yT N ak þ bk yk ; 2
ð7Þ
where the coefficients ak and bk are estimated from the bias‐
correction procedures described above. This means that the
BMA predictive distribution becomes a weighted sum of
normal distributions with equal variances but center at the
bias‐corrected forecast which can also be obtained from the
BMA distribution using the conditional expectation of y
given the forecasts:
k
X
E yj f1 . . . . . . f ; yT ¼
!k ðak þ bk fk Þ
ð8Þ
k¼1
[24] This forecast would be expected to be more skilful
than either the ensemble mean or any one member, since it
has been determined from an ensemble distribution that has
had its first and second moments bias‐corrected using recent
verification data for all the ensemble members. It is essentially an “intelligent” consensus forecast, weighted by the
recent performance results for the component models.
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3.6.2. Model Weights
[25] The BMA weights and the variance s2 are estimated
using maximum likelihood [Raftery et al., 2005]. For given
parameters to be estimated, the likelihood function is the
probability of the training data and is viewed as a function
of the parameters. The weights and variance are chosen so
as to maximize this function (i.e., the parameter values for
which the observation data were most likely to have been
observed). The algorithm used to calculate the BMA
weights and variance is called the expectation maximization
(EM) algorithm [Dempster et al., 1977]. The method is
iterative and converges to a local maximum of the likelihood. The detailed description of the BMA method is provided by Raftery et al. [2005], and more complete details of
the EM algorithm by McLachlan and Krishnan [1997]. The
value of s2 is related to the total pooled error variance over
all the models in the training data set.
3.6.3. Length of Training Period
[26] In climate research, the longer the training period is,
the better the BMA parameters are estimated. In this study, a
40‐year period (1960–2000) was used to train BMA weights
for five CGCM models under the 20C3M emission scenario.
The rest period (2001–2100) was used in generating present
and future scenarios of climate extremes in the Tarim River
Basin. As three emission scenarios (A2, A1B and B1 scenario) are involved, the period (2001–2009) was not
included in BMA training herein. Seven indices (i.e., FD,
TN90, R10, CDD, R5D, SDII, and R95) produced by five
CGCMs (Table 3) were used in constructing the present
hindcast and future projection of climate extremes.
4. Results
4.1. Observed Change of Climate Extremes in the Past
Five Decades
[27] The analysis of temperature and precipitation reveals
a variety of changes in extremes (1960–2009) in the Tarim
River Basin. Spatial patterns of trends in temperature
extremes have a much higher degree of coherence while
precipitation in the region has more variability. The results
for indices in climate extremes from the past observations
are presented as below. As some station‐based datum show
autocorrelation (only first order), the TFPW approach is
used to correct these datum. For the regional series of climate extremes, we also compared the TFPW results with the
normal M‐K results and found that they are quite similar
(due to slight auto‐correlation in the regional scale). Hence,
the Mann‐Kendall approach is used for regional series as
they are not significantly autocorrelated. Meanwhile, the
magnitudes of trend are estimated by Sen’s slope estimator
[Sen, 1968].
4.1.1. Cold Extremes (TX10, TN10, TXn, TNn, FD)
[28] Figure 2 shows the spatial pattern of the trends of cold
extremes for the 20 meteorological stations and Figure 3
demonstrates the regional annual series for indices of cold
extremes in Tarim. The regional trends in indices of cold
extremes are shown in Table 5. For cold nights (TN10,
Figures 2a and 3a) and cold days (TX10, Figures 2b and 3b),
about 80% and 75% of stations have decreasing trends which
are statistically significant. Except for some dispersed small
areas and the station Tashikuergan, significantly negative
trends for TN10 and TX10 are observed in the rest region.
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[29] Positive trends for the temperatures of coldest days
in each year (TXn, Figures 2c and 3c) are observed over
the whole Tarim except in station Tashikuergan. A similar
pattern for the temperatures of coldest nights in each year
(TNn, Figures 2d and 3d) is also found over Tarim. In
addition to the above mentioned station, a region in central
Tarim shows a negative trend for the TNn index. The TXn
and TNn show increasing trends at approximately 90–95%
of stations (Figures 2c and 3c). But only 26% and 71% of
stations for these two indices have statistically significant
trends due to the high variability. Positive trends in TNn at
these stations are generally stronger than those at most
stations.
