1. Introduction
The Bay of Bengal (BOB) and the South China Sea (SCS) are located at similar latitudes, surrounded by populous countries including China, India, Bangladesh, Vietnam, Myanmar, Sri Lanka, and the Philippines, among others. These countries are highly vulnerable to the impact of tropical cyclones (TCs), often suffering significant losses of life and property (Mendelsohn et al. 2012; Peduzzi et al. 2012; Woodruff et al. 2013; J. Zhao et al. 2018; A. Chen et al. 2019; Tu et al. 2021). Understanding the variability of regional TC activity and the responsible modulation mechanisms is crucial for improving TC prediction and possesses significant socioeconomic implications (Gu et al. 2016; Li and Zhou 2018; Chu and Murakami 2022; Wu et al. 2023).
On intraseasonal time scales (10–90 days), the 10–20-day quasi-biweekly oscillation (QBWO; Murakami and Frydrych 1974; Krishnamurti and Bhalme 1976; Chen and Chen 1993; Jia et al. 2013; Wei et al. 2019; Yan et al. 2019) and the 30–60-day Madden–Julian oscillation (MJO; Madden and Julian 1971, 1972; Madden 1986; Hendon and Liebmann 1994; Matthews 2000; Jeong et al. 2008; Zhang 2013; Liu et al. 2017; Li et al. 2018; Kikuchi 2021) have been found to be closely associated with convection and circulation phenomena over the tropics and extratropics. Gray (1979) first discovered that TCs tended to cluster in time, exhibiting a temporal pattern of alternating active and inactive periods within a couple of weeks. Subsequent studies have demonstrated the influence of intraseasonal oscillations (ISOs) on TC activity over the tropical ocean (Liebmann et al. 1994; Ho et al. 2006; Li and Zhou 2013a,b; Tao and Li 2014; H. Zhao et al. 2018; J. M. Chen et al. 2019; Zhu et al. 2021). It is believed that the frequency of TC formation is thought to be positively (negatively) correlated with the convective (nonconvective) phase of the ISO. Kim et al. (2008) demonstrated that over the western North Pacific (WNP), the axis of the preferred tropical cyclogenesis regions shifted like a seesaw according to the variations in the MJO category. Mao and Wu (2010) attributed the increase in TC genesis to the abnormal barotropic instability in the monsoon trough region during the MJO convection phase. Zhao et al. (2016) suggested that WNP TC genesis events were associated with the monsoon-related flow patterns during the QBWO convection phase. Furthermore, Bhardwaj et al. (2019) showed that TC genesis was significantly enhanced (suppressed) over the BOB when the convectively active MJO phase was positioned over the eastern Indian Ocean and the Maritime Continent (Western Hemisphere and Africa). The enhanced convection of the MJO can induce strong equatorial westerlies in the equatorial region, generating strong cyclonic vorticity in the north Indian Ocean (Krishnamohan et al. 2012). However, the research emphasizes the role of either the QBWO or the MJO on TC genesis while infrequently investigating their mutual effects.
As shown in Fig. 1, the MJO propagates eastward from the western Indian Ocean and exhibits a northward-propagating component that can influence the BOB and the SCS during boreal summer through various mechanisms associated with the lower-level temperature and moisture gradients (Gadgil and Srinivasan 1990; K. Li et al. 2013), the boundary layer moisture advection (Nitta 1987; Tseng et al. 2015), the background easterly vertical shear (Jiang et al. 2004; Bellon and Srinivasan 2006), and air–sea interaction processes (Sui and Lau 1989; Wang et al. 2018). Additionally, the QBWO unstable mode originates in the off-equatorial WNP and moves northwestward across the South Asian monsoon region, which is characterized by a northwest–southeast-oriented wave train pattern, in the presence of convection–circulation–moisture feedback under the mean monsoon flow (Kikuchi and Wang 2009; Wang and Duan 2015; Yan et al. 2019; Zhang et al. 2020; Li et al. 2022). These features imply that the TC activity over the two basins is probably modulated concurrently by the MJO and the QBWO. A recent study by Chen et al. (2023) found a critical influence of the MJO activity on the northward movement of two TCs, Yaas (2021) and Choi-wan (2021), which crossed the BOB and the SCS, respectively. Hence, it is of great interest to systematically investigate the TCs in these two neighboring basins simultaneously at intraseasonal time scales.
Schematic of the propagation path of the MJO (red) and the QBWO (blue). The purple (orange) area indicates the BOB (SCS).
Citation: Journal of Climate 37, 6; 10.1175/JCLI-D-23-0376.1
The aim of this study is to investigate the modulation on TC genesis by assessing the two types of ISO over the BOB and the SCS. The remainder of this paper is organized as follows, section 2 describes the data and statistical verification methods used in this study, section 3 presents the climatology of the TC genesis and tropical intraseasonal convection over the BOB and the SCS, section 4 outlines the relationship between MJO/QBWO and TC genesis and discusses the combined modulation of the two oscillations on TC genesis, and section 5 summarizes and discusses the main findings.
2. Data and methodology
a. Data
The TC best-track data were extracted from the Joint Typhoon Warning Center (JTWC), including the location and intensity of the TCs at 6-h intervals. Only the TCs with peak intensity greater than those of tropical storms [1-min average maximum surface wind speed exceeding 34 kt (1 kt ≈ 0.51 m s−1)] were considered. TC genesis was defined as the first record of the best-track data. The interpolated 2.5° × 2.5° resolution outgoing longwave radiation (OLR) data (Liebmann and Smith 1996) were obtained from the National Oceanic and Atmospheric Administration (NOAA). Other reanalysis data with a resolution of 1° × 1° were derived from the fifth climate reanalysis product of the European Centre for Medium-Range Weather Forecasts (ERA5; Hersbach et al. 2020). These parameters were applied at daily intervals spanning from 1979 to 2021. To investigate the anomalies associated with MJO (QBWO) variations, a second-order Butterworth bandpass filter (Butterworth 1930) with a cutoff period of 30–60 (10–20) days was applied to the original data after the daily climatology was removed.
