The Relationship between the South China Sea Summer Monsoon Onset and Pacific Meridional SST anomalies

doi:10.21203/rs.3.rs-3940493/v1

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The Relationship between the South China Sea Summer Monsoon Onset and Pacific Meridional SST anomalies

https://doi.org/10.21203/rs.3.rs-3940493/v1

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This study investigates the connection between key sea surface temperature (SST) anomalies in the North Pacific during boreal spring (February-April, FMA) and the subsequent South China Sea (SCS) summer monsoon (SCSSM) onset. The SST anomalies, similar to the Pacific meridional mode (PMM), referred to as the PMM + mode, is defined to study the new impact factor on the SCSSM onset. It is found that the (February-March-April, FMA) PMM + has a significant positive correlation with the subsequent May SCSSM onset date, which is weakly affected by the ENSO in previous winter. A strong positive PMM + in boreal spring can be maintained until May via atmosphere-ocean interaction. The cooling area over WNP would reduce in situ precipitation heating, thereby generating descending Rossby waves that reinforce the formation of the anomalous anticyclone over the SCS. As a result, the easterly winds and suppressed convection dominate the SCS, making the SCSSM tend to break out later than normal. In addition, the increase in anticyclonic vorticity anomalies also make the western North Pacific subtropical high (WNPSH) stronger and more western than the normal years, thereby blocking active convection west of the SCS. Given the weakened relationship between El Niño-Southern Oscillation (ENSO) and the SCSSM onset in recent years, the PMM + could be considered as a promising preceding signal for the SCSSM onset, which will be of great importance for the SCSSM prediction.

South China Sea summer monsoon

monsoon onset

sea surface temperature

Atmosphere-ocean interaction

The South China Sea (SCS) summer monsoon (SCSSM) is an important component of the East Asian monsoon system (Lau and Yang, 1997, Wang and Wu, 1997, Lin et al., 2004, LI, 2005, Wang et al., 2009, Zhang et al., 2017, Jiang and Zhu, 2020). The convection and circulation anomalies associated with the SCSSM play a critical role in influencing the climate variations over the East Asia (EA) and can extend their impact globally through atmospheric teleconnections (Murakami and Matsumoto, 1994, Shiyan and Longxun, 1987). The SCSSM onset typically denotes a transition in atmospheric circulation from winter to summer patterns and the commencement of the rainy season in EA (Li and Zhang, 2009, Wang et al., 2009, Kajikawa and Wang, 2012). Climatologically, the SCSSM onset occurs in the middle of May (around the pentad 28th), characterized by a reversal of the low-level easterly to westerly and enhanced convection activity over the SCS (Kajikawa and Wang, 2012, Li et al., 2012, Wang et al., 2013b, Shao et al., 2014, Liu and Zhu, 2021, Fan et al., 2022). A late SCSSM onset tends to coincidence with increased May rainfall in the middle and lower reaches of the Yangtze River basin (Jiang et al., 2018), reducing the possibility of extreme rainfall events over Southeast Asia (Hu et al., 2022c). The timing of the SCSSM onset, whether early or late, may result in economic losses for the affected regions (He and Zhu, 2015, Huangfu et al., 2018). Therefore, more attention should be paid to the monsoon onset studies.

Previous studies have demonstrated that the SCSSM onset is influenced by multiple factors, including the tropical cyclones (TCs) and cold fronts on a synoptic scale (Huangfu et al., 2018, Kajikawa and Wang, 2012, Chen, 2015); quasi-biweekly oscillation and 30-60-day oscillation on an intraseasonal timescale (Kajikawa and Wang, 2012, Shao et al., 2014, Luo et al., 2016a); as well as SST anomalies on an interannual or interdecadal timescale (Jiang and Zhu, 2020, Hu et al., 2022b). Among these factors, the impact of the SST on the SCSSM onset has always been a focus. The interdecadal shift in the SCSSM onset in the mid-1990s is believed to be linked to the changes in SST anomalies (Hu et al., 2022b). In the early stage, the SST exhibits the Eastern Pacific (EP) type of ENSO, while in the later stage, the dominance of the Central Pacific (CP) type of ENSO leads to an advancement in the SCSSM onset (Geng et al., 2022). ENSO is considered to be one of the most important predictors of SCSSM onset in both statistic predictions and dynamical predictions (Hu et al., 2020, Martin et al., 2019). ENSO can postpone the SCSSM onset via several pathways, including the large-scale divergent circulation and the Philippine Sea anticyclone (Webster and Yang, 1992, Luo et al., 2016b, Xie et al., 2016, Li et al., 2017). However, it is noted that the linkages between ENSO and the SCSSM are not consistently stable, especially in recent years (Hu et al., 2020, Jiang and Zhu, 2021, Liu and Zhu, 2021). Some studies have tried to explain the possible reasons, such as disturbances in intraseasonal oscillations or interference from other SST anomalies patterns (Hu et al., 2022a, Liu and Zhu, 2021). The relationship between the SCSSM onset or precipitation in South China and the Victoria mode (VM) has gradually strengthened in recent years (Zou et al., 2020), with ENSO is modulated by the Pacific meridional mode (PMM). This suggests a potential alteration in the relationship between SST anomalies and the SCSSM. Therefore, it is necessary to reassess the impact of the SST anomalies on the SCSSM onset.

