1. Introduction
The start of the East Asian summer monsoon (EASM) is first observed with the South China Sea summer monsoon (SCSSM) onset, which marks a sudden shift of atmospheric circulation and the beginning of the rainy season in the East Asian region (Wang and LinHo 2002; Wang et al. 2004). The onset date and intensity of the SCSSM have significant impacts on the weather and climate of East Asia and are closely related to local agriculture and crop planting (Ding 1992).
The onset of the SCSSM is characterized by a sudden shift from easterly to westerly winds and a rapid expansion of deep convection over the South China Sea (SCS) (Fig. 1) (Chang et al. 2000; Wu and Wang 2001). The onset date of the SCSSM, which typically occurs between April and June, exhibits greater variability and uncertainty compared to other regional monsoon systems (Wang et al. 2004), such as the Bay of Bengal summer monsoon (BOBSM) (Li et al. 2013), Indian summer monsoon (Wang et al. 2009), and Australian monsoon (Kajikawa et al. 2010). The SCS resides between the Indian and Pacific Oceans, making the establishment of SCSSM susceptible to the multiscale variations originating from the two nearby tropical oceans. Several studies have examined the interannual (Wu and Wang 2000; Zhou and Chan 2007; Liu et al. 2016; Luo et al. 2016; He et al. 2017) and decadal variability (Kajikawa and Wang 2012; Lin and Zhang 2020; Lin et al. 2017; Chen 2015) associated with SCSSM. It is well known that SCSSM onset varies at interannual and decadal scales, which is closely associated with the weakening or strengthening of the western North Pacific anticyclone.
The composite mean OLR (shaded; W m−2), 850-hPa wind (vector; m s−1), and 500-hPa geopotential height (contours; m) averaged over 30 days (a) before and (b) after the SCSSM onset during 42 years. The black box indicates the key area for the SCSSM index following Wang et al. (2004). The dots in two panels indicate a significant difference in the mean OLR values before and after the monsoon onset (above the 99% significance level based on the t test). The wind vectors are displayed only when the zonal wind component passes the 99% significance test based on the t test.
Citation: Journal of Climate 37, 21; 10.1175/JCLI-D-23-0182.1
In addition to the interannual and decadal variability, the SCSSM onset is significantly influenced by the intraseasonal oscillation (ISO) processes, including the northeastward-propagating 30–60-day ISO originating from the tropical Indian Ocean (Straub et al. 2006; Tong et al. 2009; Shao et al. 2015; Wu and Cao 2017; H. Wang et al. 2018) and the northwestward-propagating 10–20-day ISO from the western Pacific (Chen and Chen 1995; Fukutomi and Yasunari 1999; Li and Wang 2005; Zhou and Chan 2005; Mao and Chan 2005; Lee et al. 2013). Earlier study noticed that the above 30–60-day ISO and 10–20-day ISO are phased-locked together to trigger the SCSSM onset (Zhou and Chan 2005). It is argued that the ISO may play the critical role in triggering the SCSSM onset because the changing rate of the ISO-associated zonal winds over the SCS is about twice as large as that of the climatological zonal winds (Shao et al. 2015). Furthermore, in some years, the SCSSM onset can be triggered by the midlatitude cold front (Tong et al. 2009; Huangfu et al. 2018; Hu et al. 2020) and tropical cyclones (Mao and Wu 2008; Huangfu et al. 2017). The assessment study based on the phase 5 of Coupled Model Intercomparison Project (CMIP5) reveals the surprising fact that it is very difficult to simulate the SCSSM onset in the coupled models (Sperber et al. 2013).
Understanding the mechanism of the ISO is critical for simulating and predicting the SCSSM onset and development. There are several proposed mechanisms for the northward-propagating 30–60-day ISO during the boreal summer, including the moisture and barotropic vorticity advection in the planetary boundary layer (DeMott et al. 2013; Cheng et al. 2020), convective instability influenced by the sea surface temperature (SST) anomaly (H. Wang et al. 2018), evaporation–wind feedback under the southerly wind (T. Wang et al. 2018), vertical easterly wind shear (Wang and Xie 1997; Drbohlav and Wang 2005), convective momentum transport (Kang et al. 2010; Liu et al. 2015), and beta drift (Boos and Kuang 2010). The northwestward-propagating 10–20-day ISO is regarded to originate from equatorial mixed Rossby–gravity waves (Mao and Chan 2005), driven primarily by moisture convergence associated with barotropic vorticity anomalies during the boreal summer (Li et al. 2020).
Overall, the previous studies have recognized phase transition (from dry to wet) as the key to the SCSSM onset (Chen et al. 2022), but there is still a lack of diagnostic analysis on how the two types of ISOs propagate into the SCS in the premonsoon period as well as a comprehensive assessment of their individual or combined role in triggering the SCSSM onset. The present study further expands the understanding by addressing the below key questions: (i) Can the 10–25-day and the 30–60-day ISOs trigger the SCSSM onset individually or in a phase-locked way as previously found (Zhou and Chan 2005)? (ii) Does the premonsoon 10–25-day ISO over the western Pacific Ocean propagate westward or northwestward as widely documented in previous works? (iii) What are the dominant processes governing the propagations of the 10–25-day and the 30–60-day ISOs in the premonsoon period, and are there any differences from those during the summer monsoon? The outcomes here improve our knowledge of the complex SCSSM onset process from the view of intraseasonal scale.
This paper is organized as follows: section 2 describes the datasets and methods used in this study. In section 3, we classify the SCSSM onset into two types based on the significance of the ISOs each year. Then, we present the characteristics of the 10–25-day ISO (HISO) and 30–60-day ISO (LISO) and their propagation mechanisms in sections 4 and 5, respectively. Finally, the last section summarizes the results of the analysis and provides a brief discussion.
2. Data and methodology
a. Data
The daily mean wind in 850 hPa from the National Centers for Environmental Prediction–the National Center for Atmospheric Research (NCEP–NCAR) reanalysis data products at the Physical Sciences Laboratory (PSL) (Kalnay et al. 1996) with a resolution of 2.5° × 2.5° from 1979 to 2020 is used to document the low-level atmospheric circulation changes associated with the SCSSM onset. The daily outgoing longwave radiation (OLR) from the National Oceanic and Atmospheric Administration’s (NOAA) National Climatic Data Center (NCDC) with a resolution of 1° × 1° from 1979 to 2020 is used as a proxy for convection to illustrate the dry–wet transitions associated with the SCSSM onset. Daily SST from NOAA PSL (Reynolds et al. 2007) with a resolution of 0.25° × 0.25° from 1982 to 2020 and the SST from 1979 to 1981 from European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) data (Dee et al. 2011) are used here to check the oceanic forcing effect. Other parameters used in the analysis are derived from fifth major global reanalysis produced by ECMWF (ERA5), including 1) the three-dimensional specific humidity, temperature, geopotential height, and velocity at 25 pressure levels from 1000 to 150 hPa; 2) the latent heat (LH) and sensible heat (SH) fluxes at the sea surface and the longwave and solar radiation at the sea surface and at the top of the atmosphere; and 3) the horizontal wind at 10-m height, air temperature at 2-m height, relative humidity at 1000 hPa, surface pressure, and total precipitation (Hersbach et al. 2018). The original data in different temporal–spatial resolutions were converted to 1° × 1° resolution at daily intervals from 1979 to 2020.
