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Journal of Climate

Convective Variability associated with the Boreal Summer Intraseasonal Oscillation in the South China Sea Region --Manuscript Draft--

Manuscript Number:

JCLI-D-18-0091

Full Title:

Convective Variability associated with the Boreal Summer Intraseasonal Oscillation in the South China Sea Region

Article Type:

Article

Corresponding Author:

Weixin Xu Colorado State University Fort Collins, UNITED STATES

Corresponding Author's Institution:

Colorado State University

First Author:

Weixin Xu

Order of Authors:

Weixin Xu Steven Rutledge

Abstract:

This study investigates the convective cloud population, precipitation microphysics, and lightning activity associated with the Boreal Summer Intraseasonal Oscillation (BSISO) over the South China Sea (SCS) and surrounding landmasses. SCS rainfall shows a marked 30-60d intraseasonal variability. This variability is less evident over land. The population of mesoscale convective systems (MCSs) and the stratiform rain fraction over the SCS, Philippines, and Indochina increase remarkably after the onset of BSISO. Convection over the SCS during inactive periods exhibits a trimodal population including shallow cumulus, congestus and deep convection, mirroring the situation over tropical open oceans. The shallow mode is absent over land. Shallow cumulus clouds rapidly transition to congestus clouds over the SCS under active BSISO conditions. Over land, deep convection and lightning lead total rainfall and MCSs by 2-3 BSISO phases, while they are somewhat in phase over the SCS. Though convective instability over the SCS is larger during active periods compared to inactive periods, variability in convective intensity and precipitation microphysics is minimal, with active periods showing only higher frequency of moderate ice scattering and 30dBZ heights extending to -10 oC. Over the Philippines and Indochina, inactive phases exhibit substantially stronger ice scattering signatures, robust mixed-phase microphysics, and higher lightning flash rates, possibly due to greater convective instability and a stronger convective diurnal cycle. Total rainfall, convective environments, and convective structures over Borneo are all out of phase with that over Philippines and Indochina, while Southern China shows little BSISO variability on convective intensity and lightning frequency.

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Convective Variability associated with the Boreal Summer Intraseasonal Oscillation

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in the South China Sea Region

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Weixin Xu and Steven A. Rutledge

Department of Atmospheric Sciences, Colorado State University, Fort Collins, CO

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Submitted to:

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Journal of Climate

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Submitted: February 26, 2018

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Revised: June 7, 2018

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Accepted: June 11, 2018

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___________________________

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Corresponding Author Address: Dr. Weixin Xu, Department of Atmospheric Sciences, Colorado

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State University, 3915 West Laporte Avenue, Fort Collins, CO 80521.

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ABSTRACT

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This study investigates the convective cloud population, precipitation microphysics, and

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lightning activity associated with the Boreal Summer Intraseasonal Oscillation (BSISO) over the

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South China Sea (SCS) and surrounding landmasses. SCS rainfall shows a marked 30-60d

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intraseasonal variability. This variability is less evident over land. The population of mesoscale

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convective systems (MCSs) and the stratiform rain fraction over the SCS, Philippines, and

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Indochina increase remarkably after the onset of BSISO. Convection over the SCS during

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inactive periods exhibits a trimodal population including shallow cumulus, congestus and deep

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convection, mirroring the situation over tropical open oceans. The shallow mode is absent over

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land. Shallow cumulus clouds rapidly transition to congestus clouds over the SCS under active

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BSISO conditions. Over land, deep convection and lightning lead total rainfall and MCSs by 2-3

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BSISO phases, while they are somewhat in phase over the SCS. Though convective instability

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over the SCS is larger during active periods compared to inactive periods, variability in

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convective intensity and precipitation microphysics is minimal, with active periods showing only

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higher frequency of moderate ice scattering and 30-dBZ heights extending to -10 oC. Over the

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Philippines and Indochina, inactive phases exhibit substantially stronger ice scattering signatures,

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robust mixed-phase microphysics, and higher lightning flash rates, possibly due to greater

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convective instability and a stronger convective diurnal cycle. Total rainfall, convective

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environments, and convective structures over Borneo are all out of phase with that over

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Philippines and Indochina, while Southern China shows little BSISO variability on convective

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intensity and lightning frequency.

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1. Introduction

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The Asian summer monsoon (ASM) is one of the strongest components of the global

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monsoon system (Chang et al. 2011). The strong ASM circulation impacts a large region of the

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tropics and subtropics in the eastern hemisphere. The ASM is thought to be driven by large-scale

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thermal contrast between the Eurasian continent and Indo-Pacific Ocean (Yanai et al. 1992; Li

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and Yanai 1996; Wu and Zhang 1998). The initial onset of the ASM occurs in the Bay of Bengal

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(BOB) and the South China Sea (SCS) during early-to-mid May (Tao and Chen 1987; Wu et al.

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1998). As the monsoon trough over the SCS deepens and extends northward, the ASM marches

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northward and establishes itself over southern China and the northern SCS, marking the

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beginning of the Meiyu/Baiu rainy season in East Asia (Wu et al. 1998; Chen 2004; Ding and

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Chan 2005). After the ASM onset, active and break monsoon periods are largely determined by

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the boreal summer intraseasonal oscillation (BSISO), a specific mode of the tropical

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intraseasonal oscillation (ISO) prevailing in boreal summer (Wang and Xie 1997). While the ISO

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in boreal winter is of course the Madden-Julian Oscillation (MJO) dominated by eastward

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propagation of deep convection in the tropics, the BSISO exhibits an additional northward

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propagating feature due to effects of monsoonal vertical wind shear (Wang and Xie 1997; Jiang

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et al. 2004) and air-sea coupling (Kemball-Cook and Wang 2001, Fu et al. 2003).

