Seasonal Dependence of Cold Surges and their Interaction with the Madden–Julian Oscillation over Southeast Asia

Prince Xavier Met Office, Exeter, United Kingdom

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See Yee Lim Centre for Climate Research, Singapore

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Muhammad Firdaus Ammar Bin Abdullah Malaysian Meteorological Department, Petaling Jaya, Malaysia

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Michael Bala Philippine Atmospheric, Geophysical and Astronomical Services Administration, Quezon City, Philippines

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Sheeba Nettukandy Chenoli University of Malaya, Kuala Lumpur, Malaysia

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Asteria S. Handayani Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG), Jakarta, Indonesia

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Charline Marzin Met Office, Exeter, United Kingdom

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Donaldi Permana Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG), Jakarta, Indonesia

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Fredolin Tangang Universiti Kebangsaan Malaysia, Bangi Selangor, Malaysia

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Keith D. Williams Met Office, Exeter, United Kingdom

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Diong Jeong Yik Malaysian Meteorological Department, Petaling Jaya, Malaysia

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Abstract

Northeasterly cold surges strongly influence the rainfall patterns over the Malay Peninsula during the northeast monsoon season. This study looks at the changes in the cold surges and Madden–Julian oscillation (MJO) characteristics through the northeast monsoon season and their interaction. Nearly 75% of the cold surge events tend to cross the equator around the Java Sea area (100°–110°E) in February–March with drier conditions prevailing over the Malay Peninsula and increased rainfall over Java. Both the cold surges and the MJO undergo seasonal variations with well-defined regional features. Wavelet analysis shows that MJO amplitude and high-frequency rainfall variations over Southeast Asia peak in November–December. MJO amplitude is suppressed during February and March. This is linked to the high-frequency surges of meridional winds that are prominent during the early part of the season, but February–March is dominated by low-frequency (~20–90 days) cross-equatorial monsoon flow. These prolonged periods of strong meridional flow at the equator interact with the MJO both dynamically and thermodynamically and act as a barrier for convection from propagating from the Indian Ocean to the Maritime Continent (MC). These interactions may have implications for weather and seasonal forecasting over the region. An evaluation of the properties of cold surges and their interaction with the seasonal cycle in the Met Office Unified Model is performed. The atmosphere–ocean coupled model performs better in representing the pattern of influence of the cold surges despite the biases in intensity and spatial distribution of rainfall extremes. These diagnostics are presented with the aim of developing a set of model evaluation metrics for global and regional models.

Denotes content that is immediately available upon publication as open access.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Prince Xavier, prince.xavier@metoffice.gov.uk

Abstract

Northeasterly cold surges strongly influence the rainfall patterns over the Malay Peninsula during the northeast monsoon season. This study looks at the changes in the cold surges and Madden–Julian oscillation (MJO) characteristics through the northeast monsoon season and their interaction. Nearly 75% of the cold surge events tend to cross the equator around the Java Sea area (100°–110°E) in February–March with drier conditions prevailing over the Malay Peninsula and increased rainfall over Java. Both the cold surges and the MJO undergo seasonal variations with well-defined regional features. Wavelet analysis shows that MJO amplitude and high-frequency rainfall variations over Southeast Asia peak in November–December. MJO amplitude is suppressed during February and March. This is linked to the high-frequency surges of meridional winds that are prominent during the early part of the season, but February–March is dominated by low-frequency (~20–90 days) cross-equatorial monsoon flow. These prolonged periods of strong meridional flow at the equator interact with the MJO both dynamically and thermodynamically and act as a barrier for convection from propagating from the Indian Ocean to the Maritime Continent (MC). These interactions may have implications for weather and seasonal forecasting over the region. An evaluation of the properties of cold surges and their interaction with the seasonal cycle in the Met Office Unified Model is performed. The atmosphere–ocean coupled model performs better in representing the pattern of influence of the cold surges despite the biases in intensity and spatial distribution of rainfall extremes. These diagnostics are presented with the aim of developing a set of model evaluation metrics for global and regional models.

Denotes content that is immediately available upon publication as open access.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Prince Xavier, prince.xavier@metoffice.gov.uk

1. Introduction

The boreal winter monsoon over Southeast Asia is a complex phenomenon owing to the influences of processes operating at various temporal and spatial scales. For example, the monsoon rainfall is heavily influenced by El Niño–Southern Oscillation (ENSO, Lau and Nath 2000; Juneng and Tangang 2005), the Madden–Julian oscillation (MJO; Madden and Julian 1971, 1994; Xavier et al. 2014), northeasterly cold surges (Chang et al. 2005; Wu et al. 2007; Lim et al. 2017), the Borneo vortex (Chang et al. 2005; Tangang et al. 2008; Ooi et al. 2011; Chen et al. 2013), and other synoptic systems. The boreal winter monsoon is characterized by the large-scale northeasterly flow (Chan and Li 2004) extending from the Siberian high to the intertropical convergence zone (ITCZ) located south of the equator. Surges of cold air outbreaks are embedded in the boreal winter monsoon flow and when they flow over the warm waters of the South China Sea, they pick up large amounts of moisture. These moist low-level cold wind surges then flow across the islands of the western Maritime Continent, bringing intense rainfall spells that last for a few days with potential for widespread flooding (Lau 1982; Chang et al. 2005; Tangang et al. 2008; Lim et al. 2017).

At intraseasonal time scales (20–90 days), the MJO is the dominant mode of tropical intraseasonal variability and is most active in the boreal winter. The influences of the MJO on the patterns of precipitation in the global tropics and in portions of the extratropics have been documented in several studies (e.g., Jones et al. 2004). Xavier et al. (2014) show that the extreme rainfall over Southeast Asia is modified by the convective phase of the MJO. Hattori et al. (2011) describe the characteristics of cross-equatorial northerly surges and their interactions with the various dominant patterns of variability such as the cold surge pattern, MJO pattern, and cold surge–MJO pattern when they co-occur. They show that the cold surge–MJO pattern is responsible for greatest increase in rainfall over most parts of the western Maritime Continent. This is consistent with the findings of Lim et al. (2017) that when the cold surge and MJO coincide, even though this is rare, it produces stronger rainfall extremes, possibly due to a “vanguard” effect (Peatman et al. 2014) ahead of MJO westerlies, making it favorable for deep convection to trigger with the cold surges. This is despite a dynamical effect of opposing wind structures by which the MJO can inhibit cold surges (Chang et al. 2005; Lim et al. 2017).

Being in a monsoon climate, Southeast Asia undergoes strong seasonal variations in rainfall amounts (Lim et al. 2017), in their spatial and temporal distribution, and in the nature of circulation features. Both the Siberian high and the ITCZ undergo variations with the season and hence the evolution of the rain-bearing processes is also expected to be influenced by some of these variations. Murakami et al. (1986) studied the relationship between the seasonal cycle, low-frequency variations, and the synoptic transient disturbances. They conclude that nonlinear scale interactions may be responsible for the generation and maintenance of low-frequency modes. The synoptic variability also shows modulation with dry and wet phases of the seasonal cycle, thereby influencing the monsoon systems not only over Southeast Asia, but over Australia as well. The onset of wet westerly monsoon flow over northern Australia usually leads to heavy rainfall occurrences 2–5 days after a cold surge event over the South China Sea (McBride 1987; Suppiah and Wu 1996).

Lim et al. (2017) show that the climatological mean rainfall in February and March over the Southeast Asia region is much weaker than the two previous months, despite the presence of cold surges. Within the monsoon season, many parts of Southeast Asia experience severe drought conditions toward the later half of the monsoon season. For example, even though February is generally the drier month of the season, near-zero rainfall in February 2014 (McBride et al. 2015) caused widespread drought conditions over most parts of Indo-China and the Malay Peninsula. This was attributed to a narrower ITCZ during that period causing convecting regions to be restricted to the south of the equator. This long drought caused severe strain on water resources in the region (Ziegler et al. 2014). Therefore, understanding the seasonal and subseasonal variations is valuable for highly vulnerable regions such as Southeast Asia.

This study aims at understanding the processes that determine the wet early monsoon period versus a relatively dry February and March over Southeast Asia by carefully looking at 1) the changes in characteristics of the MJO and the cold surges (which contribute to a large fraction of total seasonal rainfall) within the monsoon season, 2) how they interact with the seasonal cycle and with each other during progression of the season, and 3) how the seasonal changes in the MJO characteristics are related to the cold surges. A basic evaluation of how the Met Office Unified Model (UM) represents these processes is also presented. This study therefore explains some of the large-scale features observed in events like those in February 2014. In the next sections seasonal variations refer to variations within the boreal winter season [November–March (NDJFM)] unless otherwise mentioned.