To isolate the components associated with high-frequency ISO (HISO) and low-frequency ISO (LISO), 10–25-day and 30–60-day Butterworth bandpass filters were applied to the daily data, respectively. The anomalies mentioned later refer specifically to these two bandpass-filtered components accordingly. In addition, the 9-day Butterworth high-pass filter is employed to remove the synoptic signals and the 80-day Gaussian low-pass filter is used to separate out the seasonal variation signals.
b. MSE budget diagnosis
From the perspective of the ISO’s “moisture mode,” the moist static energy (MSE) integrated in the column of the atmosphere has been widely used to study the tropical ISO as an important forecast variable, charging (discharging) in the front (back) of the convection (Raymond and Fuchs 2009; Kim et al. 2014; DeMott et al. 2016; Wang et al. 2017; Gao et al. 2019; L. Wang and Li 2020). The MSE budget, as an instructive diagnosis method, helps us understand the propagation dynamics of the 10–25-day and the 30–60-day ISOs.
c. SST-induced turbulent heat fluxes
The air–sea turbulent heat flux, which includes SH and LH, in the MSE budget can be calculated with five parameters (i.e., SST, air temperature at 2 m, relative humidity at 1000 hPa, wind speed at 10 m, and surface pressure) based on the state-of-the-art bulk flux algorithm, developed by the Coupled Ocean–Atmosphere Response Experiment (COARE3.0; Fairall et al. 2003). To achieve a quantitative assessment of the SST perturbation within the air–sea turbulent heat flux, three computational steps were taken (Li et al. 2020). First, the raw data of these five parameters were used to calculate the QT, denoted as QT_sst. Second, the 30–60-day anomalies were subtracted from the raw SST data; then, we calculated QT marked as QT_sstm. Third, by comparing the difference of the above results, the SST anomalies (SSTA)-induced QT including LH and SH anomalies, marked as QT_ssta, LHssta, and SHssta, could be easily estimated (i.e., LHssta = LHsst − LHsstm; SHssta = SHsst − SHsstm; and QT_ssta = LHssta + SHssta).
d. Definition of SCSSM onset date
To define the SCSSM onset, the SCSSM index is calculated as the daily mean 850-hPa zonal wind (U850) averaged over the central SCS (110°–120°E, 5°–15°N) following Wang et al. (2004). The SCSSM onset date must satisfy the following conditions: 1) it must be after 25 April, 2) the SCSSM index must be greater than zero on the onset day and for the following 5 days, 3) the cumulative 15-day-mean of the SCSSM index must be greater than 1 m s−1, and 4) the SCSSM index must be positive for at least 8 days in the following 15 days. Based on the above definition, the SCSSM onset date is identified for the period from 1979 to 2020 (Fig. S1 in the online supplemental material). The establishment processes of the SCSSM for all years can be seen in Fig. S2. The climatological-mean onset date is 21 May, which is consistent with previous results (H. Wang et al. 2018; Hu et al. 2020).
3. Classification of SCSSM onset
Figure 1 illustrates the changes in the background field of atmospheric circulation, convection, and the subtropical high before and after the SCSSM onset, representing the seasonal variations. The subtropical high over the SCS acts as a barrier of the SCSSM. The SCSSM onset is widely regarded as the dry–wet transition and the reversal of wind direction as well, along with the eastward retreat of subtropical high. The abrupt onset is one of the salient features associated with SCSSM (Ding et al. 2006). This dramatic change is attributed to the influence of ISOs and even synoptic processes (Chen et al. 2022). Figure 2 shows the influence of different time-scale processes on the onset of the SCSSM, based on the linear decomposition proposed in previous studies (Shao et al. 2015; H. Wang et al. 2018). The total U850 (black line) changes abruptly from easterly to westerly around the onset date. This abrupt change consists of the gradual transition of the seasonal cycle (green line) and the ISOs (blue and purple lines). The synoptic signal is not discussed in this context, as its contribution is negligible. Furthermore, the standard deviations (shading) of the HISO and LISO are lower before the onset than after the onset. This actually reflects the seasonal variation of the ISOs, with the intensity of the ISOs increasing significantly after the SCSSM onset (Figs. S3 and S4), which is consistent with previous results (Lau and Waliser 2012; Lee et al. 2013).
Evolution of the composite total (black), 11-day high-pass-filtered (yellow), 10–25-day bandpass-filtered (blue), 30–60-day bandpass-filtered (purple), and 80-day low-pass-filtered (green) SCSSM index over 42 years. Day 0 is the onset date. The blue and purple shading indicate the standard deviations of the 10–25-day and 30–60-day bandpass-filtered SCSSM index at each specific date, respectively.
Citation: Journal of Climate 37, 21; 10.1175/JCLI-D-23-0182.1
Figure 3 illustrates the contributions of the HISO, LISO, and seasonal variation to the total positive U850 during the first 5 days following the onset in each year. The blue and violet solid curves represent the contributions of HISO and LISO, while the green solid curve shows the seasonal contribution. Clearly, the individual contribution of the three components to the SCSSM onset varies from year to year, based on which the typical “HISO onset” year (blue stars) and “LISO onset” year (purple stars) are selected. For example, a year marked with a blue star means that the 10–25-day U850 surpasses its corresponding standard deviation (blue dashed line) and is identified as the dominant process that triggers the SCSCM onset. There are 16 HISO onset years (1979, 1982, 1989, 1994, 1995, 2001, 2003, 2004, 2006, 2007, 2010, 2011, 2015, 2016, 2017, and 2019) and 14 LISO onset years (1979, 1980, 1986, 1991, 1997, 1998, 2000, 2001, 2002, 2004, 2007, 2009, 2013, and 2018). It should be noted that there are 4 years (1979, 2001, 2004, and 2007), indicated in the italic numbers, when both the HISO and LISO contribute to the SCSCM onset. The averaged onset dates for HISO and LISO onset years are the same on 17 May, which is slightly earlier than the climatology mean date of SCSSM onset on 21 May. In addition, the seasonal variation (exceeding its standard deviation) makes the significant contribution to trigger the SCSSM onset in a slow mode for 12 years (1981, 1983, 1984, 1985, 1987, 1992, 1999, 2003, 2005, 2014, 2015, and 2018), with an averaged onset date of 2 June, which is apparently later than the climatology mean date of SCSSM onset on 21 May.
Time series of three types of filtered SCSSM index, averaged for a pentad after the SCSSM onset date. The blue, purple, and green solid lines are in 10–25-day, 30–60-day, and 80-day low-pass-filtered bands, respectively. The dotted lines indicate their standard deviations over 42 years, respectively. The blue and purple stars indicate the monsoon onsets triggered by the active HISO and LISO, respectively.
Citation: Journal of Climate 37, 21; 10.1175/JCLI-D-23-0182.1
The above three categories of SCSSM onset, including the HISO-induced onset, LISO-induced onset, and seasonal slow onset, could be grouped into two modes of SCSSM onset, i.e., the fast and slow modes. The fast mode onset occurs when either HISO and/or LISO are active to trigger the SCSSM onset, while the slow mode means none of HISO and LISO is active or they are out of phase canceling each other. In the slow mode onset, the SCSSC onset is more associated with the seasonal migration of the intertropical convergence zone (ITCZ). The significant divergence of the onset dates embedded in the fast (17 May) and slow (2 June) onset modes indicates that the fast/slow mode onsets usually correspond to the early and late onset cases. These results are similar to the previous studies categorizing monsoon onset as either early or late (Shao et al. 2015; H. Wang et al. 2018), when the ISO is active or suppressed. The present study mainly focuses on the fast mode onset due to its dominance, hard to be reproduced in models and from a manageable consideration. The contribution of seasonal variations to monsoon onset is only touched upon in a limited manner in the final discussion part, and this topic will be left for future separate study. Therefore, in the next two sections, this paper will focus on the typical characteristics of the HISO and LISO that trigger the SCSSM onset.
