Download Multi-Model Projection of July–August Climate Extreme Changes

ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 28, NO. 2, 2011, 448–463
Multi-Model Projection of July–August Climate Extreme Changes
over China under CO2 Doubling. Part II: Temperature
LI Hongmei1,2 (李红梅), FENG Lei1,2 (冯 蕾), and ZHOU Tianjun∗ 1 (周天军)
1
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
2
Graduate University of Chinese Academy of Sciences, Beijing 100049
(Received 5 May 2010; revised 18 October 2010)
ABSTRACT
This is the second part of the authors’ analysis on the output of 24 coupled climate models from the
Twentieth-Century Climate in Coupled Models (20C3M) experiment and 1% per year CO2 increase experiment (to doubling) (1pctto2x) of phase 3 of the Coupled Model Inter-comparison Project (CMIP3). The
study focuses on the potential changes of July–August temperature extremes over China. The pattern correlation coefficients of the simulated temperature with the observations are 0.6–0.9, which are higher than
the results for precipitation. However, most models have cold bias compared to observation, with a larger
cold bias over western China (>5◦ C) than over eastern China (<2◦ C). The multi-model ensemble (MME)
exhibits a significant increase of temperature under the 1pctto2x scenario. The amplitude of the MME
warming shows a northwest–southeast decreasing gradient. The warming spread among the models (∼1◦ C–
2◦ C) is less than MME warming (∼2◦ C–4◦ C), indicating a relatively robust temperature change under CO2
doubling. Further analysis of Geophysical Fluid Dynamics Laboratory coupled climate model version 2.1
(GFDL-CM2.1) simulations suggests that the warming pattern may be related to heat transport by summer
monsoons. The contrast of cloud effects also has contributions. The different vertical structures of warming
over northwestern China and southeastern China may be attributed to the different natures of vertical circulations. The deep, moist convection over southeastern China is an effective mechanism for “transporting”
the warming upward, leading to more upper-level warming. In northwestern China, the warming is more
surface-orientated, possibly due to the shallow, dry convection.
Key words: extreme temperature, coupled climate model, projection, CO2 doubling
Citation: Li, H. M., L. Feng, and T. J. Zhou, 2011: Multi-model projection of July–August climate extreme
changes over China under CO2 doubling. Part II: Temperature. Adv. Atmos. Sci., 28(2), 448–463, doi:
10.1007/s00376-010-0052-x.
1.
Introduction
Extreme temperature change has more noticeable
effect on human life and society than the mean condition. Observational evidence over continental China
indicates that extreme indicators, based on both absolute value and percentile, present significant decadal
variability; decreasing temperature trend occurred in
the 1960s and an increasing temperature trend occurred in the 1990s (see Fig. 3.1 of Li, 2007). The significant cooling region of the lower-end temperature indices was located over the middle-lower Yangtze River
valley, and the higher-end cooling region was located
∗ Corresponding
to its north (Li, 2007). In addition, the extreme temperatures show an obvious seasonal cycle and interannual variability (Li et al., 2009). Asymmetry is also
reflected in the extreme temperature changes. During the past 40 years in China, the mean minimum
temperature has increased significantly; however, the
mean maximum has not changed significantly (Zhai
and Pan, 2003). Under a global warming scenario,
Huang et al. (2010) found a close relationship between global extreme temperature anomalies and that
in three key regions of China: northeastern China and
its coastal areas, the high-latitude regions above 40◦ N,
and southwestern China.
author: ZHOU Tianjun, [email protected]
© China National Committee for International Association of Meteorology and Atmospheric Sciences (IAMAS), Institute of Atmospheric
Physics (IAP) and Science Press and Springer-Verlag Berlin Heidelberg 2011
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LI ET AL.
