1. Introduction
The Asian summer monsoon (ASM) is one of the strongest components of the global monsoon system (Chang et al. 2011). The strong ASM circulation impacts a large region of the tropics and subtropics in the Eastern Hemisphere. The ASM is thought to be driven by large-scale thermal contrast between the Eurasian continent and Indo-Pacific Ocean (Yanai et al. 1992; Li and Yanai 1996; Wu and Zhang 1998). The initial onset of the ASM occurs in the Bay of Bengal (BOB) and the South China Sea (SCS) during early-to-mid May (Tao and Chen 1987; Wu and Zhang 1998). As the monsoon trough over the SCS deepens and extends northward, the ASM marches northward and establishes itself over southern China and the northern SCS, marking the beginning of the mei-yu–baiu rainy season in East Asia (Wu and Zhang 1998; Chen 2004; Ding and Chan 2005). After the ASM onset, active and break monsoon periods are largely determined by the boreal summer intraseasonal oscillation (BSISO), a specific mode of the tropical intraseasonal oscillation (ISO) prevailing in boreal summer (Wang and Xie 1997). While the ISO in boreal winter is of course the Madden–Julian oscillation (MJO) dominated by eastward propagation of deep convection in the tropics, the BSISO exhibits an additional northward propagating feature caused by effects of monsoonal vertical wind shear (Wang and Xie 1997; Jiang et al. 2004) and air–sea coupling (Kemball-Cook and Wang 2001; Fu et al. 2003).
The BSISO consists of two major modes, the 30–60-day mode and the 10–20-day oscillation (Wang 2005; Waliser 2006; Kikuchi et al. 2012; Chu et al. 2012; Lee et al. 2013). The dominant 30–60-day mode (similar time scales to the MJO) features prominent eastward and northward/northeastward propagation and is identified by a northwest–southeast tilted rainband between BOB and the SCS (Wang and Xie 1997). The 10–20-day oscillation mainly shows northward/northwestward progression and a southwest–northeast elongated frontal-like rainband (Lee et al. 2013). The BSISO has broad impacts on the ASM including monsoon onset (Wang and Xie 1997; Kang et al. 1999), active and break monsoon periods (Goswami 2005; Hoyos and Webster 2007; Ding and Wang 2009), seasonal mean rainfall (Krishnamurthy and Shukla 2007, 2008), and precipitation extremes (Hsu et al. 2016; Gao et al. 2016; Chen and Zhai 2017; Lee et al. 2017). Virts and Houze (2016) showed that mesoscale organization, vertical cloud structure, and lightning activity within convection over and around the BOB all vary significantly across the BSISO cycle (particularly the 30–60-day mode). The BSISO also influences the midlatitude weather and climate by interacting with the midlatitude circulation directly or indirectly (Ding and Wang 2005, 2007; Lee et al. 2011; Moon et al. 2013). Unfortunately, our ability to simulate and predict the BSISO is severely limited (Waliser et al. 2003; Kim and Kang 2008; Sabeerali et al. 2013), because of model misrepresentation of processes key to the BSISO. Model improvement requires a more complete understanding that can only be obtained by extensive observations of the BSISO, such as the evolution and structure of cloud and precipitation processes, vertical profiles of tropospheric moistening and heating, surface fluxes, atmospheric boundary layer processes, upper-ocean mixing, and air–sea interaction. Indeed these outstanding problems have motivated the Propagation of the Intraseasonal Tropical Oscillation (PISTON) field experiment to be held in late summer–early fall 2018 (see http://onrpiston.colostate.edu).
The South China Sea Monsoon Experiment (SCSMEX) held in May–June 1998 collected a rich dataset of radar, atmospheric sounding, and surface observations for studying convection and convective environments associated with the SCS monsoon onset (Lau et al. 2000). SCSMEX observations have been extensively used to investigate the organization and propagation characteristics of convection, convective structures and precipitation microphysics, regional variability of convection (southern vs northern SCS), convective heating profiles, and the diurnal cycle (Johnson and Ciesielski 2002; Wang 2004; Johnson et al. 2005; Wang and Carey 2005; Ciesielski and Johnson 2006; Aves and Johnson 2008). SCSMEX studies suggested that SCS convection generally resembled other tropical oceanic systems in terms of convective organization, such as shear-parallel convective lines (Wang 2004; Johnson et al. 2005), convective intensity (Wang and Carey 2005), and ensemble precipitation microphysics (Wang and Carey 2005). However, some unique features of SCS convection were also identified. Two unique convective organizational modes were found for the SCS convection possibly owing to its interaction with subtropical frontal systems propagating from southern China (Johnson et al. 2005). The stratiform rain fraction within precipitation systems over the northern SCS (25%) was substantially smaller compared to general tropical oceanic convection (40%–50%), possibly due to reduced atmospheric instability, low sea surface temperatures (SSTs), and relatively dry upper-tropospheric conditions during SCSMEX (Johnson et al. 2005; Wang and Carey 2005). While SCSMEX focused on convection occurring before and during the onset of the SCS monsoon in May–June, SCSMEX did not observe mid-to-late summer convection over the SCS or consider the BSISO cycles (or active-break monsoon cycles).
Many studies have showed contrasting convective intensities and structures between active and break periods of various monsoons (e.g., Williams et al. 1992; Petersen and Rutledge 2001; Cifelli and Rutledge 1998; Xu and Zipser 2012), and between the low-level westerly and easterly wind regimes in the Amazon (Petersen et al. 2002; Cifelli et al. 2002; Williams et al. 2002). Convection during monsoon breaks and easterly regime in the Amazon is more intense, containing strong mixed-phase processes (owing to higher CAPE and stronger updrafts) resulting in frequent lightning activity (Mohr and Zipser 1996; Petersen and Rutledge 2001; Williams and Stanfill 2002; Xu and Zipser 2012). Monsoon (or the Amazon westerly regime) convection resembles maritime convection characterized by weak to moderate intensity, significant “warm rain” processes, and only weak mixed-phase precipitation growth (Petersen and Rutledge 2001; Williams and Stanfill 2002; Xu and Zipser 2012). Lightning is markedly reduced during this phase. These regime-based variations in convective intensity and mixed-phase microphysics are attributed to thermodynamic variability (Williams et al. 1992; Rosenfeld and Lensky 1998; Petersen and Rutledge 2001) or aerosol loading (Rosenfeld and Lensky 1998; Williams and Stanfill 2002), or both processes working in concert (Williams and Stanfill 2002; Stolz et al. 2015). Over the SCS, convective intensity is stronger before the monsoon onset than after, as indicated by larger flash rates (Yuan and Qie 2008). Ho et al. (2008) showed that inactive ISO periods (easterly conditions) exhibited more intense radar structures compared to the active ISO periods (westerly conditions). Furthermore they showed that convection over the SCS during the inactive ISO was more land based (i.e., peak convection was mainly initiated over land in the afternoon and migrated offshore in the evening). Similarly, convection over the SCS was stronger than open ocean convection (e.g., the Philippine Sea) as indicated by more intense radar reflectivity structures and microwave ice scattering signatures (Park et al. 2007). Convection over the SCS might be best described by a combination of continental and oceanic regimes, possibly impacted by aerosols advected from adjacent continents and islands or the migration of continental convective systems from land (Takayabu et al. 2006; Park et al. 2007; Ho et al. 2008). Nevertheless, the full spectrum of the convective cloud population and microphysical structures associated with the BSISO cycles over the SCS and surrounding landmasses warrants further study.
This study provides a large-scale climatological context of the convective cloud populations and microphysical characteristics throughout the BSISO cycles over the SCS and adjacent land areas. Sixteen years of measurements from the Tropical Rainfall Measuring Mission (TRMM) are used to quantify convective structures as a function of BSISO phase (30–60-day mode), specifically, convective organization, precipitation type, cold cloud-top characteristics, vertically integrated ice content, vertical precipitation structures, and lightning flash rates. These TRMM-based convective parameters are also related to environmental variables derived from reanalysis data to determine how major environmental factors may modulate convective structures throughout the 30–60-day cycles. We also examine variability in precipitating cloud populations and precipitation microphysics between regions along the active BSISO rainband (i.e., the SCS, Philippines, and Indochina) and areas outside the rainband (Borneo and southern China).
2. Data and methodology
Data used in this study include a multisatellite rainfall product, large-scale reanalysis, TRMM precipitation features (PFs), and TRMM Lightning Imaging Sensor (LIS) orbital lightning observations during BSISO peak periods (June–September) from 1998 to 2013. These data are stratified by BSISO phase defined by the BSISO index (Lee et al. 2013). This study focuses on the key BSISO region over the SCS and surrounding landmasses (5°S–30°N, 100°–130°E), including the Indochina (INDO), Philippines (PHIL), Borneo (BORN), and southern China (SCH) as marked in Fig. 1.
