Data and Method
Data from four sets are used in the present study:
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Data on sea level pressure, daily observed rainfall, and yearly onset and withdrawal dates for major regiona were gathered from the Department of Meteorology and Hydrology, Myanmar (DMH), which oversees 79 meteorological stations throughout the country.
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The NCEP/NCAR Reanalysis zonal (u) and meridional (v) wind components of atmospheric isobaric-levels of the troposphere data are used for wind analysis (Kanamitsu et al., 2002).
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The European Centre for Medium-Range Weather Forecasts (ECMWF) uses ERA5 to provide sea level pressure (SLP), moisture flux convergence (MFC), outgoing longwave radiation (OLR) for the years 1991–2020. It is a reanalysis dataset with a 0.25° geographical resolution for the global climate (Hersbach et al., 2020).
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The rainfall data of the CPC Unified Gauge-Based Analysis of Global Daily Precipitation is used (M. Chen et al., 2008; Jiao et al., 2021).
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Similary for sea surface temperature ERA5 evalutes with the Hadley Centre Sea Surface Temperature data set (HadISST) (Selman & Misra, 2014).
1.1.1 MSWM and CPM index
The MSwM region is defined as 10–30 N, 85–110 E (Fig. 2). We investigate seasonal variations in the moisture budget and large-scale atmospheric circulation, as determined by:
Equation 1
MFC = \(\:-{\int\:}_{Surfac\varvec{e}}^{300hPa}\:{\nabla\:}_{p}.\:\left(\varvec{U}q\right)\frac{\varvec{d}\varvec{p}}{\varvec{g}}\) = P – E + \(\:\frac{\partial\:W}{\partial\:t}\)
which we adapted from a prior study on the variability of the Asia Monsoon (Walker et al., 2015), In this context, MFC represents the vertically moisture flux convergence intergrated from the earth surface to the atmosphere's top. The symbol 'U' = (u,v) denotes the wind vector on horizontal, 'q' stands for specific humidity, while 'dp' and 'g' represent pressure differential values and gravity. The term '∇_p.(Uq)' signifies the horizontal flux divergence/convergence at pressure levels. On the other side, 'P' signifies precipitation rate, 'E' represents evaporating values rate, and the change in total precipitable water (W) in the column is depicted by '∂W/∂t'.
There is a strong equilibrium between MFC and TPnet (total net precipitation) (P-E) over the MIC, and there is very little storage change ('∂W/∂t'). As a result, net precipitation outcomes that are correlated with both positive and negative MFC values.
Equation 2
MSwM (CPM) = \(\:\frac{1}{5}\) * (D(U1-U2) + D(P1-P2) + D(MFC) + D(TPnet) + D(OLR))
The cumulative change of the MSwM (CPM) index of onset is defined as follows. First, we calculate the daily climatological mean of MFC, Tpnet, and OLR across the MSwM region for each year during 1991–2020. Additionally, we compute the daily climatological gradient of horizontal pressure and the meridional shear difference of the 850-hPa zonal wind. Specifically, the 850-hPa zonal winds are averaged over two regions: the southern MIC (90E-100E, 10N-15N) denoted as (U1,P1), and the northern MIC (95E-100E, 25N-30N) denoted as (U2,P2). These regions are based on Oo, (2023)’s monsoon circulation index.
We then establish the normalized values for each parameter annually for statistical analysis. The cumulative change value from positive to negative or negative to positive of variables values are count to take for further statistical calculation. The term “D” in Eq. (2) denotes the date when the state shifts of positive or negative (+ to - or - to +) values. Based on this, we determine the average change point date of the P1-P2 to measure the pressure difference, (U1-U2) (∆U) to measure the horizonal wind shear difference, MFC to measure the moisture flux transition, TpNet to measure the net rainfall values, and OLR to measure the convective and cloud situation. The first three consecutive negative or positive days was taken into consideration and verified using cumulative values change point to establish the date of change (Fig. 3). As shown in Table 1, we were able to determine not only the dates of onset or withdrawal, but also the climatology values for every term date. The correlation coefficients are assessed using a student's t-test, and the statistical significance of the correlation is ascertained at the 95% confidence level (Table 1c).
