Effects of Mountain Uplift on East Asian Summer Climate Investigated by a Coupled Atmosphere–Ocean GCM

Akio Kitoh Meteorological Research Institute, Tsukuba, Japan

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Abstract

To study the effects of progressive mountain uplift on East Asian summer climate, a series of coupled general circulation model (CGCM) experiments were performed. Eight different mountain heights were used: 0% (no mountain), 20%, 40%, 60%, 80%, 100% (control run), 120%, and 140%. The land–sea distribution is the same for all experiments and mountain heights are varied uniformly over the entire globe.

Systematic changes in precipitation pattern and circulation fields as well as sea surface temperature (SST) appeared with progressive mountain uplift. In summertime, precipitation area moves inland on the Asian continent with mountain uplift, while the Pacific subtropical anticyclone and associated trade winds become stronger. The mountain uplift resulted in an SST increase over the western tropical Pacific and the Maritime Continent and an SST decrease over the western Indian Ocean and the central subtropical Pacific. There is a drastic change in the East Asian circulations with the threshold value at the 60% mountain height. With the mountain height below 60%, the southwesterly monsoon flow from the Indian Ocean becomes strong by uplift and transports moisture toward East Asia, forming the baiu rainband. With higher mountain heights, intensified subtropical trade winds transport moisture from the Pacific into the Asian continent.

In order to investigate how the SST change affected the results presented herein, additional experiments were performed with the same experimental design but with the atmospheric GCM (AGCM). A comparison between CGCM and AGCM experiments revealed that major features such as a shift in precipitation inland and an appearance of the baiu rainband by higher orography were reproduced similarly in both the AGCM and the CGCM. However, there was a qualitatively as well as quantitatively different feature. The anticyclonic circulation anomalies in the lower troposphere, which appeared by mountain uplift in the tropical western Pacific in the CGCM associated with lowered SST, fed more moisture over East Asia and resulted in a stronger baiu rainband in the CGCM than that in the AGCM. An extent of the monsoon westerly flow is regulated by competition between the Pacific subtropical anticyclone and the southwest monsoon. The confluence zone was located near the Philippines throughout the mountain uplift in the AGCM, but it shifted backward to the west via mountain uplift in the CGCM associated with simulated SST changes. Overall the CGCM showed a larger sensitivity to mountain uplift than the AGCM due to the SST changes, thus warranting an examination of the importance of air–sea coupling and a need for the use of coupled models for such sensitivity studies.

Corresponding author address: Dr. Akio Kitoh, Climate Research Dept., Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan. Email: kitoh@mri-jma.go.jp

Abstract

To study the effects of progressive mountain uplift on East Asian summer climate, a series of coupled general circulation model (CGCM) experiments were performed. Eight different mountain heights were used: 0% (no mountain), 20%, 40%, 60%, 80%, 100% (control run), 120%, and 140%. The land–sea distribution is the same for all experiments and mountain heights are varied uniformly over the entire globe.

Systematic changes in precipitation pattern and circulation fields as well as sea surface temperature (SST) appeared with progressive mountain uplift. In summertime, precipitation area moves inland on the Asian continent with mountain uplift, while the Pacific subtropical anticyclone and associated trade winds become stronger. The mountain uplift resulted in an SST increase over the western tropical Pacific and the Maritime Continent and an SST decrease over the western Indian Ocean and the central subtropical Pacific. There is a drastic change in the East Asian circulations with the threshold value at the 60% mountain height. With the mountain height below 60%, the southwesterly monsoon flow from the Indian Ocean becomes strong by uplift and transports moisture toward East Asia, forming the baiu rainband. With higher mountain heights, intensified subtropical trade winds transport moisture from the Pacific into the Asian continent.

In order to investigate how the SST change affected the results presented herein, additional experiments were performed with the same experimental design but with the atmospheric GCM (AGCM). A comparison between CGCM and AGCM experiments revealed that major features such as a shift in precipitation inland and an appearance of the baiu rainband by higher orography were reproduced similarly in both the AGCM and the CGCM. However, there was a qualitatively as well as quantitatively different feature. The anticyclonic circulation anomalies in the lower troposphere, which appeared by mountain uplift in the tropical western Pacific in the CGCM associated with lowered SST, fed more moisture over East Asia and resulted in a stronger baiu rainband in the CGCM than that in the AGCM. An extent of the monsoon westerly flow is regulated by competition between the Pacific subtropical anticyclone and the southwest monsoon. The confluence zone was located near the Philippines throughout the mountain uplift in the AGCM, but it shifted backward to the west via mountain uplift in the CGCM associated with simulated SST changes. Overall the CGCM showed a larger sensitivity to mountain uplift than the AGCM due to the SST changes, thus warranting an examination of the importance of air–sea coupling and a need for the use of coupled models for such sensitivity studies.

Corresponding author address: Dr. Akio Kitoh, Climate Research Dept., Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan. Email: kitoh@mri-jma.go.jp

1. Introduction

Large-scale mountains such as the Tibetan Plateau, and the Rocky and Andes Mountains, play essential roles in forming the present global climate system. Therefore mountain uplift was a major event in the natural history of the earth. For example, the uplift of the Tibetan Plateau has led to the evolution and variety of the monsoon climate system in Asian regions. Accordingly, a number of numerical experiments have been conducted using general circulation models (GCMs) to study the roles of mountains on climate (e.g., Manabe and Terpstra 1974; Hahn and Manabe 1975; Kutzbach et al. 1989, 1993).

In addition to mountain–no-mountain experiments, there are a few GCM studies that investigated the sensitivity of climate on mountain height. Prell and Kutzbach (1992) conducted experiments with no mountains, half mountains, and full mountains by the National Center for Atmospheric Research Community Climate Models (CCM0 and CCM1), and concluded that at least half of the present-day elevations are a prerequisite for a strong Indian summer monsoon. An et al. (2001) used the NCAR CCM3 to conduct an experiment with four stages of the Tibetan Plateau uplift and obtained an increase in South Asian and East Asian monsoons. Liu and Yin (2002) studied the sensitivity of the East Asian monsoon climate to an idealized progressive uplift of the Tibetan Plateau by the Center for Ocean–Land–Atmosphere (COLA) atmospheric GCM (AGCM). They found a little change in the South Asian monsoon wind intensity, while a larger sensitivity was found on the East Asian monsoon. They also noted that the effect of the Tibetan Plateau uplift was more significant in winter than in summer for the East Asian monsoon. Abe et al. (2003) used the Meteorological Research Institute (MRI) coupled GCM (MRI-CGCM1) to study the sensitivity of the Asian summer monsoon to the progressive mountain uplift. They found that the South Asian summer monsoon precipitation and the zonal wind shear increased nonlinearly with progressive mountain uplift: the enhancement of these indices was larger in the first half of mountain uplift compared with that in the second half. In the first half uplift, there was an overall increase in summer precipitation in Asia. On the other hand, in the second half of the uplift, there was a contrast in precipitation changes between a decrease over Southeast Asia and an increase over India, due to an inland shift of the precipitation area.

