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Performance of the New NCAR CAM3.5 in East Asian Summer Monsoon
Simulations: Sensitivity to Modifications of the Convection Scheme
HAOMING CHEN
LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, and Graduate School of the Chinese
Academy of Sciences, Beijing, China
TIANJUN ZHOU
LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
RICHARD B. NEALE
National Center for Atmospheric Research,* Boulder, Colorado
XIAOQING WU
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa
GUANG JUN ZHANG
Division of Climate, Atmospheric Science and Physical Oceanography, Scripps Institution of Oceanography,
La Jolla, California
(Manuscript received 7 January 2009, in final form 4 February 2010)
ABSTRACT
The performance of an interim version of the NCAR Community Atmospheric Model (CAM3.5) in simulating the East Asian summer monsoon (EASM) is assessed by comparing model results against observations and reanalyses. Both the climate mean states and seasonal cycle of major EASM components are
evaluated. Special attention is paid to the sensitivity of model performance to changes in the convection
scheme. This is done by analyzing four CAM3.5 runs with identical dynamical core and physical packages but
different modifications to their convection scheme, that is, the original Zhang–McFarlane (ZM) scheme,
Neale et al.’s modification (NZM), Wu et al.’s modification (WZM), and Zhang’s modification (ZZM). The
results show that CAM3.5 can capture the major climate mean states and seasonal features of the EASM
circulation system, including reasonable simulations of the Tibetan high in the upper troposphere and the
western Pacific subtropical high (WPSH) in the middle and lower troposphere. The main deficiencies are
found in monsoon rainfall and the meridional monsoon cell. The weak meridional land–sea thermal contrasts
in the model contribute to the weaker monsoon circulation and to insufficient rainfall in both tropical and
subtropical regions of EASM. The seasonal migration of rainfall, as well as the northward jump of the WPSH
from late spring to summer, is reasonably simulated, except that the northward jump of the monsoon rain belt
still needs improvement. Three runs using modified schemes generally improve the model performance in
EASM simulation compared to the control run. The monsoon rainfall distribution and its seasonal variation
are sensitive to modifications of the ZM convection scheme, which is most likely due to differences in closure
assumptions. NZM, which uses a convective available potential energy (CAPE)-based closure assumption,
* The National Center for Atmospheric Research is sponsored by the National Science Foundation.
Corresponding author address: Tianjun Zhou, P.O. Box 9804, LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing 100029, China.
E-mail: zhoutj@lasg.iap.ac.cn
DOI: 10.1175/2010JCLI3022.1
Ó 2010 American Meteorological Society
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performs better in tropical regions where the rainfall is closely related to CAPE. However, WZM and ZZM,
which use quasi-equilibrium (QE) closure, have more realistic subtropical rainfall in the mei-yu/baiu/changma
front region, mainly because the rainfall in the subtropics is more sensitive to the rate of destabilization by the
large-scale flow.
1. Introduction
The Asian summer monsoon plays an important role
in global climate variability (e.g., Ding 1994; Webster
et al. 1998; Wang et al. 2001). Numerous studies have
documented that the expansive Asian summer monsoon
system can be divided into two subsystems: the Indian
summer monsoon (ISM) and the East Asian summer
monsoon (EASM) systems. These two subsystems are to
a large extent independent of each other, but at the same
time they interact with each other (Zhu 1934; Yeh et al.
1959; Zhu et al. 1986; Tao and Chen 1987). The EASM
is not an eastward extension of the ISM. Lying downstream of the Tibetan Plateau and in between the Eurasian continent and the Pacific Ocean, the EASM has its
own unique features. The ISM is a tropical monsoon in
which the low-level winds reverse from winter easterlies
to summer westerlies, whereas the EASM is a hybrid
type of tropical and subtropical monsoon (Zhu et al.
1986; Tao and Chen 1987; Chen et al. 1991; Ding 1994).
The generally recognized EASM system consists of three
main components, namely, the East Asian mei-yu/baiu/
changma front (a major rain-bearing system in the subtropics and midlatitudes), the western Pacific subtropical
high (WPSH), and the tropical western Pacific monsoon
trough or the western Pacific intertropical convergence
zone (ITCZ). These three components are intimately
coupled (Nitta 1987; Huang and Wu 1989; Liu et al. 2008).
Simulations of the Asian summer monsoon and its
variability are challenging issues (Kang et al. 2002; Wang
et al. 2005). Many general circulation model (GCM) results have shown that the basic distribution of monsoon
circulation, such as the subtropical high, the equatorial
jets, and the monsoon low, etc., can be reproduced reasonably, but their strengths and variations are difficult
to simulate (e.g., Hoskins and Rodwell 1995; Zhou and
Li 2002). The monsoon rainfall is even more difficult to
simulate (Kang et al. 2002). A wide skill range exists
among the GCMs in simulating monsoon precipitation,
which is largely attributed to the different subgrid-scale
parameterization schemes and horizontal resolutions
(Sperber et al. 1994). Great efforts have been devoted to
the simulations of the Indian monsoon (e.g., Yang et al.
1996; Meehl and Arblaster 1998; Loschnigg et al. 2003;
Meehl et al. 2006), but less attention has been paid to the
East Asian monsoon. Because of the complex topography and the sharp land–sea thermal contrast, the EASM
simulation provides a rigorous criterion to test model
physics (Liang and Wang 1998; Zhou and Li 2002).
The Community Atmosphere Model (CAM) has been
widely used in climate research (e.g., Ghan et al. 1996;
Zhang 2003; Collier and Bowman 2004; Zhou and Yu
2004; Deser and Phillips 2006; Hack et al. 2006). The
model has also been used for monsoon studies (e.g.,
Hoerling et al. 1990; Yu et al. 2000; Liu et al. 2002; Zhou
et al. 2008; Wu and Zhou 2008; Zhou et al. 2009a).