The Pacific meridional mode (PMM) is a leading mode of ocean-atmospheric variability in the North Pacific, whose positive mode is characterized by southwesterly wind anomalies and warm SST anomalies extending from California to the central-western equatorial Pacific (Chiang and Sobel, 2002, Larson and Kirtman, 2013, Min et al., 2017, Fan et al., 2021, Chiang and Vimont, 2004). Previous studies have found that the critical role of PMM as a subtropical precursor to the CP ENSO through air-sea interaction (Min et al., 2017, Chen et al., 2023). The SST signal of PMM experiences notable seasonal variations, which generally peaks in the boreal spring and then further develops in the boreal autumn (Chen et al., 2023). Thus, this change in seasonal signals may affect the climate over EA (Luo et al., 2020). Existing studies mostly focus on how the PMM relays the influence of extratropical atmospheric variability on tropical ENSO or TCs over the western North Pacific (Liu et al., 2019, Fan et al., 2021, Zheng et al., 2023). The wind-evaporation-SST (WES) feedback mechanism is commonly identified as a key process connecting PMM to ENSO (Chen et al., 2023, Lin et al., 2015, Amaya, 2019). Some researchers also found that the PMM has a closer linkage with the CP-type ENSO (Lin et al., 2015, Chen et al., 2023). In particular, the March-May triple SST mode over the North Pacific plays an important role in the SCSSM and precipitation in South China (Ding et al., 2018, Zou et al., 2020). Ding et al. found that the March-May triple SST mode can affect the intensity of the SCSSM (Ding et al., 2018). These SST anomalies, generated by the early spring VM via Bjerknes feedback, induce El Niño during the following winter, resulting in an increased precipitation in South China. The triple SST anomalies are similar to the PMM, but include key areas over the WNP, which we have proven in previous articles that may affect the SCSSM onset (Zhao et al., 2024). It has been proposed that the impact of this triple SST mode has become more significant in recent years, potentially explaining the weakening of the relationship between ENSO and the SCSSM (Hu et al., 2022a). However, this SST mode is generally considered to be caused by the VM, while there are few discussions about the influence of PMM on the SCSSM onset. Therefore, specific questions of interest are raised: Does PMM also exhibit a certain connection with the outbreak of the SCSSM? If so, what are the specific mechanisms underlying this relationship?

In this paper, we focus on the association between the early spring SST anomalies mode previously discussed in our prior publication and the SCSSM onset through observational data, and intend to find out the possible underlying mechanism. Section 2 describes the data and methods we used. Section 3 shows the relationship between the early spring SST anomalies mode and the SCSSM onset. Section 4 analyses the environmental anomalies related to the SCSSM onset during different SST anomalies mode years. Section 5 explores the possible physical mechanisms. Finally, a comprehensive summary of our major results is provided in section 6.

2.1 Data

The monthly SST data for the period of 1991–2020 used in this article is from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1°×1° spatial grid (Sheppard and Rayner, 2002). The monthly and daily atmospheric variables, including wind, geopotential height, vertical velocity, divergence, covering the same period are from the Japanese 55-Year Reanalysis (JRA-55) data, which have a horizontal resolution of 2.5°×2.5° (Kobayashi et al., 2015). Our previous article pointed out that the relationship between the SCSSM and SST may be more explicit when using the JRA data.

2.2 Definition of the SCSSM onset

Following our previous study, we adopt the definition of the SCSSM onset from Beijing Climate Center (BCC), see http://www.cmastd.cn/standardView.jspx?id=3725 for details. The SCSSM onset is defined by the transition of the zonal wind and the pseudo-equivalent potential temperature at 850-hPa over the SCS.

The SCSSM onset:

The time occurs after pentad 25th;

The average zonal wind at 850hPa in the SCSSM monitoring area (110–120°E, 10–20°N) is more than zero and lasts for at least 2 pentads;

The average 850-hPa pseudo-equivalent potential temperature of the SCSSM monitoring area is more than or equal to 340 K, and lasts for at least 2 pentads.

2.3 The PMM + index

Traditionally, the sea area reflected by PMM extends from California to the central-western equatorial Pacific. Following the approach of Chiang and Vimont (Chiang and Vimont, 2004), the PMM index is defined as the SST expansion coefficient of the leading singular vector decomposition (SVD) mode of monthly SST and 10 m wind fields over the central to eastern Pacific (21°S-32°N, 175°E‐95°W) after linearly removing the SST variability in the Pacific cold tongue (6°S‐6°N, 90°W‐150°W). However, according to our previous research, the influence of the central to western North Pacific on the SCSSM onset cannot be ignored. Based on the same method, we redefine the sea area for SVD as 150°E-100°W, 15°S-35°N, and the obtained mode and indices are called PMM + and PMM + index respectively (Fig. 1b). The PMM + pattern obtained by regressing monthly SST anomalies onto the monthly PMM + index is shown in Fig. 1a. Obviously, this mode also features a meridional seesaw of SST accompanied by cross-equatorial surface winds.

2.4 Statistical methods

This article we employ statistical methods, including singular value decomposition (SVD), composite analysis, correlation analysis, and linear regression. Two-tailed t test is used to assess whether the results of results are significant.

