Changes of climate extremes in a typical arid zone: Observationsand 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) intemperature 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) andCoupled General Circulation Model (CGCM) multimodel ensemble projections(2010–2100) using the Bayesian Model Average (BMA) approach are employed foranalysis in this study. Results indicate: (1) Most warm (cold) extreme temperatureindices have shown significantly positive (negative) trends in the Tarim River Basinin 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 thedesert zones than in upstream mountain zones (i.e., the Tian Shan Mountain and KunlunMountain systems which surround the basin). Whereas, there are no identical spatialpatterns for the change in extreme precipitation; (2) Ensemble of five CGCM models inPhase 3 of the Coupled Model Intercomparison Project (CMIP3) based on the BMAmethod suggests that the increasing consecutive dry days (CDD), together with thedecreasing frost day (FD) and increasing warm nights frequency (TN90) may lead to morefrequent droughts in Tarim in future. Meanwhile, slight increase of annual count ofdays 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 insummer together with frequent occurrence of droughts. The results can provide beneficialreference to water resource and eco‐environment management strategies in arid zonesfor 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 climateextremes 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; Vincentand Mekis, 2006], were conducted in past years. Based ondaily station data across the world for the second half of the20th century, Frich et al. [2002] found coherent patterns of

statistically significant changes in some indices for tem-perature extremes, such as an increase in warm summernights and a decrease in the annual number of frost days.Precipitation indices showed more mixed patterns ofchange, but significant increases were detected in the totalsderived from wet spells in some regions. In another global‐scale investigation by analyzing gridded annual and sea-sonal mean data, Horton et al. [2001] reported an increase inwarm extremes and a decrease in cold extremes in oceansurface temperatures since the late 19th century. Regionalstudies on climatic extremes have also been conducted inmany parts of the world. A variety of those reports werefound in Southeast Asia and the South Pacific [Griffithset 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 Americaand northern South America [Aguilar et al., 2005], Centraland south Asia [Klein Tank et al., 2006], Asia‐Pacific Net-work region [Choi et al., 2009], China [You et al., 2010; Zhai

1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Instituteof Ecology and Geography, Chinese Academy of Sciences, Urumqi, China.

2State Key Laboratory of Hydrology‐Water Resources and HydraulicEngineering, Hohai University, Nanjing, China.

3Department of Geoscience, University of Nevada, Las Vegas, Nevada,USA.

4Mathematics, Informatics and Statistics CSIRO, Wembley, WesternAustralia, Australia.

5Department of Geosciences, University of Oslo, Oslo, Norway.6Institute of Geographical Sciences and Natural Resources Research,

Chinese Academy of Science, Beijing, China.

Copyright 2011 by the American Geophysical Union.0148‐0227/11/2010JD015192

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and Pan, 2003; Zhai et al., 1999, 2005; Xu et al., 2009],Western central Africa [Aguilar et al., 2009] and NorthAmerica [Peterson et al., 2008]. Results obtained fromabove studies indicated that there is remarkable consistencyin temperature extremes, but less spatial coherence in pre-cipitation extremes.[3] Substantial progress in both global and regional

modeling at medium to high resolution allowed for anincreasing number of studies on modeling climate changes.Recent modeling efforts have enabled us to characterizechanges in climate extremes with closely relevance toimpacts than the traditional climate model outputs of meantemperature 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 worldconducted climate change simulation by different CoupledGeneral Circulation Models (CGCM) [IPCC, 2007]. Thisconstitutes Phase 3 of the Coupled Model IntercomparisonProject (CMIP3) [Meehl et al., 2007] ensemble of simula-tions. These models provided extreme indices/indicators forthe present and future climates, offering opportunities toconduct the multimodel ensemble analysis of the simulationand projection of climate extremes. For example, Tebaldiet al. [2006] analyzed historical and future simulations ofthese indicators derived from an ensemble of nine CMIP3‐CGCM models under a range of emission scenarios, andfound that on global and continental scales, the simulatedhistorical trends generally agree with previous observationalstudies, providing a sense of reliability for the simulations.[4] Meanwhile, the Bayesian model averaging (BMA)

approach is introduced to model evaluation and multimodelaveraging with a systematic consideration of modernuncertainty in climate impact research [Min et al., 2004,2005; Min and Hense, 2006], and its application to globalmean surface air temperature (SAT) changes is shownfrom multi Atmosphere‐Ocean General Circulation Model(AOGCM) [IPCC, 2007] ensembles of IPCC AR4 simula-tions. BMA provides a way to combine different models andis a rather promising method for calibrating ensemble inmodeling and forecasts. BMA is also a method of combin-ing forecasts from different sources into a consensus prob-ability distribution function (PDF), an ensemble analog to

