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    June climatologies of surface rainfall (color shade in mm day−1) based on (a) CMAP (1979–2009) and (b) TRMM 3B43 products (1998–2010), (c) the observed SST (color shade and white contours in °C) from AVHRR and AMSR overlapped with the wind speed (black contours at 6, 7, and 8 m s−1) at 925 hPa based on NCEP FNL during 2000–10, and (d) interannual variation of June rainfall (mm day−1) averaged over south China (20°–28°N, 110°–120°E) during 1998–2009 based on TRMM 3B43 product. The horizontal red and dashed lines show the mean value and one standard deviation, respectively, showing the two wettest years for 2005 and 2008. Black contours in (a) and (b) indicate the orography at 250, 500, 750, 1000, 2000, 3000, and 4000 m. Blue dots in (a) mark the location of the Annam Cordillera.

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    SST anomaly (color shade in °C) in June (a) 2002 and (b) 2005. The black line indicates the contour of 0°C.

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    (a) Model domain (5°S–42.8°N, 85°–140°E) and the model orography (gray shade and black contours in m), as well as the observed monthly mean SST (color shade and white contours in °C) and the observed wind vectors (m s−1) at 925 hPa in June 2005. Green rectangle shows the boundary between the model interior and the buffer zone of 5°. Blue box marks the region where the precipitation is averaged in Tables 1 and 2. (b) The model topography (gray shade and black contours in m) in the NoTop and NTSmSST simulations with the monthly mean anomalous SST (°C) over the western SCS added to the NTSmSST simulation in June 2005 shown in red contours at 0.5°, 0.75°, and 1.0°C.

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    The monthly mean rainfall rate (color shade and white contours in mm day−1) and winds (m s−1) at 850 hPa from (a) observations (TRMM 3B43 products from rainfall rate and NCEP FNL analysis for winds) and simulations from (b) CTL and (c) NTSmSST for June 2005. Also shown are the differences in rainfall (d) between CTL and observation (CTL–OBS; color shade in mm day−1) and (e) between CTL and NTSmSST (CTL–NTSmSST; blue contours at −12, −10, −8, −6, −4, and −2 mm day−1; red contours at 2, 4, 6, 8, 10 and 12 mm day−1), as well as (f) the difference between CTL and NTSmSST in percentage relative to the CTL simulation (color shade in %, contours at −15% and 15%). Orange (blue) shadings in (e) indicate regions with the positive (negative) rainfall difference in NTSmSST from CTL reaching the 95% significance level based on the two-sided Student’s t test. The black line segment AB in (e) indicates the axis for vertical cross sections displayed in Figs. 6 and 8.

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    Differences in the monthly mean rainfall rate (blue contours at −12, −10, −8, −6, −4, and −2 mm day−1; red contours at 2, 4, 6, 8, 10, and 12 mm day−1) between (a) the CTL and NoTop simulations (CTL–NoTop) and (b) the NoTop and NTSmSST simulations (NoTop–NTSmSST) for June 2005. Orange (blue) shadings mark regions where the positive (negative) differences reach the 95% significance level based on the two-sided Student’s t test.

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    Vertical cross section along line segment AB shown in Fig. 4e for the monthly mean specific humidity (Qv; black contours in g kg−1) and cloud liquid water mixing ratio (Qc; color shade and white contours in 10−2 g kg−1) for June 2005 simulated in (a) CTL and (b) NoTop and for the differences in (c) the monthly mean specific humidity (contours in g kg−1) and (d) the monthly mean vertical p velocity (contours in −1.0 × 10−2 hPa s−1) between the CTL and NoTop simulations (CTL–NoTop). Black shading marks the model topography. The orange (blue) shade in (c) and (d) indicates regions where the positive (negative) difference reaches the 95% significance level based on the two-sided Student’s t test.

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    Difference in the monthly mean surface latent heat flux and sensible heat flux (contours at −10, −5, −3, 3, 5, and 10 W m−2) between (a),(c) the CTL and NoTop simulations (CTL–NoTop) and between (b),(d) the NoTop and NTSmSST simulations (NoTop–NTSmSST) for June 2005. Orange (blue) shading marks regions where the positive (negative) difference reaches the 95% significance level based on the two-sided Student’s t test.

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    Vertical cross sections along the line segment AB shown in Fig. 4e for the differences in (a) the apparent heating Q1 (contours at 0.5 K h−1 intervals) and (b) the apparent moistening Q2 (contours at 0.3 × 10−3 K h−1 intervals) between the CTL and NoTop simulations (CTL–NoTop) for June 2005. Black shading marks the orography and the orange (blue) shading indicates regions where the positive (negative) difference reaches the 95% significance level based on the two-sided Student’s t test.

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    Differences in the monthly mean geopotential height (contours at 1-gpm intervals) and wind vectors (m s−1) at 200, 500, and 850 hPa (a) between the CTL and NoTop simulations (CTL–NoTop) and (b) between the NoTop and NTSmSST simulations (NoTop–NTSmSST) for June 2005. Only the wind differences reach the 95% confidence level are shown.

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    Differences in the column-integrated water vapor flux (900–300 hPa; kg m−1 s−1) and the associated flux divergence (contours at −3 × 10−5 and 3 × 10−5 kg m−2 s−1) between (a),(c) the CTL and NoTop simulations (CTL–NoTop) and between (b),(d) the NoTop and NTSmSST simulations (NoTop–NTSmSST) for June 2005. Only the flux differences reach the 95% significance level are shown in (a) and (b) and the orange (blue) shading in (c) and (d) marks regions where the positive (negative) differences reach the 95% significance level based on the two-sided Student’s t test.

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    (a) Difference in monthly mean rainfall between CTL and NTSmSST for June 2002 (CTL–NTSmSST; blue contours at −12, −10, −8, −6, −4, and −2 mm day−1; red contours at 2, 4, 6, 8, 10, and 12 mm day−1) and (b) difference in percentage relative to the CTL simulation (color shade in percentage; contours at −15% and 15%). Orange (blue) shadings in (a) indicate regions with the positive (negative) differences reaching the 95% significance level based on the two-sided Student’s t test.