Table 1
(a) MSwM Onset and Withdrawal dates (day of a year) compared to each parameter's MSwM index
| |
∆P
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∆U
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MFC
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TpNet
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OLR
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Mean
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DMH(MEAN)
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Onset
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135
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139
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136
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132
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135
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135 (14-May)
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139 (17-May)
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Withdrawal
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273
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271
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289
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267
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291
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278 (4-Oct)
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276 (1-Oct)
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|
Length
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139
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133
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154
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136
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158
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144
|
138
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Table 1
(b) MSwM indices results of large-scale it onset, withdrawal, and season length.
| |
Mean
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Standard
Deviation
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Maximum
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Minimum
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Onset Date
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135 (14-May)
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5
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153 (1-Jun)
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120 (29-Apr)
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|
Withdrawal Date
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278 (4-OCT)
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13
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306 (1-Nov)
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255 (11-Sep)
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|
Season Length
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144
|
14
|
168
|
117
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Table 1
(c) Correlation values of parameters btween variables
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dP
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U-Wind
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MFC
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OLR
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TP Net
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0.81 (P<0.01)
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0.8.2 (P<0.01)
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0.8.0 (P<0.01)
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- 0.80 (P<0.01)
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1.2 Climatology Outlook
Examining the spatial patterns of the onset and withdrawal back of the MSwM over MIC is interesting, even if MSwM index represents changes in the overall MIC rather than a particular area within its domain. Figure 4 shows these trends with climatological composites that highlight the MSwM index on different days during the monsoon season, by their mean onset dates. The rain belt's trajectory is consistent with the results of previous research (Q. Ding et al., 2011; B. Wang & Ho, 2002), during the first five days of May, monsoon features, particularly rainfall initiates in the Indian Ocean (IO), eastern BoB, Andaman Sea (AS), Myanmar, and Thailand. Monsoon winds and rains are well-established over, the southern coast of MIC, by May 10th. Over the next few weeks, they will gradually move northward into MIC. A distinct region emerges where regional onset dates are well-defined, with less than 7 days standard deviations. In the delta region of Myanmar, regional onset dates by the DMH, closely align with the mean onset date (day 135) (additional details are in the Appendix). Regional onset dates show an increasing delay as one moves northward from the delta region; central and northeastern MIC show regional onset duration up to 15 days after the large-scale onset.
To delve into the spatial pattern of MSwM transitions across the MIC and understand the relationship between regional onset dates and our extensive index, we employ the regression test at each variable and year. Figure 5 illustrates the scatter regression plots for each variable for MSwM onset phases during 1991–2020.
This section presents the data that define the index for the onset and changes at the synoptic and even planetary scales. The former, which in turn causes the start of the MSwM over MIC, appears to be the consequence of the latter. These links between cause and effect, along with some suggested mechanisms for these processes, will be covered in the section that follows.
Spatial rainfall contribution and the regional onset
Even while our index doesn't show changes in a particular region of the monsoon's domain, it is nevertheless interesting to look into the spatial patterns of rain belt travelling onset and widrawal phases of MSwM (Fig. 6). These are displayed by climatological composites in Fig. 6a, which are based on the mean onset (Pentad-27) of the CPC rainfall quantity on different monsoon season days. Prior research has demonstrated that the rain belt spreads similarly (Wang, 2002; Wang, Li, and Lu, 2009). Rainfall reaches the entire southern MIC region on pentad 27, and over the next pentad of rainfall, it gradually moves northward into the entire region, reaching 30°N to the north and 110°E to the east. Additionally, Fig. 6b shows a across pattern of belt in the withdrawal phases. This spatial rainfall contribution well explained the onset of MSwM.
