1. Introduction
Land surface wetness is an important factor in controlling regional-scale precipitation, as soil moisture modifies the energy partitioning into latent and sensible heat fluxes (e.g., Pielke 2001; Seneviratne et al. 2010). Higher soil moisture results in a smaller Bowen ratio and an increase in surface energy used by latent heat flux, leading to enhanced evaporation and an increase in convective instability and precipitation (Shukla and Mintz 1982; Clark and Arritt 1995; Pielke et al. 1997; Schär et al. 1999; Iwasaki et al. 2008). Meanwhile, lower soil moisture enhances the sensible heat flux and tends to warm the near-surface layer (Koster et al. 2009; Berg et al. 2014). This near-surface heating increases buoyancy and activates vertical mixing in the planetary boundary layer (PBL), which generates convective clouds (Rabin et al. 1990; Ek and Mahrt 1994; Ek and Holtslag 2004; Taylor and Ellis 2006) and leads to high precipitation (Giorgi et al. 1996; Paegle et al. 1996; Hohenegger et al. 2009). Stability and moistening in the lower troposphere determine whether convective clouds preferentially form over wetter land surfaces (Pan et al. 1996; Findell and Eltahir 2003).
Soil moisture–precipitation coupling is stronger in semiarid regions (i.e., transition zones between wet and dry climates) than in other regions (e.g., Koster et al. 2004). Many studies have therefore investigated the precipitation response to soil moisture in semiarid regions, in particular the effect of spatial and temporal land surface wetness transitions on precipitation. Wet and dry gradients and inhomogeneous land surface heating induce local-scale circulation, which contributes to the formation of convective clouds and precipitation (Segal and Arritt 1992; Chen and Avissar 1994; Cheng and Cotton 2004; Taylor et al. 2007; Sugimoto and Ueno 2010; Taylor et al. 2011). Temporal changes in soil moisture and vegetation cover influence latent heat fluxes and seasonal Bowen ratio transitions, which generate convective instability and/or moisten the subcloud layer between the land surface and the cloud base. These effects alter the vertical structure of convective clouds and, in turn, affect the precipitation amount (Fu and Li 2004; Yamada and Uyeda 2006; Yamada 2008). In terms of the temporal variations in land surface wetness, tropical rainy environments (e.g., the Asian monsoon region) also show significant seasonal changes in atmospheric environment and soil moisture between the premonsoon and monsoon seasons. However, the impact of soil moisture on precipitation in wet climate regions has not been widely investigated despite the finding of complex coupling between land surface changes and nocturnal precipitation over Southeast Asia (Takahashi et al. 2010b).
Bangladesh and its surrounding area (Fig. 1) is one of the world’s major rainy regions, with precipitation characteristics that vary substantially from the premonsoon to mature monsoon seasons. Kodama et al. (2005) and Islam and Uyeda (2008) reported the seasonal differences in precipitation characteristics over this region. Deep convective precipitation with lightning is frequently observed over the land during the premonsoon season, while stratiform precipitation and shallow convective precipitation prevail during the mature monsoon season. In situ observations also indicate that precipitation events are short in duration but intense during the premonsoon season relative to the monsoon season (Terao et al. 2008). It is therefore of interest to investigate the effects of seasonal changes in land surface wetness on precipitation in wet climate zones.
Topography with a 5-km grid spacing for the inner domain. Thick contours are the coastline and 200 and 1000 m MSL. The light blue shaded area is water.
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
Diurnal variations in precipitation differ between the southern and northern parts of Bangladesh and surrounding regions. Around the northern mountainous area (Fig. 1), a precipitating cloud system frequently occurs during nighttime because of southerly monsoon flow toward mountain slopes accelerated by the low-level jet (Barros and Lang 2003; Kataoka and Satomura 2005; Terao et al. 2006; Murata et al. 2008; Sato 2013). This system is similar to the large-scale cloud system over central Argentina (Salio et al. 2007; Boers et al. 2016) and the Great Plains (Maddox 1983; Cotton et al. 1989) in terms of the fact that moist low-level flow and/or topography is important for the formation of it. In addition, the precipitation system generated south of the Meghalaya Plateau propagates southward against the synoptic flow as a result of the gravity current associated with the development of a cold pool (Kataoka and Satomura 2005), which differs with propagating physical mechanisms of diurnal precipitation systems intensively investigated over Indochina by observations (Okumura et al. 2003; Takahashi et al. 2010a) and by simulations (Satomura 2000; Takahashi et al. 2010b). Over the southern plains, convective clouds and precipitation are frequently observed between afternoon and early evening in both the premonsoon and mature monsoon season (Ohsawa et al. 2001; Islam et al. 2004; Terao et al. 2008). Diurnal variations in precipitation across different regions should therefore be considered in any investigation of the response of precipitation characteristics to soil moisture in this area.