2. Data and methods

Various datasets and methods are used to define MJO and cold surges. Wind fields are obtained from the ECMWF interim reanalysis (ERA-Interim, herein ERA-Int; Dee et al. 2011). Daily accumulated Tropical Rainfall Measuring Mission (TRMM) 3B42 data provide TRMM-adjusted merged infrared (IR) precipitation and root-mean-square (RMS) precipitation error estimates (Kummerow et al. 2000). Daily mean data at 0.25° × 0.25° spatial resolution have also been used in this study. The SSTs used are obtained from the NOAA daily Optimum Interpolation Sea Surface Temperature (OISST) dataset. It is an analysis constructed by combining observations from different platforms (satellites, ships, buoys) on a regular global grid. A spatially complete SST map is produced by interpolating to fill in the data gaps (Reynolds et al. 2007). The phases of MJO are identified using the real-time multivariate MJO indices (RMMs), which are constructed by using NOAA OLR, and zonal winds at 850 and 200 hPa from NCEP reanalysis data (Wheeler and Hendon 2004). Based on these indices, the MJO can be classified into eight phases; phases 2, 3, and 4 generally have enhanced convective activity over the Maritime Continent region, while phases 6, 7, and 8 produce drier anomalies. This paper considers phases 2–4 as convective MJO phases over the region. The study uses data for the period 1998–2015.

Cold surges are defined based on the method of Lim et al. (2017). They are defined as days when the average northeasterly wind speed over the South China Sea (5°–10°N, 107°–115°E) exceeds a threshold equalling 0.75 standard deviations (equivalent to 2.78 m s−1) above the long-term mean (7.56 m s−1). Lim et al. (2017) also use a mean sea level pressure (MSLP) criterion (MSLP averaged over 105°–122°E, 18°–22°N exceeding 1020 hPa) to refine the definition to eliminate surge events that are not related to the cold air outbreaks from the Siberian high pressure. This is effective in removing events with easterly flow from the Philippines Sea, for example, during a typhoon. The focus of study here is the behavior of cold surges near the equator and the pure northerly cold surges over the northern South China Sea is expected to have less impact on the issues discussed here. Hence the MSLP criterion is not applied here. The cold surges defined in this manner are then checked for equatorial crossing based on a definition similar to that of Hattori et al. (2011): during a cold surge, if the normalized northerly wind over 5°S–5°N, 105°–115°E exceeds one standard deviation (equaling 2 m s−1) then it is categorized as a cross-equatorial surge (CES). Monthly statistics of cold surges and cross-equatorial surges are then computed (Table 1). Data analysis and visualization are done using the NCAR Command Language (Brown et al. 2016) and Iris/Python (Met Office 2010–2013).

Table 1.

Statistics of cold surges and cross-equatorial surges in ERA-Int and in the GA7.0 and GC3.0 simulations for each month in the November–March season. In each set, the first column is the percentage of the number of cold surge days to the total number of days in the season (CS/season), the second column is the same as column 1, but for cross-equatorial surge days (CES/season), and the third column is the percentage of cross-equatorial surge days to the cold surge days (CES/CS). Please note that all numbers are in percentage.

Table 1.

3. Seasonal cycle

Figure 1 presents a background of some of the issues this study is trying to address. Southeast Asia is a region of strong seasonal variations influenced by the reversing wind systems as part of the northeast (southwest) monsoon during boreal winter (summer). It shows the 1998–2015 climatology of daily values of 850-hPa winds and rainfall averaged over 100°–115°E, covering most parts of Indo-China, the Malay Peninsula, Indonesia, and Borneo. This is the region where the influence of cold surges and MJO is the most prominent (Lim et al. 2017). Figure 1 shows wind divergence located around 20°N in October with weak northeast monsoon flow starting where it converges around 5°N. As the season progresses both the divergence region and the near-equatorial convergence region shift southward, coinciding with regions of maximum rainfall. Note that the near-equatorial convergence zone shifts from about 5°N in October to its southernmost position around 10°S in February. The winds over the equator by the end of January and February are predominantly northerly with virtually no zonal component to it. This bears significance as it can impact zonally propagating systems such as the MJO, as will be shown later in this article.

Fig. 1.
Fig. 1.

Daily climatological evolution of 850-hPa winds and rainfall (mm day−1) over 100°–115°E. From October through September is displayed to show the flow patterns of two major monsoon systems. Month labels on the x axis are placed at the beginning of the month. Colors of arrows indicate their speed (m s−1; color bar on the right). The shaded background is the climatological evolution of rainfall (grayscale at the bottom).

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0048.1

How the seasonal cycle in winds and precipitation described above interacts with the most important rain-bearing processes over the region such as cold surges and MJO is an important question. Given that the MJO and the cold surges have different temporal and spatial scales, are there any scale interactions between them modulated by the seasonal cycle? To understand this it is important to describe the large-scale features of northeast monsoon. The spatial distribution of mean seasonal (NDJFM) rainfall and 850-hPa winds is shown in Fig. 2a. A detailed description of the rainfall and wind characteristics during the season is given in Lim et al. (2017). Important climatological features are the strong northeasterly winds over the South China Sea blowing perpendicular to the east coast of Malay Peninsula with enhanced rainfall along its coast (Hai et al. 2017). There are also regions of high rainfall in the eastern Indian Ocean along the west coast of Sumatra and over Java mostly attributed to the contribution from the MJO (Chang et al. 2005; Lim et al. 2017). Enhanced rainfall over the eastern Philippines is also related to the strong low-level wind flow across the islands (Pullen et al. 2015). Another important semipermanent feature over the northern part of Borneo is the cyclonic circulation with strong rainfall increase referred to as the Borneo vortex (Chang et al. 2005; Juneng and Tangang 2010; Chen et al. 2013; Tangang et al. 2008). This study does not explicitly document the direct impact of Borneo vortex, primarily due to the lack of an objective method of identifying and tracking these systems in a climatological dataset. It is beyond the scope of this paper to develop a new tracking method for the Borneo vortex. It is, however, assumed that the impact of the Borneo vortex is contained in the composites of cold surges and the MJO. The role of the Borneo vortex and its interactions with cold surges and MJO warrant a separate study.

Fig. 2.
Fig. 2.

(a) November–March climatological mean rainfall (shading; mm day−1) and 850-hPa winds (vectors; m s−1), and composite anomalies of (b) cold surges (cold surge composites minus NDJFM mean) and (c) cross-equatorial surges based on definitions discussed in section 2.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0048.1

The next sections attempt to understand how the large-scale subseasonal processes (e.g., MJO, cold surges) over the region evolve within the season. A detailed description of the variations of cold surges and MJO within the monsoon season is therefore important.

a. Cold surges

Figures 2b and 2c show the anomalous rainfall and 850-hPa winds compared to the NDJFM mean (Fig. 2a) during the cold surges and the cross-equatorial surges. Hattori et al. (2011) studied the patterns of cross-equatorial surges and their relationship to the precipitation patterns over the Maritime Continent using a definition of meridional winds near the equator exceeding 5 m s−1. A similar definition is used here to identify the cross-equatorial surge events (see section 2). The statistics of cold surges and cross-equatorial surge events in each month during the 18 NDJFM seasons are shown in Table 1. Cold surges are most prominent in December and January with 56.7% and 72.3% of the events categorized as cross-equatorial surges, respectively, by the above definition. The cold surge wind anomalies channel through the South China Sea with a clear dipole structure in rainfall anomalies with an enhanced Borneo vortex and associated rainfall north of Borneo and eastern parts of Malay Peninsula aligned in a southwest to northeast direction. There are suppressed rainfall regions in the eastern parts of Vietnam, the eastern Indian Ocean, and most parts of Sumatra (Figs. 2b,c).

The ocean surface is also impacted by cold surges due to various processes. Pullen et al. (2008) suggest that cold surges represent a robust forcing mechanism for oceanic eddy formation and propagation in the South China Sea in the wake of the Philippines islands. In addition to the cold subtropical waters advected by the cold surges, the relatively dry subtropical air could cool the warm tropical ocean surface in the South China Sea (Figs. 3a,b) through enhanced evaporation as evident from enhanced ocean to atmosphere (taken as negative values) latent heat flux (Figs. 3c,d) and possibly due to the ocean mixing due to strong surface winds (not shown). Enhanced surface evaporation processes can enhance the column moisture over the region, which may contribute to enhanced convection and rainfall.

Fig. 3.
Fig. 3.

