Performance of the New NCAR CAM3.5 Model in East Asian Summer
Monsoon Simulations: Sensitivity to Modifications of the Convection
Scheme
Haoming Chen 1,2, Tianjun Zhou 1, Richard B. Neale3, Xiaoqing Wu4 Guang Jun Zhang5
1 LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
2 Graduate School of the Chinese Academy of Sciences, Beijing, China
3 National Center for Atmospheric Research, Boulder, CO, USA
4 Department of Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa, USA
5 Center for Atmospheric Sciences, Scripps Institution of Oceanography, La Jolla, California, USA
Submitted to Journal of Climate
(Dec, 2008)
Corresponding author:
Tianjun ZHOU
LASG, Institute of Atmospheric Physics,
Chinese Academy of Sciences,
P. O. Box 9804,
Beijing 100029, China.
E-mail: zhoutj@lasg.iap.ac.cn
Phone: 86-10-8299-5279
Fax : 86-10-8299-5172
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Abstract
The performance of an interim version of the NCAR Community Atmospheric Model,
namely 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 its convection scheme, i.e. the original Zhang-McFarlane (ZM)
scheme, Neale and Richter’s (NZM) modification, Wu and Zhang’s (WZM) modification
and Zhang’s (ZZM) modification. The results show that CAM3.5 can capture the major
climate mean states and seasonal features of 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 of
model performance are found in monsoon rainfall and the meridional monsoon cell. The
models fail to reproduce the subtropical monsoon rain belt and the related meridional
monsoon circulation. This is caused by the weak meridional land-sea thermal contrasts in
the model which contribute to the weaker monsoon circulation, and then insufficient rainfall
in both tropical and subtropical regions of EASM. The seasonal migration of rainfall, as
well as the northward jump of WPSH from late spring to summer, is reasonably simulated,
except that the northward jump of the rain belt still needs to be improved. The monsoon
rainfall distribution and its seasonal variation are sensitive to the modification of the ZM
convection scheme, but the monsoon circulation exhibits less sensitivity. Three runs using
modified schemes generally improve the model performance in EASM compared to the
control run. The tropical rainfall distribution and the rainfall centers over the monsoon
trough are more reasonably reproduced in the NZM. The WZM and ZZM have more
realistic subtropical rainfall in the Meiyu/Baiu/Changma front as the closure assumption in
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these two modifications are explicitly linked to the large scale temperature and moisture
advection. No evidence supports the idea that the multi-run ensemble is better than
individual run.
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1. Introduction
The Asian summer monsoon is one of the most energetic components of the earth’s
climate system, and it 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 huge
Asian summer monsoon system can be divided into two subsystems: the Indian summer
monsoon (ISM) and the East Asian summer monsoon (EASM) systems, which are to a large
extent independent of each other, and at the same time, interacting 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 world’s highest Tibetan Plateau
and in between the Eurasian continent and the Pacific Ocean, 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 Meiyu/Baiu/Changma front (a major rain-bearing system in the subtropics and
mid-latitude), 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 have proven to be one of
the most challenging issues (Kang et al., 2002; Wang et al., 2005). Many general circulation
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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 hard to simulate well (e.g., Hoskins and
Rodwell, 1995; Zhou and Li, 2002). The monsoon rainfall is even harder to simulate (Kang
et al., 2002). A wide skill range exists among GCMs in simulating monsoon precipitation,
which is largely attributed to the different sub-grid 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. Due to the complex topography and the sharp land-sea thermal contrast,
the EASM is much more complex than the ISM, and the model performances for the EASM
are rather limited (Liang and Wang, 1998; Yu et al., 2000; Zhou and Li, 2002).
Atmospheric convection affects large-scale circulation and wave disturbances through
the release of latent heat of condensation and 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., 2007). The coupling of convective
processes with the large-scale dynamics is crucial for modeling the global distribution of
precipitation (Zhang, 2005). Since rainfall in the EASM system results from a mixture of
convection and stratiform clouds, and because there are planetary, synoptic and mesoscale
systems in the EASM rain belts, the simulation of EASM rainfall and circulation depends
strongly on the convection scheme (Huang et al., 2001).
The Community Atmosphere Model (CAM), as well as its previous version Community
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Climate Model (CCM), have been widely used for 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). Meehl and Arblaster (1998) compared the aspects of
the Asian–Australian monsoon system associated with El Nino–Southern Oscillation in
global coupled Climate System Model (CSM) and its atmospheric component 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
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 CAM3 and its coupled 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 still concentrated on the tropical monsoon, i.e.
the south Asian monsoon, and little attention was given to the EASM, especially its
subtropical components. EASM has proven to be a good test-bed for climate models over
large and complex topography (e.g., Zhou and Li, 2002). A comprehensive evaluation on
the performance of CAM3.5 in EASM simulation will potentially reveal strengths and
weaknesses of the model and may also serve as a useful reference for future model
improvement.