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The BSISO consists of two major modes, the 30-60 day mode and the 10-20 day

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oscillation (Wang et al. 2005; Waliser 2006; Kikuchi et al. 2012; Chu et al. 2012; Lee et al.

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2013). The dominant 30-60 day mode (similar time scales to the MJO) features prominent

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eastward and northward/northeastward propagation, and is identified by a northwest-

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southeastward tilted rain band between BOB and the SCS (Wang and Xie 1997). The 10-20 day

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oscillation mainly shows northward/northwestward progression and a southwest-northeast

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elongated frontal-like rain band (Lee et al. 2013). The BSISO has broad impacts on the ASM

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including monsoon onset (Wang and Xie 1997; Kang et al. 1999), active and break monsoon

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periods (Goswami 2005; Hoyos and Webster 2007; Ding and Wang 2009), seasonal mean

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rainfall (Krishnamurthy and Shukla 2007, 2008), and precipitation extremes (Hsu et al. 2016;

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Gao et al. 2016; Chen and Zhai 2017; Lee et al. 2017). Virts and Houze (2016) showed that

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mesoscale organization, vertical cloud structure, and lightning activity within convection over

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and around the BOB all vary significantly across the BSISO cycle (particularly the 30-60 day

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mode). The BSISO also influences the midlatitude weather and climate by interacting with the

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midlatitude circulation directly or indirectly (Ding and Wang 2005, 2007; Lee et al. 2011; Moon

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et al. 2013). Unfortunately, our ability to simulate and predict the BSISO is severely limited

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(Waliser et al. 2003; Kim and Kang 2008; Sabeerali et al. 2013), due to model misrepresentation

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of processes key to the BSISO. Model improvement requires a more complete understanding that

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can only be obtained by extensive observations of the BSISO, e.g., evolution and structure of

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cloud and precipitation processes, vertical profiles of tropospheric moistening and heating,

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surface fluxes, atmospheric boundary-layer processes, upper-ocean mixing and air-sea

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interaction. Indeed these outstanding problems have motivated the Propagation of the Intra-

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seasonal Tropical Oscillation (PISTON) field experiment to be held in late summer-early fall

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2018 (http://onrpiston.colostate.edu).

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The South China Sea Monsoon Experiment (SCSMEX) held in May-June 1998 collected

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a rich dataset of radar, atmospheric sounding, and surface observations for studying convection

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and convective environments associated with the SCS monsoon onset (Lau et al. 2000).

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SCSMEX observations have been extensively used to investigate the organization and

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propagation characteristics of convection, convective structures and precipitation microphysics,

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regional variability of convection (southern versus northern SCS), convective heating profiles,

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and the diurnal cycle (Johnson and Ciesielski 2002; Wang 2004; Johnson et al. 2005; Wang and

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Carey 2005; Ciesielski and Johnson 2006; Aves and Johnson 2008). SCSMEX studies suggested

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that SCS convection generally resembled other tropical oceanic systems in terms of convective

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organization, e.g., shear-parallel convective lines (Wang 2004; Johnson et al. 2005), convective

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intensity (Wang and Carey 2005), and ensemble precipitation microphysics (Wang and Carey

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2005). However, some unique features of SCS convection were also identified. Two unique

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convective organizational modes were found for the SCS convection possibly owing to its

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interaction with subtropical frontal systems propagating from southern China (Johnson et al.

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2005). The stratiform rain fraction within precipitation systems over the northern SCS (25%) was

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substantially smaller compared to general tropical oceanic convection (40-50%), possibly due to

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reduced atmospheric instability, low sea surface temperatures (SSTs), and relatively dry upper

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tropospheric conditions during SCSMEX (Johnson et al. 2005; Wang and Carey 2005). While

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SCSMEX focused on convection occurring before and during the onset of the SCS monsoon in

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May-June, SCSMEX did not observe mid-to-late summer convection over the SCS nor consider

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the BSISO cycles (or active-break monsoon cycles).

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Many studies have showed contrasting convective intensities and structures between

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active and break periods of various monsoons (e.g., Williams et al. 1992; Petersen and Rutledge

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2001; Cifelli and Rutledge 1998; Xu and Zipser 2012), and between the low-level westerly and

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easterly wind regimes in the Amazon (Petersen et al. 2002; Cifelli et al. 2002; Williams et al.

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2002). Convection during monsoon breaks and easterly regime in the Amazon is more intense,

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containing strong mixed-phase processes (owing to higher CAPE and stronger updrafts) resulting

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in frequent lightning activity (Mohr and Zipser, 1996; Petersen and Rutledge 2001; Williams and

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Stanfill 2002; Xu and Zipser 2012). Monsoon (or the Amazon westerly regime) convection

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resembles maritime convection characterized by weak to moderate intensity, significant “warm-

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rain” processes, and only weak mixed-phase precipitation growth (Petersen and Rutledge 2001;

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Williams and Stanfill 2002; Xu and Zipser 2012). Lightning is markedly reduced during this

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phase. These regime-based variations in convective intensity and mixed-phase microphysics are

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attributed to thermodynamic variability (Williams et al. 1992; Rosenfeld and Lensky 1998;

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Petersen and Rutledge 2001) or aerosol loading (Rosenfeld and Lensky 1998; Williams and

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Stanfill 2002), or both processes working in concert (Williams and Stanfill 2002; Stolz et al.

