Abstract

This study uses an atmospheric general circulation model to examine the relative effects of Maritime Continent (MC) orography, surface roughness, and land–sea contrast on the three cross-equatorial flows (CEF) north of Australia, including the South China Sea (SCS), Celebes-Moluccas (CM), and New Guinea (NG) CEFs, and Asian monsoon precipitation during boreal summer. Four experiments are conducted: with islands, with islands without orography, with islands with ocean roughness and no orography, and with ocean only in the MC region. At the approximately 1° horizontal resolution of these sensitivity experiments, results indicate that the land–sea contrast and orography in the MC have complicated impacts on the CEFs. The land–sea contrast creates the three CEFs. The orography is dominant in deepening, concentrating, and strengthening the CM CEF and modulating the longitudinal location of the NG CEF. For the intensity and depth of the SCS and NG CEFs, the surface roughness over the flat MC and orography are both important. In addition, the MC modulates the monsoon rainfall in tropical Asia. The decreased rainfall (by roughly 57% and 21.4% over South Asia and the SCS, respectively) is dominated by the reduced moisture availability resulting from the presence of the land–sea contrast, thereby intercepting the westward propagating quasi-biweekly convection. The surface roughness over the MC is key in reducing precipitation through reducing moisture convergence over Sumatra, Borneo, and northeastern New Guinea. However, the orography controls the intense precipitation over southwestern New Guinea and the adjacent seas through enhancing the moisture transport carried by the CM and NG CEFs.

1. Introduction

Cross-equatorial flows (CEFs) in the lower troposphere, and their counterparts in the opposite direction prevailing in the high-level atmosphere, play a pivotal role in transporting moisture, mass, momentum, and energy between the Northern and Southern Hemispheres during boreal summer, and have attracted much attention from meteorologists (e.g., Findlater 1966; Saha and Bavadekar 1973; Rodwell and Hoskins 1995; Wang and Xue 2003; He et al. 2007a). The tropospheric CEFs have been widely recognized as one of the crucial circulation components in the Asian–Australian monsoon system and exert a profound effect on the weather, precipitation, and climate in Asia (Tao and Chen 1987; Ramesh Kumar et al. 1999; Wang and LinHo 2002; Ding and He 2006; Kajikawa and Wang 2012; Zhu 2012; Li and Li 2014; Jiang et al. 2016; Lin et al. 2017). Over the equatorial Indian Ocean and western Pacific Ocean, three main branches of low-level CEFs are most prominent during boreal summer season (June–September), including the Somali CEF, Bay of Bengal (BOB) CEF, and Australian CEF (Wang and Xue 2003). The low-level Australian CEF includes three branches: the South China Sea (SCS) CEF, Celebes-Moluccas (CM) CEF, and New Guinea (NG) CEF, which are all located to the north of Australia and highly correlated with each other, and thus collectively referred to as the Australian CEF (Li and Li 2014).

Among them, the tropospheric Somali CEF is the strongest and is known to have a maximum core centered at the 850-hPa pressure level (Chakraborty et al. 2009; Li and Li 2014). Using the observed station data of two equatorial stations, Findlater (1969a,b) demonstrated that the Somali CEF originates from the southeast trade winds of the southern Indian Ocean, and then turns eastward into a westerly jet over the Arabian Sea. Since the 1970s, a number of numerical experiments have been conducted using general circulation models (GCMs) to explore the impact of several factors, including the beta effect, the East African Highlands, the Western Ghats, land–sea contrast, baroclinicity in the boundary layer, and diabatic heating on the formation of the low-level Somali jet (Krishnamurti et al. 1976; Krishnamurti et al. 1983; Hoskins and Rodwell 1995; Rodwell and Hoskins 1995; Sijikumar et al. 2013). Through employing reanalysis data and different models (Findlater 1974; Yang and Huang 1989; Xue et al. 2003; He et al. 2007b; Kitoh 2017), it has been revealed that the interhemispheric thermal contrast between the cold Mascarene high and warm Asian continent is the primary driving force of the Somali CEF, and the secondary forcing is from the differences between the surface characteristics (including the contrast in land–sea thermal features and terrain height). The strengthening effect of the East African mountains and the land–sea contrast on the Somali CEF has been demonstrated by the use of primitive equation models (Krishnamurti et al. 1976; Rodwell and Hoskins 1995) and GCMs (e.g., Slingo et al. 2005; Chakraborty et al. 2009; Xu et al. 2010). The weakening impact of the Western Ghats on the Somali CEF has been explored by the use of the WRF model (Sijikumar et al. 2013; Zhang and Smith 2018). Further numerical studies with atmospheric general circulation models (AGCMs; e.g., Srinivasan and Nanjundiah 2002; Chakraborty et al. 2002, 2009; Joseph and Sijikumar 2004) found that high correlation exists between the latent heating associated with precipitation over the BOB and the strength of the Somali jet, with the latter lagging by about three days.

The low-level Australian CEF is also an essential conveyer belt of water vapor transport into the Asian monsoon region, and each individual branch of the Australian CEF is correlated with the Asian summer monsoon to a different degree (He et al. 2007a; Zhu 2012; Song et al. 2013; Li and Li 2014). The Australian CEF originates from the Australian high, thence flowing northward across the Maritime Continent (MC) over the Indian and Pacific Oceans, splitting into three branches (i.e., the SCS, CM, and NG CEFs), and then partly arriving in the Asian continent (Li and Li 2014). It is widely accepted that the Australian CEF is driven by the pressure gradient due to the thermal contrast between the warm low over the Asian continent and the cold Australian high (Wang et al. 2005; He et al. 2007b; Kitoh 2017). Using an AGCM, Xue et al. (2003) demonstrated that the low-level Australian CEF would be enhanced with the intensification of the Australian high. Neale and Slingo (2003) conducted two experiments, removing the orography and islands of the MC using an AGCM, to study the effect of the MC on the global climate, and proposed that it is the land–sea contrast, rather than the orography forcing, that exerts the most substantial effect on the precipitation over the MC and its adjacent regions. Through diagnosing the historical station rainfall data and satellite-based observations, Chang et al. (2005, 2016) highlighted the strong interaction between the complex terrain over the MC and CEFs, which leads to intense rainfall on the windward side and less rainfall over the leeward side of the high topography. Recently, an analysis carried out by Johnson et al. (2016) and Ogata et al. (2017) indicated that a model with improved representation of the orography over the MC can simulate more realistic cross-equatorial wind and increased precipitation over the MC. Li and Li (2014), based on statistical analysis, reported that the SCS, CM, and NG CEFs are highly correlated with each other, and that they may be an integral split by the mountainous islands over the MC. From the abovementioned studies, it is obvious that sufficient studies have used CGMs to explore the relative contributions of several factors to the Somali jet. However, little effort has been devoted to investigating different factors influencing the formation, strength, location, and structure of the three branches of the Australian CEF. Previous studies focused on either the effect of the orography alone in MC (Chang et al. 2005, 2016; Johnson et al. 2016; Ogata et al. 2017) or the response of the global climate due to the land–sea contrast in MC with a coarser-resolution model (Neale and Slingo 2003). Thus, the understanding of the relative roles played by different surface properties over the MC in the three CEFs north of the Australia is limited, and a reliable analysis focusing on the relative impacts of multiple factors is needed. Therefore, in this study, by conducting four numerical experiments with an AGCM, we aim to investigate the following: 1) What factors govern the formation, strength, location, and structure of the SCS, CM, and NG CEFs—the MC’s orography, surface roughness, or land–sea thermal contrast? 2) To what extent can these three factors influence the CEFs and Asian monsoonal rainfall and water vapor budget between the two hemispheres?