(a) Composite anomalies of SST (K; shaded) and MSLP (hPa; contours range from −1 to 4 hPa by steps of 0.2) for cold surges (cold surge composites minus NDJFM mean) and (b) cross-equatorial surges. (c),(d) As in (a) and (b), but for surface latent heat flux (W m−2). Negative values suggest increased ocean to atmosphere latent heat flux compared to climatological values.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0048.1

In the later half of the season, even though there are fewer cold surges, more than 70% of the surges cross the equator (Table 1). The rainfall and wind patterns of cross-equatorial surges (Fig. 2c) show significant differences compared to the cold surges composite (Fig. 2b). The northern part of the dipole structure is much drier (over eastern Vietnam, the entire Malay Peninsula, and Sumatra), while rainfall over Java Sea and northwest of Australia is much enhanced. Even though this shift in rainfall patterns may be governed by the seasonal cycle, it can have significant impact on the water availability over the Indo-China region, the Malay Peninsula, and Indonesia in some years (e.g., 2014) despite the absence of large-scale El Niño conditions (McBride et al. 2015; Ziegler et al. 2014). It may also be noted that the wind anomalies during the cross-equatorial surges are stronger than those during the cold surges and tend to cross the equator over Karimata Strait (the wide strait that connects the South China Sea to the Java Sea). Winds in the Southern Hemisphere are also enhanced and have a stronger anomalous westerly component. SST anomalies are on the order of −1°C during cross-equatorial surges and are clearly enhanced over most of the South China Sea and Java Sea with the Indonesian islands acting as a barrier separating cold waters from the north from mixing with the warm tropical waters in the eastern Indian Ocean (Figs. 3a,b). The surface pressure gradient is also enhanced with the higher pressure north of the equator. The enhanced low-level wind convergence over Java Sea may also promote stronger rainfall anomalies (Fig. 2c).

b. MJO

Numerous studies (e.g., Zhang and Dong 2004; Wu et al. 2006; Hendon and Salby 1994) describe the changes in MJO characteristics between summer and winter seasons. MJO propagation in boreal winter season is predominantly eastward whereas in boreal summer MJO exhibits a more complex behavior, including northward propagation over the Indian and western Pacific Oceans (Lau and Chen 1986; Ferranti et al. 1999) and westward propagation over the western North Pacific (Lau and Chen 1986). These studies, however, have highlighted the large-scale differences in the seasons but the regional changes in the MJO properties are also worth addressing. The regional details of the distribution of MJO-related rainfall during the NDJFM seasonal are shown in Fig. 4. MJO phases 2, 3, and 4 (Wheeler and Hendon 2004) generally enhance the large-scale as well as local-scale rainfall over the Southeast Asia region during the season (Xavier et al. 2014). The rainfall composite for each month when MJO is in any of phase 2, 3, or 4 based on the RMM indices (Wheeler and Hendon 2004) is constructed. Lim et al. (2017) show that the MJO and cold surges can have distinct impacts on the rainfall; however, even though it happens only rarely, when they coincide it can have much larger impact on extreme rainfall than their individual impacts. Therefore to remove the impact of cold surges while constructing MJO composites, days that are classified as cold surges or cross-equatorial surges (section 2) are not considered while computing the MJO composites. Composite anomalies for each month are constructed by removing the NDJFM mean (Fig. 2a).

Fig. 4.
Fig. 4.

Rainfall anomalies (mm day−1) and 850-hPa wind anomalies (m s−1) for MJO phases 2–4 that occur in each of the NDJFM months. Anomalies are computed as the difference from the climatological mean NDJFM rainfall (Fig. 2a). Rainfall on cold surge or cross-equatorial surge days is not included in these composites.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0048.1

Figure 4 shows the rainfall anomalies during MJO phases 2–4 that occur during all of the NDJFM season and for each month separately. As shown by Xavier et al. (2014) and Lim et al. (2017), the impact of MJO on rainfall is predominantly over the area west of Sumatra and over parts of Borneo when the entire season is considered (Fig. 4a). This pattern, however, turns out to be a result of clear seasonal migration of MJO anomalies from the Northern to the Southern Hemisphere from November to March (Figs. 4b–f). Positive rainfall anomalies dominate the rainfall over the west of Sumatra, the Malay Peninsula, east of Vietnam, and most parts of the South China Sea in November, while there are large negative rainfall anomalies over the Java Sea and northwest of Australia (Fig. 4b). This pattern tends to shift southward in December when most of the region is dominated by MJO rainfall anomalies with the negative anomalies to the south getting weaker (Fig. 4c). In January, dry anomalies start to appear east of the Philippines, east of Vietnam, and over Thailand. The wet anomalies shift southward but dominate over the eastern Indian Ocean (Fig. 4d). February and March see large-scale drying over most regions north of the equator (Figs. 4e,f), especially over the Malay Peninsula and north of Borneo and east of the Philippines. The spatial extent of weaker wet anomalies is much smaller during these months.

A caveat here is in the use of large-scale indices such as RMMs (Wheeler and Hendon 2004) for regional-scale rainfall impact studies. For instance, phases 2–4 in their general definition produce enhanced convection over the region. However, in Fig. 4 even though the phases are classified as 2–4, it does not necessarily guarantee uniformly enhanced rainfall anomalies over the whole domain due to the dependence of precipitation on complex terrain features. On the contrary, there are large regions of strongly suppressed rainfall activity during most months that shift with the progression of season. Arguably, the rainfall anomaly patterns of November–December are nearly opposite to those of February–March. The reasons for the lack of uniformity in rainfall anomalies may be tracked down to the definition of RMMs (Wheeler and Hendon 2004), which is based on OLR and on zonal winds at 850 hPa (u850) and 200 hPa (u200). On several occasions the RMMs are dominated by the contribution of wind fields compared to OLR (Straub 2013). Therefore, the composites in Fig. 4 indeed show the regional-scale rainfall variations forced by the global-scale variations of OLR and circulation. The following sections look at the rainfall variations directly rather than relying on the large-scale MJO indices.

4. Seasonal modulation of MJO and cold surges

This section describes the interaction of the three time scales of variability considered in this study, namely the seasonal cycle, the MJO at an intraseasonal scale (20–90 days), and the cold surges at synoptic time scales (~5 days). Wavelet spectra using a Morlet wavelet (Torrence and Compo 1998) of rainfall time series averaged over a northern box (105°–115°E, 0°–10°N) and a southern box (105°–115°E, 10°S–0°) are shown in Fig. 5. The figure shows the 1998–2015 climatological average of daily spectral power of each individual year showing the climatological seasonal evolution of rainfall variability over the regions. Rainfall spectra show clear seasonality in both the high frequency and the MJO power in the 32–64-day period band. In the northern box (Fig. 5a) there is significant power at high frequencies (periods ≤ 8 days) during boreal winter, which is linked to the cold surges that are prominent during the season, as typical cold surge events last around 5 days (Lim et al. 2017). The high-frequency variability reduces by end of January in the northern box but remains strong in the Southern Hemisphere (Fig. 5b). Both intraseasonal and synoptic variations have clear seasonality in the northern box: amplitudes are stronger both in winter and summer, weaken in February, and stay weak until May when the summer monsoon sets in (Fig. 5a). The MJO power in the southern box peaks in December and January and remains higher than that in the northern box in February (Fig. 5b) with just one clear peak in the intraseasonal band. This suggests that the MJO signal remains strong north of the equator during the early part of the boreal winter season and it shifts southward toward the end of the season, which coincides a general weakening of MJO amplitude in the region.

Fig. 5.
Fig. 5.

Morlet wavelet spectra of rainfall time series averaged over (a) a northern box (105°–115°E, 0°–10°N) and (b) a southern box (105°–115°E, 10°S–0°). These spectra are 18-yr averages of daily spectral power (mm2 day−2) showing the climatological seasonal evolution of rainfall variability over the regions. Month labels on the x axis are placed at the beginning of the month.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0048.1

The single spectral peak in the southern box requires further investigation as the ITCZ moves toward this region around this time of the year (Fig. 1) and the mean winds are cross-equatorial. How the seasonal evolution of ITCZ and the cross-equatorial cold surges interact with the MJO is therefore an important part of the puzzle. Cold surges are often considered as synoptic-scale variations, lasting about 4–5 days for each event (Chang et al. 2005; Lim et al. 2017; Wu and Chan 1997). Hattori et al. (2011) suggest that the cross-equatorial surges have an intraseasonal MJO-related background to them as well. To investigate the interaction between cross-equatorial surges and MJO, a similar wavelet power spectra of 850-hPa meridional winds (V850) as Fig. 5 over the equatorial box (105°–115°E, 5°S–5°N) is computed and is shown in Fig. 6. It is interesting to note that even though cold surges are generally considered as synoptic high-frequency wind variability over the South China Sea during November and December, they tend to have low-frequency variations (intraseasonal time scales) in V850 by end of January and February. This suggests that there may be a modulation of cold surges characteristics to lower frequencies with the progression of the season. These seasonal changes to the cold surges characteristics add to our current understanding of the nature of northeasterly winds over the region.