[30] Strongly negative trends in the number of the frost
days (FD, Figures 2e and 3e) are found in Tarim except in
station Tashikuergan. About 78% of stations show statistically significant decreasing trends and stations with pronounced trend magnitudes are distributed in the western and
southwestern basin.
4.1.2. Diurnal Temperature Range (DTR)
[31] Figure 4 shows the spatial pattern of the trends of
diurnal temperature range (DTR) for the 20 meteorological
stations, and Figure 5 demonstrates the regional annual DTR
series. Negative trends in diurnal temperature range (DTR,
Figure 4) are found at 14 stations over Tarim. Positive
trends in 6 stations are generally strong and significant. The
strongest increasing trend at Qiemo is a linear trend of
+0.026°C per decade. But negative trends are more obvious,
approximately 60% of stations (Figure 4) show statistically
significant decreasing trends and most stations are situated
in the northern and western Tarim.
4.1.3. Warm Extremes (TX90, TN90, TXx, TNx,
GSL, SU)
[32] The spatial patterns of observed trends and the
regional annual series of warm extremes during 1961–2009
over Tarim are provided in Figures 6 and 7. The regional
trends in indices of cold extremes are shown in Table 5. For
the percentage of days exceeding the 90th percentiles
(TX90, Figure 6a, and TN90, Figure 6b), statistically significant increasing trends are observed at 55% and 75% of
stations. The patterns for the TX90 and TN90 indices are
very similar, positive trends for most stations are statistically significant. Possible reasons for having such patterns
might be due to global warming and urbanization. More
than 75% of the basin showed positive trends for TXx
(Figure 6c) including the northern Tarim, Kuluke Desert
and Taklimakan Desert. The rest area where mainly located
in the western Tarim shows negative trends. Tashikuergan
is the only station which has significant negative trend for
the index. Except for a small region in west Tarim, the rest
parts show significant positive trends for TNx (Figure 6d).
About 40% and 60% of stations have statistically significant
increasing trends for TXx and TNx. Meanwhile, positive
trends for growing season length (GSL, Figure 6e) are
found over the whole Tarim excluding Tashikuergan. 75%
of stations mainly located in the western and northwestern
Tarim for this index have statistically significant positive
trends. For the summer days indices (SU, Figure 6f), the
spatial pattern is similar to the GSL index. In addition,
Aheqi also shows negative trend, but it is very weak and not
meaningful for this region.
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Figure 2. Spatial patterns of observed trends per decade during 1960–2009 in Tarim for indices of cold
extremes: (a) TN10, (b) TX10, (c) TXn, (d) TNn and (e) FD. Positive/negative trends are shown as
up/down triangles, and the filled symbols represent statistically significant trends (significant at the
0.05 level). The size of the triangles is proportional to the magnitude of the trends.
4.1.4. Comparison of Warm and Cold Extremes
[33] In order to understand the relative changes of daily
temperature, it is essential to compare trends in warm and
cold indices, which are listed in Table 6.
[34] About 45% of stations have higher trend magnitudes in TX90 than in TX10, and the absolute value
of regional trend in TX90 is higher than that of TX10
(Table 6). For minimum temperature, the regional trend in
TN90 (0.35 d/decade, Table 6) is higher than that of
TN10 (−0.29 d/decade).While in individual stations, about
35% of stations have higher trend magnitudes in TN90
than in TN10. For TXx and TXn, regional trend in TXn
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Figure 3. Regional annual series for indices of observed cold extremes: (a) TN10, (b) TX10, (c) TXn,
(d) TNn and (e) FD. The red line is the linear trend.
(0.05°C/decade, Table 6) is higher than TXx (0.01°C/
decade, Table 6), but only 15% of stations show larger
trends in TXn. The magnitude of the regional trend in TNn
is more than 3 times higher than that of TNx. At individual
stations, about 70% of stations have greater trend magnitudes in TNn. Therefore, changes in TN90 and TX90 are
more remarkable than changes in TN10 and TX10, while
TNx and TXx seem to have smaller trend magnitudes
than TNn and TXn.
