Weather types and rainfall variability during the Northeast Monsoon over Malaysia

Xia Yan New; Liew Juneng; Fredolin Tangang

doi:10.1371/journal.pclm.0000847

Abstract

Malaysia frequently experiences extreme rainfall throughout the Northeast Monsoon season. However, the connection between extreme rainfall and distinct monsoonal synoptic circulations remains to be fully investigated. This study aims to identify the dominant synoptic circulation patterns and the associated extreme precipitation using weather type classification method. K-means algorithm was employed to classify daily weather types (WTs) over Malaysia region (3°S–10°N, 98°–122°E) during the Northeast Monsoon season, which occurs from November to February. The classification was based on 850-hPa wind data obtained from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset from 1981 to 2020. Four distinct WTs were identified, and further examined their circulation pattern, frequency of occurrence, typical progression and persistence, and associated rainfall characteristics. Among the identified synoptic circulation patterns, the Borneo Vortex, cold surge, and cross-equatorial surge were prominent within the resultant WTs. Over the past 40 years, the co-occurrence of the Borneo Vortex and cold surges has shown a significant decreasing trend, while the other three patterns, including the Borneo Vortex occurring over the South China Sea, weak cold surges, and cross-equatorial surges have exhibited increasing trends. The cold surges contribute to increased rainfall in western Borneo, particularly in the Sarawak region. The occurrence of the Borneo Vortex leads to increased rainfall in eastern Borneo, while cross-equatorial surges are associated with enhanced rainfall in northeastern Borneo. Lastly, this study looks into how El Niño–Southern Oscillation (ENSO) modulates the occurrence of each of the four WTs. Borneo Vortex events occur more frequently during La Niña years compared to El Niño years. As Malaysia continues to face the challenges of climate change, this study helps in developing strategies to manage the risks related to extreme events, helping communities and industries adapt and sustain resilience.

Citation: New XY, Juneng L, Tangang F (2026) Weather types and rainfall variability during the Northeast Monsoon over Malaysia. PLOS Clim 5(6): e0000847. https://doi.org/10.1371/journal.pclm.0000847

Editor: Lin Liu, First Institute of Oceanography Ministry of Natural Resources, CHINA

Received: January 30, 2026; Accepted: April 24, 2026; Published: June 29, 2026

Copyright: © 2026 New et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data and code to reproduce figures are available at https://github.com/ljuneng/New_et_al.

Funding: This work was funded by the Malaysian Ministry of Higher Education’s LRGS Grant (LRGS/1/2020/UKM-UKM/01/6/1 to LJ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Malaysia, which is located west of the Maritime Continent, experiences a unique climate that is influenced by significant seasonal variations throughout the year [1], as well as interannual variations [2–4] and intraseasonal variations [5]. Two distinct monsoon regimes dominate Malaysia’s climate: the boreal winter monsoon, known locally as the Northeast Monsoon, is typically associated with wetter conditions, whereas the boreal summer monsoon, known locally as the Southwest Monsoon, is comparatively drier. The period of transition between these two monsoons is referred to as the inter-monsoon phase [6].

The Northeast Monsoon has a significant impact on Malaysia's climate, primarily by triggering convection via interactions with the local sea and land breeze circulation and through the orographic lifting effect [7]. This impact typically dominates from late November to February [6,8–10], and largely affect areas that face the South China Sea, such as Peninsular Malaysia's eastern coast. However, Peninsular Malaysia's west coast can also be affected [11]. During this period, Peninsular Malaysia's east coast experiences a significant increase in rainfall, with approximately 50% of the annual precipitation occurring at the beginning of the Northeast Monsoon in November and December. In years characterized by active monsoon conditions, this contribution can rise dramatically, with up to 70% of the annual rainfall concentrated within these months [12]. This intensified monsoon rainfall has led to widespread flooding, resulting in severe mortality, displacement of communities, and damage to infrastructure. Looking ahead, the situation is expected to become even more challenging, as projections indicate that extreme monsoon rainfall will increase in both frequency and intensity, resulting from the ongoing impacts of climate change [13–15].

On average, the low-level northeasterly winds during November to February can abruptly intensify into episodes of strong and persistent winds about five to six times a year. This intensification is caused by the strengthening of the Siberian High-pressure system. This results in a phenomenon called ‘cold surge’, which is one of the most energetic monsoonal circulation systems [16]. Another prominent circulation feature during the Northeast Monsoon season is the Borneo Vortex [17]. These are the two primary features that dominate low-level circulation patterns over Malaysia on synoptic time scales [5,10,16–18]. Cold surges are pulses of strong northeasterly winds that propagate across the South China Sea toward Peninsular Malaysia, which is triggered by a strong pressure gradient between the Siberian High and the lower pressures near the South China Sea. In addition, the cold surge also enhances the near-surface northeasterly winds that rapidly progress southward, with Malaysia’s topography acting as a barrier that channels the flow equatorward. These winds begin as dry flows but gain moisture while traveling across the South China Sea, becoming more humid by the time they reach Malaysia [9,16]. The Borneo Vortex is an anti-clockwise mesoscale circulation over Borneo and its surrounding regions. It is usually formed through the interaction between shear vorticity induced by northeasterly winds over the South China Sea and the relatively weaker winds along the western coast of Borneo [7,17,19]. This feature has a significant influence on moisture recirculation, and is frequently related to intense latent heat release and deep convection [16].

