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Global and Planetary Change 80-81 (2012) 1–13
Contents lists available at SciVerse ScienceDirect
Global and Planetary Change
journal homepage: www.elsevier.com/locate/gloplacha
Multi-model ensemble projections in temperature and precipitation extremes of the
Tibetan Plateau in the 21st century
Tao Yang a, b,⁎, Xiaobo Hao b, Quanxi Shao c, Chong-Yu Xu d, Chenyi Zhao a, Xi Chen a, Weiguang Wang b
a
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
State Key Laboratory of Hydrology-Water Resources and Hydraulics Engineering, Hohai University, Nanjing 210098, China
CSIRO Mathematics, Informatics and Statistics, Private Bag 5, Wembley, WA 6913, Australia
d
Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway
b
c
a r t i c l e
i n f o
Article history:
Received 1 November 2010
Accepted 31 August 2011
Available online 17 September 2011
Keywords:
climate extremes
indices
multi-model ensemble
projection
scenarios
Bayesian model averaging (BMA)
the Tibetan Plateau
a b s t r a c t
Projections of changes in climate extreme are important in assessing the potential impacts of climate change
on social and natural systems. This article presents future projections of climate extremes in the Tibetan Plateau constructed from ensembles of coupled general circulation models (CGCMs) contributing to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4), under a range of emission
scenarios. The extremes of temperature and precipitation are described by seven indices, namely, the frost
day (FD), percentage of nights when Tmin N 90th percentile (TN90), consecutive dry days (CDD), annual
count of days when precipitation N=10 mm (R10), maximum 5-day precipitation total (R5D), simple daily
intensity index (SDII), and annual total precipitation when precipitation N 95th percentile (R95T). Results indicate that frost days decrease over the Tibetan Plateau in the 21st century. More frequent warm nights are
also projected in the plateau. The increases of these temperature extremes under A2 and A1B scenarios are
more pronounced than under B1. Heavy precipitation events for single days and pentads are projected to increase in their intensity over most parts of Tibetan Plateau. CDD, R10, R5D, R95T and SDII collectively suggest
more extreme precipitation in the region (2011–2020). In addition, impacts of climate extremes changes on
local water resources and fragile ecosystem are discussed as an extension of this article. The findings will be
beneficial to project regional responses in this unique region to global climate change, and then to formulate
regional strategies against the potential menaces of climate change.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
The global atmospheric concentrations of greenhouse gasses have
significantly increased since the pre-industrial era and will continue
to increase in the future. The concentration of greenhouse gasses
has increased by 70% from 1970 to 2004. The effect of human activities has become an important reason for global warming from 1970
with high confidence. In a series of SRES emission scenarios, global
climate will be warming in a rate of the 0.2 °C per decade in
20 years. Even if greenhouse gasses stabilized at the levels in 2000,
global temperature will increase about 0.1 °C every decade (IPCC,
2007). The issues of present climate and future climate change are
widely regarded as hot spots in climate impact study.
One of the key aspects of climate change study is to understand
the behavior of extreme events. It is widely recognized that the
changes in the frequency and intensity of extreme events are likely
⁎ Corresponding author at: State Key Laboratory of Desert and Oasis Ecology, Xinjiang
Institute of Ecology and Geography, CAS, 818, South Beijing Road, Urumqi, Xinjiang
830011, China. Tel: +86 991 7823169.
E-mail address: yang.tao@ms.xjb.ac.cn (T. Yang).
0921-8181/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.gloplacha.2011.08.006
to have more significantly negative impacts than changes in mean climate on many vulnerable aspects of human health, social organization and natural systems (e.g. Mearns et al., 2001; Katz and Brown,
2004). Meanwhile, the need for regional-scale projections of climate
variables and thresholds that are directly relevant to impact researchers and stakeholders has been strongly advised (Wetterhall
et al., 2006). Therefore, reliable future projections on extreme events
using climate models are highly recommended.
Substantial progress in both global and regional modeling at medium to high resolution has provided the basis for an increasing number
of studies that attempt to characterize the future changes. Recent
modeling efforts have also provided us with the ability to characterize
changes in term of indices with greater relevance to impacts than the
traditional climate model outputs of mean temperature, precipitation
(e.g. Meehl et al., 2005; Tebaldi et al., 2006; IPCC, 2007). Under the support of the IPCC AR4, over 20 modeling groups around the world conducted climate change simulation by different CGCMs (IPCC, 2007).
This is also known as Phase 3 of the Coupled Model Intercomparison
Project (CMIP3, Meehl et al., 2007) ensemble of simulations. Ten of
these models provide estimation for extreme indices/indicators in the
present and future climates. This offers us opportunities to conduct
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T. Yang et al. / Global and Planetary Change 80-81 (2012) 1–13
the multi-model ensemble analysis of the simulation and projection of
extreme events. For example, Tebaldi et al. (2006) analyzed historical
and future simulations of these indicators derived from ensembles of
nine models under a range of emission scenarios over the world. They
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. However, there is a growing
need to investigate the changes of climate extremes in future based
on the state-of-art climate models and ensemble approaches in individual and typical regions. Furthermore, analyzing these indices will hopefully lead to a better understanding of variability and changes in the
frequency, intensity and duration of extreme climate events. This will
be beneficial to identify different patterns of regional responses to global climate change and then to formulate regional strategies against the
potential menaces of climate change.
Bayesian model averaging (BMA), which provides a way to combine
different models, is a rather promising method for calibrating ensemble
modeling and forecasts in climate impact research. BMA has been widely used in social and health sciences and was recently applied to the
combination of NWP model forecasts in an ensemble context (Raftery
et al., 2005). BMA is adaptive in the sense that recent realizations of
the forecast system are used as a training sample to carry out the calibration. BMA is also a method of combining forecasts from different
sources into a consensus probability density function (PDF), an ensemble analog to consensus forecasting methods applied to deterministic
forecasts from different sources (Vislocky and Fritsch, 1995; Vallée
et al., 1996; 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 in climate
change detection, attribution and climate model evaluation (Min
et al., 2004, 2005; Min and Hense, 2006) as well as in future projections
of climate changes (Tebaldi et al., 2006) were reported.
Herewith, this study aims to offer the most comprehensive analysis
of global temperature and precipitation extremes in the Tibetan Plateau
using CGCM multi-model ensemble projections with BMA approach. Toward this end, this article strives to: (1) conduct an inter-comparison of
the temporal and spatial changes in climate extreme ensembles using
the state-of-art BMA and Arithmetic Mean (AM) method under the
20C3M emission scenario with HadEX observations; and (2) construct
scenarios of climate extremes using multi-model ensemble projections
(2010–2100) over the plateau provided by CMIP3 based on the Bayesian
model averaging (BMA) method. Impacts of climate extremes changes
on local water resources and fragile ecosystem will also be discussed
as an extension of this article. The results are expected to contribute in
improving our knowledge on simulation and projection of climatic extremes, which is beneficial for policymakers and stakeholders in local
water resource and eco-environment management.
2. Study region
The Tibetan Plateau (Fig. 1) is a vast, elevated plateau in Central
Asia, covering most of the Tibet Autonomous Region and Qinghai in
China, as well as smaller portions of western Sichuan, southwestern
Gansu, and northern Yunnan in Western China and Ladakh in Indiacontrolled Kashmir (Liu and Chen, 2000). It stretches approximately
1000 km north to south and 2500 km east to west. The Tibetan Plateau is surrounded by massive mountain ranges (i.e. the Kunlun,
Qilian, Hengduan, and Karakoram range of northern Kashmir). The
average elevation is over 4500 m, and all 14 of the world's 8000 m
and higher peaks are found in the region. Totally, six of the world
largest rivers including the Yellow River, Yangtze River, and Yarlung
Zangbo River originate from the Tibetan Plateau and provide the
much-needed irrigation water that feed the agricultural fields of hundreds of millions of farmers in the downstream regions. Meanwhile,
Qinghai Lake, the largest inland saltwater lake in China, is also located
in the plateau. Lying in the northeast of Qinghai Province, approximately 150 km from Xining City at 3200 m above sea level, the lake
stretches endlessly into the horizon, with an area of 4635 km 2 and
more than 360 km in circumference. The lake lies in a transitional climate zone which is sensitive to the East Asian and Indian monsoons
and the Westerly Jet Stream (Jin et al., 2009).