As described above, precipitation shows significant seasonal and diurnal differences over Bangladesh and surrounding regions. The temporal variations in the precipitation intensity and frequency are likely to be affected by the soil moisture transition, even though the total precipitation amount may not change considerably as a result of the land surface wetness (e.g., Koster et al. 2004). Hence, this study investigates the impact of soil moisture on precipitation intensity and frequency in the Bangladesh region, with a focus on seasonal and diurnal differences. Since soil moisture closely interacts with precipitation, it is difficult to isolate the effect of soil moisture on precipitation from the observation dataset (e.g., Ono and Takahashi 2016). For this reason, numerical experiments using a regional climate model are used to evaluate precipitation sensitivity to soil moisture. Furthermore, high-resolution numerical experiments are required to successfully simulate the diurnal variations in precipitation associated with complex topography. Consequently, high-spatial-resolution sensitivity experiments are conducted to understand diurnal changes in precipitation characteristics caused by differences in soil moisture.
2. Model and numerical setup
The sensitivity of precipitation to soil moisture in Bangladesh was examined using the Weather Research and Forecasting (WRF) Model, version 3.5.1 (Skamarock et al. 2008). The model was set up with 40 vertical layers and a horizontal mesh size of 20 km for the outer domain (the area is the same as in Fig. A1) and 5 km for the inner domain (Fig. 1). The numerical experiment was integrated from 20 March to 1 October each year for a 5-yr period from 2003 to 2007 to obtain the ensemble mean. The spinup period was 12 days, and the analysis period covered 6 months from April to September. This time frame was used to examine seasonal differences in precipitation frequency and intensity associated with soil moisture between the premonsoon (April and May) and mature monsoon (July and August) seasons. In this study, periods of the premonsoon and the mature monsoon seasons were determined by reference to Ahmed and Karmakar (1993) and Murata et al. (2008).
The physical schemes used in the experiment were the WRF single-moment 6-class microphysics scheme (Hong et al. 2004; Hong and Lim 2006), a shortwave radiation scheme (Dudhia 1989), a longwave radiation scheme (Mlawer et al. 1997), the Mellor–Yamada Nakanishi–Niino 2.5-level turbulent kinetic energy scheme for the PBL (Nakanishi and Niino 2004, 2006), and the Noah land surface model (Chen and Dudhia 2001). A cumulus parameterization scheme was not adopted for the outer or inner domains because it was not effective in simulating diurnal variations in the precipitation characteristics (Sugimoto and Takahashi 2016). We confirmed that simulation skill for precipitation in our numerical setup was comparable with that in the experiment using a cumulus parameterization scheme only for the outer domain.
The initial and boundary conditions for atmospheric variables and sea surface temperature (SST) were provided by 6-hourly ERA-Interim data (Dee et al. 2011) and daily OISST data (Reynolds et al. 2007). Three kinds of experiments were conducted under the same atmospheric and SST forcings to understand the impact of soil moisture on precipitation. In the control experiment, soil moisture content was calculated in the land surface scheme (CTL run; Fig. 2a). In two other experiments, soil moisture content was fixed at 0.1 m3 m−3 (DRY run) or 0.6 m3 m−3 (WET run) across the area 22°–27°N, 88°–93°E during the entire calculation period (Figs. 2b,c). Note that the soil moisture content in the DRY run is approximately half of the average during the premonsoon season in the CTL run (0.22 m3 m−3), while it was approximately 0.44 m3 m−3 at the surface layer in the WET run because of its adjustment to surface and/or underground runoff. Although there is a large gradient in surface wetness between the inside and outside of the area with fixed soil moisture, especially in the DRY run (Fig. 2b), it is not a critical issue for the simulated precipitation according to a comparison of results between the DRY run and an additional experiment with dry land surface condition over the whole of the outer and inner domains (see the appendix).
Spatial distribution of 5-yr-mean soil moisture averaged between 1 April and 30 September in the (a) CTL, (b) DRY, and (c) WET runs. The rectangle indicates the area 22°–27°N, 88°–93°E, where soil moisture is constant for 0.10 (0.44) m3 m−3 in DRY (WET) run. Contours are as in Fig. 1, and the light gray shaded area is water.
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
Two observational datasets were used to validate the WRF performance for precipitation: daily precipitation from Asian Precipitation–Highly Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE; 0.25°×0.25° resolution; Kamiguchi et al. 2010; Yatagai et al. 2012) and hourly precipitation (mm h−1) with 3-h interval from the Tropical Rainfall Measuring Mission (TRMM 3B42; 0.25° × 0.25° resolution; Huffman et al. 2007). The WRF Model provided 3-hourly output [mm (3 h)−1]; therefore, simulated daily and monthly precipitation was obtained from 3-hourly integrated precipitation. The threshold for calculating precipitation frequency and intensity was ≥0.3 mm (3 h)−1 for the WRF output and was ≥0.1 mm h−1 for TRMM 3B42 products. In this study, precipitation characteristics were investigated intensively across a “target area” of 22°–27°N, 88°–93°E below 200 m above mean sea level (MSL).