b. Determination of the ISO phase
Similar to the approach in other studies (e.g., Ho et al. 2006; Kim et al. 2008; Jeong et al. 2008; Jia and Yang 2013), a composite analysis approach was applied in this study to explore the characteristics of selected ISO events. The samples were determined based on an empirical orthogonal function (EOF) analysis performed on the ISO OLR anomalies over the 5°–25°N, 60°–130°E region in May–June during 1979–2021. Using the method of Matthews (2000), the phase and amplitude indices of the propagation vector [PC2(t), PC1(t)] are expressed as tan−1[PC2(t)/PC1(t)] and
c. Assessing the role of environmental factors
d. Quantification of TC genesis
3. Climatology of TC genesis and tropical intraseasonal convection
The climatic characteristics of tropical cyclogenesis and intraseasonal convection activity over the BOB and the SCS from 1979 to 2021 were first briefly investigated (Fig. 2). The frequency of TC occurrence exhibits different monthly distributions over the BOB and the SCS (Fig. 2a). Notably, unimodal distribution is observed in the SCS from May to November, while a distinct bimodal distribution is observed in the BOB, with one peak from April to June and the other from October to December. Combining the TC counts over the two basin regions, their proportion out of the total TCs over the northern Indo-western Pacific Ocean also follows a bimodal distribution, with the largest proportion in May, accounting for almost 40% of the total. As shown in Fig. 2b, the variability of the QBWO/MJO convection over the two basins is more active in boreal summer than in boreal winter, with the main peaks observed from May to June. The QBWO convection explains a larger fraction of the variance than the MJO convection, indicating that the variation in the QBWO convection is more dominant than that in the MJO convection over the two regions. In addition, Zhao et al. (2015) demonstrated that the modulation on TCs by the ISO in early summer (May–June) was stronger than that in late summer (July–August), increasing the number of TC formations during the active ISO phases in early summer. Therefore, by considering the seasonal distribution of TC activity and tropical intraseasonal convection, this study is aimed to explore the modulation of QBWO/MJO in the May–June TC periods over the BOB and the SCS.
Monthly climatology of (a) tropical cyclone (TC) frequency over the BOB (5°–25°N, 77.5°–100°E; blue line; refer to the right y axis; unit: yr−1) and the SCS (5°–25°N, 100°–122.5°E; orange line; refer to the right y axis; unit: yr−1), and the proportion of TCs over both basins out of the total TCs over the North Indo-western Pacific Ocean (bar; refer to the left y axis); and (b) domain averaged standard deviation of 10–20- and 30–60-day filtered OLR (W m−2) over the BOB (blue for QBWO and green for MJO) and the SCS (pink for QBWO and orange for MJO) during 1979–2021.
Citation: Journal of Climate 37, 6; 10.1175/JCLI-D-23-0376.1
To clearly distinguish the spatial patterns and propagation characteristics of both the MJO and the QBWO in May–June, the variability of the OLR for the two oscillations, as well as the first two leading EOF modes (i.e., EOF1 and EOF2) over the tropical Indo-Pacific Ocean, are investigated (Fig. 3). The higher standard deviations of the two oscillations OLR are mainly concentrated in the BOB and the SCS, suggesting the presence of noticeable intraseasonal signals in the two basins (Figs. 3a,b). In the BOB, the QBWO variation is notably located farther north compared to the MJO variation. An EOF analysis was then performed on the QBWO and MJO OLR anomalies over the vicinity of the two basins (5°–25°N, 60°–130°E) to understand the underlying variability features. The EOF1 of the QBWO exhibits an SCS convection pattern characterized by negative OLR anomalies dominating the entire SCS basin. The EOF2 shows a BOB convection mode with negative OLR anomalies occupying the entire BOB (Fig. 3c). The first and second modes explain 12.34% and 10.10% of the total variance, respectively, and are statistically significant, according to North’s criterion (North et al. 1982). Lead–lag correlation analysis between the principal components (PCs) of these two EOF modes shows a maximum correlation of 0.37 (p < 0.01), which occurs when PC1 leads PC2 by 3 days (not shown), which is approximately one-quarter of the QBWO life cycle. Thus, in conjunction with the spatial structure of the EOFs, the QBWO OLR anomalies move westward in the South Asian monsoon region from EOF1 to EOF2. The EOF results of the MJO OLR anomalies (Fig. 3d) exhibit opposite characteristics but a larger convection area than those of the QBWO, with a BOB (an SCS) convection pattern in EOF1 (EOF2) accounting for 29.68% (15.73%) of the total variance. A maximum correlation of 0.47 (p < 0.01) is also observed when PC1 leads PC2 by 9 days (not shown). Again, the two EOF modes of the MJO OLR anomalies describe the eastward propagation of the MJO in the South Asian monsoon region. These results suggest that the signals of the two oscillations are active and could encounter each other in this region.
Standard deviation of (a) 10–20- and (b) 30–60-day filtered OLR (W m−2) in May–June during 1979–2021. Blue boxes (5°–25°N, 60°–130°E) are the region used to construct the empirical orthogonal function (EOF) analysis. Spatial patterns of EOF1 and EOF2 of (c) 10–20- and (d) 30–60-day filtered OLR anomalies (W m−2). The relative contribution of each mode to the total variance is shown in the upper-right corners of (c) and (d).
Citation: Journal of Climate 37, 6; 10.1175/JCLI-D-23-0376.1
4. Modulation of MJO and QBWO on TC genesis
a. TC genesis influenced by the MJO
Corresponding to the evolution of the MJO convection, the TC genesis position and count frequency, as well as the associated large-scale environmental variables and circulation, are displayed in Figs. 4 and 5. Figure 4a shows the composite anomalies of the 30–60-day filtered OLR for different MJO phases, along with the associated TC genesis locations. During phases 3 + 4, the enhanced MJO-related convection originates in the north Indian Ocean and extends eastward into the Maritime Continent. The enhanced convection then propagates farther eastward and northward, revealing a belt of northwest–southeast-oriented negative OLR anomalies at approximately 15°N during phases 5 + 6. In phases 7 + 8, the enhanced convection dominates over the SCS and the Philippine Sea, while suppressed convection begins to appear in the north Indian Ocean. Finally, the suppressed convection region continues to shift northeastward and completely replaces the enhanced convection in phases 1 + 2. Thus, a complete cycle represents the eastward and northward propagating nature of the MJO in early boreal summer, which is consistent with previous studies (Liebmann et al. 1994; Kim et al. 2008; Li and Zhou 2013a,b; Bhardwaj et al. 2019).
Composite patterns of 30–60-day filtered (a) OLR anomalies (W m−2) and (c) genesis potential index (GPI) anomalies for different MJO phases. Only those significant anomalies exceeding the 90% confidence level are shown. Blue dots denote the positions of cyclogenesis. The number of TCs formed in each phase is shown in the upper-right corner of each panel. (b) TC counts (blue bars) and daily genesis rate (DGR; %) of TCs (green bars) for different MJO phase categories over the different subregions. The phases whose corresponding value is statistically enhanced (suppressed) at the 90% and 95% confidence levels are indicated by one and two dots (one and two asterisks), respectively. (d) Contributions of the 850-hPa absolute vorticity (V), 700-hPa relative humidity (H), potential intensity (P), vertical wind shear (S), and nonlinear effect (Non-L) to the composite 30–60-day filtered GPI anomalies for different phases over the BOB (red bars) and the SCS (blue bars). Dots denote the significant values at the 90% confidence level. The number in parentheses indicates the number of days in each MJO phase.
