Abstract

The study reported in this paper used ERA-Interim reanalysis data to investigate the intraseasonal variability of the Bay of Bengal (BOB)–East Asia–Pacific teleconnection (BEAP) during the summer between 1979 and 2016. Over this period, the intraseasonal oscillation of the BEAP fell mainly within the quasi-biweekly oscillation (QBWO) band. Variations in atmospheric circulation and precipitation, which may contribute to extreme weather events, showed a significant correlation with the phase transition of the BEAP from the BOB to East Asia and the Pacific. The evolution of the BEAP–QBWO is closely associated with the westward propagation of convective anomalies to the southwestern BOB. Dynamical analysis revealed that anomalous vertical motion coupled with anomalous convective activity over the southern BOB plays an important role in leading the phase propagation of the BEAP–QBWO, and that the horizontal advection anomalies can strengthen the BEAP–QBWO. Linear baroclinic model experiments confirmed that variations in convection over the southern BOB play a leading role in the BEAP–QBWO phase changes. Further research suggests that the boreal summer intraseasonal oscillation can trigger the BEAP–QBWO through downstream propagation of convective disturbances to the southern BOB. This study provides insights into the cause and effect of the BEAP–QBWO, which will help to improve understanding of flood and drought patterns in the Asia–Pacific region.

1. Introduction

The Bay of Bengal (BOB)–East Asia–Pacific (BEAP) teleconnection is an important teleconnection pattern that has a significant impact on the Asian–Pacific climate (Yang et al. 2019a). It is characterized by a wave train pattern of diabatic heating anomalies along the great circle path from the BOB to the western Pacific. The spatiotemporal variability of the BEAP teleconnection is closely associated with variations in atmospheric circulation and precipitation along the BEAP transmission pathway. Yang et al. (2019a) also found that convective anomalies around the southwestern BOB (the BEAP source region) can trigger the BEAP teleconnection and excite a Rossby wave that propagates from the BOB to northeastern China and then to the western Pacific.

The development of teleconnection patterns is closely associated with tropical convection anomalies over intraseasonal time scales (Pan and Li 2008; Lin et al. 2009; Zhao et al. 2012; Riddle et al. 2013). In particular, there is a robust phase relationship between the intraseasonal variability (ISV) seen in tropical convection and the ISV of some teleconnection patterns (Frederiksen and Lin 2013; Seo and Lee 2017). For example, the frequency of occurrence of the positive (negative) phase of the Pacific–North America (PNA) pattern is highest when the inactive (active) convection related to the Madden–Julian oscillation (MJO) reaches the BOB to the western Pacific (Mori and Watanabe 2008; Zhang 2015). The East Asia–Pacific (EAP) pattern also shows intraseasonal variability, with a salient westward shift and amplification of disturbances during the growth period. The boreal summer intraseasonal oscillation (BSISO) modulates convection anomalies over the Philippines and plays an important role in the initiation of low-frequency EAP pattern through Rossby wave propagation (Wang et al. 2016). The development of the low-frequency EAP is affected mainly by the atmospheric beta effect and high-frequency transient eddies.

Given the above research findings, we propose that the BEAP teleconnection may exhibit similar intraseasonal variability to the EAP and PNA patterns that can modulate atmospheric circulation. Previous studies have suggested that the probability of occurrence of tropical Northern Hemisphere teleconnections is affected by ISV-related convective activity in the tropical regions (Tyrrell et al. 1996; Stan et al. 2017). Large-scale diabatic heating in the tropics can excite a Rossby wave train that influences extratropical areas through poleward propagation (Li and Nathan 1997). Hoskins and Karoly (1981) and Webster (1981) argued that this poleward motion is enhanced by weak zonal advection of high-vorticity fluid and by the thermodynamic balance between diabatic heating and vertical motion. The extratropical response to tropical ISV becomes established in about two weeks and is accompanied by local anomalies resulting from barotropic instability and energy conversion (Sardeshmukh and Hoskins 1988; Jin and Hoskins 1995; Matthews et al. 2004). Lin et al. (2010) showed that the extratropical response in the Northern Hemisphere is sensitive to equatorial heating related to sea surface temperature anomalies (SSTAs) in the Indian Ocean and western Pacific.

As one of the most important components of tropical ISV, the quasi-biweekly oscillation (QBWO) has been studied with respect to its impact on the onset and cessation of monsoon systems, changes in monsoon intensity, and its relationship to rainfall variability (Krishnamurti and Bhalme 1976; Yasunari 1981; Chen et al. 1988, 2000; Chen and Chen 1993; Wang et al. 2017). The diabatic heating of organized convection can initiate the QBWO. Diabatic heating in the tropics and baroclinic processes in the subtropics generate eddy available potential energy (EAPE), which converts to eddy kinetic energy (EKE) through vertical motion (Fukutomi and Yasunari 1999; Chen and Sui 2010). The conversion process plays an important role in the genesis of QBWO and is accompanied by the Gill-type low-level atmospheric circulation response (Ren and Huang 2002; Wen and Zhang 2008). The QBWO convection is closely related to the propagation of the Rossby wave train along the Asian subtropical jet. It has a significant influence on moisture transport and causes rainfall variability over East Asia and the northwestern Pacific (Hu et al. 2016; Wang et al. 2019).