Meehl and Arblaster (1998) compared the aspects of
the Asian–Australian monsoon system associated with
El Niño–Southern Oscillation in the global coupled Climate System Model (CSM) and its atmospheric component, the Community Climate Model, version 3 (CCM3).
They showed that the CSM captured most major features of the monsoon system in terms of mean climatology, interannual variability, and connections to the
tropical Pacific, with the largest discrepancies between
the CSM, observations, and the CCM3 simulation in the
equatorial eastern Indian Ocean and near the Philippines. Meehl et al. (2006) also examined the simulations
of regional monsoon regimes in CAM, version 3 (CAM3)
and its coupled Community Climate System Model, version 3 (CCSM3). They noted that the major monsoon
features were well represented in all simulations, but the
details of the regional simulations were model dependent.
Some aspects of the monsoon simulations, particularly
in Asia, were improved in the coupled model compared
to the SST-forced simulations. Nevertheless, the above
analyses concentrated on the tropical monsoon, that is,
the South Asian monsoon, and less attention was given
to the EASM, especially its subtropical components. A
comprehensive evaluation of the performance of CAM3.5
in EASM simulations will potentially reveal strengths and
weaknesses of the model and serve as a useful reference
for future model improvement.
As an important energy source for atmospheric motion, cumulus convection affects large-scale circulation
and wave disturbances through the release of latent heat
of condensation and the vertical transport of heat, moisture, and momentum. The large-scale forcing in turn influences and modulates the development and organization
of convection and clouds (Wu et al. 2007a,b). The coupling of convective processes with the large-scale dynamics
is crucial for modeling the global distribution of precipitation (Zhang 2005). The choice of different cumulus
parameterization schemes has a significant influence on
1 JULY 2010
CHEN ET AL.
the simulations of both the climate and synoptic systems
(e.g., Hack 1994; Zhang and McFarlane 1995; Maloney
and Hartmann 2001; Zhang 2002; Kang and Hong 2008).
Zhang (1994) found that changes in the cumulus parameterization in a general circulation model from the
Canadian Climate Centre (CCC) have the largest effects
in the East Asian and the Indian monsoon areas, because these are regions with the most active convection
on the globe. Huang et al. (2001) examined the sensitivity of EASM rainfall and circulation to convection
schemes in a five-level spectral AGCM and found that
the Kuo scheme (Kuo 1965) generally showed the best
performance. However, Singh et al. (2006) indicated
that the Arakawa and Schubert (Arakawa and Schubert
1974) and Emanuel (Emanuel 1991) schemes simulate the summer precipitation and its variation better
than the Kuo scheme over Korea in a regional climate
model.
Recently, with the motivation of improving the performance of CAM, version 3.5 (CAM3.5), the Zhang and
McFarlane (1995) convection scheme (hereafter ZM) was
modified. There are three modifications: the Neale et al.
(2008) convection scheme (hereafter NZM), the Zhang
(2002) convection scheme (hereafter ZZM), and the Wu
et al. (2003) convection scheme (hereafter WZM). Both
the original ZM and NZM schemes employ a convective available potential energy (CAPE)-based closure
assumption and emphasize convective instability in determining convection. The NZM modification incorporates the effect of the environmental air (e.g., dry versus
moist environment) on convection. Differing from the
ZM and NZM modifications, instead of CAPE-based
closure, a quasi-equilibrium (QE)-based closure is used
in ZZM and WZM. The QE-based closure emphasizes
the destabilization of the atmosphere through tropospheric forcing (e.g., synoptic-scale disturbances) in driving convection. Because rainfall in the EASM system
results from a mix of convection and stratiform clouds,
and because there are planetary, synoptic, and mesoscale
systems in the EASM rain belts, how to describe the effect
of convective activities on rainfall and circulation simulation in the EASM region remains an open question.
The purpose of this study is to evaluate the performance of CAM3.5 in simulating the EASM and to discuss the effects of three revised versions of the ZM
convection scheme. We focus on the EASM climatology
and its seasonal variation. We want to address the following questions: 1) What are the strengths and weaknesses of the National Center for Atmospheric Research
(NCAR) CAM3.5 in EASM simulation? 2) What are
the influences of the modifications to the convection
scheme on EASM simulations? Our results show that
the main characteristics of EASM circulation is reasonably
3659
simulated by CAM3.5, but the simulation of the mei-yu/
baiu/changma rain belt and the meridional monsoon cell
still need to be improved. The precipitation is sensitive to
changes in the convection scheme, and the WZM and
ZZM schemes, which use quasi-equilibrium closure, show
better performance in EASM rainband simulations.
The rest of the paper is organized as follows: Section 2
provides a description of the model and three sets of
modifications to the ZM convection scheme, as well as
the datasets and analysis methods used in this study. The
EASM climate mean state is assessed in section 3, while
section 4 addresses seasonal and intraseasonal monsoon
variation. Section 5 discusses the possible causes of the
model biases. Section 6 follows with summary and concluding remarks.
2. Model, data, and analysis method description
a. CAM3.5
CAM3.5, developed by the NCAR in collaboration
with the climate modeling community, is the recently
improved version of the state-of-the-art atmospheric
general circulation model (AGCM) and serves as an interim version with which to improve the model physics
for the next-generation CAM, version 4 (CAM4), which
is a global primitive-equation model with 26 vertical levels.
The integration uses a finite-volume dynamical core (Lin
2004), and the horizontal resolution is approximately 1.98
latitude 3 2.58 longitude. This version is closely related
to the previous version (CAM3) and includes changes to
convective and cloud processes, the land model, and
chemistry modules (Oleson et al. 2008; Stöckli et al. 2008;
Neale et al. 2008; Richter and Rasch 2008; Gent et al.