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The Effect of Mesoscale Mountain over the East Indochina Peninsula on Downstream Summer Rainfall over East Asia

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  • 1 Key Laboratory of Meteorological Disaster of Ministry of Education, and College of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing, China, and International Pacific Research Center, and Department of Meteorology, University of Hawaii at Manoa, Honolulu, Hawaii
  • | 2 International Pacific Research Center, and Department of Meteorology, University of Hawaii at Manoa, Honolulu, Hawaii, and Key Laboratory of Meteorological Disaster of Ministry of Education, and College of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing, China
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Abstract

The mesoscale mountain over the east Indochina Peninsula, named Annam Cordillera, plays a key role in shaping the South China Sea (SCS) summer climate in both the atmosphere and the ocean. However, its effect is not limited to the SCS. Ensemble simulations using a high-resolution regional atmospheric model with or without the mountain reveals that the Annam Cordillera has a significant impact on regional climate as far as 3000 km over south and east China, and western Northwest Pacific (WNP).

By blocking/lifting the warm and moist air from the Bay of Bengal, the Annam Cordillera forces upward motion and precipitation on the windward side and subsidence on the leeward side, and a low-level southwesterly jet to the southeast tip of the Indochina Peninsula over the SCS. The latter gives rise to coastal upwelling and cold sea surface temperature (SST) filaments in the western SCS, reducing surface sensible and latent heat fluxes and thus suppressing convection over the SCS. Heating associated with the orographic rainfall forces a low-level anomalous easterly over the SCS and an anomalous cyclone and anticyclone in the midlower troposphere to the south and north, respectively. The anomalous circulation modifies the low-level moisture transport, reducing rainfall over the SCS and to the east of Taiwan Island over the WNP, while increasing rainfall as much as 15%–30% in a southwest–northeast-oriented belt extending from south China to the East China Sea. The cold SST filaments in the western SCS enhance the orographically induced circulation; however, its effect accounts for less than 50% of the direct effect of the orographic lifting/blocking.

Corresponding author address: Dr. Yuqing Wang, IPRC/SOEST, University of Hawaii at Manoa, Room POST 409G, 1680 East–West Road, Honolulu, HI 96822. E-mail: yuqing@hawaii.edu

Abstract

The mesoscale mountain over the east Indochina Peninsula, named Annam Cordillera, plays a key role in shaping the South China Sea (SCS) summer climate in both the atmosphere and the ocean. However, its effect is not limited to the SCS. Ensemble simulations using a high-resolution regional atmospheric model with or without the mountain reveals that the Annam Cordillera has a significant impact on regional climate as far as 3000 km over south and east China, and western Northwest Pacific (WNP).

By blocking/lifting the warm and moist air from the Bay of Bengal, the Annam Cordillera forces upward motion and precipitation on the windward side and subsidence on the leeward side, and a low-level southwesterly jet to the southeast tip of the Indochina Peninsula over the SCS. The latter gives rise to coastal upwelling and cold sea surface temperature (SST) filaments in the western SCS, reducing surface sensible and latent heat fluxes and thus suppressing convection over the SCS. Heating associated with the orographic rainfall forces a low-level anomalous easterly over the SCS and an anomalous cyclone and anticyclone in the midlower troposphere to the south and north, respectively. The anomalous circulation modifies the low-level moisture transport, reducing rainfall over the SCS and to the east of Taiwan Island over the WNP, while increasing rainfall as much as 15%–30% in a southwest–northeast-oriented belt extending from south China to the East China Sea. The cold SST filaments in the western SCS enhance the orographically induced circulation; however, its effect accounts for less than 50% of the direct effect of the orographic lifting/blocking.

Corresponding author address: Dr. Yuqing Wang, IPRC/SOEST, University of Hawaii at Manoa, Room POST 409G, 1680 East–West Road, Honolulu, HI 96822. E-mail: yuqing@hawaii.edu

1. Introduction

It is well known that large-scale mountains, such as the Tibetan Plateau (TP), the Rockies, and the Andes, play essential roles in shaping the global climate system through both mechanical and thermodynamic effects (e.g., Manabe and Terpstra 1974; Hoskins and Karoly 1981; Chen and Trenberth 1988a,b; Kitoh 1997, 2002, 2010; Abe et al. 2004; Song et al. 2010). In sharp contrast, it was generally considered in earlier studies that a mesoscale mountain imposes local and regional effects not far from the mountain. Such well-documented effects are the orographic lifting and windward rainfall and leeward rain shadow associated with many mesoscale mountains in different regions of the world (e.g., Grossman and Durran 1984; Ogura and Yoshizaki 1988; Jou 1994; Colle and Mass 1996; Doyle 1997; Ralph et al. 2003; Chen and Li 2005; Chien and Kuo 2006).

It was not until Xie et al. (2001) and Xie et al. (2006) that we have realized that, in addition to the local effect, mesoscale topography may have a significant effect on climate at a spatial scale much larger than previously believed. Xie et al. (2001) revealed a fingerprint of the effect from the Hawaiian Islands about 8000 km downstream in the central–western Pacific. The orographic effect of these islands could be limited to about 500 km based on earlier theoretical studies if only the dynamical terrain effect is considered. Xie et al. (2001) then hypothesized that it is the air–sea interaction under a stable trade wind regime that extends the terrain effect downstream greatly. In another study, Xie et al. (2006) demonstrated that the collective heating associated with the terrain-forced convection from a group of mesoscale mountains over South Asia contributes to the world’s largest Asian summer monsoon system through the Rossby wave response to the anomalous convective heating. Furthermore, the mesoscale topography over the TP could excite mesoscale convective systems over the TP, and those convective systems usually propagate eastward to enhance convection and precipitation in the Yangtze River basin, namely, over 3000 km downstream as demonstrated by Shi et al. (2008).