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2015). Over the SCS, convective intensity is stronger before the monsoon onset than after, as

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indicated by larger flash rates (Yuan and Qie 2008). Ho et al. (2008) showed that inactive ISO

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periods (easterly conditions) exhibited more intense radar structures compared to the active ISO

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periods (westerly conditions). Furthermore they showed that convection over the SCS during the

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inactive ISO was more land-based, i.e., peak convection was mainly initiated over land in the

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afternoon and migrated offshore in the evening. Similarly, convection over the SCS was stronger

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than open ocean convection (i.e., Philippine Sea) as indicated by more intense radar reflectivity

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structures and microwave ice scattering signatures (Park et al. 2007). Convection over the SCS

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might be best described by a combination of continental and oceanic regimes, possibly impacted

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by aerosols advected from adjacent continents/islands or the migration of continental convective

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systems from land (Takayabu 2006; Park et al. 2007; Ho et al. 2008). Nevertheless, the full

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spectrum of the convective cloud population and microphysical structures associated with the

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BSISO cycles over the SCS and surrounding landmasses warrants further study.

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This study provides a large-scale climatological context of the convective cloud

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populations and microphysical characteristics throughout the BSISO cycles over the SCS and

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adjacent land areas. Sixteen years of measurements from the Tropical Rainfall Measuring

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Mission (TRMM) are used to quantify convective structures as a function of BSISO phase (30-

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60 day mode), specifically, convective organization, precipitation type, cold cloud top

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characteristics, vertically integrated ice content, vertical precipitation structures, and lightning

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flash rates. These TRMM-based convective parameters are also related to environmental

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variables derived from reanalysis data to determine how major environmental factors may

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modulate convective structures throughout the 30-60 day cycles. We also examine variability in

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precipitating cloud populations and precipitation microphysics between regions along the active

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BSISO rain band (i.e., the SCS, Philippines, Indochina Peninsula) and areas outside the rain band

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(Borneo and Southern China).

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2. Data and Methodology

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Data used in this study include a multi-satellite rainfall product, large-scale reanalysis,

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TRMM precipitation features (PFs), and TRMM Lightning Imaging Sensor (LIS) orbital

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lightning observations during BSISO peak periods (June-September) from 1998 to 2013. These

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data are stratified by BSISO phase defined by the BSISO index (Lee et al. 2013). This study

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focuses on the key BSISO region over the SCS and surrounding landmasses [5S-30N; 100-

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130E], including the Indochina peninsula (INDO), Philippines (PHIL), Borneo (BORN), and

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Southern China (SCH) as marked in Fig. 1.

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a. Large-scale rainfall maps and environmental variables

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To study the large-scale rainfall patterns as a function of BSISO phase, we use the TRMM

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Multi-satellite Precipitation Analysis surface rainfall product (version 7) called 3B42 (Huffman

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et al. 2007). The 3B42 rainfall data have a 3 h temporal resolution and a 0.25o spatial resolution, 7

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available from 50 S to 50 N during 1998-2014. This dataset mainly uses passive-microwave

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measurements from low-earth orbit satellites, which were first calibrated by the TRMM PR.

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This study examines a set of environmental variables including SST, near surface (2 m)

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temperature, low-level (850 hPa) winds, mid-level (500 hPa) geopotential height, relative

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humidity at these two levels, vertical wind shear (150-700 hPa), and convective available

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potential energy (CAPE). SST data are taken from the TRMM Microwave Imager and Advanced

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Microwave Scanning Radiometer for EOS (Gentemann et al. 2010). SST data are on daily time

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scales, the same scale as the BSISO index data. The atmospheric environmental variables are

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derived from the ancillary data of TRMM PF database (Liu et al. 2008). The ancillary data are

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based on European Centre for Medium-Range Weather Forecasts re-analysis Interim (ERA-

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Interim) reanalysis data (Dee et al. 2009), which have a horizontal spatial resolution of 1o by 1o,

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temporal resolution of 6 h and mean vertical resolution of 25 hPa. Reanalysis data were

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interpolated into locations of PFs using linear interpolation methodology (Liu et al. 2008).

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Specifically, atmospheric variables including near surface air temperature at 2 m, convective

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available potential energy (CAPE), relative humidity (RH), and deep vertical wind shear are

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analyzed. CAPE is accumulated from the near-surface level (1000 hPa over ocean and 925 hPa

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over land) up to the level of neutral buoyancy. RH is averaged over lower troposphere (850-700

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hPa) and mid-troposphere (500-300 hPa) levels. Deep vertical wind shear is defined as the wind

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shear between 150-700 hPa (V150 – V700). Deep shear was thought to be an important factor for

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convective vigor and development of stratiform precipitation during the upscale growth of

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convective systems (Saxen and Rutledge 2000).

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b. TRMM observations

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Two different levels of TRMM data are used in this study, specifically, the TRMM PF

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database (Liu et al. 2008), and orbital lightning observations from TRMM LIS (both version 7).