2009). The calculation of cloud fraction is updated, and
new hydrology, surface datasets, and canopy integration
are introduced in the land model. The other physical
processes are the same as those in CAM3, which can be
found in Collins et al. (2006).
CAM3 uses the standard ZM convection parameterization scheme (Zhang and McFarlane 1995), which is
a mass flux scheme inspired by the convective parameterization of Arakawa and Schubert (1974). An updraft
ensemble of entraining convective plumes, all having the
same mass flux at the cloud base, relaxes the atmosphere
toward a threshold value of CAPE. Simulations are not
sensitive to the threshold used. In-cloud saturated downdrafts commence at the level of minimum saturated moist
static energy. Detrainment of ascending plumes also begins at the level of minimum saturated moist static energy.
Therefore, only ascending plumes that can penetrate
through the conditionally unstable lower troposphere
are present in the ensemble. In the NZM modification,
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TABLE 1. Description of four AMIP-type experiments using different versions of the ZM convection scheme.
Experiment
Versions of ZM scheme
Integration time
Reference(s)
ZM (CNTL)
NZM
Zhang and McFarlane
Neale and Richter
Jan 1977–Dec 2003
Jan 1977–Dec 2002
Wu and Zhang
Revised Zhang
Jan 1977–Dec 2000
Jan 1978–Dec 1999
Zhang and McFarlane (1995)
Neale et al. (2008); Richter
and Rasch (2008)
Wu et al. (2007a,b)
Zhang (2002)
WZM
ZZM
the calculation of CAPE in the ZM scheme was modified to include the effect of lateral entrainment dilution
(Neale et al. 2008). In addition, convective momentum
transport (CMT) parameterized by Gregory et al. (1997)
was included (Richter and Rasch 2008). In the ZZM
version proposed by Zhang (2002) and used in Zhang
and Mu (2005), the CAPE-based closure in the original
ZM scheme is changed to quasi-equilibrium-based closure, and a relative humidity threshold of 80% at the
parcel lifting level was imposed for convection. The WZM
version is similar to the ZZM version, but with the relative
humidity threshold replaced by a threshold in large-scale
CAPE forcing, based on cloud-resolving model simulation estimates (Wu et al. 2003, 2007a; Zhang and Wu
2003). Furthermore, CMT developed by Zhang and Cho
(1991a,b) was also included in the WZM version (Wu et al.
2007b). This CMT parameterization, which is validated
and simplified through the study of cloud-resolving model
simulations (Zhang and Wu 2003), redistributes the horizontal momentum through the subsidence-compensating
convective mass flux, detrainment of in-cloud momentum,
and the cloud-scale perturbation pressure gradient.
In this study, four Atmospheric Model Intercomparison Project (AMIP)-type integrations (Phillips 1996),
forced by observed global SSTs, are performed and compared by using the three revised convection schemes
as well as control (CNTL) run using CAM3.5 with the
CAM3 convection configuration. Table 1 shows a brief
summary of the experiments. All of the experiments are
integrated for more than 20 yr and the data from 1980 to
1999 are analyzed in this study.
b. Data
In this paper, East Asia monsoon region refers to the
area between 08 and 458N and between 908 and 1408E,
including the monsoon region over East Asia and the
western northern Pacific (Wang and Lin 2002; Ding
and Chan 2005). To evaluate the model, the following
reanalysis/observation datasets are used: 1) geopotential
heights, zonal and meridional winds, air temperature,
specific humidity, and surface pressure from the National
Centers for Environmental Prediction (NCEP)–NCAR
reanalysis during 1980–99 on a 2.58 3 2.58 grid (Kalnay
et al. 1996); and 2) the Climate Prediction Center (CPC)
Merged Analysis of Precipitation (CMAP) based on
blended satellite and in situ measurements during 1980–
99 on a 2.58 3 2.58 grid (Xie and Arkin 1997).
c. Analysis methods
The main circulation components of the EASM are
measured by specific indices. These indices are applied
to quantitatively evaluate the model performance. At
the upper level (100 hPa), the intensity of the Tibetan
high (defined as the averaged geopotential height over
208–408N, 608–1208E), the zonal wind speed of the westerly jet (defined as the zonal wind speed averaged over
408–508N, 608–1208E) and the tropical easterly jet (TEJ;
defined as the zonal wind speed averaged over 108–208N,
608–1208E) are calculated separately. In the middle level
(500 hPa), three indices, which respectively represent
the intensity IS, westward extension IW, and northern
edge IN of the WPSH, are calculated to make an objective comparison between the reanalysis and the model.
Here, IS is defined as the regional average of the grids
with geopotential height greater than 5860 gpm over the
region of 108–408N, 1008–1408E. To compare the westward extension of WPSH in reanalysis and model simulations, each of the 500-hPa geopotential height values
over the target region are subtracted by IS. Then, IW is
defined as the longitude of the westward extension of
0-gpm contours of the subtracted field (Zhou et al.
2009b). The WPSH ridge, defined as u 5 0 and ›u/›y . 0
(Li and Chou 1998), is constructed before calculating IN.
Afterward, IN is defined as the latitude of the WPSH
ridge position.
To investigate the possible causes of differences between different modifications to ZM, CAPE is estimated and its correlation
with rainfall is assessed. The
Ðp
CAPE is defined by p b Rd (T yp T y )d lnp (Zhang 2002;
t
Zhang 2003), where Rd is specific gas constant, pb is
the level in the boundary layer at which convective air
parcels originate, pt is the level at which the parcel loses
its buoyancy, and Ty is the virtual temperature. Because
the NZM uses the dilute CAPE for closure (Neale et al.