1.3 Variation of MSWM onset dates and Rainfall in May-June
Figure 7 show MSwM onset dates for each individual year, based on the thermodynamical (Fig. 7a) and dynamical (Fig. 7b) index as defined in the previous section. There is no siginificnat trend in dynamic factors for onset determination. However thermodynamic variables of index is slight down trend in last decade, mean the early onset are occurred but not siginificant by statistical test (Fig. 7a). To test the variation of MSwM onster, we prior perforemed the EOF (Empirical Orthogonal Function) analysis on both thermodynamic (MFC) and dynamic (SLP) and generated the Principal Component Analysis (PCA) timseries to test variation with MSwM onset dates and its rainfall by using pearson correlation method (Fig. 8a-d). EOF analysis, is a statistical technique used to identify patterns and variations in complex data sets (Dawson, 2016; Irannezhad et al., 2022). EOF analysis provides a powerful tool for studying variability, offering insights into spatial and temporal patterns, reducing data complexity, and aiding in decision-making processes. The correlation results also show significant moderate correltation among each standardize timeseires (Fig. 8.e). Two EOF of MFC and SLP show siginificant both spatial and temporal patternes over MIC.
Interannual variability of MSwM onset dates are significantly difference, with the ranging between the late and early onset dates exceeding 5 to 15 days (stdev = 7 days) (Fig. 8). Then, we counted +/- 5days as a anomalus onset early or late year over 1991–2020 timseires. We found 1996, 2000, 2001, 2002, 2004, 2007 and 2008 as the 7 earlear onstes years with 6 positive anomalus late years, 1991, 1994, 2005, 2017, 2018 and 2020 during this 30 years study (Fig. 8e).
1.4 Verify with the neighbouring sub-monsoon system
The monsoon season spans from June to September, marked by the highest daily and yearly rainfall. During the boreal summer, rainfall peaks four distinct times. There are four major rainfall zone can be seen clearly over South Asian region by peak daily mean rainfall such as western Indian, eastern Bay of Bengal, northern and western South China Sea region. There also several well-known monsoon indices exist for this South Asia monsoon region, including the India Monsoon Index (IMI), the West North Pacific Monsoon Index (WNPMI), and the Webster and Yang Monsoon Index for Asia (WYI) (Goswami et al., 1999; Wang et al., 2001, 2004; Webster & Yang, 1992)(Fig. 9.a). Additionally, MSWM regions can be defined as sub-regions based on homogeneous rainfall and seasonal wind variation (K. T. Oo, 2022, 2023). A similar time-series pattern and a positive moderate correlation are observed between MSWM and other South Asia monsoon indices in annual variability (Fig. 9b and c).
1.5 Relationship between the dates of the MSwM and the rainfall
The spatial distributions of the correlation coefficients between the May monsoon rainfall and the monsoon onset dates determined by MSwM index are shown in Fig. 10. The significance of the correlation coefficients over the shaded areas is moderate to strong (exceeding 0.3 or less than − 0.3). Significant positive relationships between the MSwM and rainfall are detected throughout the rest of the region, particularly over MIC, but not over the northern Indian subcontinent (NIC) and Yunnan Province, southwest China (SWC) trough to the north of the Philippines (Fig. 10a). Significantly similar patterns to the correlation pattern are also supported by the regression test (Fig. 10b).
Taking into account the fact that ENSO is a very potent interannual signal that affects other areas (Chowdary et al., 2009; Jia et al., 2013; J. Li et al., 2012; Shaw et al., 2018), particularly the Pacific Ocean's east and west coast regions(Pandey et al., 2018; Rohli et al., 2022), but also, to the fluctuation of SST in the Indian Ocean (Sreenivas et al., 2012; Yuan et al., 2014). The relationships between the May rainfall and the winter ENSO index are displayed in Fig. 10c. While there are only weakly significant relationships throughout the northern Mainland Indochina, there are substantial negative correlations throughout the southern MIC and NIC to the entirety of the Philippines. It is evident from comparing Fig. 10a and c that there is a considerable correlation signal between the MIC and SWC. Furthermore, partial correlation is employed to rule out the potential effects of ENSO because there is some covariability between ENSO and the MSwM onset (Mao & Wu, 2007). The regional distributions in Fig. 10d are very closely similar to that in Fig. 10a over MIC except NIC, indicating that the association between the late and early MSwM onset and May rainfall in South Asia is not entirely dependent on the ENSO. Thus we studied the different atmospheric circulation features of late and early monsoon onset phases in next section.