Potential future changes of climate extremes under global warming have been of great concern to
both the scientific community and society. Climate
system models have played crucial roles in understanding and simulating past, present, and future
climates (Zhou et al., 2007). Coordinated by the
World Climate Research Program’s (WCRP’s) phase
3 of the Coupled Model Intercomparison Project
(CMIP3), more than 20 state-of-the-art coupled global
climate system models have been involved in the modeling activities for the Intergovernmental Panel on
Climate Change Fourth Assessment Report (IPCC
AR4). Model projections of future climate change
under different scenarios are available (http://wwwpcmdi.llnl.gov/ipcc/about− ipcc.php). This modeling project reduces the uncertainty of future climate
change projection compared to the simulation of only
one specific model. In an attempt to assess the potential changes of summertime climate extremes over
China in a warmer climate scenario, the output of
CMIP3 models of future climate-change projection under CO2 doubling is analyzed. In the first part of this
study (Li et al., 2010a), we have shown an increasing of total and extreme precipitation under a warmer
climate, and the consistency of extreme precipitation
change among the models is higher than that of total
precipitation amount. In this part of the study, we focus on the potential changes of temperature extremes
over contiguous China.
Based on the projection of global coupled models, dramatic increases in temperature extremes were
revealed in many regions of the world (Weisheimer
and Palmer, 2005; Tebaldi et al., 2006; Kharin et
al., 2007). For the conditions over China, a preliminary analysis using CMIP3 models revealed an increase of warm extremes along the Yangtze River valley (Xu et al., 2009). CMIP3 models are actually lowresolution global climate models. Therefore, complementary regional climate models have been used to
perform regional dynamical downscaling. Regional climate model projections also showed more severe warm
extremes but less severe cold extremes under a global
warming scenario (Gao et al., 2001, 2002; Xu et al.,
2006; Zhang et al., 2006). These previous studies,
however, mainly presented the results from either a
multi-model mean or several specific models. Given
the strong limitation of the state-of-the-art climate
models in East Asia because of its complex topography (Zhou and Li, 2002; Zhou and Yu, 2006; Li et al.,
2010b; Zhou et al., 2009a, b; Chen et al., 2010), it is
desirable to examine both the relatively robust aspects
(multi-model means) and uncertainties (result spreads
among the models) with respect to the projections of
future climate change using CMIP3 global models.
The rest of the article is organized as follows. Section 2 describes the models, datasets, and methods
used in this study. In section 3, the performance
of CMIP3 models in simulating the temperature over
China is evaluated using the output of the TwentiethCentury Climate in Coupled Models (20C3M). The
projected changes of extreme temperature over China
based on the output of WCRP CMIP3 1% per year
CO2 increase experiment (to doubling) (1pctto2x) are
shown in section 4. The possible mechanism for the
temperature change is analyzed in section 5 based on
the output of Geophysical Fluid Dynamics Laboratory
coupled climate model version 2.1 (GFDL-CM2.1),
which shows similarities with the MME and also provides enough data for mechanism analyses. Summary
and discussion are given in section 6.
2.
Data, models, and methodology
The observational dataset used in this study includes the daily maximum temperatures and daily
minimum temperatures at 740 stations covering mainland China for the period 1961–2000. The corresponding daily precipitation data have been used in the analysis of precipitation extremes (Li et al., 2008). The
dataset was developed by National Climate Center of
the China Meteorological Administration. In our analysis, the data was quality controlled using the method
of Alexander et al. (2006).
Details of WCRP CMIP3 models were described in the first part of the study (Li et al.,
2010a) and also the CMIP3 data bank (http://wwwpcmdi.llnl.gov/ipcc/about− ipcc.php). In this part of
the study, the simulated daily mean and extreme temperatures are used. The 20C3M simulation is used in
the evaluation of model performance. The 1pctto2x
simulation is used to examine the potential change of
extreme temperature over China under a global warming scenario. The output of the preindustrial control
run is also used. In order to examine both the relatively robust aspects and uncertainties, we use a total
of 24 CMIP3 model outputs. For specific simulations
and variables, the data are not complete, and some
models do not provide enough data for our analyses.
For example, the outputs of 20 models are available
for the 20C3M simulation, but the outputs of only 15
models are available for the 1pctto2x simulation. The
resolutions of these models are different [see Table 1
of Li et al. (2010a)].