The plateau is a high-altitude arid steppe with annual precipitation
ranging from 100 mm to 300 mm and falls mainly as hailstorms. The
southern and eastern edges of the steppe have grasslands which can
sustainably support populations of nomadic herdsmen, although frost
occurs for six months of the year. Proceeding to the north and northwest, the plateau becomes progressively higher, colder and drier, until
reaching the remote Changthang region in the northwestern part of
the plateau. Its average altitude exceeds 5000 m and annual average
temperature is −4 °C and can be lower than −40 °C in winter. As a result of this extremely severe environment, the Changthang region is the
least populous region in Asia. Although knowledge of climate change
over the plateau is still insufficient, partly due to the lack of sufficient
observational data, research on the climate change in the region has received more and more attentions in the literature since mid-1970s (e.g.
Lin and Zhao, 1996; Tang et al., 1998; Zheng and Zhu, 2000; Zheng et al.,
2000; Zhang et al., 2009). Many studies show that the Tibetan Plateau is
one of the most sensitive areas in response to global climate change
(e.g. Zhang et al., 1996; Liu and Chen, 2000). The work by Liu and
Chen (2000) analyzing the temperature series of 97 stations showed
that the main area of the plateau has experienced statistically significant warming since the mid-1950s, especially in winter with average
rates of increase of about 0.16 °C/decade for the annual mean during
the period 1955–1996 over the plateau and 0.32 °C/decade for the winter mean. Climate impact on human activities and human impact on climate change in the plateau have great importance not only to the local
area but also to the whole Asian continent and even to the whole world.
3. Data and extreme indices
3.1. Data
Table 1 lists the IPCC AR4 global coupled climate models (CGCM)
providing annual temperature and precipitation extremes (1951–
2099), which are thoroughly addressed in Section 3.2. Five IPCC AR4
CGCMs are used, including GFDL-CM2.0, GFDL-CM2.1, INMCM 3.0,
IPSL-CM4 and MIROC3.2 (medres). They are selected because they conducted all three simulations under A2, A1B and B1 emission scenarios of
IPCC (IPCC, 2007) and shown relatively reasonable performances in
simulating the surface air temperature change over China, based on previous evaluations by Zhou and Yu (2006) and Xu et al. (2009a).
The set of projection spans almost the entire IPCC scenario range
(2000–2099), with the B1 being close to the low end of the range
(CO2 concentration of about 550 ppm by 2100), the A2 to the high
end of the range (CO2 concentration of about 850 ppm by 2100)
and the A1B to the middle of the range (CO2 concentration of about
700 ppm by 2100). Meanwhile, hindcasts for the historic period
(1951–1999) known as the 20C3M scenario (20th Century Model
Runs) from these five CGCMs are used in calibration and validation
of the ensemble approach. The models have different horizontal resolutions in the corresponding atmospheric components (Table 1).
To obtain the ensemble results of different models, the data are interpolated onto a common 1° × 1° grid. The data set (1951–2099) is
obtained from the PCMDI web site (www.pcmdi-llnl.gov) and more
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T. Yang et al. / Global and Planetary Change 80-81 (2012) 1–13
88°E
96°E
100°E
104°E
Elevation
N
0-826
827-1808
1809-2791
2792-3773
3774-4755
4756-5738
5739-6720
6721-7702
7703-8685
E
W
S
100 200
92° E
3
400 Km
Qinghai Lake
Gyaring Lake
Ye l
lo
34°N
Ngoring Lake
Ya ng tz e Ri ve r
Latitude (N)
Xining
wR
ive
r
38°N
°
nc
Cuona Lake
La
Siling Co Lake
an
30°N
Lasha
120 0E
er
100 0E
iv
80 0E
gR
NamCo Lake
0
50 N
Yarlung Zangbo River
Beijing
26°N
0
30 N
88°E
96°E Longtitude°(E)
92°E
100°E
104°E
Fig. 1. Map of the Tibetan Plateau in China and associated major rivers and lakes.
information about the participating models and data set can be found
on this web site.
3.2. Extreme indices
Extremes are more sensitive than average in responding to global
climate change. Although changes in long-term climatic means are important, extremes generally have the greatest and most direct impact
on our everyday life, community and environment. Hence, detecting
changes in extremes has become important in current climate research
(Vincent and Mekis, 2006). Indices based on daily temperature and precipitation observations have been developed to provide some insights
into changes in these extremes (Peterson et al., 2001). These indices
are valuable in studying the impact of climate changes on regional activities, agriculture and economy. They are also helpful in monitoring climate change itself and can be used as benchmarks for evaluating
climate change scenarios (Gachon et al., 2005).
Seven key indices of annual climate extremes (Table 2) according to
Frich et al. (2002) are chosen for the present day analysis. FD and TN90
represent the extreme temperature and the key indices for extreme
precipitation are CDD, R10, R5D, SDII and R95T. For the five precipitation indices, higher values indicate more extreme precipitation. The
CDD is the length of dry spell whereas R10, R5D, SDII, and R95T express
the intensity or frequency of precipitation. All the indices mentioned in
this paper were calculated on an annual basis under the three scenarios
and ensemble means of five models in the 20th century and the scenario simulations for the 21st century.
An observation data set of climate extremes indices has been developed by the Hadley Centre (HadEX indices, http://www.hadobs.org/,
Alexander et al., 2006) to compare with the 20C3M model runs. This
data set (1951–1999) is employed in our study to validate the multimodel ensemble (ME) performances. Spatial resolution of the HadEX
is originally 3.75° × 2.5°, which makes it necessary to interpolate the
data onto the common 1°× 1° grid for comparison with the model results. It is noted that the resolutions of all observation based and
model-based indices are different. These differences may cause some
differences in the spatial distribution.
4. Methodology
4.1. General formulation
Bayesian model averaging (BMA) has recently been proposed as a
way of correcting under-dispersion in ensemble forecasts (Raftery
et al., 2005; Min et al., 2006; Yang et al., 2011). 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 dataset. 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
Table 1
List of five IPCC global coupled climate models used.
Model
Country
Resolution (Atms)
Institution and reference
GFDL-CM2.0
USA
Geophysical Fluid Dynamics Laboratory (Delworth et al., 2006; Gnanadesikan et al., 2006)
GFDL-CM2.1
USA
INMCM3.0
Russia
IPSL-CM4
France
MIROC3.2(medres)
Japan
144 × 90
L24
144 × 90
L24
72 × 45
L21
96 × 72
L19
128 × 64
T42L20
Geophysical Fluid Dynamics Laboratory (Delworth et al., 2006; Gnanadesikan et al., 2006)
Institute of Numerical Mathematics, Russia (Diansky and Volodin, 2002)
Institute Pierre-Simon Laplace, France (http://dods.ipsl.jussieu.fr/omamce/IPSLCM4/DocIPSLCM4/)
Center for Climate System Research, National Institute for Environmental Studies and
Frontier Research Center for Global Change (JAMSTEC). (Hasumi and Emori, 2004)
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T. Yang et al. / Global and Planetary Change 80-81 (2012) 1–13
Table 2
Seven indices of climate extremes (Source: Frich et al., 2002).