Despite the above new insights into the large-scale effects of mesoscale mountains over South Asia as demonstrated in Xie et al. (2006), it is not clear whether individual mountains over South Asia and Southeast Asia may have any large-scale or remote effect, because Xie et al. (2006) emphasized the large-scale circulation response to collective heating due to orography-induced convection over South Asia. In this study, we choose the Annam Cordillera over the east Indochina Peninsula as an example to demonstrate whether, how, and to what degree a mesoscale mountain may have a remote effect downstream in the East Asian summer monsoon system. It is our hypothesis that heating associated with orographic convection can induce a local circulation anomaly, which in turn can lead to circulation anomalies beyond the region directly affected by the local terrain. The circulation anomalies can affect moisture transport at a larger scale, inducing a remote effect on downstream precipitation.

The Annam Cordillera over the east Indochina Peninsula is selected in this study because of its mesoscale feature and its significant local effect as well as its role in shaping the South China Sea (SCS) regional climate in both the atmosphere and the ocean (Xie et al. 2003; Xu et al. 2008). Under the control of the prevailing southwesterly monsoon in summer, the Annam Cordillera forces ascending motion on the windward side and descending motion on the leeward side, giving rise to rain belt and rain shadow, respectively (Xu et al. 2008). As shown in Fig. 1a for the 31-yr rainfall climatology obtained from the Climate Prediction Center (CPC)’s merged analysis of precipitation (CMAP) dataset (Xie and Arkin 1996), a relatively dry region with local rainfall minima is located on the leeward side of the Annam Cordillera over the western SCS. The rain belt on the windward side of the Annam Cordillera can be easily seen from the high-resolution satellite rainfall product [Tropical Rainfall Measuring Mission (TRMM 3B43); Adler et al. 2000] as shown in Fig. 1b. Moreover, on the southern tip of the mountain range, the southwesterly flow is accelerated to form an offshore low-level wind jet (Fig. 1c), causing coastal upwelling off the east coast of Vietnam in the western SCS (Xie et al. 2003). The wind-induced upwelling leads to the cold sea surface temperature (SST) filaments in the western coastal region over the SCS (Fig. 1c), which in turn suppresses convection over the western SCS (Xu et al. 2008). Both the direct mountain-induced rainfall and rain shadow and the indirect effect through the coastal cold SST filaments contribute to the anomalous atmospheric heating and induce an atmospheric circulation response at a larger horizontal scale, thus affecting precipitation in regions beyond the local scale. This effect may be significant over south and east China because these regions are located downstream of the southwesterly summer monsoon flow, much the same as the case of the effect resulting from the deforestation over the Indochina Peninsula studied by Sen et al. (2004).

Fig. 1.
Fig. 1.

June climatologies of surface rainfall (color shade in mm day−1) based on (a) CMAP (1979–2009) and (b) TRMM 3B43 products (1998–2010), (c) the observed SST (color shade and white contours in °C) from AVHRR and AMSR overlapped with the wind speed (black contours at 6, 7, and 8 m s−1) at 925 hPa based on NCEP FNL during 2000–10, and (d) interannual variation of June rainfall (mm day−1) averaged over south China (20°–28°N, 110°–120°E) during 1998–2009 based on TRMM 3B43 product. The horizontal red and dashed lines show the mean value and one standard deviation, respectively, showing the two wettest years for 2005 and 2008. Black contours in (a) and (b) indicate the orography at 250, 500, 750, 1000, 2000, 3000, and 4000 m. Blue dots in (a) mark the location of the Annam Cordillera.

Citation: Journal of Climate 25, 13; 10.1175/JCLI-D-11-00574.1

We are interested in the East Asia monsoon region; thus, the use of a high-resolution regional atmospheric model, which covers the whole of East Asia, is well justified in this study. We choose the case of June in this study because it is the month when the monsoon rain belt remains over south China. Figure 1d shows the interannual variation of the area-averaged rainfall in June during 1998–2009 over south China (20°–28°N, 110°–120°E) based on the TRMM 3B43 precipitation product. During this 12-yr period, both 2005 and 2008 are the two wettest years with the June rainfall anomalies larger than one standard deviation over south China. The wettest June (2008) over south China was mainly a result of the heavy rainfall induced by Typhoon Fengsheng (Zhou 2010). The second-wettest June (2005) over south China was induced by the anomalous early summer monsoon rainfall with severe flooding events (Souma and Wang 2009). Since our primary interest is to examine the possible impact of the Annam Cordillera on the anomalous summer monsoon rainfall in June over East Asia, the wet year of 2005 is selected in this study.

In order for a comparison with the second-wettest June (2005), June 2002 with the rainfall amount slightly below climatological mean (Fig. 1d) is also studied. The large-scale SST condition over tropical oceans in June 2002 differs greatly from that in June 2005 (Fig. 2). In June 2002, SST shows warm anomalies as high as 1°C over the central and eastern tropical Pacific, subtropical western Pacific, as well as the Southeast Pacific off South America and cold anomalies over the Northeast Pacific off the west coast of North America (Fig. 2a). In sharp contrast, in June 2005, SST shows nearly neutral conditions in the whole tropical Pacific, while it shows some cold anomalies in the coastal regions over the eastern Pacific off South and North America and alternating warm and cold anomalies in the subtropical western Pacific (Fig. 2b). Over the Indian Ocean, SST shows warm anomalies in the southeast tropical Indian Ocean in June 2002 (Fig. 2a), while systematic warm anomalies over the northwest Indian Ocean in June 2005 (Fig. 2b). Therefore, a comparison of the results from 2002 and 2005 can help determine whether the orographic effect of the Annam Cordillera on East Asian summer monsoon rainfall may be altered with different SST conditions over the tropical Pacific and Indian Oceans.