As the atmospheric ISV over the BOB is intimately related to the activation and cessation cycles of the Indian summer monsoon, as well as convection anomalies over the BOB (including the BEAP source region; Murakami 1976; Krishnamurti and Ardanuy 1980; Chen and Chen 1993), we infer that ISV disturbances may also lead to intraseasonal variability in the BEAP teleconnection. However, the main band of BEAP ISV, as well as its cause and its effect on the climate in the Asia–Pacific region, remains unclear. It is therefore necessary to study the spatiotemporal evolution of BEAP ISV and its effect on summer climate in the Asia–Pacific region. Consequently, this study aimed to determine the dominant patterns of BEAP ISV, as well as the propagating dynamics and maintenance mechanisms of the BEAP. The remainder of this paper is organized as follows. In section 2, the reanalysis data and methodology used in this study are introduced. In section 3, we describe in detail the characteristics of BEAP ISV. In section 4, we investigate the impact of BEAP ISV on summer rainfall and related physical processes in the Asia–Pacific region. In section 5, the atmospheric processes associated with BEAP ISV are analyzed via the moisture budget equation. Finally, in section 6, we present a discussion and our conclusions.

2. Data, methods, and model

a. Data

We used daily atmospheric wind, precipitation, and specific humidity data derived from the ERA-Interim reanalysis with a 2° horizontal resolution and 37 pressure levels (Simmons et al. 2004; Dee et al. 2011). Daily outgoing longwave radiation (OLR) data from the National Oceanic and Atmospheric Administration (NOAA; Liebmann and Smith 1996) at 2.5° horizontal resolution were used as a proxy for convective intensity. All datasets used in this study cover June–September (JJAS) for the period 1979–2016.

b. Methods

1) Apparent heat source and apparent moisture sink

The apparent heat source (Q1) and apparent moisture sink (Q2) were derived from the ERA-Interim reanalysis using a residual budget analysis based on the thermodynamic energy equation and the water vapor equation (Yanai et al. 1973, 1992; Cao et al. 2017):

 
Q1=cp(pps)κ(θt+VHθ+ωθp),and
(1)
 
Q2=L(qt+VHq¯+ωqp).
(2)

In Eqs. (1) and (2), θ is the potential temperature, VH is the horizontal wind velocity, ω is the vertical velocity, q is the specific humidity, t is time, p is the air pressure, and ps is the surface pressure. In Eq. (1), κ = R/Cp, where R is the gas constant of dry air and Cp is the specific heat of dry air at constant pressure. In Eq. (2), L is the latent heat of condensation. The column integrations of Q1 and Q2 (CIQ1 and CIQ2, respectively) were calculated from the surface pressure to 100 hPa:

 
Q11g100hPapsQ1dp,and
(3)
 
Q21g100hPapsQ2dp.
(4)

The term Q1 reflects the variations in total diabatic heating (i.e., the radiation, latent heating, and surface heat fluxes) and subgrid-scale heat flux convergences. The term Q2 represents changes in the latent heating during condensation or evaporation processes and subgrid-scale moisture flux convergence (Yanai and Li 1994; Hsu and Li 2011).

2) BEAP index

The BEAP teleconnection index (BEAPI) was defined by Yang et al. (2019a) with the column-integrated Q2 over five central zones across the BEAP region:

 
BEAPI=[CIQ2(80°−88°E,8°−12°N)][CIQ2(100°−108°E,22°−26°N)]+[CIQ2(118°−126°E,36°−40°N)][CIQ2(138°−146°E,30°−34°N)]+[CIQ2(152°−160°E,26°−30°N)],
(5)

where square brackets denote normalization using the standard deviation, and parentheses denote the average over a specified region. The five regions were selected based on the location of significant correlation centers on the correlation map of CIQ1 and CIQ2, which exhibit a wave-like pattern from the BOB to East Asia and the western Pacific (shown as the green boxes in Fig. 3h). The minus sign before the second and fourth terms on the right-hand side of Eq. (5) denotes an inverse correlation with the other three centers over the research domain.

We followed Yang et al. (2019a) to calculate the one-point correlation between the CIQ1 (CIQ2) at the base point (10°N, 84°E) and CIQ1 over the region of 20°S–50°N and 40°E–180° over the daily time scale. The daily CIQ1 and CIQ2 were normalized using their standard deviations. We used Student’s t test to examine the significance of correlation coefficients, in which the degrees of freedom were calculated using effective sample sizes (Hsu et al. 2017; Afyouni et al. 2019).

The spatial distributions of the correlation coefficients exhibit a wave-like pattern from the BOB to East Asia and the western Pacific (Fig. 1). There are five centers with alternate positive and negative correlation values in the BEAP region. The spatial configuration of these correlation centers resembles the BEAP teleconnection pattern, with the same sign and location of each anomaly center along the BEAP transmission pathway. Results suggest that the BEAP teleconnection can still be observed over daily time scales, and the use of Eq. (5) in the present study is justified. Therefore, we used the same method as Yang et al. (2019a) to calculate the daily BEAP index for the period 1 June–30 September for each year between 1979 and 2016.

Fig. 1.

Correlation coefficients between the CIQ1 field and (a) CIQ1 at the base point, and (b) CIQ2 at the base point (contour interval: 0.1). Red (blue) shading from light to dark denotes positive (negative) correlation at the 95% and 99% confidence levels based on Student’s t test and its effective degrees of freedom.

Fig. 1.

Correlation coefficients between the CIQ1 field and (a) CIQ1 at the base point, and (b) CIQ2 at the base point (contour interval: 0.1). Red (blue) shading from light to dark denotes positive (negative) correlation at the 95% and 99% confidence levels based on Student’s t test and its effective degrees of freedom.

3) Perturbed water vapor equation

The daily BEAPI is calculated from the daily CIQ2 over the five central zones. Consequently, we used the water vapor equation, from which Q2 was derived by Yanai et al. (1973), to perform the intraseasonal diagnosis of the BEAP-related physical and dynamic processes. Based on the formula for the apparent moisture sink (Yanai et al. 1973), the water vapor equation takes the form

 
dqdt=Q2L+Res,
(6)

where Res denotes the residual term. Applying the rule of the total derivative, the above equation can be rewritten as

 
qt=VHHqωqpQ2L+Res,
(7)

where q is the specific humidity, VH is the horizontal velocity, ω is the vertical velocity, Q2 is the apparent moisture sink by net condensation and eddy moisture transport, L is the latent heat of condensation, and Res is the residual term that represents diffusion effects and numerical error.