The characteristics of the East Asian monsoon and the Indian (South Asian) monsoon are very different (e.g., Lau and Li 1984). It is well known that most of the GCMs have difficulty in simulating the distribution and the annual cycle of the precipitation in the East Asian monsoon region (Lau 1992; Chen 2000; Kang et al. 2002). One of the most distinct characteristics in East Asia is the early summer rainfall, that is, the mei-yu in China and the baiu in Japan, which is associated with a narrow rainband with a stationary mei-yu baiu front (Tao and Chen 1987; Ninomiya and Murakami 1987). Its typical season is May to July in China, and June to early July in Japan. Mei-yu baiu rainfall is important for water resources including agriculture, but the heavy rainfall associated with the mei-yu baiu often results in disasters in East Asian countries, and thus it is important that this rainfall is simulated accurately with climate models. Although earlier model studies such as Kar et al. (1996) showed insufficient representation of the mei-yu baiu rainbelt with a medium-resolution model (T42), current models show some ability of T42 models to simulate the mei-yu baiu front precipitation in certain periods of the model integration [Center for Climate System Research/National Institute for Environmental Studies (CCSR/NIES) T42L52 resolution AGCM; Ninomiya et al. 2002] or even in the climatological mean fields (T42L30 MRI-CGCM2; Rajendran et al. 2004). The existence of the Tibetan Plateau is believed to have an influence on the baiu by its mechanical and/or thermal effect (Hahn and Manabe 1975), although Yoshikane et al. (2001) argued, with results provided by a regional atmospheric model, that the Tibetan Plateau has only a secondary role on the development of the baiu front. Therefore, the sensitivity of mountain uplift on the baiu rainband should be investigated by a state-of-the-art GCM.

As Kitoh (1997, 2002) showed, the mountain uplift can cause a change in sea surface temperature (SST) and ocean general circulation. Therefore, experimental studies with an atmosphere and ocean coupled GCM (CGCM) are required to understand the global influence, including the ocean, of the mountain uplift. Therefore, we tackled the problem of the sensitivity of monsoon climate by using a CGCM. As a prerequisite for such sensitivity experiments, the model's control climate should reasonably reproduce the observations. The current MRI CGCM2 (Yukimoto et al. 2001), a version with flux adjustments, has shown an ability to simulate Asian summer monsoons reasonably well (Rajendran et al. 2004). Also as the air–sea coupling may be a crucial factor on SST change, it would be better to use a CGCM without flux adjustments. Kitoh (2003) showed that the same model has satisfactorily simulated monsoons and El Niño–Southern Oscillation (ENSO) even without flux adjustments. Therefore, we use the MRI-CGCM2 to study the sensitivity of East Asian monsoons on progressive mountain uplift without flux adjustments. Mountain heights are changed every 20% between the no-mountain run (0%) and the control run (100%). We also conducted experiments with enhanced mountain cases, that is, 120% and 140% cases. In addition, we performed experiments with the same experimental design but with the AGCM. By comparing the CGCM and AGCM results, we can investigate how the SST changes that will be simulated by the CGCM runs by mountain uplift could produce different results from those by the atmosphere-only GCM experiments.

2. Model and experiment

The model used is the MRI global ocean-atmosphere coupled GCM (MRI-CGCM2; Yukimoto et al. 2001). The atmospheric component of the model (AGCM) has been developed based on a version of the operational weather forecasting model of the Japan Meteorological Agency (JMA). The horizontal resolution is T42 in wave truncation and 128 × 64 (about 2.8° × 2.8° grid spacing in longitude and latitude) on a transformed Gaussian grid. The vertical configuration consists of a 30-layer sigma pressure hybrid coordinate with the top at 0.4 hPa. Some of physical process schemes are replaced with those of the original JMA version. Details of the AGCM are described in Shibata et al. (1999).

The oceanic component of the model is a Bryan–Cox-type ocean general circulation model (OGCM) with a global domain. The horizontal grid spacing is 2.0° in latitude and 2.5° in longitude. Between 4°S and 4°N, the meridional grid spacing is set to 0.5° in order to have good resolution of the equatorial oceanic waves. There are 23 vertical levels with the bottom at 5000 m. The uppermost layer has a 5.2-m thickness. An eddy isopycnal mixing scheme is used in addition to subgrid mixing, using viscosities and diffusivities. A convective adjustment by mixing the whole vertical column is applied when vertical stratification becomes unstable. Solar radiation penetrates seawater with an absorptivity of 10-m-depth e-folding decay, which heats several tens of meters of the surface seawater.

The atmosphere and the ocean interact with each other by exchanging fluxes of heat, freshwater, and momentum at the sea surface. The fluxes are exchanged every 24 h in the model. Yukimoto et al. (2001) described the model climate when flux adjustment is applied. In this experiment, no flux adjustment was applied.

The control run (M10) was integrated for 50 yr with a realistic land–sea distribution and orography. Initial conditions of the control run were taken from the 1 January value of the flux-adjusted run of the same CGCM (Yukimoto et al. 2001). The elevation of the highest grid point over the Tibetan Plateau is 5536 m in M10. In the M0 run, the worldwide mountain height was set to zero, but the land–sea distribution was kept the same. The M2, M4, M6, and M8 runs used the 20%, 40%, 60%, and 80% height of the M10 orography. The initial conditions of all mountain runs are the same. Kitoh (2002) showed that the upper oceans can adjust within the first 5 yr after the change of mountain height. In this experiment, we also included the M12 and M14 runs that used the increased mountain heights of 120% and 140%, respectively. All runs were performed for 50 yr of integration without flux adjustment and the last 40 yr of the data from these eight runs are analyzed.

Additional experiments with the atmospheric GCM are done. This AGCM is the same atmospheric part of the CGCM described above. The eight runs were also performed by the AGCM: they are denoted as A0, A2, A4, A6, A8, A10, A12, and A14, respectively, where A0 run corresponds to the no-mountain run and so on. The SST data used in the AGCM are the observed climatological values of Levitus and Boyer (1994). The AGCM is integrated for 12 model years, and the mean of the last 10 years is used for analysis.

3. Coupled GCM results

a. Precipitation changes in June

Figure 1 shows the time–latitude cross sections of the climatological pentad-mean precipitation averaged for 120°–140°E for each experiment with the CGCM. From the daily precipitation data, seventy-three 5-day mean datasets are calculated for each year, and then 40-yr averages are made for each experiment. The 23-yr (1979–2001) average observed pentad values are also shown based on the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP; Xie and Arkin 1997) standard dataset. The observations show a seasonal migration of the tropical rainband that is located to the south of the equator in boreal winter and to the north of the equator in boreal summer. The latter corresponds to the rainfall maximum in the Philippine Sea, and constitutes one of the Asian summer monsoon foci (Wang and LinHo 2002). Another characteristic feature in this longitudinal band is a sudden disappearance of a dry region between 15° and 20°N in early May (Matsumoto 1992). Since then heavy rainfall appears around 25°N in mid-May. There also is a northward migration to the 30°–35°N region in late June and early July. This rainband corresponds to the baiu rainband (Ninomiya and Akiyama 1992) and characterizes the early summer climate in Japan. At 30°N, there is also a rainband beginning in March, which corresponds to a spring rain in Japan.