The remainder of this paper is structured as follows. The datasets, methods, the AGCM employed in this study, and the setup of the model experiments are described in section 2. Section 3 explores the effect of the MC on the low-level Australian and Somali CEFs by comparing the control and sensitivity runs. The influences of the MC’s orography, surface roughness, and land–sea contrast on the water vapor budget and precipitation over the Asian summer monsoon area are presented in section 4. Finally, section 5 summarizes the main conclusions.

2. Data, methods, and experiment design

a. Data

The monthly precipitation data used in this study, with a horizontal resolution of 0.25° × 0.25°, are from the Tropical Rainfall Measuring Mission 3B43, version 7, Multisatellite Precipitation Analysis (Huffman et al. 2007), which covers the period 1998–2015. The other variables, with a horizontal resolution of 1.25° × 1.25° and on 37 vertical pressure layers, including horizontal winds (zonal and meridional components) and specific humidity, are from the Japanese 55-yr Reanalysis (JRA-55) project (Ebita et al. 2011). All dataset variables comprise daily and monthly means, and cover 20 boreal summer seasons (June–September) from 1985 to 2004.

b. Methods

To diagnose the atmospheric water vapor transport, we calculate the vertically integrated moisture transport (VIMT) following previous studies (Fasullo and Webster 2002; He et al. 2007a; Xu et al. 2010; Li et al. 2018), which is linked directly to the lateral and transverse heating gradients and the basic monsoon forcing. The total vertically integrated water vapor flux vector Q can be expressed as

 
Q=1gpspTqVdp,
(1)

where q is the specific humidity and V is the horizontal wind vector, including zonal and meridional components. The units of Q are kilograms per meter per second (kg m−1 s−1). Parameter g represents gravitational acceleration; ps stands for surface pressure; and pT is the pressure of the tropopause, taken as 200 hPa in this study because the specific humidity above 200 hPa in the tropics is at least two orders of magnitude smaller than near the surface. Therefore, the moisture transport above 200 hPa has a negligible effect on the calculation of the total VIMT (Fasullo and Webster 2002). Instead of utilizing the monthly averaged wind vectors and specific humidity data to calculate the monthly mean moisture flux vector directly, we calculate the day-by-day VIMT integrated from the surface to 200 hPa for the period 1985–2004. The monthly means are further calculated from the daily mean data to avoid neglecting the contribution of transient components, which are also important sometimes.

To quantitatively analyze the latent heat released to the atmosphere associated with the precipitation in section 4c, the latent heat (LH) is calculated by precipitation via the following formula:

 
LH=Pr×ρw×Lw,
(2)

where Pr is the precipitation, ρw = 103 kg m−3 is the density of water, and Lw = 2.5 × 106 J kg−1 is the condensation heat coefficient. To avoid neglecting the contribution of transient components, we calculate the day-by-day LH from the daily precipitation anomaly and then calculate the monthly means.

c. Model and experimental design

This study employs the AGCM Finite-Volume Atmospheric Model (FAMIL; Zhou et al. 2012, 2015; Yu et al. 2014) developed by the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences. This finite-volume atmospheric model shares the same physical parameterizations with those described in Hu and Duan (2015), and has various horizontal and vertical resolution options. In this paper, we choose 32 vertical levels, with the model top at 2.16 hPa, and the horizontal resolution of C96 (0.9875° × 0.9875° longitude–latitude grid spacing; approximately 100 km ×100 km) to resolve the major topographical structure of the mountain ranges in the MC (including the Philippines). The topography and land fraction in the MC from observation and the model are shown in Figs. 1a–h. It is clear that FAMIL can describe the outlines and rough shapes of the mountains, despite underestimating the height and steepness of the terrain in the MC and the Philippines to a certain degree.

Fig. 1.

Gridpoint land fraction and topography (shaded; km) from (a),(e) observation, (b),(f) CTRL, (c),(g) NOTOPO, and (d),(h) MC_OCN. Black boxes are geographic regions defined in Table 2. Also shown are sea surface temperature (°C) from (i) CTRL, (j) NOTOPO, and (k) MC_OCN, and the momentum roughness length (m) from (l) NOTOPO and (m) MC_OCN. The terrain height and land fraction are derived from the Global Digital Elevation Model dataset on a 5-min latitude/longitude grid (ETOPO5; National Geophysical Data Center 1993).

Fig. 1.

Gridpoint land fraction and topography (shaded; km) from (a),(e) observation, (b),(f) CTRL, (c),(g) NOTOPO, and (d),(h) MC_OCN. Black boxes are geographic regions defined in Table 2. Also shown are sea surface temperature (°C) from (i) CTRL, (j) NOTOPO, and (k) MC_OCN, and the momentum roughness length (m) from (l) NOTOPO and (m) MC_OCN. The terrain height and land fraction are derived from the Global Digital Elevation Model dataset on a 5-min latitude/longitude grid (ETOPO5; National Geophysical Data Center 1993).

To investigate the impact of the orography of the MC and the land–sea contrast on the low-level Australian and Somali CEFs, three sets of AGCM numerical experiments are performed: CTRL, NOTOPO, and MC_OCN. In CTRL, the existing land–sea distribution and orography in the MC are retained. In the NOTOPO run, the mountain height in the regions 10°S–10°N, 94.5°–162.5°E and 7°–19°N, 114°–129°E (i.e., the MC region) is reduced to zero, but the land–sea distribution is retained (Figs. 1c,g). In the third experiment (MC_OCN), the land grid points comprising the MC’s islands are removed and replaced by ocean grid points with climatological annual-cycle global SST data prescribed by phase 2 of the Atmospheric Model Intercomparison Project (AMIP-II) in the model (Figs. 1d,h,j,k). NOTOPO differs from CTRL only in the absence of the MC’s orography. MC_OCN differs from NOTOPO only in the land–sea contrast. As many other parts of the AGCM as possible remain unchanged, which allows us to directly attribute changes in circulation and precipitation to the land–sea contrast or orography in the MC, and to analyze where and how the land–sea contrast and topography in the MC have the most impact on the low-level CEFs. As the warmer, saturated ocean is replaced by the flat islands (NOTOPO vs MC_OCN), the surface roughness and moisture availability change accordingly. Thus, it raises a question of whether the reduction in CEFs over the MC islands and increase in CEFs over the channels is due to the surface friction or the moisture availability. To address this, we conduct a fourth sensitivity experiment named NOTOPO_z0m, which is identical to NOTOPO except that the surface roughness over the MC’s islands is set equal to the ocean surface roughness (Figs. 1l,m). The functions calculating the surface aerodynamic roughness length Z0m (Neale et al. 2010; Oleson et al. 2013) for land are replaced by the functions calculating the surface aerodynamic roughness length for ocean over the MC region, with other processes unchanged. Thus, NOTOPO_z0m differs from NOTOPO only in surface roughness, and differs from MC_OCN in the moisture availability and forcing SST over the MC’s islands. A more detailed description of the simulations used in this study is presented in Table 1. All experiments are integrated for 22 model years (1983–2004), and the last 20 years of daily and monthly outputs are used for analysis. Given that the low-level Australian CEF and Somali CEF begin to develop in May and weaken significantly in October, we consider the June–September period as representing the boreal summer season, and all the analyses are performed for this season.