Fig. 6.
Fig. 6.

As in Fig. 5, but for meridional wind at 850 hPa (V850) averaged over 105°–115°E, 5°S–5°N (wavelet power is in m2 s−2).

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0048.1

The wind response to MJO convective heating is generally zonally oriented near the equator both at lower and upper levels (Wheeler and Hendon 2004), but there is a weaker meridional component to it away from the equator (Fig. 4a). This is associated with the anticyclonic circulations straddling the equator in response to the MJO convection in phases 2–4 over this region (Fig. 8 of Wheeler and Hendon 2004) with a southwesterly component of circulation, though weaker, acting against the direction of cold surges (Chang et al. 2005; Xavier et al. 2014). The southwesterly anomalies associated with MJO convection (Fig. 4) are particularly strong in November over the southern South China Sea, but weaken over the following months. In February and March, MJO convection signal is weak and shifted south of the equator, but the wind anomalies remain similar to the mean MJO response (Figs. 4e,f). A larger contribution of wind variance in the construction of RMM would thus identify it as MJO convective phase over the region. The southwesterly wind response to MJO convection over the South China Sea is often cited as an explanation for the mutually exclusive nature of cold surges and MJO convective phases over the region (Chang et al. 2005).

How do the intraseasonal cross-equatorial winds at the equator affect the MJO through its life cycle? To understand this, V850 is bandpass filtered between 20 and 90 days and then the lead–lag composites of rainfall and 850-hPa winds for different time leads before a peak intraseasonal northerly anomaly are computed (Fig. 7). At 12 and 9 days before the peak cross-equatorial surges (referred to as day −12 and day −9, respectively; Figs. 7a,b) MJO anomalies are strong over the eastern Indian Ocean moving across the western MC, with southwesterly wind anomalies. Wind anomalies however become more zonal at −9 days north of Borneo, and they start to get northerly at day −6 (Fig. 7c). This marks the beginning of intraseasonal cross-equatorial surges, which then take over from day −3 as northeasterly over the South China Sea and cross the equator (Figs. 7d–f). The northerly wind anomalies at day −6 push the convergence zones farther to the south of the equator to around 7°S, which is associated with notable changes in patterns of rainfall anomalies. Convection on day −9 shifts from a meridionally symmetric structure (Fig. 7b) to a zonally oriented band around 7°S by day −6 (Fig. 7c), coinciding with the regions of converging wind anomalies. Once the cross-equatorial surges are established (day −3, day 0), the wind patterns associated with cross-equatorial surges are nearly opposite to that of a typical MJO response that is required to sustain the MJO convection and can weaken the zonal convergence zones to the east of existing MJO convection to help its propagation.

Fig. 7.
Fig. 7.

(a)–(f) Composites of 20–90-day filtered rainfall (mm day−1; shading) and 850-hPa wind (m s−1; vector) anomalies at various leads with respect to a strong negative peak in 20–90-day filtered v850 averaged between 105°–115°E, 5°S–5°N [indicated as the box in (e)], indicative of a strong anomalous cross-equatorial surge. Day −12 refers to anomalies 12 days before the peak of cross-equatorial surges, day 0. Day +3 indicates 3 days after the peak cross-equatorial surges.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0048.1

More insight into the processes by which cross-equatorial surges interact with MJO may be found through the surface flux changes through modification of SSTs. Figure 8 shows composites of SST anomalies at lead times with respect to a peak cross-equatorial surge as in Fig. 7. Figures 7 and 8 reveal that the role of cross-equatorial surges over the eastern equatorial Indian Ocean is relatively weak. MJO convection over this region from day −12 to day −9 induces cold SST anomalies due to the reduced surface shortwave radiation reaching the surface, followed by cooling due to increased evaporation associated with enhanced westerly wind stress (Woolnough et al. 2000). This cooling effect continues until about day −3 and to some extent until day 0. This is consistent with the phase lag between convection and SST in the air–sea interaction processes, which is well documented (Woolnough et al. 2000; DeMott et al. 2016; Shinoda et al. 1998). However, processes over the South China Sea are different from what is discussed above. The South China Sea has warmer SST anomalies owing to the northeastward advection of SSTs up to day −6 (Figs. 8a,b) when the cross-equatorial surges sets in. It then cools the SSTs over the South China Sea and Java Sea due to a combination of advection of colder extratropical SSTs from the north/northeast and due to enhanced surface evaporation resulting from drier extratropical air moving over warm tropical oceans (Figs. 8d–f). This SST response is relatively quicker than the cooling due to reduced shortwave radiation at the surface due to convection (Xavier et al. 2008). The Indonesian islands channel the cooler SST farther south through the Java Sea. The amplitudes of SST anomalies generated by the cross-equatorial surges are larger (~0.2°–0.3°C) compared to those to the west of Sumatra, which is influenced largely by MJO convection. In summary, cross-equatorial surges generate 1) a dynamical barrier at low levels over the South China Sea by which the MJO-related equatorial zonal convergence zones and the southwesterly wind response are perturbed by strong northeasterly flow crossing the equator and 2) a thermodynamic barrier due to cooler SSTs as a result of southward wind induced advection from the East China Sea and northern parts of the South China Sea and the enhanced evaporation and upper ocean mixing by the cross-equatorial surges, suppressing MJO convection from developing over the South China Sea and Java Sea. Due to the strong effect of cross-equatorial surges and the seasonal migration of ITCZ, MJO convection preferentially occurs around 10°S with weaker amplitudes and lesser spatial organization during the later half of boreal winter.

Fig. 8.
Fig. 8.

As in Fig. 7, but the shades are SST anomalies (K). Wind anomalies (m s−1) are as in Fig. 7.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0048.1

5. Model evaluation

Some of the insight gained from the analysis of the seasonal cycle and interactions between cold surges and MJO has been extended to model evaluation in this section. There is limited scope in this study for a comprehensive model evaluation and hence some basic statistics are presented. A more detailed study on model evaluation and metrics will be conducted in the future.

The current configuration of the Met Office coupled model is known as the Global Coupled configuration 3.0 (GC3.0) documented by Williams et al. (2018). This comprises the component configurations Global Atmosphere 7.0 (GA7.0), Global Land 7.0 (GL7.0), Global Ocean 6.0 (GO6.0), and Global Sea Ice 8.0 (GSI8.0). GA7.0 and GL7.0 are fully documented by Walters et al. (2019), while GO6.0 is described by Storkey et al. (2018) and GSI8.0 by Ridley et al. (2018). The GA7.0 at low resolution (N96; 135 km in midlatitudes) has been shown to have significant mean dry biases over Southeast Asia and the Maritime Continent region (Rashid and Hirst 2017) as well as deficiencies in MJO propagation (Williams et al. 2018). Increasing the horizontal resolution to N216 (60 km in midlatitudes) reduces the mean biases (Rashid and Hirst 2017) due to enhanced orographic lifting of the moist surface air. Here we are comparing the N216 versions with 80 vertical levels of GA7.0 and GC3.0 with the observations. Twenty years of climate simulations are performed using GA7.0 with SST and sea ice forcing from HadISST (Rayner et al. 2003), while the GC3.0 simulations use fully interactive ocean and sea ice. Table 1 shows the statistics of cold surges and cross-equatorial surges through the NDJFM season. Even though the seasonal cycle of cold surges is captured by both models reasonably well, GA7.0 produces fewer cold surges and cross-equatorial surges in December. Both models tend to generate more cold surges and cross-equatorial surges in February compared to observations.

How the models fare in representing the seasonal mean and rainfall and 850-hPa wind patterns is examined in Fig. 9. The seasonal mean biases in GA7.0 and GC3.0 are shown in Figs. 9a and 9d. The large dry bias in GA7.0 over the ocean points of the eastern Indian Ocean to the west of Sumatra and Java are much reduced in the GC3.0. Similar improvement in GC3.0 is seen over northern Borneo and over the Java Sea. These improvements in the seasonal mean are also reflected in the biases of cold surges and cross-equatorial surges, with GC3.0 producing a much reduced dry bias over most of the domain. An important model bias in both models is the dry bias over land regions, particularly over Peninsular Malaysia, Sumatra, Java, northern Borneo, and the Philippines. GC3.0 targets these dry biases to a large extent, with significant reduction of the biases over most land regions. However, dry land biases continue to dominate over Peninsular Malaysia and a wet bias starts to appear to the south of the Philippines and over Sulawesi and the Celebes Sea during cross-equatorial surges. The 850-hPa wind has a northeasterly bias in GA7.0, which is slightly reduced in GC3.0. The cross-equatorial flow in GC3.0 appears to have shifted to the east (Figs. 9e,f) over to Sulawesi and the Makassar Strait (the strait between the islands of Borneo and Sulawesi in Indonesia).