Geography map of elevation (color shaded) overlaid with low-level winds at 925 hPa (vectors) from June to September in the SCS and Indo-Pacific regions. Study regions are marked by boxes, including the SCS, PHIL, INDO, BORN, SCH, and the western tropical Pacific (WPC).
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Geography map of elevation (color shaded) overlaid with low-level winds at 925 hPa (vectors) from June to September in the SCS and Indo-Pacific regions. Study regions are marked by boxes, including the SCS, PHIL, INDO, BORN, SCH, and the western tropical Pacific (WPC).
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Geography map of elevation (color shaded) overlaid with low-level winds at 925 hPa (vectors) from June to September in the SCS and Indo-Pacific regions. Study regions are marked by boxes, including the SCS, PHIL, INDO, BORN, SCH, and the western tropical Pacific (WPC).
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
a. Large-scale rainfall maps and environmental variables
To study the large-scale rainfall patterns as a function of BSISO phase, we use the TRMM Multisatellite Precipitation Analysis surface rainfall product (version 7) called 3B42 (Huffman et al. 2007). The 3B42 rainfall data have a 3-h temporal resolution and a 0.25° latitude–longitude spatial resolution, available from 50°S to 50°N during 1998–2014. This dataset mainly uses passive microwave measurements from low-Earth orbit satellites, which were first calibrated by the TRMM PR.
This study examines a set of environmental variables including SST, near-surface (2 m) temperature, low-level (850 hPa) winds, midlevel (500 hPa) geopotential height, relative humidity at these two levels, vertical wind shear (150 − 700 hPa), and convective available potential energy (CAPE). SST data are taken from the TRMM Microwave Imager and Advanced Microwave Scanning Radiometer for EOS (Gentemann et al. 2010). SST data are on daily time scales, the same scale as the BSISO index data. The atmospheric environmental variables are derived from the ancillary data of TRMM PF database (Liu et al. 2008). The ancillary data are based on European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) data (Dee and Uppala 2009), which have a horizontal spatial resolution of 1° × 1°, temporal resolution of 6 h, and mean vertical resolution of 25 hPa. Reanalysis data were interpolated into locations of PFs using linear interpolation methodology (Liu et al. 2008). Specifically, atmospheric variables including near-surface air temperature at 2 m, CAPE, relative humidity (RH), and deep vertical wind shear are analyzed. CAPE is accumulated from the near-surface level (1000 hPa over ocean and 925 hPa over land) up to the level of neutral buoyancy. RH is averaged over lower-tropospheric (850–700 hPa) and midtropospheric (500–300 hPa) levels. Deep vertical wind shear is defined as the wind shear between 150 and 700 hPa (V150 minus V700). Deep shear was thought to be an important factor for convective vigor and development of stratiform precipitation during the upscale growth of convective systems (Saxen and Rutledge 2000).
b. TRMM observations
Two different levels of TRMM data are used in this study, specifically the TRMM PF database (Liu et al. 2008) and orbital lightning observations from TRMM LIS (both version 7). LIS flash data (from all orbits) are used to calculate the lightning flash density over the entire study region (5°S–30°N, 100°–130°E). During each TRMM overpass, LIS detects total lightning (both intracloud and cloud-to-ground lightning) events (radiance based), employing a 5-km horizontal resolution and a temporal resolution of 2 ms (Christian 1999). LIS lightning events are grouped into flashes (a flash is identified as events within 6 km of one another and occurring within a 330-ms interval). The flash detection efficiency of LIS has been established to be 70%–90% (Boccippio et al. 2002; Christian et al. 2003).
The PF database utilizes various instruments on TRMM (Kummerow et al. 1998) including the PR, LIS, TRMM Microwave Imager (TMI), and Visible and IR Scanner (VIRS). These multiple sensor measurements were grouped into PFs as described by Liu et al. (2008). PFs are first defined as clusters of PR raining pixels (5-km horizontal resolution) at the near-surface level, with the centroid (based on elliptical fitting) of the cluster as the location of each PF. PR vertical profiles (250-m vertical resolution), as well as measurements of various resolutions from VIRS, TMI, and LIS, are then collocated into the PR pixels of each PF. Therefore, a PF contains quantities such as precipitation area, fraction of convective and stratiform rain, radar echo-top height, frequency of radar reflectivity values as a function of altitude, microwave brightness temperature (Tb), lightning flash rate, and so on. Horizontally and vertically polarized Tb at 85 GHz from TMI is converted into the polarization corrected temperature (PCT). PCT at 85 GHz (PCT85) is a good proxy for ice scattering (Spencer et al. 1989) or vertically integrated ice content (Vivekanandan et al. 1991). Frequencies of precipitation (PR pixels) relative to PF size, stratiform rain fraction, 20-dBZ echo-top height, infrared (IR) Tb, and PCT85, as well as 30-dBZ occurrence in the mixed-phase region and PDF of flash rates, are all derived from the PF database. Temperatures at PR vertical range heights (above sea level) are calculated through the interpolation of pressure-level temperatures and geopotential heights from ERA-Interim data.
c. BSISO indices
A primary goal of this study is to examine convection as a function of BSISO phase in the 30–60-day mode. For this purpose, we use the BSISO indices developed by Lee et al. (2013) (hereafter the Lee index), which is available from 1981 to the present (available online at http://iprc.soest.hawaii.edu/users/jylee/bsiso). The Lee index is derived from the multivariate empirical orthogonal functions (MV-EOFs) of 850-hPa zonal wind and outgoing longwave radiation over the entire ASM region (10°S–40°N, 40°–160°E). The 30–60-day mode is represented by the first and second EOF principal components (PCs). The Lee index better captures BSISO features in the ASM region compared to previous BSISO indices (e.g., Waliser et al. 2004; Annamalai and Sperber 2005; Kikuchi et al. 2012) and the MJO index (Wheeler and Hendon 2004). The BSISO index is on a daily timeframe and has eight phases, indicating various states of the ISO over the ASM region. To focus on major BSISO conditions, BSISO data are inclusive to days with significant amplitude; that is, (PC12 + PC22)1/2 > 1 during June–September. The total number of days, TRMM overpasses, and PFs in specific BSISO phases are listed in Table 1. Obviously, there are a large number of data samples in each BSISO phase (e.g., 100–200 sample days with over 1000 TRMM overpasses in each phase). These statistics provide confidence for the phase composites used in this study.
Data samples used in this study over 5°S–30°N, 100°–130°E from June to September during 1998–2013: number of BSISO periods (days), TRMM overpasses (orbits), and TRMM PFs as a function of BSISO phases (phases 1–8, labeled P1–P8). Samples are exclusive to days with BSISO index amplitude > 1.
3. Overview of 30–60-day variability
a. Large-scale rainfall and synoptic conditions
Figure 2 shows the composites of rainfall and 850-hPa winds as a function of BSISO phase during June–September. Generally, precipitation and low-level winds are suppressed over the SCS during phases 1–3 (Figs. 2a–c), whereas phases 5–7 feature bands of heavy rain and accompanying strong westerlies over the BOB, SCS, and Philippine Sea (Figs. 2e–g). In contrast, BORN and SCH, to the south and north of the SCS, show a nearly opposite rainfall pattern to the SCS (also shown in Fig. 3). Specifically, phases 1–2 mark the most suppressed conditions of precipitation (<5 mm day−1) and winds (southwesterlies at 850 hPa < 5 m s−1) over the SCS. During these same phases, heavy rainfall is located over the eastern Indian Ocean, or immediately west of Sumatra, as well as western BORN (Figs. 2a,b). The BSISO convective envelope begins propagating eastward and northward (to the northern BOB) during phase 3 (Fig. 2c) and forms a northwest–southeast-oriented rainband between the BOB and the tropical western Pacific by phase 4 (Fig. 2d). A southwest–northeast-oriented rainband accompanied by strong low-level southwesterlies (>10 m s−1) develops along the northern edge of an anticyclonic circulation (the western Pacific subtropical high) between southern China and Japan (phases 3–4). Heavy precipitation accompanied by strong low-level westerly winds (~15 m s−1) over the southern SCS and western Pacific propagates farther northward during phases 5–7 and produces peak rainfall over the central and northern SCS areas. During phases 5–7, precipitation over BORN and SCH (outside the BSISO rainband) is generally suppressed possibly owing to enhanced subsidence adjacent to the intense rainband. The heavy BSISO rainband (phases 5–7) tends to be interrupted by landmasses (i.e., INDO and the islands of the PHIL). Figure 3 clearly shows that both maximum rainfall and intraseasonal variability of precipitation are reduced over land (INDO and the PHIL) compared to over ocean (SCS). Previous studies suggested that the low thermal inertial of the land surface could suppress intraseasonal surface fluxes that help to promote the intraseasonal oscillation (Maloney and Sobel 2004; Sobel et al. 2010). Furthermore, the strong diurnal cycle over land could release the atmospheric instability on the diurnal time scale and compete with the intraseasonal time scale (BSISO) for instability (Neale and Slingo 2003; Sobel et al. 2010).