Both of these features are known as the primary drivers of severe weather events near the South China Sea region [10,16]. From December 2006 to late January 2007, the cold surge phenomenon caused one of the century's worst floods near southern Peninsular Malaysia, affecting more than 200,000 residents and resulting in 16 deaths [5]. In addition, as a cold surge travels equatorward and cross the South China Sea, it may interact with the Borneo Vortex and potentially intensify disturbances through enhanced low-level moisture convergence and organized deep cumulus convection [1,16]. When these circulations interact with the terrain, they may results in strong convection, as seen in the formation of Typhoon Vamei, which occurred on December 26, 2001, near Singapore [20].

However, the relationship between extreme precipitation and various monsoonal synoptic circulations during the Northeast Monsoon in Malaysia is not fully understood. As noted by Chang et al. [16] and Chen et al. [10], there are various recurring circulations that may affect Malaysia differently. However, how these patterns are influenced by intraseasonal oscillations and interannual variations remains to be fully investigated. Study Tangang et al. [5] highlighted the possible interactions between Madden-Julian Oscillation (MJO) and the cold surges that could intensify moisture convergence in Peninsular Malaysia. Besides that, Tangang et al. [4] also indicated that Malaysia experiences severe extreme precipitation events that are influenced by both El Niño and La Niña. In addition, study Juneng and Tangang [19] have also identified a long-term trend in the cold surge and Borneo Vortex. Hence, a key scientific question is whether different types of recurring low-level circulations exist and can be linked to episodes of extreme precipitation in Malaysia. Therefore, the objective of this study is to assess the extent to which rainfall variability is linked to various regional-scale atmospheric circulation patterns using weather types identified through cluster analysis.

Cluster analysis is a type of multivariate statistical technique used to classify daily weather patterns into distinct representative states based on their similarity [21,22]. By employing weather typing, the dominant weather patterns in a region can be objectively identified. This approach has been utilized in many studies to describe recurrent circulation patterns such as those in the North Atlantic [21,23], North America [24], and Europe [25]. In tropical regions, weather typing analysis has also been employed in studies over East Africa [26], and Indonesia [27]. In order to identify weather patterns, the k-means clustering algorithm is among the most prevalently employed methods [28], which has proven useful in identifying circulation patterns [29,30]. In addition, weather typing method can be applied to characterize the variations in rainfall anomalies and extreme events [24,31]. Hence, in this study, weather types are identified using the k-means clustering algorithm, employed to daily low-level 850-hPa winds to investigate the dominant synoptic circulation patterns during the Northeast Monsoon over Malaysia.

This paper proceeds as follows. Section 2 presents the data and methodology, such as the application of the k-means clustering technique to derive WTs in Malaysia during the Northeast Monsoon season. Section 3 presents the findings of the weather typing analysis, examining the identified WTs and their synoptic circulation patterns, frequency of occurrence, typical progression and persistence, associated precipitation characteristics, and their relationship with the El Niño–Southern Oscillation (ENSO). Section 4 concludes the paper by summarizing the key findings and highlights gaps for future studies.

2. Data and methods

2.1. Study area and data

The first step of this study involves identifying and extracting the dominant WTs over Malaysia. To achieve this, the analysis focuses on a domain extending from 3°S to 10°N in latitude and from 98°E to 122°E in longitude, as illustrated in Fig 1. This region was selected as it encompasses Malaysia (around 1°N–7°N, 100°E–119°E) as well as the primary regions influenced by the Northeast Monsoon, including the South China Sea, Peninsular Malaysia, Borneo, and adjacent areas. The selected domain is justified by its consistency with the definition of the Northeast Monsoon Index (NEMI), which is used to determine the onset and withdrawal of the Northeast Monsoon based on the method of Moten et al. [12]. The NEMI is defined over 3.75°N–6.25°N and 102.50°E–105.00°E, which lies within the study domain. In addition, the Borneo Vortex has been reported to occur within the area of 107.5°E–117.5°E and 2.5°S–7.5°N [16,19], and other studies such as studies from Howard et al. [32] and Liang et al. [33] have similarly tracked low-level vorticity within this region near Borneo. Monsoon surges are typically identified along 110°E–117.5°E, while easterly surges occur between 7.5°N and 15°N along 120°E [16,34,35]. These key features are all encompassed within the selected domain. Besides that, this study concentrates on the months from November to February during the period 1981–2020, coinciding with the Northeast Monsoon season, which is the main period for extreme rainfall in Malaysia.

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Fig 1. Study domain.