Citation: Journal of Climate 37, 6; 10.1175/JCLI-D-23-0376.1
Composite patterns of (a) 30–60-day filtered water vapor flux anomalies vertically integrated from 1000 to 500 hPa (vectors; kg m−1 s−1) and their divergence (shading; 10−4 kg m−2 s−1), (b) streamlines and 30–60-day filtered vorticity anomalies (shading; 10−5 s−1) at 850 hPa, and (c) geopotential height (contours; interval: 5 gpm) and 30–60-day filtered geopotential height anomalies (shading; gpm) at 500 hPa for different MJO phases. Only those significant anomalies exceeding the 90% confidence level are shown. The number in the parentheses indicates the number of days in each MJO phase. The green thick line in (b) indicates the approximate position of the monsoon trough.
Citation: Journal of Climate 37, 6; 10.1175/JCLI-D-23-0376.1
Obvious modulations on TC genesis over the northern Indo-western Pacific Ocean by the MJO are observed during the evolution of MJO convection. Figure 4b summarizes the corresponding TC genesis statistics over the four subregions divided by 77.5°, 100°, and 122.5°E in terms of TC counts and the DGR during different MJO phases. In particular, tropical cyclogenesis over the BOB and the SCS differs significantly from the climatological average. During phases 1 + 2 and 7 + 8 (1 + 2 and 3 + 4), few TC genesis events occur with a significant decrease, while during phases 3 + 4 and 5 + 6 (7 + 8), more TC genesis events occur with a significant increase in the BOB (SCS). There is also a change in tropical cyclogenesis associated with the development of MJO convection in the WNP and the Arabian Sea (AS), but it fails to pass the significance test. Therefore, the changes in TC genesis in the BOB and the SCS are particularly sensitive to the variations of the MJO, primarily because of the substantial variability at intraseasonal scales in these two regions (Fig. 3b). It is worth noting that the convection anomalies associated with the MJO in Fig. 4a remain evident even after removing all the TC days (not shown), implying that TCs themselves contribute minimally to the spatial structure of the MJO.
Large-scale environmental factors such as humidity, vorticity, sea surface temperature (SST), and vertical wind shear are known to influence TC generation. Given this, what are the main factors by which the MJO regulates TC generation? To answer this question, a GPI analysis is used to assess the relative role of large-scale environmental factors. Figure 4c shows the evolution of 30–60-day filtered GPI anomalies during different MJO phases, which is consistent with the spatial distribution of convective anomalies, except in the east of 140°E. Most TCs occur over the BOB and the SCS with positive GPI anomalies, although some TCs occur over the regions with negative GPI anomalies, which may be attributed to other factors that are not considered in the GPI definition or are unrelated to the MJO mode.
Considering the ability of the GPI to capture the TC genesis over the two basins associated with the MJO, the contribution of each GPI component to TC genesis is further explored (Fig. 4d). During the transitional active phases 3 + 4 (phases 5 + 6) over the BOB (SCS), all four linear components contribute positively to the GPI anomalies associated with the MJO. In the mature active phases 5 + 6 (7 + 8) over the BOB (SCS), the lower-level relative vorticity becomes more important than in the transitional active phases, while vertical wind shear and potential intensity exert a weak or opposite influence. Among these variables, the middle-level relative humidity consistently emerges as the most important contributor, highlighting that MJO convection primarily generates a humid environment. It is noted that the nonlinear component also contributes positively to the pattern of GPI anomalies, demonstrating that the function of multiscale interaction cannot be overlooked. The results for the convective inactive phases are diametrically opposite to those for the active phases.
To gain further insights into the MJO-related modulation processes, Fig. 5 presents the composite middle-lower tropospheric water vapor flux, 850-hPa streamlines, and 500-hPa geopotential height for different MJO phases. Along with the propagation of MJO convection, the MJO-induced cyclonic circulation anomalies show a clear northeastward propagation over the BOB during phases 3 + 4 to 5 + 6, and over the SCS during phases 5 + 6 to 7 + 8, then they gradually disappear in phases 1 + 2. In particular, the strong westerly anomalies on the southern flank of the extensive cyclonic circulation anomalies enhance the cross-equatorial flow, alternately leading to sufficient moisture convergence over the BOB and the SCS (Fig. 5a). Meanwhile, corresponding to the cyclonic circulation anomalies, the extension of the BOB monsoon trough apparently extends from the northeast of the Indian Peninsula to the BOB in phases 3 + 4 and 5 + 6, enhancing the lower-level relative vorticity over the BOB. During phases 5 + 6 and 7 + 8, the SCS monsoon trough strengthens and the WNP subtropical high retreats eastward, increasing the lower-level relative vorticity in the SCS and the Philippine Sea (Figs. 5b,c). By contrast, the characteristics of anticyclonic circulation anomalies exhibit an opposite pattern compared to the cyclonic circulation anomalies. During the inactive phase of MJO convective activity, there appears water vapor divergence, weakening of the monsoon trough, and westward extension of the subtropical high, all of which are unfavorable for TC development over the two basins. These results suggest that the MJO-induced wide range of cyclonic circulation anomalies facilitates TC formation by modulating moisture and vorticity in the middle to lower troposphere.
b. TC genesis influenced by the QBWO
Similar to the MJO evolution, the influence of the QBWO on TC genesis and the associated large-scale environment are examined (Figs. 6 and 7). The composite 10–20-day filtered OLR anomalies and TC genesis locations for the QBWO phases are shown in Fig. 6a. Compared with the northeastward propagation of the MJO, there is a distinct westward movement in the convection anomalies associated with the QBWO over the SCS and the BOB. During phases 1 + 2, the QBWO-induced convection moderately increases in the WNP. However, it becomes more pronounced in the SCS and Philippine Sea during phases 3 + 4, similar to the corresponding pattern in EOF1 (Fig. 3c). The enhanced convection continues to expand westward in phases 5 + 6, dominating the BOB and the SCS. Subsequently, the enhanced convection center dwells over the Indian Peninsula and the BOB in phases 7 + 8. This pattern is analogous to the corresponding EOF2 pattern (Fig. 3c). Finally, the enhanced convection gradually moves into the AS and then eastward along the southern slope of the Tibetan Plateau and southern China in phases 1 + 2, showing a clockwise propagation path, as Wang and Duan (2015) described. The suppressed QBWO-induced convection follows a path similar to that of the enhanced convection.