Table 1.

Description of numerical simulations conducted.

Description of numerical simulations conducted.
Description of numerical simulations conducted.

To check whether the differences between CTRL and sensitivity experiments are significant, the Student’s t test is applied. Unless stated otherwise, all significant differences between a pair of samples or correlations in this paper exceed the 5% significance level.

3. Experimental results

a. Model validation

The main focus of the present study is to investigate the effect of the MC on the low-level CEFs and the reasons behind the location, strength, and vertical structure of the Australian and Somali CEFs, using an AGCM that allows relatively realistic feedback between different atmospheric processes (Duan et al. 2013; Hu and Duan 2015; Liu and Duan 2017; Li et al. 2019). Before we go into comparing the results of CTRL and the sensitivity experiments, FAMIL’s representation of the summer mean low-level CEFs is evaluated. According to a previous study (Li and Li 2014), there are five branches of cross-equatorial southerly wind: the Somali CEF, BOB CEF, and the three branches of the Australian CEF (i.e., SCS, CM, and NG CEFs). The details of the definition are shown in Table 2.

Table 2.

Geographic regions defined to measure the strength of each individual CEF over the Arabian Sea, Somali coast, BOB, and the MC. SCS, CM, and NG represent the SCS CEF, the western Pacific CEF, and the New Guinea CEF, respectively—the three branches north of Australia. Land1, Land2, and Land3 refer to the land areas between the three branches of the Australian CEF.

Geographic regions defined to measure the strength of each individual CEF over the Arabian Sea, Somali coast, BOB, and the MC. SCS, CM, and NG represent the SCS CEF, the western Pacific CEF, and the New Guinea CEF, respectively—the three branches north of Australia. Land1, Land2, and Land3 refer to the land areas between the three branches of the Australian CEF.
Geographic regions defined to measure the strength of each individual CEF over the Arabian Sea, Somali coast, BOB, and the MC. SCS, CM, and NG represent the SCS CEF, the western Pacific CEF, and the New Guinea CEF, respectively—the three branches north of Australia. Land1, Land2, and Land3 refer to the land areas between the three branches of the Australian CEF.

The boreal summer multiyear mean meridional wind speed and 925-hPa winds simulated by FAMIL, along with their observed counterparts, are shown in Figs. 2a and 2b. Also, to further evaluate the model’s performance in simulating the Australian CEFs, the 925-hPa meridional wind, averaged between 2.5°S and 2.5°N, is shown in Fig. 3a. Generally, the model can simulate (Fig. 2b; blue solid line in Fig. 3a) the SCS, CM, and NG CEFs from west to east with narrow maximum meridional wind centers within 102.5°–110°, 120.5°–130°, and 142.5°–152.5°E, respectively. However, the maximum wind speed center of the CM CEF is shifted westward by about 10°, and the NG CEF is more concentrated and slightly stronger, as compared with their observational counterparts (Fig. 2a; black solid line in Fig. 3a). The vertical structure of the meridional wind component across the equator over the Indian–western Pacific Ocean from the JRA-55 reanalysis data and CTRL during the boreal summer season is displayed in Fig. 4. In the observation (Fig. 4a), there are three branches of the Australian CEF, including the SCS, CM, and NG CEFs, from west to east of the Indian–Pacific Ocean, existing in the abovementioned three regions (Fig. 3a; Table 2). The strong Australian CEF is confined mainly below the 800-hPa level, and has maximum cores at a relatively lower pressure layer, near 925 hPa. These features of the MC’s meridional flow in the lower troposphere are captured well by the model, while a thinner southerly flow over the MC and stronger and more concentrated NG CEF are found in CTRL (Fig. 4b).

Fig. 2.

Multiyear (1985–2004) mean meridional wind speed (shaded; m s−1) and horizontal winds (vectors; m s−1) of the 925-hPa low-level Australian–Asian CEFs during the boreal summer season (June–September) from the (a) observed, and (b) CTRL, (c) NOTOPO, (d) NOTOPO_z0m, and (e) MC_OCN and the differences (f) between CTRL and NOTOPO, (g) between NOTOPO and MC_OCN, (h) between NOTOPO and NOTOPO_z0m, and (i) between NOTOPO_z0m and MC_OCN. Black vectors and dotted areas in (f)–(i) exceed the 5% significance level of the Student’s t test. Regions where the orographic height is more than the height of this pressure level are left with no data.

Fig. 2.

Multiyear (1985–2004) mean meridional wind speed (shaded; m s−1) and horizontal winds (vectors; m s−1) of the 925-hPa low-level Australian–Asian CEFs during the boreal summer season (June–September) from the (a) observed, and (b) CTRL, (c) NOTOPO, (d) NOTOPO_z0m, and (e) MC_OCN and the differences (f) between CTRL and NOTOPO, (g) between NOTOPO and MC_OCN, (h) between NOTOPO and NOTOPO_z0m, and (i) between NOTOPO_z0m and MC_OCN. Black vectors and dotted areas in (f)–(i) exceed the 5% significance level of the Student’s t test. Regions where the orographic height is more than the height of this pressure level are left with no data.

Fig. 3.

(a) The 925-hPa meridional winds (m s−1) averaged between 2.5°S and 2.5°N from observations and the model results. (b) The regional-averaged 850-hPa horizontal winds (m s−1) associated with the Somali CEF and 925-hPa meridional flow (m s−1) of the BOB CEF, Australian CEF, and the MC islands between the three branches of the Australian CEF (Table 2).

Fig. 3.

(a) The 925-hPa meridional winds (m s−1) averaged between 2.5°S and 2.5°N from observations and the model results. (b) The regional-averaged 850-hPa horizontal winds (m s−1) associated with the Somali CEF and 925-hPa meridional flow (m s−1) of the BOB CEF, Australian CEF, and the MC islands between the three branches of the Australian CEF (Table 2).

Fig. 4.

Vertical structure of the low-level CEFs (m s−1) averaged between 2.5°S and 2.5°N during the boreal summer season in (a) observations, (b) CTRL, (c) NOTOPO, and (d) MC_OCN, and the differences (e) between CTRL and NOTOPO and (f) between NOTOPO and MC_OCN. Dotted areas in (e) and (f) exceed the 5% significance level of the Student’s t test.