consensus forecasting methods applied to deterministicforecasts from different sources [Krishnamurti et al., 1999].BMA naturally applies ensemble systems to make a set ofdiscrete models (such as the Canadian ensemble system). InBMA, the overall forecast PDF is a weighted average ofindividual forecast PDFs. The weights are the estimatedposterior model probabilities and reflect the forecast skill ofindividual models in the training period. The weights canalso provide a basis for selecting ensemble members: thereis only little loss by removing the ensemble member withsmall weights [Raftery et al., 2005; Wilson et al., 2007].This can be a useful strategy, given that the computationalcost of running ensembles is more affordable nowadays.Due to pronounced advantages, increasing studies usingvarious BMA methods for climate change detection andattribution [Min et al., 2004, 2005] as well as for futureprojections of climate changes [Tebaldi et al., 2006] werereported.[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 changesof climate extremes are still inadequate to understand theunique change of climate extremes in arid zones under theglobal warming conditions. Most of aforementioned effortsfocused only on the detection of variability and trends inclimate extremes. To the best of our knowledge, reports inconstructing reliable scenarios of future climate extremesin arid zones are very limited so far, motivating our researchconducted in this study. Our work strives to offer a com-prehensive analysis of changes in temperature and precipi-tation extremes in a typical arid zone using the latestobservations (1960–2009) and CMIP3‐CGCM multimodelensemble projections (2010–2100) through the BayesianModel Averaging (BMA) approach. This article seeks to: (1)identify spatial and inter‐annual changes in temperature andprecipitation 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 mul-timodel ensemble projections (2010–2100) in the basinprovided by CMIP3 based on the BMA method.

2. Study Region

[6] The Tarim River Basin in Central Asia, one of theworld’s foremost endorheic drainage systems and the mostdensely populated and dominated by an arid inland climate[Chen et al., 2006], is selected in this study to demonstratethe regional response to global climate change in arid zones.The basin is well‐known internationally for its unique andworld’s largest “Pulpous Euphratica” gene library in anextremely 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 witha drainage area of 557,000 km2 (34°∼43°N, 73°∼93°E,Figure 1). Generally, the drainage system is composed ofone mainstream (the Tarim River) and three major tributaryrivers (i.e., the Aksu River, Yarkant River, and KhotanRiver) originated from the Tian Shan Mountain and KunlunMountain systems (the northern edge of the Tibetan Pla-teau). Among these tributary streams, the Aksu River is themost important tributary, accounting for 70–80 percents of

Figure 1. Map of the Tarim River Basin, in which, namesfor the mainstream and three major tributary rivers are set inbold.

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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 rel-atively flat desert region with average annual precipitationof less than 50 mm and the total annual runoff of about280 × 108 m3. The runoff principally comes from highmountain precipitation, and seasonal snow and glaciermelting water [MWR, 1997–2000].[8] Currently, water resources in the basin have been well

developed and heavily committed for a variety of demandsin drinking water supply, irrigation, flood control, andsuppression of salinity intrusion for the past 30 years [MWR,1997–2000]. The limited water resources severely conflict

with different interests, particularly irrigation and ecologicalneeds. With the negative impacts of global warming andrapidly growing water consumption by human society in thepast, the oasis spreading along the downstream has beengradually degrading. These intensified extreme droughtshave seriously threatened the sustainable socio‐economicdevelopmental efforts and triggered a series of serious issuesof desertification in the lower part of Tarim River. Hence,the detection of historical changes and robust projection offuture spatiotemporal changes in climate extremes over thebasin is beneficial for formulating a sustainable regionalwater resources management strategy in the arid region.

3. Data and Methods

3.1. Data Sources of Observation

[9] Daily precipitation, maximum and minimum tem-peratures during 1960–2009 are provided by the NationalClimate Center, China Meteorological Administration. Thedensity of distribution and the quality of observational datain China meet the World Meteorological Organization’sstandards at a total of 20 stations (Table 1) in the data sets.Most stations were established in the 1950s, but any databefore 1960 was excluded due to quality reason.

3.2. Definition of Extreme Indices

[10] A suite of climate change indices on extremes weredeveloped 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, 27indices based on daily temperature and precipitation datawere defined and software packages were developed forend‐users. In this study, 12 temperature and 6 precipitationindices (18 indices in total) are selected, many of which arecommonly used to validate climate model simulations[Peterson et al., 2008]. Detailed descriptions are provided inTable 2.

Table 1. List of 20 Meteorological Gauges (1960–2009) in theTarim River Basina

Site Name Site Number Longitude Latitude Altitude (m asl)

Aksu 51628 80°14′E 41°10′N 1,104Baicheng 51633 81°54′E 41°47′N 1,230Luntai 51642 84°15′E 41°47′N 976Kuche 51644 82°58′E 41°43′N 1,081Kuerle 51656 86°08′E 41°45′N 931Wuqia 51705 75°15′E 39°43′N 2,175Kashi 51709 75°59′E 39°28′N 1,289Aheqi 51711 78°27′E 40°56′N 1,984Bachu 51716 78°34′E 39°48′N 1,117Keping 51720 79°03′E 40°30′N 1,161Tazhong 51747 83°40′E 39°00′N 1,099Tieganlike 51765 87°42′E 40°38′N 846Roqiang 51777 88°10′E 39°02′N 887Tashikuergan 51804 75°08′E 37°28′N 887Shache 51811 77°16′E 38°26′N 1,231Pishan 51818 78°17′E 37°37′N 1,375Hetian 51828 79°56′E 37°08′N 1,375Minfeng 51839 82°43′E 37°04′N 1,409Qiemo 51855 85°33′E 38°09′N 1,248Yutian 51931 81°39′E 36°51′N 1,422

aSource of data: National Center of Climate, China.