Fig. 2.
Fig. 2.

SST anomaly (color shade in °C) in June (a) 2002 and (b) 2005. The black line indicates the contour of 0°C.

Citation: Journal of Climate 25, 13; 10.1175/JCLI-D-11-00574.1

The rest of the paper is organized as follows. Section 2 briefly describes the regional atmospheric model used and the experimental design, as well as the observational data used in the model verification. Section 3 presents results for the case of June 2005 from the control experiment and the sensitivity experiment with the Annam Cordillera removed and its induced SST filaments over the western SCS masked out, so that the total orographic effect can be evaluated. The individual effects due to the orographic blocking/lifting and the cold SST filaments in the western SCS are discussed in section 4 based on other two well-designed sensitivity experiments for the case of June 2005, in which either the Annam Cordillera is removed or the mountain-induced cold SST filaments over the western SCS are masked out. The major findings are summarized in the last section together with a brief discussion for the case of June 2002 to demonstrate the robustness of the findings.

2. Model description, experimental design, and data

a. Model description

The regional atmospheric model (iRAM) developed at the International Pacific Research Center (IPRC), University of Hawaii at Manoa (Wang et al. 2003, 2004a, 2007), is used in this study. It is a primitive equation model with sigma (pressure normalized by surface pressure) as the vertical coordinate, solved on a longitude–latitude grid system. A detailed description of the model can be found in Wang et al. (2003) and its performance in simulating regional climate in East Asia can be found in Wang et al. (2003), Sen et al. (2004), Xu et al. (2008), and Souma and Wang (2009, 2010). The model also has been used to understand the effects of the Andean and Central American mountains on the eastern Pacific regional climate (Xu et al. 2004, 2005) and to study low clouds over the eastern Pacific (Wang et al. 2004b,c, 2005). Some improvements of the model to better simulate precipitation diurnal cycle can be found in Wang et al. (2007) and Zhou and Wang (2006).

The model includes a detailed mixed-phase cloud microphysics scheme for grid-scale moist processes (Wang 2001). The mixing ratios of water vapor, cloud water, rainwater, cloud ice, snow, and graupel are all prognostic variables in the model. Subgrid-scale convective processes, including shallow convection, midlevel convection, and penetrative deep convection, are considered based on the mass flux cumulus parameterization scheme originally developed by Tiedtke (1989) and later modified by Nordeng (1995). The subgrid-scale vertical turbulent mixing is accomplished by the so-called E-ε closure scheme, in which both the turbulence kinetic energy (TKE) and its dissipation rate are prognostic variables (Detering and Etling 1985). Turbulent fluxes at the ocean surface are calculated using the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) algorithm (Fairall et al. 1996; Wang 2002). The radiation budget is based on the package of Edwards and Slingo (1996), which was later improved by Sun and Rikus (1999). It includes seven (four) bands for longwave (shortwave) radiative flux calculations. Cloud amount is diagnosed using the semiempirical cloudiness parameterization scheme developed by Xu and Randall (1996), by which cloud amount is determined by both relative humidity and liquid/ice water content at the grid scale.

A one-way nesting is used to update the model time integration in a buffer zone near the lateral boundaries in which the model prognostic variables are nudged to the driving fields, which is provided by the National Centers for Environmental Prediction (NCEP) final analysis (FNL). An exponential nudging coefficient proposed by Liang et al. (2001) is adopted with a buffer zone of 5° in extent. The seasonal-varying climatological ozone and a constant mixing ratio of CO2 concentration for the present-day climate are specified. The U.S. Geological Survey (USGS) high-resolution topographic dataset () is used to obtain the model topography.

For the land surface processes, the Biosphere–Atmosphere Transfer Scheme, version 1e (BATS-1e) developed by Dickinson et al. (1993) is used, which includes one canopy and three soil layers. It requires land cover/vegetation (18 types) and soil texture (12 types) maps for its applications. In iRAM, these datasets are obtained from the USGS (the second version of the 1-km-resolution land cover classification) and the U.S. Department of Agriculture’s global 10-km soil data. Soil moisture is initialized using the method described in Giorgi and Bates (1989), in which the initial soil moisture depends on the vegetation and soil types defined for each grid cell.

b. Experimental design

The model domain is 5°S–42.8°N, 85°–140°E with a horizontal grid spacing of 0.2° in both zonal and meridional directions, which covers the SCS, Indochina Peninsula, eastern part of the Bay of Bengal, south China, and western part of the Northwest Pacific (Fig. 3a). This model domain is small enough to allow a strong control of the large-scale circulation in the model domain through the lateral boundary conditions while big enough to allow the internal regional response to develop so that the remote orographic effect can be evaluated through model sensitivity experiments (Wang et al. 2004a). The model has 28 vertical levels with higher resolution in the lower troposphere to better resolve the planetary boundary layer processes.

Fig. 3.
Fig. 3.

(a) Model domain (5°S–42.8°N, 85°–140°E) and the model orography (gray shade and black contours in m), as well as the observed monthly mean SST (color shade and white contours in °C) and the observed wind vectors (m s−1) at 925 hPa in June 2005. Green rectangle shows the boundary between the model interior and the buffer zone of 5°. Blue box marks the region where the precipitation is averaged in Tables 1 and 2. (b) The model topography (gray shade and black contours in m) in the NoTop and NTSmSST simulations with the monthly mean anomalous SST (°C) over the western SCS added to the NTSmSST simulation in June 2005 shown in red contours at 0.5°, 0.75°, and 1.0°C.

Citation: Journal of Climate 25, 13; 10.1175/JCLI-D-11-00574.1

The NCEP FNL data available on 1° × 1° grids with 26 vertical pressure levels at 6-h intervals are used to provide both the initial and lateral boundary conditions to the iRAM. They are interpolated onto the model grids by the cubic spline interpolation in the horizontal and linear interpolation in the vertical and in time. The National Ocean and Atmospheric Administration (NOAA) daily high-resolution SST dataset on 0.25° × 0.25° grids is used to provide the lower boundary condition. This SST dataset is retrieved based on the Advanced Very High Resolution Radiometer (AVHRR) and Advanced Microwave Scanning Radiometer (AMSR) (Reynolds et al. 2007).