Fig. 4.

Vertical structure of the low-level CEFs (m s−1) averaged between 2.5°S and 2.5°N during the boreal summer season in (a) observations, (b) CTRL, (c) NOTOPO, and (d) MC_OCN, and the differences (e) between CTRL and NOTOPO and (f) between NOTOPO and MC_OCN. Dotted areas in (e) and (f) exceed the 5% significance level of the Student’s t test.

For the Somali CEF, Fig. 5 shows the boreal summer multiyear mean wind speed and 850-hPa winds from observation and the model. Figures 5a and 5b show the Somali CEF is confined to near the East African mountains along the Somali coast, with maximum wind speed near 10°N, 50°E. However, the 850-hPa wind speed over the Indian peninsula, BOB, and south of the equator in the model is slightly overestimated and the winds over the southern parts of the Indian peninsula are more west–east oriented. Comparison between Figs. 4a and 4b also shows the model can capture the low-level Somali CEF’s location of the maximum in terms of longitude and height, except that the southerly wind is broader and the maximum values are overestimated by about 3 m s−1.

Fig. 5.

The 850-hPa horizontal wind speed (shaded; m s−1) and direction (vectors; m s−1) during the boreal summer season from the (a) observed and (b) CTRL, and the differences (c) between CTRL and NOTOPO, (d) between NOTOPO and MC_OCN, (e) between NOTOPO and NOTOPO_z0m, and (f) between NOTOPO_z0m and MC_OCN. Black vectors and dotted areas in (c)–(f) exceed the 5% significance level of the Student’s t test. Regions where the orographic height is more than the height of this pressure level are left with no data.

Fig. 5.

The 850-hPa horizontal wind speed (shaded; m s−1) and direction (vectors; m s−1) during the boreal summer season from the (a) observed and (b) CTRL, and the differences (c) between CTRL and NOTOPO, (d) between NOTOPO and MC_OCN, (e) between NOTOPO and NOTOPO_z0m, and (f) between NOTOPO_z0m and MC_OCN. Black vectors and dotted areas in (c)–(f) exceed the 5% significance level of the Student’s t test. Regions where the orographic height is more than the height of this pressure level are left with no data.

Therefore, the low-level Somali and Australian CEFs simulated by the model are sufficiently consistent with JRA-55 reanalysis data, indicating that the model can reasonably produce the low-level CEFs and can thus be used to conduct sensitivity experiments to investigate the formation of the CEFs.

b. Effect of orography and land–sea contrast on the low-level CEFs

Considering that the low-level Australian CEF comes from the Southern Hemisphere to the Northern Hemisphere and directly across the MC, we begin by investigating the impact of the MC’s orography and land–sea contrast on the three branches of the low-level Australian CEF. As indicated in Figs. 2e and 3a (MC_OCN), the low-level Australian CEF exhibits a homogeneous southerly flow from 100° to 140°E and the NG CEF decreases remarkably, with wind speeds less than 2 m s−1 when the MC is replaced by ocean. In the NOTOPO_z0m run (Figs. 2d,i), the horizontal winds extend farther northward, and three branches of the Australian CEF are clearly detected, with maximum wind speeds of about 2.0, 3.0, and 1 m s−1, respectively. With presence of the surface roughness (Figs. 2c,d,h; green and orange solid lines in Fig. 3a), the southerlies blowing into the flat Sumatra, Borneo, and New Guinea are weakened by in excess of 2 m s−1, and the SCS and NG CEFs blowing into the channels are enhanced by about 3 m s−1, with no significant change to the CM CEF. In comparing NOTOPO, NOTOPO_z0m, and MC_OCN, the reduction in CEFs over the MC islands and increase in CEFs over the channels in Fig. 2g are primarily attributed to an increase in surface friction. With the presence of orography, the southerly blowing into the MC islands in the lower troposphere weakens further and the low-level jets between the islands are further intensified (Figs. 2b,c,f; green and blue solid lines in Fig. 3a). The location of the NG CEF shifts eastward by about 6° of longitude, which might be related to the obstruction and uplifting effect of the high mountain chains in New Guinea.

The above analysis highlights the complex and different roles of the MC played on the CEFs, and indicates that the separation between the three branches of the Australian CEF is created by the presence of the islands. The orography on the islands mainly strengthens the CM CEF and governs the longitudinal location of the NG CEF. Both the land–sea contrast and orography are comparable in enhancing the SCS and NG CEFs.

The effect of the MC on the vertical structure of meridional wind at the equator is further explored (Figs. 4b–f). Note that the northward flows in the Australian CEF at the equator in NOTOPO and MC_OCN reach 850 and 875 hPa, respectively, and the peak of the maximum southerly flow over the MC is located around the 950-hPa pressure level (Figs. 4c,d). However, the southerly wind reaches 800 hPa in CTRL, with a maximum center at 925 hPa. When the flat MC is considered, the SCS and NG CEFs become much deeper, dominated by the surface roughness over the MC islands (not shown). When the orography is further retained, the three CEFs are all deepened and the axes of the maximum wind speed increase to 925 hPa. Another striking feature of the meridional winds near the equator is the concentration of isotachs between the MC islands in CTRL. The structure of the CM and NG CEFs appears to be more concentrated, and the equatorial meridional jet is much deeper with the complex terrain. One possible interpretation of this phenomenon is the impact of island uplift and the narrow tube effect associated with the distribution of the three main mountainous islands in the MC. Summarizing the above comparisons, we can conclude that both the land–sea contrast and terrain islands contribute equivalently to the depth of the SCS and NG CEFs, but the orography is the main reason for the depth increase of CM CEF and the densification of the isotachs of the CM and NG CEFs as they cross through the MC region.

For the BOB CEF, it remains almost the same, irrespective of whether the MC’s orography and land–sea contrast are present or absent in the model (Figs. 3 and 4b–f). For the Somali CEF, its vertical structure is insensitive to the land–sea contrast and orography (Figs. 4b–f), but both the Somali CEF and the westerly zonal jet over the Arabian Sea extending between 55°E and 70°E, which is defined as NSoma in Table 2, are weakened upon inclusion of the flat islands and orography (the two leftmost columns in Figs. 3b and 5c,d). The land surface roughness over the flat MC due to the land–sea contrast is the main contributor to the reduction in the Somali CEF (Figs. 5d–f). Considering the long distance between the Somali jet and the MC, the weakness in the Somali CEF is probably not due to the direct effect of the MC. Some studies have demonstrated that positive feedback exists between rainfall over the BOB and the Somali CEF, where the Somali jet lags the BOB rainfall by about three days (Srinivasan and Nanjundiah 2002; Chakraborty et al. 2002, 2009; Joseph and Sijikumar 2004). Figure 6 illustrates the lead–lag correlation between the average daily rainfall over the BOB and the 850-hPa zonal wind over the Somali coast for the NOTOPO and MC_OCN runs, in which the winds over the Somali coast correlate positively with the rainfall over the BOB, with the latter leading the wind by about three days. The result is basically consistent with the aforementioned findings of Srinivasan and Nanjundiah (2002) and Chakraborty et al. (2002, 2009). The Indian summer monsoon region becomes drier when the land–sea contrast and orography are presented (shown in the following section), with the difference being more conspicuous between NOTOPO and MC_OCN; as a consequence, the Somali jet weakens.