Mean rainfall (mm day−1) from TRMM 3B42 (color shaded) overlaid with winds at 850 hPa (vectors) derived from reanalyses as a function of BSISO phase during June–September 1998–2013.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Mean rainfall (mm day−1) from TRMM 3B42 (color shaded) overlaid with winds at 850 hPa (vectors) derived from reanalyses as a function of BSISO phase during June–September 1998–2013.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Mean rainfall (mm day−1) from TRMM 3B42 (color shaded) overlaid with winds at 850 hPa (vectors) derived from reanalyses as a function of BSISO phase during June–September 1998–2013.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Mean rainfall (mm day−1) time series for five study regions derived from TRMM 3B42 as a function of BSISO phase.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Mean rainfall (mm day−1) time series for five study regions derived from TRMM 3B42 as a function of BSISO phase.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Mean rainfall (mm day−1) time series for five study regions derived from TRMM 3B42 as a function of BSISO phase.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Analysis of the geopotential height at 500 hPa (Fig. 4) shows that precipitation-suppressed conditions (phases 1–3) over the SCS are dominated by the western Pacific subtropical high (Figs. 4a–c), whereas the active BSISO rainband (phases 5–7) over the BOB, SCS, and tropical western Pacific is associated with a train of low pressure circulations (Figs. 4e–g). The subtropical high has maximum westward extent and is the strongest during phase 3, prior to retreating eastward in phase 4. A low pressure circulation initiates over the central SCS in phase 5, accompanied by strong low-level westerlies. The strongest westerlies during active phases (5–7) are to the south of the low pressure systems, along the zone of large pressure gradients. The northward propagation of the active BSISO rainfall maxima (from phases 5 to 7) over the SCS and the western Pacific region corresponds closely to the northward movement of monsoon lows. Previous studies suggested that land surface heat fluxes into the boundary layer could destabilize the atmosphere ahead of the ascending zone and cause a northward shift of convective activity (Webster 1983; Srinivasan et al. 1993). Similarly, positive SST anomalies to the north of the BSISO convective center could induce boundary layer convergence and promote northward propagation of the intraseasonal disturbance (Back and Bretherton 2009; Hsu and Li 2012). Of course, many other propagation mechanisms for BSISO have been proposed [a summary is available in DeMott et al. (2013)].
Geopotential heights at 500 hPa (colored contours) and winds at 850 hPa (arrows) as a function of BSISO phase. Color bar represents geopotential height values.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Geopotential heights at 500 hPa (colored contours) and winds at 850 hPa (arrows) as a function of BSISO phase. Color bar represents geopotential height values.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Geopotential heights at 500 hPa (colored contours) and winds at 850 hPa (arrows) as a function of BSISO phase. Color bar represents geopotential height values.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
b. Precipitation features and lightning activity
Locations of PFs categorized by PF size in each BSISO phase are shown in Fig. 5. In Fig. 6, the quantitative PF population (density) as a function of BSISO phase is shown. In general, phases 1 and 2 feature the most suppressed convective condition (minimum population of PFs by all metrics) over the SCS (Figs. 5a,b and 6), while phases 5 and 6 are characterized by maximum number of MCSs and cold clouds over the SCS (Figs. 5e,f and 6b,c). Behaviors of the PF population, especially mesoscale convective systems (MCSs) with PF areas > 2000 km2 (Nesbitt et al. 2000; Liu et al. 2008), are consistent with rainfall patterns (shown in Fig. 2) over both the SCS and surrounding landmasses. During phases 1–3, PF frequency is relatively small and MCSs are infrequent over the SCS, INDO, and the PHIL, marking suppressed conditions in these areas (Figs. 5a–c). However, during these same periods MCSs occur more frequently over BORN and the most southern portions of the SCS. Many of the MCSs over the southern SCS west of BORN may originate from convection over Borneo, given the pronounced nocturnal offshore propagation from BORN (Ichikawa and Yasunari 2006). In the wake of active BSISO conditions (phase 5; Fig. 5e), MCSs increase substantially over INDO, the SCS, and the PHIL and exhibit a northward propagating pattern (phases 6 and 7; Figs. 5f,g). As will be shown in section 4a, MCSs contribute over 80% of the total rainfall during active periods, therefore, the propagation of these MCSs constitutes the main BSISO rainband. During these phases (5–7), BORN and SCH become convectively suppressed, marked by the minimum population of all types of PFs in these regions (Figs. 5e,f and 6b,c).
Distribution of PFs categorized by PF area (colored symbols) as a function of BSISO phase during June–September 1998–2013. Small PFs with area < 100 km2 are not displayed.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Distribution of PFs categorized by PF area (colored symbols) as a function of BSISO phase during June–September 1998–2013. Small PFs with area < 100 km2 are not displayed.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Distribution of PFs categorized by PF area (colored symbols) as a function of BSISO phase during June–September 1998–2013. Small PFs with area < 100 km2 are not displayed.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
PF population as a function of BSISO phase: (a) all PFs, (b) PFs with area > 2000 km2, (c) PFs with minimum IR Tb < −60°C, and (d) PFs with 20-dBZ echo-top height > 12 km. PF numbers are normalized by area of study region and number of days in each BSISO phase.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
PF population as a function of BSISO phase: (a) all PFs, (b) PFs with area > 2000 km2, (c) PFs with minimum IR Tb < −60°C, and (d) PFs with 20-dBZ echo-top height > 12 km. PF numbers are normalized by area of study region and number of days in each BSISO phase.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
PF population as a function of BSISO phase: (a) all PFs, (b) PFs with area > 2000 km2, (c) PFs with minimum IR Tb < −60°C, and (d) PFs with 20-dBZ echo-top height > 12 km. PF numbers are normalized by area of study region and number of days in each BSISO phase.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Populations of deep convection (minimum IR Tb < −60°C; Fig. 6c) and intense convection (20-dBZ echo tops exceeding 12 km; Fig. 6d) reveal a different pattern compared to MCSs (Fig. 6b), especially over land. The deepest convection over the PHIL and INDO leads the frequent MCS period by 1–2 phases, whereas deep convection and MCSs are more in phase over SCS and regions outside the BSISO rainband (BORN and SCH). For example, deep/intense convection frequently develops over the PHIL and INDO during phases 2–3 (Figs. 6c,d), whereas MCSs are less frequent during these same periods (Fig. 6b). Following the onset of active periods associated with BSISO over the SCS (phase 5), deep convective clouds increase over the SCS (Fig. 6c) but decrease over land including INDO, the PHIL, and BORN. However, intense convection (e.g., 20-dBZ echo top > 12 km) over the SCS only increases slightly during active periods of the BSISO, possibly because deep convection (producing cold IR Tb) over ocean has only moderate updraft that is not able to lift precipitation size particles (20 dBZ) to high altitudes (12 km).
Figure 7 shows the distribution of lightning flash density (unconditional) in each BSISO phase. Lightning activity over land is most robust during suppressed conditions (phases 2–3; Figs. 7a–c), corresponding to the presence of intense convection (Fig. 6d). However, during inactive BSISO periods, lightning activity is highly suppressed over the SCS, although there are indeed a significant population of deep convective clouds (Figs. 6c,d). This is because lightning production is more related to the mixed-phase physics (e.g., 30-dBZ echo-top height) rather than the cloud-top height (Zipser and Lutz 1994; Petersen et al. 1996, 1999). (Note that the frequency of 30 dBZ above the freezing level is very low during the inactive periods over the SCS, as will be shown in section 4c.) The increase of lightning activity over the SCS during active periods may be partially attributed to larger CAPE (to be shown in section 3c), which may in fact be enhanced by stronger heat and moisture fluxes from the ocean (discussed in section 3c). The increase in MCS organization over SCS during active periods may also promote lightning (Bang and Zipser 2015; Bang and Zipser 2016). As shown previously, deep convective systems over the SCS during inactive BSISO periods are relatively isolated; therefore, buoyant updrafts in the convective cores of these systems are more likely to be diluted by ambient air (Bang and Zipser 2016). After the onset of the BSISO (phase 5), flash density increases over the SCS corresponding to an increase in the MCS population. During active BSISO phases, flash density over land (i.e., the PHIL and INDO) decreases substantially, consistent with reduced CAPE (to be shown in section 3c).