As in Fig. 4, but for 10–20-day filtered (a) OLR anomalies (W m−2) and (c) GPI anomalies, (b) TC counts (blue bars) and DGR of TCs (green bars; %), and contributions of various terms to composite 10–20-day filtered GPI anomalies for different QBWO phases.
Citation: Journal of Climate 37, 6; 10.1175/JCLI-D-23-0376.1
As in Fig. 5 but for (a) 10–20-day filtered water vapor flux anomalies vertically integrated from 1000 to 500 hPa (vectors; kg m−1 s−1) and the divergence (shading; 10−4 kg m−2 s−1), (b) streamlines and 10–20-day filtered vorticity anomalies (shading; 10−5 s−1) at 850 hPa, and (c) geopotential height (contours; interval: 5 gpm) and 10–20-day filtered geopotential height anomalies (shading; gpm) at 500 hPa for different QBWO phases.
Citation: Journal of Climate 37, 6; 10.1175/JCLI-D-23-0376.1
Consistent with the westward propagation of the QBWO across the SCS and the BOB, there appears a clear westward movement in the TC genesis locations during different QBWO phases. As shown in Fig. 6b, the TC counts and the DGR associated with QBWO propagation over the northern Indo-western Pacific Ocean are summarized across the four subregions. It is observed that the tropical cyclogenesis over the SCS and the BOB deviates significantly from the climatological average. There is a significant decrease in TC genesis events during phases 1 + 2 and 7 + 8 (1 + 2 and 3 + 4) over the SCS (BOB), and a significant increase is observed during phases 5 + 6 over the two basins. On the other hand, tropical cyclogenesis over the WNP increases considerably from the climatological average only during phases 1 + 2, and tropical cyclogenesis over the AS generally remains constant with the movement of the QBWO. Therefore, the changes in TC genesis over the SCS and the BOB are particularly sensitive to the variations in the QBWO. It is essential to recall that the convection anomalies displayed here are persistent even after removing all of the TC days (not shown), implying that TCs exert minimal influence on the spatial structure of the QBWO.
A GPI analysis is applied to assess the relative roles of large-scale environmental factors in TC genesis (Fig. 6c). The majority of TC genesis occurs in the GPI-positive anomaly region, which is consistent with the spatial distribution of convective anomalies, indicating that the GPI also captures the signal of TC genesis on the QBWO time scales (Fig. 6a vs Fig. 6c). The contribution of each GPI component to TC generation in the two basins is further explored (Fig. 6d). It is found that relative vorticity and relative humidity are the two most important variables, and vertical wind shear and potential intensity contribute slightly. Compared to relative humidity, which is the first contributing term of the MJO, in the case of the QBWO, relative vorticity seems to be of greater importance overall, particularly in the SCS during all phases. The contributions of vertical wind shear and potential intensity can be ignored, and they also have little contribution in the MJO. In addition, the contribution of the nonlinear term cannot be ignored either, indicating that the influence of multiscale interaction cannot be overlooked.
The QBWO-induced circulation anomalies are treated similarly (Fig. 7). As the QBWO convection propagates westward, it successively induces convective cyclonic moisture convergence in the SCS and the BOB (Fig. 7a). During phases 3 + 4 and 5 + 6, the monsoon trough strengthens and extends southeastward from Hainan Island to the Philippines, and the composite subtropical high retreats from the SCS, intensifying the abnormal lower-level vorticity over the SCS (Figs. 7b,c). Similarly, during phases 5 + 6 and 7 + 8, as the QBWO convection continues to move westward, the monsoon trough deepens in the BOB, promoting increased lower-level cyclonic vorticity throughout the BOB. Thus, the QBWO-induced circulation anomalies contribute to TC formation by simultaneously providing water vapor and relative vorticity. However, in contrast with the MJO with its broad-scale circulation, the modulation processes associated with the QBWO are primarily characterized by localized convection.
c. Combined modulation of MJO and QBWO on TC genesis
Given that both the MJO and the QBWO can modulate TC genesis over the BOB and the SCS, their combined modulation is discussed in this section. Based on the convective/nonconvective phase distributions of these two ISOs in the BOB and the SCS (Figs. 4a and 6a), the combined modulation pattern can be divided into four categories. For example, the left panel of Fig. 8a represents the intersection of MJO phases 3 + 4 and 5 + 6 with QBWO phases 5 + 6 and 7 + 8, which indicates the simultaneous occurrence of the convective phase for both MJO and QBWO in the BOB. Figure 8 also illustrates the spatial distribution of TC genesis in the BOB and the SCS during the intersecting phase of the two ISOs. TC generation significantly increases when both the MJO and the QBWO are in the convective phase (Fig. 8a), but this cannot be found when they are in the nonconvective phase (Fig. 8d). Moreover, TC production is greatly reduced when one of the two oscillations is in the convective phase, and the other is in the nonconvective phase (Figs. 8b,c) because their contributions may cancel each other.
Composite patterns of 30–60-day filtered OLR anomalies (shading; W m−2) and 10–20-day filtered OLR anomalies (contours; interval: 4 W m−2, with 0 m−2 omitted) for the intersecting phase of MJO and QBWO over the BOB and the SCS. Only those significant anomalies exceeding the 90% confidence level are shown. Blue dots denote the positions of cyclogenesis. The numbers of days and TCs for the intersecting phase of MJO and QBWO are shown in the upper-left and upper-right corners.
Citation: Journal of Climate 37, 6; 10.1175/JCLI-D-23-0376.1
The mutual effect of the two ISOs on the environmental conditions for TCs genesis is further illustrated by examining the 10–60-day filtered GPI anomalies (Fig. 9). When both ISOs are simultaneously in their convective (nonconvective) phases (Figs. 9a,d), significant positive (negative) GPI anomalies appear in the BOB and the SCS, which are associated with the noteworthy occurrence (suppression) of TC generation. This consistently underscores the ability of the GPI to capture TC formation. However, as the QBWO transitions to a nonconvective phase while the MJO remains in its convective phase (Fig. 9b), the GPI anomalies weaken noticeably in both the BOB and the SCS, and the regions with positive and negative GPI anomalies become more scattered. In the central SCS, significant negative GPI anomalies are observed. Conversely, when the MJO transitions to a nonconvective phase while the QBWO is still in its convective phase (Fig. 9c), the GPI anomalies in the BOB become significantly negative, as do those in the central SCS.