Here we define the extreme temperature indices as
the days with temperatures lower than 10th percentile
or higher than 90th percentile, centered on a 5-day
window for the base period 1961–1990. Cold nights
are defined as nights when daily minimum tempera-
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PROJECTION OF EXTREME TEMPERATURE CHANGES OVER CHINA: TEMPERATURE
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Fig. 1. 1961–2000 mean July–August mean daily maximum temperatures (left) and
daily minimum temperatures (right) over China from observations (a and d), MME
(b and e), (c) and (f) are their difference between MME and observations. Light
(dark) shading indicate areas where the value is larger than 15◦ C (30◦ C) in (a and
b), 10◦ C (20◦ C) in (d and e), and smaller than −5◦ C (larger than 5◦ C) in (c and f).
ture is lower than its 10th percentile value (hereafter
TN10p); warm nights are defined as nights when daily
minimum temperature is higher than its 90th percentile value (hereafter TN90p); cold days are defined
as days when daily maximum temperature is lower
than its 10th percentile value (hereafter TX10p); warm
days are defined as days when daily maximum temperature is higher than its 90th percentile value (hereafter TX90p). These are typical indices that have been
widely used in international climate extreme studies
(Frich et al., 2002; Zhai et al., 2005; Alexander et al.,
2006). As emphasized by Li et al. (2008, 2010a), we
focus on the July–August mean condition, the central period of summertime in the East Asian monsoon
domain. The time period considered in this study is
1961–2000 for observations and the 20C3M simulation,
and the last 20 year simulations of both the 1pctto2x
run and the pre-industrial control run.
Following Zhou and Yu (2006), we use standard
deviation to measure the spread of model simulation
results quantitatively. This formula follows:
v
u n
u1 X
−
sx = t
(xi − x)2
n i=1
(1)
−
where, xi is the individual model result; x is the
multi-model mean result; n is the number of models
used. When the MME result is larger than the spread
among the models, the simulation result is considered
to be relatively robust.
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Fig. 2. 1961–2000 mean July–August mean daily maximum temperatures over China
from individual model simulation (a)–(t), units: ◦ C. Light (dark) shading indicates
areas where the values are larger than 15◦ C (30◦ C).
3.
Evaluation of model performance
We begin our analyses by evaluating model performance over contiguous China. The simulated mean
and extreme temperatures are compared with the observation data. The 1961–2000 mean July–August
daily maximum temperatures and daily minimum temperatures from the observation data, the MME, and
the differences between MME and observation are
shown in Fig. 1. The observation data shows that the
relatively high temperatures are located over southeastern and northwestern China and the lowest temperatures are located over the Tibetan Plateau. The
broad spatial pattern is well reproduced in most models; however, the difference map between the MME
and observation data (Figs. 1c and 1f) shows that
the simulated temperature is lower than the observation data, especially over western China, with a
cold bias >10◦ C. This is consistent with the IPCC
AR4 (Christensen et al., 2007), which pointed out that
the simulated temperatures in most models were too
low in all seasons over East Asia, and that the mean
cold bias was largest in winter and smallest in summer. The discrepancy between the observation data
and the simulated daily minimum temperatures is less
than those for daily maximum temperatures, which
indicates that models may have better performance
in simulating daily minimum temperatures than daily
maximum temperatures.
The spatial patterns of July–August mean daily
maximum temperatures simulated by individual models are shown in Fig. 2. Most of the coupled models
reasonably reproduce the broad-scale characteristics of
the observations; however, the simulated daily maximum temperatures over the Tibetan Plateau are generally lower than the observation data in all the models
(except for the CGCM3.1). Calculations also indicate
that the spread over western China (∼3◦ C) is larger
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PROJECTION OF EXTREME TEMPERATURE CHANGES OVER CHINA: TEMPERATURE
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Fig. 3. As Fig. 2, but for July–August mean daily minimum temperatures. Light
(dark) shading indicates areas where the values are larger than 10◦ C (20◦ C).
than that over eastern China (∼2◦ C). As stated in the
IPCC AR4 (Christensen et al., 2007), the global models had significant problems over the Tibetan Plateau
due to the difficulty in simulating the effects of the
dramatic topographic relief and the distorted albedo
feedbacks created by extensive snow cover. Generally,
the models with higher resolutions perform better than
those with lower or medium resolutions in simulating the relatively high temperatures over northwestern
China.