4. HISO onset
a. The westward-propagating HISO
Figure 4 shows the composite evolution of 10–25-day-filtered OLR and 850-hPa wind from 8 days before to 8 days after HISO onsets. The pink lines indicate the western North Pacific subtropical high. On day −8, the western North Pacific subtropical high dominates over the SCS, and there is a dry HISO accompanied by an easterly wind anomaly in the SCS. From day −8 to −2, the wet HISO is initiated at about 12°N, 150°E, and propagates westward. From day −2 to day 0, the wet HISO continues to propagate westward across the Philippines archipelago and into the SCS, triggering the SCSSM onset. From day 0 to day 4, the wet HISO accompanied by westerly anomaly is strengthening in the SCS, while the subtropical high retreats eastward from the SCS. Subsequently, as the wet HISO moves westward into the Indochina, the westerly anomaly in the SCS rapidly weakened. On day 8, the subtropical high and the dry HISO return to the central part of the SCS. The results suggest that a wet HISO initiated from the western North Pacific can cross over the Philippines archipelago and reach into the SCS to trigger the SCSSM onset. This westward propagation path is different from some previous studies (Wu 2010; Zhou and Chan 2005), which suggest the HISO propagates northwestward during the SCSSM onset.
Composite spatiotemporal evolution of 10–25-day bandpass-filtered OLR (shading; W m−2), 850-hPa wind (vector; m s−1), and raw 500-hPa geopotential height (pink contour; m) from day −8 to day 8 for the 16 HISO onset years. Day 0 denotes the onset day. The dots mark OLR anomalies over the 95% significant level, based on the t-test. Only wind anomalies greater than 1 m s−1 are shown. The black arrow lines indicate the fitted route of the HISO.
Citation: Journal of Climate 37, 21; 10.1175/JCLI-D-23-0182.1
Figure 5a shows the time evolution of the HISO along the fitted HISO track shown in Fig. 4. As can be seen, the westward-propagating HISOs are active during the premonsoon period. But their convection center can hardly cross the Philippines archipelago and enter into the SCS. Only when the first branch of westward-propagating HISO enters into the central-to-northern SCS, the SCSSM onset is triggered. Why do the premonsoon HISOs have difficulty moving into the SCS? Figure 5b shows the clues in the background SST and atmospheric vertical movement averaged in the central SCS. Under the control of the subtropical high in the premonsoon period (Fig. 1a), the strong downdraft (shading) constraints the development of convection. The SST (red line), on the other hand, is continuously increasing in response to the strong shortwave radiation due to the cloudless sky, which increases the convective instability in the SCS. This preconditions the westward intrusion of HISO into the SCS. The dry–wet transition is then realized by the first branch HISO propagating into the SCS and hence the SCSSM onset is triggered.
(a) Composite time-longitude evolutions of 10–25-day bandpass-filtered OLR (shading; W m−2), column-integrated MSE tendency (contours; W m−2), and SST (red stippling and black cross indicate above 0.1° and below −0.1°C, respectively) for the HISO onset along the HISO track. The result is based on a 7° latitude average (4°N–3°S of the black arrow in Fig. 4). Day 0 is the day of onset. The area to the left of the black line represents the SCS, and the blue box indicates the key time window for the SCSSM onset. (b) Composite time evolutions of the box-averaged (over 110°–120°E, 7°–14°N) raw SST (red line; °C) and atmospheric vertical velocity (shading; Pa s−1, positive upward) averaged between 400 and 600 hPa for the HISO onset.
Citation: Journal of Climate 37, 21; 10.1175/JCLI-D-23-0182.1
b. MSE budget analysis of the HISO
Next, we focus on what controls the westward propagation of HISO. As observed previously (Fig. 5a), the MSE tendency anomalies exhibit significant leading phases (about 4 days lead) relative to the convection anomalies. Furthermore, the zero crossing of the MSE tendency anomalies (contour) consistently coincides with the maximum or minimum of convection anomalies (shading), indicating that the recharge/discharge of the MSE controls the development of the HISO convective system. Therefore, MSE budget analysis is used to quantify the roles of advective terms, surface turbulent fluxes, and radiative heating in the evolution of the HISO.
Figure 6 illustrates the composite zonal–vertical distribution of the HISO-filtered MSE budget items and their phase relationship with the convection anomaly. In the troposphere, the largest moisture anomalies are found to coincide with HISO convection. Notably, when the HISO convection is mature over the eastern Philippines Islands (centered at 132°E), a positive (negative) MSE tendency center is located to the west (east) of the convection center (Fig. 6a). The structure of the moisture and convection anomaly tilts slightly to the east with increasing height, which is opposite to that of the eastward-propagating Madden–Julian oscillation (MJO) (Madden and Julian 1971, 1972; Hsu and Li 2012; Wang and Li 2021). This tilted structure suggests that the moisture precursor signal first appears in the lower layers and then moves upward. The leading MSE tendency west of the convective anomaly is apparently caused by the zonal advection (−〈u∂m/∂x〉′, red line), while the turbulent heat flux and radiative heating (yellow and purple lines) make almost no contribution. For the MJO, strong deep convection corresponds to a more significant tilted structure and more cumulus congestus clouds in the upper levels, which suppresses outgoing longwave radiation and allows for larger positive radiative heating anomalies in the deep convection region (Lin et al. 2004). In turn, strong deep convection also corresponds to higher near-surface wind speeds, which can lead to an increase in turbulent heat fluxes (Fuchs-Stone 2020). However, the premonsoon HISO does not exhibit a deep convective structure like the MJO and does not induce significant wind speed anomalies. Therefore, the contributions of the turbulent heat flux and radiative heating to the HISO are relatively small.
Zonal distributions and longitude–height diagrams of composite HISO averaged over 7°–14°N. (a) MSE tendency (shading), specific humidity (contours; g kg−1), and vertically overturning circulation anomalies (vectors). (b) Anomalous MSE advection due to background zonal winds and zonal gradient of MSE anomalies (shading), specific humidity (contours; g kg−1) anomalies, and background vertical overturning circulation (vectors). (c) Anomalous MSE advection due to vertical wind anomalies and vertical gradient of background MSE (shading), background MSE (contours; W g−1), and vertically overturning circulation anomalies (vectors). The one-dimensional plots at the bottom of each panel show the column-integrated MSE tendency (gray area), OLR (blue bar), radiative heating (purple line), surface turbulent fluxes (yellow line), zonal advection of column-integrated MSE (red line), the sum of the right-hand side terms in Eq. (1) (black dashed line) anomalies, column-integrated anomalous MSE advection due to background zonal winds and zonal gradient of MSE anomalies (burgundy line), and vertical wind anomalies and vertical gradient of background MSE (green line).