Based on our previous work, we found that the PMM + can affect the SCSSM onset, with a more pronounced effect in spring. Previous studies have also highlighted the influence of the Victoria mode on the SCSSM onset, with an increasing significance (Zou et al., 2020, Hu et al., 2022a). Therefore, we use the relationship between the VM index and the SCSSM onset as a reference. We calculate the correlation between the monthly PMM + index and VM index in relation to the SCSSM onset. As illustrated in Fig. 2a, the correlation coefficients between the PMM + index in February-April (FMA) and the SCSSM onset exceeds the 95% confidence level, and peaks in the March. Compared with the PMM+, VM is less closely related to the SCSSM but still exceeds the 95% confidence during January-March. This difference may be attributed to the definition of SCSSM onset from the perspective of pentads. Given that February-April is the time with stronger correlation to SCSSM onset and many studies have found that it is also with largest PMM SST variability, so the spring PMM + index refers to the PMM + SST index averaged from February to April in this article. The spatial distribution of the relationship between the FMA SST and the SCSSM onset is shown in Fig. 2b. Apparently, there are significant positive correlations over the sea area extending from California to the central-western equatorial Pacific, while negative correlations over the central to western North Pacific significantly. It can be seen from the Fig. 2c that both the time series of the spring PMM + index and the SCSSM onset show significant interannual variability, with a strong correlation between them from 1991 to 2020 (Corr = -0.49, significant at the 95% confidence level). Different lengths of moving correlations also suggest that the relationship between the spring PMM + and the SCSSM onset shows a steady increase from 1991 to 2021 (Fig. 3). Therefore, we mainly focus on the intensified impact of the PMM + on SCSSM onset in recent years. When the PMM + index shows a significant positive phase, the SCSSM onset tend be later, otherwise it will be earlier.

Previous studies have demonstrated that ENSO has a notable impact on EA climate, and the SCSSM onset is no exception (Lau et al., 2000, Zhou and Chan, 2007, Zhu and Li, 2017). Therefore, we calculate the correlation between the SCSSM onset and the spring PMM + index after removing the previous winter’s ENSO signal (represented by the December-February Niño 3.4 index). The resulting partial correlation coefficient reaches − 0.47, which still passes the 95% significance test. These results indicate that the relationship between the spring PMM + and the SCSSM onset is strong and only weakly affected by the ENSO in the preceding winter.

Based on the above discussion, next we use composite analysis to better understand the relationship between the spring PMM + and the SCSSM onset. The year with standardized PMM + index larger (smaller) than 1 (-1) was defined as a strong positive (negative) PMM + case. The categorization of early or late SCSSM onset follows the BCC standard. Generally, the SCSSM onset is considered to be early on or before pentad 27th and late on or after pentad 30th. Given that there may be fewer cases with normalized PMM + indices greater than 1, greater than 0.7 cases are selected instead. This gives six strong positive and six strong negative PMM + cases, along with nine early and eight late SCSSM onset cases (Table 1). Among the 12 abnormal PMM + years, the SCSSM onset occurs earlier or later in 10 years, indicating that the potential linkage between the PMM + and the SCSSM onset.

Figure 4a shows the composite difference of the FMA SST anomalies between positive and negative PMM + years. It can be seen that significant positive SST anomalies difference are evident over the sea area extending from California to the central-western equatorial Pacific, while the negative is over the central to western North Pacific. The composite difference pattern of FMA SST anomalies closely resemble the PMM+ (similar to the Fig. 1a), characterized by marked positive SST anomalies appear off the west coast of North America extending southwestward to the tropical central Pacific, together with cold SST anomalies in the western North Pacific, forming a meridional SST gradient. This SST anomalies pattern is similar to the traditional PMM. To detect the difference in dynamic and thermal conditions between the positive and negative PMM + years, the time-longitude distribution of difference in average 850hPa pseudo-equivalent potential temperature and wind along 10–20 °N are shown in Fig. 4b. Prominent easterly wind anomalies in middle May are observed over the SCS (110–120°E), indicating that during positive PMM + phases, easterly winds dominate the SCS, or characterized by weak westerly winds. Additionally, negative pseudo-equivalent potential temperature anomalies in middle May, which suggests that the convection conditions when the SCSSM outbreaks are poor in the positive PMM + phase. The presence of stronger easterly winds or weaker westerly winds, coupled with less favorable convective conditions, increases the possibility of a later SCSSM onset during the positive PMM + years.

Table 1

Selected strong positive and negative PMM + cases and early and late SCSSM onset cases during the period 1991–2021.
Cases	Years
Positive PMM+	1991, 1993, 1994, 2014, 2015, 2018
Negative PMM+	1999, 2000, 2001, 2008, 2012, 2019
Early SCSSM onset	1995, 1996, 1999, 2000, 2001, 2008, 2012,2013,2019
Late SCSSM onset	1991, 1993, 1994, 2006, 2009, 2014, 2018, 2021

To examine the differences in the different PMM + phases of the mean SCSSM onset, we calculated the composite of 850hPa pseudo-equivalent potential temperature and winds based on the SCSSM onset pentad. We mainly focus on the circulation characteristics during pentads 27th-29th, because this period is the time when the SCSSM typically breaks out. Figure 5 shows the composite map of the SCSSM onset processes in both positive and negative PMM + years from pentad 27th to pentad 29th. It can be concluded from the Fig. 5a-c that during positive PMM + phases, the SCS monitoring area is mainly controlled by easterly winds from pentad 27th to 29th, while the westerly winds are relatively weak. At this time, a small anticyclone controls the SCS, and the monsoon trough is not established in the upstream of SCS until the pentad 28th-29th. In addition, the western extension of the WNPSH is notable at 500hPa, which dominates the SCS, making it difficult for the westerly winds to pass through the SCS. However, the situation is completely different during the negative phase year of PMM+ (Fig. 5d-f). By pentad 27th, a cyclone pair near 110 °E is already present, with the cyclone in the SCS gradually intensifying and evolving into a monsoon trough. Subsequently, the zonal wind field undergoes a transformation from easterly to westerly. Therefore, the WNPSH is more westward and the SCS is dominated by easterly winds and suppressed convection during PMM + positive years.