The larger box encompass Malaysia and surrounding land areas. The smaller box represents the region selected as input for the k-means clustering algorithm, which was applied to identify the dominant weather types. The background shading shows the mean wind speed, and the arrows indicate 850-hPa wind vectors averaged over the period 1981–2020. The basemap of Malaysia, including administrative boundaries and the study area, was produced in GrADS using publicly available high resolution (hires) land-ocean polygon datasets from ftp://grads.iges.org/grads/scripts/lpoly_hires.asc and ftp://grads.iges.org/grads/scripts/opoly_hires.asc.

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The atmospheric data used for k-means clustering consists of the daily 850-hPa zonal and meridional ( component winds, which were obtained from the Fifth Generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset, provided by Copernicus Climate Change Service (C3S) [36]. The 850-hPa level is selected due to its effectiveness in representing monsoonal circulation in tropical regions and its widespread use in previous studies [27]. These wind fields are particularly relevant for analyzing the large-scale flow patterns that govern monsoonal weather systems. For example, Hassim and Timbal [37] used 850-hPa wind data to characterize monsoonal weather types in Singapore and the broader Maritime Continent. The frequent application of 850-hPa winds in such studies highlights their effectiveness in capturing key atmospheric circulations during the Northeast Monsoon season over Malaysia. Furthermore, 850-hPa winds are considered effective for identifying synoptic-scale disturbances, such as the cold surge index, which is based on the daily area-averaged wind speed at this pressure level [16].

The rainfall data used cover the same period, which is from November to February during 1981–2020 and are sourced from the gridded precipitation dataset provided by the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). CHIRPS is a processed observational precipitation dataset that provides daily, pentadal and monthly precipitation climatology (CHPclim) blended from multiple sources including rain gauge data from various sources, such as the Cold Cloud Duration (CCD)-based precipitation on thermal infrared (IR) data archived in the NOAA National Climate Data Center (NCDC), Version 7 TRMM 3B42 data and the Version 2 atmospheric model rainfall field of the NOAA Climate Forecast System (CFS) [38]. The gridded dataset is generated by blending satellite-based precipitation estimates with in situ rain gauge observations. The final product is provided at 0.05° and 0.25° horizontal resolutions covering a period spanning from 1981 until present. In this study, CHIRPS05 product at 0.05° resolution is used. This dataset is openly accessible online at https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/. This rainfall dataset which has the highest spatial resolution of 5 km has been evaluated in various countries including Malaysia [39], Indonesia [40] and China [41], and was recently used by Zakaria et al. [42] for drought evaluation in Malaysia.

In addition to analyzing rainfall variability, this study also examines the influence of ENSO variations on the occurrence of WTs. For this purpose, the Oceanic Niño Index (ONI) is used. The ONI dataset is obtained from the Climate Prediction Center (CPC) of the National Oceanic and Atmospheric Administration (NOAA) and can be accessed at https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php. Lastly, to examine the influence of the Indian Ocean Dipole (IOD), the Dipole Mode Index (DMI) is used, which can be obtained from https://www.cpc.ncep.noaa.gov/products/international/ocean_monitoring/indian/IODMI/DMI_month.html.

2.2. Classification of weather types

To determine the dominant WTs over Malaysia, k-means algorithm was applied to the daily low-level wind data at 850-hPa level, following the methodologies by Cheng and Wallace [43] and Michelangeli et al. [23]. Before conducting the k-means analysis, standardization of the wind dataset was performed. To standardize the wind dataset, the climatological daily mean was removed and the result was divided by the climatological daily standard deviation, giving values with zero mean and unit variance. Standardizing the data before conducting Empirical Orthogonal Function (EOF) analysis enables small-scale wind field fluctuations comparable to large-scale circulation patterns. The standardized anomalies of the 850-hPa and wind components were then subjected to EOF analysis, retaining 90% of the total variance in the combined matrix. This dimensionality reduction step decreases the degrees of freedom and enhances computational efficiency. The resulting transformed dataset, structured as an matrix, was then subjected to k-means clustering to objectively classify the dominant WTs.

By minimizing the within-cluster variance, the k-means clustering algorithm is intended to separate the dataset into a predetermined number of clusters, , denoted by the function . The purpose is to find the smallest value of , where indicates the partitioning of the data. The function is defined in Equation (1) below:

(1)

𝑃 represents a specific grouping of the data, where all daily data was grouped into clusters ,, , . Each cluster has a centroid . The distance between a data point in cluster and its centroid is measured using squared Euclidean distance), which reflects how similar data point is to its centroid.

The function represents the intra-cluster sum of variances for a given partition 𝑃. The optimal partition is the one that minimizes . The process of minimizing provides the optimal division of the data into 𝑘 clusters. This minimization is carried out iteratively and made more efficient by selecting new centroids from a new subset of the data.