Citation: Journal of Climate 37, 21; 10.1175/JCLI-D-23-0182.1
To identify the relative contributions of different time scales to the intraseasonal MSE budget, a decomposition method is adopted, referring to a previous study (Hsu and Li 2012). Here, a time filter is applied to u (zonal wind) and m (MSE) to isolate four independent components: synoptic perturbations with periods shorter than 10 days, 10–25-day HISO, 30–60-day LISO, and mean states with periods longer than 80 days (marked by an overbar). Thus, the zonal advection can be decomposed into 16 cross-products between different u and m components. Figure 6b shows the detailed calculations and reveals anomalous MSE advection due to background zonal winds and zonal gradient of the HISO-filtered MSE (
Several studies have highlighted the role of vertical MSE advection in the eastward propagation of the MJO and the northward propagation of the quasi-biweekly oscillation (QBWO) (Wang et al. 2017; Li et al. 2020). At the front of the convective anomalies, ascending (descending) anomalies appear in the lower (upper) troposphere, while the opposite occurs at the back. Since the mean MSE is minimized in the midtroposphere, such a distribution of vertical velocity anomalies causes the advection of positive (negative) MSE in the front (back) of the convection and promotes the propagation of the MJO and QBWO. However, this mechanism is not effective for the westward HISO due to its lower slope (Fig. 6c). The anomalous MSE advection by ascending anomalies and the vertical gradient of mean MSE corresponds to the convergence of moisture anomalies in the lower troposphere and their dissipation in the upper layers, but it does not show a clear leading phase for the convection anomaly. Figure 7 displays the final diagnostic results when the positive MSE tendency reaches its maximum before the SCSSM onset. Except for
Diagnosed results of column-integrated MSE tendency budget averaged over (7°–14°N, 115°–125°E) and from day −4 to day 0 for the composite HISO onset. The sum term indicates the adding up of the five right-hand-side terms in Eq. (1).
Citation: Journal of Climate 37, 21; 10.1175/JCLI-D-23-0182.1
Based on this result, the estimated speed of the HISO (4.8° of longitude per day) from Fig. 5a can be reasonably explained by the mean trade wind (about 4.3° of longitude per day) along 10°N for 30 days before the SCSSM onset (Fig. 1a). In addition, the dry phase of the HISO is usually followed by a weak warm SST anomaly in the SCS during the premonsoon period (Fig. 5a). This is likely because the oceanic feedback takes more time and is less responsive to the HISO, as also found by previous studies (Wu 2010). Although the SST is not considered important for the westward-propagating HISO, the warm SST can increase atmospheric instability in the SCS by heating and moistening the planetary boundary layer through sensible and latent heat release (Stephens et al. 2004; Fu et al. 2006). This in turn creates a wetter and warmer background field that facilitates the development of deep convection and the onset of the SCSSM.
Overall, the process by which HISO leads to the rapid establishment of the SCSSM can be summarized. The convergence of moisture anomalies in the SCS, driven by the advection of mean easterly winds, leads to the development of convection, effectively breaking the control of the subtropical high pressure over the SCS (Fig. 4). This, in turn, causes the mean downward circulation to shift to an upward circulation (Fig. 5b), with shallow convection transporting moisture upward to moisten and warm the midtroposphere. Once the moisture in the midtroposphere reaches its maximum, deep convection and the SCSSM onset are triggered (Kemball-Cook and Weare 2001; Kikuchi and Takayabu 2004). Subsequently, similar to the development of the MJO, the positive feedback mechanisms of radiative heating and surface turbulent heating, especially the latter, contribute significantly to the development of deep convection (Wang and Li 2021). With the eastward retreat of the subtropical high from the SCS, the lower tropospheric southeasterly winds change to southwesterly winds (Fig. 1). As a result, the westerly wind anomaly associated with the deep convection increases the mean wind speed. This increased wind speed leads to more latent heat release (Fairall et al. 2003), which supports the intensification of convection. The enhanced convection, in turn, induces stronger westerly anomalies, creating positive feedback. However, the westerly wind anomalies accompanying the convection prior to the SCSSM onset only weaken the mean easterly winds at the edge of the subtropical high (Fig. 2). This results in less latent heat release, and thus weaker development of convection. Therefore, the transition from negative to positive feedback to some extent explains why the establishment of SCSSM is an abrupt transition and why the amplitude of the ISOs becomes more pronounced after the monsoon onset.
5. LISO onset
a. The northward-propagating LISO
The composite evolution pattern of 30–60-day-filtered OLR and 850-hPa wind for the LISO onset is shown in Fig. 8. The moist LISO often originates in the central equatorial Indian Ocean and propagates eastward prior to the onset of the SCSSM (H. Wang et al. 2018; Zheng and Huang 2019). From days −12 to −4, the maximum of the convection anomaly accompanied by the westerly anomaly in the eastern equatorial Indian Ocean propagates northward into the Bay of Bengal. In addition, the eastward-propagating equatorial convection anomaly can cross over the Maritime Continent and reach the western equatorial Pacific. From days −8 to 0, the weak convective anomaly in the southern SCS slowly propagates northward into the central SCS. At day 0, the convection anomaly strengthens in the central SCS and triggers the SCSSM onset. A previous study (H. Wang et al. 2018) shows that the onset of the SCSSM is triggered by the northwestward-propagating Rossby wave of the wet ISO in the western Pacific. However, their analysis was based on 12–80-day intraseasonal variability, which may have confounded the two signals.
LISO evolution associated with 14 LISO onset years. As in Fig. 4, but for the LISO onset and 30–60-day-filtered bands. The black arrow lines indicate the fitted path of the LISO.
Citation: Journal of Climate 37, 21; 10.1175/JCLI-D-23-0182.1
Figure 9a shows the composite time–latitude section of the OLR, column-integrated MSE tendency, and the SST anomalies along the fitted LISO track as shown in Fig. 8. As with the HISO, a wet phase of the weak LISO emerges in the northern SCS about 30 days before the monsoon onset, but it quickly dissipates due to the suppression by the downdraft (Fig. 9b). The subsequent dry period is followed by an intense warming of the SST, which enhances the convective instability of the atmosphere and creates an environment conducive to the monsoon onset. Subsequently, the SCSSM onset is triggered by the first LISO that propagates northward into the northern SCS, which is comparable to the BOBSM onset (Li et al. 2013).
(a) Composite time–latitude evolutions of 30–60-day bandpass-filtered OLR (shading; W m−2), column-integrated MSE tendency (contours; W m−2), and SST (red dots and black cross indicate above 0.3° and below −0.3°C, respectively) for the LISO onset along the LISO route. The result is based on a 10° longitude average (5°W–5°E of the black arrow in Fig. 8). Day 0 denotes the onset day. The blue box indicates the key time window for the SCSSM onset. (b) Composite time evolutions of raw SST (red line; °C) and vertical circulation (shading; Pa s−1, positive upward, averaged between 400 and 600 hPa) for the LISO onset (110°–120°E, 10°–16°N).