Fig. 9.
Fig. 9.

GA7.0 and GC3.0 biases (model minus observations) in rainfall (mm day−1) and 850-hPa winds (m s−1) for (a),(d) the entire NDJFM season, (b),(e) cold surge events, and (c),(f) cross-equatorial surge events.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0048.1

Since cold surges and cross-equatorial surges are important contributors of the extreme rainfall over the region (Lim et al. 2017), this section looks at the extremes in precipitation over the region and how the models represent them (Fig. 10). The top row shows the 90th percentile values at each grid point from observations and the models for all days during the NDJFM season. As seen in the mean seasonal bias (Figs. 10a,b) GA7.0 only produces less than half the value of the 90th percentile rainfall, which is much improved in GC3.0, despite the common issue in both models that most of the high rainfall regions are off the coast rather than over the land (e.g., eastern parts of Peninsular Malaysia). The difference in 90th percentile values during cold surges (and cross-equatorial surges) days compared to all days in NDJFM is shown in Figs. 10d and 10g and similar figures for the models are shown in Figs. 10e, 10h, 10f, and 10i. In the observations there is more than 10 mm day−1 increase in the 90th percentile value over a large region over eastern parts of Peninsular Malaysia and north of Borneo during a cold surge. These extreme values shift farther south to the Java Sea and Java during cross-equatorial surges, with a significant reduction in the extreme values over Sumatra and Peninsular Malaysia. Models capture these relative changes between cold surges and cross-equatorial surges even though the 90th percentile values are much smaller than in the observations. GC3.0 does a better job than GA7.0 overall in simulating the mean rainfall and extreme values, as well as their changes due to cold surges and cross-equatorial surges. A notable feature is that the impact of cold surges on the Philippines is a reduction of extreme precipitation over the northeast regions and an increase over the southeast regions (Figs. 10a,d,g). This does not appear to be influenced by whether the event is cold surges or cross-equatorial surges and the models simulate these changes with lesser accuracy than over Indonesia or Malaysia. Some of the diagnostics presented here will be used along with more process-based metrics for detailed model evaluation studies in the future.

Fig. 10.
Fig. 10.

(top) The 90th percentile rainfall values (mm day−1) at each grid point for all days in the NDJFM season, (middle) differences between 90th percentile rainfall only for cold surge events and for all days in the NDJFM season, and (bottom) as in the middle row, but for cross-equatorial surge events, from (a),(d),(g) observations, (b),(e),(h) GA7.0, and (c),(f),(i) GC3.0.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0048.1

6. Summary

Rainfall and circulation over Southeast Asia is driven by a strong seasonal cycle that marks the northeast monsoon season in Boreal winter and southwest monsoon in summer. A large fraction of northeast monsoon rainfall is also controlled by subseasonal processes such as the northeasterly cold surges and the eastward propagating MJO and many other synoptic processes. Satellite-derived rainfall and reanalyzed winds are used to understand the interplay between the seasonal cycle, MJO, and cold surges.

The MJO and cold surges exhibit substantial differences in character during their evolution through the seasonal cycle over the western Maritime Continent region. Notably, the MJO anomalies tend to be located around the equator and north of it during November–December, but weaken and shifts southward with large negative rainfall anomalies in the South China Sea during January–February. This evolution coincides with the seasonal changes in cold surge characteristics. During the early half of the season, the surges bring rainfall to a large part of the Indo-China Peninsula, the Malay Peninsula, and northern Borneo. With the shift of the season, surges become more northerly and cross the equator around Karimata Strait with drier conditions over the above regions and enhanced rainfall over Java Sea and northern Australia.

How the seasonal cycle ties these two large-scale systems is further explored. Wavelet analysis shows that the low-frequency MJO signal weakens significantly by the end of January over regions north of the equator and shifts southward slightly. The two subsequent months show very little MJO activity over the region, which coincides with a strong intraseasonal signal in the meridional component of low-level winds. This suggests that the cold surge winds are predominantly at high-frequency synoptic scales during the early part of the season, but shift to lower frequencies toward the later part of the season. This clear shift in the frequency of meridional flow may be linked to its interaction with the seasonal cycle and the MJO.

The mechanisms of how these low-frequency cross-equatorial flows may suppress MJO convection are explored. Southwesterly wind anomalies over the South China Sea are a feature of the MJO, which, in the absence of cold surges, propagate over the Maritime Continent with a reduced phase speed and amplitude compared to the open ocean. In the presence of the strong cross-equatorial surges, the MJO wind field tends to be reversed by the stronger cold surge wind anomalies and hence suppresses the rainfall anomalies in the northern parts of western Maritime Continent. The cross-equatorial component may also act to shift the convergence zones to the Java Sea with enhanced rainfall there. In addition to this dynamical barrier generated by the cross-equatorial surges, the cold SSTs resulting from the enhanced evaporation, mixing, and advection from midlatitudes by the cold surges can also act as a thermodynamic barrier for the convection to develop in the southern parts of the South China Sea and may produce drought like conditions as happened in 2014.

A basic evaluation of the latest atmospheric and atmosphere–ocean coupled models from the Met Office (U.K.) is also performed. The cold surges and cross-equatorial surges definitions are applied on model fields and metrics are computed to compare the seasonal variations of cold surges and cross-equatorial surges in models with observations. The coupled atmosphere–ocean model does a better job at representing the pattern of impact of both cold surges and cross-equatorial surges despite the persisting dry bias over many land regions. The relative increases in extreme rainfall due to cold surges and cross-equatorial surges for Peninsular Malaysia and Indonesia are captured reasonably well in both models despite the biases in the intensity and spatial distribution of the extremes. The improvements in the coupled model will need to be understood by exploring the processes involving the atmosphere and ocean interaction as well as the impact of different mean state.

The diagnostics developed in this study will benefit in investigating mechanisms of interactions with various time scales in the models and also in understanding the biases in simulating the MJO propagation over the Maritime Continent (Kim et al. 2014). The diagnostics presented here will be developed for investigating these processes in global models with the aim of improving predictions over the Maritime Continent. There are some important processes that are not explicitly considered in this study, for example, the Borneo vortex (Chang et al. 2005; Ooi et al. 2011) and diurnal cycle of convection over the region. Birch et al. (2016) show that the diurnal cycle gets modified with the passage of the MJO primarily through the changes in incoming shortwave radiation and surface heating due to varying cloud cover. The resulting modifications in the land–sea breeze system influence the diurnal cycle of rainfall. How the interactions presented here could impact the diurnal cycle would require further studies. This study also suggests that air–sea interaction may be an important process contributing to the scale interactions and there is need for detailed understanding of these processes for better representation in global and regional models.

Acknowledgments

This work and its contributors (PX, CM, and KDW) were supported by the Met Office Weather and Climate Science for Service Partnership (WCSSP) Southeast Asia as part of the Newton Fund. MJO RMM indices are obtained from http://www.bom.gov.au/climate/mjo/graphics/rmm.74toRealtime.txt F. Tangang was funded by the Universiti Kebangsaan Malaysia Grant DIP-2017-008.

REFERENCES

  • Birch, C., S. Webster, S. Peatman, D. Parker, A. Matthews, Y. Li, and M. Hassim, 2016: Scale interactions between the MJO and the western Maritime Continent. J. Climate, 29, 24712492, https://doi.org/10.1175/JCLI-D-15-0557.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, D., R. Brownrigg, M. Haley, and W. Huang, 2016: The NCAR Command Language (NCL), version 6.3. 0. UCAR/NCAR Computational and Information Systems Laboratory, https://doi.org/10.5065/D6WD3XH5.

    • Crossref
    • Export Citation
  • Chan, J. C. L., and C. Li, 2004: The East Asia winter monsoon. East Asian Monsoon, C.-P. Chang, Ed., World Scientific, 54–106, https://doi.org/10.1142/9789812701411_0002.