4.1.5. Precipitation Extremes (SDII, RX1day, RX5day,
R95, CDD, and PRCPTOT)
[35] Spatial distribution of temporal trends is shown
in Figure 8 and regional annual series for precipitation
indices are shown in Figure 9. In contrast to the temperature
extremes, the significance of changes in precipitation
extremes is low as suggested in Figures 8 and 9.
[36] Eight out of 20 stations show negative trends and
the rest 12 stations show positive trends for the simply
daily intensity index (SDII, Figure 8a). The strongest
negative and positive trends for the index occurred in
stations Tashikuergan and Shache respectively. The Shache
station in the western Tarim is the only station that shows
statistically significant trend.
[37] For the maximum 1‐day and 5 day precipitation
(RX1day and RX5day, Figures 8b and 8c), if Tarim
is separated into two non‐equal parts by a northeast‐
southwestward line, slightly negative trends can be observed
in the southwest part and mixed negative and positive trends
in northeast. This can be explained by that the south region
surrounded by the Taklimagan and Kuleke Desert is
extremely arid and has experienced serious droughts. About
55% and 50% of stations indicate negative trends for
RX1day and RX5day, and the highest negative trends are
detected at stations Kuerle and Kashi. Positive trends for
the RX5day index are observed in a vast area located in the
north and northwest Tarim.
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Table 5. Annual Trends, With 95% Confidence Intervals in
Parentheses, for Regional Indices of Temperature and Precipitation
Extremesa
Index
Units
1960–2009
TN10
TX10
TN90
TX90
DTR
TXx
TNx
TXn
TNn
FD
GSL
SU
Temperature
days/year
days/year
days/year
days/year
°C/year
°C/year
°C/year
°C/year
°C/year
days/year
days/year
days/year
−0.29 (−0.68 to 1.66)
−0.15 (−0.41 to 1.20)
0.35 (−0.21 to 1.07)
0.24 (−0.36 to 1.40)
−0.12 (−0.12 to 0.23)
0.01 (−0.20 to 0.19)
0.03 (−0.20 to 0.19)
0.15 (−0.12 to 0.30)
0.18 (−0.22 to 0.44)
−0.40 (−1.81 to 1.36)
0.34 (−1.25 to 1.63)
0.41 (−2.56 to 2.70)
PRCPTOT
SDII
RX1day
Rx5day
R95
CDD
Precipitation
mm/year
mm/year
mm/year
mm/year
mm/year
days/year
0.003 (−0.045 to 0.045)
0 (−0.02 to 0.01)
0 (−0.014 to 0.011)
0 (−0.035 to 0.018)
0.002 (−0.017 to 0.02)
2.59 (−10.03 to 22.17)
a
Values for trends significant at the 5% level (t test) are set in bold face.
[38] Positive trends of very wet days (R95, Figure 8d) are
detected in the northern and western Tarim. The proportion
of stations with positive trends for this index is 65%. Stations in the eastern Tarim have slightly positive or negative
trends. The strongest positive and negative trends for R95
are found at stations Kashi and Kuerle, respectively.
[39] Positive trends of the consecutive dry days index
(CDD, Figure 8e) are found in many parts of Tarim. In
addition to the two stations (Tazhong and Roqiang) located
in the Taklimakan Desert and Kuluke Desert, Kashi in the
western Tarim, also has statistically significant increasing
trend.
[40] In the northern and western Tarim, annual total
precipitation (PRCPTOT, Figure 8f) shows positive trends
Figure 4. Spatial patterns of observed trends per decade
during 1960–2009 in Tarim for DTR index. Positive/negative
trends are shown as up/down triangles, and the filled symbols
represent statistically significant trends (significant at the
0.05 level). The size of the triangles is proportional to the
magnitude of the trends.
Figure 5. Regional annual series for observed DTR indices.