Distribution of lightning flash density (flashes km−2 yr−1) derived from orbital TRMM LIS data as a function of BSISO phase. Flash density is unconditional, including both raining and not raining times sampled by LIS.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Distribution of lightning flash density (flashes km−2 yr−1) derived from orbital TRMM LIS data as a function of BSISO phase. Flash density is unconditional, including both raining and not raining times sampled by LIS.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Distribution of lightning flash density (flashes km−2 yr−1) derived from orbital TRMM LIS data as a function of BSISO phase. Flash density is unconditional, including both raining and not raining times sampled by LIS.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
c. Composites of inactive versus active BSISO periods
Figure 8 shows composites of rainfall, 850-hPa winds, and lightning density during “inactive” (phases 1–3) and “active” periods (phases 5–7). Obviously, inactive periods are characterized by minimum rainfall (Fig. 8a) and infrequent lightning activity (Fig. 8c) over the SCS, while a remarkable west–east rainband spanning INDO, the SCS, and the PHIL marks the active phases (Fig. 8b). However, BORN and SCH show a rainfall pattern opposite to INDO, the SCS, and the PHIL, as these locations are outside the BSISO rainband region.
Mean rainfall overlaid with 850-hPa winds and lightning flash density during inactive (phases 1–3) and active (phases 5–7) BSISO periods: (a) rainfall during inactive, (b) rainfall during active, (c) lightning during inactive, and (d) lightning during active. Study regions have been marked in Fig. 1.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Mean rainfall overlaid with 850-hPa winds and lightning flash density during inactive (phases 1–3) and active (phases 5–7) BSISO periods: (a) rainfall during inactive, (b) rainfall during active, (c) lightning during inactive, and (d) lightning during active. Study regions have been marked in Fig. 1.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Mean rainfall overlaid with 850-hPa winds and lightning flash density during inactive (phases 1–3) and active (phases 5–7) BSISO periods: (a) rainfall during inactive, (b) rainfall during active, (c) lightning during inactive, and (d) lightning during active. Study regions have been marked in Fig. 1.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Figure 9 shows mean values of several environmental variables during inactive and active BSISO periods as a function of location (marked in Fig. 1). SST (the SCS only) and surface air temperature in the BSISO rainband regions (the SCS, INDO, and the PHIL) are reduced (by 0.5°–1°C) during active periods compared to inactive phases, likely owing to cloud shading, precipitation cooling, and stronger upper-ocean mixing owing to enhanced low-level winds (Fig. 9a). As a result, the inactive BSISO induces larger CAPE relative to the active phases, particularly for the INDO and PHIL regions (Fig. 9b), likely due to stronger surface heating over land. It is intriguing that the SCS shows higher CAPE during active periods even though SST and air temperature are lower compared to inactive periods. A possible mechanism is that higher sea surface fluxes (because of stronger surface winds) and stronger low-level moisture convergence during active BSISO phases (as in active MJO phases; Johnson and Ciesielski 2013) lead to larger CAPE during these periods. Petersen et al. (1996) found that CAPE was highly correlated to the mixing ratio wm of the lowest 50 hPa (i.e., CAPE = 747.7wm − b, where b is an analytical constant based on regression) over the tropical western Pacific Ocean, suggesting that CAPE over warm oceans is largely moisture driven. Over the SCS, mixing ratio in the lowest 50 hPa is 17.6 g kg−1 during inactive periods and 18.2 g kg−1 during active periods. Using the relationship reported by Petersen et al. (1996), the derived CAPE differences are approximately 400 J kg−1 between active and inactive periods, consistent with the difference shown in Fig. 9b.
Environmental variables during inactive (blue) and active (red) BSISO periods: (a) SST and 2-m air temperature, (b) CAPE, (c) RH at lower levels (850–700 hPa) and mid-to-upper levels (500–300 hPa), and (d) vertical wind shear between 700 and 150 hPa. Error bars represent 0.5 standard deviation.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Environmental variables during inactive (blue) and active (red) BSISO periods: (a) SST and 2-m air temperature, (b) CAPE, (c) RH at lower levels (850–700 hPa) and mid-to-upper levels (500–300 hPa), and (d) vertical wind shear between 700 and 150 hPa. Error bars represent 0.5 standard deviation.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Environmental variables during inactive (blue) and active (red) BSISO periods: (a) SST and 2-m air temperature, (b) CAPE, (c) RH at lower levels (850–700 hPa) and mid-to-upper levels (500–300 hPa), and (d) vertical wind shear between 700 and 150 hPa. Error bars represent 0.5 standard deviation.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Relative humidity in the lower-to-middle troposphere and vertical wind shear over the SCS, INDO, and the PHIL are all enhanced during active periods (Figs. 9c,d) with influences from rainbands, low pressure circulations, and strong low-level westerly winds. Convective environments over BORN also vary significantly between inactive and active periods, showing patterns opposite to INDO and the PHIL (e.g., higher air temperature and larger CAPE during active BSISO periods). Note that active BSISO periods correspond to dry periods over BORN (and SCH) and vice versa. So it is not surprising that the CAPE over BORN is high during active periods as there is little convection to remove the CAPE. Deep shear over BORN during active phases becomes exceptionally large (25 m s−1), as the South Asian upper-level anticyclonic circulation is enhanced by active BSISO deep convection leading to strengthened upper-level easterlies or northeasterlies over BORN. In contrast, SCH shows the minimum BSISO-related variability in most of the environmental variables. Compared to other regions, SCH shows lower rainfall maxima and less variability in total precipitation between active and inactive periods (Figs. 3 and 8a,b) and therefore minimal variability in cloud shading, precipitation cooling, and condensational heating across the BSISO cycle.
4. BSISO variations on convective and microphysical structures
While section 3 provided a large-scale overview of the variability in synoptic conditions, convective environments, rainfall, precipitation features, and lightning activity over the BSISO cycle, we now discuss the detailed convective and microphysical structures during inactive and active BSISO periods. Specifically, we compare convective organization, radar echo-top heights, IR Tb frequency, vertically integrated ice content, mixed-phase microphysical properties, and lightning flash rates of convective clouds over various regions impacted by the BSISO including the SCS, INDO, the PHIL, BORN, and SCH.
a. Convective organization
Convective organization is examined in terms of the size and convective/stratiform proportions of precipitation systems. Figure 10 shows the frequency of precipitation occurrence (PR raining pixels) as a function of precipitation area and areal fraction of stratiform precipitation (FSP) of PFs. Generally, precipitation systems over the SCS, the PHIL, and INDO are larger and consist of larger FSP during active phases compared to inactive periods, possibly due to greater shear and more moist midtropospheric environments during active periods (Fig. 9). Isolated systems contribute 20%–30% of precipitation frequency (similar contribution from mostly convective systems with FSP < 20%) over the SCS, the PHIL, and INDO during inactive periods, which is twice that during active phases. This is consistent with higher CAPE, reduced deep shear, and drier midtropospheric conditions over the SCS, the PHIL, and INDO during inactive phases (Fig. 9). In these same regions, large systems with broad stratiform precipitation (e.g., PF size > 10 000 km2 and FSP > 50%) are responsible for nearly 40% of the precipitation frequency during active phases, yet they comprise only 15%–25% of the precipitation frequency during inactive periods. The precipitation contribution from large MCSs during active BSISO phases is similar to what has been observed by shipborne radar during active MJO periods over the Indian Ocean (Xu and Rutledge 2014) and the western Pacific warm pool (Rickenbach and Rutledge 1998). BORN and SCH exhibit an opposite trend regarding precipitation area and FSP to the SCS, as the wet and dry periods in these regions correspond respectively to inactive and active BSISO phases over the SCS.
Joint PDF of TRMM PR raining pixels as a function of PF area and PF stratiform rain fraction during (a)–(e) inactive and (f)–(j) active BSISO periods. Numbers within each box represent the percentage of occurrence in the specific value ranges of PF area and stratiform rain fraction.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Joint PDF of TRMM PR raining pixels as a function of PF area and PF stratiform rain fraction during (a)–(e) inactive and (f)–(j) active BSISO periods. Numbers within each box represent the percentage of occurrence in the specific value ranges of PF area and stratiform rain fraction.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Joint PDF of TRMM PR raining pixels as a function of PF area and PF stratiform rain fraction during (a)–(e) inactive and (f)–(j) active BSISO periods. Numbers within each box represent the percentage of occurrence in the specific value ranges of PF area and stratiform rain fraction.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
b. Convective echo-top heights
Figure 11 shows the probability distribution function (PDF) of 20-dBZ echo-top heights (ETH20) of convective cells (within rain areas identified as convective). Generally, ETH20 maximizes near the freezing level (5 km), corresponding to the “congestus mode” over the tropical oceans (Johnson et al. 1999). The congestus mode is caused by the near 0°C stable layer, owing to cooling and moistening effects associated with extensive regions of melting within MCSs. SCS exhibits a broad peak (2–5 km) of ETH20 during inactive BSISO periods (Fig. 11a). The lower-level (2–3 km) echo-top maximum represents the “shallow cumulus mode,” as reported over the Pacific warm pool–western Atlantic (Johnson et al. 1999), Manus Island (Hollars et al. 2004), and the central Indian Ocean (Powell and Houze 2015). It is not surprising that this shallow cumulus mode disappears over land (Figs. 11b–e), given the strong surface heating and greater instability over land. The shallow cumulus mode is better defined over more open oceans during inactive periods, such as the WPC (Fig. 11f). An examination of stability profiles shows that a low-level stable layer (850–750 hPa) associated with trade inversion apparently exists over both the SCS and WPC (Fig. 12). Of course, the midlevel stable layer is also evident over these regions. The midlevel stable layer over the SCS is stronger during active periods (Fig. 12a), because of the substantial increase of the MCS population in active BSISO periods, thus enhanced melting and subsidence within stratiform regions.