Table 2. List of the 18 Climate Indices for the Tarim River Basina

Index Name Definition Units

TemperatureFD Frost day Annual count when TN(daily minimum) < 0°C daysSU Summer Annual count when TX(daily maximum) > 25°C daysGSL Growing season length 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°Cdays

TXx Max Tmax Annual maximum value of daily maximum temperature °CTNx Max Tmin Annual maximum value of daily minimum temperature °CTXn Min Tmax Annual minimum value of daily maximum temperature °CTNn Min Tmin Annual minimum value of daily minimum temperature °CTN10 Cold nights frequency Percentage of nights when TN < 10th percentile %TX10 Cold days frequency Percentage of days when TX < 10th percentile %TN90 Warm nights frequency Percentage of nights when TN > 90th percentile %TX90 Warm days frequency Percentage of days when TX > 90th percentile %DTR Diurnal temperature range Annual mean difference between TX and TN °C

PrecipitationRX1day Max 1‐day precipitation Annual maximum 1‐day precipitation mmRx5day Max 5‐day precipitation Annual maximum consecutive 5‐day precipitation mmSDII Simple daily precipitation intensity index Average precipitation on wet days mm/dayCDD Consecutive dry days Maximum number of consecutive days with RR < 1 mm daysR95 Very wet day precipitation Annual total precipitation when RR > 95th percentile mmPRCPTOT Wet‐day precipitation Annual total precipitation in wet days (PR ≥ 1 mm) mm

aAll 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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[11] The 18 indices were used primarily for the assess-ment of the many aspects of a changing global climatecovering changes in intensity, frequency and duration oftemperature and precipitation events [Alexander et al., 2006;You et al., 2010]. According to Alexander et al. [2006], theindices are grouped into five different categories: (1) per-centile‐based indices, such as occurrence of cold nights(TN10), (2) absolute indices represent maximum or mini-mum values within a season or a year, such as maximumdaily maximum temperature (TXx) and maximum 1‐dayprecipitation amount (RX1day), (3) threshold indicesdefined as the number of days on which a temperature orprecipitation value falls above or below a fixed threshold,such as the number of frost days (FD), (4) duration indiceswhich define periods of excessive warmth, cold, wetness ordryness (or in the case of growing season length, periods ofmildness), 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 previousstudies [Aguilar et al., 2005; Alexander et al., 2006; KleinTank 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 climatemodels providing temperature and precipitation extremes.Three IPCC AR4 global coupled climate models were usedin this study, including GFDL‐CM2.0, CNRM‐CM3 andMIROC3.2 (medres). These three models were selectedbecause they showed relatively reasonable performancesin simulating the temperature and precipitation extremesover Tarim.

[13] This set of scenarios spans almost the entire IPCCscenario range, with B1 being close to the low end of therange (CO2 concentration of about 550 ppm by 2100), A2to the high end of the range (CO2 concentration of about850 ppm by 2100) and A1B to the middle of the range (CO2

concentration of about 700 ppm by 2100). The modelshave different horizontal resolutions in the correspondingatmospheric components as shown in Table 3. To obtain theensemble results of different models, the data were inter-polated onto a common 1° × 1°grid. The data set wasobtained from the PCMDI web site (www.pcmdi‐llnl.gov)and more information about the participating models anddata 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 climateextremes in the basin. FD and TN90 represent the extremetemperature and the key indices for extreme precipitation areCDD, R10, R5D, SDII and R95. For the latter indices,higher values indicate more extreme precipitation. The CDDis the length of dry spell whereas R10, R5D, SDII, and R95stand for the intensity or frequency of precipitation. Allindices mentioned in this paper were calculated on an annualbasis under three emission scenarios and 5 models ensemblemeans of the 20th century and the scenario simulations of21st century, respectively.

3.4. Trend Free Pre‐whitening (TFPW) Approach

[15] Results of partial auto‐correlation tests indicated thefirst‐order auto‐correlation exist in the climate series ofsome stations in Tarim. Hence, the station‐based data werecorrected to reduce the effects of serial correlation throughthe Trend Free Pre‐Whitening (TFPW) approach developed

Table 3. List of Six IPCC AR4 Global Coupled Climate Models Providing Temperature and Precipitation Extremesa

Model Country InstitutionCorrelation Coefficient for

Daily TemperatureCorrelation Coefficient for

Daily Precipitation

CNRM‐CM3 France Center National Weather Research,CNRM, METEO‐FRANCE

0.98 0.75

MIROC3.2 medres Japan Center for Climate System Research(The University of Tokyo),National Institute for Environmental Studies,and Frontier Research Center for Global Change(JAMSTEC)

0.95 0.58

GFDL‐CM2.0 USA Geophysical Fluid Dynamics Laboratory, NOAA 0.86 0.17NCAR‐PCM USA The National Center for Atmospheric Research, NCAR −0.25 −0.47MRI‐CGCM2.3.2 Japan Meteorological Research Institute,

Japan Meteorological Agency, Japan0.74 −0.50

IPSL_CM4 France Institut Pierre Simon Laplace 0.36 −0.11aThree 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.

Table 4. Seven Indices of Climate Extremes as Described by Frich et al. [2002]a

Index Definitions Units

FD Annual count when TN(daily minimum) < 0°C daysTN90 Percentage of days when Tmin > 90th percentile daysR10 Annual count of days when RR > =10 mm daysCDD Maximum number of consecutive dry days with RR < 1 mm daysR5D Maximum 5‐day precipitation total mmSDII Simple daily intensity index: annual total precipitation divided by the number

of wet days (defined as RR > =1.0 mm) in the yearmm/day

R95 Annual total precipitation when RR > 95th percentile mm

aAcronyms are same as in Table 2.