Index
Definitions
Definitions
1.
2.
3.
4.
5.
6.
Annual count when Tmin (daily minimum) b 0 °C
Annual percentage of days when Tmin N 90th percentile
Annual count of days when PR N=10 mm
Annual maximum number of consecutive dry days with RR b 1 mm
Annual maximum 5-day precipitation total
Simple daily intensity index: annual total precipitation divided
by the number of wet days (defined as PR N=1.0 mm) in the year
Annual total precipitation when precipitation N95th percentile
Days
%
Days
Days
mm
mm/day
FD
TN90
R10
CDD
R5D
SDII
7. R95T
the relative contributions of the component models to the predictive
skill over a training sample. The combined forecast pdf of a variable y is:
T
K
T
T
pðyjy Þ ¼ ∑ pðyjMk ; y ÞpðMk jy Þ
ð1Þ
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:
p
yT Mk pðMk Þ
T
pðMk jy Þ ¼
k
∑ p yT Ml pðMl Þ:
ð2Þ
l¼1
Considering the application of BMA to bias-corrected forecasts
from the k models, Eq. (1) can be rewritten as:
T
k
T
pðyjf1 ……fk ; y Þ ¼ ∑ ωk pk ðyjfk ; y Þ
ð3Þ
k¼1
where ωk = p(Mk|y T) is the BMA weight for model k computed from
the training dataset and reflects the relative performance of models
k on the training period. The weights ωk 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 y T. These
conditional pdfs are assumed to be normally distributed as:
T
2
y fk ; y eN ak þ bk yk ; σ
ð4Þ
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
T
E½yj f1 ……fk ; y ¼ ∑ ωk ðak þ bk fk Þ:
ð5Þ
k¼1
This forecast would be expected to be more skillful 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.
4.2. Model weights
The BMA weights and the variance σ 2 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
mm
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 can be found in Raftery et al. (2005), and more complete details of the EM algorithm in McLachlan and Krishnan
(1997). The value of σ 2 is related to the total pooled error variance
over all the models in the training dataset.
4.3. Training period
In climate change research, the longer the training period is, the
better the BMA parameters are estimated (Yang et al., 2011). In this
study, a 49-year period (1951–1999) is used to train BMA weights
for five CGCM models under the 20C3M emission scenario. The rest
period (2000–2099) is used in generating present and future scenarios of climate extremes in the Tibetan Plateau. As three emission scenarios (A2, A1B and B1 scenarios) are involved, the period (2000–
2010) is not included in BMA training herein. Seven indices (i.e. FD,
TN90, R10, CDD, R5D, SDII, and R95) produced by five CGCMs
(Table 1) are used in constructing the present hindcast and future
projection of climate extremes.
5. Results and analysis
5.1. Inter-comparison of ensemble performance by BMA and Arithmetic
Mean (AM) methods
Fig. 2 presents an inter-comparison of ensemble performance by
BMA and AM methods over the Tibetan Plateau. Generally, it shows
that the FD (frost days, Fig. 2a), R10 (annual count of days when daily
precipitation N=10 mm, Fig. 2d) and R5D (annual total precipitation
when PR N 95th percentile, Fig. 2e) ensemble by BMA are more strongly
consistent with HadEX than that by AM over the simulation periods
(1951–1999). Whereas, comparison in the other 4 indices (TN90,
CDD, R95T, and SDII; Fig. 2b, c, f, g) suggests slightly better performance
by BMA than by AM. Table 3 provides a summarized result of intercomparison of model skills for the BMA and AM ensemble. As shown
by Min and Hense (2006), biases for the BMA and AM ensemble in
reproducing the global monthly mean surface temperatures are
−0.047 and −0.032. Table 3 shows that there are more considerable
uncertainties in reproducing annual climate extremes than monthly climate variables. Whereas, biases for BMA and AM are 0.833 and 0.979 in
reproducing annual percentage of days when Tmin N 90th percentile
(TN90). Meanwhile, biases and RMSE of the BMA ensemble for all
seven indices of climate extremes are rather lower than that of the
AM ensemble. Because there is remarkable consistency among the
five CMIP3-CGCMs projections in these 4 indices, the BMA and AM
ensembles are similar with HadEX observations to a certain degree in
some cases. The inter-comparison of the various extreme indices by
BMA and AM shows that BMA ensemble produced the lowest bias in
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T. Yang et al. / Global and Planetary Change 80-81 (2012) 1–13
a
5
b
FD
32
26
HadEX
HadEX
BMA
22
AM
26
TN90
BMA
AM
18
20
14
14
10
8
6
2
1950
1960
1970
1980
1990
2000
c
2
1950
1960
1970
1980
1990
2000
1980
1990
2000
1980
1990
2000
d
80
CDD
70
HadEX
60
BMA
55
AM
R10
45
50
40
BMA
AM
50
60
HadEX
35
40
30
30
20
1950
25
1960
1970
1980
1990
2000
e
20
1950
1960
1970
f
90
R5D
80
HadEX
65
BMA
55
AM
70
45
60
35
50
25
40
15
30
1950
1960
1970
1980
R95T
1990
2000
1990
2000
5
1950
HadEX
BMA
AM
1960
1970
g
22
SDII
HadEX
BMA
AM
18
14
10
6
1950
1960
1970
1980
Fig. 2. Comparison of climate extreme ensembles using BMA and Arithmetic Mean (AM) method between HadEX observations.
comparison with AM ensemble. Therefore, the skill-weighted average
by the Bayes factors (Bayesian model averaging, BMA) is superior to
the arithmetic ensemble mean based on conventional statistics, illuminating applicability of BMA to projections of climate extremes.
5.2. Present-day modeled and observation-based indices
Multi-model means of the selected indices for 1951–1999 from the
20C3M scenario runs (twenty century simulations) are calculated
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T. Yang et al. / Global and Planetary Change 80-81 (2012) 1–13
Table 3
Inter-comparison of model skills for the BMA and AM ensemble.
Indices
1. FD
2. CDD
3. R5D
4. R10
5. R95T
6. SDII
7. TN90
Mean
BMA
AM
RMSE
Bias
RMSE
Bias
0.692
1.766
1.573
0.914
1.582
0.841
0.491
1.123
0.695
− 0.322
−0.473
−0.473
0.460
−4.952
0.833
−0.605
2.516
2.275
2.023
2.011
1.616
1.102
0.486
1.718
17.26
−8.906
−11.34
−11.34
−9.714
−7.163
0.979
−4.318
using BMA method to demonstrate the spatial pattern over the Tibetan
Plateau in present climate. The analyses of the change in spatial distribution focus on the A1B scenario to keep the length of the paper. The spatial
pattern of the observed (HadEX) and ME simulation of the extreme indices of FD and TN90 for temperature and CDD, R10, R5D, SDII and R95T for
precipitation over the plateau during 1951–1999 are presented in Fig. 3.
Fig. 3a and b suggests that the spatial pattern of the observed
number of frost days (FD) is not reasonably simulated by ME. A
higher FD is found over the whole plateau in ME, which indicates
FD is overestimated. Fig. 3c and d indicates that the simulated TN90
in the 20C3M are generally higher (approximately 1 °C) than that in
the HadEX observations, referring to more warm nights in the region.
This is represented by ME, but with an overestimation of one to a few
days. The ME overestimates warm nights over the whole Qinghai
province and the south-eastern part of the Tibetan Autonomous region. Similarly, Fig. 3e and f indicates that the pattern of the observed
number of consecutive dry days (CDD) is not reasonably simulated by
ME. A higher CDD is found in the eastern part of the plateau in the
HadEX, while a higher CDD is found in the western part of the plateau
in ME. This is to some extent related to the coarse resolution of the
models. The Tibetan Plateau is an area with the most complex topography to simulate. The coarse resolution of the models may lead to a
great bias in this area. This emphasizes the importance of using high
resolution models in better simulating present climate and future climate changes (Christensen et al., 2007).