Fig. 6.

Lead–lag correlation between daily precipitation over the BOB (83.5°–95.5°E, 10°–20.0°N) and 850-hPa zonal wind over the Somali coast (60°–70°E, 10°–20°N) for the NOTOPO and MC_OCN runs in June–September. The correlation coefficient exceeds the 5% significance level.

Fig. 6.

Lead–lag correlation between daily precipitation over the BOB (83.5°–95.5°E, 10°–20.0°N) and 850-hPa zonal wind over the Somali coast (60°–70°E, 10°–20°N) for the NOTOPO and MC_OCN runs in June–September. The correlation coefficient exceeds the 5% significance level.

As the Australian/Somali CEF is driven by the pressure gradient between the Asian low and Australian/Mascarene high (Wang et al. 2005; He et al. 2007b; Kitoh 2017), there is a high positive correlation between the Somali/Australian CEF and the pressure gradient (Li et al. 2017). Thus, it is also desirable to investigate whether the pressure gradient force changes coherently with the three CEFs north of the Australia. The south-minus-north sea level pressure gradients from CTRL, NOTOPO, and MC_OCN presented in Fig. 7a are all positive across the three different latitudinal boxes used to calculate the meridional pressure gradient from western Indian Ocean to the MC, which supports the importance of the local meridional pressure gradients in driving the CEFs. Comparing the NOTOPO to MC_OCN (Fig. 7c), there is a subtle reduction in the pressure gradients from 40° to 65°E and a slight increase over the channels in MC region, which are responsible for the slightly weakened Somali CEF and the intensive three CEFs north of the Australia, respectively. The differences between the CTRL and NOTOPO in Fig. 7b are basically the same as in Fig. 7c. Although the magnitude of the change in pressure gradient is small, the sign of change is consistent with the response seen for the Somali CEF and three CEFs north of the Australia, which indicates that the pressure gradient force also impacts the CEFs.

Fig. 7.

(a) South-minus-north sea level pressure gradient force (Pa) across the equator from CTRL, NOTOPO, and MC_OCN. The differences of south-minus-north sea level pressure gradient (Pa) (b) between CTRL and NOTOPO and (c) between NOTOPO and MC_OCN.

Fig. 7.

(a) South-minus-north sea level pressure gradient force (Pa) across the equator from CTRL, NOTOPO, and MC_OCN. The differences of south-minus-north sea level pressure gradient (Pa) (b) between CTRL and NOTOPO and (c) between NOTOPO and MC_OCN.

4. Water vapor budget and Asian summer monsoon rainfall

a. Regional water vapor budget

The water vapor transport associated with precipitation in the monsoonal regions of Asia is the energy source for the latent heat of condensation, and is considered one of the crucial components of the Asian monsoon system. The CEFs serve as the conveyor belts controlling the exchange of moisture and mass between the two hemispheres. Therefore, it is meaningful to investigate how the moisture transport belts change when the MC’s orography and islands are removed. The VIMT into and out of a region can generally describe the water vapor budget. Therefore, we calculate the VIMT from the surface (1000 hPa) to 200 hPa, and the VIMT integrated across key boundaries in the Asian–Australian monsoon region, following the procedure of Fasullo and Webster (2002).

Figure 8 shows the VIMT integrated across several key boundaries and the regional net moisture budget, climatological mean VIMT, and moisture flux divergence during boreal summer from the model results. In the CTRL run (Figs. 8a,d), the net moisture convergence can be found from the Indian peninsula to the tropical western Pacific Ocean, where the maximum centers of the rainfall are located (Fig. 9b). In contrast, the net moisture divergence can be found mainly in the southern tropical Indian Ocean and Arabian Sea. Both the Somali and Australian CEFs play a key role in transporting moisture from the Southern Hemisphere to the Northern Hemisphere. The South Asian and SCS regions obtain moisture mainly from the western boundary. The westerly and easterly VIMT is the main contributor to the net positive VIMT over the region east of the Philippines.

Fig. 8.

(a) Mean VIMT across key boundaries (small-sized numbers) and regional net moisture budget (107 kg s−1; large-sized numbers) for the two tropical hemispheres in June–September, as depicted by the CTRL run. The arrows indicate the direction of moisture transport. Differences in boundary VIMT and net moisture budget (b) between CTRL and NOTOPO and (c) between NOTOPO and MC_OCN. The black arrows represent the direction of the differences relative to the boundary VIMT in CTRL. (d) VIMT (vectors; 107 kg m−1 s−1) and vertically integrated moisture flux divergence (shaded; mm day−1) from the CTRL run. (e),(f) As in (d), but for the differences between CTRL and NOTOPO, and between NOTOPO and MC_OCN, respectively. Red/blue boxes in (b) and (c) and black vectors and dotted areas in (e) and (f) exceed the 5% significance level of the Student’s t test.

Fig. 8.

(a) Mean VIMT across key boundaries (small-sized numbers) and regional net moisture budget (107 kg s−1; large-sized numbers) for the two tropical hemispheres in June–September, as depicted by the CTRL run. The arrows indicate the direction of moisture transport. Differences in boundary VIMT and net moisture budget (b) between CTRL and NOTOPO and (c) between NOTOPO and MC_OCN. The black arrows represent the direction of the differences relative to the boundary VIMT in CTRL. (d) VIMT (vectors; 107 kg m−1 s−1) and vertically integrated moisture flux divergence (shaded; mm day−1) from the CTRL run. (e),(f) As in (d), but for the differences between CTRL and NOTOPO, and between NOTOPO and MC_OCN, respectively. Red/blue boxes in (b) and (c) and black vectors and dotted areas in (e) and (f) exceed the 5% significance level of the Student’s t test.

Fig. 9.

Multiyear (1985–2004) mean precipitation (shaded; mm day−1) during the boreal summer season (June–September) from (a) observations and (b) CTRL, and the differences (c) between CTRL and NOTOPO and (d) between NOTOPO and MC_OCN. Also shown are surface evaporation (shaded; mm day−1) from (e) observations and (f) CTRL, and the differences (g) between CTRL and NOTOPO and (h) between NOTOPO and MC_OCN. Dotted areas in (c), (d), (g), and (h) exceed the 5% significance level of the Student’s t test.

Fig. 9.

Multiyear (1985–2004) mean precipitation (shaded; mm day−1) during the boreal summer season (June–September) from (a) observations and (b) CTRL, and the differences (c) between CTRL and NOTOPO and (d) between NOTOPO and MC_OCN. Also shown are surface evaporation (shaded; mm day−1) from (e) observations and (f) CTRL, and the differences (g) between CTRL and NOTOPO and (h) between NOTOPO and MC_OCN. Dotted areas in (c), (d), (g), and (h) exceed the 5% significance level of the Student’s t test.