Three simulations are performed to evaluate the overall and individual effects of the Annam Cordillera on summer rainfall downstream over East Asia. In the first simulation [control (CTL)], the model is configured as described above with all default model settings. In CTL, the Annam Cordillera is represented well in the model with the terrain height over 900 m (Fig. 3a). In the second simulation (NTSmSST), both the Annam Cordillera and the cold SST filaments in the coastal region of the western SCS are removed. Specifically, over the land, the elevation over the east Indochina Peninsula south of 17.5°N is set to be 1 m, while over the ocean, daily SST is set to 30°C if it is lower than 30°C off the southeast coast of Vietnam, followed by five applications of a two-dimensional five-point smoother in the coastal region. Figure 3b shows the topography in NTSmSST and the difference in the monthly mean SST relative to SST in the CTL simulation. Since the Annam Cordillera may have both the direct orographic blocking/lifting effect and the indirect effect through its induced cold SST filaments over the western SCS, the difference between the CTL and NTSmSST simulations may infer the bulk effect of the Annam Cordillera. In the third simulation (NoTop), only the Annam Cordillera is removed by setting the elevation over the east Indochina Peninsula south of 17.5°N at 1 m while SST over the SCS is kept the same as that used in CTL (Fig. 3b).

Therefore, the three simulations are designed to identify the total and individual—and the direct and indirect—effects of the Annam Cordillera. A comparison between simulations CTL and NoTop can help unravel the direct lifting/blocking effect of the Annam Cordillera, while a comparison between NTSmSST and NoTop can help identify the contribution of the cold SST filaments over the western SCS induced by the indirect orographic effect. For each simulation above, we performed seven ensemble runs, initialized at 0000 UTC with a one-day shift from 22 to 28 May of the year, respectively, and integrated through the end of June. The same experiments are performed for June 2002 and 2005. Therefore, a total of 42 model runs were made. In our discussion in sections 3 and 4 below, we will focus on the ensemble mean for the month of June based on the daily mean outputs for each simulation and for the case of 2005. A brief discussion on the results for the case of 2002 will be given in the last section.

c. Data

In addition to the NCEP FNL data used to drive the regional atmospheric model and for verification of the simulated large-scale atmospheric circulation, we also used the monthly mean TRMM merged best-estimated precipitation product 3B43, which is on 0.25° grids covering 50°S–50°N (Adler et al. 2000), to verify the model simulations of precipitation. The 3B43 product combines four independent precipitation estimates: (i) the monthly average unclipped TRMM satellite’s Microwave Imager (TMI) estimate, (ii) the monthly average Special Sensor Microwave Imager (SSM/I) estimate, (iii) the pentad-average adjusted merged-infrared (IR) estimate, and (iv) the monthly accumulated Climate Assessment and Monitoring System (CAMS) and Global Precipitation Climatology Centre (GPCC) rain gauge analysis. Note that the TRMM 3B43 precipitation product was already used to show the climatological rainfall in the region in Fig. 1b.

3. Overall effect of the Annam Cordillera

a. Control simulation

Figure 4 shows the simulated precipitation and winds at 850 hPa from the CTL simulation for June 2005 and the corresponding TRMM 3B43 rainfall and NCEP FNL winds. The observed heavy rainfall occurred in south China, eastern SCS, and western Northwest Pacific (WNP). The model simulates well the precipitation pattern and 850-hPa winds over the SCS, south China, and WNP. The model captures the rain belt on the west slope of the Annam Cordillera and the rain shadow on the leeside and the relatively dry condition in the coastal region over the western SCS (Xie et al. 2006, 2008), indicating that the CTL simulation reproduces realistically the regional climate in the region as well as the local effect of the mountain.

Fig. 4.
Fig. 4.

The monthly mean rainfall rate (color shade and white contours in mm day−1) and winds (m s−1) at 850 hPa from (a) observations (TRMM 3B43 products from rainfall rate and NCEP FNL analysis for winds) and simulations from (b) CTL and (c) NTSmSST for June 2005. Also shown are the differences in rainfall (d) between CTL and observation (CTL–OBS; color shade in mm day−1) and (e) between CTL and NTSmSST (CTL–NTSmSST; blue contours at −12, −10, −8, −6, −4, and −2 mm day−1; red contours at 2, 4, 6, 8, 10 and 12 mm day−1), as well as (f) the difference between CTL and NTSmSST in percentage relative to the CTL simulation (color shade in %, contours at −15% and 15%). Orange (blue) shadings in (e) indicate regions with the positive (negative) rainfall difference in NTSmSST from CTL reaching the 95% significance level based on the two-sided Student’s t test. The black line segment AB in (e) indicates the axis for vertical cross sections displayed in Figs. 6 and 8.

Citation: Journal of Climate 25, 13; 10.1175/JCLI-D-11-00574.1

To quantitatively assess the overall model performance in the CTL simulation, we have done statistical analysis for the simulated precipitation in the domain of 10°–30°N, 100°–130°E as shown in Fig. 3a (Table 1), and over the land and the ocean, respectively, in the domain (Table 2), following Wang et al. (2003). The model overestimated monthly mean rainfall by 1.67 mm day−1 (7.66 mm day−1 in CTL simulation versus 5.99 mm day−1 in TRMM 3B43; Table 1), mainly over most of the ocean areas (1.97 mm day−1 over ocean versus 1.23 mm day−1 over land; Table 2), in particular west of the Philippines (Fig. 4d). The simulated rain belt to the northeast of Taiwan Island is about 2°–3° latitude north of the observed, leading to negative rainfall difference over north SCS. It is probably due to the slightly stronger southerly flow in the lower troposphere in the simulation than in the observation (OBS). For the land area only, the bias between the CTL simulation and the observation is smaller (1.23 mm day−1; Table 2). The spatial correlation coefficient between the CTL simulation and the TRMM 3B43 product is as high as 0.79 (with confidence level above 95%) in the interested region and reaches 0.84 for the land area only (Tables 1 and 2). This indicates that the CTL simulation reasonably reproduced the observed rainfall distribution, although it overestimated the rainfall amount by about 25%–30% over the ocean.