Citation: Journal of Climate 37, 21; 10.1175/JCLI-D-23-0182.1
Due to the mean southerly and SST feedback, the LISO is known to propagate northward after the onset of the SCSSM (T. Wang et al. 2018). However, unlike the HISO, which typically propagates to the west, the LISO has difficulty in propagating to the north during the premonsoon period. A few instances of the northward-propagating LISO have been observed during the premonsoon season (Zheng and Huang 2019), but they are hardly able to propagate to the northern SCS. In the next section, an analysis of the MSE is used to investigate why LISO can propagate to the north and intensify when it triggers the onset of SCSSM.
b. MSE budget analysis of the LISO
Figure 10 shows the composite meridional–vertical distribution of the LISO-filtered MSE budget items and their phase relationship with the convective anomaly. In the midtroposphere, the largest moisture anomalies occur over the equator, while in the lower levels, the largest moisture anomalies correspond to the center of the convective anomaly (located at 8°N). A positive MSE tendency center is located to the north of the convective center. However, the tilted structure of the moisture anomaly and the associated anomalous circulation are different from those of the MJO. Therefore, the effect of the vertical advection of the MSE is not the primary driver of the northward propagation of the LISO (Wang et al. 2017). The leading MSE tendency north of the convective anomaly is apparently caused by the mean meridional winds and the meridional gradient of the MSE anomalies (
Meridional distributions and latitude–height diagrams of the composite LISO averaged over 110°–120°E. (a) MSE tendency (shading), specific humidity (contours; g kg−1), and vertically overturning circulation anomalies (vectors). (b) Anomalous MSE advection due to background meridional winds and meridional gradient of MSE anomalies (shading) and background vertically overturning circulation (vectors). (c) The one-dimensional plots show the column-integrated MSE tendency (gray area), OLR (blue bar), column-integrated background meridional winds and meridional gradient of MSE anomalies (wine red line), and sum of the right-hand side terms in Eq. (1) (black dashed line) anomalies. (d) Surface turbulent flux (black line) in ERA5, surface turbulent flux calculated from SST (gray dashed line), and SSTA-induced surface turbulent flux (cyan line).
Citation: Journal of Climate 37, 21; 10.1175/JCLI-D-23-0182.1
However, it is important to note that the southerly winds are very weak during the premonsoon period. They only become stronger when the subtropical high is about to leave the SCS. Therefore, the first northward LISO often triggers the SCSSM onset. Additionally, the anomalous MSE advection of the LISO in the midtroposphere is much smaller compared to that of the HISO, which suggests that MSE advection processes alone may be insufficient to moisten and warm the midtroposphere over the SCS.
We have observed significant warm SST anomalies (above 0.3°C) prior to the wet LISO (Fig. 9a). Warm SST anomalies play an important role in the development of the ISOs by inducing turbulent heating and low-level convergence (Lindzen and Nigam 1987; Hsu et al. 2004; Hsu and Li 2012; H. Wang et al. 2018; Gao et al. 2019). To understand the contribution of SST, we used the COARE3.0 algorithm mentioned in section 2 to determine the air–sea turbulent exchange induced by the SST anomaly. As shown in Fig. 10d, the surface turbulent flux anomaly based on the COARE3.0 algorithm (gray dashed line) approaches the value derived from ERA5 (black line), and its maximum values coincide with convective anomalies. Similar to the HISO, radiative heating and surface turbulent heating contributions are weak in the absence of deep convection. In particular, the SSTA-induced turbulent heat flux (cyan line) coincides with warm SST anomalies (Fig. 9a) and has apparent positive contributions to the MSE tendency based on a comparison Figs. 10c and 10d.
The results of the quantitative evaluation of the main contribution terms are illustrated in Fig. 11. The anomalous MSE advection due to mean meridional winds and the meridional gradient of MSE anomalies (
Diagnosed results of the column-integrated MSE tendency budget averaged over 10°–16°N, 110°–120°E, day −8 to 0 for the composite LISO onset. Sum indicates the sum of the right-hand side terms in Eq. (1). Minor items are not shown here.
Citation: Journal of Climate 37, 21; 10.1175/JCLI-D-23-0182.1
6. Summary and discussion
This study illustrates the complex processes associated with the SCSSM onset. The primary factors influencing the onset of the SCSSM include the 10–25-day HISO, the 30–60-day LISO, and seasonal variation as well (Fig. 2). The ISO’s developing phase (from dry to wet) is crucial for the rapid establishment of the SCSSM. Compared to the extensive studies on the boreal summer ISOs, there is less focus on the premonsoon ISOs over the tropical Indo-Pacific Ocean and how they propagate into the SCS triggering the monsoon onset. Based on newly released reanalysis and remote sensing data from 1979 to 2020, this study gives the comprehensive investigation of the characteristics and propagation mechanisms of the premonsoon ISOs and their role in triggering the SCSSM onset. The SCSSM onset represents the abrupt seasonal transition that is influenced by both the ISOs and the background fields (Fig. 12), which can be summarized into three cases as below.
Schematic diagram showing how the onset of SCSSM is triggered by the ISOs. The base diagram shows the mean 850-hPa wind (vector; m s−1) and the 500-hPa geopotential height (contours; m) for 10 days before the SCSSM onset for 42 years.
Citation: Journal of Climate 37, 21; 10.1175/JCLI-D-23-0182.1
The first case is that the SCSSM onset is triggered by the westward-propagating HISO from the western Pacific. During the premonsoon period, the subtropical high dominates the SCS, thereby hindering the penetration of ISOs into the SCS. In response to this background subtropical high system, the SCS SST continues to increase to its annual peak (Wu 2002), which acts as a precursor to the monsoon onset. Meanwhile, the HISO, under the favorable condition of the westward advection of the anomalous MSE by the background easterly wind, propagates westward from the northwestern Pacific Ocean and transports the moisture to SCS. The convective instability over the SCS increases due to warmer SST and increased atmospheric moisture (Wu 2010). Along with the slowly weakening subtropical high, there is one branch of HISO finally crossing the Philippines archipelago into the SCS. Then, the SCSSM onsets accordingly represented by the dry–wet transition and the quick reverse of wind direction from the easterly to the westerly. There are 16 years from the total 42 years belonging to this case (Fig. 3).
There is another case that the LISO from the equatorial Indian Ocean can also trigger the SCSSM onset (Fig. 8). There are 14 years from the total 42 years belonging to this case. An earlier study (Zhou and Chan 2005) found that the SCSSM onset tends to be triggered by the phase-locked LISO and HISO. Here, it turns out that both HISO and LISO can trigger the SCSSM onset individually and there are only 4 years (1979, 2001, 2004, and 2007) when they came into place together. This difference may be due to the different criteria used to determine the significance of the ISOs to the SCSSM onset. It is interesting to note that this LISO-triggered SCSSM onset follows the earlier monsoon onset (day −8 in Fig. 8) over Bay of Bengal (BOB), which is well documented in a previous study (Li et al. 2013). Along with the development of BOB monsoon, the deep convection signal extends into the southern SCS. Figure 9 describes well its further northward propagation and finally triggers the SCSSM onset, during which the SST is found to play the dominant role, supplemented further by the significant contribution from the northward advection of the anomalous MSE by the background southerly wind.
Third, it is interesting to note that in 12 of the 42 years (1981, 1983, 1984, 1985, 1987, 1992, 1999, 2003, 2005, 2014, 2015, and 2018), the SCSSM onset is triggered not by HISO or LISO, but by the slow mode of the seasonal variations (Fig. 3). In fact, the retreat of subtropical high from SCS is part of the seasonal variations, which involve the complex land–ocean–atmosphere interactions (Miyasaka and Nakamura 2010). The seasonal cycle of the subtropical high is of significant importance to understand the monsoon onset and its subsequent development. In addition, such cases generally respond to the fact that the HISO and LISO are in opposite phases and thus canceling each other out, when the seasonal westerlies have been established prior to the monsoon onset (Fig. S5). But this fundamental topic is beyond the scope of the present study and will be further addressed in the future. Here, only one striking fact is emphasized that the SCSSM usually exhibits the significant late onset if triggered by the seasonal variations. The mean onset date of this category is 2 June, compared to 17 May for the first two categories, with the climatological mean date being 21 May.