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LIS flash data (from all orbits) are used to calculate the lightning flash density over the entire

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study region [5S-30N; 100-130E]. During each TRMM overpass, LIS detects total lightning

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(both intracloud and cloud-to-ground lightning) events (radiance-based), employing a 5 km

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horizontal resolution and a temporal resolution of 2 ms (Christian 1999). LIS lightning events are

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grouped into flashes (a flash is identified as events within 6 km of one another and occurring

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within a 330 ms interval). The flash detection efficiency of LIS has been established to be 70%–

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90% (Boccippio et al. 2002; Christian et al. 2003).

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The PF database utilizes various instruments on TRMM (Kummerow et al. 1998)

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including the PR, LIS, TRMM Microwave Imager (TMI), and Visible and IR Scanner (VIRS).

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These multiple sensor measurements were grouped into PFs as described by Liu et al. (2008).

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PFs are first defined as clusters of PR raining pixels (5 km horizontal res.) at the near surface

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level, with the centroid (based on elliptical fitting) of the cluster as the location of each PF. PR

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vertical profiles (250 m vertical res.), as well as measurements of various resolutions from VIRS,

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TMI, and LIS, are then collocated into the PR pixels of each PF. Therefore, a PF contains

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quantities such as precipitation area, fraction of convective/stratiform rain, radar echo top height,

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frequency of radar reflectivity values as a function of altitude, microwave brightness temperature

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(Tb), lightning flash rate, etc. Horizontally and vertically polarized Tb at 85 GHz from TMI is

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converted into the polarization corrected temperature (PCT). PCT at 85 GHz (PCT85) is a good

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proxy for ice scattering (Spencer et al. 1989) or vertically integrated ice content (Vivekanandan

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et al. 1991). Frequencies of precipitation (PR pixels) relative to PF size, stratiform rain fraction,

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20-dBZ echo-top height, infrared (IR) Tb, and PCT85, as well as 30-dBZ occurrence in the

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mixed-phase region and PDF of flash rates, are all derived from the PF database. Temperatures

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at PR vertical range heights (above sea level) are calculated through the interpolation of

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pressure-level temperatures and geopotential heights from ERA-Interim reanalysis data.

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c. BSISO indices

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A primary goal of this study is to examine convection as a function of BSISO phase in

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the 30-60 day mode. For this purpose, we use the BSISO indices developed by Lee et al. (2013)

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(hereafter,

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(http://iprc.soest.hawaii.edu/users/jylee/bsiso). The Lee index is derived from the Multivariate

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empirical orthogonal functions (MV-EOFs) of 850-hPa zonal wind and outgoing long-wave

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radiation over the entire ASM region [10S-40N, 40-160E]. The 30-60 day mode is represented

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by the first and second EOF principal components (PC). The Lee index better captures BSISO

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features in the ASM region compared to previous BSISO indices (e.g., Waliser et al. 2004;

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Annamalai and Sperber 2005; Kikuchi et al. 2012) and the MJO index (Wheeler and Hendon

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2004). BSISO index is on a daily timeframe and has eight phases, indicating various states of the

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ISO over the ASM region. In order to focus on major BSISO conditions, BSISO data are

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inclusive to days with significant amplitude, i.e., (PC12 + PC22)1/2 > 1 during June-September.

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The total number of days, TRMM overpasses, and PFs in specific BSISO phases is listed

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in Table 1. Obviously, there are a large number of data samples in each BSISO phase (e.g., 100-

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200 sample days with over 1000 TRMM overpasses in each phase). These statistics provide

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confidence for the phase composites used in this study.

Lee

index),

which

is

available

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from

1981

to

present

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3. Overview of 30-60 Days Variability a. Large-scale rainfall and synoptic conditions

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Figure 2 shows the composites of rainfall and 850-hPa winds as a function of BSISO

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phase during June-September. Generally, precipitation and low-level winds are suppressed over

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the SCS during phases 1-3 (Figs. 2a-c), whereas phases 5-7 feature bands of heavy rain and

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accompanying strong westerlies over the BOB, SCS, and Philippine Sea (Figs. 2e-g). In contrast,

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BORN and SCH, to the south and north of the SCS, show a nearly opposite rainfall pattern to the

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SCS (also shown in Fig. 3). Specifically, phases 1-2 mark the most suppressed conditions of

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precipitation (< 5 mm d-1) and winds (southwesterlies at 850 hPa < 5 m s-1) over the SCS. During

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these same phases, heavy rainfall is located over the eastern Indian Ocean, or immediately west

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of Sumatra, as well as western BORN (Figs. 2a-b). The BSISO convective envelope begins

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propagating eastward and northward (to northern BOB) during phase 3 (Fig. 2c), and forms a

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northwest-southeastward oriented rain band between the BOB and the tropical western Pacific by

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phase 4 (Fig. 2d). A southwest-northeast oriented rain band accompanied by strong low-level

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southwesterlies (> 10 m s-1) develops along the northern edge of an anticyclonic circulation (the

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western Pacific subtropical high) between southern China and Japan (phases 3-4). Heavy

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precipitation accompanied by strong low-level westerly winds (~15 m s-1) over the southern SCS

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and western Pacific propagates further northward during phases 5-7, and produces peak rainfall

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over the central and northern SCS areas. During phases 5-7, precipitation over BORN and SCH

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(outside the BSISO rain band) is generally suppressed possibly owing to enhanced subsidence

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adjacent to the intense rain band. The heavy BSISO rain band (phases 5-7) tends to be

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interrupted by landmasses, i.e., the INDO and the islands of the PHIL. Figure 3 clearly shows

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that both maximum rainfall and intraseasonal variability of precipitation are reduced over land

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(INDO and PHIL) compared to over ocean (SCS). Previous studies suggested that the low

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thermal inertial of the land surface could suppress intraseasonal surface fluxes that help to

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promote the intraseasonal oscillation (Maloney and Sobel 2004; Sobel et al. 2010). Furthermore,

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the strong diurnal cycle over land could release the atmospheric instability on the diurnal

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timescale and compete with the intraseasonal timescale (BSISO) for instability (Neale and Slingo

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2003; Sobel et al. 2010).