The spatial patterns of July–August mean daily
minimum temperatures simulated by individual models are shown in Fig. 3. The distribution pattern is
similar to that of the daily maximum temperatures.
The highest value over southeastern China is reasonably well simulated by all the models; however, most
models underestimate the daily minimum temperatures over the Tibetan Plateau. The results of some
models are even <−5◦ C, and the differences between
the simulation and the observation data are >10◦ C. A
large discrepancy between the simulated and observed
daily minimum temperatures occurs in northwestern
China. The values of some models are stronger than
the observation data; the values of the other models
are, however, weaker than the observation data, indicating a spread among the models. Calculations indicate that the consistency among the models in simulation of the daily minimum temperatures is not as good
as that among daily maximum temperatures, with the
spread >5◦ C (3◦ C) over western (eastern) China.
As the analysis of precipitation presented in Part I
of the study (Li et al., 2010a), we employ the Taylor
diagram to quantitatively evaluate the performances
of the models in temperature simulations (Fig. 4).
The concentration of dots represents resemblance of
model simulations. Clearly, the resemblance of simulated daily minimum temperatures to the observation
data is higher than that of daily maximum temperatures. The Taylor diagram shows that most of the
pattern correlations between the specific model simu-
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Fig. 4. Taylor diagram of 1961–2000 mean July–August daily maximum and
minimum temperatures, the number marks represent the resemblance between
the spatial pattern from single model simulation and observation. The radial
distance from the origin indicates the standard deviation of each model simulation, normalized by the observed value. The angle from the horizontal axis
represents the inverse cosine of the spatial correlation between the simulation of certain model and the observations. Dots represent daily maximum
temperatures and stars represent daily minimum temperatures.
lations and the observation data are between 0.6 and
0.9, which are much higher than the corresponding results for precipitation (0.3–0.6) shown in Part I of this
study (Li et al., 2010a). The correlation coefficients
for daily minimum temperatures (0.8–0.9) are larger
than those for daily maximum temperatures (0.6–0.8),
suggesting that the performance of the models in simulating the spatial pattern of daily minimum temperatures is better than that of daily maximum temperatures. The high pattern correlation may be affected
by the surface altitude. The radial distances are >1.0,
which means the spatial variations of temperature over
China from model simulation are larger than those of
the observation data.
4.
Projected future changes
Although there are discrepancies between the observation data and the simulations, evaluation of
model performance shows that the CMIP3 coupled
models simulate the broad characteristics of temperature distribution over contiguous China reasonably
well except for the plateau area. Hence, we further
examine how the temperature, especially extreme temperature, would change over contiguous China in response to CO2 doubling.
The July–August mean daily maximum temperature changes over China under CO2 doubling relative
to the preindustrial control run are shown in Fig. 5.
The MME shows a consistent warming trend with its
highest value over the northwestern Tibetan Plateau;
all the changes are statistically significant at the 5%
level (Fig. 5a). A regional climate modeling study by
Gao et al. (2003) and an analysis of A1B scenario simulations of IPCC AR4 (Christensen et al., 2007) also
showed a larger warming over the Tibetan Plateau
compared to surrounding areas due to the effects of
greenhouse gases. Giorgi et al. (1997) stated that the
greater temperature increase over high-altitude areas
can be explained by the decrease in surface albedo associated with melting snow and ice. A further examination of individual model simulations (Figs. 5b–p)
shows that the locations of the largest warming centers
are different among the models. The largest spread is
located over the Tibetan Plateau (∼1.9◦ C), indicating
a comparatively larger uncertainty of climate change
projection over that area.
The July–August mean daily minimum temperatures changes over China are shown in Fig. 6. A consistent warming trend is also evident in all the models
except for the Bjerknes Centre for Climate ResearchBergen Climate Model (BCM) Version 2 (BCCRBCM2.0). In the other specific models, the spatial
distribution and amplitudes of change in daily maximum temperatures and daily minimum temperatures
are similar. The spatial pattern and magnitude of
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Fig. 5. Simulated changes of July–August daily maximum temperatures over China
under CO2 doubling concentration relative to pre-industry control run. (a) MME,
(b)–(p) individual simulation. Light (dark) shading indicates areas where the values
are larger than 2◦ C (4◦ C).
spread among the models in the simulation of the daily
minimum temperatures are almost the same as that of
the daily maximum temperatures, being larger over
western China and smaller over eastern China (not
shown).