Table 1.

Statistics (biases and spatial correlation coefficients) of the simulated monthly mean precipitation against the TRMM 3B43 products averaged in the domain of 10°–30°N, 100°–130°E as shown in Fig. 3a for June 2005 in the CTL, NoTop, and NTSmSST simulations. The bias means the simulated rainfall minus the TRMM observation. All differences are statistically significant at 95% confidence level.

Table 1.
Table 2.

As in Table 1, but for the land area only and for the ocean only averaged in the domain of 10°–30°N, 100°–130°E. All differences are statistically significant at 95% confidence level.

Table 2.

Despite the above discrepancies, the model captures the terrain-induced rain belt and rain shadow on the windward side and the leeward side, respectively, as well as the early summer monsoon rainfall belt in the region downstream of the mountain reasonably well. This allows us to use the model to evaluate the remote effect of the Annam Cordillera on early summer monsoon rainfall over south and east China and over the WNP through well-designed sensitivity experiments as discussed below.

b. Overall effect of the Annam Cordillera

Figure 4c illustrates the simulated precipitation from the NTSmSST simulation for June 2005. With the Annam Cordillera and the cold SST filaments over the western SCS removed, the precipitation distribution is markedly changed, in particular in the area with the surface forcing changes. Now both the rainband on the windward side and the rain shadow on the leeward side of the Annam Cordillera almost disappeared (Fig. 4c). This can be more easily seen from the difference field in the monthly mean daily rainfall between the CTL and NTSmSST simulations as shown in Fig. 4e. A dipole of rainfall anomalies appears across the mountain range, positive (negative) on the windward (leeward) side and over the SCS, with the maximum difference larger than 12 mm day−1 over the SCS.

In addition to the local changes in rainfall, the most noticeable feature is the obvious influence of the Annam Cordillera on rainfall farther downstream over south and east China and over the WNP (Fig. 4e). The presence of the Annam Cordillera and the cold SST filaments over the western SCS increases rainfall in a southwest–northeast-oriented band, extending from south China to east China and then to the East China Sea, which reduces rainfall immediately to the south over the SCS and to the northeast of Taiwan Island. As we can see from the percentage change relative to the monthly mean rainfall in the CTL simulation (Fig. 4f), rainfall over south China and the East China Sea is increased by as much as 15%–30% and that over the SCS is reduced by more than 50% because of the presence of the Annam Cordillera. These changes are statistically significant over the 95% confidence level. The results thus suggest that the Annam Cordillera not only exerts a strong forcing near the mountain area, but also has a significant effect on summer monsoon rainfall over south and east China, and even over the WNP—namely, as far as 3000 km downstream in the East Asian summer monsoon system. This latter effect is beyond the local effect of the mountain itself, which is only about 100 km wide in the east–west direction and about 600 km long in the north–south direction. This implies that the Annam Cordillera has a remote effect on summer rainfall over East Asia.

In addition, in the NTSmSST simulation, the monthly mean rainfall rate increases to 8.84 mm day−1 in the domain of 10°–30°N, 100°–130°E (Table 1), 6.33 mm day−1 over the land area, and 10.7 mm day−1 over the ocean in the domain (Table 2). The difference in the NTSmSST simulation and the observation is considerably larger than that between the CTL simulation and the observation (Tables 1 and 2). This indicates that the overall performance of the model is degraded with the Annam Cordillera removed and the cold SST filaments over the western SCS masked out. This demonstrates that high model resolution to resolve the mesoscale topography is important for the simulation/prediction of the East Asian summer monsoon rainfall, which is in agreement with earlier studies (Xie et al. 2006; Shi et al. 2008).

4. Direct and indirect effects of the Annam Cordillera

In this section, the individual effects of the orographic lifting/blocking and the cold SST filaments over the western SCS will be evaluated and understood with the help of the results from the NoTop simulation. With the mountain Annam Cordillera removed but the cold SST filaments over the western SCS retained in NoTop, the difference between the simulated rainfall and the TRMM observed is slightly reduced both over the land and over the ocean compared to that in NTSmSST (Table 2), indicating that the removal of the Annam Cordillera in NoTop degraded the overall simulation of precipitation over East Asia.

Figure 5 shows the monthly mean rainfall differences in June 2005 between CTL and NoTop and between NoTop and NTSmSST. They infer the direct orographic lifting/blocking effect of the Annam Cordillera and its indirect effect through its induced cold SST filaments over the western SCS, respectively. A close comparison among the three simulations (Figs. 4e and 5) suggests that the direct effect from the orographic lifting/blocking accounts for most of the overall effect of the Annam Cordillera. This is true because the rainfall difference between the NoTop and NTSmSST simulations (Fig. 5b) is considerably smaller than that between the CTL and NoTop simulations (Fig. 5a). To understand how the mesoscale mountain Annam Cordillera with only 900-m height can extend its impact on precipitation downstream as far as 3000 km, we examined circulation anomalies and vertical cross sections along the line segment AB across the mountain range as marked in Fig. 4e.

Fig. 5.
Fig. 5.

Differences in the monthly mean rainfall rate (blue contours at −12, −10, −8, −6, −4, and −2 mm day−1; red contours at 2, 4, 6, 8, 10, and 12 mm day−1) between (a) the CTL and NoTop simulations (CTL–NoTop) and (b) the NoTop and NTSmSST simulations (NoTop–NTSmSST) for June 2005. Orange (blue) shadings mark regions where the positive (negative) differences reach the 95% significance level based on the two-sided Student’s t test.