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by Yue et al. [2002, 2003]. The TFPW involves estimating amonotonic trend for the series, removing this trend prior topre‐whitening the series and finally adding the monotonictrend calculated in the first step to the pre‐whitened dataseries. In essence, the TFPW approach attempts to separatethe serial correlation that arises from a (linear) trend fromthe remaining serial correlation and then only removes thelatter portion of the serial correlation. The TFPW‐MKprocedure of Yue et al. [2002] is applied in the followingmanner to detect a significant trend in a serially correlatedtime series:[16] 1. The slope (b) of a trend in sample data is estimated

using the approach proposed by Sen [1968]. The originalsample data Xt were unitized by dividing each of their valueswith the sample mean E(Xt) prior to conducting the trendanalysis [Yue et al., 2002]. By this treatment, the mean ofeach data set is equal to one and the properties of theoriginal sample data remain unchanged. The trend isassumed 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 thedetrended series Yt is computed. If r1 is not significantlydifferent from zero, the sample data are considered to beserially independent and the MK test is directly applied tothe original sample data. Otherwise, it is considered to beserially correlated and pre‐whitening is used to remove theAR(1) process from the detrended series as follows:

Yt′ ¼ Yt � r1 � Yt�1 ð2Þ

[18] The residual series after applying the TFPW proce-dure should be an independent series.[19] 3. The identified trend (Tt) and the residual Y ′t are

combined as:

Yt′′ ¼ Yt′þ Tt ð3Þ

[20] The blended series (Y″t ) just includes a trend and anoise and is no longer influenced by serial correlation. Thenthe MK test is applied to the blended series to assess thesignificance 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 detecttrends in regional series of annual climate extremes in thisstudy, because serial correlation of regional series is notstatistically 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 toestimate 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

ensemble forecasts [Raftery et al., 2005; Min and Hense,2006]. BMA is a standard statistical procedure for com-bining predictive distributions from different sources andprovides a way of combining statistical models and at thesame time calibrating them using a training data set. Theoutput of BMA is a probability density function (pdf), whichis a weighted average of pdfs centered on the bias‐correctedforecasts. The BMA weights reflect the relative contribu-tions of the component models to the predictive skill overa training sample. The combined forecast pdf of a variabley is:

p yjyT� � ¼XK

k¼1

p yjMk ; yT

� �p Mk jyT� � ð4Þ

where p(y|Mk, yT) is the forecast pdf based on model Mk

alone, estimated from the training data; k is the number ofmodels being combined. p(Mk|y

T) is the posterior probabilityof model Mk being correct given the training data. This termis computed with the aid of Bayes’ theory:

p Mk jyT� � ¼ p yT jMkð Þp Mkð Þ

Pk

l¼1p yT jMlð Þp Mlð Þ

ð5Þ

[23] Considering the application of BMA to bias‐correctedforecasts from the kmodels, equation (4) can be rewritten as:

p yjf1 . . . . . . f k ; yT� � ¼

Xk

k¼1

!kpk yjfk ; yT� � ð6Þ

where wk = p(Mk|yT) is the BMA weight for model k com-

puted from the training data set and reflects the relativeperformance of models k on the training period. The weightswk add up to 1, the conditional probabilities pk[y|(fk, y

T)]may be interpreted as the conditional pdf of y given fk (i.e.,model k has been chosen) and training data yT. Theseconditional pdfs are assumed to be normally distributed as:

yj fk ; yT� � � N ak þ bkyk ; �

2� � ð7Þ

where the coefficients ak and bk are estimated from the bias‐correction procedures described above. This means that theBMA predictive distribution becomes a weighted sum ofnormal distributions with equal variances but center at thebias‐corrected forecast which can also be obtained from theBMA distribution using the conditional expectation of ygiven the forecasts:

E yj f1 . . . . . . f ; yT� �� � ¼

Xk

k¼1

!k ak þ bkfkð Þ ð8Þ

[24] This forecast would be expected to be more skilfulthan either the ensemble mean or any one member, since ithas been determined from an ensemble distribution that hashad its first and second moments bias‐corrected using recentverification data for all the ensemble members. It is essen-tially an “intelligent” consensus forecast, weighted by therecent 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 givenparameters to be estimated, the likelihood function is theprobability of the training data and is viewed as a functionof the parameters. The weights and variance are chosen soas to maximize this function (i.e., the parameter values forwhich the observation data were most likely to have beenobserved). The algorithm used to calculate the BMAweights and variance is called the expectation maximization(EM) algorithm [Dempster et al., 1977]. The method isiterative and converges to a local maximum of the likeli-hood. The detailed description of the BMA method is pro-vided by Raftery et al. [2005], and more complete details ofthe EM algorithm by McLachlan and Krishnan [1997]. Thevalue of s2 is related to the total pooled error variance overall 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, a40‐year period (1960–2000) was used to train BMA weightsfor five CGCM models under the 20C3M emission scenario.The rest period (2001–2100) was used in generating presentand future scenarios of climate extremes in the Tarim RiverBasin. As three emission scenarios (A2, A1B and B1 sce-nario) are involved, the period (2001–2009) was notincluded in BMA training herein. Seven indices (i.e., FD,TN90, R10, CDD, R5D, SDII, and R95) produced by fiveCGCMs (Table 3) were used in constructing the presenthindcast and future projection of climate extremes.