The seasonal changes of the tropical rainfall band are more or less reproduced in all experiments, since the seasonal change of the solar insolation and resultant SST seasonal change are the main contributors to this seasonal migration in precipitation. It is noted that the baiu rainband found in the observations in late May through early July does not appear in the M0, M2, and M4 runs. This rainband only appears when the mountain height is higher than 60% of the standard height. Its northward propagation is simulated in the M6–M14 cases. Similarly, the spring rain at 30°N does not appear when the mountain height is lower, while it becomes distinct with mountain uplift. Broccoli and Manabe (1992) showed in their mountain (M) and no-mountain (NM) experiments that this midlatitude precipitation maximum in spring is associated with considerable synoptic disturbance activity when the mountains exist. In cases M12 and M14, this precipitation is larger than the observations in winter, and continues into the baiu season seamlessly.

Correlation coefficients are calculated for the time–latitude cross sections between each of the eight experiments and the observations shown in Fig. 1. They are +0.71, +0.74, +0.75, +0.79, +0.81, +0.79, +0.74, and +0.66 for M0–M14, respectively. Thus, the M8 run has the largest correlation coefficient (+0.81) with the observations. This indicates that both lower mountains and higher mountains deteriorate the model climate in terms of the seasonal march of East Asian precipitation. The fact that the control run has a lower correlation coefficient than the M8 case suggests that the perfect mountain height may not be the optimum standard mountain height used in the control run.

Figure 2 shows the horizontal distributions of the June mean precipitation for the M0–M14 runs, and the 1979–2001 averaged observations. The observations show five distinct precipitation maxima over the western coast of India, the Bay of Bengal, Thailand, the South China Sea, and the Caroline Islands (western North Pacific). The observations also show a rainbelt from the South China Sea through Taiwan to the south of Japan, corresponding to the baiu rainband. The mei-yu baiu front is a low-level baroclinic zone extending zonally from eastern China toward the south of Japan, coinciding with the maximum precipitation zone. The mei-yu front over China has a large meridional temperature contrast, but the baiu front over Japan is characterized with a large moisture gradient, rather than a temperature gradient. There is a large moisture flux convergence within the baiu front, which is mainly transported by the southwesterly flow along the periphery of the subtropical anticyclone (Akiyama 1973). This baiu rainband is successfully simulated by the model control run (M10) in its climatological features.

In order to check the reproducibility of the baiu front structure, we plotted the latitude–height cross sections of zonal, meridional, and vertical winds and equivalent potential temperature of the control run in June at 130°E (Fig. 3). The subtropical jet core of 40 m s−1 is located at 35°N, 150 hPa, which extends down to a low-level wind maximum at 27°N, 850 hPa. Near the surface, a strong southerly wind component is found just to the south of the baiu rainband. Intense upward motion and near moist-neutral vertical stratification are seen at the baiu rainband latitude. These structures are similar to the observed (Ninomiya 2000) and simulated ones (Ninomiya et al. 2002; Kawatani and Takahashi 2003), although the 40-yr climatological means yield weaker motions, that is, maximum meridional and vertical motions being about half of the value of typical observed events (Ninomiya 2000).

The sensitivity of mountain uplift on the June mean precipitation is shown in Fig. 2. In the no-mountain (M0) run, heavy precipitation in June is confined in the deep Tropics, within 10° latitude. To the north, a dry region, with precipitation of less than 1 mm day−1, almost entirely covers the Eurasian continent, yielding a dry climate in East Asia. The appearance of monsoon rain over the oceans in the no-mountain case has been identified by previous studies with AGCMs (e.g., Manabe and Terpstra 1974). In M2 and M4, precipitation increases in Southeast Asia and East Asia, and a dry area retreats westward. In M6, a clear maximum rainband is simulated from Taiwan to southern Japan. A northward shift of this large precipitation area continues into M8, where a local precipitation maximum appears over south China. In M10, the baiu rainband develops and reaches 140°E, off the southern coast of Japan.

Therefore, the present model results support the previous AGCM experiments, such as those of Hahn and Manabe (1975), that the Tibetan Plateau is essential for the existence of the baiu rainband. Ose (1998) and Rodwell and Hoskins (2001) used an idealized model to investigate the northern summer circulation responses to heat source and orography. Ose (1998) showed that the deep heat source in South and Southeast Asia in June formed a background condition (upward motion) over the baiu area, while the sole topographic effect did not produce such an effect. His result and our experiments imply that the northward displacement of the convection centers due to the existence of the Tibetan Plateau is essential for creating the baiu rainband. Although Yoshikane et al. (2001) reached a different conclusion, that the zonal mean field and the land–sea contrast are more important than the orography, we believe that the effect of orography is implicitly included in the change of their zonal mean field because the existence of the Tibetan Plateau modifies the seasonal change of the circulation.

In M12 and M14, an overall increase in precipitation is found over land. In particular, in M14, precipitation greater than 4 mm day−1 covers the Indian subcontinent. It is noted that over India there is a northward downgradient of precipitation in M0, which turned into a northwestward gradient in M4 and becomes a westward gradient in M8 and M10. In M14, a large precipitation area occupies the whole of India.

With respect to the horizontal distribution of the June precipitation, the M8 run is more close to the observations than the other runs. This is shown in Fig. 4, which is a Taylor diagram (Taylor 2001) of the June precipitation over Asia. This is defined as the pattern correlations of each run with the observations, and the relative magnitude of the spatial standard deviations to the observations, calculated for the region 10°S–50°N, 30°E–180° (i.e., the area shown in Fig. 2). The 1979–2001 climatological precipitation values from the CMAP data are used as the observations. The M0 has the smallest pattern correlation coefficient compared with the observations. When increasing the mountain height, it gets closer to the observations; at M6 and M8, it is the closest to the observations; from M10 through M14, the pattern correlation gets worse. Therefore, as in a visual inspection of Fig. 2, the M8 shows the most reasonable precipitation simulation as far as the June mean Asian precipitation is concerned.

As there exist precipitation data other than those of CMAP, we also used the 11-yr (1986–96) averaged precipitation from the Global Precipitation Climatology Project (GPCP; Huffman et al. 1997). The label GPCP in Fig. 4 indicates the pattern correlation coefficient and the normalized standard deviation of the GPCP climatology versus the CMAP climatology. It is noted that the two observation points are not very close each other. The pattern correlation between the two observations is only about 0.8. This kind of uncertainty in the observations was pointed out elsewhere (Taylor 2001).

b. Atmospheric circulation changes in June

Figure 5 shows the global distributions of the June mean sea level pressure. The National Centers for Environmental Prediction/Department of Energy (NCEP/DOE) Atmospheric Model Intercomparison Project-II (AMIP-II) reanalysis (NCEP-2) monthly mean data (Kistler et al. 2001) are used for the observations. In all the experiments, the initial conditions are taken from the control run so that the total atmospheric mass is the same as in the control run. Therefore, the sea level reduction of the surface pressure results in quite a large difference among the experiments; a lower mountain case has a lower global mean sea level pressure value. For plotting purposes only, we adjusted the sea level pressure by adding or subtracting a constant value so that the global mean sea level pressure is the same as that in the control run.