Figures 8b and 8c show the differences in the boundary VIMT and net moisture budget between CTRL and NOTOPO, and between CTRL and MC_OCN. The differences in the VIMT and vertically integrated moisture flux divergence are given in Figs. 8e and 8f. With the presence of the orography (Figs. 8b,e), the moisture convergence shows a slight decrease over the southern SCS, and east of the Philippines, and is largely weakened over the BOB and significantly enhanced over northern Australia. The net moisture budget over the BOB and northern Australia region is mainly contributed by the zonal and meridional VIMT anomaly, but the decreased VIMT over the SCS and east of the Philippines are mainly attributable to the meridional VIMT associated with the overall weakened southerly flow.

Compared with the minor difference between CTRL and NOTOPO, the vertically integrated moisture flux convergence from the Indian peninsula to the southern SCS is distinctly weakened in the NOTOPO experiment compared with that in MC_OCN (Figs. 8c,f), indicating that the land–sea contrast in the MC plays a dominant role in reducing the moisture transport into the BOB and SCS regions. About 4.3 × 107 and 3.4 × 107 kg s−1 of moisture contributed by the negative zonal VIMT anomalies are carried out of these two regions, accounting for about 8.6% and 12.5% of the total in the CTRL run, respectively. As a consequence, the significantly weakened moisture convergence reduces the release of the latent heat of condensation, and is thus conducive to a cooling of the air and driving of the circulation over southern Asia and the SCS. The net moisture transport over other regions is insensitive to the presence of the MC islands.

b. Intensity of Asian summer monsoon rainfall

The water vapor transport is directly associated with the precipitation of the Asian monsoon; plus, it exhibits marked differences between the results of the CTRL run and sensitivity experiments. Figure 9 shows the spatial distribution of precipitation and evaporation during boreal summer, together with the differences between the CTRL and the two sensitivity runs. The CTRL run reproduces the overall precipitation over the Asian summer monsoon region and MC well, albeit with some biases in terms of the magnitude of the rainfall maxima (Figs. 9a,b). For example, the model is too wet over the southwestern BOB and SCS, and overestimates the precipitation maximum over the central Indian Ocean near the equator and the intertropical convergence zone. This is probably related to the cumulus convective parameterization scheme used in the model (Song and Zhang 2009; Mukhopadhyay et al. 2010; Hu et al. 2011), the lack of negative air–sea interaction feedback in the Asian monsoon regions (Kitoh 2004; Wang et al. 2005; Duan et al. 2008; Song and Zhou 2014), and the deficient representation of the orography, which can lead to less (more) rainfall on the leeward (windward) side of the highland (Chang et al. 2005, 2016; Johnson et al. 2016; Ogata et al. 2017). The local evaporation in the model is slightly underestimated from the Indian Ocean and western Pacific Ocean (Figs. 9e,f).

Despite the overall precipitation patterns over the Asian monsoon region remaining unchanged among the three experiments, the rainfall magnitude does show some differences. A comparison between NOTOPO and CTRL (Figs. 9b,c) reveals that the summer mean precipitation from the western Indian peninsula to the equatorial western Pacific Ocean decreases by about 15% with the presence of the MC’s orography. Moreover, significantly increased rainfall over the main MC islands and seas, with the exception of parts of southwestern Sumatra, northern Celebes, and northern New Britain, which have less rainfall, is detected. The moisture convergence anomalies in Fig. 8e are consistent with the overall precipitation changes in Fig. 9c. However, the surface evaporation remains almost unchanged from the western Indian peninsula to the MC region in Fig. 9g. Therefore, the precipitation changes between CTRL and NOTOPO are mainly attributed to the moisture convergence. The changes in rainfall over the MC and southern SCS are likely dominated by the interaction between the CEFs and orography (Chang et al. 2005, 2016). On the one hand, mountainous islands can force stronger southerlies flowing into the channels, and the interaction between the orography and increasing onshore winds leads to moisture convergence on the windward side of the islands, thereby enhancing the convection activity over the main MC islands and seas. On the other hand, the sheltering effect of the topography can inhibit the moisture transport to the leeward side and then give rise to the decreased rainfall over the southern SCS, northern Celebes, and northern New Britain. This is similar to the hypothesis in Johnson et al. (2016) and Ogata et al. (2017) that the orography of the MC results in more precipitation over the MC and precipitation decrease farther north through increasing the moisture convergence over the MC and reducing cross-equatorial moisture transport into the southern SCS with increasing resolution.

When only the flat islands of the MC are considered (cf. NOTOPO and MC_OCN; Fig. 9d), a significantly decreased northwest–southeast-extending rainfall belt is found from the western Indian peninsula to the equatorial western Pacific Ocean. In particular, the summer mean rainfall over South Asia, the southern SCS, and northern New Guinea decreases by nearly 21.4%, 57%, and 44.4%, respectively. In Fig. 9h, the local surface evaporation shows a coherent decrease by in excess of 2 mm day−1 over the main MC islands but remains unchanged over other regions. Compared to the moisture divergence in Fig. 8f, it is clear that both the evaporation and moisture divergence are important to the decreased rainfall over the Sumatra and New Guinea. However, the local evaporation is dominant for the decreased rainfall over the Java and Sulawesi, and the moisture divergence provides a larger contribution toward the decreased rainfall over the Borneo and regions from the western Indian peninsula to the western Pacific.

In contrast, both the complex orography and land–sea contrast in the MC exert important effects on the rainfall from the western Indian peninsula to the southern SCS, but the differences are more substantial in the MC_OCN run than in the NOTOPO run, indicating that the MC’s land–sea thermal contrast plays a more crucial role than orography in modulating the amount of the South Asian and SCS precipitation. However, the increased rainfall in the southwestern New Guinea and surrounding seas southwest of the New Guinea is governed by the orography.

As NOTOPO differs from MC_OCN in the surface roughness and moisture availability, it is essential to further analyze which factor is key in determining the precipitation over the South Asia and MC region. Figure 10 presents the moisture budget including the precipitation, evaporation, and moisture divergence from the differences between the NOTOPO and NOTOPO_z0m, and between NOTOPO_z0m and MC_OCN. With the presence of surface roughness, the precipitation is slightly enhanced from the northeastern BOB to western Pacific and is severely weakened over the MC islands, except Java and its surrounding seas, which is mainly contributed by the moisture convergence (Figs. 10a,c,e). This is mainly because the surface roughness weakens the southerly winds flowing over the islands and intensifies the SCS and NG CEF, then intercepts the northward moisture transport into the MC and promotes the westerly moisture transported into the region between 10° and 20°N. The lack of the warm and saturated ocean over the MC results in remarkably suppressed precipitation from South Asia to the western Pacific Ocean, and a precipitation increase over the MC (Fig. 10b). This is primarily due to the weakened southerly and westerly moisture transport (the enhanced southeasterly moisture transport) into the BOB, SCS, and western Pacific Ocean (the northern MC; Figs. 10d,f). It is worth noting that the precipitation over Java and the surrounding sea north of Java is decreased; this is jointly contributed by the prominent negative evaporation anomalies and moisture divergence. In contrast, the pattern between NOTOPO_z0m and MC_OCN is almost reversed in the differences between NOTOPO and NOTOPO_z0m, which indicates that the surface roughness and moisture availability play opposite roles in the South Asian and MC climate. However, the surface roughness outweighs the moisture availability in decreasing the rainfall over the MC region, which acts to intercept the moisture transport into the MC islands and then weakens the precipitation there. For the decreased precipitation from South Asia to the western Pacific, and over Java, the reduction of the moisture availability has a more considerable effect, contributed mainly by the suppressed moisture convergence and evaporation, respectively.