2008), the CAPE of NZM is directly derived from model
output. Furthermore, to examine the effect of diabatic
heating and surface sensible heat flux, the apparent
heatsource Q1 is calculated following Yanai et al. (1992).
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FIG. 1. Northern Hemisphere summer [June–August (JJA)] mean precipitation (mm day21) in (a) observations
(CMAP data) and (b) the CAM3.5 control run. (c) The difference field between the CAM3.5 control run and the
observations. (d)–(f) As in (c), but for the differences between the three revised versions of the ZM scheme and the
control run, respectively. The regions shaded by black dots in (c)–(f) denote areas exceeding a confidence level of 99%.
Moreover, a Student’s t test is applied to check the significance of the differences between observations and
simulations as well as those between model experiments.
3. Climatological mean pattern
a. Precipitation
Precipitation is one of the most important variables
used to measure monsoon activities. From the observations (Fig. 1a), two major rain belts are evident. One
is the tropical monsoon trough located between 08 and
208N, which reflects the intense rainfall corresponding
to the ITCZ in the tropical western Pacific. The other,
known as the subtropical mei-yu/baiu/changma front,
exhibits a zonally elongated structure extending from
East China toward the northwestern Pacific near 308N,
with heavy rainfall along the Yangtze River (1008–1208E)
in China, and extending over South Korea and southwestern Japan. These two rain belts are closely related to
each other, with a relatively dry region dominated by the
WPSH between them. The summer mean precipitation
in the CAM3.5 control run has a spatial pattern similar
to that of CMAP (Fig. 1b), but some biases are also
obvious. The largest deficiency is the significant underestimation of precipitation over both the tropical and
subtropical rain belt areas (Fig. 1c). In the tropical region, the model simulates much weaker precipitation
over the Bay of Bengal, South China Sea, and the subtropical western Pacific, with the main rainband shifting
to the Southern Hemisphere along 58S. The rainfall centers associated with mei-yu/baiu/changma front over the
middle and lower reaches of the Yangtze River valley,
South Korea, and southwestern Japan are almost absent. The observed rain belt extends from the eastern
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interpolated to the spatial grid of the observations. The
rainfall distributions over the tropical (58–158N) and
subtropical (258–358N) regions are also compared. The
standard deviations of the simulations are smaller than
those of the observations except for the subtropical
rainfall in the ZZM run, suggesting that the spatial
variance in the model is smaller than the observation.
Compared to the control run, the three revised versions
improve the monsoon rainfall simulations. This improvement is evident in both tropical and subtropical rainfall.
The NZM run simulates the EASM rainfall more realistically in its tropical rain belt, but not as well in the
subtropical rainfall. The subtropical rainfall simulated
by WZM is the most reasonable.
b. EASM horizontal circulation
FIG. 2. Taylor diagram evaluating simulations of Northern Hemisphere summer precipitation over the East Asia region (08–458N,
908–1408E), as well as its tropical (08–208N, 908–1408E) and subtropical (258–358N, 90–1408E) rain belts. The angular coordinate is
the correlation coefficient between model results and the observations (CMAP). The radial coordinate is the standard deviation of
model results divided by the standard deviation of the observations.
flank of the Tibetan Plateau to the mid-Pacific, while the
simulated rainband is mainly located along the landward
side of the eastern coast of the Asian continent. Meanwhile, the model produces excessive precipitation over
most land areas of the Asian continent. Overestimation
of the rainfall over the Tibetan Plateau and its eastern
flank is also evident.
The differences between the three modifications to
the convection scheme and the control run are shown in
Figs. 1d–f. Overall, all three modifications improve the
simulation of EASM precipitation. The rain rate over
the two main rainbands is increased. All of these runs
show similar biases to those seen in the control run, for
example, the underestimated precipitation over the South
China Sea and Philippines Sea and the excessive rainfall
over the Tibetan Plateau and its eastern periphery. However, regional details are different among the three runs.
In the NZM run, the precipitation rate over the subtropical rainband is still weaker than the observations,
while in the WZM and ZZM runs, the amplitude is more
realistic along the mei-yu/baiu/changma front. The rainfall centers over South Korea and southwestern Japan
are only evident in the WZM and ZZM runs, but with
a weaker intensity.
To quantitatively evaluate the model’s performance
in simulating the geographical distribution of monsoon
precipitation, a Taylor diagram (Taylor 2001) is employed (Fig. 2). For better comparison, model results are
Monsoon rainfall distribution is closely related to monsoon circulation. At the upper level (100 hPa), the most
outstanding feature of the EASM is the huge anticyclonic circulation (the so-called Tibetan high) centered
over the southern edge of the Tibetan Plateau, with the
axis of the anticyclone along 308N, a westerly jet to the
north along 408N, and the TEJ to the south of 258N
(Fig. 3a). The intensity of the Tibetan high simulated in
the control run is stronger than in the reanalysis, and
its center shifts westward with a northward-extending
ridge (Fig. 3b). As a result, there is an anomalous anticyclone over northeastern China (408–508N, 1108–1308E)
and strong northerlies west of 908E in the model (Fig. 3c).
The unrealistic anticyclone and northerlies are improved
in all of the three revised schemes, but the Tibetan high
shows similar patterns in all the runs, suggesting that the
upper-level circulation is less sensitive to changes in the
convection scheme. The indices related to the Tibetan
high are listed in Table 2. In the control run, the intensity
of the Tibetan high and the westerly jet are stronger than
that in the reanalysis, while the TEJ is weaker. The intensities of the high, westerly jet, and TEJ simulated by
the three revised versions of the ZM scheme are weaker
than those in the reanalysis. The weak high indicates a
weak divergence in the upper troposphere. The weak
TEJ in the simulation is consistent with the weak rainfall
over the tropical monsoon trough, because the TEJ is
closely linked to monsoon rainfall in Asia through the
meridional vertical circulation (Zeng and Guo 1982).