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Analysis of the geopotential height at 500 hPa (Fig. 4) shows that precipitation-

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suppressed conditions (phases 1-3) over the SCS is dominated by the western Pacific subtropical

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high (Figs. 4a-c), whereas the active BSISO rain band (phases 5-7) over the BOB, SCS, and

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tropical western Pacific is associated with a train of low-pressure circulations (Figs. 4e-g). The

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subtropical high has maximum westward extent and is the strongest during phase 3, prior to

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retreating eastward in phase 4. A low-pressure circulation initiates over the central SCS in phase

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5, accompanied by strong low-level westerlies. The strongest westerlies during active phases (5-

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7) are to the south of the low-pressure systems, along the zone of large pressure gradients. The

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northward propagation of the active BSISO rainfall maxima (phase 5 to phase 7) over the SCS

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and the western Pacific region corresponds closely to the northward movement of monsoon lows.

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Previous studies suggested that land surface heat fluxes into the boundary layer could destabilize

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the atmosphere ahead of the ascending zone and cause a northward shift of convective activity

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(Webster, 1983; Srinivasan et al., 1993). Similarly, positive SST anomalies to the north of the

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BSISO convective center could induce boundary layer convergence and promote northward

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propagation of the intraseasonal disturbance (Back and Bretherton 2009; Hsu and Li 2012). Of

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course, many other propagation mechanisms for BSISO have been proposed (summary in

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DeMott et al. 2012).

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b. Precipitation features and lightning activity

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6, the quantitative PF population (density) as a function of BSISO phase is shown. In general,

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phases 1-2 feature the most suppressed convective condition (minimum population of PFs by all

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metrics) over the SCS (Figs. 5a-b and Fig. 6), while phases 5-6 are characterized by maximum

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number of MCSs and cold clouds over the SCS (Figs. 5e-f and Figs. 6b-c). Behaviors of the PF

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population, especially mesoscale convective systems (MCSs) with PF areas > 2000 km2 (Nesbitt

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et al. 2000; Liu et al. 2008), are consistent with rainfall patterns (shown in Fig. 2) over both the

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SCS and surrounding landmasses. During phases 1-3, PF frequency is relatively small and MCSs

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are infrequent over the SCS, INDO, and the PHIL, marking suppressed conditions in these areas

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(Figs. 5a-c). However, during these same periods MCSs occur more frequently over BORN and

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the most southern portions of the SCS. Many of the MCS over the southern SCS west of BORN

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may originate from convection over Borneo, given the pronounced nocturnal offshore

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propagation from BORN (Ichikawa and Yasunari 2006). In the wake of active BSISO conditions

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(phase 5, Fig. 5e), MCSs increase substantially over INDO, the SCS, and PHIL, and exhibit a

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northward propagating pattern (phases 6-7, Figs. 5f-g). As will be shown in Sec. 4a, MCSs

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contribute over 80% of the total rainfall during active periods, therefore, the propagation of these

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MCSs constitutes the main BSISO rain band. During these phases (5-7), BORN and SCH

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become convectively suppressed, marked by the minimum population of all types of PFs in these

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regions (Figs. 5e-f and Figs. 6b-c).

Locations of PFs categorized by PF size in each BSISO phase are shown in Fig. 5. In Fig.

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Populations of deep convection (min IR Tb < -60 oC, Fig. 6c) and intense convection (20-

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dBZ echo-tops exceeding 12 km, Fig. 6d) reveal a different pattern compared to MCSs (Fig. 6b),

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especially over land. The deepest convection over PHIL and INDO leads the frequent MCS

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period by 1-2 phases, whereas deep convection and MCSs are more in phase over SCS and

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regions outside the BSISO rain band (BORN and SCH). For example, deep/intense convection

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frequently develops over PHIL and INDO during phases 2-3 (Figs. 6c-d), whereas MCSs are less

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frequent during these same periods (Fig. 6b). Following onset of active periods associated with

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BSISO over the SCS (phase 5), deep convective clouds increase over the SCS (Fig. 6c), but

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decrease over land including INDO, PHIL, and BORN. However, intense convection (e.g., 20-

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dBZ echo-top > 12 km) over SCS only increases slightly during active periods of the BSISO,

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possibly because deep convection (producing cold IR Tb) over ocean have only moderate updraft

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that is not able to lift precipitation size particles (20-dBZ) to high altitudes (12 km).

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Figure 7 shows the distribution of lightning flash density (unconditional) in each BSISO

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phase. Lightning activity over land is most robust during suppressed conditions (phases 2-3, figs.