3. Performance of the WRF Model in simulating precipitation characteristics
Average climatological daily precipitation over the target area was compared between the APHRODITE dataset and the CTL run (Fig. 3). The seasonal variation in daily precipitation was successfully computed in the CTL run. Simulated daily precipitation is quite low during April (<7.0 mm day−1) and gradually increases in May. After early June, it reaches approximately 20 mm day−1 but decreases to less than 10 mm day−1 during August and September. These results are quantitatively consistent with APHRODITE. The simulation also captures heavy daily precipitation between 40 and 60 mm day−1, which is eventually observed.
Seasonal variations in daily precipitation averaged over the target area (22°–27°N, 88°–93°E; below 200 m MSL). Thick brown (black) lines indicate 5-yr-average daily precipitation between 2003 and 2007 for APHRODITE observations (CTL simulation) (right y axis). Thin yellow (gray) lines indicate daily precipitation for each year from 2003 to 2007 in observations (CTL simulation) (left y axis).
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
There is also consistency in the spatial distribution of precipitation between observations and simulations. During the premonsoon season, precipitation amount gradually increases from west to east in APHRODITE (Fig. 4a). In particular, precipitation amount is large around and over the Meghalaya Plateau. This precipitation distribution is qualitatively simulated in the CTL run, although an absolute value of precipitation is overestimated (Fig. 4b). Indeed, simulated maximum precipitation amount in the target area is 1387 mm month−1 over the southern Meghalaya Plateau during the premonsoon season, which is approximately twice as much as the observation (788 mm month−1). The spatial correlation coefficients and the mean absolute errors (MAEs) of monthly precipitation are examined in the CTL run relative to APHRODITE (Table 1; note that the simulated precipitation is regridded to same grid spacing with APHRODITE; i.e., 0.25° × 0.25° resolution). The spatial correlation coefficients of monthly precipitation are 0.77 and 0.68 for April and May, and their MAEs are 57 and 72 mm month−1 relative to 140 and 230 mm month−1 of monthly precipitation averaged over the target area in APHRODITE.
Spatial distribution of seasonal mean precipitation (mm month−1) during the premonsoon season in the (a) APHRODITE and (b) CTL run. (c),(d) As in (a),(b), but for the mature monsoon season. The rectangle indicates the area within 22°–27°N, 88°–93°E. Thick contours are 200 and 1000 m MSL. Dashed line is the coastline. Note that the maximum precipitation is 788 (1387) mm month−1 at the southern Meghalaya Plateau in the observation (simulation) during the premonsoon season and 1700 (1798) mm month−1 during the mature monsoon season.
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
Correlation coefficient r and MAE for the spatial distribution of simulated monthly precipitation over the target area (<200 m) relative to APHRODITE.
The east and west precipitation gradient is removed during the mature monsoon season for both APHRODITE and the CTL run (Figs. 4c and 4d, respectively), indicating that the CTL run captures a seasonal transition in the spatial distribution of precipitation. Although simulated precipitation at coastal regions of the Bay of Bengal is too much relative to that in observations, the simulation accuracy for precipitation is reflected in the spatial correlation coefficients (i.e., 0.74 in July and 0.58 in August) and the MAEs of 97 and 85 mm month−1 for each month, which are comparable to 20%–30% of observed monthly precipitation averaged over the target area.
Simulated diurnal variations in precipitation frequency were spatially evaluated using TRMM 3B42 products. During the premonsoon season, observed precipitation frequently occurs over the northern mountainous region from night to morning (Figs. 5a,d), while precipitation frequency increases over the southern plains during afternoon and evening (Figs. 5b,c). This north–south shift in the enhanced region of precipitation frequency is found in the CTL run, although the precipitation frequency is overestimated at the southern edge of the Meghalaya Plateau (Figs. 5e–h). In contrast, a diurnal variation in precipitation frequency is not remarkable during the mature monsoon season for both observations and simulation (Fig. 6). The high precipitation frequency at the southern part of the Meghalaya Plateau is observed in the early morning (Fig. 6d), which tends to be maintained from night to morning in the CTL run (Figs. 6e,g,h).
Spatial distribution of precipitation frequency (month−1) at (a) 0600, (b) 1200, (c) 1800, and (d) 0000 LT averaged for the premonsoon season in TRMM 3B42 products. (e)–(h) As in (a)–(d), but for 0600–0900, 1200–1500, 1800–2100, and 0000–0300 LT in the CTL run, respectively.
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
As in Fig. 5, but for the mature monsoon season at (a) 0900, (b) 1500, (c) 2100, and (d) 0300 LT in TRMM 3B42 products and for (e) 0900–1200, (f) 1500–1800, (g) 2100–0000, and (h) 0300–0600 LT in the CTL run.