The red line is the linear trend.
and about 35% of stations have decreasing trends mostly
occurring in the central and southern Tarim. No station has
statistically significant trends. The strongest positive trend
is found at the station Baicheng and the strongest negative
trend is found at the station Kuerle. Both of these 2 stations
are located in the northern Tarim.
4.2. Multimodel Ensemble Projections of Climate
Extreme in the 21st Century
[41] In this section, multimodel ensemble projected future
changes for the 7 temperature and precipitation‐based
indices (Table 4) over Tarim based on the BMA method are
presented. To answer the growing public concerns on climate change of Tarim in the forthcoming decade of 21st
century, we mainly demonstrated and addressed the spatial
changes in the 2010s under A1B scenario for sake of brevity
as well. However, the temporal change processes in climate
extremes (1980–2100) under all the four scenarios (20C3M,
A2, A1B and B1) are addressed to show all changes.
4.2.1. Temperature‐Based Extremes
[42] A general increase of TN90 is observed in Tarim,
indicating longer warm nights (Figure 10a) in the second
decade of 21st century. The increase is more pronounced in
the southern Tarim. The growth rate is about 4% to 5% over
Tarim. As indicated by Figure 10b, consistent increases of
TN90 can be commonly found in the 21st century under all
three emission scenarios (A2, A1B and B1). However, the
increases are not sensitive to the emission scenarios up till
2050. Since then, more pronounced increase of the TN90
index can be found in A2 and A1B scenario when compared
with B1. In the end of 21st century, the increase of TN90
is around 63% (A2), 53% (A1B), and 39% (B1). There are
some differences of the increase among the 3 scenarios
and they are usually consistent with emission values.
[43] Changes of FD are presented in Figures 10c and 10d.
Pronounced negative change is found in the western basin
(Figure 10c). The decrease is around 9–20 days. Slight
decrease (4–9 days) is found significant in the eastern
Tarim. The temporal evolution of FD (Figure 10d) shows
an obviously consistent decrease in the 21st century. The
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Figure 6. Spatial patterns of observed trends per decade during 1960–2009 in Tarim for indices of warm
extremes: (a) TX90, (b) TN90, (c) TXx, (d) TNx, (e) GSL and (f) SU. Positive/negative trends are shown
as up/down triangles, and the filled symbols represent statistically significant trends (significant at the
0.05 level). The size of the triangles is proportional to the magnitude of the trends.
decrease is similar under A1B and A2 scenarios while relatively smaller decrease is found under B1 scenario.
4.2.2. Precipitation‐Based Extremes
[44] Over the whole Tarim, CDD (consecutive dry days,
Figure 11a) shows a negative change in the 2020s compared
with 2000s. In the eastern Tarim, CDD tends to reduce
notably to 129 days. CDD is decreasing (94 days/decade) at
stations Aheqi and Keping, indicating a higher possibility of
future humid in future ten years induced by impacts of
global warming on glacier recession and snowmelt from the
Tianshan Mountains. From north to south, these decreasing
trends become slight. The regional mean CDD shows a
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Figure 7. Regional annual series for indices of warm extremes: (a) TX90, (b) TN90, (c) TXx, (d) TNx,
(e) GSL and (f) SU. The red line is the linear trend.
slight increase in the temporal processes during the 21st
century (Figure 11b). The trend magnitude is much smaller
than TN90. Difference among the 3 emission scenarios
is small and the curves overlap each other until the end of
the century.
[45] Changes in R10 are presented in Figure 11c, which
shows a negative changes. A decrease around 4–8 days is
found in the south Tarim. Meanwhile, increasing R10 is also
found in the southwest Tarim, indicating more floods.
Temporal change of R10 shows similar features with CDD
(Figure 11d). The non‐significant change in the time series
may be due to the incoherence of changes.