The daily maximum temperatures and daily minimum temperatures are only part of the extreme temperature indices. We further examine the extreme
temperature change in Figs. 7–10. All models (except
for BCCR-BCM2.0) show that the cold nights tend to
decrease by ∼5 days in July–August over China (Fig.
7). In the meantime, warm nights increase by ∼30
days under the CO2 doubling scenario relative to the
preindustrial control run (Fig. 8). The MME results
show that the increase of warm nights over the Tibetan
Plateau is larger than that over the other regions of
China. Nevertheless, there are still significant spreads
among the spatial distribution of changes among the
models (Figs. 8b–p).
The potential mean-state warming would lead to
different change of cold days and warm days. The
projected cold days show decreasing trends (Fig. 9),
while the projected warm days show increasing trends
(Fig. 10) in a CO2 doubling scenario. The magnitudes
of change for cold nights and cold days are almost the
same. However, the increase of warm nights based
on daily minimum temperatures is much larger than
that of warm days based on daily maximum temperatures. This indicates an asymmetric change of warm
extreme events. The asymmetric change of the daily
maximum and minimum temperatures are universal
on the global scale (Karl et al., 1993). Within China,
this is the most evident over the Tibetan Plateau and
northwestern regions (Ma, 1999; Duan and Wu, 2006;
Liu et al., 2006). Observation data indicates that cloud
amount greatly affects the change of diurnal temperature range, mainly by influencing the daily maximum temperatures (Karl et al., 1993; Duan and Wu,
2006). A similar result was also found in the climate
change projection under the A2 scenario by model
inter-comparison (Lobell et al., 2007). Furthermore,
the atmospheric and surface boundary conditions are
shown to differentially affect the daily maximum and
minimum temperatures, i.e., the desertification would
increase the maximum temperatures and decrease the
minimum temperatures (Karl et al., 1993). Under CO2
doubling, the different responses of daily maximum
and minimum temperatures to the large-scale climate
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Fig. 6. As Fig. 5, but for the daily minimum temperatures.
Fig. 7. As Fig. 5, but for the cold nights, units: d. Light (dark) shading indicates
areas where the values are smaller than −4 days (larger than 4 days).
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PROJECTION OF EXTREME TEMPERATURE CHANGES OVER CHINA: TEMPERATURE
Fig. 8. As Fig. 5, but for the warm nights, units: d. Light (dark) shading indicates
areas where the values are smaller than −5 days (larger than 20 days).
Fig. 9. As Fig. 5, but for the cold days, units: d. Shading indicates areas where the
values are smaller than −4 days.
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Fig. 10. As Fig. 5, but for the warm days, units: d. Shading indicates areas where
the values are larger than 15 days.
forcing in the coupled models deserves further study.
Extreme temperature changes over China have
unique spatial distributions, and the model simulations have shown large discrepancies. As the
analysis of extreme precipitation presented in Part
I of the study (Li et al., 2010a), changes of extreme temperature over five typical sub-regions of
contiguous China are calculated (Fig. 11). These
five sub-regions are northwestern China (35◦ –50◦ N,
80◦ –100◦ E), Tibetan Plateau (28◦ –35◦ N, 80◦ –100◦ E),
northeastern China (43◦ –54◦ N, 117.5◦ –130◦ E), northern China (35◦ –43◦ N, 100◦ –122.5◦ E), and southeastern China (22.5◦ –35◦ N, 100◦ –122.5◦ E). All the models
(except for BCCR-BCM2.0) project more warm days
and warm nights and less cold days and cold nights
over northwestern China under CO2 doubling relative
to the preindustrial control run (Fig. 11a), and the
decreases in cold days and cold nights are <10 days.