However, R10, R5D, R95T and SDII in ME are closely consistent with
HadEX observations in general. To a certain degree (10 to 22 mm), the
ME overestimates the amount of R10 in the upper stream of the Yellow
River (Fig. 3g, h) but underestimates it in the Yarlung Zangbo River.
Nevertheless, the simulated R10 still shows an overall similar pattern
compared to the observation (Fig. 3g, n). Similar performances are
also observed for R5D, R95T and SDII in ME when compared with
HadEX (Fig. 3i–n). In addition, the spatial patterns for R10, R5D, SDII
and R95T are characterized by a gradient directed from south part of
the region to the north. This is also reproduced by ME, suggesting that
ME simulations based on BMA method are capable of capture spatial
patterns of climate extremes in most cases.
5.3. Projected change in extremes in the 21st century
In this section, projected changes in future for the seven temperature and precipitation indices over the Tibetan Plateau are presented
(Fig. 4). For sake of brevity, only the changes in spatial pattern in future
ten years (2011–2020) under A1B scenario are presented. The time series of the regional mean indices from the three scenarios are considered from 1951 to 1999 year in the 20th century and for the whole
21st century to demonstrate the temporal evolution of the indices. As
for temporal evolution, changes for all the three scenarios and period
extending from 2000 to 2099 are provided.
5.3.1. Temperature-based extremes
A general decreasing FD can be found in the Tibetan Plateau, indicating the tendency of decreasing frost days and increasing temperature in
the plateau (Fig. 4a) in the future (2011–2020). The decreasing FD is
more pronounced in south Tibetan. The decrease (in a range of 5.7%–
2.6%) is more significant than that of HadEX (1991–1999), in the source
region of Yellow River, Yangtze River, Lancang River, Yarlung Zangbo
River and Qinghai Lake. According to Fig. 4c, increasing TN90 is also
dominated over the plateau, the increasing is more pronounced in
south Tibetan with amount of 38.9%–73.6% comparing with HadEX
(1991–1999). The change of TN90 shows opposite tendency compared
with FD, characterized by a general increase.
As shown in Fig. 4b, consistent decrease of FD can be found in the
21st century under all 3 scenarios, indicating increasing tendency of
temperature in the future in the region (28–33°N, 88–94°E). In the
end of 21st century, FDs are around 250, 235 and 230 days under
B1, A1B and A2, respectively. Consistent increasing of TN90 can be
found in the 21st century for all 3 scenarios (Fig. 4d), although
some stabilization of B1 is observed in the end of the century. The difference is slight among the 3 scenarios until 2030. After that, obvious
increases of the two indices can be found under A2 and A1B compared to B1. In the end of 21st century, increasing TN90 are around
63%, 55% and 40% under A2, A1B and B1, respectively.
The spatial patterns of FD and TN90 in other periods of the century
are similar to that in the last 20 years, but with less extent. There are
some differences in the increasing patterns across the scenarios but
are usually consistent with the values of emissions (not shown).
5.3.2. Precipitation-based extremes
The number of CDD is found decreasing in the central Tibetan Plateau while increases are observed in the south and north plateaus
(Fig. 4e) in comparison with HadEX. The maximum increase of CDD
is up to 38.6% in the south plateau, indicating a higher possibility of
future drought there. A slight increase is found in most northern
part of Qinghai province, around 5.4%–18.6% comparing with HadEX
of 1991–2000, indicating consecutive dry days tends to increase
while the decreasing tendency of extreme precipitation in this region.
However, the regional mean CDD does not show positive or negative
trend in the temporal evolution in the whole 21st century (Fig. 4f).
Differences among the 3 scenarios are small and the time series
plots overlap to each other until the end of the 21st century. Changes
in R10 are presented in Fig. 4g, which shows a spatial pattern of almost positive changes. A remarkable increase of around 68.7%–
105.3% is found in the central part of the Tibetan Plateau. Slightly increasing R10 (0%–30.2%) is projected over the source region of Yellow
River, Yangtze River, and Lancang River in comparison with HadEX
during 1991–2000, indicating more heavy rain days and consequently
more floods there in the forthcoming ten years. The temporal evolution of R10 (Fig. 4h) shows significantly increasing trend in the 21st
century for all the 3 scenarios, and the increasing is insensitive to
the choice of scenarios till 2040.
The rainfall intensity as measured by R5D (Fig. 4i), R95T (Fig. 4k)
and SDII (Fig. 4m) shows positive changes over most part of Tibetan
Plateau. The maximum 5 d precipitation total (R5D) is an indicator
of flood-producing events and shows a general increase in the
whole plateau. The increase is obvious in the north-west, indicating
a higher possibility of heavy rain the next ten years. The increase of
R95T (Fig. 4k), defined as the fraction of annual total precipitation
from events wetter than the 95th percentile of wet days (≥1 mm),
is generally in the range of 0%–34.3% over the plateau. The increasing
of SDII, defined as the mean daily intensity for events ≥ 1 mm/day, is
greater in most part except the west (Fig. 4m). Increasing trend is
found in headstream of the Yellow River, Yangtze River, and Lancang
River. Temporal evolution of R5D (Fig. 4l) and SDII (Fig. 4n) shows
similar features, characterized by a consistent increase across the scenarios in the 21st century. Compared to the temperature indices (FD
and TN90), the separation of the trajectories of the precipitation indices under the different scenarios appears less clearly.
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T. Yang et al. / Global and Planetary Change 80-81 (2012) 1–13
The discrepancy between the observed and simulated precipitation indices may indicate the uncertainty associated with the ME projections. However, the general increase of the precipitation extreme
events is also reported by some RCM simulations (Zhang et al.,
2006; Song, 2007), although difference of the spatial distribution
and magnitude of the changes exists among these simulations.
6. Conclusions and discussions
General findings and results in detecting changes of climate extremes over the eastern and central Tibetan Plateau during 1961–
2005 using a wide range of statistical testing methods were well presented by Liu et al. (2005) and You et al. (2008). However, reports
with particular highlights in constructing reliable scenarios of future climate extremes over the Tibetan Plateau, the highest zone across the
world with unique ecosystem very sensitive to climate change, are
very limited so far. Even in a similar study by Xu et al. (2009a) on projections in temperature and precipitation extremes over the Yangtze
River Basin of China in the 21st century, the arithmetic ensemble
mean is too simple and consequently will lead to more uncertainties
in constructing future scenarios of climate extremes. In contrast to
these previous publications in the similar topic, this study strives to:
(1) construct more reliable scenarios of future climate extremes over
the plateau using multi-model ensemble projections (2011–2099)
based on the state-of-art Bayesian model averaging (BMA) approach,
and (2) discuss potential impacts of climate extremes changes on
local hydrological processes and ecosystem. These two parts collectively
constitute the distinctive deliverables to provide beneficial insights in
developing more robust ensemble scenarios of climate extremes and
understanding associated potential impacts on local water resources
and ecosystem for the unique zone. The major points are summarized
and discussed as following:
(1) By using the results from an ensemble of five CIMP3-CGCMs, the
projection of future annual climate extremes over the plateau is
investigated. The extreme indices include FD and TN90 for temperature, and CDD, R10, R5D, SDII and R95T for precipitation.