    • Crossref
    • Export Citation
  • Chang, C.-P., P. A. Harr, and H.-J. Chen, 2005: Synoptic disturbances over the equatorial South China Sea and western Maritime Continent during boreal winter. Mon. Wea. Rev., 133, 489503, https://doi.org/10.1175/MWR-2868.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, T.-C., J.-D. Tsay, M.-C. Yen, and J. Matsumoto, 2013: The winter rainfall of Malaysia. J. Climate, 26, 936958, https://doi.org/10.1175/JCLI-D-12-00174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 83508373, https://doi.org/10.1002/2016JD025098.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferranti, L., J. M. Slingo, T. N. Palmer, and B. J. Hoskins, 1999: The effect of land-surface feedbacks on the monsoon circulation. Quart. J. Roy. Meteor. Soc., 125, 15271550, https://doi.org/10.1002/qj.49712555704.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hai, O. S., A. A. Samah, S. N. Chenoli, K. Subramaniam, and M. Y. Ahmad Mazuki, 2017: Extreme rainstorms that caused devastating flooding across the east coast of peninsular Malaysia during November and December 2014. Wea. Forecasting, 32, 849872, https://doi.org/10.1175/WAF-D-16-0160.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hattori, M., S. Mori, and J. Matsumoto, 2011: The cross-equatorial northerly surge over the Maritime Continent and its relationship to precipitation patterns. J. Meteor. Soc. Japan, 89A, 2747, https://doi.org/10.2151/JMSJ.2011-A02.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., and M. L. Salby, 1994: The life cycle of the Madden–Julian Oscillation. J. Atmos. Sci., 51, 22252237, https://doi.org/10.1175/1520-0469(1994)051<2225:TLCOTM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, C., D. E. Waliser, K. Lau, and W. Stern, 2004: Global occurrences of extreme precipitation and the Madden–Julian oscillation: Observations and predictability. J. Climate, 17, 45754589, https://doi.org/10.1175/3238.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Juneng, L., and F. T. Tangang, 2005: Evolution of ENSO-related rainfall anomalies in Southeast Asia region and its relationship with atmosphere–ocean variations in Indo-Pacific sector. Climate Dyn., 25, 337350, https://doi.org/10.1007/s00382-005-0031-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Juneng, L., and F. T. Tangang, 2010: Long-term trends of winter monsoon synoptic circulations over the Maritime Continent: 1962–2007. Atmos. Sci. Lett., 11, 199203, https://doi.org/10.1002/asl.272.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, D., and Coauthors, 2014: Process-oriented MJO simulation diagnostic: Moisture sensitivity of simulated convection. J. Climate, 27, 53795395, https://doi.org/10.1175/JCLI-D-13-00497.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C., and Coauthors, 2000: The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J. Appl. Meteor., 39, 19651982, https://doi.org/10.1175/1520-0450(2001)040<1965:TSOTTR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., 1982: Equatorial response to northeasterly cold surges as inferred from satellite cloud imagery. Mon. Wea. Rev., 110, 13061313, https://doi.org/10.1175/1520-0493(1982)110<1306:ERTNCS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., and P. H. Chen, 1986: Aspects of 40–50 day oscillation during the northern summer as inferred from outgoing longwave radiation. Mon. Wea. Rev., 114, 13541367, https://doi.org/10.1175/1520-0493(1986)114<1354:AOTDOD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, N.-C., and M. J. Nath, 2000: Impact of ENSO on the variability of the Asian-Australian monsoons as simulated in GCM experiments. J. Climate, 13, 42874309, https://doi.org/10.1175/1520-0442(2000)013<4287:IOEOTV>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lim, S. Y., C. Marzin, P. Xavier, C.-P. Chang, and B. Timbal, 2017: Impacts of boreal winter monsoon cold surges and the interaction with MJO on Southeast Asia rainfall. J. Climate, 30, 42674281, https://doi.org/10.1175/JCLI-D-16-0546.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 702708, https://doi.org/10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madden, R. A., and P. R. Julian, 1994: Observations of the 40–50-day tropical oscillation—A review. Mon. Wea. Rev., 122, 814837, https://doi.org/10.1175/1520-0493(1994)122<0814:OOTDTO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McBride, J. L., 1987: The Australian summer monsoon. Monsoon Meteorology, C.-P. Chang and T. N. Krishnamurti, Eds., Oxford University Press, 203–231.

  • McBride, J. L., S. Sahany, M. E. Hassim, C. M. Nguyen, S.-Y. Lim, R. Rahmat, and W.-K. Cheong, 2015: The 2014 record dry spell at Singapore: An intertropical convergence zone (ITCZ) drought [in “Explaining Extremes of 2014 from a Climate Perspective”]. Bull. Amer. Meteor. Soc., 96 (12), S126S130, https://doi.org/10.1175/BAMS-D-15-00117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Met Office, 2010–2013: Iris: A Python library for analysing and visualising meteorological and oceanographic data sets, v1.2, https://scitools.org.uk/iris/docs/latest/.

  • Murakami, T., L.-X. Chen, and A. Xie, 1986: Relationship among seasonal cycles, low-frequency oscillations, and transient disturbances as revealed from outgoing longwave radiation data. Mon. Wea. Rev., 114, 14561465, https://doi.org/10.1175/1520-0493(1986)114<1456:RASCLF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ooi, S. H., A. A. Samah, and P. Braesicke, 2011: A case study of the Borneo vortex genesis and its interactions with the global circulation. J. Geophys. Res., 116, D21116, https://doi.org/10.1029/2011JD015991.

    • Search Google Scholar
    • Export Citation
  • Peatman, S. C., A. J. Matthews, and D. P. Stevens, 2014: Propagation of the Madden–Julian oscillation through the Maritime Continent and scale interaction with the diurnal cycle of precipitation. Quart. J. Roy. Meteor. Soc., 140, 814825, https://doi.org/10.1002/qj.2161.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pullen, J., J. D. Doyle, P. May, C. Chavanne, P. Flament, and R. A. Arnone, 2008: Monsoon surges trigger oceanic eddy formation and propagation in the lee of the Philippine Islands. Geophys. Res. Lett., 35, L07604, https://doi.org/10.1029/2007GL033109.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pullen, J., A. L. Gordon, M. Flatau, J. D. Doyle, C. Villanoy, and O. Cabrera, 2015: Multiscale influences on extreme winter rainfall in the Philippines. J. Geophys. Res. Atmos., 120, 32923309, https://doi.org/10.1002/2014JD022645.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rashid, H. A., and A. C. Hirst, 2017: Mechanisms of improved rainfall simulation over the Maritime Continent due to increased horizontal resolution in an AGCM. Climate Dyn., 49, 17471764, https://doi.org/10.1007/s00382-016-3413-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N., D. E. Parker, E. Horton, C. K. Folland, L. V. Alexander, D. Rowell, E. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 54735496, https://doi.org/10.1175/2007JCLI1824.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ridley, J. K., E. W. Blockley, A. B. Keen, J. G. L. Rae, A. E. West, and D. Schroeder, 2018: The sea ice model component of HadGEM3-GC3.1. Geosci. Model Dev., 11, 713723, https://doi.org/10.5194/gmd-11-713-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shinoda, T., H. H. Hendon, and J. Glick, 1998: Intraseasonal variability of surface fluxes and sea surface temperature in the tropical western Pacific and Indian Oceans. J. Climate, 11, 16851702, https://doi.org/10.1175/1520-0442(1998)011<1685:IVOSFA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Storkey, D., and Coauthors, 2018: UK Global Ocean GO6 and GO7: A traceable hierarchy of model resolutions. Geosci. Model Dev., 11, 31873213, https://doi.org/10.5194/gmd-11-3187-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Straub, K. H., 2013: MJO initiation in the real-time multivariate MJO index. J. Climate, 26, 11301151, https://doi.org/10.1175/JCLI-D-12-00074.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Suppiah, R., and X. Wu, 1996: Surges, cross-equatorial flows and their links with the Australian summer monsoon circulation and rainfall. Proc. Second Australian Conf. on Agricultural Meteorology: The Impact of Weather and Climate on Agriculture, Brisbane, Australia, University of Queensland, 146–150.