The CAM model is recently improved by modifying the Zhang and McFarlane (1995,
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hereafter ZM) convection scheme and the representation of other physical processes. The
purpose of this paper is to evaluate the performance of CAM3.5 in simulating the EASM.
We focus on the EASM climatology and seasonal variation. We want to address the
following questions: 1) What are the strengths and weaknesses of the NCAR CAM3.5
model in EASM simulation? 2) What are the influences of the modifications to the
convection scheme on EASM simulations? Our results show that the model can capture the
main characteristics of EASM circulation, but the simulation of Meiyu/Baiu/Changma rain
belt and the meridional monsoon cell still needs to be improved. The precipitation is
sensitive to changes in convection scheme, whereas the monsoon circulation is less so. The
multi-run ensemble is not always superior to the individual simulation.
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 schemes, as well as the
observational and reanalysis datasets used in this study. The EASM climate mean state is
depicted in section 3, while section 4 addresses seasonal and intraseasonal monsoon
variation. Section 5 follows with conclusions and a brief discussion.
2. Description of model and observational datasets
a. CAM3.5
The CAM3.5, developed by the National Center for Atmospheric Research (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 to improve the model physics for the next generation CAM4. It is a global primitive
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equation model with 26 vertical levels. The integration used finite-volume dynamical core
(Lin, 2004) and the horizontal resolution is about 2.5° longitude by 1.9° latitude. This
version is closely related to its previous version CAM3 and includes changes to convection
and cloud processes, land model and chemistry modules (Oleson et al., 2008; Stockli et al.,
2008; Neale and Mapes, 2008; Neale et al., 2008; Richter and Rasch, 2008). The calculation
of cloud fraction is updated and new hydrology, surface datasets, canopy integration are
introduced in the land model. The other physical processes are the same as those of CAM3,
which can be found in Collins et al. (2006). The CAM3 uses the standard Zhang-McFarlane
(ZM, Zhang and McFarlane, 1995) convection parameterization scheme, 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
cloud base, relaxes the atmosphere toward a threshold value of convective available
potential energy (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 an effort that led to the
development of CAM3.5, three revised versions of the Zhang-McFarlane convection
scheme were tested. One was the version revised by Neale and Richter (hereafter NZM), in
which the calculation of CAPE in the ZM scheme was modified to include the effect of
lateral entrainment dilution (Neale and Mapes, 2008). In addition, convective momentum
transport parameterized by Gregory et al. (1997) was included (Richter and Rasch, 2008).
The second was the free tropospheric quasi-equilibrium version (hereafter ZZM) of the ZM
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scheme proposed by Zhang (2002) and used in Zhang and Mu (2005). In this version, in
addition to changing the CAPE-based closure in the original ZM scheme to
quasi-equilibrium-based, a relative humidity threshold of 80% at the parcel lifting level was
imposed for convection. The third one is the Wu and Zhang version (hereafter WZM),
which uses 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, 2007; Zhang and Wu, 2003). Furthermore, as in the NZM
version, convective momentum transport was also included, but using the more
sophisticated parameterization of Zhang and Cho (1991). In this study, four AMIP-type
integrations, forced by observed SSTs, are performed and compared by using the three
revised versions as well as the original ZM scheme. They will be referred to as the NZM,
WZM, ZZM and control runs, respectively. Table 1 shows a brief summary of each
experiment. All the experiments are integrated more than 20 years and the data of
1980-1999 are used in this study.
b. Data
In this paper, East Asia refers to the area between 90°E and 140°E and between 0°N
and 45°N, including the monsoon region over the East Asia and 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 from ERA40 reanalysis during 1980-1999 on a
2.5° grid (Uppala et al., 2005); and 2) CPC Merged Analysis of Precipitation (CMAP)
based on blended satellite and in situ measurements during 1980-1999 on a 2.5° grid (Xie
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and Arkin, 1997).
3. Climatological mean pattern
a. Precipitation
Precipitation is one of the most important variables to measure monsoon activities. The
20-year mean JJA precipitation over East Asia is shown in Fig. 1. From the observation (Fig.