PDF of 20-dBZ echo-top height over convective area of precipitation systems observed by TRMM PR during inactive (blue bars) and active (red bars) BSISO periods. Frequency is relative to convective rain pixels near the surface.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
PDF of 20-dBZ echo-top height over convective area of precipitation systems observed by TRMM PR during inactive (blue bars) and active (red bars) BSISO periods. Frequency is relative to convective rain pixels near the surface.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
PDF of 20-dBZ echo-top height over convective area of precipitation systems observed by TRMM PR during inactive (blue bars) and active (red bars) BSISO periods. Frequency is relative to convective rain pixels near the surface.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Cumulative frequency (%) of stability (dT/dZ) greater than −5°C km−1 over ocean for (a) SCS and (b) WPC. The frequency is calculated in every 50-hPa bin.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Cumulative frequency (%) of stability (dT/dZ) greater than −5°C km−1 over ocean for (a) SCS and (b) WPC. The frequency is calculated in every 50-hPa bin.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Cumulative frequency (%) of stability (dT/dZ) greater than −5°C km−1 over ocean for (a) SCS and (b) WPC. The frequency is calculated in every 50-hPa bin.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
During active BSISO periods the frequency of shallow cumulus over the SCS (e.g., 20-dBZ echo-top heights < 3 km) decreases (Fig. 11a), as the large-scale BSISO disturbance likely disrupts the trade stable layer near 2 km (reduction of stability as shown in Fig. 12a), promoting the growth of cumulus into congestus. The modulation of the shallow cumulus cloud population caused by the active BSISO is even more evident over the WPC (Fig. 11f). During inactive periods, convective echo tops over the WPC show a clear bimodal structure with maxima at 2–3 and 5 km. After the onset of BSISO, shallow cumulus decreases substantially and congestus clouds (with tops at 4–6 km) increase correspondingly. Hence the shallow cumulus mode virtually disappears.
There is little variability of shallow cumulus and congestus echo tops over land between inactive and active periods (Figs. 11b–e). Over land, especially the PHIL and INDO, the frequency of deep convection (e.g., ETH20 > 8 km) is higher during inactive periods compared to active periods. Nevertheless, ETH20 does not show a deep convective mode (cloud tops >12 km; Johnson et al. 1999), possibly because ETH20 does not represent actual cloud tops [which may be closer to 0 dBZ as in Johnson et al. (1999)]. Xu and Rutledge (2014) compared convective top heights from 20 and 0 dBZ and IR Tb using shipborne radar data and satellite imagery collected during DYNAMO. They found that the discrepancies between these heights increase as a function of altitude; for example, these proxies are near in height for shallow to medium convection (<5–6 km), but 20-dBZ echo-top height could be 4–5 km lower than 0-dBZ echo-top height or IR-based cloud-top height when the cloud top reaches above 10 km. In fact, the deep convective mode could be seen in cold IR Tb (<−60°C or 213 K) as shown in the IR Tb distribution (Fig. 13). The frequency of IR-inferred deep convection is 30%–40% over ocean and 20%–30% over land.
Frequency of infrared Tb during inactive (blue bars) and active (red bars) BSISO periods observed by TRMM VIRS during 1998–2013.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Frequency of infrared Tb during inactive (blue bars) and active (red bars) BSISO periods observed by TRMM VIRS during 1998–2013.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Frequency of infrared Tb during inactive (blue bars) and active (red bars) BSISO periods observed by TRMM VIRS during 1998–2013.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
c. Precipitation microphysics and lightning flash rates
Precipitation microphysics is investigated in terms of microwave-based vertically integrated ice content and radar structures in the mixed-phase region. Figure 14 shows the PDF of microwave PCT85, a close proxy for vertically integrated ice content. In general, oceanic convection exhibits substantially lower frequencies of cold PCT85 [e.g., <200 K, a threshold for significant ice content suggested by Mohr and Zipser (1996)] compared to land-based convection, presumably due to reduced updraft speeds in oceanic convection. Recall that SCS convection produces the coldest IR-inferred cloud tops of all regions (Fig. 13). These opposite PCT85 and IR Tb patterns suggest that convective clouds over the SCS may indeed have a deep ice column but columnar ice contents are low. Oceanic convection also shows smaller variability on PCT85 between inactive and active periods. Over the SCS, active periods correspond to a slight increase in the frequency of moderate PCT85 (200–225 K) compared to inactive periods (Fig. 14a). In contrast, land-based convection can induce large PCT85 depressions (e.g., PCT85 < 175 K) as a result of strong ice scattering (Mohr and Zipser 1996), suggesting a very deep column of significant ice water contents, or the presence of large ice particles in a portion of the column. The PHIL and INDO exhibit the greatest BSISO-related variability on PCT85 (Figs. 14b,c); that is, inactive periods have substantially higher frequency (~20%) of convection with large PCT85 depression (<200 K) compared to active periods (~5%). Over BORN, convection during active BSISO periods induces slightly higher frequencies of moderate-to-cold PCT85 (Fig. 14c). Distributions of PCT85 are very similar between inactive and active periods over SCH (Fig. 14d).
Frequency of TMI microwave polarization-corrected Tb at 85 GHz during inactive (blue bars) and active (red bars) BSISO periods. Frequency is relative to TMI raining pixels.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Frequency of TMI microwave polarization-corrected Tb at 85 GHz during inactive (blue bars) and active (red bars) BSISO periods. Frequency is relative to TMI raining pixels.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Frequency of TMI microwave polarization-corrected Tb at 85 GHz during inactive (blue bars) and active (red bars) BSISO periods. Frequency is relative to TMI raining pixels.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Figure 15 depicts the frequency of 30-dBZ radar echoes at subfreezing temperatures within convective precipitation areas, an accepted proxy for the existence of precipitation-sized ice particles and/or mixed-phase precipitation production (Zipser and Lutz 1994; Petersen et al. 1996). Over the SCS (Fig. 15a), active-BSISO convection exhibits a higher frequency of 30 dBZ echoes at temperatures between 0° and −10°C compared to convection during inactive periods. However, SCS convection exhibits only marginal values and little BSISO variation regarding the frequency of 30 dBZ at temperatures colder than −10°C, a pseudothreshold for lightning production (Petersen et al. 1996). These mixed-phase 30-dBZ patterns are consistent with patterns of PCT85 (Fig. 14a). Together they suggest that active BSISO convection over the SCS may have slightly stronger updrafts relative to inactive BSISO convection (consistent with higher CAPE in active periods; Fig. 9b), but the active-BSISO updrafts are evidently not strong enough to loft precipitation-sized ice particles to temperatures colder than −10°C. In contrast, the PHIL and INDO show the largest BSISO variations in mixed-phase 30-dBZ frequency with inactive periods twice that of the active phases (Figs. 15b,c). This finding is consistent with observed stronger surface heating, higher CAPE, and drier midtropospheric conditions denoting inactive periods (Figs. 9a–c). Another intriguing point is that during the same BSISO active periods, distributions of 30-dBZ frequency above the freezing level are almost identical across INDO, the SCS, and the PHIL, suggesting very little land–sea contrast in mixed-phase microphysics (convective intensity) along the active BSISO rainband (further discussion will be provided in section 5). For regions outside the BSISO rainband, BORN (Fig. 15d) exhibits a higher mixed-phase 30-dBZ frequency during active BSISO periods (locally drier conditions) compared to inactive periods (locally wetter conditions), consistent with the higher CAPE over BORN during active phases (Fig. 9b). Again, SCH shows almost no change in mixed-phase 30-dBZ frequency throughout the BSISO cycle (Fig. 15e), possibly due to marginal BSISO variability in thermodynamic conditions over this region (Fig. 9).