To disentangle the effects of intraseasonal perturbations and variations at other scales, each variable in Eq. (7) was decomposed into its mean and anomaly fields. Then, as in previous studies (Hsu and Li 2011, 2012; Seo et al. 2016; T. Wang et al. 2018), we separated the anomaly fields into ISV and non-ISV components:

 
A=A¯+A andA={A}+A*.
(8)

Here, the overbar and prime denote the mean and total anomaly, respectively, of a given variable A, and the curly braces and asterisk represent the ISV and non-ISV components, respectively, of the total anomaly. The ISV component was extracted using the Butterworth filtering method (Russell 2006) based on the dominant ISV period in the BEAP power spectrum.

Substituting each variable in Eq. (7) with Eq. (8), a simple mathematical derivation yields the perturbed water vapor equation:

 
{q}t=VH¯H{q}{VH}Hq¯{VH}H{q}ω¯{q}p{ω}q¯p{ω}{q}p{Q2}/L+{RES}.
(9)

Integrating both sides of this equation from the surface pressure to 100 hPa and combining like terms, the perturbed equation can be written as

 
{CIQ2}=LgST(VH¯H{q}I+{VH}Hq¯II+{VH}H{q}III)dpLgST(ω¯{q}pIV+{ω}q¯pV+{ω}{q}pVI)dpLgtST{q}dpVII+{RES*},
(10)

where {CIQ2} denotes the ISV component of the column-integrated apparent moisture sink, and the terms on the right-hand side of the equation represent dynamic processes related to the CIQ2 ISV. The first three of these terms represent the effects of horizontal advection, namely, advection of specific humidity ISV component by the mean wind (VH¯H{q}), advection of the mean specific humidity by the wind ISV components ({VH}Hq¯), and the interaction between the ISV components of specific humidity and wind ({VH}H{q}). The next three terms represent the effects of convection, namely, convection of the specific humidity ISV component by the mean vertical velocity [ω¯({q}/p)], convection of the mean specific humidity by the vertical wind ISV component [{ω}(q¯/p)], and the interaction between the ISV components of vertical velocity and specific humidity [{ω}(q¯/p)]. The seventh term denotes the intraseasonal part of the specific humidity tendency ({q}/t). The residual term is relatively small and is thus neglected in the following diagnostic analysis.

4) Significance testing in the composite analysis

Following Murakami (1988) and Wang et al. (2016), significance testing was conducted using Student’s t test and the null hypothesis that two series with a 7-day time difference share the same mean. The t statistic is given by

 
t=(X¯iX¯i7)(Si2+Si72N1)1/2,
(11)

where N denotes the number of QBWO cases, X¯i represents the mean, and Si2 is the variance for day i.

c. Model

We used a linear baroclinic model (LBM; Watanabe and Kimoto 2000) to verify the key physical processes of the BEAP–QBWO. A dry version of the LBM (DLBM) was used because previous studies have suggested that the DLBM performs better than a moist LBM when analyzing the steady response to regional diabatic heating (Lu and Lin 2009; Tao et al. 2016; Cao et al. 2017; Yang et al. 2019b). The model’s response to the diabatic heating pattern can serve as a proof of its important role in modulating the large-scale circulation associated with the BEAP–QBWO. The model resolution was set to the standard T42L20, where the horizontal resolution is based on triangular truncation with wavenumber 42 (T42) and the vertical resolution is 20 levels on the model sigma coordinate. The values used for Rayleigh’s friction and Newtonian damping were 0.5 day−1 for σ ≥ 0.90, 1 day−1 for σ ≤ 0.03, and 30 day−1 for other levels in between 0.03 and 0.90. The basic state climatology in use was calculated from the ERA-Interim reanalysis data for the boreal summer over the period 1979–2016. Following previous studies (Cao et al. 2017; Yang et al. 2019b), as the model simulation approaches a steady state after 10 days, we used the model results from day 15 as the steady-state response.

3. Daily BEAPI and its power spectra

The daily BEAPI was calculated for each boreal summer between 1979 and 2016. The index shows intense fluctuations in all years, with maximum amplitudes of more than 2 units, which we attribute to the strong and rapid adjustment of convective activity (figure not shown). We used spectral analysis to identify the dominant periodicities within the BEAP ISV. The mean power spectrum estimates are similar for the raw and detrended cases (Figs. 2a,b). Following Gilman et al. (1963), significance testing of the mean power spectra was carried out based on the Markov red noise spectrum, and we identified a dominant peak within the 10–20-day period band, which was significant at the 95% confidence level. The ISV at periods between 20 and 60 days was weak and statistically insignificant. The results indicate that BEAP ISV has a dominant periodicity in the quasi-biweekly band. Therefore, our analysis focused mainly on QBWO events.

Fig. 2.

Ensemble power spectra of daily BEAPI (solid black line) (a) without detrending and (b) after detrending. Also shown is the Markov red noise spectrum (dashed green line), and its confidence bound at the 95% (dashed red line) and 5% (dashed blue line) levels. (c) Ensemble composite phase of 10–20-day-filtered BEAPI during the boreal summer over the period 1979–2016.

Fig. 2.

Ensemble power spectra of daily BEAPI (solid black line) (a) without detrending and (b) after detrending. Also shown is the Markov red noise spectrum (dashed green line), and its confidence bound at the 95% (dashed red line) and 5% (dashed blue line) levels. (c) Ensemble composite phase of 10–20-day-filtered BEAPI during the boreal summer over the period 1979–2016.