The 1961–1999 period serves as a reference for future changes
and projection. Validation of the performances of the model
mean is firstly conducted by comparing the model results with
the HadEX observations. Spatial pattern of the future changes
in the 2011–2020 is analyzed and the temporal evolution of the
regional indices in the whole 21st century is also presented. Results indicate that although discrepancies can still be found between the ME and HadEX observations (e.g. overestimation of
FD and TN90, underestimation of CDD), the ME captures the spatial patterns of most indices under present day climate. This
means that the ME based on the state-of-art CIMP3-CGCMs and
BMA ensemble approach is capable of producing ensembles
with the lowest bias in comparison with other conventional
methods. However, high-resolution projections of extremes constructed by regional climate models are highly recommended to
replace a range of coarse-resolution CGCMs in offering more reliable scenarios.
In the climate projection for the 21st century, temperature-based
index FD shows a significant decrease while TN90 shows an increase in the 21st century, indicating less frost days and more
warm nights in the future. The increase under A2 and A1B scenarios is generally more pronounced than that under B1, in correspondence with their greenhouse gas (GHG) concentration
levels. Most probably, there are two main reasons accounting
for the temperature warming in Tibetan Plateau. One is that the
contribution from increasing anthropogenic GHG in the plateau
(Duan and Wu, 2006), and a model study also testifies that enhanced climatic warming in the region due to doubling carbon
dioxide (Chen et al., 2003). The other is the change of cloud
7
amount. The low-level cloud amount in the region exhibits a significant increasing trend during the night times, leading to the
strong nocturnal surface warming, and both the total and lowlevel cloud amounts during daytime display decreasing trends,
resulting in surface warming (Duan and Wu, 2006; You et al.,
2008). Meanwhile, slight decrease of CDD and general increase
of R5D, R10 and R95T are found over most parts of the plateau,
together with increasing temperature index (TN90) may lead
to more snowmelt and glacial recession, indicating more runoff
in this region. This finding is similar with the tropical and subtropical monsoon dominated regions, where significant increase
of precipitation extremes was projected. According to Xu et al.
(2009a), some global models projected that during the warmer
21st century, precipitation will increase in the subtropical regions and become more concentrated in intense rainfall events
with a greater risk of droughts over the Yangtze River basin. In
addition, it is found that while the changes of extreme temperature indices are more in proportion to the GHG concentration
levels of the scenarios, changes in precipitation indices can exhibit markedly different response to the GHG concentrations.
(2) Considering Tibetan Plateau as the source region for the major
rivers in Asia, including the Yellow River, Yangtze River, Mekong
River, and Salween River, estimating future potential changes in
temperature and precipitation extremes can provide essential
input to the plateau adaptation and planning strategies for
China, India, Burma, Thailand, Cambodia and Vietnam. The findings from the analysis presented here are expected to contribute
to this effort.
The physical processes determining the conversion of glaciers, ice,
and snow into runoff and downstream flow are complex, but the impact
of climate change on river regimes will very likely be profound. Glaciers
located on the Tibetan Plateau are likely to shrink from 500,000 km2
(the 1995 baseline) to 100,000 km2 or less by the year 2035 (Ye and
Yao, 2008). Initially, increased melting will result in growing discharge.
However, when glaciers completely disappear or approach new equilibriums, long-term effects will be increasing water shortages and limited
supplies for downstream communities, particularly during the dry season. Based on current knowledge, the rivers most likely to experience
the greatest loss in water availability due to melting glaciers in the
Yangtze, and Yarlung Zangbo River (Xu et al., 2009b). Water-related
hazards and risks will be omnipresent over the plateau, and landslides,
debris flows, and flash floods are projected to increase in frequency.
Mountain ecosystems have a significant role in biospheric carbon
storage and carbon sequestration, particularly in the cold and elevated
Tibetan Plateau (Piao et al., 2006). Mountain ecosystem services such
as water purification and climate regulation extend beyond their geographical boundaries and affect all continental mainlands (Woodwell,
2004). Related impacts included an earlier and shortened snow-melt
period, with rapid water release and downstream floods which, in combination with reduced glacier extent, could cause water shortage during
the growing season. The third and fourth assessment reports (IPCC,
2000, 2007) collectively suggested that these impacts may be exacerbated by ecosystem degradation pressures such as land-use changes,
over-grazing, trampling, pollution, vegetation destabilization and soil
losses, in particular in highly diverse regions such as the Caucasus and
Himalayas. Because adaptive capacities are generally limited, and the
high vulnerabilities were attributed to the highly endemic alphine
biota of the plateau. Meanwhile, where warmer and wetter conditions
are projected over most part of the region, vegetation is expected to be
subjected to increased evapotranspiration. This will lead to increasing
flood events particularly in the north-eastern part of Tibetan Plateau in
the forthcoming decade (2011–2020), which is beneficial to increase
streamflows and water-levels in the source region of Yellow River and
Qinghai Lake in wet seasons (June to August). This is also good to maintain essential ecological water demands from the significantly degrading
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8
T. Yang et al. / Global and Planetary Change 80-81 (2012) 1–13
low-stature alpine meadows in such regions (Liu and Chen, 2000). Potential ecological cascading effects in the region may include secondary
extinctions triggered by losses of key species in the ecosystems (Xu et
al., 2009b). The Tibetan Plateau includes many plant species that may
not respond successfully to projected rates and scale of climate change
(Salick et al., 2009). One of the obvious risks is species extinctions from
mountains not high enough to offer escape routes in the case of upward
shifts of taxa (Baker and Moseley, 2007). In general, the response of natural vegetation to projected climate change will be complex; some species will decrease, some increase, and new ones may also appear (Chen
et al., 2003; Xu et al., 2009b). Invasions of weedy and exotic species
from lower elevations are likely (McCarty, 2001).
Although some preliminary results of changes in extreme indices
over the plateau are obtained in the present work, a lot of uncertainties exist in assessing the regional-scale extreme indices changes.
More research work in the future, particularly the ensemble projections by higher resolution CGCMs and especially regional climate
models, as well as analyzing the uncertainties related to the model
spread, are indeed needed for a better understanding of the futures
changes of extremes over the unique zone.
a
b
0
0
88 E
92 E
0
0
96 E
100 E
0
104 E
0
88 E
N
W
FD(HadEX)
E
N
W
96 0E
100 0E
104 0E
FD(20C3M)
E
S
S
100
92 0E
N
N
200
100
400 Km
200
400 Km
0
38 N
38 0N
Xining
Xining
34 0N
Latitudeo(N)
Latitudeo(N)
34 0N
30 0N
0
0
80 E
100 E
< 97
97 - 145
145 - 165
165 - 230
Lasha
0
120 E
0
50 N
Beijing
0
26 N
30 0N
80 0E
0
c
0
0
0
88 E
92 E
96 E
Longtitudeo(E) 100 E
0
88 E
92 0E
96 0E
100 0E
0
30 N
0
0
d
0
88 E
N
0
0
0
92 E
96 E
Longtitudeo(E) 100 E
104 E
92 0E
96 0E
100 0E
104 0E
N
N
W
TN90(HadEX)
E
N
W
S
100
0
88 E
104 E
104 0E
< 97
97 - 145
145 - 165
165 - 230
Lasha
120 0E
Beijing
26 0N
0
30 N
100 0E
50 0N
TN90(20C3M)
E
S
200
400 Km
100
0
200
400 Km
0
38 N
38 N
Xining
0
Xining
34 0N
Latitudeo(N)
Latitudeo(N)
34 N
30 0N
0
0
80 E
< 10.0
10.0 - 10.4
10.4 - 10.8
10.8 - 11.2
Lasha
0
100 E
120 E
50 0N
Beijing
26 0N
30 0N
80 0E
e
88 E
0
92 E
0
96 E
0
Longtitudeo(E) 100 E
0
104 E
0
88 E
92 0E
96 0E
100 0E
104 0E
30 0N
0
0
88 E
f
0
88 E
N
N
W
200
0
0
0
0
92 E
96 E
Longtitudeo(E) 100 E
104 E
92 0E
96 0E
100 0E
104 0E
N
CDD(HadEX)
E
N
W
S
100
< 10.0
10.0 - 10.4
10.4 - 10.8
10.8 - 11.2
Lasha
120 0E
Beijing
26 0N
0
30 N
100 0E
0
50 N
CDD(20C3M)
E
S
400 Km
100
0
38 N
200
400 Km
0
38 N
Xining
34 0N
Latitudeo(N)
Latitudeo(N)
30 0N
80 0E
100 0E
< 45
45 - 55
55 - 60
Lasha
120 0E
0
50 N
Beijing
26 0N
Xining
34 0N
30 0N
30 0N
80 0E
0
0
92 E
0
96 E
0
Longtitudeo(E) 100 E
0
104 E
< 45
45 - 55
55 - 60
Lasha
120 0E
Beijing
26 0N
88 E
100 0E
50 0N
0
30 N
0
88 E
0
92 E
0
96 E
0
Longtitudeo(E) 100 E
0
104 E
Fig. 3. Spatial pattern of climate extremes in 1951–1999: (a) FD of observation (HadEX),(b) FD of multi-model ensemble, (c) TN90 of observation (HadEX), (d) TN90 of multi-model
ensemble. (e) CDD of observation (HadEX), (f) CDD of multi-model ensemble, (g) R10 of observation (HadEX), (h) R10 of multi-model ensemble, (i) R5D of observation (HadEX),
(j) R5D of multi-model ensemble. (k) R95T of observation (HadEX), (l) R95T of multi-model ensemble, (m) SDII of observation (HadEX) and (n) SDII of multi-model ensemble.