2.3. Determination of the optimal number of clusters

The ideal number of clusters needed to achieve an adequate amount of separation of the data can be determined using the classifiability index (CI) [23,24]. The CI is obtained based on anomaly correlation coefficients (ACC) of the partitioned clusters. Equation (2) below defines the ACC between two partitioned clusters and :

(2)

and

In this context, , , with . The terms and indicate the cluster’s centroid belonging to partitions and respectively. Accordingly, ACC value will be ranges from -1–1, where ACC = 1 suggests that two partitioned clusters are identical to one another. Following Moron et al. [27], each cluster is assigned an ACC score. Each ACC score is determined by averaging the highest ACC values between and every cluster , across partitions where , with Given partitions, this results in ACC scores. Thus, the partition achieving the highest ACC score is regarded as offering the optimal division of the data into clusters. The value of CI is then computed by averaging the ACC scores across all partitions. The CI is evaluated for different values of 𝑘 to determine the ideal number of clusters needed.

The statistical significance of the CI values is examined by repeatedly applying the k-means algorithm to datasets constructed from randomly generated red noise. This approach provides a baseline against which the observed CI values can be compared. Using 100 partitions is considered sufficient to ensure stable CI estimates. According to Michelangeli et al. [23], the optimal number of clusters (value of 𝑘) is the smallest value for which the CI exceeds 90% of the CI values obtained from random red noise.

3. Results and discussion

3.1. Identification and characteristics of WTs

Fig 2 presents the CIs obtained from k-means clustering analysis for = 2 to = 10. The grey shading indicates the lower 90% of CI values generated from random red noise and provides a statistical baseline. A comparison between the observed CI values and those from random red noise shows that the 850-hPa wind patterns can be effectively grouped into four distinct clusters = 4), as this value exceeds the red noise threshold. This finding suggests that the= 4 partitioning captures meaningful and robust atmospheric patterns, indicating that four WTs provide an optimal representation of the synoptic-scale circulation variability over Malaysia during the Northeast Monsoon season.

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Fig 2. Classifiability index (CI) for each value of k.

The blue solid line indicates the CI value and the grey shading represents the one-sided 90% confidence interval of CI values generated through random noise.

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In this study, each day during the study period was grouped into a specific WT based on similarities in their atmospheric circulation patterns. Days exhibiting similar atmospheric characteristics were grouped into the same cluster, ensuring that each WT represents a coherent set of circulation features. The identified WTs are illustrated in Fig 3 as composite representations of low-level wind patterns at the 850-hPa level. These composites describe and summarize the spatial variability and structure of the dominant weather patterns.

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Fig 3. Mean wind speed (shading) and 850-hPa wind vectors for each of the four WTs.

The base map of Malaysia, including administrative boundaries and the study area, was produced in GrADS using publicly available high resolution (hires) land-ocean polygon datasets from ftp://grads.iges.org/grads/scripts/lpoly_hires.asc and ftp://grads.iges.org/grads/scripts/opoly_hires.asc.

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Fig 3a illustrates WT1, which is characterized by the simultaneous occurrence of two key synoptic-scale features during the Northeast Monsoon: a cold surge and the Borneo Vortex. The cold surge, as identified by Lim et al. [44], is marked with the strengthened northeasterly winds over the northern South China Sea extending toward Borneo. This cold surge coincides with the development of a counterclockwise circulation over the South China Sea, centered near western Borneo, which likely represents the Borneo Vortex. The formation of the Borneo Vortex is attributed to wind-terrain interactions and the conservation of potential vorticity during the Northeast Monsoon [5,16,20]. This simultaneous occurrence is likely due to the cold surges intensifying northeasterly winds, increasing relative vorticity and mass convergence, which in turn promotes the formation of the Borneo Vortex. In fact, studies suggest that the Borneo Vortex develops due to the high vorticity background created by the horizontal cyclonic shear of the cold surge [8,17]. As a result, these two phenomena frequently co-occur, reinforcing each other’s development [20,45]. Additionally, the observed cyclonic circulation aligns with well-documented Borneo Vortex formation regions, particularly along western Borneo. Over the past 20 seasons, more than 120 vortex centers have been recorded in this area, particularly around 1.5°N, 111°E [8]. Furthermore, Koseki et al. [7] reported that when a Borneo Vortex coincides with a cold surge, it typically forms in the western Borneo, which aligns with the location of the Borneo Vortex observed in this WT.

A notable characteristic of WT2 (Fig 3b) is the occurrence of a single cold surge event, which is weaker than WT1 and does not lead to the formation of the Borneo Vortex. This WT represents a weakening cold surge where northeasterly winds intensify but remain weaker than those in a typical cold surge event. As the wind crosses the equator, it undergoes an eastward deflection due to the planetary vorticity gradient. This deflection is further modified by the interaction of the wind with topographic features, which cause additional blocking and deflection effects [46].

Observations in WT3 (Fig 3c) show the occurrence of a well-developed Borneo Vortex over northern Borneo, located over the South China Sea. This Borneo Vortex is accompanied by a broad belt of westerly winds extending zonally across southern Malaysia, between 4°N and 3°S. Studies have shown that the Borneo Vortex in WT3 is situated in one of its most common formation regions, which is slightly north of the equator along the eastern coast of Borneo, near 7.5°N and 112.5°E [16]. According to Hassim and Timbal [37], the Borneo Vortex near the South China Sea is formed because of the interaction between near-equatorial westerlies which are deflected by the topography of Kalimantan, and the easterly flow originating from Vietnam. Additionally, its development may involve tropical storms crossing the southern Philippines, which propagate westward and interact with the northeast monsoonal flow.