  • Tangang, F. T., L. Juneng, E. Salimun, P. Vinayachandran, Y. K. Seng, C. Reason, S. K. Behera, and T. Yasunari, 2008: On the roles of the northeast cold surge, the Borneo vortex, the Madden–Julian Oscillation, and the Indian Ocean Dipole during the extreme 2006/2007 flood in southern Peninsular Malaysia. Geophys. Res. Lett., 35, L14S07, https://doi.org/10.1029/2008GL033429.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torrence, C., and G. P. Compo, 1998: A practical guide to wavelet analysis. Bull. Amer. Meteor. Soc., 79, 6178, https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walters, D., and Coauthors, 2019: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations. Geosci. Model Dev., 12, 19091963, https://doi.org/10.5194/gmd-12-1909-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wheeler, M. C., and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 19171932, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, K., and Coauthors, 2018: The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) configurations. J. Adv. Model. Earth Syst., 10, 357380, https://doi.org/10.1002/2017MS001115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woolnough, S. J., J. M. Slingo, and B. J. Hoskins, 2000: The relationship between convection and sea surface temperature on intraseasonal timescales. J. Climate, 13, 20862104, https://doi.org/10.1175/1520-0442(2000)013<2086:TRBCAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, M., and J. C. Chan, 1997: Upper-level features associated with winter monsoon surges over South China. Mon. Wea. Rev., 125, 317340, https://doi.org/10.1175/1520-0493(1997)125<0317:ULFAWW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, M.-L. C., S. D. Schubert, M. J. Suarez, P. J. Pegion, and D. E. Waliser, 2006: Seasonality and meridional propagation of the MJO. J. Climate, 19, 19011921, https://doi.org/10.1175/JCLI3680.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, P., M. Hara, H. Fudeyasu, M. D. Yamanaka, J. Matsumoto, F. Syamsudin, R. Sulistyowati, and Y. S. Djajadihardja, 2007: The impact of trans-equatorial monsoon flow on the formation of repeated torrential rains over Java Island. SOLA, 3, 9396, https://doi.org/10.2151/SOLA.2007-024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xavier, P. K., J.-P. Duvel, and F. J. Doblas-Reyes, 2008: Boreal summer intraseasonal variability in coupled seasonal hindcasts. J. Climate, 21, 44774497, https://doi.org/10.1175/2008JCLI2216.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xavier, P. K., R. Rahmat, W. K. Cheong, and E. Wallace, 2014: Influence of Madden–Julian Oscillation on Southeast Asia rainfall extremes: Observations and predictability. Geophys. Res. Lett., 41, 44064412, https://doi.org/10.1002/2014GL060241.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., and M. Dong, 2004: Seasonality in the Madden–Julian oscillation. J. Climate, 17, 31693180, https://doi.org/10.1175/1520-0442(2004)017<3169:SITMO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ziegler, A. D., J. P. Terry, G. J. Oliver, D. A. Friess, C. J. Chuah, W. T. Chow, and R. J. Wasson, 2014: Increasing Singapore’s resilience to drought. Hydrol. Processes, 28, 45434548, https://doi.org/10.1002/hyp.10212.

    • Crossref
    • Search Google Scholar
    • Export Citation
Save
  • Birch, C., S. Webster, S. Peatman, D. Parker, A. Matthews, Y. Li, and M. Hassim, 2016: Scale interactions between the MJO and the western Maritime Continent. J. Climate, 29, 24712492, https://doi.org/10.1175/JCLI-D-15-0557.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, D., R. Brownrigg, M. Haley, and W. Huang, 2016: The NCAR Command Language (NCL), version 6.3. 0. UCAR/NCAR Computational and Information Systems Laboratory, https://doi.org/10.5065/D6WD3XH5.

    • Crossref
    • Export Citation
  • Chan, J. C. L., and C. Li, 2004: The East Asia winter monsoon. East Asian Monsoon, C.-P. Chang, Ed., World Scientific, 54–106, https://doi.org/10.1142/9789812701411_0002.

    • Crossref
    • Export Citation
  • Chang, C.-P., P. A. Harr, and H.-J. Chen, 2005: Synoptic disturbances over the equatorial South China Sea and western Maritime Continent during boreal winter. Mon. Wea. Rev., 133, 489503, https://doi.org/10.1175/MWR-2868.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, T.-C., J.-D. Tsay, M.-C. Yen, and J. Matsumoto, 2013: The winter rainfall of Malaysia. J. Climate, 26, 936958, https://doi.org/10.1175/JCLI-D-12-00174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 83508373, https://doi.org/10.1002/2016JD025098.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferranti, L., J. M. Slingo, T. N. Palmer, and B. J. Hoskins, 1999: The effect of land-surface feedbacks on the monsoon circulation. Quart. J. Roy. Meteor. Soc., 125, 15271550, https://doi.org/10.1002/qj.49712555704.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hai, O. S., A. A. Samah, S. N. Chenoli, K. Subramaniam, and M. Y. Ahmad Mazuki, 2017: Extreme rainstorms that caused devastating flooding across the east coast of peninsular Malaysia during November and December 2014. Wea. Forecasting, 32, 849872, https://doi.org/10.1175/WAF-D-16-0160.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hattori, M., S. Mori, and J. Matsumoto, 2011: The cross-equatorial northerly surge over the Maritime Continent and its relationship to precipitation patterns. J. Meteor. Soc. Japan, 89A, 2747, https://doi.org/10.2151/JMSJ.2011-A02.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., and M. L. Salby, 1994: The life cycle of the Madden–Julian Oscillation. J. Atmos. Sci., 51, 22252237, https://doi.org/10.1175/1520-0469(1994)051<2225:TLCOTM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, C., D. E. Waliser, K. Lau, and W. Stern, 2004: Global occurrences of extreme precipitation and the Madden–Julian oscillation: Observations and predictability. J. Climate, 17, 45754589, https://doi.org/10.1175/3238.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Juneng, L., and F. T. Tangang, 2005: Evolution of ENSO-related rainfall anomalies in Southeast Asia region and its relationship with atmosphere–ocean variations in Indo-Pacific sector. Climate Dyn., 25, 337350, https://doi.org/10.1007/s00382-005-0031-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Juneng, L., and F. T. Tangang, 2010: Long-term trends of winter monsoon synoptic circulations over the Maritime Continent: 1962–2007. Atmos. Sci. Lett., 11, 199203, https://doi.org/10.1002/asl.272.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, D., and Coauthors, 2014: Process-oriented MJO simulation diagnostic: Moisture sensitivity of simulated convection. J. Climate, 27, 53795395, https://doi.org/10.1175/JCLI-D-13-00497.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C., and Coauthors, 2000: The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J. Appl. Meteor., 39, 19651982, https://doi.org/10.1175/1520-0450(2001)040<1965:TSOTTR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., 1982: Equatorial response to northeasterly cold surges as inferred from satellite cloud imagery. Mon. Wea. Rev., 110, 13061313, https://doi.org/10.1175/1520-0493(1982)110<1306:ERTNCS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., and P. H. Chen, 1986: Aspects of 40–50 day oscillation during the northern summer as inferred from outgoing longwave radiation. Mon. Wea. Rev., 114, 13541367, https://doi.org/10.1175/1520-0493(1986)114<1354:AOTDOD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, N.-C., and M. J. Nath, 2000: Impact of ENSO on the variability of the Asian-Australian monsoons as simulated in GCM experiments. J. Climate, 13, 42874309, https://doi.org/10.1175/1520-0442(2000)013<4287:IOEOTV>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lim, S. Y., C. Marzin, P. Xavier, C.-P. Chang, and B. Timbal, 2017: Impacts of boreal winter monsoon cold surges and the interaction with MJO on Southeast Asia rainfall. J. Climate, 30, 42674281, https://doi.org/10.1175/JCLI-D-16-0546.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 702708, https://doi.org/10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madden, R. A., and P. R. Julian, 1994: Observations of the 40–50-day tropical oscillation—A review. Mon. Wea. Rev., 122, 814837, https://doi.org/10.1175/1520-0493(1994)122<0814:OOTDTO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McBride, J. L., 1987: The Australian summer monsoon. Monsoon Meteorology, C.-P. Chang and T. N. Krishnamurti, Eds., Oxford University Press, 203–231.

  • McBride, J. L., S. Sahany, M. E. Hassim, C. M. Nguyen, S.-Y. Lim, R. Rahmat, and W.-K. Cheong, 2015: The 2014 record dry spell at Singapore: An intertropical convergence zone (ITCZ) drought [in “Explaining Extremes of 2014 from a Climate Perspective”]. Bull. Amer. Meteor. Soc., 96 (12), S126S130, https://doi.org/10.1175/BAMS-D-15-00117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Met Office, 2010–2013: Iris: A Python library for analysing and visualising meteorological and oceanographic data sets, v1.2, https://scitools.org.uk/iris/docs/latest/.

  • Murakami, T., L.-X. Chen, and A. Xie, 1986: Relationship among seasonal cycles, low-frequency oscillations, and transient disturbances as revealed from outgoing longwave radiation data. Mon. Wea. Rev., 114, 14561465, https://doi.org/10.1175/1520-0493(1986)114<1456:RASCLF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ooi, S. H., A. A. Samah, and P. Braesicke, 2011: A case study of the Borneo vortex genesis and its interactions with the global circulation. J. Geophys. Res., 116, D21116, https://doi.org/10.1029/2011JD015991.