As was found in previous studies, low pressure covers the whole Eurasian continent and there is no deep cyclonic circulation in India in the no-mountain case (M0). There still exist subtropical anticyclones over the Northern Hemisphere oceans, but their strength is weak. At around 40°N there is a large meridional pressure gradient both over land and over the oceans. Particularly this gradient is large around Japan, resulting in strong surface winds there. From M0 to M2, there is an increase in sea level pressure to the north of 30°N, and a decrease in the Tropics. A similar change continues from M2 to M4 with a large pressure drop in India that is associated with an increased precipitation. An intensification of the subtropical anticyclone can be seen in M4 and M6. In M8 and M10, a large increase in sea level pressure is found over the northern North Pacific, which resulted in the shape of the subtropical anticyclone being much closer to the observed pattern. Ose (1998) and Rodwell and Hoskins (2001) showed that the Asian monsoon heating influences not only the Indian monsoon trough but also the subtropical anticyclone. In southern Asia, a monsoon trough extends westward into the Persian Gulf. In M12 and M14, the North Pacific subtropical anticyclone intensifies further, and the 1020- and 1012-hPa contours reach 160° and 130°E, respectively. The trade winds in the western Pacific become stronger and reach their most westward longitude in these highest mountain cases.

Figure 6 shows the June mean wind vectors at 850 hPa over Asia and the western Pacific region. In the no-mountain case (M0), the midlatitude winds flow nearly zonally and are very strong, constituting a westerly jet at 40°N over the Eurasian continent. It is strongest over Japan, where the southerly flow near the North Pacific subtropical high merges into the midlatitude flow. The trade winds exist over the subtropical Pacific Ocean even in the M0 case, but extend into the Indochina Peninsula at 15°N. Therefore, Southeast Asia is under the influence of the Pacific Ocean circulation system in M0. But with a mere 20% uplift in M2, a westerly core appears at the southern tip of the Indian subcontinent, and this westerly covers the Indochina Peninsula. At the same time, easterlies over the Philippines become weaker. Both in M0 and M2, the northerly wind component dominates over the Arabian Sea. From M2 to M4, there is a large change in the monsoon westerly flow over the northern Indian Ocean. Also M4 shows a southwesterly flow from the Bay of Bengal through the Indochina Peninsula, which extends farther into East Asia. This southwesterly flow merges with the southeasterly flow from the Pacific Ocean over the Philippines. As was shown in Fig. 2, the rainbelt extends northeastward from the South China Sea to the south of Japan, although the baiu rainband is not yet clearly established. In M6, there is another increase in westerlies at the mouth of the Bay of Bengal and in the southwesterly flow over the South China Sea. The midlatitude westerly component drastically reduces its strength, and from M6 onward, the tropical component becomes dominant in the flow around Japan. From M8 to M10, there appears to be a more easterly component in the trade wind zone. Thus the westerlies in M10 over the South China Sea are weaker than those in M8. The easterly trade winds become stronger and stronger by further mountain uplift in M12 and M14, where even the westerlies over the Bay of Bengal become weaker than in M10. At the same time, the jet core region moves from the Bay of Bengal in M10 to the Somali coast in M14. The African mountains should be responsible for forming the Somali jet. The model mountains in Africa in the control run may still not be enough to create this coastal jet.

As shown in Fig. 6, the East Asian climate is affected by the two tropical circulation systems: the westerly monsoon from the Indian Ocean and the easterly trade wind system. These two wind systems meet around the Philippines. It has been shown that the mountain uplift affects both the intensity of the southwesterly Indian monsoon (e.g., Hahn and Manabe 1975), and the intensity of the North Pacific subtropical anticyclone and associated trade winds (Kitoh 2002). The competition between the two systems can easily be seen in Fig. 7, which shows the longitude versus mountain height cross sections of the 850-hPa zonal wind averaged between 5° and 15°N. The easterlies over the Pacific Ocean intensity and their core shifts westward with mountain uplift. This is related to the intensification of the North Pacific subtropical anticyclone. The South Asian monsoon westerlies at this latitude have a maximum core at 80°E and increase in strength from M0 to M10 with mountain uplift. As shown in Fig. 6, with more mountain uplift, westerlies at 60°E over the Arabian Sea increase.

The zero wind position in Fig. 7, which is situated around 120°E, corresponds to the convergence line. In M0, it is located at 100°E. As shown in Fig. 6, the Indochina Peninsula and the South China Sea are under the influence of the Pacific trade winds. As the South Asian monsoon westerlies become strong with mountain uplift, this convergence line shifts eastward and lies around 120°E for M4–M10. The case of M8 has the zero wind line at the most eastward longitude. In M12 and M14, the influence of the Pacific subtropical anticyclone surpasses that of the southwest monsoon, and the confluence zone shifted backward to the west. Thus this competition between the two systems determines the convergence zone and the resultant precipitation pattern.

To investigate more thoroughly the precipitation change dependence on mountain uplift shown in Fig. 2, the column-integrated moisture flux and its convergence are calculated. Figure 8 compares the June mean moisture flux and its convergence for the no-mountain (M0) run and for the control (M10) run. The moisture flux in the control run roughly follows the 850-hPa wind vector (Fig. 6) as the lower atmosphere contains most of the moisture. Also, regions with large precipitation in Fig. 2 correspond to regions with a large moisture flux convergence.

In M10, at around 10°N, there is an eastward moisture flux over the Indian Ocean and over the South China Sea, and a westward moisture flux over the tropical western Pacific Ocean. They converge over the Philippines and reflect northward and then flow northeastward along the periphery of the Pacific subtropical high. Large moisture flux divergence is found over the tropical Indian Ocean, the Arabian Sea, and the subtropical Pacific Ocean. In M0, the meridional moisture flux component is very weak. In the control run, the meridional component transports moisture from the South China Sea and the Philippine Sea to the rainband over south China, the East China Sea, and Japan. These are not seen in the no-mountain run, and heavy precipitation is limited between the equator and 10°N over the Maritime Continent.

Figure 9 shows the changes of the vertically integrated moisture flux and its convergence between the successive mountain runs. From M0 through M6, there is a continuous strengthening in the intensity of the moisture flux from the Indian Ocean to the western Pacific Ocean along the periphery of the Asian continent, which is associated with an increased cyclonic circulation in the lower troposphere by mountain uplift. Details in the spatial pattern of the difference are, however, different. From M0 to M2, the moisture flux convergence and precipitation increased over the Bay of Bengal, the South China Sea, and the subtropical western Pacific Ocean. The increase is largest from M2 to M4, forming a rainbelt extending from the South China Sea toward Japan (see Fig. 2). The increase from M4 to M6 is more confined from Taiwan to the south of Japan, resulting in a clear baiu rainband in M6. The contributions of the cyclonic circulation changes and the southwesterly moisture flux are evident from M0 through M4, but the contribution from the intensified subtropical Pacific anticyclone becomes important for the M6 case for the baiu rainband region. Therefore, the major contributor to establishing the baiu rainband is the southwesterly moisture flux from the Indian Ocean, with an additional contribution from the moisture flux from the subtropical Pacific Ocean.