In the middle and lower troposphere, the WPSH greatly
influences the climate of the EASM. The position, shape,
and strength of the WPSH dominate the large-scale quasistationary frontal zones and associated rainband in East
Asia (Tao and Chen 1987; Ding 1994; Zhou and Yu 2005).
Here we use the 500-hPa geopotential height to measure
the WPSH, which has been widely used in previous
studies (e.g., Yu et al. 2000; Liu et al. 2002; Zhou and Li
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CHEN ET AL.
FIG. 3. Northern Hemisphere summer mean horizontal wind fields (vector; m s21) and geopotential height
(contour, dagpm) at 100 hPa in (a) NCEP–NCAR reanalysis and (b) the CAM3.5 control run. (c)–(f) The differences
between the control run and the reanalysis and between the three revised versions of the ZM scheme and the control
run, respectively. The shading denotes regions with significance at the 1% level.
2002; Zhou et al. 2009b). In the NCEP–NCAR reanalysis,
a strong anticyclone dominates the subtropical western
Pacific and a weak trough appears over northeastern
China (Fig. 4a).
The spatial pattern of WPSH in the control run resembles the reanalysis (Fig. 4b). The intensity of WPSH
is stronger than that in reanalysis, and it extends westward approximately 58 and the ridge shifts northward
more than 58 (Table 3). The westward extension of the
WPSH, along with the downward motion, is consistent
with the underestimated precipitation over the subtropical regions of East Asia. Corresponding to the strong
WPSH, the westerlies in the higher latitudes and the
southerlies in the tropics are both stronger than those
in the reanalysis (Fig. 4c). The stronger southwesterly
monsoon flows reach relatively higher latitudes in East
Asia. The northward shift of the WPSH ridge results in
the southerlies deeply penetrating into northern China.
TABLE 2. The intensity of Northern Hemisphere summer mean 100-hPa Tibetan high (TPI), as well as wind speed of westerly jet and TEJ
from the NCEP–NCAR reanalysis, and the four different versions of the ZM convection scheme, respectively.
Index
Averaged region
Units
NCEP–NCAR
CNTL
NZM
WZM
ZZM
TPI
U wind
U wind (TEJ)
208–408N, 608–1208E
408–508N, 608–1208E
108–208N, 608–1208E
dagpm
m s21
m s21
1677.5
14
224.7
1678.7
16
223.6
1670.4
14.6
222.4
1670.9
14.4
219
1672.2
14.1
222.8
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FIG. 4. As in Fig. 3, but for 500-hPa wind and geopotential height.
This corresponds to the excessive precipitation in North
China, but deficient mei-yu/baiu/changma rainfall along
the Yangtze River (Fig. 1). The midlatitude westerlies
in the model seem to be more flat. No trough is evident
in the midlatitude westerlies, because an anomalous anticyclone dominates northern China where there is a
weak trough in the reanalysis. Although the simulations
are improved in the three revised versions of the ZM
scheme (Figs. 4d–f), the northward shift of the WPSH still
exists, especially in the ZZM run (Table 3). The westward
extension is also obvious in all three revised schemes.
Generally, the stronger WPSH is associated with a westward extension and northward shift. According to Rodwell
and Hoskins (2001), the westward extension can be attributed to the intensified rainfall over the ISM region
(Fig. 1) in terms of the Sverdrup vorticity balance. This
bias of the WPSH then causes the drier bias of rainfall
over the western Pacific, which is dominated by the ridge
of WPSH.
The water vapor transport is crucial to monsoon rainfall, and closely resembles the large-scale monsoon circulation in the lower troposphere (Zhou and Yu 2005).
From Fig. 5a, three main branches of water vapor transport to East Asia are evident: a strong transport by
southwesterlies from the ISM, a moderate transport by
southeasterlies from the western Pacific, and a weak
branch linked to the cross-equator flow straddling 1058–
1508E. The southwesterly transport in all runs is weaker
than that in the reanalysis (Figs. 5b–f), which leads to
TABLE 3. As in Table 2, but for the indices of 500-hPa WPSH
over 108–408N, 1008–1408E. The units for IS, IN, and IW are dagpm,
8N, and 8E, respectively.
Index
NCEP–NCAR
CNTL
NZM
WZM
ZZM
IS
IN
IW
5868.3
22.5
112.5
5886.9
29.37
107.5
5868.5
27.47
112.5
5868.9
27.47
117.5
5871.8
31.26
102.5
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3665
FIG. 5. Vertically integrated summer mean water vapor transport (kg m21 s21) in (a) NCEP–NCAR reanalysis,
(b) control run, (c) the difference between the control run and reanalysis, and (d)–(f) the differences between the
three revised versions and the control run, respectively. The gray shading and the black dots denote regions with
zonal and meridional components that are significant at the 1% level, respectively.
weak rainfall centers over the Bay of Bengal and South
China Sea. The simulated moisture transport turns northwestward to the north of the Bay of Bengal near 208N
and causes a strong northward transport to the midlatitudes (Fig. 5b). This leads to a weaker contribution
of the southwesterly monsoon flow to the water vapor
transport over the EASM. The southeasterly transport
from the western Pacific is also weak, and shifts westward and extends northward as a result of the biases in
WPSH simulation. The southwesterly transport from the
ISM and the southeasterly from the western Pacific are
strengthened and the northward transport is weakened
in all revised model versions, but the spatial distribution
of the biases are similar to the control run. Therefore,
the deficiencies of the water vapor transport are greatly
related to the weak rainfall associated with the subtropical
mei-yu/baiu/changma front and the relatively strong rainfall over the midlatitude continent north of 458N.