As in Fig. 8, but for 10–60-day filtered GPI anomalies. White multiplication signs denote the significant values at the 90% confidence level.
Citation: Journal of Climate 37, 6; 10.1175/JCLI-D-23-0376.1
Subsequently, the diagnostic analysis of GPI anomalies indicates that when both ISOs share the same convective/nonconvective phase, relative humidity exerts a more substantial influence compared to relative vorticity over the BOB and the SCS (Fig. 10). When the MJO (QBWO) resides in the convective phase but the QBWO (MJO) is in the nonconvective phase, the QBWO (MJO) exerts a more pronounced influence on the SCS (BOB), while its effect on the BOB (SCS) is less pronounced. This feature is primarily due to the mutual cancellation of the GPI anomalies in the BOB (SCS). However, it is noteworthy that the negative relative humidity (relative vorticity) anomalies play the most significant role during the MJO (QBWO) nonconvective phase, when the influence of the nonlinear term is not taken into account. This conclusion further supports the notion that the MJO and QBWO each play a distinct role, with one being associated with relative humidity and the other with relative vorticity. Thus, the MJO convection of relatively longer time scales provides more extensive humidity for TC genesis through cross-equatorial flow, while the QBWO exerts a fairly localized convergence. These different characteristics appear to indicate that the QBWO is prone to exert local convergence on the background of the MJO, which supports the view of Li and Zhou (2013a).
Contributions of various terms to the composite 10–60-day filtered GPI anomalies for the intersecting phase of MJO and QBWO over the BOB (red bars) and the SCS (blue bars). Dots denote the significant values at the 90% confidence level.
Citation: Journal of Climate 37, 6; 10.1175/JCLI-D-23-0376.1
5. Conclusions and discussion
The MJO and the QBWO are the major components of the ISO over the tropical Indo-Pacific Ocean during summer. The MJO typically propagates northeastward from its origin in the western Indian Ocean, while the QBWO propagates westward from its origin in the northwestern Pacific. These two ISOs can intersect in the BOB and the SCS. As a result, both basins exhibit strong variability in the corresponding convection with respect to the two oscillations, with the early boreal summer being the active period. This study shows that TC genesis in these two basins is influenced by the simultaneous presence of the two oscillations in May–June during 1979–2021. As the convection associated with the MJO/QBWO propagates, alternating TC formation occurs between the BOB and the SCS. There is a notable increase (decrease) in the frequency of TC formation during the convective (nonconvective) phases of the two oscillations.
The role of environmental factors in determining TC genesis is further investigated using an anomalous GPI associated with the two oscillations. Middle-level relative humidity and lower-level relative vorticity are found to be the key features of the MJO/QBWO modulation on TC genesis. The MJO exerts a strong influence on humidity by enhancing the large-scale cross-equatorial moisture transport, resulting in sufficient moisture convergence, while the QBWO has a strong influence on local vorticity by strengthening the monsoon troughs over the two basins, leading to an eastward retreat of the WNP subtropical high. Relatively, potential intensity and vertical wind shear show weak or opposite influences on TC genesis. Consequently, when the convective phases of the MJO and the QBWO overlap in time and space, they can effectively enhance TC generation by intensifying the abnormal moisture and vorticity in the middle-lower troposphere.
This study provides useful information for better understanding of regional TCs. TC genesis over the BOB and the SCS is respectively and significantly sensitive to the variations of MJO and QBWO. The TCs can form sequentially in these two basins with the propagation of the ISO, suggesting a temporal relationship in which they may develop first in one basin and then in the other. Although this study has explored the individual and combined modulations of the two oscillations on TC genesis, additional investigation is required to elucidate their potential interactions. Understanding the interplay between the MJO and the QBWO may provide valuable insights into the dynamics of TC formation and improve forecasting capabilities at intraseasonal scales. Furthermore, the nonlinear term has a positive influence on the pattern of GPI anomalies, indicating the importance of considering the interactions among different variables. Therefore, future research should aim to develop appropriate approaches to deepen understanding of the role of nonlinear effects.
Apart from the above considerations, the role of tropical SST modes (i.e., El Niño–Southern Oscillation and the Indian Ocean dipole) should also be taken into account in the future. Recent studies have suggested that the tropical Indo-Pacific Oceans play an important role in modulating the ISO-TC relationship. The MJO–TC relationship over the WNP is significantly strengthened during El Niño years, when the ratio of TC enhancement to suppression is twice as high in El Niño years compared to neutral and La Niña years (Li et al. 2012). Girishkumar et al. (2015) identified that an increase in middle-tropospheric humidity and a decrease in vertical wind shear were the primary and secondary factors, respectively, which increased the likelihood of rapid intensification of TCs in October–December over the BOB during the active phase of the MJO under the La Niña condition. Wang et al. (2019) found that the strong ISO activity centers were located over the BOB and the SCS during the boreal winter in the positive phase of the southern Indian Ocean dipole, which increased the probability of cyclogenesis in these regions. The influence of tropical SST on the QBWO–TC relationship still remains unclear, and it will be of great interest to further explore the dynamics of the MJO–QBWO–TC relationship for different tropical SST modes.
Acknowledgments.
The authors are grateful to the three anonymous reviewers for providing thorough and insightful comments, which are helpful for improving the overall quality of the manuscript. This work was funded by the Korea Meteorological Administration Research and Development Program (Grant RS-2023-00236880), the Guangdong Major Project of Basic and Applied Basic Research (Grant 2020B0301030004), the “111-Plan” Project of China (Grant B17049), and the Science and Technology Planning Project of Guangdong Province (Grant 2023B1212060019). WZC was also supported by the School of Earth and Environmental Sciences, Seoul National University, South Korea, and the China Scholarship Council.
Data availability statement.
The TC best track data of the JTWC are obtained at https://www.metoc.navy.mil/jtwc/jtwc.html?best-tracks, the OLR data of the NOAA at https://psl.noaa.gov/data/gridded/data.olrcdr.interp.html, and other reanalysis data of the ERA5 at https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset.
REFERENCES
Bellon, G., and J. Srinivasan, 2006: Comments on “Structures and mechanisms of the northward propagating boreal summer intraseasonal oscillation.” J. Climate, 19, 4738–4743, https://doi.org/10.1175/JCLI3861.1.
Bhardwaj, P., O. Singh, D. R. Pattanaik, and P. J. Klotzbach, 2019: Modulation of Bay of Bengal tropical cyclone activity by the Madden-Julian Oscillation. Atmos. Res., 229, 23–38, https://doi.org/10.1016/j.atmosres.2019.06.010.