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7a-c), corresponding to the presence of intense convection (Figs. 6d). However, during inactive

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BSISO periods, lightning activity is highly suppressed over the SCS, although there are indeed a

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significant population of deep convective clouds (Figs. 6c-d). This is due to the fact that

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lightning production is more related to the mixed-phase physics (e.g., 30-dBZ echo-top height)

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rather than the cloud-top height (Zipser and Lutz 1994; Petersen et al. 1996, 1999). [Note: the

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frequency of 30 dBZ above the freezing level is very low during the inactive periods over the

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SCS as will be shown in Sec. 4c] The increase of lightning activity over the SCS during active

331

periods may be partially attributed to larger CAPE (will be shown in Sec. 3c), which may in fact

332

be enhanced by stronger heat and moisture fluxes from the ocean (discussed in Sec. 3c). The

333

increase in MCS organization over SCS during active periods may also promote lightning (Bang

334

and Zipser 2015; Bang and Zipser 2016). As shown previously, deep convective systems over

335

the SCS during inactive BSISO periods are relatively isolated, therefore, buoyant updrafts in the

14

336

convective cores of these systems are more likely to be diluted by ambient air (Bang and Zipser

337

2016). After the onset of the BSISO (phase 5), flash density increases over the SCS

338

corresponding to an increase in the MCS population. During active BSISO phases, flash density

339

over land (i.e., PHIL and INDO) decreases substantially, consistent with reduced CAPE (will be

340

shown in Sec. 3c).

341

c. Composites of “inactive” versus “active” BSISO periods

342

Figure 8 shows composites of rainfall, 850-hPa winds, and lightning density during

343

“inactive” (phases 1-3) and “active” periods (phases 5-7). Obviously, inactive periods are

344

characterized by minimum rainfall (Fig. 8a) and infrequent lightning activity (Fig. 8c) over the

345

SCS, while a remarkable west-eastward rain band spanning over INDO, SCS, and PHIL marks

346

the active phases (Fig. 8b). However, BORN and SCH show a rainfall pattern opposite to INDO,

347

SCS, and PHIL, as these locations are outside the BSISO rain band region.

348

Figure 9 shows mean values of several environmental variables during inactive and

349

active BSISO periods as a function of location (marked in Fig. 1). SST (SCS only) and surface

350

air temperature in the BSISO rain band regions (SCS, INDO, and PHIL) are reduced (by 0.5-1

351

o

352

precipitation cooling, and stronger upper-ocean mixing owing to enhanced low level winds (Fig.

353

9a). As a result, the inactive BSISO induces larger CAPE relative to the active phases,

354

particularly for the INDO and PHIL regions (Fig. 9b), likely due to stronger surface heating over

355

land. It is intriguing that SCS shows higher CAPE during active periods even though SST/air

356

temperature is lower compared to inactive periods. A possible mechanism is that higher sea

357

surface fluxes (due to stronger surface winds) and stronger low-level moisture convergence

358

during active BSISO phases (as in active MJO phases, Johnson and Ciesielski 2013) lead to

C) during active periods compared to inactive phases, likely owing to cloud shading,

15

359

larger CAPE during these periods. Petersen et al. (1996) found that CAPE was highly correlated

360

to the mixing ratio (wm) of the lowest 50 hPa (i.e., CAPE = 747.7*wm – b) over the tropical

361

western Pacific Ocean, suggesting that CAPE over warm oceans is largely moisture driven. Over

362

SCS, mixing ratio in the lowest 50 hPa is 17.6 g kg-1 during inactive periods and 18.2 g kg-1

363

during active periods. Using the relationship reported by Petersen et al. (1996), the derived

364

CAPE differences are ~400 J kg-1 between active and inactive periods, consistent to the

365

difference shown in Fig. 9b.

366

Relative humidity in the lower-to-mid troposphere and vertical wind shear over SCS,

367

INDO, and PHIL are all enhanced during active periods (Figs. 9c-d) with influences from rain

368

bands, low-pressure circulations, and strong low-level westerly winds. Convective environments

369

over BORN also vary significantly between inactive and active periods, showing patterns

370

opposite to INDO and PHIL, e.g., higher air temperature and larger CAPE during active BSISO

371

periods. Note that active BSISO periods correspond to dry periods over BORN (and SCH), vice

372

versa. So it is not surprising the CAPE over BORN is high during active periods as there is little

373

convection to remove the CAPE. Deep shear over BORN during active phases becomes

374

exceptionally large (25 m s-1), as the South-Asia upper-level anticyclonic circulation is enhanced

375

by active BSISO deep convection leading to strengthened upper-level easterlies/northeasterlies

376

over BORN. In contrast, SCH shows the minimum BSISO-related variability in most of the

377

environmental variables. Compared to other regions, SCH shows lower rainfall maxima and less

378

variability in total precipitation between active and inactive periods (Fig. 3 and Figs. 8a-b),

379

therefore minimal variability in cloud shading, precipitation cooling, and condensational heating

380

across the BSISO cycle.

381

16

382

4. BSISO Variations on Convective and Microphysical Structures

383 384

While Sec. 3 provided a large-scale overview of the variability in synoptic conditions,

385

convective environments, rainfall, precipitation features, and lightning activity over the BSISO

386

cycle, we now discuss the detailed convective and microphysical structures during inactive and

387

active BSISO periods. Specifically, we compare convective organization, radar echo-top heights,

388

IR Tb frequency, vertically integrated ice content, mixed-phase microphysical properties, and

389

lightning flash rates of convective clouds over various regions impacted by the BSISO including

390

SCS, INDO, PHIL, BORN, and SCH.