4. Results

4.1. Observed Change of Climate Extremes in the PastFive Decades

[27] The analysis of temperature and precipitation revealsa variety of changes in extremes (1960–2009) in the TarimRiver Basin. Spatial patterns of trends in temperatureextremes have a much higher degree of coherence whileprecipitation in the region has more variability. The resultsfor indices in climate extremes from the past observationsare presented as below. As some station‐based datum showautocorrelation (only first order), the TFPW approach isused to correct these datum. For the regional series of cli-mate extremes, we also compared the TFPW results with thenormal 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 asthey are not significantly autocorrelated. Meanwhile, themagnitudes 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 3demonstrates the regional annual series for indices of coldextremes in Tarim. The regional trends in indices of coldextremes 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 whichare statistically significant. Except for some dispersed smallareas and the station Tashikuergan, significantly negativetrends for TN10 and TX10 are observed in the rest region.

[29] Positive trends for the temperatures of coldest daysin each year (TXn, Figures 2c and 3c) are observed overthe whole Tarim except in station Tashikuergan. A similarpattern for the temperatures of coldest nights in each year(TNn, Figures 2d and 3d) is also found over Tarim. Inaddition to the above mentioned station, a region in centralTarim shows a negative trend for the TNn index. The TXnand TNn show increasing trends at approximately 90–95%of stations (Figures 2c and 3c). But only 26% and 71% ofstations for these two indices have statistically significanttrends due to the high variability. Positive trends in TNn atthese stations are generally stronger than those at moststations.[30] Strongly negative trends in the number of the frost

days (FD, Figures 2e and 3e) are found in Tarim except instation Tashikuergan. About 78% of stations show statisti-cally significant decreasing trends and stations with pro-nounced trend magnitudes are distributed in the western andsouthwestern 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 meteorologicalstations, and Figure 5 demonstrates the regional annual DTRseries. Negative trends in diurnal temperature range (DTR,Figure 4) are found at 14 stations over Tarim. Positivetrends in 6 stations are generally strong and significant. Thestrongest 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 statisticallysignificant decreasing trends and most stations are situatedin 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 1961–2009over Tarim are provided in Figures 6 and 7. The regionaltrends in indices of cold extremes are shown in Table 5. Forthe percentage of days exceeding the 90th percentiles(TX90, Figure 6a, and TN90, Figure 6b), statistically sig-nificant increasing trends are observed at 55% and 75% ofstations. The patterns for the TX90 and TN90 indices arevery similar, positive trends for most stations are statisti-cally significant. Possible reasons for having such patternsmight be due to global warming and urbanization. Morethan 75% of the basin showed positive trends for TXx(Figure 6c) including the northern Tarim, Kuluke Desertand Taklimakan Desert. The rest area where mainly locatedin the western Tarim shows negative trends. Tashikuerganis the only station which has significant negative trend forthe index. Except for a small region in west Tarim, the restparts show significant positive trends for TNx (Figure 6d).About 40% and 60% of stations have statistically significantincreasing trends for TXx and TNx. Meanwhile, positivetrends for growing season length (GSL, Figure 6e) arefound over the whole Tarim excluding Tashikuergan. 75%of stations mainly located in the western and northwesternTarim for this index have statistically significant positivetrends. For the summer days indices (SU, Figure 6f), thespatial pattern is similar to the GSL index. In addition,Aheqi also shows negative trend, but it is very weak and notmeaningful for this region.

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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 andcold indices, which are listed in Table 6.[34] About 45% of stations have higher trend magni-

tudes 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 inTN90 (0.35 d/decade, Table 6) is higher than that ofTN10 (−0.29 d/decade).While in individual stations, about35% of stations have higher trend magnitudes in TN90than in TN10. For TXx and TXn, regional trend in TXn

Figure 2. Spatial patterns of observed trends per decade during 1960–2009 in Tarim for indices of coldextremes: (a) TN10, (b) TX10, (c) TXn, (d) TNn and (e) FD. Positive/negative trends are shown asup/down triangles, and the filled symbols represent statistically significant trends (significant at the0.05 level). The size of the triangles is proportional to the magnitude of the trends.

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(0.05°C/decade, Table 6) is higher than TXx (0.01°C/decade, Table 6), but only 15% of stations show largertrends in TXn. The magnitude of the regional trend in TNnis more than 3 times higher than that of TNx. At individualstations, about 70% of stations have greater trend magni-tudes in TNn. Therefore, changes in TN90 and TX90 aremore remarkable than changes in TN10 and TX10, whileTNx and TXx seem to have smaller trend magnitudesthan 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 precipitationindices are shown in Figure 9. In contrast to the temperatureextremes, the significance of changes in precipitationextremes 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 strongestnegative and positive trends for the index occurred instations Tashikuergan and Shache respectively. The Shachestation in the western Tarim is the only station that showsstatistically significant trend.[37] For the maximum 1‐day and 5 day precipitation

(RX1day and RX5day, Figures 8b and 8c), if Tarimis separated into two non‐equal parts by a northeast‐southwestward line, slightly negative trends can be observedin the southwest part and mixed negative and positive trendsin northeast. This can be explained by that the south regionsurrounded by the Taklimagan and Kuleke Desert isextremely arid and has experienced serious droughts. About55% and 50% of stations indicate negative trends forRX1day and RX5day, and the highest negative trends aredetected at stations Kuerle and Kashi. Positive trends forthe RX5day index are observed in a vast area located in thenorth and northwest Tarim.

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.

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[38] Positive trends of very wet days (R95, Figure 8d) aredetected in the northern and western Tarim. The proportionof stations with positive trends for this index is 65%. Sta-tions in the eastern Tarim have slightly positive or negativetrends. The strongest positive and negative trends for R95are 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. Inaddition to the two stations (Tazhong and Roqiang) locatedin the Taklimakan Desert and Kuluke Desert, Kashi in thewestern Tarim, also has statistically significant increasingtrend.[40] In the northern and western Tarim, annual total

precipitation (PRCPTOT, Figure 8f) shows positive trends

and about 35% of stations have decreasing trends mostlyoccurring in the central and southern Tarim. No station hasstatistically significant trends. The strongest positive trendis found at the station Baicheng and the strongest negativetrend is found at the station Kuerle. Both of these 2 stationsare located in the northern Tarim.