1a), two major rain belts can be seen. One is the tropical monsoon trough located between
10-20°N, as the reflection of intense rainfall corresponding to the ITCZ in tropical western
Pacific. The other, known as the subtropical Meiyu/Baiu/Changma front, exhibits a zonally
elongated structure extending from East China to northwestern Pacific around 30°N, with
heavy rainfall in the lower reaches of the Yangtze River in China, 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. Overall, all simulations can reproduce a
spatial pattern similar to that of the CMAP precipitation, although there are certain
differences. Before examining the results from each modification of the scheme, the
ensemble mean precipitation is calculated by averaging the results of four runs. The mean
precipitation has a spatial pattern similar to that of CMAP, but the biases are also obvious,
especially over the western Pacific (Fig. 1b). The largest deficiency is the underestimation
of precipitation over the subtropical rain belt. The observed rain belt extends from the east
flank of the Tibetan Plateau to the mid-Pacific, while the simulated rain band is located
along the landward side of the eastern coast of Asian continent. The rainfall centers
associated with Meiyu/Baiu/Changma front over the middle and lower reaches of the
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Yangtze River valley, South Korea and southwestern Japan are missing in the model. In the
tropical region, the model simulates much weaker precipitation over the South China Sea
and the subtropical western Pacific. The model produces excessive precipitation over the
Tibetan Plateau and its eastern flank. This may be a systematic bias in NCAR atmospheric
models, e.g. Yu et al. (2000) found that the NCAR CCM3 simulated unrealistically strong
precipitation on the eastern periphery of the Tibetan Plateau. They attribute the heavy
rainfall to the unrealistically strong surface sensible heating over the southeast and northeast
of Tibetan Plateau that favors the forming of a powerful subtropical anticyclone over the
eastern China.
The results for different modifications of the convection scheme are shown in Figs.
1c-1f. They all show similar biases to those in the ensemble average, e.g., the landward
distribution rather than zonally elongated subtropical rain belt, the missing of rainfall
centers associated with the Meiyu/Baiu/Changma front, the underestimated precipitation
over the South China Sea and Philippines Sea, and the excessive rainfall over the Tibetan
Plateau and its eastern periphery. The landward distribution of rainfall is obvious in these
runs, leading to the heavy rainfall shift northward over the continent. However, regional
details are different among the four runs. In the control run, the tropical rainfall over the
western Pacific is greatly underestimated, i.e., the precipitation amount over the South
China Sea and the Philippines Sea is less than 6 mm day-1, which is only about half of that
in the observation. The rainfall over the central eastern China (110-120°E, 30-40°N) is
overestimated, but that over the southeast coastal regions is underestimated. The NZM run
reproduces the two major rain belts in EASM, but the amplitude is weaker than the
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observation, especially over the Meiyu/Baiu/Changma rain belt. Similar results are also
evident in the WZM and ZZM runs, but they have more realistic Meiyu/Baiu/Changma rain
belts compared to the NZM run. The rainfall centers over the South Korea and southwestern
Japan are only evident in the WZM run, except with a weaker intensity. The tropical rain
belt is separated into two parts in the ZZM run, as the rainfall over the South China Sea is
dramatically decreased. The simulation of EASM precipitation is generally improved in
three modifications compared to the ZM scheme, particularly in the tropical region. None of
the simulations realistically reproduces the observed Meiyu/Baiu/Changma rain band in the
region from East China to the mid-Pacific, but the WZM and ZZM runs show better results
than the NZM run. The simulation of the Meiyu/Baiu/Changma rain band seems to be a
universal problem in current state-of-the-art AGCMs (Kang et al. 2002). The
Meiyu/Baiu/Changma front is a rain band of thousands of kilometers long across East Asia
and the northwestern Pacific, and is closely related to the planetary scale circulation.
Precipitation along the Meiyu/Baiu/Changma front is usually caused by convection and
clouds organized into eastward moving mesoscale convective systems (Chen et al., 1991).
Because WZM and the ZZM are explicitly related to the large scale forcing, they do better
than the NZM over the subtropical rain belt.
To quantitatively evaluate the model’s performance in simulating the geographical
distribution of monsoon precipitation, a Taylor diagram (Taylor, 2001) is employed to
compare the ability of different runs in East Asia (Fig.2). Before the comparison, model
results are interpolated to the spatial grid of the observation. The rainfall distributions over
the tropical (5-15°N) and subtropical (25-35°N) regions are also compared. The standard
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deviations of the simulations are smaller than those of the observation except for the
subtropical rainfall in the ZZM run, suggesting that the spatial variance in the model is
smaller than the observation. In comparison with the control run, the three revised schemes
improve the monsoon rainfall simulations. This improvement is evident in both tropical and
subtropical rainfalls. The NZM run simulates the EASM rainfall more realistically in its
tropical rain belt, but weakly in the subtropical rainfall. The subtropical rainfall simulated
by WZM is the most reasonable, which may be related to its closure assumption. The
performance of multi-run ensemble is generally better than most individual schemes,
indicating that the multi-run ensemble can overcome some biases in the individual run.