Further mountain uplift resulted in qualitatively different changes in the circulation, moisture flux, and precipitation. The meridional (northward) component of the column-integrated moisture flux averaged for the baiu rainbelt region (25°–30°N, 120°–140°E) almost linearly increased from M0 through M6, but remained almost the same from M6 through M14. Changes in the moisture flux convergence and precipitation are relatively small over the western Pacific from M6 to M8, but large changes are seen over China. From M8 through M14, the trade winds become stronger associated with the intensified subtropical anticyclone, and there is a decrease in precipitation over the western Pacific, while the precipitation over the Asian continent increases. In particular, an increase in precipitation over India is large from M10 through M14 mostly due to moisture transported from the Pacific by intensified trade winds.

Therefore, there is a drastic change in the East Asian circulation, moisture flux, and precipitation fields with the threshold value at the 60% level. With the mountain height below 60%, the southwesterly monsoon flow becomes strong by uplift and transports more moisture toward East Asia, forming the baiu rainband. With higher mountain heights, intensified trade winds over the subtropical Pacific transport moisture from the Pacific Ocean into the Asian continent, by reducing precipitation over the western Pacific region. As we are using a coupled GCM in this experiment, the SST is not the same in the course of the mountain uplift. Actually there are large changes in the simulated SST, which should be related to the above changes in moisture flux and precipitation. In the following sections, we investigate how SST is changed by mountain uplift, and also its role in the circulation and precipitation changes by comparing the additional experiment where the SST is kept constant.

4. Effect of SST changes

a. SST changes by mountain uplift

The mountain uplift changes the atmospheric circulations, which thus affect the heat, momentum, and water fluxes at the surface. If the atmospheric GCM is used for such experiments, the SST is among the prescribed boundary conditions and there is no feedback from air–sea interactions. This feedback should be explored by a coupled GCM, as has been done in our experiment. Kitoh (1997, 2002) used an older version of the MRI CGCM (MRI-CGCM1) and found that the Pacific SST, particularly over the subtropical eastern oceans, in the mountain case (control run) is lower than that in the no-mountain case. This SST cooling occurred because the mountain case has a stronger subtropical anticyclone and stronger trade winds, which cause larger evaporation, and also because the mountain case creates more low-level clouds under strong subsidence over the subtropical eastern oceans, which reduces solar radiation reaching the ocean surface. An intensification of the subtropical anticyclone and the associated SST changes due to mountain uplift, are also the case in the present experiment. The changes in the seasonal cycle and the interannual variations of SST are an interesting and important aspect of this experiment, as we use a global coupled GCM where SSTs are allowed to vary according to changes in mountain height. However, full discussions will be given elsewhere, and we restrict ourselves to only describing the mean SST changes in this article.

Figure 10 shows the June mean SST for experiments M0–M14. Over the Indian Ocean, the SST is rather flat in the zonal direction in M0. The zonal gradient in SST over the tropical Indian Ocean increases with mountain uplift from M0 to M14 as the SST decreases along the eastern coast of Africa and increases in the equatorial eastern Indian Ocean. The former is associated with increasing Somali jet strength (Fig. 6). The latter is related with a change from southeasterly winds along Sumatra in M0, which generates upwelling there, to westerly winds along the equator in the high mountain cases, which suppress upwelling and accumulate warm water in the equatorial eastern Indian Ocean.

The SST over the Maritime Continent in the tropical western Pacific reveals a more complicated change with mountain uplift. The western Pacific warm pool SST increases from the lowest value in M0 (29.4°C over 0°–20°N, 120°–150°E) to the highest value in M8 (30.5°C), and then decreases toward M14 (29.4°C). Both the air–sea heat fluxes and ocean dynamical changes are responsible for these SST changes. For the changes in air–sea heat fluxes, those in the latent heat flux and net shortwave radiative flux are dominant, with the former being larger than the latter for this area in our experiment. An investigation of the June mean heat fluxes for the western Pacific warm pool (0°–20°N, 120°–150°E) reveals that both of the components act to reduce the SST there by mountain uplift. The mountain uplift caused stronger trade winds and the associated larger evaporation. The June mean latent heat flux was 128 W m−2 in M0 but increased almost linearly with mountain uplift to 146 W m−2 in M14. Net shortwave radiation flux into the ocean decreased from 241 W m−2 in M0 to 234 W m−2 in M14. Thus, as far as the air–sea heat fluxes are concerned, the mountain uplift would make the western Pacific SST cooler. Therefore, ocean dynamics should be responsible for making the warm pool SST warner from M0 to M8. In M0, the zonal SST gradient in the central and western tropical Pacific is very small. Strengthened trade winds with mountain uplift pile up warm water in the western Pacific SST, resulting in a La Niña-like mean state. This background situation still holds with further mountain uplift from M8 to M14, but at these stages increased evaporation with stronger winds surpasses the dynamical effect and SST decreased from M8 to M14.

b. Precipitable water

The SST in the western Pacific warm pool was changed nonlinearly by mountain uplift, and the maximum SST appeared in the M8 case. This would influence atmospheric moisture content, because a warmer atmosphere can retain more moisture. Figure 11 shows the horizontal distributions of the June mean precipitable water (total moisture content in the atmosphere). The observations [NCEP reanalysis; Kistler et al. (2001)] show the largest moisture content over the Bay of Bengal in June. The area with precipitable water of more than 50 kg m−2 cover the northeastern Indian Ocean, the Indochina Peninsula, and the South China Sea. It extends into southern China, Taiwan, and the Ryukyu Island. Moreover, there appears to be a northeastward intrusion of a moist tongue to the south of Japan. There is a dry region around 20°N over the subtropical western Pacific, which is under the influence of the subtropical anticyclone. These overall features are well reproduced in the control run (M10).

Generally, the model experiments show northward movement of the most humid area by mountain uplift. The no-mountain case (M0) has its maximum precipitable water near the equator in June. The moisture content to the south of Japan is only about 30 kg m−2. In the continental interiors, the moisture content is just below 20 kg m−2 and is not so dry, and an east–west contrast is rather small. With mountain uplift, the region with the maximum precipitable water shifts northward, and from the 60% case (M6), it is located over the Bay of Bengal, which is consistent with the observations. At the same time, the moisture content over and to the northwest of the Tibetan Plateau monotonically decreases. In East Asia, the precipitable water increases drastically from M0 to M6. It is noted that the M6 case shows a distinct “moist tongue” extending from the Philippines to the south of Japan. The moist tongue is one of the characteristic features of the baiu.