Over the Tibetan Plateau (Fig. 11b), most models
project a decrease of cold nights (except for BCCRBCM2.0 and CSIRO-MK3.5) and a decrease of cold
days (except for CSIRO-MK3.5). All model simulations exhibit an increase of warm nights and warm
days; in particular, 12 of 15 models project that the
increase of warm nights would >25 days. All model
simulations (except for BCCR-BCM2.0) exhibit a decrease in cold nights and cold days and an increase in
warm nights and warm days over northeastern China
(Fig. 11c). Similar changes are evident in northern
China (Fig. 11d) and southeastern China (Fig. 11e),
where consistent warming is projected by most simulations. Thirteen of 15 models show that the increase
of warm nights would >25 days, with a more consistent projection over southeastern China than the other
regions.
In summary, most of the model simulations and the
MME show consistent warming over contiguous China
under CO2 doubling relative to the preindustrial control run. Although there are discrepancies in spatial
distributions of extreme temperature changes among
the models, the spread is much smaller than that of extreme precipitation shown in Part I of this study (Li
et al., 2010a). The largest spread among the model
simulations is located over the Tibetan Plateau. Most
models project a decrease in cold nights and cold days,
and an increase in warm nights and warm days over
contiguous China under CO2 doubling relative to the
preindustrial control run.
5.
Possible
change
mechanism
for
temperature
The MME results of extreme temperature change
under CO2 doubling feature an obviously northwest–
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Fig. 11. Number of model which simulated extreme temperature changes in different
change value bin over China, (a)–(e) shows the results from 5 sub-regions over China,
the locations of sub-regions are shown in (f). The red, light blue, blue and green bar
indicates changes of TN90p, TN10p, TX90p and TX10p, respectively.
southeast (NW–SE) decreasing gradient. To further
understand this temperature change, we focus on the
GFDL-CM2.1 model for deep analysis. This model
produces a similar spatial pattern to that of MME and
provides enough data for our further study.
The patterns in extreme temperature changes, especially the daily maximum temperature and daily
minimum temperature changes, are partly related to
the change in mean temperature. A comparison
of mean and extreme temperature changes in July–
August and December–January–February is shown in
Fig. 12. During summer, the daily maximum temperatures show the largest warming amplitude, followed
by similar changes in both the mean and minimum
temperatures. All three indices show a NW–SE de-
creasing gradient, indicating that the largest climate
sensitivity to CO2 doubling is located over northwestern China.
Why is the highest sensitivity located in northwestern China? The larger direct surface radiative forcing
associated with the higher altitude and less cloud may
be one reason for the largest warming over Northwest
China (J. H. Lu, personal communication). Although
the water vapor feedback tends to allow greater surface warming across southeastern China, the weaker
warming amplitude suggests that the cloud effect may
have more negative effect on the warming. In addition, the monsoonal transport also reduces the climate
sensitivity over southeastern China and enhances the
warming in the north end of the monsoonal domain
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Fig. 12. Simulated changes of July–August (left) and Dec–Jan–Feb (right) mean
daily temperatures over China by GFDL-CM2.1 under CO2 doubling concentration
relative to pre-industry control run. (a and b) daily mean temperatures, (c and d)
daily maximum temperatures, (e and f) daily minimum temperatures. Light (dark)
shading indicates areas where the values are larger than 2◦ C (4◦ C).
(Cai and Lu, 2007). A further comparison of temperature change between winter and summer (Fig. 12b,
d, f) shows that the winter monsoon also induces a
poleward heat transport, which leads to the amplified
warming in high latitudes, and the largest warming
center is located over northeastern China. Poleward
amplification is one of the most prominent characteristics of global warming (Trenberth et al., 2007). The
meridional asymmetry of the climate sensitivity can
be successfully reproduced in GCM simulations with
the presence of anthropogenic greenhouse gases (Meehl
et al., 2000). However, there has been no consen-
sus on the mechanisms of stronger warming in higher
latitudes. Changes in ice/snow albedo in high latitudes and evaporation in low latitudes might be the
two leading local thermodynamic processes that act
to amplify the high-latitude response and reduce the
low-latitude response to climate forcing, respectively
(Hassol, 2004). Changes in poleward heat transport
also contribute to polar warming amplification (Cai
and Lu, 2007). The different warming patterns between winter and summer monsoons in Fig. 12 may
be related to the different direction of monsoonal heat
transport. Additionally, the warming amplitude in
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Fig. 13. Vertical profiles of simulated changes of July–
August mean air temperatures averaged over Northwest
China (solid line) and Southeast China (dashed line) by
GFDL-CM2.1 under CO2 doubling concentration relative to pre-industry control run. The abscissa axis is the
change of air temperature.
winter is generally larger than that in summer by
>0.5◦ C. Therefore, the summer monsoonal heat transport may be important in determining the NW–SE
decreasing gradient of temperature change.