However, the ensemble result is not always the best, which may be caused by the high
resemblance among the four versions of the ZM scheme compared here.
b. EASM horizontal circulation
Monsoon rainfall distribution is closely related to monsoon circulation. In the upper
level (100 hPa), the most outstanding feature of 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 30°N, westerly jet to the north along 40°N and the Tropical
Easterly Jet (TEJ) to the south of 25°N (Fig. 3a). This anticyclone provides a favorable
divergent field for the underlying convective activity along the Meiyu/Baiu/Changma front.
The multi-run ensemble reasonably reproduces the structure of the Tibetan High except
with a weaker strength (Fig.3b), indicating a weaker heating from the Tibetan Plateau.
Contrary to the ensemble mean, the intensity of the High simulated in the control run is
stronger than the reanalysis, and the center of the High shifts westward (Fig.3c). The
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Tibetan High simulated by three revised versions of the ZM scheme (Figs.3d-f) shows
similar patterns to the ensemble mean, suggesting that the upper level circulation is less
sensitive to changes in the convection scheme.
To quantify the model performance, the intensity of Tibetan High (defined as the
averaged geopotential height over 60-120°E, 20-40°N), the zonal wind speed of westerly jet
(defined as the zonal wind speed averaged over 60-120°E, 40-50°N) and the TEJ (defined
as the zonal wind speed averaged over 60-120°E, 10-20°N) are listed in Table 2. In the
control run, the intensity of the Tibetan High and the westerly jet are stronger than the
reanalysis, while the TEJ is slightly weaker. The intensities of the High, westerly jet and
TEJ simulated by the three revised versions of the ZM scheme are weaker than 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,
as 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, WPSH greatly influences the climate of EASM.
The position, shape and strength of WPSH dominate the large-scale quasi-stationary frontal
zones and associated rain band in East Asia (Tao and Chen, 1987; Ding, 1994; Zhou and Yu,
2005). Here we use 500-hPa geopotential height to measure the WPSH, which is widely
used in previous studies (e.g., Yu et al., 2000; Liu et al., 2002; Zhou and Li, 2002). In the
ERA40 reanalysis, a strong anticyclone dominates the subtropical western Pacific and a
weak trough appears over the northeastern China (Fig.4a). Three indices, which
respectively represent WPSH intensity (IS), westward extension (IW) and northern edge (IN),
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are calculated to make an objective comparison between the reanalysis and the model. IS is
defined as the regional average of the grids with geopotential height greater than 5860 gpm
over the region 100-140°E and 10-40°N. To compare the westward extension of WPSH in
reanalysis and model simulations, each of the 500-hPa geopotential height values over the
selected region are subtracted by IS. IW is then defined as the longitude of the westward
extension of zero gpm contours of the subtracted field (Zhou et al., 2008). The WPSH ridge,
defined as u  0 and
u
 0 (Li and Chou, 1998), is constructed before calculating IN.
y
Afterward, IN is defined as the latitude of WPSH ridge position. The values of three indices
are listed in Table 3.
The WPSH in the multi-run ensemble resembles the reanalysis (Fig. 4b), except that it
extends westward about 20°and the ridge shifts northward more than 5° (Table 3). The
westward extension of WPSH, along with the downward motion, is consistent with the
underestimated precipitation over the subtropical regions of East Asia. The WPSH can also
accelerate the southwestern monsoon flows to reach relatively higher latitudes of East Asia.
The northward shift of the WPSH ridge results in the southerlies deeply penetrating into the
northern China. This corresponds to the excessive precipitation in North China but deficient
Meiyu/Baiu/Changma rainfall along the Yangtze River (Fig. 1). The mid-latitude westerly
in the model seems to be more flat. No trough is evident in this zone. The WPSH simulated
by the control run is much stronger than the reanalysis, and the subtropical Eurasian
continent is dominated by the anticyclone (Fig.4c). Although the simulations are improved
in three revised versions of ZM scheme (Figs.4d-4f), the northward shift of the WPSH still
exist, especially in the ZZM version (Table 3). The westward extension is obvious in all the
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three revised schemes, with the WZM run slightly better. 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 term of the Sverdrup vorticity balance. This bias of WPSH then
causes the drier bias of rainfall over the western Pacific, which is dominated by the ridge.
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 can be found: a strong transport
by southwesterly from ISM, a moderate transport by southeasterly from the western Pacific,
and a weak one linked to the cross-equator flow straddling 105°E-150°E. The southwesterly
transport in all three simulations is weaker than the reanalysis (Figs. 5b- 5f) which lead to
the weak rainfall centers over the Bay of Bengal and South China Sea. The simulated
moisture transport turns northwestward in the north of the Bay of Bengal around 20° N and
this leads to a weaker contribution of the southwesterly monsoon flow to the water vapor
transport over the EASM. The southeasterly transport of the water vapor from the western
Pacific shifts westward and extends northward as a result of the biases in WPSH simulation.