Fig. 10.

(a),(b) Mean precipitation (shaded; mm day−1), (c),(d) the surface evaporation (shaded; mm day−1), and (e),(f) VIMT (vectors; 107 kg m−1 s−1) and vertically integrated moisture flux divergence (shaded; mm day−1) during the boreal summer season from the differences (top) between NOTOPO and NOTOPO_z0m and (bottom) between NOTOPO_z0m and MC_OCN. Dotted areas and black arrows exceed the 5% significance level of the Student’s t test.

Fig. 10.

(a),(b) Mean precipitation (shaded; mm day−1), (c),(d) the surface evaporation (shaded; mm day−1), and (e),(f) VIMT (vectors; 107 kg m−1 s−1) and vertically integrated moisture flux divergence (shaded; mm day−1) during the boreal summer season from the differences (top) between NOTOPO and NOTOPO_z0m and (bottom) between NOTOPO_z0m and MC_OCN. Dotted areas and black arrows exceed the 5% significance level of the Student’s t test.

c. Reason behind the decreased rainfall over South Asia

The above analysis implies that the decrease in precipitation over South Asia, the SCS, and the western Pacific is mainly due to the land–sea thermal contrast induced by the presence of the MC, which can directly weaken the moisture transport into the area north of Australia associated with the SCS and CM CEFs, hence leading to the reduction in rainfall over the SCS and east of Philippines. However, the inclusion of the flat islands cannot directly influence the zonal moisture transport anomaly over South Asia, which is the principal factor leading to the negative rainfall anomaly over the BOB and Indian peninsula. Accordingly, in this section we further investigate the reason behind the decrease in rainfall over South Asia with the land–sea contrast.

In Fig. 11, the spatial distribution of the lead–lag correlation between the daily precipitation anomaly over the southern SCS (2.5°–12.5°N, 100°–122.5°E) and the precipitation anomaly and 850-hPa winds anomalies is plotted for the differences between the NOTOPO and MC_OCN. In terms of the notation, day −2 or day 2, for example, means the regional mean rainfall over the SCS lags or leads the 850-hPa horizontal winds by two days. From day −3 to day 0, rainfall accompanied by anticyclonic circulation propagates northwest from the southeastern Philippines to the southern SCS. As a negative rainfall anomaly exists over the SCS between NOTOPO and MC_OCN, the latent heat release into the atmosphere is reduced by about −46.92 W m−2 over the SCS. Consequently, cooler air associated with the reduced latent heat release will induce an anticyclone over the BOB on day 0, which then becomes continuously strengthened and integrates with the SCS anticyclone on day 3. During this period, the convective activity can be suppressed by the strengthening anticyclonic anomaly extending from the Indian peninsula to the SCS, leading to less rainfall over South Asia during boreal summer in the NOTOPO run. Considering the positive feedback between the BOB rainfall anomaly and the Somali CEF, the suppressed convective activity over South Asia induces weaker westerly flow over the Arabian Sea, and this in turn weakens the advection of moisture into the BOB, which further contributes to the reduction in precipitation.

Fig. 11.

Spatial distribution of the lead–lag correlation between the SCS (2.5°–12.5°N, 100°–122.5°E) precipitation anomaly (shaded) and the precipitation anomaly and 850-hPa horizontal winds (vectors) anomaly between NOTOPO and MC_OCN during boreal summer. The shaded area and vectors exceed the 5% significance level of the Student’s t test.

Fig. 11.

Spatial distribution of the lead–lag correlation between the SCS (2.5°–12.5°N, 100°–122.5°E) precipitation anomaly (shaded) and the precipitation anomaly and 850-hPa horizontal winds (vectors) anomaly between NOTOPO and MC_OCN during boreal summer. The shaded area and vectors exceed the 5% significance level of the Student’s t test.

The quasi-biweekly oscillation (QBWO; 10–20 days; Murakami 1976) is found to have maximum variance over the tropical and off-equator region and propagate westward from the western tropical Pacific to the Indian peninsula, closely related to the SCS and Indian summer monsoon (Krishnamurti and Ardanuy 1980; Annamalai and Slingo 2001; Chen et al. 2004). Synoptic variability interacts with the QBWO on an intraseasonal scale over South Asia (Mak 1987; Annamalai and Slingo 2001; Goswami et al. 2003). Thus, the decreased rainfall from the SCS to South Asia may involve the quasi-biweekly and synoptic disturbances affected by the MC. Figures 12a–c show the phase–longitude (averaged between 2.5° and 22.5°N) cross section of composite 10–20-day-filtered precipitation anomalies, which exhibits westward propagation during boreal summer, consistent with previous studies (Mao and Chan 2005; Wang and Chen 2017). Little difference is detected in the intensity and propagation of the QBWO convection, except for the rainfall shifting slightly westward (around 75°E) with the presence of orography. However, the difference between NOTOPO and MC_OCN is substantial. Apart from the more conspicuous westward propagation to the Indian peninsula in MC_OCN, the QBWO convection is enhanced from the western Pacific to South Asia, especially over the SCS and the Western Ghats, which is coherent with the changes in climate mean precipitation. Inspection of the standard deviation of 3–9-day-filtered precipitation variability presented in Figs. 12d–h indicates that the synoptic signal over northern Indian peninsula (New Guinea) decreases (increases) substantially with the presence of orography, and the land–sea contrast is detrimental to the synoptic variability over the northwest West Ghats and MC islands. The analysis above suggests that the orography of the MC can lead to less rainfall over northern Indian peninsula by reducing synoptic-time scale disturbances. The land–sea contrast exerts an essential impact on the decreased rainfall over the SCS and South Asia through weakening the westward propagation and intensity of the QBWO and then inhibiting the moisture transported to the SCS and South Asia. Besides, it can also cause a decrease in the rainfall over the MC by suppressing the synoptic-scale convection.

Fig. 12.

(a),(c) Phase–longitude (averaged between 2.5°S and 22.5°N) cross sections of composite 10–20-day-filtered precipitation anomalies (shaded; mm day−1) from CTRL, NOTOPO, and MC_OCN. The dotted area exceeds the 5% significance level of the Student’s t test. (d)–(f) Standard deviation of synoptic-scale (3–9-day band-passed) rainfall anomalies during June–September (shaded; mm day−1) from CTRL, NOTOPO, and MC_OCN. (g),(h) As in (d)–(f), but for the differences between CTRL and NOTOPO, and between NOTOPO and MC_OCN.