4.2. Multimodel Ensemble Projections of ClimateExtreme in the 21st Century

[41] In this section, multimodel ensemble projected futurechanges for the 7 temperature and precipitation‐basedindices (Table 4) over Tarim based on the BMA method arepresented. To answer the growing public concerns on cli-mate change of Tarim in the forthcoming decade of 21stcentury, we mainly demonstrated and addressed the spatialchanges in the 2010s under A1B scenario for sake of brevityas well. However, the temporal change processes in climateextremes (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 seconddecade of 21st century. The increase is more pronounced inthe southern Tarim. The growth rate is about 4% to 5% overTarim. As indicated by Figure 10b, consistent increases ofTN90 can be commonly found in the 21st century under allthree emission scenarios (A2, A1B and B1). However, theincreases are not sensitive to the emission scenarios up till2050. Since then, more pronounced increase of the TN90index can be found in A2 and A1B scenario when comparedwith B1. In the end of 21st century, the increase of TN90is around 63% (A2), 53% (A1B), and 39% (B1). There aresome differences of the increase among the 3 scenariosand 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. Slightdecrease (4–9 days) is found significant in the easternTarim. The temporal evolution of FD (Figure 10d) showsan obviously consistent decrease in the 21st century. The

Figure 4. Spatial patterns of observed trends per decadeduring 1960–2009 in Tarim for DTR index. Positive/negativetrends are shown as up/down triangles, and the filled symbolsrepresent statistically significant trends (significant at the0.05 level). The size of the triangles is proportional to themagnitude of the trends.

Figure 5. Regional annual series for observed DTR indices.The red line is the linear trend.

Table 5. Annual Trends, With 95% Confidence Intervals inParentheses, for Regional Indices of Temperature and PrecipitationExtremesa

Index Units 1960–2009

TemperatureTN10 days/year −0.29 (−0.68 to 1.66)TX10 days/year −0.15 (−0.41 to 1.20)TN90 days/year 0.35 (−0.21 to 1.07)TX90 days/year 0.24 (−0.36 to 1.40)DTR °C/year −0.12 (−0.12 to 0.23)TXx °C/year 0.01 (−0.20 to 0.19)TNx °C/year 0.03 (−0.20 to 0.19)TXn °C/year 0.15 (−0.12 to 0.30)TNn °C/year 0.18 (−0.22 to 0.44)FD days/year −0.40 (−1.81 to 1.36)GSL days/year 0.34 (−1.25 to 1.63)SU days/year 0.41 (−2.56 to 2.70)

PrecipitationPRCPTOT mm/year 0.003 (−0.045 to 0.045)SDII mm/year 0 (−0.02 to 0.01)RX1day mm/year 0 (−0.014 to 0.011)Rx5day mm/year 0 (−0.035 to 0.018)R95 mm/year 0.002 (−0.017 to 0.02)CDD days/year 2.59 (−10.03 to 22.17)

aValues for trends significant at the 5% level (t test) are set in bold face.

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decrease is similar under A1B and A2 scenarios while rel-atively 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 comparedwith 2000s. In the eastern Tarim, CDD tends to reduce

notably to 129 days. CDD is decreasing (94 days/decade) atstations Aheqi and Keping, indicating a higher possibility offuture humid in future ten years induced by impacts ofglobal warming on glacier recession and snowmelt from theTianshan Mountains. From north to south, these decreasingtrends become slight. The regional mean CDD shows a

Figure 6. Spatial patterns of observed trends per decade during 1960–2009 in Tarim for indices of warmextremes: (a) TX90, (b) TN90, (c) TXx, (d) TNx, (e) GSL and (f) SU. Positive/negative trends are shownas up/down triangles, and the filled symbols represent statistically significant trends (significant at the0.05 level). The size of the triangles is proportional to the magnitude of the trends.

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slight increase in the temporal processes during the 21stcentury (Figure 11b). The trend magnitude is much smallerthan TN90. Difference among the 3 emission scenariosis small and the curves overlap each other until the end ofthe century.[45] Changes in R10 are presented in Figure 11c, which

shows a negative changes. A decrease around 4–8 days isfound in the south Tarim. Meanwhile, increasing R10 is alsofound 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 seriesmay be due to the incoherence of changes.[46] The maximum 5d precipitation total (RX5day,

Figure 12a) is an important index for flood events. Asimplied by Figure 12a, we can found increasing RX5dayin East and decreasing RX5day in West. Increase ofRX5day ranges from 0 to 14 mm, and decrease is found in0–25 mm. However, increase of RX5day (0–14 mm) in theTaklimakan and Kuluke Desert cannot be transformed into

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.

Table 6. Number and Proportion of Individual Stations Where theTrend in One Index is of Greater Magnitude Than the Trend in aSecond Indexa

Index Comparison Percentage (%)

TX90 > TX10 abs 45TN90 > TN10 abs 35TXx > TXn rel 15TNx > TNn rel 30TXx > TNx rel 15TXn > TNn rel 25Tx90 > TN90 abs 25TX90 > TN10 abs 20TX10 > TN10 abs 20TN90 > TX10 abs 65

aAbbreviations are as follows: abs indicates that the absolute magnitudesof trends are compared; rel indicates that the signs of trends are retainedduring comparison.