c. Meridional monsoon circulation
One unique characteristic in the East Asian monsoon
region is that the normal Hadley cell is replaced by a
meridional circulation of the opposite sense, which is
often referred to as the monsoonal meridional cell (Chen
et al. 1964; Ye and Yang 1979). The monsoonal meridional cell has been used as an observational metric for
evaluating climate models (e.g., Zhou and Li 2002). The
averaged meridional circulation in the monsoon region
is shown in Fig. 6 (from 908 to 1408E). Strong upward
motion controls the region between 108 and 308N of the
Northern Hemisphere, while strong low-level convergence
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FIG. 6. Northern Hemisphere summer mean meridional circulations for 908–1408E in (a) NCEP–NCAR reanalysis
and (b) the control run. (c)–(f) The differences between the control run and the reanalysis and between the three
revised versions of the ZM scheme and the control run, respectively. The black dots and gray shadings denote regions
with meridional and vertical components that are significant at the 1% level, respectively.
dominates the region near 358N, which is closely related
to the mei-yu/baiu/changma rain belt. None of the simulations reasonably reproduces the EASM meridional
monsoon circulation (Figs. 6b–f). In the simulations,
strong upward motion dominates the tropical area, and
weak ascent is seen in the lower troposphere between
208 and 408N. The middle and upper levels of the subtropical troposphere north of 308N are controlled by the
subsidence, which is the opposite compared to the reanalysis. In the three revised versions, the subsidence flow
is improved, because an anomalous upward motion dominates the region north of 458N, but the intensity of the
upward flow is still much weaker than the reanalysis (Figs.
6d–f). This subsidence corresponds to the westward extension of WPSH shown in Fig. 4. Therefore, the biases of
rainfall and circulation in subtropical East Asia are closely
related to each other, which may be caused by the weak
land–sea thermal contrast in the model in the monsoon
region (see the discussion below).
4. Seasonal variation
The dominant characteristic of monsoon climate is
the seasonal cycle, especially in rainfall (Ding 1994). The
seasonal march of the climate mean precipitation averaged over 1108–1258E is shown in Fig. 7. In observations
(Fig. 7a), prior to mid-May, southern China experiences
a premonsoon rainy season. The monsoon rains extend
from southern Asia to the Yangtze River valley in June,
and finally penetrate to the northern China in July. The
rainy season in northern China lasts for approximately
1 month and ends in August. From August to September,
the monsoon rain belt rapidly moves back to southern
China. The differences among different model versions
are also evident. In the control run (Fig. 7b), both the
tropical and subtropical rainfalls are weaker than that
observed. These features are separated into two bands,
with a dry tongue located between 208 and 258N from
February to June. In midsummer, the strong rainfall
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CHEN ET AL.
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FIG. 7. Seasonal migration of the monsoon rainband (mm day21) zonally averaged between 1108 and 1258E in (a)
observations (CMAP data) and (b)–(e) for four different versions of the ZM convection scheme, respectively.
extends northward and the rainband withdraws more
rapidly than in the observations. In the three revised
versions of the ZM scheme, the northward progress and
southward withdrawal of the monsoon rainband are more
reasonable. The subtropical rainband simulated by NZM
shifts northward, and the rainfall along the mei-yu/baiu/
changma front near 308N decreases dramatically after
July, which is approximately 3 months earlier than that
observed (Fig. 7c). The simulations of the WZM and
the ZZM are more realistic, although the strong raining
periods last from June to August over the area north of
408N, which is approximately 2 months longer than that
observed (Figs. 7d,e).
The seasonal progression of the rain belt is closely related to the seasonal change of large-scale circulation.
The circulation and related rainfall over Asia undergo
abrupt seasonal changes, which are linked to tropospheric
warming over the Asian landmass (Murakami and Ding
1982; He et al. 1987; Yanai et al. 1992). The ridge of the
WPSH from May to August and 3 mm day21 precipitation rates are shown in Fig. 8. Because the mei-yu/baiu/
changma rainfall is more closely related to the WPSH, we
show the seasonal march of the rainband in subtropical
regions. Two northward jumps of the WPSH ridge in
June and July are evident in Fig. 8a. Correspondingly, the
rainband over East Asia undergoes two northward jumps
(Fig. 8b). In June, the strong rainfall advances from
southern China to the Yangtze River valley, and then
the mei-yu begins. In July, the strong rainfall further
jumps to northern China.
All simulations reasonably reproduce the poleward
jumps of the WPSH ridge (left panel, Fig. 8), but the
position of the ridge shifts northward. The ridge is flat in
the zonal direction, which is different from the southwest–
northeast-tilted pattern in the reanalysis. The simulated
northward jumps of the rain belt are not well simulated
(right panel, Fig. 8) except for the WZM run (Fig. 8j).
The simulated rain belts all shift northward approximately 108, which is inconsistent with the bias of the
WPSH ridge. Meanwhile, it is worth noting that the evolution of the rain fronts with month is much more smooth
for ZZM and WZM (Figs. 8h,j). This is because they
are more weakly coupled to surface inhomogeneities
resulting from the closure and relative humidity minimum closure conditions that are not present in CNTL
and NZM.
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FIG. 8. Seasonal evolution of (left) 500-hPa WPSH ridge and (right) the corresponding 3 mm day21
precipitation rate contour line averaged for May (black solid line), June (blue dashed line), July (red dotted
line), and August (purple dotted–dashed line).
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CHEN ET AL.