This deficiency is related to the weak rainfall associated with the subtropical
Meiyu/Baiu/Changma front and the relatively strong rainfall over the mid-latitude continent
north of 45°N. Therefore, the bias of water vapor transport is consistent with that of
precipitation shown above.
c. Meridional monsoon circulation
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One unique characteristic in the East Asian monsoon region is that the normal Hadley
cell is replaced by a meridional circulation of 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 90°E to 140°E). Strong upward motion controls the region between 10-30°N of
the Northern Hemisphere, while strong low level convergence dominates the region around
35°N which is closely related to the Meiyu/Baiu/Changma rain belt. None of the
simulations reasonably reproduces the EASM meridional monsoon circulation (Figs. 6b- 6f).
In the simulations, strong upward motions dominate the tropical area, and weak ascent flow
is seen in the lower troposphere between 20-40°N. The middle and upper levels of
subtropical troposphere north of 30°N are controlled by the subsidence flow, which is
opposite to the reanalysis. 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 weakly simulated land-sea
thermal contrast 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
East Asia (110°E~125°E) is shown in Fig.7. In observations (Fig.7a), prior to mid May,
southern China experiences a pre-monsoon rainy season. The monsoon rain extends from
southern Asia to the Yangtze River valley in June, and finally penetrates to the northern
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China in July. The rainy season in northern China lasts for about one month and ends in
August. From August to September, the monsoon rain belt rapidly moves back to the
southern China. The multi-run ensemble reproduces the poleward progress of the rainfall
band and the southward withdraw (Fig.7b), but also shows some deficiencies. For example,
the tropical rainfall in spring is overestimated, while the subtropical rainfall in summer is
underestimated. The rainfall periods in northern China lasts much longer in the model,
corresponding to the northward shift of the WPSH. The differences among three revised
schemes are also evident. In the control run (Fig.7c), both the tropical and subtropical
rainfall is weaker than the observation. They are separated into two bands, with a dry
tongue located between 20°N-25°N from February to June. In mid-summer, the strong
rainfall extends northward and the rain band withdraws more rapidly than the observation.
In three revised versions of the ZM scheme, the northward progress and southward
withdraw of monsoon rain band are more reasonable. The subtropical rain band simulated
by NZM shifts northward, and the rainfall along the Meiyu/Baiu/Changma front near 30°N
decreases dramatically from July, which is about 3 months earlier than the observation
(Fig.7d). 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 40°N which is about two
months longer than the observation (Figs.7e-7f).
The seasonal transition of rain belt shown above is closely related to seasonal change
of large-scale circulation. Actually, the seasonal cycle of monsoon is not simply
characterized by smooth variation, but the circulation and related rainfall over Asia undergo
abrupt seasonal changes, which are linked to the tropospheric warming over the Asian land
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mass (Murakami and Ding, 1982; He et al., 1987; Yanai et al., 1992). The ridge of WPSH
from May to August, defined as u  0 and
u
 0 (Li and Chou, 1998), and 3 mm/day
y
precipitation rates, are shown in Fig. 8. Two northward jumps of WPSH ridge in June and
July are evident in Fig.8a. Correspondingly, the rain band over East Asia undergoes two
northward jumps (Fig.8b). In June, the strong rainfall advances from the southern China to
the Yangtze River valley, and then the Meiyu begins. In July, the strong rainfall further
jumps to the northern China.
All simulations can reasonably reproduce the poleward jumps of the WPSH ridge (left
panel, Fig.8), but the position of the ridge is located a bit northward compared to the
reanalysis. The ridge is flat in zonal direction, as opposed to a southwest-northeast tilted
pattern in the reanalysis. The simulated northward jumps of rain belt in none of the runs are
reasonable (right panel, Fig.8) except for the WZM run (Fig.8j), which is also better than
the multi-run ensemble (Fig.8d). The simulated rain belts all shift northward about 10°
which is consistent with the bias of WPSH ridge.
5. Summary and discussion
a. Summary
In this study, the EASM simulated by the CAM3.5 model is evaluated in terms of
climate mean pattern and seasonal variation. To examine the model results in EASM, both
monsoon rainfall and circulation should be taken into consideration. The rain belts
associated with the tropical western Pacific ITCZ and subtropical Meiyu/Baiu/Changma are
19
two of the most important components of the EASM. The Tibetan High in the upper
troposphere and the WPSH in the middle and lower troposphere, as well as the meridional
monsoon cell, need to be well represented. The seasonal cycle and the abrupt jumps of the
rain belts (caused by the jumps of WPSH) from late spring to summer are also metrics for
evaluating model results. The sensitivity of simulations to three modified versions of the
Zhang-McFarlane convection scheme as well as the standard version is also examined. Both
the strengths and weaknesses of the four versions are documented. The results should be
helpful to the climate modeling community for improving the simulations of the East Asian
monsoon. The major conclusions are summarized below.