1.6 Atmospheric circulation anomalies affecting rainfall and the possible causes
Composite studies are performed for late and early onset years in order to investigate the circulation anomalies that are responsible for the correlation between the MSwM onset and MIC precipitation. We defied when the anomaly of the MSwM onset date is greater than 0.5 positive standard deviation for late onset or less than 0.5 negative standard deviation for early, respectively. The composite in May for the late and early MSwM years, along with the composite difference with respect to the MSwM index, are displayed in Fig. 11. Anomaly cyclonic (anticyclonic) circulation in the lower troposphere is observed over the northwest of the Arabian Sea (AS), the BoB, and the south China Sea (SCS) when the MSwM happens early (late). In addiational, the results also clearly show the anomalous positioning of low-level westerlies over AS and BoB is siginificantly different in two scenarios, while southwesterlies (northeasterlies) are found over SC (Fig. 11c).
Hence, anomalous cyclonic circulation over the AS to the west of the IC strengthens local westerly components and decreases local northerly components when the MSwM begins sooner. The convergence at lower-tropospheric over the MIC is aided by these southwesterlies anomalies, which leads to localized heavy rainfall. While the anomalous cyclonic motion over the South China Sea runs counter to local climatological winds, it corresponds well with the anomalous cyclonic mothion over the BoB. Heavy rain is experienced over the MIC due to vertical moisture anomaly transport caused by anomalous low-level convergent wind and ascending flows connected to anomalous cyclonic circulation over the BoB and SCS. Additionally, anomalous southeasterlies carry additional water vapor all the way to south-west China. Furthermore, south China has less rainfall due to reduced moisture transport brought about by low-level northeasterlies and descending flow anomalies, and vice versa.
The subtropical highs in the middle troposphere exhibit notable interannual variation of anomalous circulation at the lower troposphere. Owing to variations in intensity for two scenarios, we gauge the subtropical highs using zonal ridgelines and contour lines for 5700 or 5750 gpm. As shown in Fig. 12, there are remarkable differences in extent of monsoon trough (warm toungue) and the two subtropical ridge lines over the Indian and western Pacific ocean for both onset cases. When the MSwM begins early, the trough line extends from Tibet to Sri Lanka, although both ridge ranges collectively get smaller in comparison to the climatology values (Fig. 12a). In contrast, the merditional direction of monsoon tough end in northern BoB with the zonal direction of two ridges shifts eastward (westward) into the southern MIC from the eastern BoB, and the western Pacific during late onset years (Fig. 11b). For earlier onset years (Fig. 12a, b), these modifications are more conducive to anomalous cyclonic circulation over the AS and SCS, which causes descending flows and northeasterlies over SC; for later onset years, the situation is the opposite (Fig. 11c, d).
To address the query, however, what impact do late and early MSwM onsets have on anomalous atmospheric circulation? Composite SST anomaly (SSTA) trends for the late and early start years are displayed in Fig. 13. Significant negative anomalies, or SST being colder than the it climatological mean, are seen in the BoB and tropical Indian ocean (TIO) to the south China sea (SCS) when the MSwM occurs earlier. (Fig. 13a). When the MSwM subsequently occurred, the opposite SSTA (Fig. 13b). Stastical difference tests were run to confirm this alteration, and the results showed a significantly similar SSTA pattern for the two phases at a 95% confidence level. In the tropics, atmospheric heating closely relates to surface steam temperature anomalies (SSTA), and both positive and negative heating cause cyclonic and anticyclonic circulation anomalies along tropical and subtropical (G. T. J. Chen et al., 2008; Hu et al., 2019). Therefore, anomalous cyclonic (anticyclonic) circulation is inconsistent with negative (positive) SSTA between the TIO and the SCS for early (late) MSwM onsets. The findings suggest that simultaneous SSTA, particularly over BoB, is not able to detect the low-level circulation change that occurred in May.