The extreme climate changes are closely related to
the mean climate change (Meehl et al., 2005; Turner
and Slingo, 2009). An examination of the mechanism
for the mean temperature change would also enrich our
understanding of the extreme temperature change. By
comparing the horizontal structures of warming at 700
hPa and 500 hPa (not shown), the main differences are
seen in northwestern China and southeastern China.
In northwestern China, the warming amplitude at 700
hPa is larger than that at 500 hPa. In southeastern
China, the warming amplitude at 500 hPa is larger
than that at 700 hPa. No obvious difference is evident
in other regions. Therefore, northwestern China and
southeastern China are two typical regions that exhibit completely different vertical structures of mean
temperature change.
The vertical structures of warming over northwestern China and southeastern China are further examined by showing the area-averaged (see Fig. 11) vertical profiles (Fig. 13). The warming changes of temperature over the two regions penetrate the entire troposphere below 200 hPa. In southeastern China, the amplitude of the warming increases with the height. For
example, the warming change in southeastern China is
<2.0◦ C at the surface, 2.5◦ C at 700 hPa, and >3.0◦ C
above 500 hPa. In northwestern China, however, the
amplitude of the warming decreases with the height.
For example, the warming change is ∼3.5◦ C at the
surface, but only 2.5◦ C at 500 hPa. Thus a significant
difference exists in the vertical structures of temperature change over the two regions. In the dry areas of
northwestern China, few or no moist convections are
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detected. The dry convection, if any, is usually shallow and is not an effective mechanism for “transporting” the climate sensitivity upward. Thus the warming is more surface-orientated, as evidenced in Fig. 13.
In southeastern China, however, frequent deep, moist
convections are found. The deep, moist convection
is effective in transporting the climate sensitivity upward. Thereby the warming is more upper-level orientated, as witnessed in Fig. 13. The different vertical
patterns of warming clearly show the importance of
vertical energy transport, consistent with the conclusions of Cai and Lu (2007) and Lu and Cai (2010).
In summary, the response of summer temperature
change to CO2 doubling exhibits a NW–SE decreasing gradient. We suggest that this gradient is related
to the heat transport of summer monsoons. In our
coupled model, more direct forcing of CO2 doubling
due to less cloud leads to stronger surface warming
over northwestern China, while the negative effect of
cloud on the warming results in weaker surface warming over southeastern China. The deep, moist convection over southeastern China is more effective in
transporting the warming upward, evidenced by the
contrast of vertical structures of temperature changes
over southeastern China and northwestern China. The
former increases with the height and is thus more evident at upper levels, while the latter decreases with
the height and is therefore more evident at the surface.
6.
Summary and concluding remarks
We evaluate the performances of the state-of-theart climate models in simulating the present July–
August mean temperature extremes with daily maximum temperatures and daily minimum temperatures
data from both in-situ observations and 20C3M simulations. Potential changes in July–August mean temperature extremes over China under CO2 doubling are
estimated with the output of 1pctto2x simulations.
Both the 20C3M and 1ptto2x simulations are coordinated by the WCRP CMIP3 for IPCC AR4. The
possible mechanism for the change of extreme temperature is further analyzed based on the outputs of
GFDL− CM2.1, which shows a pattern similar to the
MME and provides enough data for our deep analysis.
The major results are summarized here.