4. Evolution of the BEAP–QBWO during the boreal summer

a. Phase transitions in BEAP–QBWO composite fields

Previous studies have suggested that the evolution of the QBWO during the boreal summer can be described by composites of related fields based on filtered time series (Wang and Zhang 2018). Following the phase compositing method used in these previous studies, we used Butterworth bandpass filtering (Russell 2006) to extract the 10–20-day signal of the BEAPI, before selecting typical QBWO events from the filtered BEAPI. We defined the reference date for the QBWO composites as the time when the maximum BEAPI anomaly appears in one phase cycle (day 0). Our selection criteria for the QBWO events were as follows: 1) there must be a complete phase cycle within the specified time period that consists of lagged days from −7 to 7; 2) the maximum BEAPI anomaly during the cycle (i.e., on day 0) must be greater than one standard deviation of the mean BEAPI; and 3) the previous and subsequent minimum BEAPI anomalies during the same cycle must be less than minus one standard deviation of the mean BEAPI. A total of 64 typical cases were selected on the basis of the above criteria. The average evolution of these QBWO events is shown in Fig. 2c. Composites from day −7 to day 6 were calculated for the main relevant meteorological fields, all of which show a significant correlation with the BEAP–QBWO.

b. Convective heating

The BEAP-related convective heating features a positive–negative–positive–negative–positive wave train pattern that runs along the line of the BOB/northern Indochina Peninsula, northeastern China, southern Japan, and the western Pacific (Yang et al. 2019a). This wave train pattern is also significant in the composite maps of CIQ2 around the positive BEAP–QBWO peak (day 0), whereas the opposite pattern appears around the negative peak (day −7; Fig. 3). These phase patterns confirm that quasi-biweekly is the predominant periodicity within the BEAP–ISV time series. As the phase transition proceeds from the negative to the positive phase, the negative anomalies over the southwestern BOB decrease and almost vanish on day −4. Consequently, the entire BEAP pattern along its transmission pathway becomes indiscernible. In the meantime, positive anomalies grow stronger over the South China Sea and propagate westward to the southern BOB. A significant positive anomaly center over the BEAP energy source region (southwestern BOB) becomes established on day −2 and keeps growing until day 0. Accordingly, the positive BEAP pattern from the BOB to the western Pacific becomes clear. The evolution of the CIQ2 composites from the positive phase to the negative phase follows the opposite track, with westward propagation of negative anomalies from the South China Sea to the southwestern BOB (Figs. 3h–n). The apparent heat source and apparent moisture sink represent different aspects of the convective activity associated with the BEAP–QBWO, so that the phase evolution of the apparent heat source (see Fig. S1 in the online supplemental material) is similar to that in the apparent moisture sink.

Fig. 3.

(a)–(n) Composites of 10–20-day-filtered CIQ2 (contours) from day −7 to day 6 over one life cycle of the QBWO during the boreal summer. The contour interval is 20 W m−2. Light to dark shading denotes statistical significance at the 95% and 99% confidence levels based on Student’s t test, with red (blue) color for positive (negative) values. The green box in (a) marks the southern BOB region for dynamical decomposition. The five green boxes in (h) represent the key areas of the BEAP teleconnection.

Fig. 3.

(a)–(n) Composites of 10–20-day-filtered CIQ2 (contours) from day −7 to day 6 over one life cycle of the QBWO during the boreal summer. The contour interval is 20 W m−2. Light to dark shading denotes statistical significance at the 95% and 99% confidence levels based on Student’s t test, with red (blue) color for positive (negative) values. The green box in (a) marks the southern BOB region for dynamical decomposition. The five green boxes in (h) represent the key areas of the BEAP teleconnection.

To further reveal the lead–lag relationship of convective anomalies over the key areas of the BEAP teleconnection, in Fig. 4 we show the time–longitude and time–latitude cross sections of CIQ1, CIQ2, and OLR over 0°–50°N, 70°E–180°. There is a significant westward propagation of the CIQ1 and OLR anomalies between 80° and 120°E. However, the CIQ2 composites show westward propagation of significant positive (negative) anomalies between 80° and 90°E (90° and 120°E), which covers the area from the southeastern BOB to the southwestern BOB (from the South China Sea to the southeastern BOB). The time–longitude evolutions of CIQ1, CIQ2, and OLR exhibit a quasi-stationary transition of significant anomalies over the western Pacific (east of 120°E). These results confirm that the BEAP–QBWO is closely associated with the westward propagation of convective anomalies west of 120°E. As the BEAP index is defined based on the CIQ2 variations, we can conclude that the BEAP–QBWO is affected primarily by the propagation of convective anomalies between 80° and 90°E (covering the southern BOB).

Fig. 4.

Longitude–time evolution of (a) CIQ1, (b) CIQ2, and (c) OLR averaged over 0°–50°N. Latitude–time evolution of (d) CIQ1, (e) CIQ2, and (f) OLR averaged over 70°E–180°. The green dots denote the regions where values are significant at the 95% level based on Student’s t test.

Fig. 4.

Longitude–time evolution of (a) CIQ1, (b) CIQ2, and (c) OLR averaged over 0°–50°N. Latitude–time evolution of (d) CIQ1, (e) CIQ2, and (f) OLR averaged over 70°E–180°. The green dots denote the regions where values are significant at the 95% level based on Student’s t test.