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T. Yang et al. / Global and Planetary Change 80-81 (2012) 1–13
g
0
0
88 E
92 E
0
0
96 E
100 E
h
0
104 E
0
88 E
N
92 0E
96 0E
100 0E
104 0E
N
N
W
R10(HadEX)
E
N
W
S
100
9
R10(20C3M)
E
S
200
100
400 Km
0
200
400 Km
0
38 N
38 N
Xining
Xining
0
0
34 N
Latitudeo(N)
Latitudeo(N)
34 N
0-5
5 - 10
10 - 17
17 - 22
22 - 30
0
30 N
0
0
80 E
Lasha
0
100 E
120 E
50 0N
Beijing
0
26 N
80 0E
0
0
88 E
92 E
0
96 E
0
Longtitudeo(E) 100 E
100 0E
Beijing
30 0N
0
104 E
0
92 E
0
92 E
88 E
i
Lasha
120 0E
50 0N
26 0N
30 0N
0-5
5 - 10
10 - 17
17 - 22
22 - 30
30 0N
0
96 E Longtitudeo(E)
0
100 E
0
96 E
0
104 E
0
100 E
0
0
104 E
j
0
0
88 E
92 E
0
0
96 E
100 E
0
104 E
88 E
N
W
R5D(HadEX)
E
N
W
S
100
0
N
N
R5D(20C3M)
E
S
200
100
400 Km
0
200
400 Km
0
38 N
38 N
Xining
Xining
0
0
34 N
Latitudeo(N)
Latitudeo(N)
34 N
< 32
32 - 47
47 - 69
69 - 98
98 - 134
30 0N
80 0E
100 0E
Lasha
120 0E
50 0N
Beijing
26 0N
30 0N
80 0E
0
92 E
0
96 E
Longtitudeo(E) 100 E
104 E
0
92 0E
96 0E
100 0E
104 0E
88 E
0
0
0
Beijing
30 0N
0
0
0
88 E
k
< 32
32 - 47
47 - 69
69 - 98
98 - 134
Lasha
120 0E
50 N
26 0N
30 0N
100 0E
0
0
0
92 E
96 E
Longtitudeo(E) 100 E
104 E
92 0E
96 0E
100 0E
104 0E
l
88 E
0
88 E
N
N
N
W
R95T(HadEX)
E
N
W
R95T(20C3M)
E
S
S
100
200
100
400 Km
200
400 Km
0
38 N
0
38 N
Xining
Xining
34 0N
0
Latitudeo(N)
Latitudeo(N)
34 N
30 0N
0
0
80 E
< 16
16 - 19
19 - 22
22 - 24
Lasha
0
100 E
120 E
50 0N
Beijing
26 0N
30 0N
0
0
0
88 E
m
92 E
0
96 E
0
Longtitudeo(E) 100 E
0
30 N
0
0
0
0
0
0
92 E
96 E
100 E
104 E
92 E
0
96 E
0
Longtitudeo(E) 100 E
0
104 E
n
0
0
88 E
92 E
0
0
96 E
100 E
0
104 E
N
N
W
0
88 E
0
N
SDII(HadEX)
E
N
W
S
100
120 E
104 E
88 E
< 16
16 - 19
19 - 22
22 - 24
Lasha
0
100 E
Beijing
26 0N
0
30 N
0
80 E
50 0N
SDII(20C3M)
E
S
200
100
400 Km
0
200
400 Km
0
38 N
38 N
Xining
Xining
0
0
34 N
Latitude o(N)
Latitudeo(N)
34 N
30 0N
0
80 E
0
< 3.1
3.1 - 4.7
4.7 - 6.0
6.0 - 8.5
8.5 - 10.3
Lasha
0
100 E
120 E
50 0N
Beijing
26 0N
0
80 E
0
88 E
0
92 E
0
96 E
0
Longtitudeo(E) 100 E
0
Lasha
0
100 E
120 E
0
50 N
Beijing
26 0N
0
30 N
< 3.1
3.1 - 4.7
4.7 - 6.0
6.0 - 8.5
8.5 - 10.3
30 0N
0
30 N
0
0
104 E
88 E
Fig. 3 (continued).
0
92 E
0
96 E
0
Longtitudeo(E) 100 E
0
104 E
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T. Yang et al. / Global and Planetary Change 80-81 (2012) 1–13
a
0
0
88 E
0
92 E
0
96 E
100 E
b
0
104 E
N
N
W
FD
E
FD
S
100
200
400 Km
0
38 N
Xining
0
Latitude o(N)
34 N
(%)
0
-8.3 - -5.7
30 N
-5.7 - -3.8
0
0
80 E
Lasha
0
100 E
120 E
-3.8 - -2.6
0
50 N
-2.6 - 0.0
Beijing
0.0 - 1.0
0
0
26 N
30 N
0
0
88 E
0
92 E
0
96 E
100 E
0
104 E
Longtitude o(E)
c
d
0
0
88 E
92 E
0
0
96 E
100 E
0
104 E
N
N
W
TN90
TN90
E
S
100
200
400 Km
0
38 N
Xining
0
Latitude o(N)
34 N
(%)
0
-2.2 - 0.0
30 N
0.0 - 31.7
0
0
80 E
Lasha
0
100 E
120 E
31.7 - 38.9
0
50 N
38.9 - 47.0
Beijing
47.0 - 57.8
0
26 N
0
30 N
0
0
88 E
92 E
0
0
96 E
100 E
0
104 E
Longtitude o(E)
e
f
0
0
88 E
92 E
0
0
96 E
100 E
0
104 E
CDD
N
N
W
CDD
E
S
100
200
400 Km
0
38 N
Xining
0
Latitude o(N)
34 N
( %)
-21.9 - -13.2
0
30 N
-13.2 - -7.1
0
80 E
0
Lasha
0
100 E
120 E
-7.1 - 0.0
0
50 N
0.0 - 5.4
Beijing
5.4 - 18.6
0
26 N
0
30 N
0
88 E
0
92 E
0
96 E
0
100 E
0
104 E
Longtitude o(E)
Fig. 4. Spatial pattern of (a) FD (days), (c) TN90 (%), (e) CDD (days), (g) R10 (days), (i) R5D (mm), (k) R95T (%), and (m) SDII (mm/d) change of multi-model ensemble under A1B
scenarios (the difference between two ten-year averages: 2011–2020 minus 1991–2000), and the temporal evolution of (b) FD (days), (d) TN90 (%),(f) CDD (days), (h) R10 (days),
(j) R5D (mm), (l) R95T (%), and (n) SDII (mm/d) over Tibetan Plateau under A2, A1B and B1 scenarios (three scenarios are shown in different styles or colors for the years from 1951
to 2099).