1)
The CAM3.5 simulations in all four versions examined are able to capture major
characteristics of the EASM circulation, including the Tibetan High in the upper
troposphere and the WPSH in the middle and lower troposphere. The model’s main
deficiency lies in the simulation of subtropical monsoon precipitation and its related
circulation. All the schemes simulate weaker rainfall related to Meiyu/Baiu/Changma front
extending from the eastern flank of the Tibetan Plateau to the mid-Pacific. This deficiency
is closely related to the weak low level upward flow motion around 30°N, since the model
fails in reproducing the pronounced meridional monsoon circulation over East Asia. In
addition, the tropical rainfall centers over South China Sea and western Pacific are also
weak in the model.
2)
The CAM3.5 can reasonably capture the springtime northward advance and late
summer southward withdrawal of monsoon rain belt over East Asia. The model also
reasonably reproduces two abrupt north jumps of WPSH from May to August. The
20
northward jump of the rain belt is poorly simulated, especially for the northward jump in
July. The deficiencies in seasonal cycle are closely related to the climatological pattern. For
example, the northward extension of WPSH results in longer periods of rainy monsoon
season at higher latitudes around 40°N.
3)
Both the distribution and seasonal cycle of monsoon rainfall simulations depend on
convection scheme, while the monsoon circulation shows less sensitivity. The three revised
ZM convective scheme, viz. the NZM version, WZM version and ZZM version, generally
improve the simulation of the EASM relative to the control run. The NZM run is slightly
better in simulating tropical rainfall, and the WZM and ZZM runs are better in simulating
the subtropical rainfall and its seasonal variation. The ensemble of four runs shows no
superiority in simulating the monsoon circulation and rainfall, and this may be due to the
similarities among them.
b. Discussion
The different responses to the revised versions of the ZM convection scheme indicate
the importance of convective processes involved in the model in EASM simulation. The
differences between the NZM and WZM may be attributed to closure assumptions, as these
two modifications both have momentum transport parameterization. The WZM and ZZM
both use the quasi-equilibrium closure, and the differences between them may reflect the
roles of convective-scale momentum transport. The results above show that NZM run
improve EASM tropical rainfall distribution, especially in the Indian Ocean, indicating that
the NZM may have advantages in simulating tropical deep convection. However, over East
21
Asian subtropical regions, the tropospheric large-scale forcing is important to convection,
especially for the Meiyu/Baiu/Changma front. The NZM does not perform well over these
regions. The WZM and ZZM runs generally do better in the simulations of EASM
subtropical rainfall associated with the Meiyu/Baiu/Changma front, because these two
revised versions of the ZM scheme are explicitly linked to large scale forcing through the
quasi-equilibrium assumption. Zhang (2003) point out that the diurnal variation of the
tropospheric large-scale forcing has a strong in-phase relationship with convection, and this
is also the case over the Meiyu/Baiu/Changma front (Yu et al., 2007). Therefore, the
quasi-equilibrium closure is more favorable in simulating the EASM subtropical rainfall.
However, the intensity of the rainfall is still too weak especially in the ZZM run, which
does not include the convective momentum transport parameterization as in the WZM
version. But the role of convective-scale momentum transport is subtle, because there are
no significant differences between WZM and ZZM runs. Nevertheless, there are common
biases in all of the runs, indicating some missing feedbacks may have an important
contribution to EASM simulations. Furthermore, the physical processes involved still need
further examination.
Although there are differences among the results of different runs, the model has some
systematical biases, in which the weaker 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).
The monsoon circulation is driven by the diabatic processes, particularly by the sensible
22
heating and deep convection heating, and at the same time, the diabatic processes are
influenced by the monsoon circulation (Liu et al., 2004). As tropical latent heat and plateau
sensible heating are the main thermal sources in EASM, the systematical biases in the
CAM3.5 model should be linked to the biases in heating sources. To examine this
hypothesis, the horizontal distribution of 200-500 hPa mean temperature is shown in Fig.9.