    • Search Google Scholar
    • Export Citation
  • Peatman, S. C., A. J. Matthews, and D. P. Stevens, 2014: Propagation of the Madden–Julian oscillation through the Maritime Continent and scale interaction with the diurnal cycle of precipitation. Quart. J. Roy. Meteor. Soc., 140, 814825, https://doi.org/10.1002/qj.2161.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pullen, J., J. D. Doyle, P. May, C. Chavanne, P. Flament, and R. A. Arnone, 2008: Monsoon surges trigger oceanic eddy formation and propagation in the lee of the Philippine Islands. Geophys. Res. Lett., 35, L07604, https://doi.org/10.1029/2007GL033109.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pullen, J., A. L. Gordon, M. Flatau, J. D. Doyle, C. Villanoy, and O. Cabrera, 2015: Multiscale influences on extreme winter rainfall in the Philippines. J. Geophys. Res. Atmos., 120, 32923309, https://doi.org/10.1002/2014JD022645.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rashid, H. A., and A. C. Hirst, 2017: Mechanisms of improved rainfall simulation over the Maritime Continent due to increased horizontal resolution in an AGCM. Climate Dyn., 49, 17471764, https://doi.org/10.1007/s00382-016-3413-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N., D. E. Parker, E. Horton, C. K. Folland, L. V. Alexander, D. Rowell, E. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 54735496, https://doi.org/10.1175/2007JCLI1824.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ridley, J. K., E. W. Blockley, A. B. Keen, J. G. L. Rae, A. E. West, and D. Schroeder, 2018: The sea ice model component of HadGEM3-GC3.1. Geosci. Model Dev., 11, 713723, https://doi.org/10.5194/gmd-11-713-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shinoda, T., H. H. Hendon, and J. Glick, 1998: Intraseasonal variability of surface fluxes and sea surface temperature in the tropical western Pacific and Indian Oceans. J. Climate, 11, 16851702, https://doi.org/10.1175/1520-0442(1998)011<1685:IVOSFA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Storkey, D., and Coauthors, 2018: UK Global Ocean GO6 and GO7: A traceable hierarchy of model resolutions. Geosci. Model Dev., 11, 31873213, https://doi.org/10.5194/gmd-11-3187-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Straub, K. H., 2013: MJO initiation in the real-time multivariate MJO index. J. Climate, 26, 11301151, https://doi.org/10.1175/JCLI-D-12-00074.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Suppiah, R., and X. Wu, 1996: Surges, cross-equatorial flows and their links with the Australian summer monsoon circulation and rainfall. Proc. Second Australian Conf. on Agricultural Meteorology: The Impact of Weather and Climate on Agriculture, Brisbane, Australia, University of Queensland, 146–150.

  • Tangang, F. T., L. Juneng, E. Salimun, P. Vinayachandran, Y. K. Seng, C. Reason, S. K. Behera, and T. Yasunari, 2008: On the roles of the northeast cold surge, the Borneo vortex, the Madden–Julian Oscillation, and the Indian Ocean Dipole during the extreme 2006/2007 flood in southern Peninsular Malaysia. Geophys. Res. Lett., 35, L14S07, https://doi.org/10.1029/2008GL033429.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torrence, C., and G. P. Compo, 1998: A practical guide to wavelet analysis. Bull. Amer. Meteor. Soc., 79, 6178, https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walters, D., and Coauthors, 2019: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations. Geosci. Model Dev., 12, 19091963, https://doi.org/10.5194/gmd-12-1909-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wheeler, M. C., and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 19171932, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, K., and Coauthors, 2018: The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) configurations. J. Adv. Model. Earth Syst., 10, 357380, https://doi.org/10.1002/2017MS001115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woolnough, S. J., J. M. Slingo, and B. J. Hoskins, 2000: The relationship between convection and sea surface temperature on intraseasonal timescales. J. Climate, 13, 20862104, https://doi.org/10.1175/1520-0442(2000)013<2086:TRBCAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, M., and J. C. Chan, 1997: Upper-level features associated with winter monsoon surges over South China. Mon. Wea. Rev., 125, 317340, https://doi.org/10.1175/1520-0493(1997)125<0317:ULFAWW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, M.-L. C., S. D. Schubert, M. J. Suarez, P. J. Pegion, and D. E. Waliser, 2006: Seasonality and meridional propagation of the MJO. J. Climate, 19, 19011921, https://doi.org/10.1175/JCLI3680.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, P., M. Hara, H. Fudeyasu, M. D. Yamanaka, J. Matsumoto, F. Syamsudin, R. Sulistyowati, and Y. S. Djajadihardja, 2007: The impact of trans-equatorial monsoon flow on the formation of repeated torrential rains over Java Island. SOLA, 3, 9396, https://doi.org/10.2151/SOLA.2007-024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xavier, P. K., J.-P. Duvel, and F. J. Doblas-Reyes, 2008: Boreal summer intraseasonal variability in coupled seasonal hindcasts. J. Climate, 21, 44774497, https://doi.org/10.1175/2008JCLI2216.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xavier, P. K., R. Rahmat, W. K. Cheong, and E. Wallace, 2014: Influence of Madden–Julian Oscillation on Southeast Asia rainfall extremes: Observations and predictability. Geophys. Res. Lett., 41, 44064412, https://doi.org/10.1002/2014GL060241.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., and M. Dong, 2004: Seasonality in the Madden–Julian oscillation. J. Climate, 17, 31693180, https://doi.org/10.1175/1520-0442(2004)017<3169:SITMO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ziegler, A. D., J. P. Terry, G. J. Oliver, D. A. Friess, C. J. Chuah, W. T. Chow, and R. J. Wasson, 2014: Increasing Singapore’s resilience to drought. Hydrol. Processes, 28, 45434548, https://doi.org/10.1002/hyp.10212.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    Daily climatological evolution of 850-hPa winds and rainfall (mm day−1) over 100°–115°E. From October through September is displayed to show the flow patterns of two major monsoon systems. Month labels on the x axis are placed at the beginning of the month. Colors of arrows indicate their speed (m s−1; color bar on the right). The shaded background is the climatological evolution of rainfall (grayscale at the bottom).

  • Fig. 2.

    (a) November–March climatological mean rainfall (shading; mm day−1) and 850-hPa winds (vectors; m s−1), and composite anomalies of (b) cold surges (cold surge composites minus NDJFM mean) and (c) cross-equatorial surges based on definitions discussed in section 2.

  • Fig. 3.

    (a) Composite anomalies of SST (K; shaded) and MSLP (hPa; contours range from −1 to 4 hPa by steps of 0.2) for cold surges (cold surge composites minus NDJFM mean) and (b) cross-equatorial surges. (c),(d) As in (a) and (b), but for surface latent heat flux (W m−2). Negative values suggest increased ocean to atmosphere latent heat flux compared to climatological values.

  • Fig. 4.

    Rainfall anomalies (mm day−1) and 850-hPa wind anomalies (m s−1) for MJO phases 2–4 that occur in each of the NDJFM months. Anomalies are computed as the difference from the climatological mean NDJFM rainfall (Fig. 2a). Rainfall on cold surge or cross-equatorial surge days is not included in these composites.

  • Fig. 5.

    Morlet wavelet spectra of rainfall time series averaged over (a) a northern box (105°–115°E, 0°–10°N) and (b) a southern box (105°–115°E, 10°S–0°). These spectra are 18-yr averages of daily spectral power (mm2 day−2) showing the climatological seasonal evolution of rainfall variability over the regions. Month labels on the x axis are placed at the beginning of the month.

  • Fig. 6.

    As in Fig. 5, but for meridional wind at 850 hPa (V850) averaged over 105°–115°E, 5°S–5°N (wavelet power is in m2 s−2).

  • Fig. 7.

    (a)–(f) Composites of 20–90-day filtered rainfall (mm day−1; shading) and 850-hPa wind (m s−1; vector) anomalies at various leads with respect to a strong negative peak in 20–90-day filtered v850 averaged between 105°–115°E, 5°S–5°N [indicated as the box in (e)], indicative of a strong anomalous cross-equatorial surge. Day −12 refers to anomalies 12 days before the peak of cross-equatorial surges, day 0. Day +3 indicates 3 days after the peak cross-equatorial surges.

  • Fig. 8.

    As in Fig. 7, but the shades are SST anomalies (K). Wind anomalies (m s−1) are as in Fig. 7.

  • Fig. 9.

    GA7.0 and GC3.0 biases (model minus observations) in rainfall (mm day−1) and 850-hPa winds (m s−1) for (a),(d) the entire NDJFM season, (b),(e) cold surge events, and (c),(f) cross-equatorial surge events.

  • Fig. 10.

    (top) The 90th percentile rainfall values (mm day−1) at each grid point for all days in the NDJFM season, (middle) differences between 90th percentile rainfall only for cold surge events and for all days in the NDJFM season, and (bottom) as in the middle row, but for cross-equatorial surge events, from (a),(d),(g) observations, (b),(e),(h) GA7.0, and (c),(f),(i) GC3.0.