Bister, M., and K. A. Emanuel, 2002: Low frequency variability of tropical cyclone potential intensity 1. Interannual to interdecadal variability. J. Geophys. Res., 107, 4801, https://doi.org/10.1029/2001JD000776.
Bruyère, C. L., G. J. Holland, and E. Towler, 2012: Investigating the use of a genesis potential index for tropical cyclones in the North Atlantic basin. J. Climate, 25, 8611–8626, https://doi.org/10.1175/JCLI-D-11-00619.1.
Butterworth, S., 1930: On the theory of filter amplifiers. Exp. Wireless Wireless Eng., 7, 536–541.
Camargo, S. J., A. H. Sobel, A. G. Barnston, and K. A. Emanuel, 2007: Tropical cyclone genesis potential index in climate models. Tellus, 59A, 428–443, https://doi.org/10.1111/j.1600-0870.2007.00238.x.
Camargo, S. J., M. C. Wheeler, and A. H. Sobel, 2009: Diagnosis of the MJO modulation of tropical cyclogenesis using an empirical index. J. Atmos. Sci., 66, 3061–3074, https://doi.org/10.1175/2009JAS3101.1.
Chen, A., C.-H. Ho, D. Chen, and C. Azorin-Molina, 2019: Tropical cyclone rainfall in the Mekong River basin for 1983–2016. Atmos. Res., 226, 66–75, https://doi.org/10.1016/j.atmosres.2019.04.012.
Chen, J. M., C.-H. Wu, J. Gao, P.-H. Chung, and C.-H. Sui, 2019: Migratory tropical cyclones in the South China Sea modulated by intraseasonal oscillations and climatological circulations. J. Climate, 32, 6445–6466, https://doi.org/10.1175/JCLI-D-18-0824.1.
Chen, T.-C., and J.-M. Chen, 1993: The 10–20-day mode of the 1979 Indian monsoon: Its relation with the time variation of monsoon rainfall. Mon. Wea. Rev., 121, 2465–2482, https://doi.org/10.1175/1520-0493(1993)121<2465:TDMOTI>2.0.CO;2.
Chen, W., S. Yang, Z. Wu, and F. Cai, 2022: Large-scale atmospheric features favoring the tropical cyclone activity affecting the Guangdong-Hong Kong-Macao Greater Bay area of China. Environ. Res. Lett., 17, 104057, https://doi.org/10.1088/1748-9326/ac9744.
Chen, Y., N. Jiang, Y. Ai, K. Xu, and L. Mao, 2023: Influences of MJO-induced tropical cyclones on the circulation-convection inconsistency for the 2021 South China Sea summer monsoon onset. Adv. Atmos. Sci., 40, 262–272, https://doi.org/10.1007/s00376-022-2103-5.
Chu, P.-S., and H. Murakami, 2022: Climate Variability and Tropical Cyclone Activity. Cambridge University Press, 320 pp.
Efron, B., 1979: Bootstrap methods: Another look at the jackknife. Ann. Stat., 7 (1), 1–26, https://doi.org/10.1214/aos/1176344552.
Emanuel, K. A., and D. S. Nolan, 2004: Tropical cyclone activity and the global climate system. 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 10A.2, https://ams.confex.com/ams/26HURR/techprogram/paper_75463.htm.
Gadgil, S., and J. Srinivasan, 1990: Low frequency variation of tropical convergence zones. Meteor. Atmos. Phys., 44, 119–132, https://doi.org/10.1007/BF01026814.
Girishkumar, M. S., K. Suprit, S. Vishnu, V. P. T. Prakash, and M. Ravichandran, 2015: The role of ENSO and MJO on rapid intensification of tropical cyclones in the Bay of Bengal during October–December. Theor. Appl. Climatol., 120, 797–810, https://doi.org/10.1007/s00704-014-1214-z.
Gray, W. M., 1979: Hurricanes: Their formation, structure and likely role in the tropical circulation. Meteorology over the Tropical Oceans, D. B. Shaw, Ed., Royal Meteorological Society, 155–218.
Gu, C., J. Kang, G. Yan, and Z. Chen, 2016: Spatial and temporal variability of northwest Pacific tropical cyclone activity in a global warming scenario. J. Trop. Meteor., 22, 15–23, https://doi.org/10.16555/j.1006-8775.2016.S1.002.
Hall, J. D., A. J. Matthews, and D. J. Karoly, 2001: The modulation of tropical cyclone activity in the Australian region by the Madden–Julian oscillation. Mon. Wea. Rev., 129, 2970–2982, https://doi.org/10.1175/1520-0493(2001)129<2970:TMOTCA>2.0.CO;2.
Hendon, H. H., and B. Liebmann, 1994: Organization of convection within the Madden-Julian oscillation. J. Geophys. Res., 99, 8073–8083, https://doi.org/10.1029/94JD00045.
Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803.
Ho, C.-H., J.-H. Kim, J.-H. Jeong, H.-S. Kim, and D. Chen, 2006: Variation of tropical cyclone activity in the South Indian Ocean: El Niño–Southern Oscillation and Madden–Julian oscillation effects. J. Geophys. Res., 111, D22101, https://doi.org/10.1029/2006JD007289.
Hsu, P.-C., P.-S. Chu, H. Murakami, and X. Zhao, 2014: An abrupt decrease in the late-season typhoon activity over the western North Pacific. J. Climate, 27, 4296–4312, https://doi.org/10.1175/JCLI-D-13-00417.1.
Jeong, J.-H., B.-M. Kim, C.-H. Ho, and Y.-H. Noh, 2008: Systematic variation in wintertime precipitation in East Asia by MJO-induced extratropical vertical motion. J. Climate, 21, 788–801, https://doi.org/10.1175/2007JCLI1801.1.
Jia, X., and S. Yang, 2013: Impact of the quasi-biweekly oscillation over the western North Pacific on East Asian subtropical monsoon during early summer. J. Geophys. Res. Atmos., 118, 4421–4434, https://doi.org/10.1002/jgrd.50422.
Jia, X., S. Yang, X. Li, Y. Liu, H. Wang, X. Liu, and S. Weaver, 2013: Prediction of global patterns of dominant quasi-biweekly oscillation by the NCEP Climate Forecast System version 2. Climate Dyn., 41, 1635–1650, https://doi.org/10.1007/s00382-013-1877-7.
Jiang, X., T. Li, and B. Wang, 2004: Structures and mechanisms of the northward propagating boreal summer intraseasonal oscillation. J. Climate, 17, 1022–1039, https://doi.org/10.1175/1520-0442(2004)017<1022:SAMOTN>2.0.CO;2.