WT4 (Fig 3d) initially features an intensification of winds over the northern South China Sea, followed by a weakening phase and a subsequent second intensification near the Java Sea. This wind pattern, which is characterized by winds crossing the equator near Singapore, likely represents a cross-equatorial surge. Its features are consistent with previous studies showing that cross-equatorial surges generate stronger wind anomalies than cold surges, with their influence extending from the South China Sea down to the Java Sea [47].

Lastly, a comparison was made between the WTs identified in this study and those from previous research, including Hassim and Timbal [37], to enhance the interpretation of the findings. Two of the WTs from their study (see Fig 9) closely match those identified here: WT1 corresponds to their R4, and WT3 aligns with their R6. This consistency across studies strengthens the reliability of the classification and provides additional validation of the identified weather patterns.

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Fig 4. Percentage of days assigned to each WT.

(a) November to February and (b–e) individual months.

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Fig 5. Progression of each WT.

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Fig 6. Persistence of each WT.

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Fig 7. Annual frequency of occurrence for each WT with estimated linear trends (black lines).

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Fig 8. Mean climatology daily precipitation over Malaysia region for each WT.

The base map of Malaysia, including administrative boundaries and the study area, was produced in GrADS using publicly available high resolution (hires) land-ocean polygon datasets from ftp://grads.iges.org/grads/scripts/lpoly_hires.asc and ftp://grads.iges.org/grads/scripts/opoly_hires.asc.

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3.2. Occurrence Frequency of WTs

An analysis of the occurrence frequency of each WT is presented in Fig 4. Fig 4a shows the average annual occurrence frequency for each WT, providing insights into the overall prevalence of each WT throughout the study period, while Fig 4b-e show the monthly occurrence frequencies from November to February, highlighting the seasonal variations in the distribution of WTs. The all-year average frequency distribution of WTs shows that each WT occurs on approximately 15% to 28% of the total number of days during the study period (Fig 4a). Among the four WTs, WT2 has the lowest frequency, while WT1 and WT3 occur at similar frequencies, each accounting for around 28% of the days, with WT3 occurring slightly more frequently than WT1.

Significant intraseasonal variations in WT frequency are observed at the monthly scale. WT1, which is associated with both cold surges and Borneo Vortex, shows a pronounced increase in occurrence during the late Northeast Monsoon period, particularly in January and February. This seasonal pattern is in agreement with previous studies. For example, Cheang [48] reported that cold surges peak in January, highlighting their dominance during the latter part of the monsoon season. Similarly, Xavier et al. [47] observed that cold surges occur most frequently in December (56.7%) and January (72.3%), further supporting the observed trend.

WT2, which represents a weakening cold surge, shows peak occurrences in November. This timing may indicate the onset of the cold surge season, which typically spans from November to February [48]. In November, cold surges are in their early stages of development and are generally weaker compared to those later in the season, which explains the peak in WT2 occurrence during this month. WT3, which is characterized by the presence of the Borneo Vortex occurring over the South China Sea exhibits a distinct seasonal cycle. Its frequency peaks in November, gradually declines from November to January, and then increases again in February. This pattern suggests that WT3 may be influenced by both early and late phases of the Northeast Monsoon. By comparing the Borneo Vortex located in the north in WT1 and in the south in WT3, a seasonal southward migration of the Borneo Vortex from November to January is observed. WT3 (southern Borneo Vortex) peaks in November, while WT1 (northern Borneo Vortex) peaks in January and February. This pattern is consistent with the southward migration of the low-level trough and the seasonal evolution of cold surge flow [18].

WT 4, which is associated with cross-equatorial surges, gradually increases in frequency from November to January, becoming more prominent during the late monsoon season. This trend suggests that cross-equatorial surges intensify as the northeast monsoon matures, peaking toward the season’s end. While studies such as Hassim and Timbal [37] and Liang et al. [49] identify February as the peak period for these surges, this reinforces the idea that cross-equatorial flow strengthens as the monsoon transitions toward its end.

3.3. Progression and persistence of WTs

Fig 5 illustrates the progression between different WTs over Malaysia, while Fig 6 shows the persistence of each WT. Understanding how these patterns evolve from one day to the next offers deeper insight into the dynamics of monsoonal circulation [24]. In this study, progression is defined as the percentage of a given WT shifting to a different WT on the following day, while persistence refers to the percentage of days a specific WT remains the same on the next day.

In terms of WT progression, all WTs predominantly exhibit self-persistence, indicating a strong tendency to maintain the same pattern from one day to the next. Among the transitions, WT1 most frequently evolves into WT3, followed by WT4. WT2 shows a higher likelihood of transitioning into WT4 and to a lesser extent into WT3, suggesting that the dissipation of a cold surge may be followed by the intensification of cross-equatorial flow or the development of a Borneo Vortex event. WT3 commonly transitions into either WT1 or WT4, implying that the presence of the Borneo Vortex may be followed by cold surges or be overtaken by cross-equatorial surges. WT4, on the other hand, most often progresses into WT1, indicating that cross-equatorial surges may play a role in initiating or modulating subsequent cold surge events and Borneo Vortex activity.