The subtropical anticyclone monotonically intensifies from M0 to M14, and so does the zonal pressure gradient around 130°E. A relatively dry area intrudes westward at 20°N over the Pacific as the subtropical anticyclone intensifies. It is also noted that the humid area with precipitable water greater than 55 kg m−2 in M10 shrinks considerably compared to that in M8. This decrease in precipitable water over the western Pacific continues in the further mountain uplift of M12 and M14. These changes in precipitable water are consistent with the SST changes wehre the western Pacific warm pool SST is a maximum in the M8 case.

5. Comparison between AGCM and CGCM experiments

In the previous section, we showed that the SST has also changed due to mountain uplift so that the higher mountain cases have more warm SST anomalies over the western Pacific and the eastern Indian Ocean, and more cold SST anomalies over the subtropical Pacific and the western Indian Ocean than the lower mountain cases. Therefore, the changes in the precipitation and circulation discussed so far implicitly include the effects of these SST changes besides the dynamical effects of mountain uplift. In order to investigate how the SST change has affected our results, the results obtained by the CGCM are compared with those of additional experiments from the AGCM. Note that in the AGCM experiments the SSTs are the same for all of the mountain runs (A0–A14), although the changes in orography are exactly the same as in the CGCM runs (M0–M14).

Figures 12a and 12b show the June precipitation difference between the control (100%) run and the no-mountain (0%) run of the CGCM and AGCM, respectively. The AGCM experiment shows an increase in precipitation to the southeast of the Tibetan Plateau, that is, over the Bay of Bengal, the South China Sea, the Philippine Sea, the area south of Japan, and mainland China in the control run (A10) compared to the no-mountain run (A0). The general pattern of precipitation change by mountain uplift is similar between the AGCM and CGCM experiments, including an increase in precipitation over the South Asian land areas and an appearance of the baiu rainband to the south of Japan by mountain uplift. However, there are different aspects in the response. The difference map (Fig. 12c) shows that the CGCM obtained larger precipitation increases between 15° and 30°N compared to those of the AGCM. Therefore the precipitation increase over the baiu region is more pronounced by the mountain uplift in the CGCM (Fig. 12a) than in the AGCM (Fig. 12b). On the other hand, the precipitation increase over the South China Sea and the Philippine Sea seen in the AGCM experiment is not found in the CGCM. The difference map (Fig. 12c) shows a broad negative area between the equator and 15°N. These differences between the CGCM and AGCM experiments mainly come from the SST difference (Fig. 13), which only exists in the CGCM experiment. In other words, the AGCM result (Fig. 12a) is a sole dynamical effect of mountain uplift while the difference (Fig. 12c) is an additional effect caused by SST changes. As discussed in the previous section, the mountain uplift caused a positive SST anomaly over the western tropical Pacific and the Maritime Continent, while the SST decreased over the central subtropical Pacific and the western Indian Ocean.

Figures 14 and 15 show the differences in the precipitable water and the total moisture flux and its convergence between the control run and the no-mountain run by CGCM and AGCM, respectively. Although the AGCM also simulated an increase in precipitable water over East Asia by mountain uplift, it is substantially lower compared to that obtained by the CGCM experiment. The difference map (Fig. 14c) roughly follows the SST difference by mountain uplift in CGCM (Fig. 13). As warmer air can contain more water vapor, it is plausible that the SST change by mountain uplift produced an additional precipitable water increase (Fig. 14c) in the CGCM, resulting in a larger precipitable water increase in the CGCM than that in the AGCM where no SST change effect is included. Therefore, in this case, use of the CGCM has induced large sensitivity by mountain uplift manifested by precipitation changes, which is not obtained by using the AGCM.

Over the Indian Ocean, the CGCM shows a larger westerly moisture flux than the AGCM. The AGCM experiment shows an intensified southwesterly monsoon flow over the northern Indian Ocean. In the coupled system, this westerly flow induces the eastern Indian Ocean SST to be warmer and the western Indian Ocean SST cooler (Fig. 10). The resultant zonal SST gradient with the eastern ocean being warmer than the western ocean would give rise to an eastward shift of convections and feed back to more westerly flow near the surface. This positive feedback exists only in the CGCM, and is also working in the western Pacific. The anticyclonic circulation anomaly by mountain uplift is simulated in the AGCM, but is stronger in the CGCM. The lowered SST over the subtropical Pacific in M10 can explain a stronger subtropical anticyclone and trade winds in the CGCM experiment than in the AGCM experiment. The anticyclonic circulation difference between the CGCM and the AGCM (Fig. 15c) is located to the northwest of the negative precipitation difference (Fig. 12c). The region from Taiwan to the Ryukyu Islands is located to the northwest of this anticyclonic circulation and the southwesterly moisture flux contributes to create distinct baiu rainbands there in the CGCM mountain uplift experiment. This mechanism is similar to what Wang et al. (2000, 2003) proposed for the impacts of atmosphere–warm ocean interaction on the interannual variability of the ENSO–monsoon system.

Finally, Fig. 16 shows the changes of selected June precipitation indices by progressive mountain uplift in the CGCM and AGCM. Three rainfall indices are plotted: IMR is defined as the precipitation averaged over the land grid points for 10°–30°N, 60°–100°E, corresponding to the all-Indian monsoon rainfall; SEAM is the precipitation over Southeast Asia (5°–25°N, 100°–130°E); and EAM is the precipitation in East Asia including the baiu are (25°–35°N, 120°–140°E). The summertime precipitation in the last two regions is negatively correlated in the present-day observations (Nitta 1987).

The Indian rainfall index (IMR) almost linearly increases with progressive mountain uplift in both of the experiments, but the sensitivity in the AGCM is lower than that in the CGCM. The IMR in the AGCM changed from 2.4 (A0) to 4.7 mm day−1 (A14), while the IMR in the CGCM changed from 0.9 (M0) to 5.8 day−1 mm (M14). The larger increase by mountain uplift of the atmospheric moisture content over South Asia, which is due to SST changes, may explain this difference in sensitivity between the two models. The SEAM, which is a June mean rainfall is Southeast Asia covering Indochina, the South China Sea, and the Philippines, changed very differently between the AGCM and CGCM. The SEAM in the AGCM almost monotonically increased from 5.7 (A0) to 9.8 mm day−1 (A14). On the other hand, the SEAM in the CGCM increased from M0 (3.5 mm day−1) to M8 (8.7 mm day−1), and then decreases to M14 (6.8 mm day−1). This is related to an SST decrease over Southeast Asia in the higher mountain cases in the CGCM. The baiu rainfall, here defined as EAM, shows a nearly identical from the no-mountain case to the 60% case in the AGCM and CGCM. However, the EAM in the AGCM is nearly unchanged with further mountain uplift, while the EAM in the CGCM increases further. As was shown in Fig. 9 in section 3b, the increase in precipitation over the baiu region from M0 (A0) to M6 (A6) is mainly due to the strengthening southwesterly moisture flux from the Indian Ocean that flows around the uplifting Tibetan Plateau. This mechanism worked similarly both in the AGCM and the CGCM. The effect of the SST changes with warmer SST in the EAM region and cooler SST to the south only worked in the CGCM, where this meridional SST gradient helps to further increase precipitable water and precipitation over the baiu region. Therefore, the sensitivity of the mountain uplift found in the AGCM experiment could be quite different from in the CGCM experiment where the effect of changing the SST can be included.