Jiang, X., M. Zhao, and D. E. Waliser, 2012: Modulation of tropical cyclones over the eastern Pacific by the intraseasonal variability simulated in an AGCM. J. Climate, 25, 6524–6538, https://doi.org/10.1175/JCLI-D-11-00531.1.
Kikuchi, K., 2021: The Boreal Summer Intraseasonal Oscillation (BSISO): A review. J. Meteor. Soc. Japan, 99, 933–972, https://doi.org/10.2151/jmsj.2021-045.
Kikuchi, K., and B. Wang, 2009: Global perspective of the quasi-biweekly oscillation. J. Climate, 22, 1340–1359, https://doi.org/10.1175/2008JCLI2368.1.
Kim, J.-H., C.-H. Ho, H.-S. Kim, C.-H. Sui, and S. K. Park, 2008: Systematic variation of summertime tropical cyclone activity in the western North Pacific in relation to the Madden–Julian oscillation. J. Climate, 21, 1171–1191, https://doi.org/10.1175/2007JCLI1493.1.
Krishnamohan, K. S., K. Mohanakumar, and P. V. Joseph, 2012: The influence of Madden–Julian oscillation in the genesis of North Indian Ocean tropical cyclones. Theor. Appl. Climatol., 109, 271–282, https://doi.org/10.1007/s00704-011-0582-x.
Krishnamurti, T. N., and H. N. Bhalme, 1976: Oscillations of a monsoon system. Part I. Observational aspects. J. Atmos. Sci., 33, 1937–1954, https://doi.org/10.1175/1520-0469(1976)033<1937:OOAMSP>2.0.CO;2.
Li, K., W. Yu, T. Li, V. S. N. Murty, S. Khokiattiwong, T. R. Adi, and S. Budi, 2013: Structures and mechanisms of the first-branch northward-propagating intraseasonal oscillation over the tropical Indian Ocean. Climate Dyn., 40, 1707–1720, https://doi.org/10.1007/s00382-012-1492-z.
Li, K., Q. He, Y. Yang, Z. Li, and W. Yu, 2022: Maintenance of the basin-dependent quasi-biweekly mode in the Indian Ocean during summer. J. Climate, 35, 3587–3601, https://doi.org/10.1175/JCLI-D-21-0686.1.
Li, R. C. Y., and W. Zhou, 2013a: Modulation of western North Pacific tropical cyclone activity by the ISO. Part I: Genesis and intensity. J. Climate, 26, 2904–2918, https://doi.org/10.1175/JCLI-D-12-00210.1.
Li, R. C. Y., and W. Zhou, 2013b: Modulation of western North Pacific tropical cyclone activity by the ISO. Part II: Tracks and landfalls. J. Climate, 26, 2919–2930, https://doi.org/10.1175/JCLI-D-12-00211.1.
Li, R. C. Y., and W. Zhou, 2018: Revisiting the intraseasonal, interannual and interdecadal variability of tropical cyclones in the western North Pacific. Atmos. Oceanic Sci. Lett., 11, 198–208, https://doi.org/10.1080/16742834.2018.1459460.
Li, R. C. Y., W. Zhou, J. C. L. Chan, and P. Huang, 2012: Asymmetric modulation of western North Pacific cyclogenesis by the Madden–Julian oscillation under ENSO conditions. J. Climate, 25, 5374–5385, https://doi.org/10.1175/JCLI-D-11-00337.1.
Li, T., L. Wang, M. Peng, B. Wang, C. Zhang, W. Lau, and H.-C. Kuo, 2018: A paper on the tropical intraseasonal oscillation published in 1963 in a Chinese journal. Bull. Amer. Meteor. Soc., 99, 1765–1779, https://doi.org/10.1175/BAMS-D-17-0216.1.
Li, Z., W. Yu, T. Li, V. S. N. Murty, and F. Tangang, 2013: Bimodal character of cyclone climatology in the Bay of Bengal modulated by monsoon seasonal cycle. J. Climate, 26, 1033–1046, https://doi.org/10.1175/JCLI-D-11-00627.1.
Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 1275–1277, https://doi.org/10.1175/1520-0477-77.6.1274.
Liebmann, B., H. H. Hendon, and J. D. Glick, 1994: The relationship between tropical cyclones of the western Pacific and Indian Oceans and the Madden-Julian oscillation. J. Meteor. Soc. Japan, 72, 401–412, https://doi.org/10.2151/jmsj1965.72.3_401.
Liu, X., and Coauthors, 2017: MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate center. Climate Dyn., 48, 3283–3307, https://doi.org/10.1007/s00382-016-3264-7.
Madden, R. A., 1986: Seasonal variations of the 40–50 day oscillation in the tropics. J. Atmos. Sci., 43, 3138–3158, https://doi.org/10.1175/1520-0469(1986)043<3138:SVOTDO>2.0.CO;2.
Madden, R. A., and P. R. Julian, 1971: Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28, 702–708, https://doi.org/10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2.
Madden, R. A., and P. R. Julian, 1972: Description of global-scale circulation cells in the tropics with a 40–50 day period. J. Atmos. Sci., 29, 1109–1123, https://doi.org/10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2.
Mao, J., and G. Wu, 2010: Intraseasonal modulation of tropical cyclogenesis in the western North Pacific: A case study. Theor. Appl. Climatol., 100, 397–411, https://doi.org/10.1007/s00704-009-0195-9.
Matthews, A. J., 2000: Propagation mechanisms for the Madden-Julian oscillation. Quart. J. Roy. Meteor. Soc., 126, 2637–2651, https://doi.org/10.1002/qj.49712656902.
Mendelsohn, R., K. Emanuel, S. Chonabayashi, and L. Bakkensen, 2012: The impact of climate change on global tropical cyclone damage. Nat. Climate Change, 2, 205–209, https://doi.org/10.1038/nclimate1357.
Murakami, H., and B. Wang, 2010: Future change of North Atlantic tropical cyclone tracks: Projection by a 20-km-mesh global atmospheric model. J. Climate, 23, 2699–2721, https://doi.org/10.1175/2010JCLI3338.1.
Murakami, T., and M. Frydrych, 1974: On the preferred period of upper wind fluctuations during the summer monsoon. J. Atmos. Sci., 31, 1549–1555, https://doi.org/10.1175/1520-0469(1974)031<1549:OTPPOU>2.0.CO;2.
Nitta, T., 1987: Convective activities in the tropical western Pacific and their impact on the Northern Hemisphere summer circulation. J. Meteor. Soc. Japan, 65, 373–390, https://doi.org/10.2151/jmsj1965.65.3_373.