Figure 6 shows the composite evolution of 850hPa relative vorticity and its difference between different PMM + cases from pentad 27th to pentad 29th. In the strong positive PMM + years (Fig. 6a-c), the center of negative vorticity anomalies dominates the SCS and gradually weakens by pentad 29th, seemingly splitting from the edge of the WNPSH, which hinders the development of the convection over the SCS. However, a strong positive relative vorticity field is evident in pentad 27th of the negative PMM + years (Fig. 6d-f). This vorticity configuration favors ascending motion and enhanced convection over the SCS, potentially triggering an early onset of the SCSSM. The difference between two strong PMM + cases further demonstrated that anticyclonic relative vorticity tends to appear in the SCS during the strong positive phase (Fig. 6g-i). This negative vorticity, peaking in the pentad 27th, fails to provide a favorable vorticity field before the SCSSM onset.

Figure 7 depicts the composite evolution of the 10–20°N-averaged pressure-longitude cross section of zonal circulation, vertical movement and its difference between two kinds of PMM + cases from pentad 27th to pentad 29th. It can be seen that an obvious sinking movement in the SCS in the pentad 27th of positive PMM + years (Fig. 7a), especially in the lower level. The strong ascending movement area is blocked to its west side. An eastward-moving and gradually developing upwelling and downwelling at 180 − 150 °W constitute the entire zonal circulation in strong positive PMM + years (Fig. 7a-c). Such a circulation configuration blocks the ascending movement and convection development from the west in mid-early May. However, in negative PMM + years (Fig. 7d-f), strong upwelling throughout the entire height, controls the SCS during pentad 27th-29th, and moves eastward gradually, providing favorable dynamic conditions for the SCSSM onset. In addition, the zonal circulation tends to be wider, especially the ascending branch over the SCS. The difference between two strong PMM + cases also revealed that the sinking motion is more likely to occur in pentad 27th of PMM + positive years, making it difficult for the development of the convection before the SCSSM onset (Fig. 7g-i).

In conclusion, the patterns in Figs. 5–7 collectively suggest that a strong positive PMM + in early spring is likely to be followed by poor dynamic and thermal conditions in May over the SCS. It is worth noting that the environmental configuration difference between positive and negative PMM + years tends to be greater in pentad 27th, which indicates that PMM + has a greater impact before the SCSSM onset.

As speculated above, different PMM + cases show different circulation configurations around the SCS, suggesting a potential correlation between spring PMM + and the SCSSM onset. Previous studies have shown how the spring PMM affects the following winter ENSO or tropical cyclone genesis. However, the mechanism through which the PMM and the SST anomalies in the WNP jointly influence the monsoon system remains unexplored. Therefore, we aim to investigate the underlying mechanisms by which the spring PMM + affects the SCSSM onset in this section.

We first explore the mechanisms from the perspective of months. Figure 8 shows the correlation maps of SCSSM onset with SST anomalies during February to May. Figure 9 depicts the regressions of 850hPa winds and pseudo-equivalent potential temperature in May with respect to the spring PMM + index. It can be seen that the SST anomalies show a PMM + like pattern, with a negative SST anomalies band over central North Pacific, and U-shaped positive SST anomalies extending from east North Pacific to the central-western equatorial Pacific in February (Fig. 8a). This pattern of SST anomalies increases the zonal SST difference between the western equatorial Pacific and the central equatorial Pacific, promoting the development of anomalous westerly winds over the equatorial Pacific. The cooling, combined with negative SST anomalies over the WNP, also further strengthens the anomalous easterly winds over the WNP. Under these anomalous wind fields, it is conductive to the formation of the low-level anomalous cyclone over the tropical central Pacific. The interaction between the anomalous cyclone and the SST anomalies via the positive feedback mechanism of wind- evaporation-SST (WES) allows the PMM + to be maintained until May. On the other hand, an anomalous anticyclone is observed in the SCS (Fig. 9d). How can this anomalous anticyclone sustain itself? We argue that the positive feedback between the negative SST anomalies over the WNP and the anomalous anticyclone plays a central role. To the southeast of the anticyclone anomalies, the SST over the WNP is cool because of the dominance of the easterly wind anomalies. However, the resultant ocean cooling would reduce in situ precipitation heating, thereby generating descending Rossby waves that reinforce the formation of the anomalous anticyclone. This positive thermodynamic feedback is confirmed by Wang et al. through numerical experiments with a coupled model, which demonstrates that a spring SST cooling in the WNP can indeed maintain an anomalous anticyclone over the SCS in the ensuing summer (Wang et al., 2013a). In addition, the negative pseudo-equivalent potential temperature anomalies related to PMM + dominates the SCS during February to May, which is not conductive to the development of the convection. Therefore, the easterly winds, combined with the suppressed convection over the SCS, result in a late SCSSM onset.