[46] The maximum 5d precipitation total (RX5day,
Figure 12a) is an important index for flood events. As
implied by Figure 12a, we can found increasing RX5day
in East and decreasing RX5day in West. Increase of
RX5day ranges from 0 to 14 mm, and decrease is found in
0–25 mm. However, increase of RX5day (0–14 mm) in the
Taklimakan and Kuluke Desert cannot be transformed into
Table 6. Number and Proportion of Individual Stations Where the
Trend in One Index is of Greater Magnitude Than the Trend in a
Second Indexa
Index
Comparison
Percentage (%)
TX90 > TX10
TN90 > TN10
TXx > TXn
TNx > TNn
TXx > TNx
TXn > TNn
Tx90 > TN90
TX90 > TN10
TX10 > TN10
TN90 > TX10
abs
abs
rel
rel
rel
rel
abs
abs
abs
abs
45
35
15
30
15
25
25
20
20
65
a
Abbreviations are as follows: abs indicates that the absolute magnitudes
of trends are compared; rel indicates that the signs of trends are retained
during comparison.
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Figure 8. Spatial patterns of observed trends per decade during 1960–2009 in Tarim for indices of precipitation extremes: (a) SDII, (b) RX1Day, (c) RX5Day, (d) R95, (e) CDD, and (f) PRCPTOT. Positive/
negative trends are shown as up/down triangles, and the filled symbols represent statistically significant
trends (significant at the 0.05 level). The size of the triangles is proportional to the magnitude of the trends.
effective runoff because of the strong potential evaporation
(PET) in the desert. As showed in the Figure 12b, change
of temporal processes of RX5day is different in three
scenarios before 2060. After that, Difference across the 3
scenarios is small and the curves overlap each other until
the end of the century.
[47] Changes in SDII (Figure 12c) show a general
decreasing pattern over the Tarim. The decrease is more
significant, especially in the Tashikuegan, Wuqiai, Kashi
and Shache station located in the west Tarim (in the range of
3.4–4.8 mm, indicating a higher possibility of future drought
there). The increase of R95 (Figure 12e), defined as the
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Figure 9. Regional annual series for observed precipitation indices: (a) SDII, (b) RX1Day, (c) RX5Day,
(d) R95, (e) CDD, and (f) PRCPTOT. The red line is the linear trend.
fraction of annual total precipitation from wetter than the
95th percentile of wet days (≧1 mm), is generally in the
range of 10% in the east Tarim. Decreasing R95 is significant in west Tarim.
[48] Temporal changes of SDII (Figure 12d) and R95
(Figure 12e) show similar features, characterized by slight
increases under all three scenarios in the 21st century. The
increase is similar under A1B and A2, while they are not
significant under the B1 scenario, particularly in the last
20 years of the 21st century. Compared to the temperature
indices (TN90 and FD, Figures 10b and 10d), changes of the
precipitation indices under the different scenarios are not
distinct.
5. Conclusion and Discussions
[49] General findings and results in detecting changes of
climate extremes using a wide range of statistical testing
methods were presented by Easterling et al. [2000a, 2000b]
and Alexander et al. [2006] for the whole world, Choi et al.
[2009] for the Asia‐Pacific Network (APN) region, Aguilar
et al. [2005, 2009] for the Central America and northern
South America, western central Africa, Zhai et al. [2005]
and You et al. [2010] for China, Yang et al. [2009, 2010]
for south China, and You et al. [2008] for the Tibetan Plateau. However, reports in constructing reliable scenarios of
future climate extremes in the Tarim River Basin of
Northwest China are highly inadequate so far. Even in the
study by Klein Tank et al. [2006] on change of climate
extremes in central and south Asia based on observations
from 116 meteorological stations (1961–2000), only 1–2
stations were used for the basin. Hence, it is hard to help us
in understanding the spatiotemporal change patterns of climate extremes in Tarim. Meanwhile, the observations in the
second half of 20th century were out of date, major changes
in the first decade in the 21st century need to be re‐investigated using the latest datum. This work strives to present
changes in temperature and precipitation extremes in Tarim
using the latest observations (1960–2009) and CMIP3‐
CGCM multimodel ensemble projections (2010–2100). The
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Figure 10. Spatial patterns of (a) TN90 (%) and (c) FD (days) change of multimodel ensemble using
BMA method under A1B scenario (shown is the difference between two ten–years averages: 2011–
2020 minus 1991–2000), and (b, d) their temporal change in Tarim under A2, A1B and B1 scenarios
(three scenarios are shown in different styles or color for the years from 1980–2100, compared with
the 1980–1999 mean).
major findings of the article are summarized and discussed
as following:
[50] 1. The 18 indices defined by the ETCCDI derived
from stations over Tarim River basin during 1960–2009
were analyzed. In general, both warm and cold extremes (12
indices) have shown stronger trends in past five decades.