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
Spatial distribution in the simulated precipitation intensity was qualitatively compared with TRMM 3B42 products since absolute values of precipitation intensity are difficult to evaluate because of the difference of output intervals between two datasets, as described section 2. A simulated diurnal pattern of precipitation intensity over the target area is consistent with that in the observations during the premonsoon season (Fig. 7). The precipitation intensity is enhanced over the southern plains from morning to afternoon (Figs. 7a,b,e,f), and the area with strong precipitation intensity gradually shifts north (Figs. 7c,d,g,h) for both simulations and observations. During the mature monsoon season, the observed precipitation intensity weakens during the afternoon relative to that in the morning and nighttime (Figs. 8a–d), while it is not simulated in the CTL run because of its small diurnal variation (Figs. 8e–h). The observed enhancement of precipitation intensity during night and early morning is related with the southward propagation of convective systems, which are formed over the southern regions of the Meghalaya Plateau, as described in section 1 (Kataoka and Satomura 2005; Sato 2013). We suggest that the CTL run cannot simulate well the development and southward propagation of the nocturnal convective systems during the mature monsoon season.
As in Fig. 5, but for the precipitation intensity at (a) 0900, (b) 1500, (c) 2100, and (d) 0300 LT in TRMM 3B42 products (mm h−1) and for (e) 0900–1200 LT, (f) 1500–1800 LT, (g) 2100–0000 LT, and (h) 0300–0600 LT in the CTL run [mm (3 h)−1].
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
As in Fig. 5, but for the precipitation intensity during the mature monsoon season at (a) 0900, (b) 1500, (c) 2100, and (d) 0300 LT in TRMM 3B42 products (mm h−1) and for (e) 0900–1200, (f) 1500–1800, (g) 2100–0000, and (h) 0300–0600 LT in the CTL run [mm (3 h)−1].
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
The results from high-resolution numerical simulations show good performance for seasonal variations in precipitation amount and spatial pattern over the target area. The WRF Model also captures diurnal variations in precipitation frequency and intensity depending on seasons, except for nighttime precipitation intensity during the mature monsoon season. In summary, the results show that the WRF Model is capable of simulating realistic seasonal and diurnal variations in precipitation.
4. Seasonal and diurnal variations in the influence of soil moisture on precipitation
Seasonal and diurnal variations in precipitation characteristics were simulated in the CTL run over Bangladesh and the surrounding regions. To identify the maximum amplitude of the precipitation response to soil moisture, this section examines the diurnal variation in precipitation intensity and frequency differences between the WET and DRY runs in the target area. A dependent t test was conducted to confirm statistical significance at the 90% confidence level in the differences of precipitation characteristics between the two runs at the grid points with values for more than three out of five years for both the DRY and WET runs because precipitation greater than the threshold (>0.3 mm h−1) sometimes does not occur over grid points during premonsoon season. The difference in precipitation characteristics between the WET and CTL runs is discussed in section 5d.
a. Precipitation intensity over the southern plains
Differences occur in daytime precipitation intensity in the WET and DRY runs over the southern plains (23°–25°N, 89°–92°E; rectangle in Fig. 9b), although they are not statistically significant. For the premonsoon season, precipitation intensity tends to weaken during the afternoon and evening in the WET run relative to the DRY run (Figs. 9a–c). Grid points with lower precipitation intensity in the WET run than the DRY run account for 65%–73% of the total grid points in this area during 1200–2100 LT. The difference of area-averaged precipitation intensity (the WET minus DRY runs) is therefore from −2.2 to −1.4 mm (3 h)−1 between 1200 and 2100 LT (black bars in Fig. 10a). This represents a 15%–35% weakening in precipitation intensity over wet relative to dry land surface. The tendency of the weakening of daytime precipitation intensity in the WET run is also confirmed during the mature monsoon season, although its change between the two runs is smaller than that in the premonsoon season (Figs. 9e–g and gray bars in Fig. 10a). Changes in nighttime precipitation intensity between the two experiments spatially varied over the southern plains for both seasons, except for slightly higher nocturnal precipitation intensities south of the Meghalaya Plateau during the premonsoon season (Figs. 9d and 10a).
Spatial distribution of the difference in 3-hourly precipitation intensity between the WET and DRY runs at (a) 1200–1500, (b) 1500–1800, (c) 1800–2100, (d) 0000–0300 LT averaged for April and May (i.e., the premonsoon season). (e)–(h) As in (a)–(d), but at 1200–1500, 1500–1800, 1800–2100, and 0300–0600 LT for July and August, respectively (i.e., the mature monsoon season). Thin contours are 200 m MSL and indicate the coastline. The rectangles in (a) and (b) indicate the northern mountainous area (25°–26.5°N, 89°–92°E) and the southern plains area (23°–25°N, 89°–92°E), respectively, for the average precipitation intensity anomaly (shown in Fig. 10). Hatched areas indicate the 90% confidence level of statistical significance (dependent t test).