Previous studies have explained how the VM mode affects the SCSSM onsetq, they emphasized the influence of convergence and divergence at high and low levels, which induced by triple-like SST anomalies (Zou et al., 2020, Hu et al., 2022a). The correlation maps of the spring PMM + index with the divergence and relative vorticity in May are shown in Fig. 10. The PMM + related divergence field is significantly positive at 850hPa and significant negative at 200hPa over SCS and WNP, although these distributions are relatively scattered. Therefore, the spring PMM + could modulate the SCSSM onset via large-scale divergent circulation. Obvious results are also shown in the vorticity regression pattern, characterized by strong negative vorticity correlations at 850hPa over the SCS while strong positive vorticity correlations at 200hPa, which is conducive to the development of the anomalous anticyclone in the SCS, blocking the SCSSM onset (Fig. 8). Combined with the analysis of Fig. 9, we can further conclude that the suppression of convection in the SCS is closely associated with the emergence of PMM+.

Considering that the SCSSM onset often occurs in the middle of May, and there are differences between the positive and negative phase of PMM + years in pentad 27th-29th, it is necessary to focus on the impacts of variables over the SCS from the perspective of pentads. Figure 11 gives the zonal distribution of correlation coefficients between PMM + indices against zonal wind anomalies, meridional gradient of zonal winds, and vertical gradient of pseudo-equivalent potential temperature anomalies. Our main concern is the period during 27th-29th, when the PMM + affects the SCSSM onset more strongly. In terms of the zonal winds, the influence of the PMM + on it is mainly through anomalous anticyclone. During pentad 28th-29th, significant correlation coefficients exist in the SCS monitoring area, which making the SCSSM outbreaks later than normal (Fig. 11a).

Previous studies have discussed the role of the meridional gradient of zonal wind in the interdecadal variability of SCSSM. Lin and Zhang found that SST anomalies in the key region (140° -150° E, 5° S-2.5° N) have a strong correlation with the meridional gradient of zonal wind in the SCS, resulting in an obvious shift of SCSSM onset. Generally, vorticity contains two terms, the meridional gradient of zonal winds (\(-\partial u/\partial y\)) and the zonal gradient of meridional winds (\(\partial v/\partial x\)). In Fig. 9 we can see that the term \(-\partial u/\partial y\) plays an important role in the mean vorticity anomalies over the SCS. The easterly anomalies around the SCS while the westerly anomalies over the South China, contribute to an increase in the term of \(-\partial u/\partial y\), resulting in an enhancement of anticyclonic anomaly over the SCS. The correlation between PMM + index and the negative value of meridional of zonal winds over the SCS is examined in Fig. 11b. We can conclude that in the area around SCS (110°-120° E, 0°-20° N), the significant negative correlation lasts for three pentads from pentad 27th to 29th. Therefore, this explains from a dynamic perspective why the SCSSM onset is later when the PMM + occurs.

On the other hand, since the pseudo-equivalent potential temperature is conservative in both dry and wet adiabatic processes, the distribution of it with height can be used to judge the stratification stability. Generally, when the vertical gradient of the pseudo-equivalent potential temperature less than 0, it means instability, and vice versa. Previously we only focused on the convection conditions at 850hPa (Fig. 5 and Fig. 9). Next we consider the influence of the PMM + at different vertical height levels. Figure 11c shows the correlation between PMM + indices and the negative vertical gradient of pseudo-equivalent potential temperature during pentad 27th-29th, it can be seen that the relationship between them shows a significant negative correlation in the SCS, which suggest that the PMM + can affect the vertical distribution of the pseudo-equivalent potential temperature by increasing the term of \(\partial {\theta }_{se}/\partial z\), making the atmospheric stratification in the SCS more inclined to be a stable state. This denotes the air can return to its original state when disturbed. Therefore, the development of convection over the SCS would be suppressed, which is unfavorable to the SCSSM onset. This explains from a thermodynamic perspective how the PMM + affects the SCSSM onset.

This study has focused on the relationship between the SCSSM onset and the PMM + mode of SST anomalies in North Pacific Ocean. The PMM + index is defined as the expansion coefficient time series of SST in 150°E-100°W, 15°S-35°N corresponding to the first SVD mode. This mode emphasizes the combination of PMM and SST anomalies in the central to western North Pacific, which is more obvious when considering the SCSSM onset dates from the perspective of pentads. Results show that the PMM+, reaching its peak during February-April, exerts a profound influence on SCSSM onset in May. When the early spring PMM + is positive, the subsequent May SCSSM onset tends to be later than normal.

The possible mechanism for the effect of the PMM + on the SCSSM onset is depicted in Fig. 12. When the PMM + appears in FMA, significant positive SST anomalies with the PMM + extend from California to the central-western equatorial Pacific while significant negative SST anomalies are over the central to western North Pacific. The SST anomalies not only increase the zonal SST difference between the western equatorial Pacific and the central equatorial Pacific, but also increase the SST difference over the WNP, which promotes the development of anomalous westerly winds in the equatorial central Pacific (ECP) and anomalous easterly winds over the WNP. These anomalous zonal winds jointly cause the occurrence of anomalous cyclone over the western tropical Pacific. The PMM + is maintained until May because of the air-ocean interaction between anomalous cyclone and SST anomalies. On the other hand, the cooling area over WNP would also reduce the precipitation heating, thereby generating descending Rossby waves that contribute to the development of the anomalous anticyclone over the SCS. The SCS is controlled by the easterly winds and is inclined to be a stable atmosphere, suppressing the convection, which makes the SCSSM later than normal. In addition, the increase of anticyclonic vorticity also make the WNPSH stronger than the PMM + negative years, thereby blocking active convection in the west of the SCS.