6. Concluding remarks

The effects of progressive mountain uplift on East Asian climate were investigated by the MRI coupled GCM (MRI-CGCM2) without flux adjustments, and compared with similar experiments done using an AGCM. We used eight different mountain heights: 0% (no mountain), 20%, 40%, 60%, 80%, 100% (control run), and enhanced at 120% and 140%. The land–sea distribution was the same for all experiments and the mountain heights are varied uniformly over the entire globe.

The CGCM experiment showed that there were systematic changes in the precipitation and circulation fields as well as in SST with progressive mountain uplift. The southerly winds along a periphery of the Pacific subtropical anticyclone and the southwesterly winds from the Indian Ocean are the two dominant factors affecting the summertime East Asian climate. These two compensating effects determine the subdivisions of Asian–Pacific monsoons. The precipitation area moved inland with mountain uplift, while the Pacific subtropical anticyclone and the associated trade winds became stronger with mountain height.

Ninomiya et al. (2002) indicated that a proper simulation of the large-scale circulation systems is needed for a realistic mei-yu/baiu simulation. The model used in our experiment (MRI-CGCM2) reproduces a reasonable baiu rainband when incorporating a flux adjustment (Rajendran et al. 2004). We showed that this model successfully reproduced the large-scale circulation systems as well as the baiu rainband without flux adjustment (M10 run). It was also shown that there appears to be no baiu rainband in the no-mountain case, while the baiu rainband attains features similar to the observations at the 60% mountain height where a wet southwesterly moisture tongue is reproduced, and the baiu rainband became stronger and sharper with further mountain height increases. Thus orography is indeed essential for creating the baiu rainband. A northward movement of the convection center due to the Tibetan Plateau uplift may be important.

The SST also changed by progressive mountain uplift. In the no-mountain case, the local SST maximum is located over the central equatorial Pacific, while it has shifted toward the Maritime Continent with the high mountain cases. This is clearly associated with changes in the intensity of the subtropical anticyclone and trade winds. Thus, the formation of the western Pacific warm pool is controlled by mountain uplift. In northern summer, the western Pacific warm pool SST reaches a maximum in the 80% case due to compensating effects of the ocean dynamics and evaporative heat loss. Precipitable water amount, therefore, reaches its maximum in the 80% case.

The comparison between the CGCM and AGCM experiments revealed the effects of these changing SSTs on the sensitivity of mountain uplift. It was found that both the Indian rainfall and the baiu rainfall in the CGCM are more sensitive than are those in the AGCM. By definition, the circulation changes with mountain uplift in the AGCM experiments occur with fixed SST distribution. On the other hand, those in the CGCM experiments can occur more freely because SST and ocean circulation can change, too. In the CGCM, the mountain uplift resulted in an SST increase over the western tropical Pacific and the Maritime Continent and an SST decrease over the western Indian Ocean and the central subtropical Pacific. Anticyclonic circulation anomalies in the lower troposphere, which appeared to be caused by mountain uplift in the tropical western Pacific in the CGCM, thus feed more moisture over East Asia, which results in a stronger baiu rainband in the CGCM than in the AGCM. This ocean–monsoon interaction mechanism also works in the present climate and constitutes an important part of the monsoon interannual variability (Wang et al. 2003).

It was also shown that, in the CGCM, the extent of the monsoon westerly flow is regulated by a competition between the Pacific subtropical anticyclone and the southwest monsoon, and the confluence zone is shifted backward to the west by further mountain uplift. This is not seen in the AGCM experiments where the convergence zone is always located near the Philippines irrespective of the mountain height, which is regulated by the prescribed SST distribution (not shown). Thus this competition between the two systems is also closely related with whether SST can change or not. Because air–sea coupling and feedback are inherent within the climate system, our experiments show the importance of properly reproducing air–sea coupling in the model and the need for using coupled models in such sensitivity studies.

In this paper, we have restricted ourselves to showing the mean fields, but the model results also show the changes in interannual variability including the El Niño–Southern Oscillation (ENSO). The model ENSO was the strongest and had its most power in the lower-frequency range in the M0 case. The ENSO characteristics systematically changed with mountain uplift toward less power and higher-frequency ranges (Kitoh 2003). A full discussion of this result will be reported on elsewhere. Our results clearly demonstrate the importance of coupled systems in climate change and, thus, encourages the use of more coupled models, even in classical studies.

Acknowledgments

The author would like to thank K. Rajendran of MRI for useful comments on the manuscript. The perceptive comments of the two anonymous reviewers are greatly acknowledged.

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Fig. 1.
Fig. 1.

Time–latitude sections of the climatological pentad-mean precipitation averaged for 120°–140°E for the observations, and the M0 (no orography run), M2 (20%), M4 (40%), M6 (60%), M8 (80%), M10 (control run), M12 (120%), and M14 (140%) runs. The observations are the 23-yr averages for 1979–2001 from Xie and Arkin (1997)

Citation: Journal of Climate 17, 4; 10.1175/1520-0442(2004)017<0783:EOMUOE>2.0.CO;2

Fig. 2.
Fig. 2.

Horizontal distributions of the Jun mean precipitation for the observations and for all experiments (M0, M2, M4, M6, M8, M10, M12, and M14)

Citation: Journal of Climate 17, 4; 10.1175/1520-0442(2004)017<0783:EOMUOE>2.0.CO;2

Fig. 3.
Fig. 3.

Latitude–height cross sections of Jun climatology at 130°E for M10 (control run): (a) zonal wind, (b) meridional wind, (c) vertical p velocity, and (d) equivalent potential temperature

Citation: Journal of Climate 17, 4; 10.1175/1520-0442(2004)017<0783:EOMUOE>2.0.CO;2

Fig. 4.
Fig. 4.

Taylor-style diagram for the Jun mean precipitation in the region 10°S–50°N, 30°E–180°. CMAP is the observed reference data (Xie and Arkin 1997), and 0, 2, 4, 6, 8, 10, 12, and 14 show the pattern correlation between M0–M14 and the CMAP data and the normalized standard deviation. GPCP shows the observed Jun mean climatology based on GPCP (Huffman et al. 1997). See Taylor (2001) for diagram details

Citation: Journal of Climate 17, 4; 10.1175/1520-0442(2004)017<0783:EOMUOE>2.0.CO;2

Fig. 5.
Fig. 5.

Horizontal distributions of the Jun mean sea level pressure for the observations and for all experiments. The contour interval is 4 hPa. Values greater than 1020 hPa and those less than 1000 hPa are shaded dark and light, respectively. For clarification in the plotting, the global mean sea-level pressure values are adjusted in M0, M2, M4, M6, M8, M12, and M14 to be equal to that in M10. The adjustment value is approximately 5 hPa for one-step mountain uplift

Citation: Journal of Climate 17, 4; 10.1175/1520-0442(2004)017<0783:EOMUOE>2.0.CO;2

Fig. 6.
Fig. 6.