Occurrence frequency of TRMM PR 30-dBZ radar reflectivity above the freezing altitude during inactive (blue bars) and active (red bars) BSISO periods. Frequency is relative to PR convective rain pixels at 2 km.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Occurrence frequency of TRMM PR 30-dBZ radar reflectivity above the freezing altitude during inactive (blue bars) and active (red bars) BSISO periods. Frequency is relative to PR convective rain pixels at 2 km.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Occurrence frequency of TRMM PR 30-dBZ radar reflectivity above the freezing altitude during inactive (blue bars) and active (red bars) BSISO periods. Frequency is relative to PR convective rain pixels at 2 km.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Distributions of PFs with lightning (as a function of flash rate; Fig. 16) mimic the mixed-phase 30-dBZ patterns discussed above. Flash rates over the SCS are overall lower in comparison to surrounding landmasses (e.g., the PHIL and INDO, by roughly a factor of 2), which is expected because of the substantially lower 30-dBZ frequencies at altitudes colder than −10°C (a well-accepted threshold for lightning production; Petersen et al. 1996) over ocean. Over the SCS, convection during active BSISO periods produces slightly higher flash rates relative to inactive periods (Fig. 16a), consistent with slightly stronger convective intensities during the active periods (indicated by PCT85 and mixed-phase 30-dBZ frequency). Flash rates during inactive periods over the PHIL and INDO are a factor of 2 larger than during active periods (Figs. 16b,c), reflecting the previously demonstrated stronger convection over land areas during inactive periods. Flash rates over BORN shows an opposite trend to the PHIL and INDO (Fig. 16d). It is not surprising that inactive and active BSISO periods in SCH have the same flash rate patterns (Fig. 16e), given their similar 30-dBZ frequencies in the mixed-phase region (Fig. 15).
Accumulated frequency of PFs as a function of lightning flash rates for PFs during inactive (blue bars) and active (red bars) BSISO periods. All PFs are included for the frequency calculation.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Accumulated frequency of PFs as a function of lightning flash rates for PFs during inactive (blue bars) and active (red bars) BSISO periods. All PFs are included for the frequency calculation.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
Accumulated frequency of PFs as a function of lightning flash rates for PFs during inactive (blue bars) and active (red bars) BSISO periods. All PFs are included for the frequency calculation.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
5. Summary and conclusions
The BSISO is the most dominant intraseasonal mode in the SCS and surrounding landmasses during the summer monsoon. Rainfall patterns, large-scale circulations, precipitation extremes, monsoon onset, and monsoon active and break periods have previously been shown to vary substantially over the BSISO cycle. However, the full spectrum of the convective cloud population and microphysical structures associated with the BSISO cycles in these regions was relatively unknown prior to the current study. Using 16 years of TRMM satellite convective measurements and reanalysis data, this study investigated the BSISO-associated intraseasonal and regional variations in convective environments, convective cloud populations, precipitation microphysics, and lightning activity over the SCS and surrounding landmasses. For this purpose, large-scale satellite rainfall, environmental variables, TRMM PFs, and TRMM lightning data were stratified by BSISO indices (focusing on the 30–60-day mode). Figure 17 summarizes the convective structures and microphysical properties in regions across the BSISO rainband during inactive and active BSISO periods. Generally, land–sea contrasts of convective structures and microphysical properties are substantial during inactive periods. Land–sea contrasts in these parameters are only marginal during active periods (Fig. 17, left). For example, during inactive periods land convection induces higher echo tops, stronger microwave ice scattering, and higher 30-dBZ heights in the mixed-phase zone compared to convection over the SCS. However, during active periods, all these convective and microphysical metrics are almost identical across INDO, the SCS, and the PHIL, suggesting very little land–sea contrast in the convective intensity and mixed-phase microphysics. One explanation for this is related to impact of the BSISO on the diurnal cycle of convection over land (Fig. 17, right). During inactive periods, convection over land shows a remarkable diurnal cycle with copious amounts of intense convection. In contrast, the diurnal cycle amplitude over land is reduced significantly during active BSISO conditions (perhaps owing to cloud shading, weaker surface heating, and reduced instability). As a result, intense convection is infrequent during these periods.
(left) Conceptual model of convective structures and microphysical properties across the BSISO rainband region (INDO, SCS, and PHIL) during (top) suppressed condition and (bottom) active BSISO periods, and (right) corresponding diurnal variations on 30-dBZ occurrence frequency above 6 km in convective precipitation areas.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
(left) Conceptual model of convective structures and microphysical properties across the BSISO rainband region (INDO, SCS, and PHIL) during (top) suppressed condition and (bottom) active BSISO periods, and (right) corresponding diurnal variations on 30-dBZ occurrence frequency above 6 km in convective precipitation areas.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
(left) Conceptual model of convective structures and microphysical properties across the BSISO rainband region (INDO, SCS, and PHIL) during (top) suppressed condition and (bottom) active BSISO periods, and (right) corresponding diurnal variations on 30-dBZ occurrence frequency above 6 km in convective precipitation areas.
Citation: Journal of Climate 31, 18; 10.1175/JCLI-D-18-0091.1
In conclusion, major findings from this study are summarized as follows:
Active periods of the BSISO (30–60-day mode) are characterized by a northwest–southeast-tilted rainband between the BOB and the tropical western Pacific, but this rainband is somewhat disrupted over land (rainfall over land show less variability).
During the active BSISO phases (phases 5–7), precipitation systems over the rainband regions (SCS, PHIL, and INDO regions) are larger in size and contain larger fractions of stratiform precipitation. We suggest that this behavior is due to greater shear and more moist midtropospheric environments in active periods.
The deepest convection over the PHIL and INDO leads the frequent MCS period by 1–2 phases, whereas deep convection and MCSs are more in phase over the SCS and regions outside the BSISO rainband (BORN and SCH).
Convection over the SCS during inactive periods exists in a trimodal population including shallow cumulus, congestus, and deep convection, whereas land convection only shows the congestus and deep convection modes. Shallow cumulus clouds over the SCS decrease substantially under active BSISO conditions. The shallow cumulus and congestus modes over SCS are consistent to stable layers at 2–3 km and 0°C.
Although active BSISO periods over the SCS have higher CAPE compared to inactive periods, convection in active periods is only slightly more intense as revealed by PCT85, 30-dBZ frequency at subfreezing temperatures, and lightning flash rates.
Convection over the PHIL and INDO induces substantially higher 30-dBZ heights, stronger PCT85 depressions, and larger flash rates during inactive periods than active periods. We suggest that enhanced surface heating and drier middle troposphere conditions lead to stronger convection during inactive phases.
Total rainfall, convective environments, and convective structures over BORN are out of phase with convection over the BSISO rainband regions. Specifically, BORN convection is stronger and exhibits more robust mixed-phase microphysics during active BSISO periods than inactive periods.
SCH shows little BSISO variability in system size, convective intensity, and mixed-phase microphysics, even though rainfall amounts and atmospheric circulations over SCH are significantly different between active and inactive BSISO periods.
Acknowledgments
This research was supported by the ONR PISTON program (Grant N000141613092) and the NASA PMM program (Grant NNX16AD85G). We thank the three anonymous reviewers for their constructive comments and suggestions. We thank Professors Edward Zipser (University of Utah) and Chuntao Liu (TAMU-CC) for providing the TRMM precipitation feature dataset. Thanks also go to Prof. June-Yi Lee for providing the BSISO index data. We also thank colleagues for insightful discussions of this work, especially Dr. Timothy Lang from NASA MSFC and Paul Ciesielski and Prof. Eric Maloney from CSU. The TRMM precipitation features and orbital lightning data can be downloaded from NASA Goddard Earth Sciences Data and Information Services Center (http://disc.sci.gsfc.nasa.gov/TRMM) and the BSISO index data from http://iprc.soest.hawaii.edu/users/jylee/bsiso.
REFERENCES
Annamalai, H., and K. R. Sperber, 2005: Regional heat sources and the active and break phases of boreal summer intraseasonal (30–50 day) variability. J. Atmos. Sci., 62, 2726–2748, https://doi.org/10.1175/JAS3504.1.
Aves, S. L., and R. H. Johnson, 2008: The diurnal cycle of convection over the northern South China Sea. J. Meteor. Soc. Japan, 86, 919–934, https://doi.org/10.2151/jmsj.86.919.
Back, L. E., and C. S. Bretherton, 2009: On the relationship between SST gradients, boundary layer winds, and convergence over the tropical oceans. J. Climate, 22, 4182–4196, https://doi.org/10.1175/2009JCLI2392.1.
Bang, S. D., and E. J. Zipser, 2015: Differences in size spectra of electrified storms over land and ocean. Geophys. Res. Lett., 42, 6844–6851, https://doi.org/10.1002/2015GL065264.
Bang, S. D., and E. J. Zipser, 2016: Seeking reasons for the differences in size spectra of electrified storms over land and ocean. J. Geophys. Res. Atmos., 121, 9048–9068, https://doi.org/10.1002/2016JD025150.
Boccippio, D. J., W. J. Koshak, and R. J. Blakeslee, 2002: Performance assessment of the Optical Transient Detector and Lightning Imaging Sensor. Part I: Predicted diurnal variability. J. Atmos. Oceanic Technol., 19, 1318–1332, https://doi.org/10.1175/1520-0426(2002)019<1318:PAOTOT>2.0.CO;2.