The average meridional transition of CIQ1 composites shows southward propagation from 30° to 10°N, whereas the CIQ2 and OLR composites over the same area remain quasi-stationary during the entire QBWO cycle (Figs. 4d–f). Besides, the CIQ1 anomalies are much stronger within the 10°–15°N region than in the northern subtropics. Our results suggest that the southward propagation of CIQ1 anomalies from the midlatitudes may trigger the development of convective anomalies over the southern BOB. However, the time–latitude evolution of CIQ2 and OLR suggests that the local BEAP–QBWO signal is affected more by the convection pattern in tropical areas. The evolution patterns of the time–longitude and time–latitude sections point to the southern BOB as a key area of the BEAP–QBWO phase change. The dynamical processes underlying the BEAP–QBWO are discussed in section 5.

c. Atmospheric circulation and precipitation

The atmospheric circulation response to the BEAP–QBWO exhibits a mainly baroclinic structure. There are anticyclonic (cyclonic) anomalies over southern India and the southwestern BOB, as well as northeastern Asia, at 200 hPa (700 hPa) on day 0 (Figs. 5h and 6h). A pair of cyclonic and anticyclonic circulations appears over the western Pacific at 700 hPa, which shifts northeast at 200 hPa. These spatial features resemble the BEAP-related circulation pattern (Yang et al. 2019a). During days 1 and 2, the upper-tropospheric circulation features a westward propagation of anticyclonic and cyclonic anomalies over the northwestern Pacific (Figs. 5i,j). These circulation anomalies strengthen over the next few days and develop into a conspicuous anticyclone (cyclone) to the east (west) of Japan (Figs. 5k–n). In the lower troposphere, the cyclonic circulation over the BEAP source region gradually weakens and turns into southerly anomalies from day 1 to day 3 (Figs. 6i–k). In the meantime, an anomalous cyclone develops over Japan, accompanied by an anticyclonic circulation to the west. Over the next few days, an anticyclonic circulation develops over the southwestern BOB and becomes statistically significant on day 6. The phase transition from negative peak to positive peak is the reverse of the process outlined above (Figs. 6h–n).

Fig. 5.

(a)–(n) Composite fields of 10–20-day-filtered 200-hPa winds (vectors) from day −7 to day 6 over one life cycle of the QBWO during the boreal summer. Thick black vectors denote differences significant at the 95% confidence level.

Fig. 5.

(a)–(n) Composite fields of 10–20-day-filtered 200-hPa winds (vectors) from day −7 to day 6 over one life cycle of the QBWO during the boreal summer. Thick black vectors denote differences significant at the 95% confidence level.

Fig. 6.

As in Fig. 5, but for 700-hPa winds.

Fig. 6.

As in Fig. 5, but for 700-hPa winds.

The phase evolution of horizontal winds exhibits significant variations from the southwestern BOB to northeastern China and then to the northwestern Pacific. These spots coincide with the inflection points on the propagation path of the Rossby wave associated with BEAP teleconnection (Yang et al. 2019a). Therefore, we examined the vertical structure of the atmospheric circulation along the same route, where the starting point (A) is 14°N, 84°E, the middle point (B) is 40°N, 122°E, and the end point (C) is 26°N, 156°E. Composites of 10–20-day-filtered vorticity and vertical velocity along the ABC line exhibit a baroclinic structure (Fig. 7). The vorticity anomalies exhibit a wave train pattern with alternating positive and negative centers slightly slanted in the southwest (northeast) direction along the AB (BC) segment. During the BEAP–QBWO, the vorticity wave train propagates east, with anomalous ascending (descending) motion corresponding to the low-level cyclonic (anticyclonic) centers. The low-level cyclonic (anticyclonic) anomalies are paired with negative (positive) OLR anomalies during the phase transition (Fig. S2).

Fig. 7.

Vertical cross section of vorticity (contours; s−1, contour interval: 10−6) and vertical velocity (shaded; Pa s−1) composites for lags of (a) −7, (b) −4, (c) 0, and (d) +3 days, along the blue line shown in Fig. 6h.

Fig. 7.

Vertical cross section of vorticity (contours; s−1, contour interval: 10−6) and vertical velocity (shaded; Pa s−1) composites for lags of (a) −7, (b) −4, (c) 0, and (d) +3 days, along the blue line shown in Fig. 6h.

When the BEAP–QBWO reaches its positive peak (day 0), precipitation increases over southern India/BOB, northern China/North Korea, and the northwestern Pacific, but decreases over southern China, southern Japan, and the tropical western Pacific (Fig. 8). When the BEAP–QBWO reaches its negative peak (day −7), the opposite variations occur over the above areas. Variations in precipitation are consistent with the changes in low-level atmospheric circulation, where the cyclonic (anticyclonic) circulations increase (decrease) moisture transport and enhance (weaken) the convective activity to facilitate (reduce) precipitation. From the composite maps of significant precipitation variations, we conclude that the BEAP-related QBWO in precipitation has the potential to alter flood and drought patterns in the Asia–Pacific region.

Fig. 8.

As in Fig. 3, but for precipitation (mm day−1). The contour interval is 4 mm day−1.

Fig. 8.

As in Fig. 3, but for precipitation (mm day−1). The contour interval is 4 mm day−1.

5. Key dynamical processes of the BEAP–QBWO

According to Yang et al. (2019a), the energy source of the BEAP teleconnection is located around the southwestern BOB. Our composite phase results also suggest that the convective anomalies over the southern BOB are closely associated with the BEAP–QBWO. Consequently, we diagnosed the propagation dynamics of the BEAP–QBWO over the southern BOB. The calculation region was 8°–12°N, 80°–100°E (marked by the green box in Fig. 3a), and is an extension of the BEAP source region to the southeastern BOB. As mentioned in section 2b(3), the dynamical diagnosis was based on the perturbed water vapor equation [Eq. (10)]. The zonal average of each term was calculated along the propagation route to help identify the cause of QBWO evolution. Each dynamical term has a distinct pattern of time–longitude evolution (Fig. 9). Variation in the magnitudes of CIQ2, the horizontal advection term ({VH}Hq¯), the convection term [{ω}(q¯/p)], and the tendency term (∂{q}/∂t) are an order of magnitude larger than other terms in the perturbed equation. Therefore, the following analysis focuses mainly on these four terms.

Fig. 9.