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effective runoff because of the strong potential evaporation(PET) in the desert. As showed in the Figure 12b, changeof temporal processes of RX5day is different in threescenarios before 2060. After that, Difference across the 3scenarios is small and the curves overlap each other untilthe end of the century.

[47] Changes in SDII (Figure 12c) show a generaldecreasing pattern over the Tarim. The decrease is moresignificant, especially in the Tashikuegan, Wuqiai, Kashiand Shache station located in the west Tarim (in the range of3.4–4.8 mm, indicating a higher possibility of future droughtthere). The increase of R95 (Figure 12e), defined as the

Figure 8. Spatial patterns of observed trends per decade during 1960–2009 in Tarim for indices of pre-cipitation 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 significanttrends (significant at the 0.05 level). The size of the triangles is proportional to the magnitude of the trends.

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fraction of annual total precipitation from wetter than the95th percentile of wet days (≧1 mm), is generally in therange of 10% in the east Tarim. Decreasing R95 is signifi-cant in west Tarim.[48] Temporal changes of SDII (Figure 12d) and R95

(Figure 12e) show similar features, characterized by slightincreases under all three scenarios in the 21st century. Theincrease is similar under A1B and A2, while they are notsignificant under the B1 scenario, particularly in the last20 years of the 21st century. Compared to the temperatureindices (TN90 and FD, Figures 10b and 10d), changes of theprecipitation indices under the different scenarios are notdistinct.

5. Conclusion and Discussions

[49] General findings and results in detecting changes ofclimate extremes using a wide range of statistical testingmethods 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, Aguilaret al. [2005, 2009] for the Central America and northernSouth 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 Pla-teau. However, reports in constructing reliable scenarios offuture climate extremes in the Tarim River Basin ofNorthwest China are highly inadequate so far. Even in thestudy by Klein Tank et al. [2006] on change of climateextremes in central and south Asia based on observationsfrom 116 meteorological stations (1961–2000), only 1–2stations were used for the basin. Hence, it is hard to help usin understanding the spatiotemporal change patterns of cli-mate extremes in Tarim. Meanwhile, the observations in thesecond half of 20th century were out of date, major changesin the first decade in the 21st century need to be re‐inves-tigated using the latest datum. This work strives to presentchanges in temperature and precipitation extremes in Tarimusing the latest observations (1960–2009) and CMIP3‐CGCM multimodel ensemble projections (2010–2100). The

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.

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major findings of the article are summarized and discussedas following:[50] 1. The 18 indices defined by the ETCCDI derived

from stations over Tarim River basin during 1960–2009were analyzed. In general, both warm and cold extremes (12indices) have shown stronger trends in past five decades.Meanwhile, only slight changes in precipitation extremescan be observed, which means there were no overwhelmingtrends in precipitation extremes over Tarim in 1960–2009.From the spatial perspective, more significantly upward(downward) warm (cold) extremes are found around thedesert zones than in upstream mountain zones (i.e., the TianShan and Kunlun Mountains systems which surround thebasin). However, there are no identical governing spatialpatterns for extreme precipitation change over Tarim,although some increases (decreases) are observed in lowerTarim River course (some upstream mountain zones).

[51] More specifically, negative trends of indicesrepresenting cold temperature extremes (i.e.TX10, TN10,FD) and conversely, positive trends for indices representingwarm maximum and minimum temperature extremes (i.e.,TX90, TN90, GSL, and SU25) are found in many regions ofthe 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 moresignificant than maximum temperature extremes, which isconsistent with the observed decreases in DTR index[Easterling et al., 1997]. The decrease in Tarim is strongerlike other parts of China than reported in all other regions[Alexander et al., 2006; You et al., 2010]. This may beassociated with rapid urbanization, increased aerosol load-ing and other land use change. Same as other regions inChina, Tarim has experienced rapid urbanization and dra-matic economic growth since 1970s. Population of Tarimincreased sharply from 1.7 million in 1970 to 4.2 million in

Figure 10. Spatial patterns of (a) TN90 (%) and (c) FD (days) change of multimodel ensemble usingBMA 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 withthe 1980–1999 mean).

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2000 [Tong, 2004], and rapid growth of urbanization andeconomy exerted effects on local climate. Many studieshave shown these changes have strong effects on regionalclimate [e.g., You et al., 2008]. The warming climate causesthe number of frost days (FD) to decrease significantly. Thisagrees with the findings in the World, APN region andChina in past 50 years [Alexander et al., 2006; Choi et al.,2009; You et al., 2008]. Generally, increasing frequency andintensity of droughts are attributed to the warming climate insummer, normally drier months and ENSO events, particu-larly in the inland and arid zones like Tarim. Previousstudies have confirmed this point in many parts of Asia[IPCC, 2007]. In addition, most temperature indices showspatially uniform patterns over the basin, even though theclimate 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 andextreme precipitation events when averaged across themiddle and high latitudes [IPCC, 2007], our analyses indi-

cated that the change of rainfall statistics through the secondhalf of 20th century is dominated by variations on the inter‐annual to inter‐decadal time scale and that trend estimatesare spatially incoherent. This result is consistent with anumber of available reports [Alexander et al., 2006; Youet al., 2008; Choi et al., 2009]. Besides, though IPCCfound increasing trends for extreme precipitation for manylocations throughout the world [IPCC, 2007], both slightlypositive and negative trends are found over Tarim. How-ever, more than half of Tarim exhibited a positive trend forthe 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 [Rafteryet al., 2005], the scenarios of future climate extremes (2010–2100) over Tarim are constructed and analyzed. Generally,trends of these multimodel ensemble projections are con-sistent with the observations in past five decades (1960–2009), which means the projections can reproduce the mainfeatures of climate extremes in past. This further confirms

Figure 11. Spatial patterns of (a) CDD (days) and (c) R10 (days) change of multimodel ensemble usingBMA method under A1B scenarios (shown is the difference between two ten–years averages: 2011–2020minus 1991–2000), and (b, d) their temporal change in Tarim under A2, A1B and B1 scenarios (threescenarios are shown in different styles or color for the years from 1980–2100, compared with the1980–1999 mean).