Meanwhile, only slight changes in precipitation extremes
can be observed, which means there were no overwhelming
trends in precipitation extremes over Tarim in 1960–2009.
From the spatial perspective, more significantly upward
(downward) warm (cold) extremes are found around the
desert zones than in upstream mountain zones (i.e., the Tian
Shan and Kunlun Mountains systems which surround the
basin). However, there are no identical governing spatial
patterns for extreme precipitation change over Tarim,
although some increases (decreases) are observed in lower
Tarim River course (some upstream mountain zones).
[51] More specifically, negative trends of indices
representing cold temperature extremes (i.e.TX10, TN10,
FD) and conversely, positive trends for indices representing
warm maximum and minimum temperature extremes (i.e.,
TX90, TN90, GSL, and SU25) are found in many regions of
the basin. Meanwhile, the magnitudes of trends for cold/
warm nights are higher than those for cold/warm days,
thus trends in minimum temperature extremes are more
significant than maximum temperature extremes, which is
consistent with the observed decreases in DTR index
[Easterling et al., 1997]. The decrease in Tarim is stronger
like other parts of China than reported in all other regions
[Alexander et al., 2006; You et al., 2010]. This may be
associated with rapid urbanization, increased aerosol loading and other land use change. Same as other regions in
China, Tarim has experienced rapid urbanization and dramatic economic growth since 1970s. Population of Tarim
increased sharply from 1.7 million in 1970 to 4.2 million in
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Figure 11. Spatial patterns of (a) CDD (days) and (c) R10 (days) change of multimodel ensemble using
BMA method under A1B scenarios (shown is the difference between two ten–years averages: 2011–2020
minus 1991–2000), and (b, d) their temporal change in Tarim under A2, A1B and B1 scenarios (three
scenarios are shown in different styles or color for the years from 1980–2100, compared with the
1980–1999 mean).
2000 [Tong, 2004], and rapid growth of urbanization and
economy exerted effects on local climate. Many studies
have shown these changes have strong effects on regional
climate [e.g., You et al., 2008]. The warming climate causes
the number of frost days (FD) to decrease significantly. This
agrees with the findings in the World, APN region and
China in past 50 years [Alexander et al., 2006; Choi et al.,
2009; You et al., 2008]. Generally, increasing frequency and
intensity of droughts are attributed to the warming climate in
summer, normally drier months and ENSO events, particularly in the inland and arid zones like Tarim. Previous
studies have confirmed this point in many parts of Asia
[IPCC, 2007]. In addition, most temperature indices show
spatially uniform patterns over the basin, even though the
climate varies across the region.
[52] Although it is likely that there has been a statistically
significant 2% to 4% increase in the frequency of heavy and
extreme precipitation events when averaged across the
middle and high latitudes [IPCC, 2007], our analyses indi-
cated that the change of rainfall statistics through the second
half of 20th century is dominated by variations on the inter‐
annual to inter‐decadal time scale and that trend estimates
are spatially incoherent. This result is consistent with a
number of available reports [Alexander et al., 2006; You
et al., 2008; Choi et al., 2009]. Besides, though IPCC
found increasing trends for extreme precipitation for many
locations throughout the world [IPCC, 2007], both slightly
positive and negative trends are found over Tarim. However, more than half of Tarim exhibited a positive trend for
the annual wet‐day precipitation (PRCPTOT) index.