Fig. 12.

(a),(c) Phase–longitude (averaged between 2.5°S and 22.5°N) cross sections of composite 10–20-day-filtered precipitation anomalies (shaded; mm day−1) from CTRL, NOTOPO, and MC_OCN. The dotted area exceeds the 5% significance level of the Student’s t test. (d)–(f) Standard deviation of synoptic-scale (3–9-day band-passed) rainfall anomalies during June–September (shaded; mm day−1) from CTRL, NOTOPO, and MC_OCN. (g),(h) As in (d)–(f), but for the differences between CTRL and NOTOPO, and between NOTOPO and MC_OCN.

5. Conclusions and discussion

This study investigates the relative roles played by the land–sea contrast and orography in the MC in the nature of the low-level CEFs and rainfall of the Asian monsoon, with the aid of an AGCM that can realistically simulate the major features of these low-level CEFs and Asian precipitation. By comparing the results of four sensitivity experiments (i.e., with islands, with islands without orography, with islands with ocean roughness and no orography, and with ocean only) we can draw the following conclusions.

The results highlight the complex influence exerted by the MC in the formation, intensity, locations, and structure of three CEFs north of Australia. Without the land–sea contrast and topography of the MC, the Australian CEF is a homogeneous, thin southerly flow from 100° to 140°E. The land–sea thermal contrast of the MC, rather than the orography, creates the occurrence of the three relatively narrow branches north of Australia. This leads to a noticeably weakened northward wind flowing over the flat islands and to intensive and deepened winds over the ocean channels, especially the SCS and NG CEFs, which is mainly due to the surface roughness. Upon further including the orography, the CM and NG CEFs are narrowed and become deeper and stronger, and the NG CEF shifts eastward by about 6°, which may be a consequence of the narrow tube and uplift effects of the orography. Besides, it is worth noting that the orography has a remote effect by weakening the strength of the Somali CEF.

The complex orography and land–sea contrast in the MC also exert a profound impact on the precipitation from the western Indian peninsula to the western Pacific, and play opposite roles in the precipitation over the MC, by modulating the moisture convergence. The orography is dominant in increasing the rainfall over southwestern New Guinea and the surrounding seas southwest of New Guinea, through enhancing the CM and NG CEFs and the wind–terrain interaction. However, the land–sea contrast plays an essential role for precipitation decreases by nearly 21.4%, 57%, and 44.4% over South Asia, the SCS, and other parts of the MC, respectively. The reason for this is markedly weakened southerlies over the landmass from 100° to 140°E and as a consequence, abundant moisture flowing out of the western boundary at 70°E and southern boundaries. Further analysis indicates that the surface roughness over the flat MC governs the reduced precipitation over Sumatra, Borneo, and northeastern New Guinea by suppressing the moisture convergence. However, the decreased rainfall over South Asia is attributable to the reduction of moisture availability when replacing the ocean by the MC islands, which can suppress the moisture convergence. Especially, the decreased rainfall over Java and its surrounding sea is dominated by the deficient local evaporation caused by the reduced moisture availability.

Another new finding from our analysis is the indication of an indirect relation between the MC and the precipitation over South Asia. The decreased rainfall over the SCS primarily due to the land–sea contrast in the MC can lead to a reduction in latent heat release and resultant cooler air, which can induce an anticyclonic circulation response at 850 hPa over the BOB, and then suppress the convective activity over South Asia. This process involves the QBWO and synoptic-scale variability, whereby the land–sea contrast weakens the westward propagation and intensity of the QBWO and synoptic-scale disturbances.

Note that the design of the sensitivity experiments, including CTRL, NOTOPO, and MC_OCN, in this paper and Neale and Slingo (2003, hereafter NS03) is similar, but this study shows a big difference in the rainfall changes and relative contributions of the MC. In contrast to a dramatically coherent increase in precipitation over the MC and adjacent seas in their “no-islands” experiment, explained by the land–sea contrast alone in NS03, negative (positive) rainfall anomalies over the central MC (Sumatra, Java, and northeastern New Guinea) are detected after replacing the MC by ocean in our study, which is primarily due to the influence of the orography (land–sea contrast). Further, the land–sea contrast in FAMIL also contributes to a remarkably intensive rainfall over South Asia, which just shows a subtle increase in NS03. In addition, it is worth noting that we conduct another new experiment NOTOPO_z0m, which is lacking in NS03, to separate the influence of the surface roughness from the moisture availability caused by the land–sea contrast.

However, further numerical studies are needed, since the coupled air–sea processes are not included in the AGCM used in our study. Coupled processes are thought to be essential to realistically simulate the Asian monsoon (Kitoh 2004; Wang et al. 2005; Duan et al. 2008; Song and Zhou 2014). In addition, a number of studies have addressed the resolution sensitivity of the Asian monsoon and tropical precipitation simulated in GCMs and have suggested that a higher horizontal resolution can improve these model errors to some extent (Kobayashi and Sugi 2004; Kitoh et al. 2010; Sabin et al. 2013; Schiemann et al. 2014). By conducting resolution sensitivity assessment, several studies (Johnson et al. 2016; Ogata et al. 2017; Vannière et al. 2019) suggested that there is a systematic increasing precipitation in regions of the high orography, including the main MC islands, and a decreasing precipitation over the northern MC oceans in multimodels with increasing resolution, which are largely attributed to an improved representation of the orography. Similar changes occur in this study when introducing the orography of the MC to the flat islands, which supports the published resolution sensitivity studies. Besides, we also highlight the remotely weakening effect of the orography over the MC on the Somali jet and rainfall over South Asia, which is opposite to the enhanced impact of the East African Highlands and Western Ghats in the global GCMs with increasing resolution and may be partially responsible for the varying resolution sensitivity of rainfall over India (Johnson et al. 2016; Ogata et al. 2017).

Furthermore, the horizontal resolution of FAMIL employed in this study is still relatively coarse for the islands of the MC and cannot perfectly resolve the surface boundary conditions, and our results may depend on the model resolution. Based on the previous diagnostic, we can speculate that increasing the resolution of FAMIL would aggravate the changes in the differences between CTRL and NOTOPO in this study, further enhancing the increased rainfall over the main MC islands and weakening the decreased rainfall over southern SCS and South Asia. An increased portion of the precipitation changes would be more confined to the coastlines of the high orography. Apart from the profound effect of the orography, this study also highlights the importance of the joint effect of the surface roughness and moisture availability due to the land–sea contrast in reducing the rainfall over Sumatra and South Asia. As the land fraction of the MC is also better resolved in higher resolution, it is difficult to diagnose the relative roles of the increasing influence of the orography and land–sea contrast on rainfall over the South Asia and MC. Thus, more dedicated simulations with a higher resolution would be needed to reach conclusions as to the relative contributions of the surface properties over the MC.

Acknowledgments

This work was jointly supported by the National Natural Science Foundation of China under Grants 41725018 and 91637312. The authors thank Dr. Joel Hirschi for providing insightful suggestions, and thank three anonymous reviewers for their constructive comments.

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Footnotes

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