A planetary-scale warm air mass centers on the Asian continent with the maximum
temperature (≥ -20℃) over the southern Tibetan Plateau, resulting in a strong temperature
gradient in both the meridional and zonal directions (Fig. 9a). All simulations produce
warmer temperature in the tropical region but colder air in the subtropical region eastward
the Tibetan Plateau surrounding 30°N except for the control run. At higher latitudes (north
of 40°N), the model temperature is generally warmer than the reanalysis, with the warm
center over the northern China (100-130°N, 40-45°N). This significant zonal “+ - +” bias
pattern dominates the Asia-Pacific region (Figs. 9c- 9f), and strongly decreases the
meridional land-sea thermal contrast. Due to the weaker thermal contrasts between tropical
ocean and subtropical land in the model, the monsoon meridional circulation driven by this
thermal effect is much weaker than the reanalysis (Fig.6). The weaker monsoon circulation
then leads to less rainfall over East Asian tropical and subtropical region. The model
produces excessive rainfall in continental higher latitudes around 40°N (Figs. 1c-1f). As a
consequence, this region has warmer air compared to the reanalysis (Figs. 9c-9f).
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 other two centers over the South China Sea and western Pacific (Fig.1a). This indicates
23
a strong heating and associated upper-tropospheric divergent centers over these regions (Fig.
10). In the simulations, although there are rainfall centers surrounding the Bay of Bengal,
stronger rainfall center is evident in the northern Indian ocean in both the control run and
NZM run (Figs.1c and 1d), but in the northern Indian ocean and the west of Philippines Sea
in both WZM and ZZM runs (Figs.1e-1f). Since the mean position and intensity of tropical
convection are closely related to the atmospheric general circulation (Branstator 1983),
these spurious equatorial rainfall centers (and the associated latent heating) should have
negative effects on the large-scale circulation patterns. The upper-level divergence center
over the western Pacific shifts westward to the Indian in both the control run and NZM run
(Figs. 10c and 10d), while the divergence center shifts eastward to the mid-Pacific in both
the WZM and ZZM runs (Figs. 10e-10f). No divergence center is seen over the EASM
region, and this may explain the weak rainfall over the EASM tropical region. However,
although the tropical rainfall is underestimated in the model, the tropical air is still warmer
than the reanalysis. While cloud-radiation processes may be one source for the bias, the lack
of air-sea interaction in the AMIP-type experiments can also have contributions. Wang et al.
(2005) found that coupled ocean-atmosphere processes are crucial in the monsoon regions
where atmospheric feedback on SST is critical. Therefore, the simulations of stand-alone
atmospheric model need to be compared with those of the air-sea coupled model. This is
ongoing work and the results will be reported elsewhere.
24
Acknowledgments: This work was jointly supported by National Natural Science
Foundation of China under grant No. 40523001, 40625014, the Major State Basic Research
Development Program of China (973 Program) under grant No. 2006CB403603, and
Chinese COPES project (GYHY200706005).
25
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Figure Captions
Fig. 1. Northern hemispheric summer (June-August) mean precipitation in (a) observation
(CMAP data), (b) multi-run ensemble and (c)-(f) four different versions of the ZM
convection scheme, respectively.
Fig. 2. Taylor diagram evaluating simulations of northern hemispheric summer precipitation
over East Asia region (90-140°E, 0-45°N), as well as its tropical (90-140°E, 5-15°N) and
subtropical (90-140°E, 25-35°N) rain belts. The angular coordinate is the correlation
coefficient between model results and the observation (CMAP). The radial coordinate is the
standard deviation of model results divided by the standard deviation of the observation.
Fig. 3. Northern hemispheric summer mean horizontal wind fields (vector) and geopotential
height (contour, unit: dagpm) at 100 hPa in (a) ERA40 reanalysis, (b) multi-run ensemble,
and (c)-(f) four different versions of the ZM convection scheme, respectively.
Fig. 4. Same as Fig. 3 except for 500 hPa wind and geopotential height.
Fig. 5. Vertically integrated summer mean water vapor transport (kg×m-1 s-1) in (a) ERA40
reanalysis, (b) multi-run ensemble and (c)-(f) four different versions of the ZM convection
scheme, respectively.
Fig. 6. Northern hemispheric summer mean meridional circulations for the longitudinal
range of 90–140°E in (a) ERA40 reanalysis, (b) multi-run ensemble and (c)-(f) four
different versions of the ZM convection scheme, respectively.
Fig. 7. Seasonal migration of the monsoon rain band zonally averaged between
110°E~125°E in (a) observation (CMAP data), (b) multi-run ensemble and (c)-(f) four
35
different versions of the ZM convection scheme, respectively.
Fig. 8. Seasonal evolution of 500 hPa WPSH ridge (left panel) and corresponding 3mm/day
precipitation rate contour line (right panel) averaged for May (black solid line), June (blue
dashed line), July (purple dotted line) and August (red long and short dashed line).