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
Diurnal variations in the 3-hourly precipitation intensity difference between the WET and DRY runs during the premonsoon (April and May) and mature monsoon (July and August) seasons [mm (3 h)−1]. Precipitation intensity is averaged over (a) the southern plains (rectangle in Fig. 9b) and (b) the northern mountainous regions (lower than 200 m MSL; rectangle in Fig. 9a).
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
b. Precipitation intensity in the northern mountains
Precipitation intensity is also modified by the land surface wetness in the northern mountains for only a limited time. During the premonsoon season, precipitation intensity is significantly stronger between the Himalayas and Meghalaya Plateau from 2100 to 0300 LT in the WET run than in the DRY run (Fig. 9d) and tends to be weaker from 0300 to 0900 LT (not shown). This result suggests a timing shift in the occurrence of intense precipitation between the two runs (i.e., strong precipitation intensity is simulated earlier in the wet experiment than in the dry one). The difference of area-averaged precipitation intensity between WET and DRY runs is negative during the morning and afternoon (Fig. 10b), although this change is not significant. Meanwhile, in the mature monsoon season, precipitation intensity over the northern Meghalaya Plateau is significantly enhanced from 1500 to 1800 LT in the WET run relative to the DRY run (Fig. 9f), which moves and expands north during 1800–0000 LT (e.g., Fig. 9g). Indeed, area-averaged precipitation intensity over the northern regions increases during 1500 and 0000 LT (Fig. 10b).
c. Precipitation frequency
The spatial distribution of precipitation frequency was compared between the WET and DRY runs. The precipitation frequency response to soil moisture clearly differs between regions and seasons. Around the northern mountains and south of the Meghalaya Plateau, precipitation frequency increases throughout the day in both the premonsoon and mature monsoon seasons, except during nighttime in the mature monsoon season (Figs. 11a–h and 12b).
As in Fig. 9, but for precipitation frequency integrated for two months [(2 month)−1] during premonsoon and mature monsoon seasons. The time in (d) differs from Fig. 9 at 0300–0600 LT. The open rectangle in (b) indicates the southern plains (22.5°–24.5°N, 88.5°–91.5°E) for the area-averaged precipitation frequency anomaly (shown in Fig. 12).
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
As in Fig. 10, but for 3-hourly precipitation frequency [(2 month)−1] averaged over (a) the southern plains (rectangle in Fig. 11b) and (b) the northern mountainous regions (lower than 200 m MSL; rectangle in Fig. 9a).
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
Over the southern plains, the daytime change in precipitation frequency is greater than the nighttime change. In the WET run, daytime precipitation frequency decreases during the premonsoon season and increases during the mature monsoon season (Figs. 11a–c,e–g). Differences in area-averaged precipitation frequency (22.5°–24.5°N, 88.5°–91.5°E; rectangle in Fig. 11b) during the premonsoon season between the WET and DRY runs indicate the infrequent occurrence of precipitation over wet land surfaces (i.e., 78% of the DRY run) between 1500 and 1800 LT (Fig. 12a). The reduction in precipitation frequency in the WET run is also found during 1200–1500 and 1800–2100 LT, although statistical significance is not confirmed in the early afternoon (Figs. 11a,c). On the other hand, the increase in precipitation frequency during the mature monsoon season is maintained during the day and night over the southern plains in the WET run (Figs. 11e–h) with a maximum peak during 1200–1500 LT (Fig. 12a), which is quite different from that in the premonsoon season as shown in Figs. 11a–d. The area-averaged precipitation frequency in the WET run reaches up to 136% of the DRY run at the maximum peak.
Noteworthy changes in precipitation characteristics between the WET and DRY runs are summarized as follows (see also Table 2) for wet land surface experiments:
Over the southern plains, daytime precipitation intensity weakens during both the premonsoon and mature monsoon seasons.
Nocturnal intense precipitation occurs frequently in the premonsoon season south of the Meghalaya Plateau.
Precipitation frequency over the southern plains decreases during daytime in the premonsoon season but increases all day in the mature monsoon season.
North of the Meghalaya Plateau, high precipitation intensity is found during nighttime for both seasons in the wet land surface experiment compared with the dry one.
In the northern mountainous area, precipitation frequency increases regardless of the time and season, except for nighttime during the mature monsoon season.
Precipitation response over wet land surfaces relative to dry land surfaces.
Changes in the atmospheric environment and its effect on precipitation are discussed in the next section in order to understand seasonal and diurnal differences in the precipitation response to soil moisture. We focus particularly on the systematic modification described by points 1, 3, and 5 above because shorter model output interval and higher spatial resolution would be required to reveal a change in local-scale precipitation characteristics around the Meghalaya Plateau with complex topography as illustrated by points 2 and 4.