Time–longitude diagram of lagged composites for each term of the perturbed water vapor equation (filled contours): (a) CIQ2, (b) horizontal advection of biweekly specific humidity by the time mean flow, (c) horizontal advection of time mean specific humidity by the biweekly horizontal flow, (d) horizontal advection of biweekly specific humidity by the biweekly horizontal flow, (e) convection of biweekly specific humidity by the time mean vertical flow, (f) convection of time mean specific humidity by the biweekly vertical flow, (g) convection of biweekly specific humidity by the biweekly vertical flow, and (h) tendency of biweekly specific humidity. All variables are averaged over 8°–12°N. The contours in (b)–(h) denote the evolution of CIQ2, and the green dots in (a) mark regions where values are significant at the 95% level based on Student’s t test. Dashed gray lines denote the propagation route of the terms with high variability.

Fig. 9.

Time–longitude diagram of lagged composites for each term of the perturbed water vapor equation (filled contours): (a) CIQ2, (b) horizontal advection of biweekly specific humidity by the time mean flow, (c) horizontal advection of time mean specific humidity by the biweekly horizontal flow, (d) horizontal advection of biweekly specific humidity by the biweekly horizontal flow, (e) convection of biweekly specific humidity by the time mean vertical flow, (f) convection of time mean specific humidity by the biweekly vertical flow, (g) convection of biweekly specific humidity by the biweekly vertical flow, and (h) tendency of biweekly specific humidity. All variables are averaged over 8°–12°N. The contours in (b)–(h) denote the evolution of CIQ2, and the green dots in (a) mark regions where values are significant at the 95% level based on Student’s t test. Dashed gray lines denote the propagation route of the terms with high variability.

The CIQ2 presents a northwest–southeast slanted structure in which the maximum and minimum centers are located around 85°E on days 0 and −7, respectively (Fig. 9a). During the phase transition, significant positive (negative) anomalies first appear in southeastern BOB (east of 95°E) on day −4 (day 4). Over the next three days, these anomalies propagate to the west and develop into anomalous centers on the peak days of the phase cycle (days 0 and −7). The evolution of the horizontal advection term ({VH}Hq¯) is generally in phase with that of CIQ2 (Fig. 9c) and is dominated by a northwest–southeast slanted structure. Unlike the time–longitude pattern of CIQ2, the peak values of {VH}Hq¯ are located in the southeastern BOB (east of 95°E), which may be the result of the Rossby wave response to convective heating and local air–sea interaction (Wen and Zhang 2008; Hu et al. 2015). The evolutions of the convection term [{ω}(q¯/p)] and the tendency term ({q}/t) are out of phase with that of CIQ2 (Figs. 9f,h). The in-phase and out-of-phase variations of the three terms [{VH}Hq¯,{ω}(q¯/p),({q}/t)] suggest that the corresponding dynamical processes play different roles in the initiation and maintenance of the BEAP–QBWO.

To quantitatively measure the lead–lag relationship between the dynamical terms and CIQ2, a cross-spectral analysis was applied to the spatial average of these terms over one QBWO cycle (Fig. 10). The time delay of each term can be determined from the period with the maximum value in the orthogonal spectra. All terms share the same period of approximately −14 days when their orthogonal spectra reach the peak (figure not shown). The two biweekly interaction terms {VH}H{q} and {w}({q}/p) exhibit a sharp decrease in the coherence spectrum, suggesting that the variations in the biweekly interaction terms and CIQ2 are unrelated to each other. The remaining five terms have a similar V-shaped curve with an insignificant trough at approximately 7 days, and two peaks at both ends that are significant at the 95% confidence level (Fig. 10a). This implies that the dynamical processes are most associated with the CIQ2 variations over the quasi-biweekly time scale.

Fig. 10.

Cross-spectral components of each term in the water vapor equation, including (a) coherence squared, (b) phase, and variance of (c) CIQ2 and of (d) other dynamical terms. The dotted black line in (a) denotes the 95% confidence bound. The terms with high variability are represented by solid lines of different colors, with other terms displayed using dashed lines.

Fig. 10.

Cross-spectral components of each term in the water vapor equation, including (a) coherence squared, (b) phase, and variance of (c) CIQ2 and of (d) other dynamical terms. The dotted black line in (a) denotes the 95% confidence bound. The terms with high variability are represented by solid lines of different colors, with other terms displayed using dashed lines.

Given the significant frequency-domain correlation at quasi-14 days, the phase estimates should be reliable for determining the time delay of each term. The phase variations differ greatly among the terms (Fig. 10b). The variability of the tendency term ∂{q}/∂t and the horizontal advection term VH¯H{q} clearly delays the evolution of CIQ2, whereas the convection term {ω}(q¯/p) markedly precedes the CIQ2 transition. The CIQ2 and each of the dynamical terms also show maximum variance at the quasi-14-day period (Figs. 10c,d), which is in accordance with the peak period in the orthogonal spectra.

The time delay for each term during the quasi-14-day period is presented in Table 1. Among the three terms with large variability, the horizontal advection term {VH}Hq¯ shows no remarkable phase delay, the convection term {ω}(q¯/p) leads the CIQ2 evolution by 3.87 days, and the tendency term −(∂{q}/∂t) lags behind it by 2.81 days. According to Lau and Lau (1992), the in-phase forcing of {VH}Hq¯ contributes to the exponential growth of the CIQ2 QBWO. For a sinusoidal-like disturbance of the quasi-14-day period, the 3.5-day phase delay is one quadrature phase, which is very close to the lead time of {ω}(q¯/p). This implies that the phase propagation of CIQ2 is affected by the convection term {ω}(q¯/p). The delay of ({q}/t) is understandable because CIQ2 acts as a forcing term in the water vapor equation, which changes the specific humidity in conjunction with other dynamical terms. The phase delays of the other terms are relatively small, except that for the horizontal advection term VH¯H{q}. The lead–lag sequence of dynamical terms can be determined by arranging their time delays in descending order:

 
{ω}q¯p>{ω}{q}p>{VH}Hq¯>CIQ2>{VH}H{q}>ω¯{q}p>VH¯H{q}>{q}t.