In general, all WTs tend to persist for a few days to approximately one week. For the WTs that are associated with Borneo Vortex occurrences (WT1 and WT3), persistence typically persists for around eight days, as both WT1 and WT3 exhibit a significant decline in persistence after eight and nine days respectively. This behavior aligns with the nature of the Borneo Vortex, which tends to persist for several days to more than one week during each episode [7,16]. The WT which shows a weakening cold surge (WT2) exhibits the shortest persistence, with a noticeable drop in persistence beyond seven days. According to Ramage [1], cold surges generally last from a few days to more than a week. The reduced persistence of WT2 may be due to the rapid dissipation of weak cold surges or their transition into stronger events. Finally, WT4, which represents a cross-equatorial surge, generally persists for about six days.

3.4. Trends in WTs

Fig 7 shows the annual frequency trends of each WT during the November–February period and indicates notable variations over time. Among the four WTs, WT1, which represents the simultaneous occurrence of cold surges and Borneo Vortex exhibits a statistically significant downward trend at the 5% significance level, indicating a notable decline in the frequency of the co-occurrence of cold surges and the Borneo Vortex during the study period. In contrast, the other WTs display weak upward trends. These include WT2, which is associated with weakening cold surges; WT3, which represents Borneo Vortex events; and WT4, which is linked to cross-equatorial surges.

The observed increase in the frequency of WT3, which is associated with the Borneo Vortex, is consistent with the findings of Juneng and Tangang [19], who reported a 7% per-decade increase in Borneo Vortex occurrences between 1962 and 2007. This trend suggests a long-term enhancement in the occurrence of the Borneo Vortex over the region. Additionally, the positioning of WT3, where the Borneo Vortex is located in the central South China Sea, aligns with previous studies indicating a northward shift in the vortex’s position [19]. This displacement also likely explains the decline in WT1, as the Borneo Vortex, previously centered over western Borneo is now occurring farther north. This northward shift has important climatic implications for the region. As the Borneo Vortex moves northward into the South China Sea, its interaction with land decreases, reducing the steering effect that typically directs it toward Borneo. Consequently, the vortex system remains over the sea for extended periods, potentially contributing to an increase in the occurrence of Borneo Vortex days and influencing associated rainfall over both land and ocean regions.

The significant reduction in WT1 may be partly associated with decadal climate variability in the tropical Pacific [50] as well as changes in the strength of the Northeast Monsoon flow over North Borneo [49]. A weakening of the northeast monsoon reduces the interaction between low-level winds and regional orography south of the South China Sea, thereby inhibiting the formation of the Borneo Vortex. In addition, changes in the large-scale pressure gradient have led to a strengthening of low-level winds over 10–20°N and a relative weakening over the southern flank. This spatial contrast limits the southward propagation of cold surges, making it less favourable for triggering Borneo Vortex through orographic effects [5,7].

3.5 Relationship between WTs and Precipitation

Fig 8 and Fig 9 shows the climatological mean daily precipitation and the corresponding precipitation anomalies for each identified WT, highlighting the spatial differences in rainfall patterns under distinct synoptic circulation. This approach identifies regions that consistently experience high or low precipitation across various WTs. Meanwhile, Fig 10 shows the 99^th percentile of precipitation, illustrating the intensity and spatial extent of extreme rainfall events associated with each WT.

WT1 (Fig 3a) is characterized by the Borneo Vortex and a cold surge occurring simultaneously, with prevailing northeasterly 850-hPa winds over the South China Sea leading to a notable increase in convective activity. This synoptic configuration results in intensified precipitation along the eastern coast of Peninsular Malaysia, as well as over western and central Borneo (Fig 8a). Furthermore, the northeastern tip of Borneo also exhibits an increase in rainfall. The intensified rainfall over western and central Borneo is attributed to the proximity of the Borneo Vortex, which enhances relative vorticity, strengthens upward motion, and promotes moisture convergence, thereby further intensifying convection in the region. This finding is consistent with Koseki et al. [7], who reported that deep cumulus convection near the center of the Borneo Vortex is associated with intense rainfall. On the other hand, Robertson et al. [51] showed that the co-occurrence of Borneo Vortex and cold surge may suppress convective activity over Peninsular Malaysia while enhancing convergence over Borneo. Nevertheless, the result of precipitation anomalies (Fig 9a) also shows that intense rainfall continues along the east coast of Peninsular Malaysia, implying that the interaction between the cold surge and the Borneo Vortex may be modulated by specific atmospheric conditions. For example, Liang et al. [33] shows that the Borneo Vortex during the October-March period can lead to significant precipitation increases along the east coast of Peninsular Malaysia and Borneo region, with rainfall increases up to 20%–25% in southeastern Peninsular Malaysia. Hence, the occurrence of the Borneo Vortex enhances rainfall over both Borneo and Peninsular Malaysia. The findings in this study, which show an extension of increased rainfall further south along the east coast of Malaysia, are consistent with these observations. Besides that, the heaviest daily rainfall in WT1 aligns with areas of strong shear vorticity, high convergence, and a positive convective index, when a cold surge and the Borneo Vortex occur together in other studies such as Chang et al. [16] and Chen et al. [18]. For extreme 99^th percentile rainfall events (Fig 10a), the simultaneous occurrence of a cold surge and Borneo Vortex produces intense precipitation along the east coast of Peninsular Malaysia, extending from northern Malaysia into southern Thailand and further southward to southern Malaysia. The highest extreme rainfall is observed in Terengganu, Malaysia. The other regions experiencing extreme precipitation include the westernmost and northeastern coastal areas of Borneo.