It is worth noting that some scholars emphasize the relationship between the VM and the SCSSM onset (Ding et al., 2018, Zou et al., 2020, Hu et al., 2022a). They have also pointed out that the early spring VM mode may strengthen via Bjerknes feedback, and then shows a triple-like SST anomalies pattern during March-May over the North Pacific basin, which is induced by a thermoclinic feedback (Zou et al., 2020). That suggests there may be a potential connection between VM and the PMM + we defined and this study is an important supplement to the study of VM mode. However, the onset dates in this article are based on pentads, so the results are more inclined to the PMM + SST anomalies mode that we defined.

In conclusion, it is clear that the PMM + during FMA plays an important role in the SCSSM onset. Previous studies have pointed out that the relationship between the SCSSM and SST anomalies has changed, particularly in the weakening correlation with ENSO (Jiang and Zhu, 2021, Ding et al., 2018, Hu et al., 2022a). We also calculate the correlation coefficient between PMM + indices and the SCSSM onset from 1960 to 1990, and the value is -0.033 (while it is -0.49 during 1991–2020). This indicates that the relationship between the two has become more significant in recent years (Fig. 3). Therefore, further research is required to prove why PMM + delays the SCSSM onset significantly in recent years. And whether the relationship between PMM + and the SCSSM onset can be used to explain the weakening correlation between the SCSSM and ENSO. In addition, the mechanism of PMM + for the SCSSM onset also needs model validation.

Acknowledgment: The study is supported by the National Natural Science Foundation of China Project (Nos. 42130610, 42075057, and 42275050) and the National Key Research and Development Program of China (2022YFE0136000).

Funding: The study is supported by the National Natural Science Foundation of China Project (Nos. 42130610, 42075057, and 42275050) and the National Key Research and Development Program of China (2022YFE0136000).

Competing Interests: The authors have no relevant financial or non-financial interests to disclose.

Author Contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Zhao Yuxuan, Liu Ruoyu and Yao Chenwei. The first draft of the manuscript was written by Zhao Yuxuan and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data Availability: All datasets are available publicly in the main text. JRA-55 reanalysis data are available at https://rda.ucar.edu/datasets/ds628.0/index.html#sfol-wl-/data/ds628.0?g=19819. HadlSST data is available at https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html.