Of the three terms before CIQ2, {ω}(q¯/p) and {VH}Hq¯ are highly variable, which suggests that the quasi-biweekly variability of CIQ2 is derived from the quasi-biweekly variability of the vertical motion and horizontal advection of the mean specific humidity over the southern BOB.

Table 1.

Time delay between the evolution of each term in the perturbed water vapor equation and that of CIQ2. The three terms with large variability are shown in bold.

Time delay between the evolution of each term in the perturbed water vapor equation and that of CIQ2. The three terms with large variability are shown in bold.
Time delay between the evolution of each term in the perturbed water vapor equation and that of CIQ2. The three terms with large variability are shown in bold.

6. Model results

We used the DLBM, forced by the apparent moisture sink (Q2) anomalies from low to high levels, to examine the role of Q2 anomalies in modulating the BEAP–QBWO. The spatial distribution of the average forcing along the model sigma coordinate is shown in Fig. S3. The steady response on day 0 resembles the results of our observational analysis described above, including a strong cyclonic circulation over the southern BOB, and a weak but discernible cyclonic circulation over the western Pacific at 700 hPa (Fig. 11h). The model response in the upper troposphere manifests a quasi-baroclinic correspondence to the low-level circulation anomalies, with anticyclonic circulations over the tropical Indian Ocean and northern China (Fig. S4). As the forcing transforms from the positive to the negative phase of the BEAP–QBWO, the modeled circulation at 700 hPa shows a decreasing cyclonic circulation over the southern BOB up to day 3, followed by an increasing anticyclonic circulation for the following days. The circulation anomalies in other parts of the research area vary accordingly from the positive phase pattern to the negative phase pattern. The inverse forcing on day −7 results in a circulation pattern that is essentially the opposite of that seen on day 0 (Figs. 11a,h).

Fig. 11.

(a)–(n) Model response of 700-hPa wind anomalies from day −7 to day 6 over one life cycle of the BEAP–QBWO during the boreal summer. The geographical location of point A is 14°N, 84°E; point B is 40°N, 122°E; and point C is 26°N, 156°E.

Fig. 11.

(a)–(n) Model response of 700-hPa wind anomalies from day −7 to day 6 over one life cycle of the BEAP–QBWO during the boreal summer. The geographical location of point A is 14°N, 84°E; point B is 40°N, 122°E; and point C is 26°N, 156°E.

The vertical structure of the modeled vorticity is shown along the same route (line ABC in Figs. 3h and 11h) as in our observational analysis (see section 4c). Vorticity anomalies exhibit a tilted wave train pattern from the southwestern BOB to western Pacific on the peak days of the BEAP–QBWO (Figs. 12a,c). The wave train on day 0 is composed of positive–negative–positive (negative–positive–negative) anomaly centers. There is alternating upward and downward motion associated with the vorticity anomaly wave train. The positive (negative) forcing over the BEAP source region excites upward (downward) motion over the southwestern BOB, northern China, and the western Pacific. All of these characteristics of the modeled circulation resemble their reanalysis counterparts (see Fig. 7). During the middle of the BEAP–QBWO phase transition, the forcing over the BEAP energy source region becomes weak, and the resultant vorticity wave train pattern diminishes (Figs. 12b,d). These results indicate that the transition of apparent moisture sink anomalies over the southern BOB can excite the in-phase changes of atmospheric circulation along the BEAP transmission pathway. Consequently, we have confirmed that the BEAP–QBWO is led by in-phase variations of convective anomalies over the southern BOB.

Fig. 12.

Model response of vorticity (contours; s−1, contour interval: 2 × 10−7) and vertical velocity (shaded; dPa s−1) cross sections along the blue line shown in Fig. 3h for lags of (a) −7, (b) −4, (c) 0, and (d) +3 days.

Fig. 12.

Model response of vorticity (contours; s−1, contour interval: 2 × 10−7) and vertical velocity (shaded; dPa s−1) cross sections along the blue line shown in Fig. 3h for lags of (a) −7, (b) −4, (c) 0, and (d) +3 days.

7. The impact of the BSISO on triggering of the BEAP–QBWO

According to Lee et al. (2013), the BSISO includes a 10–30-day mode (BSISO2) that covers the QBWO band of ISV. The BSISO2 is characterized by the northward and northwestward propagation of convective disturbances across the Asian summer monsoon (ASM) region. To quantitatively analyze the relationship between the BSISO2 and the BEAP–QBWO, we calculated the correlation coefficients between the BSISO2 index and the BEAP index for each year. The maximum correlation coefficient was 0.27 and the minimum was −0.31. The mean correlation coefficient averaged between 1979 and 2016 was −0.03, which is much lower than the threshold value at the 90% confidence level (0.15). This implies that the intraseasonal variability of the BEAP during the boreal summer is intrinsically different from that associated with BSISO2.

Figure 13 shows the phase space composite curves of BSISO2 (Fig. 13a) and the phase points for the onset dates of the BEAP–QBWO (Fig. 13b). The occurrence frequency of the BEAP–QBWO is higher in phases 1–4 than in the rest of the phase diagram. There are 40 out of 64 cases of typical BEAP–QBWO events in BSISO2 phases 1–4. During the phase interval, the BSISO2-related convection propagates to the southern BOB from upstream areas on the BSISO2 propagation path, including the Philippine Sea and South China Sea. If only the strong BSISO2 events are considered (amplitude outside the unit circle), then 61% of the BEAP–QBWO initiation occurs within the phase interval. Our results suggest that the BSISO2 modulates the onset of the BEAP–QBWO mainly via the downstream propagation of convective disturbances into the southern BOB.