Chang, C., Y. Ding, N.-C. Lau, R. H. Johnson, B. Wang, and T. Yasunari, 2011: The Global Monsoon System: Research and Forecast. 2nd ed. World Scientific Series on Asia-Pacific Weather and Climate, Vol. 5, World Scientific, 590 pp.
Chen, G. T.-J., 2004: Research on the phenomena of Meiyu during the past quarter century: An overview. The East Asian Monsoon, C.-P. Chang, Ed., World Scientific, 357–403.
Chen, Y., and P. Zhai, 2017: Simultaneous modulations of precipitation and temperature extremes in southern parts of China by the boreal summer intraseasonal oscillation. Climate Dyn., 49, 3363–3381, https://doi.org/10.1007/s00382-016-3518-4.
Christian, H. J., 1999: Optical detection of lightning from space. Proc. 11th Int. Conf. on Atmospheric Electricity (NASA/CP-1999-209261), Guntersville, AL, NASA, 715–718.
Christian, H. J., and Coauthors, 2003: Global frequency and distribution of lightning as observed from space by the Optical Transient Detector. J. Geophys. Res., 108, 4005, https://doi.org/10.1029/2002JD002347.
Chu, J.-E., S. N. Hameed, and K.-J. Ha, 2012: Nonlinear, intraseasonal phases of the East Asian summer monsoon: Extraction and analysis using self-organizing maps. J. Climate, 25, 6975–6988, https://doi.org/10.1175/JCLI-D-11-00512.1.
Ciesielski, P. E., and R. H. Johnson, 2006: Contrasting characteristics of convection over the northern and southern South China Sea during SCSMEX. Mon. Wea. Rev., 134, 1041–1062, https://doi.org/10.1175/MWR3113.1.
Cifelli, R., and S. A. Rutledge, 1998: Vertical motion, diabatic heating and rainfall characteristics in north Australia convective systems. Quart. J. Roy. Meteor. Soc., 124, 1133–1162, https://doi.org/10.1002/qj.49712454806.
Cifelli, R., W. A. Peterson, L. D. Carey, S. A. Rutledge, and M. A. F. Silva Dias, 2002: Radar observations of the kinematic, microphysical, and precipitation characteristics of two MCSs in TRMM LBA. J. Geophys. Res., 107, 8077, https://doi.org/10.1029/2000JD000264.
Dee, D. P., and S. Uppala, 2009: Variational bias correction of satellite radiance data in the ERA-Interim reanalysis. Quart. J. Roy. Meteor. Soc., 135, 1830–1841, https://doi.org/10.1002/qj.493.
DeMott, C. A., C. Stan, and D. A. Randall, 2013: Northward propagation mechanisms of the boreal summer intraseasonal oscillation in the ERA-Interim and SP-CCSM. J. Climate, 26, 1973–1992, https://doi.org/10.1175/JCLI-D-12-00191.1.
Ding, Q.-H., and B. Wang, 2005: Circumglobal teleconnection in the Northern Hemisphere summer. J. Climate, 18, 3483–3505, https://doi.org/10.1175/JCLI3473.1.
Ding, Q.-H., and B. Wang, 2007: Intraseasonal teleconnection between the summer Eurasian wave train and the Indian monsoon. J. Climate, 20, 3751–3767, https://doi.org/10.1175/JCLI4221.1.
Ding, Q.-H., and B. Wang, 2009: Predicting extreme phases of the India summer monsoon. J. Climate, 22, 346–363, https://doi.org/10.1175/2008JCLI2449.1.
Ding, Y., and J. C.-L. Chan, 2005: The East Asian summer monsoon: An overview. Meteor. Atmos. Phys., 89, 117–142, https://doi.org/10.1007/s00703-005-0125-z.
Fu, X., B. Wang, T. Li, and J. P. McCreary, 2003: Coupling between northward-propagating, intraseasonal oscillations and sea surface temperature in the Indian Ocean. J. Atmos. Sci., 60, 1733–1753, https://doi.org/10.1175/1520-0469(2003)060<1733:CBNIOA>2.0.CO;2.
Gao, J., H. Lin, L. You, and S. Chen, 2016: Monitoring early-flood season intraseasonal oscillations and persistent heavy rainfall in South China. Climate Dyn., 47, 3845–3861, https://doi.org/10.1007/s00382-016-3045-3.
Gentemann, C. L., T. Meissner, and F. J. Wentz, 2010: Accuracy of satellite sea surface temperatures at 7 and 11 GHz. IEEE Trans. Geosci. Remote Sens., 48, 1009–1018, https://doi.org/10.1109/TGRS.2009.2030322.
Goswami, B. N., 2005: South Asian monsoon. Intraseasonal Variability of the Atmosphere–Ocean Climate System, K. M. Lau and D. E. Waliser, Eds., Springer Praxis, 19–61.
Ho, C.-H., M.-S. Park, Y.-S. Choi, and Y. N. Takayabu, 2008: Relationship between intraseasonal oscillation and diurnal variation of summer rainfall over the South China Sea. Geophys. Res. Lett., 35, L03701, https://doi.org/10.1029/2007GL031962.
Hollars, S., Q. Fu, J. Comstock, and T. Ackerman, 2004: Comparison of cloud-top height retrievals from ground-based 35 GHz MMCR and GMS-5 satellite observations at ARM TWP Manus site. Atmos. Res., 72, 169–186, https://doi.org/10.1016/j.atmosres.2004.03.015.
Hoyos, C. D., and P. J. Webster, 2007: The role of intraseasonal variability in the nature of Asian monsoon precipitation. J. Climate, 20, 4402–4424, https://doi.org/10.1175/JCLI4252.1.
Hsu, P.-C., and T. Li, 2012: Role of the boundary layer moisture asymmetry in causing the eastward propagation of the Madden–Julian oscillation. J. Climate, 25, 4914–4931, https://doi.org/10.1175/JCLI-D-11-00310.1.
Hsu, P.-C., J.-Y. Lee, and K.-J. Ha, 2016: Influence of boreal summer intraseasonal oscillation on rainfall extremes in southern China. Int. J. Climatol., 36, 1403–1412, https://doi.org/10.1002/joc.4433.
Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 38–55, https://doi.org/10.1175/JHM560.1.
Ichikawa, H., and T. Yasunari, 2006: Time–space characteristics of diurnal rainfall over Borneo and surrounding oceans as observed by TRMM-PR. J. Climate, 19, 1238–1260, https://doi.org/10.1175/JCLI3714.1.
Jiang, X., T. Li, and B. Wang, 2004: Structures and mechanisms of the northward propagating boreal summer intraseasonal oscillation. J. Climate, 17, 1022–1039, https://doi.org/10.1175/1520-0442(2004)017<1022:SAMOTN>2.0.CO;2.
Johnson, R. H., and P. E. Ciesielski, 2002: Characteristics of the 1998 summer monsoon onset over the northern South China Sea. J. Meteor. Soc. Japan, 80, 561–578, https://doi.org/10.2151/jmsj.80.561.
Johnson, R. H., and P. E. Ciesielski, 2013: Structure and properties of Madden–Julian oscillations deduced from DYNAMO sounding arrays. J. Atmos. Sci., 70, 3157–3179, https://doi.org/10.1175/JAS-D-13-065.1.
Johnson, R. H., T. M. Rickenbach, S. A. Rutledge, P. E. Ciesielski, and W. H. Schubert, 1999: Trimodal characteristics of tropical convection. J. Climate, 12, 2397–2418, https://doi.org/10.1175/1520-0442(1999)012<2397:TCOTC>2.0.CO;2.
Johnson, R. H., S. L. Aves, P. E. Ciesielski, and T. D. Keenan, 2005: Organization of oceanic convection during the onset of the 1998 East Asian summer monsoon. Mon. Wea. Rev., 133, 131–148, https://doi.org/10.1175/MWR-2843.1.
Kang, I.-S., C.-H. Ho, Y.-K. Lim, and K.-M. Lau, 1999: Principal modes of climatological seasonal and intraseasonal variations of the Asian summer monsoon. Mon. Wea. Rev., 127, 322–340, https://doi.org/10.1175/1520-0493(1999)127<0322:PMOCSA>2.0.CO;2.
Kemball-Cook, S., and B. Wang, 2001: Equatorial waves and air–sea interaction in the boreal summer intraseasonal oscillation. J. Climate, 14, 2923–2942, https://doi.org/10.1175/1520-0442(2001)014<2923:EWAASI>2.0.CO;2.
Kikuchi, K., B. Wang, and Y. Kajikawa, 2012: Bimodal representation of the tropical intraseasonal oscillation. Climate Dyn., 38, 1989–2000, https://doi.org/10.1007/s00382-011-1159-1.