Citation: Journal of Climate 25, 13; 10.1175/JCLI-D-11-00574.1

A comparison between the CTL and NoTop simulations can provide insights into the direct effect of the Annam Cordillera on summer precipitation over East Asia. Consistent with the results in Xu et al. (2008), the Annam Cordillera forces ascending motion on the windward side with elevated cloud liquid water (Figs. 6a,d), giving rise to a rainband on the windward side of the mountain (Fig. 4b). On the leeward side, air sinks along the mountain slope, suppressing convection (Xu et al. 2008). The subsidence warming over the cold SST filaments in the western SCS stabilizes the surface layer and suppresses surface latent and sensible heat fluxes over the SCS (Figs. 7a,c). With the Annam Cordillera removed in the NoTop simulation, although a local maximum in cloud liquid water still appears in the original mountain area associated with rainfall over the peninsula, the specific humidity becomes smooth along the axis AB across the mountain range (Fig. 6b) with more moist air transported to the leeward side of the Annam Cordillera and to the SCS. This leads to upward motion and convection over the original region of rain shallow on the leeward side and over the SCS (Figs. 4c and 6d). Therefore, with the presence of the mesoscale mountain, the apparent heat source Q1 increases on the windward side and decreases on the leeward side, accompanied by negative and positive anomalous apparent moisture sink Q2, respectively (Fig. 8, where the definition of Q1 and Q2 follows Yanai et al. 1973). A direct response to this orographically induced anomalous heating is the low-level anomalous easterly over the SCS and an anticyclonic circulation to the north, which can be clearly seen at 500 hPa and turns to an anomalous cyclonic circulation displaced to the southeast of the mountain (Fig. 9). The low-level anomalous circulation modifies the moisture transport (Fig. 10a), leading to more water vapor being transported and converged over south China and the East China Sea (Fig. 10c), and thus a significant rainfall increase (Fig. 5a), while less water vapor is transported to the SCS and the east of Taiwan Island (Figs. 10a,c), causing a decrease in rainfall by more than 50% over the SCS.

Fig. 6.
Fig. 6.

Vertical cross section along line segment AB shown in Fig. 4e for the monthly mean specific humidity (Qv; black contours in g kg−1) and cloud liquid water mixing ratio (Qc; color shade and white contours in 10−2 g kg−1) for June 2005 simulated in (a) CTL and (b) NoTop and for the differences in (c) the monthly mean specific humidity (contours in g kg−1) and (d) the monthly mean vertical p velocity (contours in −1.0 × 10−2 hPa s−1) between the CTL and NoTop simulations (CTL–NoTop). Black shading marks the model topography. The orange (blue) shade in (c) and (d) indicates regions where the positive (negative) difference reaches the 95% significance level based on the two-sided Student’s t test.

Citation: Journal of Climate 25, 13; 10.1175/JCLI-D-11-00574.1

Fig. 7.
Fig. 7.

Difference in the monthly mean surface latent heat flux and sensible heat flux (contours at −10, −5, −3, 3, 5, and 10 W m−2) between (a),(c) the CTL and NoTop simulations (CTL–NoTop) and between (b),(d) the NoTop and NTSmSST simulations (NoTop–NTSmSST) for June 2005. Orange (blue) shading marks regions where the positive (negative) difference reaches the 95% significance level based on the two-sided Student’s t test.

Citation: Journal of Climate 25, 13; 10.1175/JCLI-D-11-00574.1

Fig. 8.
Fig. 8.

Vertical cross sections along the line segment AB shown in Fig. 4e for the differences in (a) the apparent heating Q1 (contours at 0.5 K h−1 intervals) and (b) the apparent moistening Q2 (contours at 0.3 × 10−3 K h−1 intervals) between the CTL and NoTop simulations (CTL–NoTop) for June 2005. Black shading marks the orography and the orange (blue) shading indicates regions where the positive (negative) difference reaches the 95% significance level based on the two-sided Student’s t test.

Citation: Journal of Climate 25, 13; 10.1175/JCLI-D-11-00574.1

Fig. 9.
Fig. 9.

Differences in the monthly mean geopotential height (contours at 1-gpm intervals) and wind vectors (m s−1) at 200, 500, and 850 hPa (a) between the CTL and NoTop simulations (CTL–NoTop) and (b) between the NoTop and NTSmSST simulations (NoTop–NTSmSST) for June 2005. Only the wind differences reach the 95% confidence level are shown.

Citation: Journal of Climate 25, 13; 10.1175/JCLI-D-11-00574.1

Fig. 10.
Fig. 10.

Differences in the column-integrated water vapor flux (900–300 hPa; kg m−1 s−1) and the associated flux divergence (contours at −3 × 10−5 and 3 × 10−5 kg m−2 s−1) between (a),(c) the CTL and NoTop simulations (CTL–NoTop) and between (b),(d) the NoTop and NTSmSST simulations (NoTop–NTSmSST) for June 2005. Only the flux differences reach the 95% significance level are shown in (a) and (b) and the orange (blue) shading in (c) and (d) marks regions where the positive (negative) differences reach the 95% significance level based on the two-sided Student’s t test.

Citation: Journal of Climate 25, 13; 10.1175/JCLI-D-11-00574.1

The difference between the NoTop and NTSmSST simulations gives an estimate of the indirect effect of the orographically induced cold SST filaments over the western SCS. As we can see from Fig. 5b, the removal of the cold SST filaments over the western SCS caused the precipitation anomalies in the model domain to be generally less than 50% of those caused by the orographic lifting/blocking effect alone (Fig. 5a). Nevertheless, the presence of the cold SST filaments over the western SCS leads to a decrease in rainfall over the SCS, while it has little effect on rainfall over south China except for over parts of the southern south China. This indicates that the coastal cold SST filaments over the western SCS affect precipitation mainly over the SCS, showing a dominant local effect within about 1500 km downstream to the east.