WT2 (Fig 3b) represents a single cold surge event, but it illustrates a weakened surge that does not fully capture the typical rainfall distribution associated with a strong cold surge. The results suggest that during a weakening cold surge, rainfall is primarily confined to the upper east coast of Peninsular Malaysia, rather than extending further south (Fig 8b). This pattern may be attributed to reduced moisture transport from the South China Sea as a result of the weaker cold surge. Additionally, substantial rainfall is observed over western Borneo, with the highest precipitation concentrated in northern Borneo. This finding is consistent with Lim et al. [44], who demonstrated that during cold surge events, rainfall is predominantly concentrated over northern and western Borneo. Specifically, in WT2, the rainfall distribution over Borneo is notably focused in the western and central regions of Sarawak. This result aligns with Moten et al. [12], who reported that cold surge events contribute to heavy rainfall along the east coast of Peninsular Malaysia and western Sarawak. The 99^th percentile rainfall pattern (Fig 10b) also mirrors that of WT1. However, the extent of extreme rainfall is more limited, as it does not extend into southern Peninsular Malaysia.

WT3 (Fig 3c) represents the occurrence of the Borneo Vortex over the South China Sea. In this WT, rainfall over Peninsular Malaysia is primarily concentrated along the upper east coast (Fig 8c). This pattern is likely attributed to the circulation associated with the Borneo Vortex, which suppresses rainfall transported by the northeasterly monsoon winds, resulting in reduced convective activity over much of Peninsular Malaysia [16]. However, under this weather pattern, southern Thailand experiences increased rainfall. Meanwhile, rainfall over Borneo is predominantly concentrated in the eastern part of the island, as the Borneo Vortex induces low-level wind convergence toward Borneo. Notably, rainfall decreases over both Sabah and Sarawak in Malaysia, while northeastern Kalimantan experiences enhanced precipitation. Overall, above-average rainfall is observed across much of Borneo, with the most significant increases occurring in central Kalimantan. The rainfall anomalies (Fig 9c) clearly show negative anomalies over Peninsular Malaysia, and positive anomalies over the eastern Borneo, reflecting the influence of the Borneo Vortex, which alters the wind circulation structure by blocking and deflecting moisture flow from the Northeast Monsoon [16]. Although the Borneo Vortex is typically associated with reduced rainfall over Peninsular Malaysia, the 99^th percentile rainfall (Fig 10c) distribution reveals a concentration of extreme precipitation along the east coast, particularly in the Terengganu region. This suggests that, under certain conditions, the Borneo Vortex can still contribute to localized extreme rainfall events in Peninsular Malaysia.

WT4 (Fig 3d), characterized by a cross-equatorial surge, is associated with increased rainfall over the east coast of Peninsular Malaysia, with a slight westward expansion along the coastline (Fig 8d). In Borneo, a notable increase in rainfall is observed across most of Sarawak and northeastern Kalimantan, extending southward into central Kalimantan. Although cross-equatorial surges are typically associated with drier conditions over Peninsular Malaysia [47], the present findings do not clearly exhibit such drying. Instead, they indicate a general enhancement of rainfall across Borneo, along with a localized increase in precipitation in northeastern Borneo. The 99^th percentile rainfall (Fig 10d) associated with a cross-equatorial surge reveals extreme precipitation along the east coast of Peninsular Malaysia, with the highest intensities observed near the northeastern region, particularly around Terengganu. Notably, in Borneo, extreme rainfall affects a larger area compared to other weather types, especially covering most of Sarawak, with the greatest intensity recorded in the northeastern tip of the island.

3.6. Relationships between WTs, ENSO, and IOD

Malaysia is strongly influenced by ENSO, with various local impacts observed during boreal winter [4,46,51,52]. To better understand this influence, the frequency of each WT during different phases of ENSO is evaluated, as shown in Fig 11 and Table 1. This analysis provides valuable insights into how ENSO modulates synoptic weather patterns over Malaysia, potentially affecting rainfall distribution and overall climatic variability.