Fig. 9. (a) Northern hemispheric summer mean temperature averaged between 200 and 500
hPa in ERA40 reanalysis; (b)-(f) the differences between model results and ERA40
reanalysis.
Fig. 10. Northern hemispheric summer mean velocity potential at 200 hPa in (a) ERA40
reanalysis, (b) multi-run ensemble and (c)-(f) four different versions of the ZM convection
scheme, respectively. Contour interval is 2×106 m2 s–1. Vectors indicate the divergent wind.
36
Table 1 Description of 4 AMIP-type experiments using different versions of the ZM
convection scheme.
Experiment Versions of ZM Scheme
Integration Time
References
Zhang and McFarlane
(1995)
Neale and Mapes (2008);
Richter and Rasch (2008)
CNTL
Zhang and McFarlane
1977.01 - 2003.12
NZM
Neale and Richter
1977.01 - 2002.12
WZM
Wu and Zhang
1977.01 - 2000.12
Wu et al.(2007)
ZZM
Revised Zhang
1978.01 - 1999.12
Zhang et al. (2002)
37
Table 2 The intensity of northern hemispheric summer mean 100 hPa Tibetan High (TPI),
as well as wind speed of westerly and TEJ from ERA40 reanalysis, multi-run ensemble and
the four different versions of the ZM convection scheme, respectively.
Averaged
Units ERA40 Ensemble CNTL NZM WZM ZZM
Region
20-40°N,
TPI
dagpm 1676
1673.1
1678.7 1670.4 1670.9 1672.2
60-120°E
40-50°N,
U-wind
m s-1
14
14.8
16
14.6
14.4
14.1
60-120°E
10-20°N,
V-wind
m s-1
-26.1
-21.6
-23.6
-22.4
-19
-22.8
60-120°E
Index
38
Table 3 Same as Table 2, but for the indices of 500 hPa WPSH over 100-140°E, 10-40°N.
Index
ERA40
Ensemble
CNTL
NZM
WZM
ZZM
IS
5870.0
5871.6
5886.9
5868.5
5868.9
5871.8
IN
22.5
29.37
29.37
27.47
27.47
31.26
IW
120
100
107.5
112.5
117.5
102.5
39
Fig. 1. Northern hemispheric summer (June-August) mean precipitation in (a) observation
(CMAP data), (b) multi-run ensemble and (c)-(f) four different versions of the ZM
convection scheme, respectively.
40
Fig. 2. Taylor diagram evaluating simulations of northern hemispheric summer precipitation
over East Asia region (90-140°E, 0-45°N), as well as its tropical (90-140°E, 5-15°N) and
subtropical (90-140°E, 25-35°N) rain belts. The angular coordinate is the correlation
coefficient between model results and the observation (CMAP). The radial coordinate is the
standard deviation of model results divided by the standard deviation of the observation.
41
Fig. 3. Northern hemispheric summer mean horizontal wind fields (vector) and geopotential
height (contour, unit: dagpm) at 100 hPa in (a) ERA40 reanalysis, (b) multi-run ensemble,
and (c)-(f) four different versions of the ZM convection scheme, respectively.
42
Fig. 4. Same as Fig. 3 except for 500 hPa wind and geopotential height.
43
Fig. 5. Vertically integrated summer mean water vapor transport (kg×m-1 s-1) in (a) ERA40
reanalysis, (b) multi-run ensemble and (c)-(f) four different versions of the ZM convection
scheme, respectively.
44
Fig. 6. Northern hemispheric summer mean meridional circulations for the longitudinal
range of 90–140°E in (a) ERA40 reanalysis, (b) multi-run ensemble and (c)-(f) four
different versions of the ZM convection scheme, respectively.
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Fig. 7. Seasonal migration of the monsoon rain band zonally averaged between
110°E~125°E in (a) observation (CMAP data), (b) multi-run ensemble and (c)-(f) four
different versions of the ZM convection scheme, respectively.
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Fig. 8. Seasonal evolution of 500 hPa WPSH ridge (left panel) and corresponding 3mm/day
precipitation rate contour line (right panel) averaged for May (black solid line), June (blue
dashed line), July (purple dotted line) and August (red long and short dashed line).
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Fig. 9. (a) Northern hemispheric summer mean temperature averaged between 200 and 500
hPa in ERA40 reanalysis; (b)-(f) the differences between model results and ERA40
reanalysis.
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Fig. 10. Northern hemispheric summer mean velocity potential at 200 hPa in (a) ERA40
reanalysis, (b) multi-run ensemble and (c)-(f) four different versions of the ZM convection
scheme, respectively. Contour interval is 2×106 m2 s–1. Vectors indicate the divergent wind.
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