The propagation mechanisms are carefully examined based on the diagnosis of the column-integrated MSE. It is emphasized that HISO and LISO have different governing processes for their premonsoon propagations, which is less understood in contrast to the in-depth study of their propagating behaviors during the boreal summer monsoon. For premonsoon HISO, it has been found that it propagates westward rather than northwestward, differing from previous reports (Wu 2010; Zhou and Chan 2005). This difference may be attributed to the different definitions of the SCSSM onset or different compositing samples. Through the MSE budget diagnosis, this westward propagation is dominantly attributed to the anomalous MSE advection by the mean easterly (Fig. 6), contributing to 76% of the MSE tendency (Fig. 7). The westward propagation speed of the HISO is approximately 4.8° longitude per day, roughly equivalent to the speed of the mean easterly. In contrast, the diagnosis revealed that the SSTA-induced turbulent heat flux plays a crucial role in LISO’s northward propagation during the SCSSM onset (Fig. 10), accounting for 59% of the MSE tendency (Fig. 11). Another crucial factor is the anomalous MSE advection by the mean southerly wind, which accounts for 49%. However, in summer, southwesterly winds’ anomalous MSE advection primarily drives LISO’s northeastward propagation (T. Wang and Li 2020), while warm SST plays a secondary role (Gao et al. 2019).
It is worth noting that the SSTA-induced turbulent heat flux contributes minimally to HISO MSE tendency, amounting to only about 6% during the onset of the monsoon (Fig. S6). This is much less than its contribution to the LISO, which exceeds 50%. There are two reasons that could explain this difference. First, the positive SST anomalies induced by the dry HISO are smaller, only slightly surpassing 0.1°C (Fig. 5), compared to those of the LISO, which tend to be slightly over 0.3°C (Fig. 9). Given the considerable thermal inertia of the oceanic mixed layer, its time scale for adjustment to these anomalies is protracted compared to the atmosphere. With the inherently shorter cycle of the HISO compared to the LISO, the SST may not have sufficient time to adequately respond to the dry HISO influences. In addition, the HISO MSE tendency (about 21 W m−2 in Fig. 7) is larger than that associated with the LISO (about 12 W m−2 in Fig. 11). This disparity is due to the different intensities of the HISO and LISO over the SCS (Figs. S3 and S4). In fact, the intensity of the HISO exceeds that of the LISO in many regions (Ye and Wu 2015; Wu and Cao 2017). Because of these differences, the contribution of the SSTA-induced turbulent heat flux becomes relatively less significant for HISO.
The present study has further implication to the simulation and prediction of the Asian monsoon including the SCSSM onset. It has been the challenge to simulate the Asian monsoon onset and the delayed onset is identified as the common bias in the Coupled Model Intercomparison Project (CMIP3 and CMIP5) (Sperber et al. 2013) and in CMIP6 (Hu et al. 2023). Also, the unsatisfactory simulation of ISOs is known in CMIP5 (Sabeerali et al. 2013) and CMIP6 (Li et al. 2022). The outcomes here provide the intraseasonal view of SCSSM onset, which could serve as the physical framework for the model assessment and guide the model bias correction.
Acknowledgments.
This work was jointly supported by the National Natural Science Foundation of China (42230408, 42088101) and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies (Grant 2020B1212060025). We really appreciate the comments from the anonymous reviewers that really improve the quality of the manuscript. We would like to acknowledge the support of the following datasets: the NCEP–NCAR reanalysis data products, the NOAA–NCDC dataset, the ERA5 dataset, and the NOAA OI-SST v2 dataset.
Data availability statement.
The wind field data are obtained from the NCEP–NCAR reanalysis data products at the PSL (https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html). The OLR data are available at the NOAA–NCDC (https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00875). The ERA5 dataset is available at Copernicus Climate Change Service Climate Data Store (CDS) (https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset). The NOAA OI-SST v2 dataset is available at https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html.
REFERENCES
Boos, W. R., and Z. Kuang, 2010: Mechanisms of poleward propagating, intraseasonal convective anomalies in cloud system-resolving models. J. Atmos. Sci., 67, 3673–3691, https://doi.org/10.1175/2010JAS3515.1.
Chang, C.-P., Y. Zhang, and T. Li, 2000: Interannual and interdecadal variations of the East Asian summer monsoon and tropical Pacific SSTs. Part I: Roles of the subtropical ridge. J. Climate, 13, 4310–4325, https://doi.org/10.1175/1520-0442(2000)013<4310:IAIVOT>2.0.CO;2.
Chen, G., 2015: Comments on “Interdecadal change of the South China Sea summer monsoon onset”. J. Climate, 28, 9029–9035, https://doi.org/10.1175/JCLI-D-14-00732.1.
Chen, T.-C., and J.-M. Chen, 1995: An observational study of the South China Sea monsoon during the 1979 summer: Onset and life cycle. Mon. Wea. Rev., 123, 2295–2318, https://doi.org/10.1175/1520-0493(1995)123<2295:AOSOTS>2.0.CO;2.
Chen, W., P. Hu, and J. Huangfu, 2022: Multi-scale climate variations and mechanisms of the onset and withdrawal of the South China Sea summer monsoon. Sci. China Earth Sci., 65, 1030–1046, https://doi.org/10.1007/s11430-021-9902-5.
Cheng, Y., L. Wang, and T. Li, 2020: Causes of interdecadal increase in the intraseasonal rainfall variability over southern China around the early 1990s. J. Climate, 33, 9481–9496, https://doi.org/10.1175/JCLI-D-20-0047.1.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828.
DeMott, C. A., C. Stan, and D. A. Randall, 2013: Northward propagation mechanisms of the boreal summer intraseasonal oscillation in the ERA-Interim and SP-CCSM. J. Climate, 26, 1973–1992, https://doi.org/10.1175/JCLI-D-12-00191.1.
DeMott, C. A., J. J. Benedict, N. P. Klingaman, S. J. Woolnough, and D. A. Randall, 2016: Diagnosing ocean feedbacks to the MJO: SST-modulated surface fluxes and the moist static energy budget. J. Geophys. Res. Atmos., 121, 8350–8373, https://doi.org/10.1002/2016JD025098.
Ding, Y., 1992: Summer monsoon rainfalls in China. J. Meteor. Soc. Japan, 70, 373–396, https://doi.org/10.2151/jmsj1965.70.1B_373.
Ding, Y., and Coauthors, 2006: South China Sea Monsoon Experiment (SCSMEX) and the East Asian monsoon. Acta Meteor. Sin., 20, 159–190.
Drbohlav, H.-K. L., and B. Wang, 2005: Mechanism of the northward-propagating intraseasonal oscillation: Insights from a zonally symmetric model. J. Climate, 18, 952–972, https://doi.org/10.1175/JCLI3306.1.
Fairall, C. W., E. F. Bradley, J. E. Hare, A. A. Grachev, and J. B. Edson, 2003: Bulk parameterization of air-sea fluxes: Updates and verification for the COARE algorithm. J. Climate, 16, 571–591, https://doi.org/10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2.
Fu, X., B. Wang, and L. Tao, 2006: Satellite data reveal the 3-D moisture structure of tropical intraseasonal oscillation and its coupling with underlying ocean. Geophys. Res. Lett., 33, L03705, https://doi.org/10.1029/2005GL025074.