North, G. R., T. L. Bell, R. F. Cahalan, and F. J. Moeng, 1982: Sampling errors in the estimation of empirical orthogonal functions. Mon. Wea. Rev., 110, 699–706, https://doi.org/10.1175/1520-0493(1982)110<0699:SEITEO>2.0.CO;2.
Peduzzi, P., B. Chatenoux, H. Dao, A. De Bono, C. Herold, J. Kossin, F. Mouton, and O. Nordbeck, 2012: Global trends in tropical cyclone risk. Nat. Climate Change, 2, 289–294, https://doi.org/10.1038/nclimate1410.
Sui, C.-H., and K.-M. Lau, 1989: Origin of low-frequency (intraseasonal) oscillations in the tropical atmosphere. Part II: Structure and propagation of mobile wave-CISK modes and their modification by lower boundary forcings. J. Atmos. Sci., 46, 37–56, https://doi.org/10.1175/1520-0469(1989)046<0037:OOLFOI>2.0.CO;2.
Tao, L., and S. Li, 2014: Impact of tropical intraseasonal oscillation on the tracks of tropical cyclones in the western North Pacific. J. Trop. Meteor., 20, 26–34.
Tseng, K.-C., C.-H. Sui, and T. Li, 2015: Moistening processes for Madden–Julian oscillations during DYNAMO/CINDY. J. Climate, 28, 3041–3057, https://doi.org/10.1175/JCLI-D-14-00416.1.
Tu, S., J. Xu, J. C. L. Chan, K. Huang, F. Xu, and L. S. Chiu, 2021: Recent global decrease in the inner-core rain rate of tropical cyclones. Nat. Commun., 12, 1948, https://doi.org/10.1038/s41467-021-22304-y.
Wang, B., and H. Murakami, 2020: Dynamic genesis potential index for diagnosing present-day and future global tropical cyclone genesis. Environ. Res. Lett., 15, 114008, https://doi.org/10.1088/1748-9326/abbb01.
Wang, M., and A. Duan, 2015: Quasi-biweekly oscillation over the Tibetan Plateau and its link with the Asian summer monsoon. J. Climate, 28, 4921–4940, https://doi.org/10.1175/JCLI-D-14-00658.1.
Wang, T., X.-Q. Yang, J. Fang, X. Sun, and X. Ren, 2018: Role of air–sea interaction in the 30–60-day boreal summer intraseasonal oscillation over the western North Pacific. J. Climate, 31, 1653–1680, https://doi.org/10.1175/JCLI-D-17-0109.1.
Wang, T., X. Lu, and S. Yang, 2019: Impact of south Indian Ocean dipole on tropical cyclone genesis over the South China Sea. Int. J. Climatol., 39, 101–111, https://doi.org/10.1002/joc.5785.
Wei, W., R. Zhang, S. Yang, W. Li, and M. Wen, 2019: Quasi-biweekly oscillation of the South Asian high and its role in connecting the Indian and East Asian summer rainfalls. Geophys. Res. Lett., 46, 14 742–14 750, https://doi.org/10.1029/2019GL086180.
Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. Academic Press, 648 pp.
Woodruff, J. D., J. L. Irish, and S. J. Camargo, 2013: Coastal flooding by tropical cyclones and sea-level rise. Nature, 504, 44–52, https://doi.org/10.1038/nature12855.
Wu, Z., C. Hu, L. Lin, W. Chen, L. Huang, Z. Lin, and S. Yang, 2023: Unraveling the strong covariability of tropical cyclone activity between the Bay of Bengal and the South China Sea. npj Climate Atmos. Sci., 6, 180, https://doi.org/10.1038/s41612-023-00506-z.
Yan, X., S. Yang, T. Wang, E. D. Maloney, S. Dong, W. Wei, and S. He, 2019: Quasi-biweekly oscillation of the Asian monsoon rainfall in late summer and autumn: Different types of structure and propagation. Climate Dyn., 53, 6611–6628, https://doi.org/10.1007/s00382-019-04946-3.
Yu, J., T. Li, Z. Tan, and Z. Zhu, 2016: Effects of tropical North Atlantic SST on tropical cyclone genesis in the western North Pacific. Climate Dyn., 46, 865–877, https://doi.org/10.1007/s00382-015-2618-x.
Yuan, J., Y. Gao, D. Feng, and Y. Yang, 2019: The zonal dipole pattern of tropical cyclone genesis in the Indian Ocean influenced by the tropical Indo-Pacific Ocean sea surface temperature. J. Climate, 32, 6533–6549, https://doi.org/10.1175/JCLI-D-19-0042.1.
Zhang, C., 2013: Madden–Julian oscillation: Bridging weather and climate. Bull. Amer. Meteor. Soc., 94, 1849–1870, https://doi.org/10.1175/BAMS-D-12-00026.1.
Zhang, Y., T. Li, J. Gao, and W. Wang, 2020: Origins of quasi-biweekly and intraseasonal oscillations over the South China Sea and Bay of Bengal and scale selection of unstable equatorial and off-equatorial modes. J. Meteor. Res., 34, 137–149, https://doi.org/10.1007/s13351-020-9109-7.
Zhao, H., X. Jiang, and L. Wu, 2015: Modulation of Northwest Pacific tropical cyclone genesis by the intraseasonal variability. J. Meteor. Soc. Japan, 93, 81–97, https://doi.org/10.2151/jmsj.2015-006.
Zhao, H., C. Wang, and R. Yoshida, 2016: Modulation of tropical cyclogenesis in the western North Pacific by the quasi-biweekly oscillation. Adv. Atmos. Sci., 33, 1361–1375, https://doi.org/10.1007/s00376-016-5267-z.
Zhao, H., G. B. Raga, and P. J. Klotzbach, 2018: Impact of the boreal summer quasi-biweekly oscillation on eastern North Pacific tropical cyclone activity. Int. J. Climatol., 38, 1353–1365, https://doi.org/10.1002/joc.5250.
Zhao, J., R. Zhan, and Y. Wang, 2018: Global warming hiatus contributed to the increased occurrence of intense tropical cyclones in the coastal regions along East Asia. Sci. Rep., 8, 6023, https://doi.org/10.1038/s41598-018-24402-2.
Zhu, L., F. Lin, and C. Liang, 2021: Modulation of tropical cyclone activity over the northwestern Pacific through the quasi-biweekly oscillation. J. Trop. Meteor., 27, 125–135, https://doi.org/10.46267/j.1006-8775.2021.012.