[53] 2. By using the results from an ensemble of five
CMIP3‐CGCM models based on the BMA method [Raftery
et al., 2005], the scenarios of future climate extremes (2010–
2100) over Tarim are constructed and analyzed. Generally,
trends of these multimodel ensemble projections are consistent with the observations in past five decades (1960–
2009), which means the projections can reproduce the main
features of climate extremes in past. This further confirms
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Figure 12. Spatial patterns of (a) RX5day (mm), (c) SDII (mm) and (e) R95 (%) change of multimodel
ensemble using BMA method under A1B scenarios (shown is the difference between two ten–years
averages: 2011–2020 minus 1991–2000), and (b, d, f) there temporal change in Tarim under A2, A1B
and B1 scenarios (three scenarios are shown in different styles or color for the years from 1980–2100,
compared with the 1980–1999 mean).
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our findings in the spatiotemporal changes of climate
extremes in the past five decades, namely, the most significant changes of extreme precipitation and temperature over
Tarim are projected to show high variations in spatiotemporal scale which results in more frequent droughts with
higher intensity and floods as well.
[54] In the climate projections for the 21st century, the
extreme temperature indices (e.g., FD and TN90) show
significant increases over Tarim in the end of the 21st
century, indicating more frequent warm nights in the future.
The increase under A2 and A1B scenarios is generally more
pronounced than under B1, in correspondence to their
greenhouse gas (GHG) concentration levels. Meanwhile,
general increase of CDD is found over Tarim in the future.
The increased CDD, together with the increases in the
temperature indices may lead to more frequent droughts in
the future. Slight increase of R10 in some areas, and
increase of R5D, SDII, and R95 in the whole region indicate
a probability of flood occurrence in Tarim along with the
drought dominated in future. This finding is different from
the tropical and sub‐tropical monsoon dominated regions,
where significant increase of precipitation extremes was
projected. According to IPCC [2007], some global models
projected that during the warmer 21st century, precipitation
will decrease in the subtropical regions and become more
concentrated in intense rainfall events with a greater risk of
droughts. In contrast, precipitation is projected to increase in
the mid‐high latitude regions (e.g., the Yangtze River basin
in China [Xu et al., 2009]). From a regional and local perspective, there are many influences on climate in addition to
broad global changes, including urbanization and terrain,
and proximity to water bodies. For instance, the magnitudes
of changes in temperature extremes over Tarim in continental
regions are generally higher than island countries, e.g., Fiji
and New Zealand [Choi et al., 2009]. This is partly because
the moist atmosphere near the oceans may subdue the
occurrences of extreme temperature events due to its high
heat capacity compared with the drier inland atmosphere.
While the magnitudes of changes in precipitation extremes
over Tarim are smaller than island countries however,
induced by the far proximity to water bodies.
[55] Although some preliminary results of changes in
extreme indices over Tarim are obtained in the present work,
a number of uncertainties still exist in assessing the changes
of regional‐scale extreme indices. More research work in
the future, particularly the ensemble projections by higher
resolution CGCMs or especially regional climate models
(RCMs), as well as analyzing the uncertainties related to the
model spread, are needed for a more profound understanding of the futures changes in climate extremes over the arid
region. Meanwhile, for a large study region or a region with
high heterogeneity in climate change, field significance
using appropriate methods (e.g., the bootstrap resampling
technique [Burn and Hag Elnur, 2002]) should be used in
trend analysis in order to obtain better understanding of the
spatial pattern of change.
[56] Acknowledgments. The work was jointly supported by grants
from the National Natural Science Foundation of China (40901016,
40830639, 40830640), a grant from the State Key Laboratory of Hydrology‐Water Resources and Hydraulic Engineering (2009586612,
2009585512), and grants of Special Public Sector Research Program of
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Ministry of Water Resources (201001066, 201001057), the National Basic
Research Program of C hina “973 Program” (2010CB428405,
2010CB951101, 2010CB951003), and the Fundamental Research Funds
for the Central Universities (2010B00714). The authors acknowledge the
modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modeling
(WGCM) for their roles in making the WCRP CMIP3 multimodel data set
available. The IPCC Data Archive at Lawrence Livermore National Laboratory is supported by the Office of Science, U.S. Department of Energy.
Finally, cordial thanks are also extended to the editor, Sara C. Pryor, and
two referees for their valuable comments which greatly improved the quality of this paper.
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