5. Physical processes that modify precipitation characteristics
a. Precipitation intensity
According to the comparison of 3-hourly precipitation between the WET and DRY runs over the southern plains, daytime precipitation intensity weakens over wet land surfaces during the premonsoon season as well as the mature monsoon season. This indicates that wet land surfaces suppress convective activity even though they enhance evapotranspiration and increase water vapor in the lower troposphere (not shown). Therefore, we need to consider other controls on daytime precipitation intensity over the plains.
Land surface heating is an important factor in the generation of convective clouds because it produces high buoyancy and active vertical mixing in the PBL (Rabin et al. 1990; Ek and Mahrt 1994; Giorgi et al. 1996; Paegle et al. 1996; Ek and Holtslag 2004; Taylor and Ellis 2006; Hohenegger et al. 2009). The difference in surface temperature between the WET and DRY runs is used to examine the impact of land surface heating on the precipitation intensity.
In the DRY run, the average surface temperature in the premonsoon season over the southern plains (23°–25°N, 89°–92°E) is 318 K during 1200–1500 LT, and it decreases by 14 K (to 304 K) in the WET run (Fig. 13a). Weakening of land surface heating in the WET run decreases the daytime sensible heat flux from the land surface to the atmosphere (Fig. 13b) and suppresses the development of vertical mixing in the PBL (Fig. 13c). The difference in PBL height between the two experiments changes the vertical profile of enthalpy CpΔT and CpΔT with latent energy LΔq. In the WET run, CpΔT below 700 hPa is lower than in the DRY run, but CpΔT + LΔq increases in the near-surface layer and decreases between 850 and 500 hPa during the premonsoon and mature monsoon seasons (Fig. 14).
Diurnal variation in the 5-yr average of (a) surface temperature, (b) sensible heat flux, and (c) PBL height for the DRY (red lines) and the WET (black lines) runs averaged over the southern plains. Red open (filled) square and black open (filled) triangles indicate data for April (May).
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
Vertical profiles of difference in 5-yr-average enthalpy (red) and enthalpy plus latent energy (black) over the southern plains (rectangle in Fig. 9b) at 1500 LT between the WET and DRY runs for (a) the premonsoon and (b) the mature monsoon seasons.
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
The PBL height reaches to the layer with the highest CpΔT + LΔq energy in each experiment in the premonsoon season (i.e., below 900 hPa for the WET run and between 850 and 500 hPa for the DRY run). In this layer, the mixing ratio of cloud hydrometeors (cloud water and cloud ice) is larger in both experiments than the CTL run (Fig. 15a). In addition, total hydrometeors (cloud water, cloud ice, rain, snow, and graupel) increase between 750 and 150 hPa in the DRY run relative to that in the CTL run (Fig. 15b); as a result, condensation heating intensifies vertical upward motion between 650 and 200 hPa (i.e., in the mid- and upper troposphere; Fig. 15c). This effect is limited to below 850 hPa in the WET run, which causes weakening of precipitation intensity compared with the DRY run. Similar precipitation intensity responses to soil moisture are also found during the mature monsoon season, although the amplitude is smaller than in the premonsoon season (Figs. 13, 14, and 16). In other words, surface heating is more important than low-level moistening as a trigger for daytime cloud convection and intense precipitation regardless of the season.
Vertical profiles of 5-yr-mean difference in (a) mixing ratio of cloud hydrometeors (cloud water and cloud ice), (b) mixing ratio of total hydrometeors (cloud water, cloud ice, rain, snow, and graupel), and (c) vertical upward motion during the premonsoon season, averaged over 23°–25°N, 89°–92°E (rectangle in Fig. 9b) at 1500 LT. Black, blue, and red lines indicate the results in the CTL, WET, and DRY runs, respectively.
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
As in Fig. 15, but for the mature monsoon season.
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
b. Precipitation frequency over the southern plains
Over the southern plains, the daytime precipitation frequency response to soil moisture decreases during the premonsoon and increases during the mature monsoon seasons (Figs. 11 and 12a). The modification of daytime precipitation frequency associated with land surface wetness is explained by a seasonal difference in relative humidity in the lower troposphere. During the premonsoon season, the suppression of vertical mixing in the WET run, as discussed in section 5a, causes higher relative humidity below 900 hPa compared with the DRY run (Fig. 17a). As a result, the formation of cloud hydrometeors is enhanced at 900 hPa in the WET run (Fig. 15a). However, there are fewer precipitation hydrometeors (rain, snow, and graupel) in the lower troposphere in the WET run (Fig. 15b minus Fig. 15a). This suggests that the increase in relative humidity at the lower troposphere of the WET run (89% at 950 hPa and lower than 70% at 900 hPa averaged over the southern plains) is insufficient for cloud droplet growth to cause frequent precipitation. In addition, precipitation hydrometeors also do not develop well at the mid- and upper troposphere in the WET run compared with in the DRY run, implying their fall to the lower troposphere is not effective.