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T. Yang et al. / Global and Planetary Change 80-81 (2012) 1–13
g
11
h
0
0
88 E
0
92 E
0
96 E
0
100 E
104 E
N
N
W
R10
E
S
100
200
400 Km
R1 0
0
38 N
Xining
0
o
Latitude (N)
34 N
(%)
-12.3 - 0.0
0
30 N
0.0 - 16.5
0
0
80 E
Lasha
0
100 E
120 E
16.5 - 30.2
0
50 N
30.2 - 48.2
Beijing
0
26 N
48.2 - 68.7
0
30 N
0
0
88 E
0
92 E
0
96 E
0
100 E
104 E
Longtitude o(E)
i
j
0
0
88 E
92 E
0
0
96 E
100 E
0
104 E
N
N
W
R5 D
E
R5 D
S
100
200
400 Km
0
38 N
Xining
0
o
Latitude (N)
34 N
(%)
-11. 5 - 0.0
0
30 N
0.0 - 2.4
0
0
80 E
Lasha
0
100 E
120 E
2.4 - 6.1
0
50 N
6.1 - 11. 0
Beijing
0
26 N
11. 0 - 17.9
0
30 N
0
0
88 E
92 E
0
0
96 E
100 E
0
104 E
Longtitude o(E)
k
l
0
0
88 E
92 E
0
0
96 E
100 E
0
104 E
R95T
N
N
W
R95T
E
S
100
200
400 Km
0
38 N
Xining
0
o
Latitude ( N)
34 N
(%)
0
-16.2 - 0.0
30 N
0.0 - 6.1
0
80 E
0
Lasha
0
100 E
120 E
6.1 - 13.3
0
50 N
13.3 - 19.4
Beijing
19.4 - 25.9
0
26 N
0
30 N
0
88 E
0
92 E
0
96 E
0
100 E
0
104 E
Longtitude o(E)
Fig. 4 (continued).
Author's personal copy
12
T. Yang et al. / Global and Planetary Change 80-81 (2012) 1–13
m
n
0
0
88 E
92 E
0
0
96 E
100 E
0
104 E
N
N
W
SDII
SDII
E
S
100
200
400 Km
0
38 N
Xining
0
Latitude o(N)
34 N
(%)
0
-6.4 - -2.8
30 N
-2.8 - 0.0
0
80 E
0
Lasha
0
100 E
120 E
0.0 - 1.7
0
50 N
1.7 - 3.6
Beijing
3.6 - 6.0
0
26 N
0
30 N
0
88 E
0
92 E
0
96 E
0
100 E
0
104 E
Longtitude o(E)
Fig. 4 (continued).
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), grants of the Special
Public Sector Research Program of Ministry of Water Resources
(201001066, 201001057), the National Basic Research Program of
China “973 Program” (2010CB428405, 2010CB951101), and the Fundamental Research Funds for the Central Universities (2010B00714), the
Australian Endeavour Fellowship Program, and the CSIRO Computational
and Simulation Sciences Transformational Capability Platform. 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 multi-model data set available. The IPCC Data Archive at Lawrence
Livermore National Laboratory is supported by the Office of Science, US
Department of Energy. Finally, cordial thanks are also extended to the editor, Professor K. McGuffie and two anonymous referees for their valuable
comments which greatly improved the quality of this paper.
References
Alexander, L.V., et al., 2006. Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research 111, 1–22.
Baker, B.B., Moseley, R.K., 2007. Advancing treeline and retreating glaciers: implications for
conservation in Yunnan, P. R. China. Arctic, Antarctic, and Alpine Research 39, 200–209.
Chen, B., et al., 2003. Enhanced climatic warming in the Tibetan plateau due to doubling
CO2: a model study. Climate Dynamics 20, 401–413.
Christensen, J.H., et al., 2007. Regional climate projections. Climate change 2007: The
physical science basis. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis,
M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), Contribution of Working Group I to
the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.
Cambridge University Press, Cambridge. United Kingdom and New York, NY, USA.
Delworth, T.L., et al., 2006. GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics. Journal of Climate 19, 643–674.
Dempster, A.P., Laird, N.M., Rubin, D.B., 1977. Maximum likelihood from incomplete
data via the EM algorithm. Journal of the Royal Statistical Society 39B, 1–39.
Diansky, N.A., Volodin, E.M., 2002. Simulation of present-day climate with a coupled
atmosphere-ocean general circulation model. Izvestiya, Atmospheric and Oceanic
Physics 38, 732–747.
Duan, A.M., Wu, G.X., 2006. Change of cloud amount and the climate warming on the Tibetan Plateau. Geophysical Research Letters 33, L22704. doi:10.1029/2006GL027946.
Frich, P., Alexander, L.V., Della-Marta, P., Gleason, B., Haylock, M., Klein Tank, A.M.G.,
Peterson, T., 2002. Observed coherent changes in climatic extremes during the second half of the twentieth century. Climate Research 19, 193–212.
Gachon, P., et al., 2005. A first evaluation of the strength and weaknesses of statistical
downscaling methods for simulating extremes over various regions of eastern
Canada. Subcomponent, Climate Change Action Fund (CCAF), Environment Canada, Final report, Montréal, Québec, Canada. 209 pp.
Gnanadesikan, A., et al., 2006. GFDL’s CM2 global coupled climate models. Part II: the
baseline ocean simulation. Journal of Climate 19, 675–697.
Hasumi, H., Emori, S., 2004. K-1 Coupled Model (MIROC) Description. K-1 Tech. Rep. 1.
Center for Climate System Research, University of Tokyo. 34 pp.
IPCC, 2000. In: Nakicenovic, H. (Ed.), Special Report on Emissions Scenarios. Cambridge
Univ. Press, Cambridge.
IPCC, 2007. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor,
M., Miller, H.L. (Eds.), Climate Change 2007: The Physical Science Basis. Contribution
of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel
on Climate Change. Cambridge University Press, Cambridge. United Kingdom and
New York, NY, USA.
Jin, Z.D., You, C.F., Wang, Y., Shi, Y.W., 2009. Hydrological and solute budgets of Lake
Qinghai, the largest lake on the Tibetan Plateau. Quaternary International 218
(2010), 151–156.
Katz, R.W., Brown, B.G., 2004. Extreme events in a changing climate: variability is more
important than averages. Climatic Change 21, 289–302.
Krishnamurti, T.N., Kishtawal, C.M., LaRow, T., Bachiochi, D., Zhang, Z., Williford, C.E.,
Gadgil, S., Surendran, S., 1999. Improved weather and seasonal climate forecasts
from multimodel superensembles. Science 285, 1548–1550.
Lin, Z., Zhao, X., 1996. Spatial characteristics of changes in temperature and precipitation of the Qinghai-Xizang (Tibet) Plateau. Science in China. Series B
39, 442–448.
Liu, X., Chen, B., 2000. Climatic warming in the Tibetan Plateau during recent decades.
International Journal of Climatology 20 (14), 1729–1742.
Liu, B., Xu, M., Henderson, M., Qi, Y., 2005. Observed trends of precipitation amount, frequency, and intensity in China, 1960–2000. Journal of Geophysical Research 110,
D08103. doi:10.1029/2004JD004864.