Fuchs-Stone, Ž., 2020: WISHE-moisture mode in a vertically resolved model. J. Adv. Model. Earth Syst., 12, e2019MS001839, https://doi.org/10.1029/2019MS001839.
Fukutomi, Y., and T. Yasunari, 1999: 10-25 day intraseasonal variations of convection and circulation over East Asia and western North Pacific during early summer. J. Meteor. Soc. Japan, 77, 753–769, https://doi.org/10.2151/jmsj1965.77.3_753.
Gao, Y., N. P. Klingaman, C. A. DeMott, and P.-C. Hsu, 2019: Diagnosing ocean feedbacks to the BSISO: SST-modulated surface fluxes and the moist static energy budget. J. Geophys. Res. Atmos., 124, 146–170, https://doi.org/10.1029/2018JD029303.
He, B., Y. Zhang, T. Li, and W.-T. Hu, 2017: Interannual variability in the onset of the South China Sea summer monsoon from 1997 to 2014. Atmos. Ocean. Sci. Lett., 10, 73–81, https://doi.org/10.1080/16742834.2017.1237853.
Hersbach, H., and Coauthors, 2018: ERA5 hourly data on pressure levels from 1979 to the present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), accessed 1 May 2021, https://doi.org/10.24381/cds.bd0915c6.
Hsu, H.-H., C.-H. Weng, and C.-H. Wu, 2004: Contrasting characteristics between the northward and eastward propagation of the intraseasonal oscillation during the boreal summer. J. Climate, 17, 727–743, https://doi.org/10.1175/1520-0442(2004)017<0727:CCBTNA>2.0.CO;2.
Hsu, P.-c., and T. Li, 2012: Role of the boundary layer moisture asymmetry in causing the eastward propagation of the Madden–Julian oscillation. J. Climate, 25, 4914–4931, https://doi.org/10.1175/JCLI-D-11-00310.1.
Hu, D., A. Duan, Y. Tang, and W. Yu, 2023: Delayed onset of the tropical Asian summer monsoon in CMIP6 can be linked to the cold bias over the Tibetan Plateau. Environ. Res. Lett., 18, 114005, https://doi.org/10.1088/1748-9326/acff79.
Hu, P., W. Chen, S. Chen, Y. Liu, and R. Huang, 2020: Extremely early summer monsoon onset in the South China Sea in 2019 following an El Niño event. Mon. Wea. Rev., 148, 1877–1890, https://doi.org/10.1175/MWR-D-19-0317.1.
Huangfu, J., R. Huang, and W. Chen, 2017: Statistical analysis and a case study of tropical cyclones that trigger the onset of the South China Sea summer monsoon. Sci. Rep., 7, 12732, https://doi.org/10.1038/s41598-017-13128-2.
Huangfu, J., W. Chen, X. Wang, and R. Huang, 2018: The role of synoptic-scale waves in the onset of the South China Sea summer monsoon. Atmos. Sci. Lett., 19, e858, https://doi.org/10.1002/asl.858.
Kajikawa, Y., and B. Wang, 2012: Interdecadal change of the South China Sea summer monsoon onset. J. Climate, 25, 3207–3218, https://doi.org/10.1175/JCLI-D-11-00207.1.
Kajikawa, Y., B. Wang, and J. Yang, 2010: A multi-time scale Australian monsoon index. Int. J. Climatol., 30, 1114–1120, https://doi.org/10.1002/joc.1955.
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
Kang, I.-S., D. Kim, and J.-S. Kug, 2010: Mechanism for northward propagation of boreal summer intraseasonal oscillation: Convective momentum transport. Geophys. Res. Lett., 37, L24804, https://doi.org/10.1029/2010GL045072.
Kemball-Cook, S. R., and B. C. Weare, 2001: The onset of convection in the Madden–Julian oscillation. J. Climate, 14, 780–793, https://doi.org/10.1175/1520-0442(2001)014<0780:TOOCIT>2.0.CO;2.
Kikuchi, K., and Y. N. Takayabu, 2004: The development of organized convection associated with the MJO during TOGA COARE IOP: Trimodal characteristics. Geophys. Res. Lett., 31, L10101, https://doi.org/10.1029/2004GL019601.
Kim, D., J.-S. Kug, and A. H. Sobel, 2014: Propagating versus nonpropagating Madden–Julian oscillation events. J. Climate, 27, 111–125, https://doi.org/10.1175/JCLI-D-13-00084.1.
Lau, W. K. M., and D. E. Waliser, 2012: Intraseasonal Variability in the Atmosphere–Ocean Climate System. Springer, 437 pp.
Lee, J.-Y., B. Wang, M. C. Wheeler, X. Fu, D. E. Waliser, and I.-S. Kang, 2013: Real-time multivariate indices for the boreal summer intraseasonal oscillation over the Asian summer monsoon region. Climate Dyn., 40, 493–509, https://doi.org/10.1007/s00382-012-1544-4.
Li, B., L. Zhou, J. Qin, and Z. Meng, 2022: Key process diagnostics for monsoon intraseasonal oscillation over the Indian Ocean in coupled CMIP6 models. Climate Dyn., 59, 2853–2870, https://doi.org/10.1007/s00382-022-06245-w.
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., Y. Yang, F. Lin, W. Yu, and S. Liu, 2020: Structures and northward propagation of the quasi-biweekly oscillation in the western North Pacific. J. Climate, 33, 6873–6888, https://doi.org/10.1175/JCLI-D-19-0752.1.
Li, T., and B. Wang, 2005: A review on the western North Pacific monsoon synoptic-to-interannual variabilities. Terr. Atmos. Oceanic Sci., 16, 285–314, https://doi.org/10.3319/TAO.2005.16.2.285(A).
Lin, A., and R. Zhang, 2020: Climate shift of the South China Sea summer monsoon onset in 1993/1994 and its physical causes. Climate Dyn., 54, 1819–1827, https://doi.org/10.1007/s00382-019-05086-4.
Lin, A., R. Zhang, and C. He, 2017: The relation of cross-equatorial flow during winter and spring with South China Sea summer monsoon onset. Int. J. Climatol., 37, 4576–4585, https://doi.org/10.1002/joc.5106.
Lin, J., B. Mapes, M. Zhang, and M. Newman, 2004: Stratiform precipitation, vertical heating profiles, and the Madden–Julian oscillation. J. Atmos. Sci., 61, 296–309, https://doi.org/10.1175/1520-0469(2004)061<0296:SPVHPA>2.0.CO;2.
Lindzen, R. S., and S. Nigam, 1987: On the role of sea surface temperature gradients in forcing low-level winds and convergence in the tropics. J. Atmos. Sci., 44, 2418–2436, https://doi.org/10.1175/1520-0469(1987)044<2418:OTROSS>2.0.CO;2.
Liu, B., C. Zhu, Y. Yuan, and K. Xu, 2016: Two types of interannual variability of South China Sea summer monsoon onset related to the SST anomalies before and after 1993/94. J. Climate, 29, 6957–6971, https://doi.org/10.1175/JCLI-D-16-0065.1.
Liu, F., B. Wang, and I.-S. Kang, 2015: Roles of barotropic convective momentum transport in the intraseasonal oscillation. J. Climate, 28, 4908–4920, https://doi.org/10.1175/JCLI-D-14-00575.1.
Luo, M., Y. Leung, H.-F. Graf, M. Herzog, and W. Zhang, 2016: Interannual variability of the onset of the South China Sea summer monsoon. Int. J. Climatol., 36, 550–562, https://doi.org/10.1002/joc.4364.