However, the regression distributions of OLR anomalies with the MSwM onset dates are explained in Fig. 14. Convective activity is quite significant in the early start years from the BoB to the SCS and AS, although it is rather moderate in the Indian Ocean in May while the situation is conversely during late onset years (Fig. 9). Since atmospheric heating is what propels air circulation, convective activities have a tight relationship to atmospheric heating and could strengthen it (González et al., 2002; Hung & Yanai, 2004). Theoretical work by Gill, (1980) and Xing et al., (2017) explained how local low-level cyclonic (anticyclonic) circulation is induced by positive (negative) heat sources over the northwest of the heating and equatorial westerlies (easterlies). This suggests that circulation anomalies are influenced by abnormal convective activity linked to the late and early MSwM start. These patterns are also observed across the AS, BoB, and SCS (Fig. 11). Furthermore, for earlier (later) onset years, the anomalous strong (weak) convective activities over the SCS and AS closely match the smaller (greater) area of the ridges and reverent trough patterns over BoB, respectively (Fig. 12).
How does convective process occuered over the AS and SCS, and subsequently local atmospheric circulation over both sides, get influenced by the BoB's convective activity following the onset of the MSwM? The development of vertically integrated MFC and meridionally averaged (10°–30°N) OLR, obtained by Eq. (1), from surface to 100 hPa for the climatological, early, and late MSwM onset phases, is depicted in Fig. 15. Prior to the MSwM commencement, particularly over MIC (85°–110°E), convective activity (less OLR values) steadily increases and MFC gradually intensifies in the climatological mean depicted in Fig. 15a.d. The OLR value drops below the critical value of 240 W m− 2 in the BoB in late April, indicating during early onset phases (Fig. 15.b) while atmospheric heating significantly exceed over the MIC and BoB similarly (Fig. 15.e). However, weak and intermittently patterns are found during late onset years (Fig. 15.c,f). This information suggest the cloudiness and rainfall intensity different of two pahses (Annamalai & Slingo, 2001; Shen et al., 2017).
Figure 16 explained to understanding the different of vertical motion between late and early onst years. Horizontal divergence at the 850 hPa level is a crucial parameter for vertical motion and airflow in the atmosphere (C. W. Newton, 1950; Yadav et al., 2007). Negative 850-hPa wind divergence often occurs in regions where low-pressure systems are forming or intensifying. The siginificant shifting of these divergence activities are forund over between southern Indochina region and MIC for both phases (Fig. 16a,b). The anticyclonic is slight occurred in AS and it connect as a ridge with western Pacific anticyclone during late years. this mechanism has the potential to break the monsoon by extension. as explained in Fig. 12. After separating the two climatologies of the late and early years, three cyclonics are significantly identified (Fig. 12.c).
Similary vertical velocity associated with the late and early onset of the MSwM over the MIC has significant implications for it rainfall in May. These variations in monsoon onset can significantly impact rainfall patterns (Mansouri Daneshvar et al., 2015; Smith & Ummenhofer, 2018). Understanding these dynamics is crucial for managing seasonal monsson pattern. The zoanl and merditional average of vertical velocity from surface to 100 hPa level are exhibited in Fig. 17. Furthermore, the composites in vertical velocity at 850 hPa based on the MSwM index (Fig. 17.a-f) show that, when the MSwM occurs early (later), there is anomalous ascending (descending) flow over study regions, accompanying anomalous descending (ascending) flow over the eastern part of the central MIC and southern China in the lower troposphere 850 hPa. The weak and strong of ascending motion over zonal and meridesional average are siginificantly found in Fig. 17.a-d. However, their spatial distribution map also show siginificant values (Fig. 17e-f).