McCarty, J.P., 2001. Ecological consequences of recent climate change. Conservation Biology 15, 320–331.
McLachlan, G.J., Krishnan, T., 1997. The EM Algorithm and Extensions. Wiley. 274 pp.
Mearns, L.O., Hulme, M., Carter, T.R., Leemans, R., Lal, M., Whetton, P., 2001. Climate
scenario development. In: Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van
der Linden, P.J., Dai, X., Maskell, K., Johnson, C.A. (Eds.), Climate Change 2001:
The Scientific Basis, Contribution of Working Group I to the Third Assessment Report of the IPCC. Cambridge U. Press, Cambridge, pp. 583–638.
Meehl, G.A., Arblaster, J.M., Tebaldi, C., 2005. Understanding future patterns of increased precipitation intensity in climate model simulations. Geophysical Research
Letters 32 (L18719), 1–4.
Meehl, G.A., Covey, C., Delworth, T., Latif, M., McAvaney, B., Mitchell, J.F.B., Stouffer,
R.J., Taylor, K.E., 2007. The WCRP CMIP3 multi-model dataset: a new era in climate change research. Bulletin of the American Meteorological Society 88,
1383–1394.
Min, S.K., Hense, A., 2006. A Bayesian approach to climate model evaluation and multimodel averaging with an application to global mean surface temperatures from
IPCC AR4 coupled climate models. Geophysical Research Letters 33, L08708.
doi:10.1029/2006GL025779.
Min, S.-K., Hense, A., Paeth, H., Kwon, W.-T., 2004. A Bayesian decision method for climate
change signal analysis. Meteorologische Zeitschrift 13, 421–436.
Min, S.-K., Hense, A., Kwon, W.-T., 2005. Regional-scale climate change detection using a
Bayesian decision method. Geophysical Research Letters 32, L03706. doi:10.1029/
2004GL021028.
Peterson, T.C., Folland, C., Gruza, G., Hogg, W., Mokssit, A., Plummer, N., 2001. Report of
the Activities of the Working Group on Climate Change Detection and Related Rapporteurs, Tech. Doc. 1071. World Meteorol. Organ., Geneva, Switzerland. 146 pp.
Author's personal copy
T. Yang et al. / Global and Planetary Change 80-81 (2012) 1–13
Piao, S.L., Fang, J.Y., He, J.S., 2006. Variations in vegetation net primary production in the
Qinghai-Xizang Plateau, China, from 1982 to 1999. Climatic Change 74, 253–267.
Raftery, A.E., Gneiting, T., Balabdaoui, F., Polakowski, M., 2005. Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review 133, 1155–1174.
Salick, J., Fang, Z.D., Byg, A., 2009. Eastern Himalayan alpine plant ecology, Tibetan ethnobotany, and climate change. Global Environmental Change 19 (2), 147–155.
Song, R.Y., 2007. Projected future changes of extreme precipitation climate events over
China by a high resolution regional climate model (RegCM3). MSc thesis, Chinese
Academy of Meteorological Sciences, Beijing, 92 pp.
Tang, M., Cheng, G., Lin, Z. (Eds.), 1998. Contemporary Climatic Variations over
Qinghai-Xizang (Tibetan) Plateau and their Influences on Environments (in Chinese). Guangdong Science and Technology Press, Guangzhou.
Tebaldi, C., Hayhoe, K., Arblaster, J.M., Meehl, G.A., 2006. Going to the extremes, an
intercomparison of model-simulated historical and future changes in extreme
events. Climatic Change 79, 185–211.
Vallée, M., Wilson, L., Bourgouin, P., 1996. New statistical methods for the interpretation of NWP output at the Canadian Meteorological Center. Preprints, 13th Conf.
on Probability and Statistics in the Atmospheric Sciences. Amer. Meteor. Soc., San
Francisco, CA, pp. 37–44.
Vincent, L., Mekis, É., 2006. Changes in daily and extreme temperature and precipitation
indices for Canada over the twentieth century. Atmosphere-Ocean 44, 177–193.
Vislocky, R.L., Fritsch, J.M., 1995. Improved model output statistics forecasts through
model consensus. Bulletin of the American Meteorological Society 76, 1157–1164.
Wetterhall, F., Bárdossy, A., Chen, D., Halldin, S., Xu, C.-Y., 2006. Daily precipitation
downscaling techniques in different climate regions in China. Water Resources Research 42, W11423. doi:10.1029/2005WR004573.
Wilson, L.J., Beauregard, S., Raftery, A.E., Verret, R., 2007. Calibrated surface temperature
forecasts from the Canadian ensemble prediction system using Bayesian model averaging. Monthly Weather Review 135, 1364–1385. doi:10.1175/MWR3347.1.
Woodwell, G.M., 2004. Mountains: top down. Ambio Special Report 13, 35–38.
Xu, Y., Xu, C., Gao, X., Luo, Y., 2009a. Projected changes in temperature and precipitation
extremes over the Yangtze River basin of China in the 21st century. Quaternary International 208, 44–52.
13
Xu, J., Grumbine, R., Shrestha, A., Eriksson, M., Yang, X., Wang, Y., Wilkes, A., 2009b. The
melting Himalayas: cascading effects of climate change on water, biodiversity, and
livelihoods. Conservation Biology 23 (3), 520–530.
Yang, T., Wang, X., Zhao, C., Chen, X., Yu, Z., Shao, Q., Xu, C., Xia, J., Wang, W., 2011.
Changes of climate extremes in a typical arid zone: observations and multimodel ensemble projections. Journal of Geophysical Research. doi:10.1029/
2010JD015192.
Ye, Q., Yao, T.D., 2008. Glacier and lake variations in some regions on Tibetan Plateau
using remote sensing and GIS technologies. Geophysical research abstracts, Volume 10. Copernicus Publications, Katlenburg-Lindau, Germany (EGU2008-A01760, 2008. SRef-ID:1607–792/gra/EGU2008-A-01760).
You, Q., Kang, S., Aguilar, E., Yan, Y., 2008. Changes in daily climate extremes in the
eastern and central Tibetan Plateau during 1961–2005. Journal of Geophysical Research 113, D07101. doi:10.1029/2007JD009389.
Zhang, X., Yang, D., Zhou, G., Liu, C., Zhang, J., 1996. Model expectation of impacts of
global climate change on Biomes of the Tibetan Plateau. In: Omasa, K., Kai, K.,
Taoda, H., Uchijima, Z., Yoshino, M. (Eds.), Climate Change and Plants in East
Asia. Springer-Verlag, Tokyo, pp. 25–38.
Zhang, Y., Xu, Y.L., Dong, W.J., Cao, L.J., Sparrow, M., 2006. A future climate scenario of
regional changes in extreme climate events over China using the PRECIS climate
model. Geophysical Research Letters 33, L24702.
Zhang, Q., Xu, C.-Y., Zhang, Z.X., Chen, Y.D., 2009. Changes of temperature extremes for
1960–2004 in Far-West China. Stochastic Environmental Research & Risk Assessment 23, 721–735.
Zheng, D., Zhu, L. (Eds.), 2000. Formation and Environmental Changes and Sustainable
Development on the Tibetan Plateau: Proceedings of International Symposium on
the Qinghai-Tibetan Plateau, Xining, China, 21–24 July 1998. Academy Press,
Beijing.
Zheng, D., Zhang, Q., Wu, S. (Eds.), 2000. Mountain Genecology and Sustainable Development of the Tibetan Plateau. Kluwer Academic Publishing, Dordrecht.
Zhou, T.J., Yu, R.C., 2006. Twentieth-century surface air temperature over China and
the globe simulated by coupled climate models. Journal of Climate 19 (22),
5843–5858.