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Figure 12. Spatial patterns of (a) RX5day (mm), (c) SDII (mm) and (e) R95 (%) change of multimodelensemble using BMA method under A1B scenarios (shown is the difference between two ten–yearsaverages: 2011–2020 minus 1991–2000), and (b, d, f) there temporal change in Tarim under A2, A1Band 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 climateextremes in the past five decades, namely, the most signif-icant changes of extreme precipitation and temperature overTarim are projected to show high variations in spatiotem-poral scale which results in more frequent droughts withhigher intensity and floods as well.[54] In the climate projections for the 21st century, the

extreme temperature indices (e.g., FD and TN90) showsignificant increases over Tarim in the end of the 21stcentury, indicating more frequent warm nights in the future.The increase under A2 and A1B scenarios is generally morepronounced than under B1, in correspondence to theirgreenhouse gas (GHG) concentration levels. Meanwhile,general increase of CDD is found over Tarim in the future.The increased CDD, together with the increases in thetemperature indices may lead to more frequent droughts inthe future. Slight increase of R10 in some areas, andincrease of R5D, SDII, and R95 in the whole region indicatea probability of flood occurrence in Tarim along with thedrought dominated in future. This finding is different fromthe tropical and sub‐tropical monsoon dominated regions,where significant increase of precipitation extremes wasprojected. According to IPCC [2007], some global modelsprojected that during the warmer 21st century, precipitationwill decrease in the subtropical regions and become moreconcentrated in intense rainfall events with a greater risk ofdroughts. In contrast, precipitation is projected to increase inthe mid‐high latitude regions (e.g., the Yangtze River basinin China [Xu et al., 2009]). From a regional and local per-spective, there are many influences on climate in addition tobroad global changes, including urbanization and terrain,and proximity to water bodies. For instance, the magnitudesof changes in temperature extremes over Tarim in continentalregions are generally higher than island countries, e.g., Fijiand New Zealand [Choi et al., 2009]. This is partly becausethe moist atmosphere near the oceans may subdue theoccurrences of extreme temperature events due to its highheat capacity compared with the drier inland atmosphere.While the magnitudes of changes in precipitation extremesover 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 changesof regional‐scale extreme indices. More research work inthe future, particularly the ensemble projections by higherresolution CGCMs or especially regional climate models(RCMs), as well as analyzing the uncertainties related to themodel spread, are needed for a more profound understand-ing of the futures changes in climate extremes over the aridregion. Meanwhile, for a large study region or a region withhigh heterogeneity in climate change, field significanceusing appropriate methods (e.g., the bootstrap resamplingtechnique [Burn and Hag Elnur, 2002]) should be used intrend analysis in order to obtain better understanding of thespatial pattern of change.

[56] Acknowledgments. The work was jointly supported by grantsfrom the National Natural Science Foundation of China (40901016,40830639, 40830640), a grant from the State Key Laboratory of Hydrol-ogy‐Water Resources and Hydraulic Engineering (2009586612,2009585512), and grants of Special Public Sector Research Program of

Ministry of Water Resources (201001066, 201001057), the National BasicResearch Program of China “973 Program” (2010CB428405,2010CB951101, 2010CB951003), and the Fundamental Research Fundsfor the Central Universities (2010B00714). The authors acknowledge themodeling groups, the Program for Climate Model Diagnosis and Intercom-parison (PCMDI) and the WCRP’s Working Group on Coupled Modeling(WGCM) for their roles in making the WCRP CMIP3 multimodel data setavailable. The IPCC Data Archive at Lawrence Livermore National Labo-ratory is supported by the Office of Science, U.S. Department of Energy.Finally, cordial thanks are also extended to the editor, Sara C. Pryor, andtwo referees for their valuable comments which greatly improved the qual-ity of this paper.

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X. Chen, T. Yang, and C. Zhao, State Key Laboratory of Desert andOasis Ecology, Xinjiang Institute of Ecology and Geography, ChineseAcademy of Sciences, Urumqi 100864, China. (enigama2000@hhu.edu.cn)Q. Shao, CSIRO Mathematics, Informatics and Statistics, Private Bag 5,

Wembley, WA 6913, Australia.W. Wang and X. Wang, State Key Laboratory of Hydrology‐Water

Resources and Hydraulic Engineering, Hohai University, Nanjing 210098,China.J. Xia, Institute of Geographical Sciences and Natural Resources

Research, Chinese Academy of Science, Beijing 100101, China.C.‐Y. Xu, Department of Geosciences, University of Oslo, PO Box 1047,

Blindern, N‐0316 Oslo, Norway.Z. Yu, Department of Geoscience, University of Nevada, Las Vegas, NV

89154‐4010, USA.

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