As in Fig. 15, but for relative humidity averaged over 22.5°–24.5°N, 88.5°–91.5°E (rectangle in Fig. 11b) during (a) the premonsoon and (b) the mature monsoon seasons.
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
On the other hand, during the mature monsoon season, the lower troposphere is wetter than in the premonsoon season for both the WET and DRY runs in association with a background atmospheric environment (Fig. 17b). Area-averaged relative humidity reaches approximately 94% at 950 hPa and 90% at 900 hPa over the southern plains in the WET run. The higher relative humidity in the near-surface layer in the WET run favors the formation of cloud hydrometeors and the growth of precipitation hydrometeors relative to that in the DRY run (Figs. 16a,b). Therefore, precipitation frequency increases during the mature monsoon season if the land surface will be wet and will supply additional water vapor to the atmosphere.
c. Precipitation frequency over the northern mountain regions
The response of precipitation frequency over the northern mountain regions on the land surface wetness differs from that over the southern plains during the premonsoon season. Daytime precipitation does not occur effectively over the southern plains in the WET run as described above, while precipitable water increases throughout the day around the northern mountainous area resulting from active evapotranspiration and moisture convergence (Fig. 18). Since the mountain slopes force upward motion, convective clouds and precipitation frequently occur in this region. In contrast, during the mature monsoon season, the differences in the precipitable water between the two experiments are small relative to the premonsoon season (not shown). This result suggests that additional water vapor supplied from the land surface during the daytime quickly affects the increase in daytime precipitation frequency over both the southern plains and the mountain slopes in the WET run during the mature monsoon season, which causes the relatively small modification of nighttime precipitation frequency.
Spatial distribution of precipitable water (shaded) and vertical integrated water vapor flux (vectors) differences between the WET and DRY runs at (a) 1500 and (b) 0300 LT during April. (c),(d) As in (a),(b), but for May. Solid contours show 200 and 1000 m MSL, and the dashed contour indicates the coastline.
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
d. Impacts of land surface wetness on precipitation
The WET and DRY runs clearly capture seasonal, diurnal, and geographical differences in the sensitivity of precipitation intensity and frequency to soil moisture over Bangladesh and surrounding regions. Our result supports the idea that land surface wetness can control precipitation characteristics even in a wet climate region. Ono and Takahashi (2016) and Takahashi (2016) have showed the higher precipitation intensity over the southern plains in Bangladesh during the premonsoon season compared with the mature monsoon season using observation datasets. Although their result suggests soil moisture–precipitation coupling, an effect of seasonal transition in atmospheric environment is not negligible. The sensitivity experiments in this study could have qualitatively identified their suggestion (i.e., soil moisture affects the precipitation intensity in this region).
The response of diurnal variation in precipitation amount to soil moisture is similar to that in precipitation frequency in our experiments (i.e., the increase of nighttime precipitation over the southern plains and precipitation increase throughout the day over the northern mountainous regions over the wetter land surface). As a result, the 5-yr mean of monthly precipitation amount averaged over the target area increases in the WET run relative to that in the DRY run. In addition, the precipitation amount sensitivity to land surface wetness is smaller during the wetter season (i.e., the mature monsoon season) than during the drier season (i.e., the premonsoon season). On these two points, our finding is qualitatively consistent with previous studies such as Koster et al. (2004); that is, there is a positive correlation between monthly scale precipitation amount and soil moisture, and drier environment as the semiarid condition has a larger precipitation–soil moisture coupling.
Note that there is little variance in the difference in precipitation frequency and intensity between the WET and CTL runs compared with the WET and DRY runs, although its amplitude is slightly small. The exception is daytime precipitation intensity over the southern plains during the premonsoon season, which is stronger in the CTL run than in the DRY run, suggesting that slightly higher evapotranspiration in the CTL run effectively enhances convection. The difference in precipitation characteristics between the WET and DRY or between the WET and CTL runs indicates that land surface wetness before April is likely to modify precipitation intensity and frequency at the beginning of the premonsoon season. Furthermore, a fixed dry land surface is unrealistic during the rainy season while moistening of the land surface can occur from external forcings such as floods or the expansion of irrigated paddy areas. The land-cover modification and a change in flood frequency due to climate variability may affect long-term precipitation variation over Bangladesh. In other words, we need to consider a soil moisture–precipitation coupling when long-term variations in precipitation characteristics are evaluated over this rainy region, even though the effect of land–atmosphere interaction on local precipitation is not the major factor, as discussed in, for example, Wei et al. (2016).
Sensitivity experiments were conducted in this study under two extreme land surface conditions. However, as shown by Barthlott and Kalthoff (2011), the relationship between soil moisture and precipitation is not always linear. Additional experiments that change soil moisture over finer intervals are required to deeply understand soil moisture–precipitation coupling in rainy regions. Furthermore, we should conduct similar numerical experiments in other regions with different background climates to examine differences of precipitation response to land surface condition depending on the regions (e.g., Sugimoto et al. 2015).