AMAYA, D. J. 2019. The Pacific Meridional Mode and ENSO: a Review. CURRENT CLIMATE CHANGE REPORTS, 5, 296-307.
CHEN, G. 2015. Comments on "Interdecadal Change of the South China Sea Summer Monsoon Onset''. JOURNAL OF CLIMATE, 28, 9029-9035.
CHEN, Y., DU, Y. & CHEN, Z. 2023. Indo-western Pacific Ocean capacitor events recorded by coral proxies in the South China Sea. PALAEOGEOGRAPHY PALAEOCLIMATOLOGY PALAEOECOLOGY, 609.
CHIANG, J. C. H. & SOBEL, A. H. 2002. Tropical tropospheric temperature variations caused by ENSO and their influence on the remote tropical climate. JOURNAL OF CLIMATE, 15, 2616-2631.
CHIANG, J. C. H. & VIMONT, D. J. 2004. Analogous Pacific and Atlantic meridional modes of tropical atmosphere-ocean variability. JOURNAL OF CLIMATE, 17, 4143-4158.
DING, R., LI, J., TSENG, Y.-H., LI, L., SUN, C. & XIE, F. 2018. Influences of the North Pacific Victoria Mode on the South China Sea Summer Monsoon. ATMOSPHERE, 9.
FAN, H., HUANG, B., YANG, S. & DONG, W. 2021. Influence of the Pacific Meridional Mode on ENSO Evolution and Predictability: Asymmetric Modulation and Ocean Preconditioning. JOURNAL OF CLIMATE, 34, 1881-1901.
FAN, Y., ZHU, S., WANG, L. & WANG, X. 2022. Subseasonal dynamical prediction of South China Sea summer monsoon. Atmospheric Research, 278.
GENG, T., CAI, W., WU, L., SANTOSO, A., WANG, G., JING, Z., GAN, B., YANG, Y., LI, S., WANG, S., CHEN, Z. & MCPHADEN, M. J. 2022. Emergence of changing Central-Pacific and Eastern-Pacific El Nino-Southern Oscillation in a warming climate. NATURE COMMUNICATIONS, 13.
HE, J. & ZHU, Z. 2015. The relation of South China Sea monsoon onset with the subsequent rainfall over the subtropical East Asia. International Journal of Climatology, 35, 4547-4556.
HU, P., CHEN, W., CHEN, S., LIU, Y. & HUANG, R. 2020. Extremely Early Summer Monsoon Onset in the South China Sea in 2019 Following an El Nino Event. MONTHLY WEATHER REVIEW, 148, 1877-1890.
HU, P., CHEN, W., CHEN, S., WANG, L. & LIU, Y. 2022a. The Weakening Relationship between ENSO and the South China Sea Summer Monsoon Onset in Recent Decades. ADVANCES IN ATMOSPHERIC SCIENCES, 39, 443-455.
HU, P., CHEN, W., CHEN, S., WANG, L. & LIU, Y. 2022b. The Weakening Relationship between ENSO and the South China Sea Summer Monsoon Onset in Recent Decades. ADVANCES IN ATMOSPHERIC SCIENCES, 39, 443-455.
HU, P., CHEN, W., LI, Z., CHEN, S., WANG, L. & LIU, Y. 2022c. Close Linkage of the South China Sea Summer Monsoon Onset and Extreme Rainfall in May over Southeast Asia: Role of the Synoptic-Scale Systems. Journal of Climate, 35, 4347-4362.
HUANGFU, J., CHEN, W., WANG, X. & HUANG, R. 2018. The role of synoptic‐scale waves in the onset of the South China Sea summer monsoon. Atmospheric Science Letters, 19, e858.
JIANG, N. & ZHU, C. 2020. Seasonal Forecast of South China Sea Summer Monsoon Onset Disturbed by Cold Tongue La Niña in the Past Decade. Advances in Atmospheric Sciences, 38, 147-155.
JIANG, N. & ZHU, C. 2021. Seasonal Forecast of South China Sea Summer Monsoon Onset Disturbed by Cold Tongue La Nina in the Past Decade. ADVANCES IN ATMOSPHERIC SCIENCES, 38, 147-155.
JIANG, X., WANG, Z. & LI, Z. 2018. Signature of the South China Sea summer monsoon onset on spring-to-summer transition of rainfall in the middle and lower reaches of the Yangtze River basin. CLIMATE DYNAMICS, 51, 3785-3796.
KAJIKAWA, Y. & WANG, B. 2012. Interdecadal Change of the South China Sea Summer Monsoon Onset. JOURNAL OF CLIMATE, 25, 3207-3218.
KOBAYASHI, S., OTA, Y., HARADA, Y., EBITA, A., MORIYA, M., ONODA, H., ONOGI, K., KAMAHORI, H., KOBAYASHI, C., ENDO, H., MIYAOKA, K. & TAKAHASHI, K. 2015. The JRA-55 Reanalysis: General Specifications and Basic Characteristics. JOURNAL OF THE METEOROLOGICAL SOCIETY OF JAPAN, 93, 5-48.
LARSON, S. & KIRTMAN, B. 2013. The Pacific Meridional Mode as a trigger for ENSO in a high-resolution coupled model. GEOPHYSICAL RESEARCH LETTERS, 40, 3189-3194.
LAU, K. M., DING, Y. H., WANG, J. T., JOHNSON, R., KEENAN, T., CIFELLI, R., GERLACH, J., THIELE, O., RICKENBACH, T., TSAY, S. C. & LIN, P. H. 2000. A report of the field operations and early results of the South China Sea Monsoon Experiment (SCSMEX). BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 81, 1261-1270.
LAU, K. M. & YANG, S. 1997. Climatology and interannual variability of the Southeast Asian summer monsoon. Advances in Atmospheric Sciences, 14, 141-141.
LI, J.-P. 2005. A new monsoon index, its interannual variability and relation with monsoon precipitation. Climatic and Environ. Res., 10, 351-365.
LI, J., FENG, J. & LI, Y. 2012. A possible cause of decreasing summer rainfall in northeast Australia. International Journal of Climatology, 32, 995-1005.
LI, J. & ZHANG, L. 2009. Wind onset and withdrawal of Asian summer monsoon and their simulated performance in AMIP models. CLIMATE DYNAMICS, 32, 935-968.
LI, T., WANG, B., WU, B., ZHOU, T., CHANG, C.-P. & ZHANG, R. 2017. Theories on Formation of an Anomalous Anticyclone in Western North Pacific during El Nino: A Review. JOURNAL OF METEOROLOGICAL RESEARCH, 31, 987-1006.
LIN, A. L., LIANG, J. Y., GU, D. J. & WANG, D. X. 2004. On the relationship between convection intensity of South China Sea summer monsoon and air-sea temperature difference in the tropical oceans. ACTA OCEANOLOGICA SINICA, 23, 267-278.