The cold SST filaments reduce the latent and sensible heat fluxes (Figs. 7b,d), suppressing convection and precipitation over the SCS. This results in an anomalous anticyclonic circulation in the midlower troposphere (Figs. 9d,f), but it is much weaker than that forced by the direct orographic heating anomalies as shown in Fig. 9c, contributing insignificantly to the anomalous moisture transport except for in the region along the south coast of south China (Fig. 10b). Nevertheless, although the effect of the cold SST filaments is secondary, it enhances the direct orographic effect on the local and regional circulation and precipitation.

5. Conclusions and discussion

The remote effect of the Annam Cordillera—a mesoscale mountain over the east Indochina Peninsula—has been investigated based on ensemble simulations for June 2005 using a high-resolution regional atmospheric model. The Annam Cordillera is found not only to have a significant control on the local climate in the east Indochina Peninsula and over the western SCS, but also to have a considerable effect on summer monsoon rainfall downstream over south and east China, and the East China Sea. Our case study for June 2005 indicates that the Annam Cordillera substantially suppresses convection and precipitation over the SCS and even to the northeast of Taiwan Island over the WNP, and increases precipitation over south and east China and the East China Sea. That means the orographically induced anomalous circulation in the Annam Cordillera case can lead to a significant change in precipitation downstream over the WNP as far as 3000 km away from the mesoscale mountain, namely, showing a significant remote effect.

The Annam Cordillera influences the regional climate downstream through both the direct orographic lifting/blocking and the indirect mountain-triggered coastal upwelling and the cold SST filaments over the western SCS. Standing in the path of the prevailing southwesterly monsoon, the Annam Cordillera directly forces upward motion, convection, and rainband on the windward side and subsidence and rain shadow on the leeward side. The terrain-induced convection and the associated anomalous convective heating trigger a large-scale anomalous midlower tropospheric anticyclonic circulation over the SCS, which modifies the large-scale moisture transport associated with the summer monsoon and further affects precipitation downstream remotely. In addition, the Annam Cordillera indirectly affects summer monsoon precipitation over the SCS and south and east China through its triggered cold SST filaments over the western SCS. Our results show that this indirect effect is secondary compared to the direct orographic lifting/blocking effect. Nevertheless, the indirect effect plays a role in enhancing the direct orographic effect, contributing to the overall orographically induced remote precipitation over south and east China.

The above findings are reached based on a case study for June 2005. To check the robustness of the results, we also performed the same numerical experiments for June 2002—a year with the SST state being quite different from 2005 in the tropical Pacific and Indian Oceans. The results (Fig. 11) are generally consistent with those discussed in this study for June 2005 (Fig. 4); namely, the Annam Cordillera has a remote effect on summer monsoon precipitation downstream over south and east China and over the WNP. Similar results for 2002 and 2005 suggest that the remote effect of the mesoscale mountain over the east Indochina Peninsula is independent of the larger-scale SST conditions in the tropical oceans. However, because of the weaker summer monsoon and the associated less monsoon rainfall over south China in June 2002, the anomalous rainfall forced by the Annam Cordillera becomes less distinct east of 120°E.

Fig. 11.
Fig. 11.

(a) Difference in monthly mean rainfall between CTL and NTSmSST for June 2002 (CTL–NTSmSST; blue contours at −12, −10, −8, −6, −4, and −2 mm day−1; red contours at 2, 4, 6, 8, 10, and 12 mm day−1) and (b) difference in percentage relative to the CTL simulation (color shade in percentage; contours at −15% and 15%). Orange (blue) shadings in (a) indicate regions with the positive (negative) differences reaching the 95% significance level based on the two-sided Student’s t test.

Citation: Journal of Climate 25, 13; 10.1175/JCLI-D-11-00574.1

There are several limitations in this study. First, the experiments were performed in a limited area with the lateral boundary conditions given by reanalysis. The removal of the mesoscale mountain could cause changes in the large-scale atmospheric circulation, which might in turn affect the simulated results through the lateral boundary conditions. This effect was ignored and considered to be secondary and would not be expected to alter the main conclusions in this study. Second, SSTs were prescribed in our simulations and thus any possible feedback from the ocean due to changes in the atmospheric circulation was neglected in this study. We considered that this might not change our overall findings either. Last, the removal of the mesoscale mountain in the NoTop and NTSmSST simulations might introduce some unbalance to the initial model atmosphere. This imbalance may be adjusted in several hours and thus should not affect the subsequent simulation considerably, as already indicated in other studies using regional climate models (e.g., Yhang and Hong 2008, 2011; Sun et al. 2011).

In summary, this study demonstrates that even the mesoscale mountain Annam Cordillera with only 900-m height may have a significant impact on the large-scale circulation and precipitation remotely downstream as far as 3000 km in the East Asian summer monsoon system. This also suggests that models with high resolutions to resolve the mesoscale topography over South and Southeast Asia are critical to accurately simulate/predict the summer monsoon rainfall over East Asia and the SCS. Most of the state-of-the-art climate models show relatively low skill in simulating the summer monsoon rainfall in the region (Lau et al. 1996; Lee and Suh 2000; Zhou et al. 2009). This may be partly because of their use of relatively coarse spatial resolutions that could not properly resolve mesoscale mountains over South and Southeast Asia (Gao et al. 2006; Xie et al. 2006).

Acknowledgments

The authors are grateful to three anonymous reviewers for their constructive comments, which helped improve the manuscript. This study has been supported in part by grants from the National Natural Science Foundation of China (40975024 and 41075068) and the Chinese Ministry of Science and Technology 973 Program (2010CB428505) and in part by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) through its sponsorship to the International Pacific Research Center (IPRC) in the School of Ocean and Earth Science and Technology (SOEST) at the University of Hawaii at Manoa.

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