6. Conclusions
This study investigated the response of precipitation characteristics to soil moisture over Bangladesh and surrounding regions using a regional climate model based on sensitivity experiments with fixed soil moisture. The numerical experiments simulate seasonal and diurnal variations in precipitation frequency and intensity and suggest that land surface wetness strongly contributes to changes in precipitation characteristics for both the premonsoon and mature monsoon seasons.
Over the southern plains, daytime precipitation intensity weakens over wet land surfaces during the premonsoon and mature monsoon seasons. This is a result of the reduction in surface heating and suppression of vertical mixing in the PBL that controls vertical expansion of the layer with high CpΔT + LΔq energy and upward motion associated with condensation heating in the mid- and upper troposphere. The fact that precipitation intensity is higher over drier land surfaces in both seasons suggests enhanced seasonal variations in precipitation intensity; that is, there is stronger (weaker) precipitation intensity in the premonsoon (mature monsoon) season over drier (wetter) land surfaces, as observed by Kodama et al. (2005) and Islam and Uyeda (2008).
Daytime precipitation frequency decreases over the southern region during the premonsoon season and increases during the mature monsoon season over wet land surfaces. This difference in the precipitation frequency response to soil moisture between the two seasons is explained by water vapor condensation associated with relative humidity in the lower troposphere. Around the northern mountains, precipitation triggered by topographically forced upward motion occurs frequently over wet land surfaces as a result of the water vapor supply in both the premonsoon and mature monsoon seasons.
Acknowledgments
This work was supported in part by the Green Network of Excellence (GRENE) and Social Implementation Program on Climate Change Adaptation Technology (SI-CAT) programs of the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. ERA-Interim datasets were provided by the European Centre for Medium-Range Weather Forecasts (ECMWF; http://www.ecmwf.int/). OISSTv2 SST data were provided by the National Climatic Data Center (NCDC; http://www.ncdc.noaa.gov/sst/). Daily APHRODITE precipitation data were obtained from the project website (http://www.chikyu.ac.jp/precip/). Hourly TRMM 3B42 precipitation datasets were downloaded from their FTP site (
APPENDIX
An Additional Numerical Experiment with Dry Land Surface over the Entire Simulated Area
As shown in Fig. 2, there is a large gradient in the surface wetness of our numerical design between the inside and outside of the region with fixed soil moisture, in particular for the DRY run. We conducted an additional numerical experiment to confirm an impact of spatial gradient in land surface wetness on local-scale precipitation. The numerical setup is the same as the DRY experiment, except that soil moisture is modified in the entire simulated area for both the outer and inner domains (allDRY run; Fig. A1).
Spatial distribution of soil moisture on 0000 UTC 1 August 2003 in the outer domain of the allDRY run. The rectangle indicates the area of 22°–27°N, 88°–93°E. Contours are as in Fig. 1, and the light gray shaded area is water.
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
Spatial distribution of seasonal precipitation is quite similar between the DRY and allDRY runs for both of the premonsoon and mature monsoon seasons (Fig. A2), not only the inside of the target area (22°–27°N, 88°–93°E; rectangle) but also the outside of it. The simulated precipitation in these two sensitivity experiments clearly differ with the CTL run as shown in Fig. 4. In addition, diurnal variations in anomalies of precipitation intensity and frequency between the WET and allDRY runs are examined over the southern plains (Fig. A3), where we found an active precipitation–soil moisture coupling between the WET and DRY runs as described in section 4. Daytime precipitation intensity in the WET run tends to weaken during both two seasons relative to that in the allDRY run, and an opposite response of daytime precipitation frequency on soil moisture is found between the premonsoon and the mature monsoon seasons. This result is consistent with the precipitation–soil moisture coupling as shown between the WET and DRY runs, suggesting that an effect of strong moisture gradients on the simulated precipitation characteristics is small over the moist South Asian regions, which differs from the previous studies indicating that a local- or regional-scale circulation generated by the spatial heterogeneity in land surface wetness affects precipitation over the semiarid regions (Segal and Arritt 1992; Chen and Avissar 1994; Cheng and Cotton 2004; Taylor et al. 2007; Sugimoto and Ueno 2010; Taylor et al. 2011).
Spatial distribution of seasonal mean precipitation (mm month−1) during the premonsoon season in the (a) DRY and (b) allDRY runs. (c),(d) As in (a),(b), but for the mature monsoon season.
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
Diurnal variations in the (a) 3-hourly precipitation intensity difference between the WET and allDRY runs during the premonsoon and mature monsoon seasons [mm (3 h)−1]. (b) As in (a), but for the precipitation frequency [(2 month)−1]. Precipitation intensity (frequency) is averaged over the southern plains; rectangle in Fig. 9b (Fig. 11b).
Citation: Journal of Climate 30, 3; 10.1175/JCLI-D-15-0800.1
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