5. Discussion
The different responses to the revised versions of the
ZM convection scheme indicate the important role of
convective processes in EASM simulation. The disparities between the NZM and WZM runs may be attributed
to closure assumptions, because these two modifications
both have momentum transport parameterization, even
though they are formulated differently (Neale et al. 2008;
Wu et al. 2003). The WZM and ZZM runs both use QE
closure. The differences between these two may reflect
the roles of convective-scale momentum transport as
well as threshold of relative humidity. The above results
show that the NZM run improves the EASM tropical
rainfall distribution, especially in the Indian Ocean, indicating that NZM may have advantages in simulating
tropical deep convection. However, over the East Asian
subtropical regions, the tropospheric large-scale forcing
is important to convection, especially for the mei-yu/
baiu/changma front. The mei-yu/baiu/changma front is a
rainband thousands of kilometers long, stretching across
East Asia and the northwestern Pacific, and is closely
related to the planetary-scale circulation. Precipitation
along the mei-yu/baiu/changma front is usually caused
by convection and clouds organized into eastward-moving
mesoscale convective systems, and the large-scale dynamic circulation associated with baroclinic instability is
the driver of the system (Chen et al. 1991). In ZM and
NZM, the convection is determined by the amount of
CAPE in the atmosphere. Because CAPE strongly depends on the boundary layer equivalent potential temperature, which in turn is largely governed by surface
heat and moisture fluxes, this closure ties convection
closely to boundary layer forcing (Zhang 2003). Therefore, the NZM does not perform well over these regions.
In WZM and the ZZM modifications, convection is determined by the CAPE generation rate from the freetropospheric large-scale forcing, which has explicitly tied
convection to the large-scale forcing through the QE assumption. Thus, they perform better in this region.
To further discuss the influences of different closure
assumptions, the spatial correlations between CAPE and
precipitation in tropical and subtropical regions are shown
in Fig. 9. For comparison, the CAPE is calculated based
on NCEP–NCAR reanalysis data, while the precipitation is derived from CMAP data. As shown in section 2,
the CAPE calculation uses only temperature T and moisture q of reanalysis. Because both T and q are observable quantities, the impact of model parameterizations
in the NCEP–NCAR reanalysis system on CAPE calculation should not be very large. Along the tropical
rain belt (08–208N, 1008–1408E), the precipitation and
CAPE are positively correlated (Fig. 9a), while along
3669
the subtropical rainband (258–358N, 1008–1408E) the correlation is negative (Fig. 9b). In ZM and NZM, which use
a CAPE-based closure, the CAPE is positively correlated
with the precipitation in both the tropical and subtropical
regions. In WZM and ZZM, which use a QE-based closure, the correlation is relatively weak in both the tropics
and along the mei-yu/baiu/changma rain belt. This may
explain why the NZM simulation performs better in the
tropical region but not as well in subtropical regions.
The QE closure based on the large-scale forcing should
be more favorable in simulating the EASM subtropical
rainfall.
Although there are differences among the results of
the different runs, the model has some common biases in
which the weaker-than-observed land–sea thermal contrasts may play an important role. The Asian summer
monsoon is closely related to large-scale land–sea thermal contrasts. The key driving force for the summer
monsoon is the available potential energy generated by
the differential heating between land and sea (Li and
Yanai 1996; Zhang et al. 1997). Because tropical latent
heat and plateau sensible heating are the main thermal
sources in the EASM, the systematic biases in CAM3.5
should be linked to the biases in heating sources. The
zonally averaged (908–1408E) cross section of Q1 is shown
in Fig. 10 to evaluate the diabatic heating and sensible heat flux. In the Northern Hemisphere, Q1 is positive throughout the troposphere, and intense heating
(.3 K day21) is located at the middle levels (Fig. 10a),
consistent with previous studies that show sensible heating from the plateau surface is the major source of heating during this season (Luo and Yanai 1984; Yanai et al.
1992). In the control run, Q1 is greatly underestimated
(Fig. 10b). The tropical heating is positive, but only approximately half of the intensity in the reanalysis. The
three modifications improve the simulated heat source
mainly in the tropics (Figs. 10c–f). Because all of the
model experiments share the same surface land model,
the land–sea contrast biases are shared and the Q1 profile
in the midlatitude show similar pattern. The significant
zonal bias pattern strongly decreases the meridional
thermal contrast; thus, the monsoon meridional circulation driven by this thermal effect is much weaker than
that in the reanalysis (Fig. 6). The weaker monsoon
circulation in the model leads to less rainfall over the East
Asian tropical and subtropical regions.
To estimate tropical heating we use the proxy of total
rainfall (Zhou and Li 2002). The observed rainfall has
a pronounced center over the northern portion of the
Bay of Bengal, with two other centers over the South
China Sea and western Pacific (Fig. 1a). This indicates a
strong heating associated with upward motion (Fig. 11)
and upper-tropospheric divergence (Fig. 12) over these
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FIG. 9. (a)–(b) Scatterplot of the estimated JJA mean CAPE (J kg21) from NCEP–NCAR reanalysis vs corresponding CMAP precipitation (mm day21) in tropical (08–208N, 1008–1408E) and subtropical (258–358N, 1008–1408E)
regions. (c)–( j) As in (a)–(b), but for each run using different modifications of the ZM scheme. The spatial correlation
coefficients are shown above the top-right boundary of each plot.
regions. There are rainfall centers surrounding the Bay
of Bengal in all of the simulations; however, a stronger
rainfall center is evident in the northern Indian Ocean in
both the control run and NZM run (Figs. 1c,d), while
strong rainfall centers are located both in the northern
Indian Ocean and the west edge of the Philippines Sea in
both the WZM and ZZM runs (Figs. 1e,f). Because the
mean position and intensity of tropical convection are
closely related to the atmospheric general circulation
(Branstator 1983), these spurious equatorial rainfall centers (and their associated latent heating) should have
negative effects on the large-scale circulation patterns.