Fig. 13.

(a) Phase space composite curves of BSISO2 based on normalized PC3 and PC4 of multivariate empirical orthogonal function (MV-EOF) analysis of OLR and 850-hPa zonal wind. Blue (red) curves denote the evolution of strong cases for odd (even) number initial phases with (PC32 + PC42)1/2 ≥ 1.5. Data from the initial day to the next 30 days are averaged over all strong cases for each phase. (b) Phase points (marked by the small numbers) for the onset dates of typical BEAP–QBWO events during the research period.

Fig. 13.

(a) Phase space composite curves of BSISO2 based on normalized PC3 and PC4 of multivariate empirical orthogonal function (MV-EOF) analysis of OLR and 850-hPa zonal wind. Blue (red) curves denote the evolution of strong cases for odd (even) number initial phases with (PC32 + PC42)1/2 ≥ 1.5. Data from the initial day to the next 30 days are averaged over all strong cases for each phase. (b) Phase points (marked by the small numbers) for the onset dates of typical BEAP–QBWO events during the research period.

Note that the BSISO2-related convective anomalies during phases 1–4 also appear over the Indian Ocean and India, which are located downstream of the BOB on the BSISO2 propagation path. It remains unclear how these downstream perturbations contribute to the BEAP–QBWO. This question is left for future research. It is also noteworthy that the propagation route and spatial patterns of the BEAP–QBWO differ from its BSISO2 counterparts. The BSISO2-related rainfall anomalies exhibit a northwestward propagation from the coastal areas of southeast China to the Yangtze River during phases 5–7 (Hsu et al. 2016). In contrast, the BEAP-related precipitation variations manifest mainly as changes to the extent and intensity of the precipitation that are caused by the in situ strengthening or weakening of convective activity.

8. Discussion and conclusions

In this study, we investigated the intraseasonal variability of the BEAP during the boreal summer over the period 1979–2016. The ISV of the BEAP shows a prominent QBWO mode. The BEAP–QBWO displays distinct features during the evolution of convection-related thermodynamic variables and low-level circulations. Significant variations in rainfall appear across the Asia–Pacific region during the phase transition of the BEAP–QBWO. Dynamic diagnosis revealed three terms with large variability, namely, the horizontal advection term {VH}Hq¯, the convection term {ω}(q¯/p), and the tendency term ({q}/t). The variability of {VH}Hq¯ shows an in-phase transition with that of CIQ2, which strengthens the intensity of the BEAP–QBWO. The fluctuations of {ω}(q¯/p) are in quadrature with CIQ2 and thus lead to phase propagation. The tendency of specific humidity that lags behind the phase transition of CIQ2 is a result of their forcing–response relationship in the water vapor equation. Our modeling results confirmed that the in-phase variations of convective anomalies over the southern BOB play a strong role in modulating the BEAP–QBWO. Further analysis showed that BSISO2 can modulate the onset of the BEAP–QBWO mainly through downstream propagation of convective disturbances from the Philippine Sea and South China Sea to the southern BOB.

One noteworthy point about the BEAP–QBWO is that the origin of the QBWO signal may not develop locally over the southern BOB. The BEAP–QBWO could be propagated from the western Pacific across the South China Sea through the western North Pacific (WNP) wave train (Wang and Duan 2015) and Indo–western Pacific convection oscillation (Li et al. 2013; Q. Wang et al. 2018). The excitation and propagation mechanisms associated with the BEAP–QBWO could be further clarified with the help of an atmospheric general circulation model (AGCM). An in-depth analysis based on AGCM experiments is needed to reveal the origin of convective anomalies and the feedbacks between the BEAP and other teleconnections over the western Pacific. According to Yang et al. (2014), the BEAP–QBWO may also be enhanced over the exit region of the westerly jet core and WNP tropical monsoon trough. The westward propagation of tropical convection around the southern BOB can be treated as a tropical atmospheric process driven by the convectively coupled equatorial Rossby wave (Wang and Xu 1997; Chatterjee and Goswami 2004) or the mixed Rossby–gravity wave (Goswami and Mathew 1994; Mao and Chan 2005; Li et al. 2019). The enhancement and suppression of convective disturbances can be attributed to intraseasonal air–sea interaction around the area and the Rossby wave response to the SST-induced convective heating. The role of air–sea interactions in the Rossby wave response that drives the westward propagation of the QBWO remains uncertain. Further theoretical analysis is also needed to reveal the role of tropical waves in the propagation of the BEAP–QBWO.

Like the ISVs of other teleconnection patterns, the BEAP–QBWO is closely associated with monsoon climate variability. The BEAP–QBWO behaviors may be affected by the circulation background around East Asia, such as the eastward extension of the South Asian high, the westward extension of the WNP subtropical high, and the northward migration of the westerly jet (Li et al. 2015; Yang et al. 2017; Gao et al. 2018; Qi et al. 2019). Alongside the phase change of the BEAP–QBWO, the associated extreme precipitation events can lead to flood and drought events that cause substantial economic damage to countries in the Asia–Pacific region (Bureau for Crisis Prevention and Recovery 2004; Yang et al. 2010; Chen et al. 2015). The coordinated variations related to the BEAP–QBWO are also a potential source of subseasonal predictability (Vitart and Robertson 2018).

Acknowledgments

This work was supported by the National Natural Science Foundation of China (41861144012, 41875103, and U1502233), the Natural Science Foundation of Yunnan Province (2018FY001-018, 2018FB081, and 2018BC007), and the program for provincial innovative team of the climate change study of Greater Mekong Subregion (2019HC027). It is also co-supported by the Natural Science Foundation of Yunnan Provincial Education Department (2019J0019).

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