Horizontal distributions of the Jun mean wind fields at 850 hPa for the observations and for all experiments. Wind magnitudes larger than 6, 10, and 14 m s−1 are shaded with increasing darkness

Citation: Journal of Climate 17, 4; 10.1175/1520-0442(2004)017<0783:EOMUOE>2.0.CO;2

Fig. 7.
Fig. 7.

Longitude vs mountain height cross sections of the Jun mean zonal wind at 850 hPa averaged for 5°–15°N

Citation: Journal of Climate 17, 4; 10.1175/1520-0442(2004)017<0783:EOMUOE>2.0.CO;2

Fig. 8.
Fig. 8.

Horizontal distributions of the Jun mean total (vertically integrated) moisture flux and its convergence for M0 and M10. The moisture convergence area is shaded

Citation: Journal of Climate 17, 4; 10.1175/1520-0442(2004)017<0783:EOMUOE>2.0.CO;2

Fig. 9.
Fig. 9.

Differences in the Jun mean total (vertically integrated) moisture flux and its convergence between each experiment

Citation: Journal of Climate 17, 4; 10.1175/1520-0442(2004)017<0783:EOMUOE>2.0.CO;2

Fig. 10.
Fig. 10.

Horizontal distributions of the Jun mean SST for the observations and for all experiments

Citation: Journal of Climate 17, 4; 10.1175/1520-0442(2004)017<0783:EOMUOE>2.0.CO;2

Fig. 11.
Fig. 11.

Horizontal distributions of the Jun mean precipitable water for the observations and for all experiments

Citation: Journal of Climate 17, 4; 10.1175/1520-0442(2004)017<0783:EOMUOE>2.0.CO;2

Fig. 12.
Fig. 12.

(a) The precipitation difference in Jun between the control run (M10) and the no-mountain run (M0) using the coupled GCM. (b) As in (a) except using the atmospheric GCM. (c) Difference between (a) and (b)

Citation: Journal of Climate 17, 4; 10.1175/1520-0442(2004)017<0783:EOMUOE>2.0.CO;2

Fig. 13.
Fig. 13.

The SST difference in Jun between the control run (M10) and the no-mountain run (M0)

Citation: Journal of Climate 17, 4; 10.1175/1520-0442(2004)017<0783:EOMUOE>2.0.CO;2

Fig. 14.
Fig. 14.

(a) The precipitable water difference in Jun between the control run (M10) and the no-mountain run (M0) using the coupled GCM. (b) As in (a) except using the atmospheric GCM. (c) Difference between (a) and (b)

Citation: Journal of Climate 17, 4; 10.1175/1520-0442(2004)017<0783:EOMUOE>2.0.CO;2

Fig. 15.
Fig. 15.

(a) The difference in moisture and its convergence in Jun between the control run (M10) and the no-mountain run (M0) using the coupled GCM. (b) As in (a) except using the atmospheric GCM. (c) Difference between (a) and (b)

Citation: Journal of Climate 17, 4; 10.1175/1520-0442(2004)017<0783:EOMUOE>2.0.CO;2

Fig. 16.
Fig. 16.

Jun precipitation indices: (a) IMR, land precipitation for 10°–30°N, 60°–100°E; (b) SEAM, precipitation over Southeast Asia (5°–25°N, 100°–130°E), and (c) precipitation over East Asia (25°–35°N, 120°–140°E). Solid lines denote the CGCM, and dashed lines denote the AGCM

Citation: Journal of Climate 17, 4; 10.1175/1520-0442(2004)017<0783:EOMUOE>2.0.CO;2

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  • Fig. 1.

    Time–latitude sections of the climatological pentad-mean precipitation averaged for 120°–140°E for the observations, and the M0 (no orography run), M2 (20%), M4 (40%), M6 (60%), M8 (80%), M10 (control run), M12 (120%), and M14 (140%) runs. The observations are the 23-yr averages for 1979–2001 from Xie and Arkin (1997)

  • Fig. 2.

    Horizontal distributions of the Jun mean precipitation for the observations and for all experiments (M0, M2, M4, M6, M8, M10, M12, and M14)

  • Fig. 3.

    Latitude–height cross sections of Jun climatology at 130°E for M10 (control run): (a) zonal wind, (b) meridional wind, (c) vertical p velocity, and (d) equivalent potential temperature

  • Fig. 4.

    Taylor-style diagram for the Jun mean precipitation in the region 10°S–50°N, 30°E–180°. CMAP is the observed reference data (Xie and Arkin 1997), and 0, 2, 4, 6, 8, 10, 12, and 14 show the pattern correlation between M0–M14 and the CMAP data and the normalized standard deviation. GPCP shows the observed Jun mean climatology based on GPCP (Huffman et al. 1997). See Taylor (2001) for diagram details

  • Fig. 5.

    Horizontal distributions of the Jun mean sea level pressure for the observations and for all experiments. The contour interval is 4 hPa. Values greater than 1020 hPa and those less than 1000 hPa are shaded dark and light, respectively. For clarification in the plotting, the global mean sea-level pressure values are adjusted in M0, M2, M4, M6, M8, M12, and M14 to be equal to that in M10. The adjustment value is approximately 5 hPa for one-step mountain uplift

  • Fig. 6.

    Horizontal distributions of the Jun mean wind fields at 850 hPa for the observations and for all experiments. Wind magnitudes larger than 6, 10, and 14 m s−1 are shaded with increasing darkness

  • Fig. 7.

    Longitude vs mountain height cross sections of the Jun mean zonal wind at 850 hPa averaged for 5°–15°N

  • Fig. 8.

    Horizontal distributions of the Jun mean total (vertically integrated) moisture flux and its convergence for M0 and M10. The moisture convergence area is shaded

  • Fig. 9.

    Differences in the Jun mean total (vertically integrated) moisture flux and its convergence between each experiment

  • Fig. 10.

    Horizontal distributions of the Jun mean SST for the observations and for all experiments

  • Fig. 11.

    Horizontal distributions of the Jun mean precipitable water for the observations and for all experiments

  • Fig. 12.

    (a) The precipitation difference in Jun between the control run (M10) and the no-mountain run (M0) using the coupled GCM. (b) As in (a) except using the atmospheric GCM. (c) Difference between (a) and (b)

  • Fig. 13.

    The SST difference in Jun between the control run (M10) and the no-mountain run (M0)

  • Fig. 14.

    (a) The precipitable water difference in Jun between the control run (M10) and the no-mountain run (M0) using the coupled GCM. (b) As in (a) except using the atmospheric GCM. (c) Difference between (a) and (b)

  • Fig. 15.

    (a) The difference in moisture and its convergence in Jun between the control run (M10) and the no-mountain run (M0) using the coupled GCM. (b) As in (a) except using the atmospheric GCM. (c) Difference between (a) and (b)

  • Fig. 16.

    Jun precipitation indices: (a) IMR, land precipitation for 10°–30°N, 60°–100°E; (b) SEAM, precipitation over Southeast Asia (5°–25°N, 100°–130°E), and (c) precipitation over East Asia (25°–35°N, 120°–140°E). Solid lines denote the CGCM, and dashed lines denote the AGCM

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