LIN, C.-Y., YU, J.-Y. & HSU, H.-H. 2015. CMIP5 model simulations of the Pacific meridional mode and its connection to the two types of ENSO. INTERNATIONAL JOURNAL OF CLIMATOLOGY, 35, 2352-2358.
LIU, B. & ZHU, C. 2021. Subseasonal-to-Seasonal Predictability of Onset Dates of South China Sea Summer Monsoon: A Perspective of Meridional Temperature Gradient. JOURNAL OF CLIMATE, 34, 5601-5616.
LIU, C., ZHANG, W., STUECKER, M. F. & JIN, F.-F. 2019. Pacific Meridional Mode-Western North Pacific Tropical Cyclone Linkage Explained by Tropical Pacific Quasi-Decadal Variability. GEOPHYSICAL RESEARCH LETTERS, 46, 13346-13354.
LUO, M., LAU, N.-C., ZHANG, W., ZHANG, Q. & LIU, Z. 2020. Summer High Temperature Extremes over China Linked to the Pacific Meridional Mode. JOURNAL OF CLIMATE, 33, 5905-5917.
LUO, M., LEUNG, Y., GRAF, H.-F., HERZOG, M. & ZHANG, W. 2016a. Interannual variability of the onset of the South China Sea summer monsoon. INTERNATIONAL JOURNAL OF CLIMATOLOGY, 36, 550-562.
LUO, M., LEUNG, Y., GRAF, H. F., HERZOG, M. & ZHANG, W. 2016b. Interannual variability of the onset of the South China Sea summer monsoon. INTERNATIONAL JOURNAL OF CLIMATOLOGY, 36, 550-562.
MARTIN, G. M., CHEVUTURI, A., COMER, R. E., DUNSTONE, N. J., SCAIFE, A. A. & ZHANG, D. 2019. Predictability of South China Sea Summer Monsoon Onset. ADVANCES IN ATMOSPHERIC SCIENCES, 36, 253-260.
MIN, Q., SU, J. & ZHANG, R. 2017. Impact of the South and North Pacific Meridional Modes on the El Nino-Southern Oscillation: Observational Analysis and Comparison. JOURNAL OF CLIMATE, 30, 1705-1720.
MURAKAMI, T. & MATSUMOTO, J. 1994. Summer monsoon over the Asian continent and western North Pacific. Journal of the Meteorological Society of Japan. Ser. II, 72, 719-745.
SHAO, X., HUANG, P. & HUANG, R.-H. 2014. Role of the phase transition of intraseasonal oscillation on the South China Sea summer monsoon onset. Climate Dynamics, 45, 125-137.
SHEPPARD, C. & RAYNER, N. A. 2002. Utility of the Hadley Centre sea ice and sea surface temperature data set (HadISST1) in two widely contrasting coral reef areas. MARINE POLLUTION BULLETIN, 44, 303-308.
SHIYAN, T. & LONGXUN, C. 1987. A review of recent research on the East Asian summer monsoon in China. Monsoon Meteorology, 92.
WANG, B., HUANG, F., WU, Z., YANG, J., FU, X. & KIKUCHI, K. 2009. Multi-scale climate variability of the South China Sea monsoon: A review. DYNAMICS OF ATMOSPHERES AND OCEANS, 47, 15-37.
WANG, B. & WU, R. 1997. Peculiar temporal structure of the South China Sea summer monsoon. Advances in Atmospheric Sciences, 14, 177-194.
WANG, B., XIANG, B. & LEE, J.-Y. 2013a. Subtropical High predictability establishes a promising way for monsoon and tropical storm predictions. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 110, 2718-2722.
WANG, X., JIANG, X., YANG, S. & LI, Y. 2013b. Different impacts of the two types of El Nino on Asian summer monsoon onset. ENVIRONMENTAL RESEARCH LETTERS, 8.
WEBSTER, P. J. & YANG, S. 1992. MONSOON AND ENSO - SELECTIVELY INTERACTIVE SYSTEMS. QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 118, 877-926.
XIE, S.-P., KOSAKA, Y., DU, Y., HU, K., CHOWDARY, J. S. & HUANG, G. 2016. Indo-western Pacific ocean capacitor and coherent climate anomalies in post-ENSO summer: A review. ADVANCES IN ATMOSPHERIC SCIENCES, 33, 411-432.
ZHANG, R., MIN, Q. & SU, J. 2017. Impact of El Niño on atmospheric circulations over East Asia and rainfall in China: Role of the anomalous western North Pacific anticyclone. Science China Earth Sciences, 60, 1124-1132.
ZHAO, Y., TUO, Y., YANG, Z., WU, Z., GONG, Z. & FENG, G. 2024. Uncertainties of the South China Sea summer monsoon and its relationship with sea surface temperature from different reanalysis datasets. INTERNATIONAL JOURNAL OF CLIMATOLOGY.
ZHENG, Y., CHEN, S., CHEN, W. & YU, B. 2023. A Continuing Increase of the Impact of the Spring North Pacific Meridional Mode on the Following Winter El Nino and Southern Oscillation. JOURNAL OF CLIMATE, 36, 585-602.
ZHOU, W. & CHAN, J. C. L. 2007. ENSO and the South China Sea summer monsoon onset. INTERNATIONAL JOURNAL OF CLIMATOLOGY, 27, 157-167.
ZHU, Z. & LI, T. 2017. Empirical prediction of the onset dates of South China Sea summer monsoon. CLIMATE DYNAMICS, 48, 1633-1645.
ZOU, Q., DING, R., LI, J., TSENG, Y.-H., HOU, Z., WEN, T. & JI, K. 2020. Is the North Pacific Victoria Mode a Predictor of Winter Rainfall over South China? JOURNAL OF CLIMATE, 33, 8833-8847.

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The Relationship between the South China Sea Summer Monsoon Onset and Pacific Meridional SST anomalies

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Abstract

Figures

1. Introduction

2. Data and methods

2.1 Data

2.2 Definition of the SCSSM onset

2.3 The PMM + index

2.4 Statistical methods

3. Relationship between PMM + and SCSSM onset

4. Environmental factor anomalies

5. Possible mechanisms

6. Summary and discussion

Declarations

Data Availability: All datasets are available publicly in the main text. JRA-55 reanalysis data are available at https://rda.ucar.edu/datasets/ds628.0/index.html#sfol-wl-/data/ds628.0?g=19819. HadlSST data is available at https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html.

References

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