1 JULY 2010
CHEN ET AL.
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FIG. 10. Height–latitude cross section of mean JJA Q1 averaged over 908–1408E in the (a) NCEP–NCAR reanalysis and (b) the control run. (c)–(f) The differences between the control run and the reanalysis and between the
three modifications of the ZM scheme and the control run, respectively. Contour interval is 1 K day21. The gray
shading denotes regions exceeding a confidence level of 99%.
Both upper-level divergence centers and low-level upward motion over the western Pacific shift westward to
the Indian in the control run. The NZM run simulates
a similar pattern, because its differences with the control
run show an almost reversed pattern compared to that
between the control run and reanalysis (Figs. 11c,d and
12c,d). In the WZM and ZZM runs, the convection in
the open ocean of the western Pacific (east of 1408E),
and the upward motion in the Maritime Continent region are suppressed. Thus, the divergence center shifts
eastward to the mid-Pacific (Figs. 12e,f) and the low-level
convergence and upward motion are mainly located west
of 1208E (Figs. 11e,f). No evident divergence center is
seen over the EASM region, and this may partly explain the weak rainfall over the EASM tropical region.
Finally, it should be noted that the EASM is a complex phenomenon including interactions between tropical and subtropical systems. Based on the above analyses,
for a better simulation of EASM, the following four
factors need to be considered: 1) the influences of the ISM
and the moist transport along the south flank of the Tibetan Plateau; 2) the dynamical response to the plateau
warming, especially in the downstream region; 3) reasonable simulations of convection related to the mesoscale
and synoptic systems in the EASM subtropical region;
and 4) a reasonable simulation of the ITCZ and related
circulation in the tropical western Pacific. Further efforts
are required to improve these factors in current GCMs to
obtain more realistic simulation of the EASM.
6. Summary and concluding remarks
In this study, the EASM simulated by CAM3.5 is evaluated in terms of climate mean pattern and seasonal variation. The sensitivity of simulations to ZM convection
scheme as well as its three modifications is also examined.
Both the strengths and weaknesses of the four versions
are documented. The results are potentially helpful to
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FIG. 11. Northern Hemisphere summer mean zonal circulations for 58–158N in (a) NCEP–NCAR reanalysis and
(b) the control run. (c)–(f) The differences between the control run and the reanalysis and between the three revised
versions of the ZM scheme and the control run, respectively. The black dotted and gray shadings denote regions with
meridional and vertical components that are significant at the 1% level, respectively.
the climate modeling community in improving the simulation of the East Asian monsoon. The major conclusions are summarized below.
1) The major characteristics of the EASM circulation,
including the Tibetan high in the upper troposphere
and the WPSH in the middle and lower troposphere,
are reasonably simulated by CAM3.5. The model’s
main deficiency lies in the simulation of tropical and
subtropical monsoon rain belt. All four runs simulate
weaker-than-observed rainfall related to the mei-yu/
baiu/changma front. This deficiency is closely related
to the weak meridional monsoon circulation over East
Asia, which is attributed to the weak heating over
the subtropical continent in the model. The tropical
rainfall centers over the South China Sea and western Pacific are also weak in the model.
2) The springtime northward advance and late summer
southward withdrawal of the monsoon rain belt over
East Asia can be reproduced by the model. Two abrupt
northward jumps of the WPSH from May to August
are also well simulated but the northward jump of the
rain belt is poorly simulated, especially for the northward jump in July. The deficiencies in the seasonal
cycle are closely related to the climatological pattern,
because the northward extension of the WPSH results in longer periods of the rainy monsoon season at
higher latitudes near 408N.
3) Both the distribution and seasonal cycle of monsoon
rainfall simulations depend on the convection scheme,
while the monsoon circulation shows less sensitivity.
The three revised ZM modifications generally improve the simulation of the EASM relative to the
control run. The NZM run is better at simulating
tropical rainfall partly because it uses a CAPE-based
closure assumption. The WZM and ZZM runs are
better at simulating the subtropical rainfall and its
seasonal variation, implying that the QE-based assumption is more favorable in simulating rain belts
associated with large-scale forcing.
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3673
CHEN ET AL.
FIG. 12. Northern Hemisphere summer mean velocity potential at 200 hPa in (a) the NCEP–NCAR reanalysis and
(b) the control run. (c) The diffidence field between the CAM3.5 control run and the observations. (d)–(f) As in (c),
but for the differences between the three revised modifications of the ZM scheme and the control run, respectively.
Contour interval is 2 3 106 m2 s21. Vectors indicate the corresponding divergent wind (mm day21). The regions
shaded in (c)–(f) denote those exceeding a confidence level of 99%.
By assessing the model performance in EASM simulation, the present study is a useful step toward a better
understanding and simulation of the climate system in
East Asia. It is our hope that the results shown here can be
helpful for future model improvement and development.
project (GYHY200706005); U.S. Department of Energy
under Grants DE-FG02-08ER64559 (Wu) and DEFG02-09ER64736 (Zhang); and U.S. National Sciences
Foundation under Grants ATM-0935263 (Wu) and ATM0601781 (Zhang).
Acknowledgments. The authors are thankful to the
anonymous referees whose insightful and valuable suggestions contributed a great deal to improving the quality
of this paper. The simulations were performed at the
National Center for Atmospheric Research. This work
was jointly supported by China Meteorological Administration under Grant 2007BAC29B03; the National
Natural Science Foundation of China under Grants
40625014, 40821092, 40890054; the Chinese COPES
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