Kim, H.-M., and I.-S. Kang, 2008: The impact of ocean–atmosphere coupling on the predictability of boreal summer intraseasonal oscillation. Climate Dyn., 31, 859–870, https://doi.org/10.1007/s00382-008-0409-3.
Krishnamurthy, V., and J. Shukla, 2007: Intraseasonal and seasonally persisting patterns of Indian monsoon rainfall. J. Climate, 20, 3–20, https://doi.org/10.1175/JCLI3981.1.
Krishnamurthy, V., and J. Shukla, 2008: Seasonal persistence and propagation of intraseasonal patterns over the Indian monsoon region. Climate Dyn., 30, 353–369, https://doi.org/10.1007/s00382-007-0300-7.
Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and J. Simpson, 1998: The Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15, 809–817, https://doi.org/10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2.
Lau, K. M., and Coauthors, 2000: A report of the field operations and early results of the South China Sea Monsoon Experiment (SCSMEX). Bull. Amer. Meteor. Soc., 81, 1261–1270, https://doi.org/10.1175/1520-0477(2000)081<1261:AROTFO>2.3.CO;2.
Lee, J.-Y., B. Wang, Q. Ding, K.-J. Ha, J.-B. Ahh, A. Kumar, B. Stern, and O. Alves, 2011: How predictable is the Northern Hemisphere summer upper-tropospheric circulation? Climate Dyn., 37, 1189–1203, https://doi.org/10.1007/s00382-010-0909-9.
Lee, J.-Y., B. Wang, M. C. Wheeler, X. Fu, D. E. Waliser, and I.-S. Kang, 2013: Real-time multivariate indices for the boreal summer intraseasonal oscillation over the Asian summer monsoon region. Climate Dyn., 40, 493–509, https://doi.org/10.1007/s00382-012-1544-4.
Lee, S., J. Moon, B. Wang, and H. Kim, 2017: Subseasonal prediction of extreme precipitation over Asia: Boreal summer intraseasonal oscillation perspective. J. Climate, 30, 2849–2865, https://doi.org/10.1175/JCLI-D-16-0206.1.
Li, C., and M. Yanai, 1996: The onset and interannual variability of the Asian summer monsoon in relation to land–sea thermal contrast. J. Climate, 9, 358–375, https://doi.org/10.1175/1520-0442(1996)009<0358:TOAIVO>2.0.CO;2.
Liu, C., E. J. Zipser, D. J. Cecil, S. W. Nesbitt, and S. Sherwood, 2008: A cloud and precipitation feature database from nine years of TRMM observations. J. Appl. Meteor., 47, 2712–2728, https://doi.org/10.1175/2008JAMC1890.1.
Maloney, E. D., and A. H. Sobel, 2004: Surface fluxes and ocean coupling in the tropical intraseasonal oscillation. J. Climate, 17, 4368–4386, https://doi.org/10.1175/JCLI-3212.1.
Mohr, K. I., and E. J. Zipser, 1996: Mesoscale convective systems defined by their 85-GHz ice scattering signature: Size and intensity comparison over tropical oceans and continents. Mon. Wea. Rev., 124, 2417–2437, https://doi.org/10.1175/1520-0493(1996)124<2417:MCSDBT>2.0.CO;2.
Moon, J.-Y., B. Wang, K.-J. Ha, and J.-Y. Lee, 2013: Teleconnections associated with Northern Hemisphere summer monsoon intraseasonal oscillation. Climate Dyn., 40, 2761–2774, https://doi.org/10.1007/s00382-012-1394-0.
Neale, R., and J. Slingo, 2003: The Maritime Continent and its role in the global climate: A GCM study. J. Climate, 16, 834–848, https://doi.org/10.1175/1520-0442(2003)016<0834:TMCAIR>2.0.CO;2.
Nesbitt, S. W., E. J. Zipser, and D. J. Cecil, 2000: A census of precipitation features in the tropics using TRMM: Radar, ice scattering, and lightning observations. J. Climate, 13, 4087–4106, https://doi.org/10.1175/1520-0442(2000)013<4087:ACOPFI>2.0.CO;2.
Park, M.-S., Y.-S. Choi, C.-H. Ho, C.-H. Sui, S. K. Park, and M.-H. Ahn, 2007: Regional cloud characteristics over the tropical northwestern Pacific as revealed by Tropical Rainfall Measuring Mission (TRMM) precipitation radar and TRMM Microwave Imager. J. Geophys. Res., 112, D05209, https://doi.org/10.1029/2006JD007437.
Petersen, W. A., and S. A. Rutledge, 2001: Regional variability in tropical convection: Observation from TRMM. J. Climate, 14, 3566–3586, https://doi.org/10.1175/1520-0442(2001)014<3566:RVITCO>2.0.CO;2.
Petersen, W. A., S. A. Rutledge, and R. E. Orville, 1996: Cloud-to-ground lightning observations from TOGA COARE: Selected results and lightning location algorithms. Mon. Wea. Rev., 124, 602–620, https://doi.org/10.1175/1520-0493(1996)124<0602:CTGLOF>2.0.CO;2.
Petersen, W. A., R. C. Cifelli, S. A. Rutledge, B. S. Ferrier, and B. F. Smull, 1999: Shipborne dual-Doppler operations during TOGA COARE: Integrated observations of storm kinematics and electrification. Bull. Amer. Meteor. Soc., 80, 81–98, https://doi.org/10.1175/1520-0477(1999)080<0081:SDDODT>2.0.CO;2.
Petersen, W. A., S. W. Nesbitt, R. J. Blakeslee, R. Cifelli, P. Hein, and S. A. Rutledge, 2002: TRMM observations of intraseasonal variability in convective regimes over the Amazon. J. Climate, 15, 1278–1294, https://doi.org/10.1175/1520-0442(2002)015<1278:TOOIVI>2.0.CO;2.
Powell, S. W., and R. A. Houze Jr., 2015: Evolution of precipitation and convective echo top heights observed by TRMM radar over the Indian Ocean during DYNAMO. J. Geophys. Res. Atmos., 120, 3906–3919, https://doi.org/10.1002/2014JD022934.
Rickenbach, T. M., and S. A. Rutledge, 1998: Convection in TOGA COARE: Horizontal scale, morphology, and rainfall production. J. Atmos. Sci., 55, 2715–2729, https://doi.org/10.1175/1520-0469(1998)055<2715:CITCHS>2.0.CO;2.
Rosenfeld, D., and I. M. Lensky, 1998: Satellite-based insights into precipitation formation processes in continental and maritime convective clouds. Bull. Amer. Meteor. Soc., 79, 2457–2476, https://doi.org/10.1175/1520-0477(1998)079<2457:SBIIPF>2.0.CO;2.
Sabeerali, C. T., A. Ramu Dandi, A. Dhakate, K. Salunke, S. Mahapatra, and S. A. Rao, 2013: Simulation of boreal summer intraseasonal oscillations in the latest CMIP5 coupled GCMs. J. Geophys. Res. Atmos., 118, 4401–4420, https://doi.org/10.1002/jgrd.50403.
Saxen, T. R., and S. A. Rutledge, 2000: Surface rainfall–cold cloud fractional coverage relationship in TOGA COARE: A function of vertical wind shear. Mon. Wea. Rev., 128, 407–415, https://doi.org/10.1175/1520-0493(2000)128<0407:SRCCFC>2.0.CO;2.
Sobel, A. H., E. D. Maloney, G. Bellon, and D. M. Frierson, 2010: Surface fluxes and tropical intraseasonal variability: A reassessment. J. Adv. Model. Earth Syst., 2, 2, https://doi.org/10.3894/JAMES.2010.2.2.
Spencer, R. W., H. G. Goodman, and R. E. Hood, 1989: Precipitation retrieval over land and ocean with the SSM/I: Identification and characteristics of the scattering signal. J. Atmos. Oceanic Technol., 6, 254–273, https://doi.org/10.1175/1520-0426(1989)006<0254:PROLAO>2.0.CO;2.
Srinivasan, J., S. Gadgil, and P. J. Webster, 1993: Meridional propagation of large-scale monsoon convective zones. Meteor. Atmos. Phys., 52, 15–35, https://doi.org/10.1007/BF01025750.
Stolz, D. C., S. A. Rutledge, and J. R. Pierce, 2015: Simultaneous influences of thermodynamics and aerosols on deep convection and lightning in the tropics. J. Geophys. Res. Atmos., 120, 6207–6231, https://doi.org/10.1002/2014JD023033.
Takayabu, Y. N., J. Yokomori, and K. Yoneyama, 2006: A diagnostic study on interactions between atmospheric thermodynamic structure and cumulus convection over the tropical western Pacific Ocean and over the Indochina Peninsula. J. Meteor. Soc. Japan, 84A, 151–169, https://doi.org/10.2151/jmsj.84A.151.
Tao, S., and L.