391

a. Convective organization

392

Convective organization is examined in terms of the size and convective/stratiform

393

proportions of precipitation systems. Figure 10 shows the frequency of precipitation occurrence

394

(PR raining pixels) as a function of precipitation area and areal fraction of stratiform

395

precipitation (FSP) of PFs. Generally, precipitation systems over SCS, PHIL, and INDO are

396

larger and consist of larger FSP during active phases compared to inactive periods, possibly due

397

to greater shear and more moist mid-tropospheric environments during active periods (Fig. 9).

398

Isolated systems contribute 20-30% of precipitation frequency (similar contribution from mostly

399

convective systems with FSP < 20%) over SCS, PHIL, and INDO during inactive periods, which

400

is twice that during active phases. This is consistent with higher CAPE, reduced deep shear, and

401

drier mid-tropospheric conditions over SCS, PHIL, and INDO during inactive phases (Fig. 9). In

402

these same regions, large systems with broad stratiform precipitation (e.g., PF size > 10,000 km2

403

and FSP > 50%) are responsible for nearly 40% of the precipitation frequency during active

404

phases, yet comprise only 15-25% of the precipitation frequency during inactive periods. The

405

precipitation contribution from large MCSs during active BSISO phases is similar to what has 17

406

been observed by shipborne radar during active MJO periods over the Indian Ocean (Xu and

407

Rutledge 2014) and the western Pacific warm pool (Rickenbach and Rutledge 1998). BORN and

408

SCH exhibit an opposite trend regarding precipitation area and FSP to the SCS, as the wet and

409

dry periods in these regions correspond to inactive and active BSISO phases over the SCS,

410

respectively.

411

b. Convective echo-top heights

412

Figure 11 shows the probability distribution function (PDF) of 20-dBZ echo-top heights

413

(ETH20) of convective cells (within rain areas identified as convective). Generally, ETH20

414

maximizes near the freezing level (5 km), corresponding to the “congestus mode” over the

415

tropical oceans (Johnson et al. 1999). The congestus mode is caused by the near 0o C stable

416

layer, owing to cooling and moistening effects associated with extensive regions of melting

417

within MCSs. SCS exhibits a broad peak (2-5 km) of ETH20 during inactive BSISO periods

418

(Fig. 11a). The lower-level (2-3 km) echo-top maximum represents the “shallow cumulus”

419

mode, as reported over the Pacific warm pool/western Atlantic (Johnson et al. 1999), Manus Is.

420

(Hollars et al. 2004), and the central Indian Ocean (Powell et al. 2015). It is not surprising that

421

this shallow cumulus mode disappears over land (Figs. 11b-e), given the strong surface heating

422

and greater instability over land. The shallow cumulus mode is better defined over more open

423

oceans during inactive periods, i.e., western tropical Pacific (WPC, Fig. 11f). An examination of

424

stability profiles shows that a low-level stable layer (850-750 hPa) associated with trade

425

inversion apparently exists over both SCS and WPC (Fig. 12). Of course, the mid-level stable

426

layer is also evident over these regions. The mid-level stable layer over the SCS is stronger

427

during active periods (Fig. 12a), due to the substantial increase of MCS population in active

428

BSISO thus enhanced cooling and subsidence within broad stratiform regions.

18

429

During active BSISO periods, the frequency of shallow cumulus over the SCS (e.g.,

430

MAXHT20 < 3 km) decreases (Fig. 11a), as the large-scale BSISO disturbance likely disrupts

431

the trade stable layer near 2 km (reduction of stability as shown in Fig. 12a), promoting the

432

growth of cumulus into congestus. The modulation of the shallow cumulus cloud population due

433

to active BSISO is even more evident over WPC (Fig. 11f). During inactive periods, convective

434

echo tops over WPC show a clear bimodal structure with maxima at 2-3 km and 5 km. After the

435

onset of BSISO, shallow cumulus decreases substantially and congestus clouds (with tops at 4-6

436

km) increase correspondingly. Hence the shallow cumulus mode virtually disappears.

437

There is little variability of shallow cumulus and congestus echo tops over land

438

between inactive and active periods (Figs. 11b-e). Over land, especially PHIL and INDO, the

439

frequency of deep convection (e.g., ETH20 > 8 km) is higher during inactive periods compared

440

to active periods. Nevertheless, ETH20 does not show a deep convective mode (cloud tops > 12

441

km, Johnson et al. 1999), possibly because ETH20 does not represent actual cloud tops (which

442

may be closer to 0 dBZ as in Johnson et al. 1999). Xu and Rutledge (2016) compared convective

443

top heights from 20 dBZ, 0 dBZ, and IR Tb using shipborne radar data and satellite imagery

444

collected during DYNAMO. They found that the discrepancies between these heights increase as

445

a function of altitude, e.g., these proxies are near in height for shallow to medium convection (
1.

826 827 P1

P2

P3

P4

P5

P6

P7

P8

Days

152

192

157

112

182

136

139

165

Orbits

1416

1710

1446

1035

1680

1259

1212

1499

PFs

46535

59106

52602

37225

50184

36267

33485

45914

828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851

36

852 853 854 855 856

Figures

857 858 859

Fig. 1. Geography map of elevation (color shaded) overlaid with low-level winds at 925 hPa

860

(arrows) from June to September in the South China Sea and Indo-Pacific regions. Study regions

861

are marked by boxes, including South China Sea (SCS), Philippines (PHIL), Indochina (INDO),

862

Borneo (BORN), Southern China (SCH), and Western Pacific (WPC).