(1) The majority of CMIP3 models reasonably
simulate the observed broad-scale spatial patterns of
July–August temperature characteristics over contiguous China. The results of temperature simulation are
obviously better than those for precipitation (see Part
I of the study). Almost all of the pattern correlation
coefficients of July–August mean daily maximum temperatures and daily minimum temperatures between
NO. 2
461
LI ET AL.
the simulations and observations are between 0.6 and
0.9, which are much larger than that of the precipitation (0.3–0.6) shown in Part I of this study (Li et al.,
2010a). The spatial correlation coefficients between
the observation and the simulation for daily minimum
temperatures (0.8–0.9) are larger than those for daily
maximum temperatures (0.6–0.8). Thus the state-ofthe-art climate models have better performances in
simulating the spatial pattern of daily minimum temperatures than that of the daily maximum temperatures over contiguous China.
(2) The July–August temperatures over contiguous
China simulated by most models are generally lower
than those of observation data. The cold bias in western China is ∼10◦ C, which is almost five times larger
than the cold bias in eastern China. A large spread
(2◦ C–5◦ C) among the models could be seen in the simulations of mean states and extreme temperatures. For
specific models, the climate mean and extreme indices
have similar spatial distributions. The models with
higher resolutions generally perform better than those
with lower or medium resolutions.
(3) Most models and the MME show significant
warming of July–August mean temperature and an
increase of warm extremes under CO2 doubling relative to the preindustrial control run. The largest
warming center is located over the Tibetan Plateau in
the MME; however, individual model simulations reveal large discrepancies in the locations of the largest
warming center. The magnitudes of the decrease in
cold nights and cold days are almost identical, but the
increase of warm nights based on daily minimum temperatures (20–40 days) is much larger than the increase
of warm days based on daily maximum temperatures
(20–25 days), indicating an asymmetric change of extreme warm events.
(4) The projected temperature changes over five
specific regions of contiguous China, i.e., northwestern
China (35◦ –50◦ N, 80◦ –100◦ E), Tibetan Plateau (28◦ –
35◦ N, 80◦ –100◦ E), northeastern China (43◦ –54◦ N,
117.5◦ –130◦ E), northern China (35◦ –43◦ N, 100◦ –
122.5◦ E), and southeastern China (22.5◦ –35◦ N, 100◦ –
122.5◦ E), are also assessed. These results indicate a
homogeneous change; all five sub-regions exhibit similar changes under CO2 doubling relative to the preindustrial control run, i.e., more warm nights and warm
days (increasing by ∼20–40 days) but less cold nights
and cold days (decreasing by ∼4–5 days).
(5) The response of summer temperature change
to CO2 doubling exhibits a NW–SE decreasing gradient, which may be associated with the summer monsoonal heat transport and the contrast of cloud effects
in northwestern China and southeastern China. The
deep, moist convection in southeastern China is more
effective in transporting the warming upward, thus the
temperature change over southeastern China is more
evident in the upper level. The shallow, dry convection
in northwestern China is less effective in transporting
the warming upward, thus temperature change over
northwestern China is more evident at the surface.
The potential change of climate is of great concern
to the society. Climate system models have been useful
tools for projecting future climate change associated
with an increase in greenhouse gases. Information on
the projected climate changes should be useful to policymakers. However, given the strong limitations of the
state-of-the-art climate models, not only the relatively
robust aspects (multi-model mean) but also the uncertainties (spread among the models) in the projections
of future climate change with CMIP3 global models
should be clarified. Results presented here should be
useful in this regard. Given the discrepancies among
the climate models, Christensen et al. (2007) reported
that the simulated temperatures in most models were
too low in all seasons over East Asia. Randall et al.
(2007) pointed out there is a tendency for the models
to show a global mean cold bias at all levels; their latitudinal distributions also show that almost all models
are too cold in both hemispheres of the extra-tropical
lower stratosphere. The impact of the temperature
bias on the model’s response to anthropogenic forcing
agents warrants further investigation.
Acknowledgements. We gratefully acknowledge the
CMIP modeling groups for conducting the CMIP model
simulations and making the model data available. This
work was jointly supported by R&D Special Fund for Public Welfare Industry (meteorology) (GYHY200806010),
China–UK–Swiss Adapting to Climate Change in China
Project (ACCC)–Climate Science, and the National Key
Technologies R&D Program under Grant No. 2007BAC29
B03.
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