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 J. C. L. Chan, 2005: Intraseasonal variability of the South China Sea summer monsoon. J. Climate, 18, 2388–2402, https://doi.org/10.1175/JCLI3395.1.
Mao, J., and G. Wu, 2008: Influences of typhoon Chanchu on the 2006 South China Sea summer monsoon onset. Geophys. Res. Lett., 35, L12809, https://doi.org/10.1029/2008GL033810.
Miyasaka, T., and H. Nakamura, 2010: Structure and mechanisms of the Southern Hemisphere summertime subtropical anticyclones. J. Climate, 23, 2115–2130, https://doi.org/10.1175/2009JCLI3008.1.
Neelin, J. D., and I. M. Held, 1987: Modeling tropical convergence based on the moist static energy budget. Mon. Wea. Rev., 115, 3–12, https://doi.org/10.1175/1520-0493(1987)115<0003:MTCBOT>2.0.CO;2.
Raymond, D. J., and Ž. Fuchs, 2009: Moisture modes and the Madden–Julian oscillation. J. Climate, 22, 3031–3046, https://doi.org/10.1175/2008JCLI2739.1.
Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 5473–5496, https://doi.org/10.1175/2007JCLI1824.1.
Sabeerali, C. T., A. Ramu Dandi, A. Dhakate, K. Salunke, S. Mahapatra, and S. A. Rao, 2013: Simulation of boreal summer intraseasonal oscillations in the latest CMIP5 coupled GCMs. J. Geophys. Res. Atmos., 118, 4401–4420, https://doi.org/10.1002/jgrd.50403.
Shao, X., P. Huang, and R.-H. Huang, 2015: Role of the phase transition of intraseasonal oscillation on the South China Sea summer monsoon onset. Climate Dyn., 45, 125–137, https://doi.org/10.1007/s00382-014-2264-8.
Sperber, K. R., H. Annamalai, I.-S. Kang, A. Kitoh, A. Moise, A. Turner, B. Wang, and T. Zhou, 2013: The Asian summer monsoon: An intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century. Climate Dyn., 41, 2711–2744, https://doi.org/10.1007/s00382-012-1607-6.
Stephens, G. L., P. J. Webster, R. H. Johnson, R. Engelen, and T. L’Ecuyer, 2004: Observational evidence for the mutual regulation of the tropical hydrological cycle and tropical sea surface temperatures. J. Climate, 17, 2213–2224, https://doi.org/10.1175/1520-0442(2004)017<2213:OEFTMR>2.0.CO;2.
Straub, K. H., G. N. Kiladis, and P. E. Ciesielski, 2006: The role of equatorial waves in the onset of the South China Sea summer monsoon and the demise of El Niño during 1998. Dyn. Atmos. Oceans, 42, 216–238, https://doi.org/10.1016/j.dynatmoce.2006.02.005.
Tong, H. W., J. C. L. Chan, and W. Zhou, 2009: The role of MJO and mid-latitude fronts in the South China Sea summer monsoon onset. Climate Dyn., 33, 827–841, https://doi.org/10.1007/s00382-008-0490-7.
Wang, B., and X. Xie, 1997: A Model for the boreal summer intraseasonal oscillation. J. Atmos. Sci., 54, 72–86, https://doi.org/10.1175/1520-0469(1997)054<0072:AMFTBS>2.0.CO;2.
Wang, B., and LinHo, 2002: Rainy season of the Asian–Pacific summer monsoon. J. Climate, 15, 386–398, https://doi.org/10.1175/1520-0442(2002)015<0386:RSOTAP>2.0.CO;2.
Wang, B., LinHo, Y. Zhang, and M.-M. Lu, 2004: Definition of South China Sea monsoon onset and commencement of the East Asian summer monsoon. J. Climate, 17, 699–710, https://doi.org/10.1175/2932.1.
Wang, B., Q. Ding, and P. V. Joseph, 2009: Objective definition of the Indian summer monsoon onset. J. Climate, 22, 3303–3316, https://doi.org/10.1175/2008JCLI2675.1.
Wang, H., F. Liu, B. Wang, and T. Li, 2018: Effects of intraseasonal oscillation on South China Sea summer monsoon onset. Climate Dyn., 51, 2543–2558, https://doi.org/10.1007/s00382-017-4027-9.
Wang, L., T. Li, E. Maloney, and B. Wang, 2017: Fundamental causes of propagating and nonpropagating MJOs in MJOTF/GASS models. J. Climate, 30, 3743–3769, https://doi.org/10.1175/JCLI-D-16-0765.1.
Wang, L., and T. Li, 2020: Effect of vertical moist static energy advection on MJO eastward propagation: Sensitivity to analysis domain. Climate Dyn., 54, 2029–2039, https://doi.org/10.1007/s00382-019-05101-8.
Wang, T., and T. Li, 2020: Diagnosing the column-integrated moist static energy budget associated with the northward-propagating boreal summer intraseasonal oscillation. Climate Dyn., 54, 4711–4732, https://doi.org/10.1007/s00382-020-05249-8.
Wang, T., and T. Li, 2021: Factors controlling the diversities of MJO propagation and intensity. J. Climate, 34, 6549–6563, https://doi.org/10.1175/JCLI-D-20-0859.1.
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.
Wu, R., 2002: Processes for the northeastward advance of the summer monsoon over the western North Pacific. J. Meteor. Soc. Japan, 80, 67–83, https://doi.org/10.2151/jmsj.80.67.
Wu, R., 2010: Subseasonal variability during the South China Sea summer monsoon onset. Climate Dyn., 34, 629–642, https://doi.org/10.1007/s00382-009-0679-4.
Wu, R., and B. Wang, 2000: Interannual variability of summer monsoon onset over the western North Pacific and the underlying processes. J. Climate, 13, 2483–2501, https://doi.org/10.1175/1520-0442(2000)013<2483:IVOSMO>2.0.CO;2.
Wu, R., and B. Wang, 2001: Multi-stage onset of the summer monsoon over the western North Pacific. Climate Dyn., 17, 277–289, https://doi.org/10.1007/s003820000118.
Wu, R., and X. Cao, 2017: Relationship of boreal summer 10–20-day and 30–60-day intraseasonal oscillation intensity over the tropical western North Pacific to tropical Indo-Pacific SST. Climate Dyn., 48, 3529–3546, https://doi.org/10.1007/s00382-016-3282-5.
Ye, K., and R. Wu, 2015: Contrast of local air–sea relationships between 10–20-day and 30–60-day intraseasonal oscillations during May–September over the South China Sea and western North Pacific. Climate Dyn., 45, 3441–3459, https://doi.org/10.1007/s00382-015-2549-6.
Zheng, B., and Y. Huang, 2019: Mechanisms of northward-propagating intraseasonal oscillation over the South China Sea during the pre-monsoon period. J. Climate, 32, 3297–3311, https://doi.org/10.1175/JCLI-D-18-0391.1.
Zhou, W., and J. C. L. Chan, 2005: Intraseasonal oscillations and the South China Sea summer monsoon onset. Int. J. Climatol., 25, 1585–1609, https://doi.org/10.1002/joc.1209.
Zhou, W., and J. C. L. Chan, 2007: ENSO and the South China Sea summer monsoon onset. Int. J. Climatol., 27, 157–167, https://doi.org/10.1002/joc.1380.