863 864 865 866 867 868 869 870 871 872 873 874

37

875 876

Fig. 2. Mean rainfall (mm d-1) from TRMM 3B42 (color shaded) overlaid with winds at 850 hPa

877

(arrows) derived from reanalyses as a function of BSISO phase during June-September 1998-

878

2013.

879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 38

897 898 899

900 901 902

Fig. 3. Mean rainfall (mm d-1) time series for five study regions derived from TRMM 3B42 as a

903

function of BSISO phase.

904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 39

922 923

924 925

Fig. 4. Geopotential heights at 500 hPa (contours) and winds at 850 hPa (arrows) as a function of

926

BSISO phase. Colorbar represents geopotential height.

927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944

40

945 946

947 948 949

Fig. 5. Distribution of Precipitation Features (PFs) categorized by PF area (color) as a function

950

of BSISO (30-60 days oscillation) phase during June-September, 1998-2013. Small PFs with

951

area < 100 km2 are not displayed.

952 953 954 955 956 957 958 959 960 961 962 963 964 965 966

41

967

968 969 970

Fig. 6. PF population as a function of BSISO phase: (a) all PFs, (b) PFs with area > 2000 km2,

971

(c) PFs with minimum IR Tb < -60 oC, and (d) PFs with 20dBZ echo-top height > 12 km. PF

972

numbers are normalized by area of study region and number of days in each BSISO phase.

973 974 975 976 977 978 979 980 981 982 983 984

42

985 986

987 988 989

Fig. 7. Distribution of lightning flash density (fl km-2 yr-1) derived from orbital TRMM LIS data

990

as a function of BSISO phase. Flash density is unconditional, including both raining and not

991

raining times sampled by LIS.

992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007

43

1008 1009 1010

Fig. 8. Mean rainfall overlaid with 850 hPa winds and lightning flash density during inactive

1011

(phases 1-3) and active (phases 5-7) BSISO periods: (a) rainfall during inactive, (b) rainfall

1012

during active, (c) lightning during inactive, and (d) lightning during active. White boxes in

1013

panels (a) and (b) mark the selected study regions.

1014 1015 1016 1017

44

1018 1019 1020

Fig. 9. Environmental variables during inactive (blue) and active (red) BSISO periods: (a) SST

1021

and 2-m air temperature, (b) convective available potential energy (CAPE), (c) relative humidity

1022

(RH) at lower-levels (850-700 hPa) and mid-to-upper levels (500-300 hPa), and (d) vertical wind

1023

shear between 700 and 150 hPa. Error bars represent 0.5 standard deviation.

1024 1025 1026 1027 1028 1029 1030 45

1031

1032 1033 1034

Fig. 10. Joint PDF of TRMM PR raining pixels as a function of PF area and PF stratiform rain

1035

fraction during (a)-(e), inactive BSISO, and (f)-(j), active BSISO periods. Number within each

1036

box represents the percentage of occurrence in the specific value ranges of PF area and stratiform

1037

rain fraction.

1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054

46

1055 1056 1057

Fig. 11. PDF of 20-dBZ echo-top height over convective area of precipitation systems observed

1058

by TRMM PR during inactive (blue bars) and active (red bars) BSISO periods. Frequency is

1059

relative to convective rain pixels near the surface.

1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073

47

1074 1075 1076 1077

1078 1079 1080

Fig. 12. Cumulative frequency (%) of stability (dT/dZ) greater than -5 oC km-1 over ocean for (a)

1081

SCS and (b) WPC. The frequency is calculated in every 50 hPa bin.

1082 1083 1084 1085 1086

48

1087 1088 1089

Fig. 13. Frequency of infrared brightness temperature during inactive (blue bars) BSISO and

1090

active (red bars) BSISO periods observed by TRMM VIRS during 1998-2013.

1091 1092 1093 1094 1095 1096 1097 1098

49

1099 1100

1101 1102 1103

Fig. 14. Frequency of TRMM TMI microwave polarization-corrected brightness temperature at

1104

85 GHz during inactive (blue bars) BSISO and active (red bars) BSISO periods. Frequency is

1105

relative to TMI raining pixels near the surface.

1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 50

1120 1121

1122 1123 1124

Fig. 15. Occurrence frequency of PR 30-dBZ radar reflectivity above the freezing altitude during

1125

inactive (blue bars) BSISO and active (red bars) BSISO periods. Frequency is relative to PR

1126

convective rain pixels at 2 km.

1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 51

1143 1144

1145 1146 1147

Fig. 16. Accumulated frequency of precipitation features as a function of PF’s lightning flash

1148

rates during inactive (blue bars) BSISO and active (red bars) BSISO periods. All PFs are

1149

included for the frequency calculation.

1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 52

1164 1165 1166

Fig. 17. Conceptual model (left panels) of convective structures and microphysical properties

1167

across the BSISO rainband region (INDO, SCS, and PHIL) during suppressed conditions and

1168

active BSISO periods, and corresponding diurnal variations (right panels) on 30-dBZ occurrence

1169

frequency above 6 km in convective precipitation areas.

1170 1171 1172 1173 1174

53