Download:

Table 1. Frequency (%) of each WT during different ENSO phases.

https://doi.org/10.1371/journal.pclm.0000847.t001

In WT1, where the cold surge and Borneo Vortex co-occur, occurs more frequently during La Niña and less frequently during El Niño years. WT3, associated with the Borneo Vortex without a significant cold surge influence, also shows a strong relationship with ENSO. It is more frequent during La Niña years and occurs less often during El Niño years. Hence, the Borneo Vortex occurs more frequently during La Niña years. Similar findings were reported by Braesicke et al. [53], which stated that the Borneo Vortex near Malaysia is significantly correlated with ENSO, with fewer detection during El Niño. In contrasts, WT2, characterized by a weakening cold surge and WT4, which characterized by a cross-equatorial surge is most prominent in El Niño years, and occurs less frequently in La Niña years.

Besides ENSO, the IOD may also play a contributing role in modulating Malaysia’s climate, although ENSO is generally recognized as the primary driver. For example, the Maritime Continent typically experiences reduced precipitation during the positive phase of the IOD [54], and the 2006 flooding event has been linked to the termination of the 2006 IOD event [5]. To further investigate these influences, the frequency of each weather type (WT) under different ENSO phases is examined, as shown in Fig 12 and Table 2.

Download:

Table 2. Frequency (%) of each WT during different IOD phases.

https://doi.org/10.1371/journal.pclm.0000847.t002

Download:

Fig 9. Precipitation anomalies for each WT.

The base map of Malaysia, including administrative boundaries and the study area, was produced in GrADS using publicly available high resolution (hires) land-ocean polygon datasets from ftp://grads.iges.org/grads/scripts/lpoly_hires.asc and ftp://grads.iges.org/grads/scripts/opoly_hires.asc.

https://doi.org/10.1371/journal.pclm.0000847.g009

Download:

Fig 10. Mean 99^th percentile daily precipitation over Malaysia region for each WT.

The base map of Malaysia, including administrative boundaries and the study area, was produced in GrADS using publicly available high resolution (hires) land-ocean polygon datasets from ftp://grads.iges.org/grads/scripts/lpoly_hires.asc and ftp://grads.iges.org/grads/scripts/opoly_hires.asc.

https://doi.org/10.1371/journal.pclm.0000847.g010

Download:

Fig 11. WT frequency during various phase of ENSO.

https://doi.org/10.1371/journal.pclm.0000847.g011

Download:

Fig 12. WT frequency during various phase of IOD.

https://doi.org/10.1371/journal.pclm.0000847.g012

Both WT (WT1 and WT3) associated with the Borneo Vortex, occur more frequently during the positive IOD phase compared to the negative phase, suggesting a possible enhancement of Borneo Vortex activity under positive IOD conditions. In contrast, WT2, which is associated with cold surge events, shows a higher frequency during the negative IOD phase, indicating a potential preference under cooler western Indian Ocean conditions. For WT4, which is associated with cross-equatorial surge remains relatively stable across positive and negative IOD implying weaker sensitivity to IOD variability.

4. Conclusion

Malaysia, located over the equatorial region experiences significant spatial and temporal rainfall variability due to interactions between different monsoonal circulation systems. The key drivers of monsoon rainfall variability include the ENSO [3,55], cold surges [16,44], Borneo vortex [5,10,16,17], and other synoptic-scale circulations. These interacting factors contribute to the complexity of rainfall patterns during the Northeast Monsoon in Malaysia which underscoring the need to investigate the underlying synoptic circulation patterns driving the rainfall variability. Therefore, the purpose of this study is to enhance our understanding of monsoonal synoptic circulation by classifying daily weather patterns into distinct WTs. These WTs were identified through cluster analysis using the k-means algorithm. The k-means algorithm was applied to 850-hPa wind data covering the period from 1981 to 2020. By examining these WTs, we characterize different atmospheric conditions that occur throughout the Northeast Monsoon season, offering deeper insight into the synoptic circulation influencing monsoonal weather patterns. Furthermore, the frequency distribution of these WTs provides valuable information on seasonal rainfall variability. By identifying the specific WTs and their corresponding rainfall patterns, this provides a clearer understanding of the drivers of rainfall variability during the Northeast Monsoon season.

In our study, we identified four WTs that predominantly occur during the Northeast Monsoon. The results show four main WTs, each associated with an important synoptic circulation system: (i) simultaneous occurrence of a cold surge and a Borneo Vortex, (ii) a weak cold surge, (iii) Borneo Vortex located in northern Borneo, and (iv) a cross-equatorial surge. Notably, the research builds upon earlier work by Hassim and Timbal [37] and Qian et al. [56], which explored the role of synoptic circulations in the region. The WTs identified in this study show remarkable consistency with those from their research, particularly during the Northeast Monsoon, demonstrating the robustness and stability of these weather patterns. Besides that, the WTs identified in this study have a direct influence on observed rainfall patterns in Malaysia. For example, the simultaneous occurrence of the Borneo vortex and a cold surge result in the highest rainfall, particularly over the east coast of Peninsular Malaysia east coast and western Borneo, and cross-equatorial surge increases rainfall across north Borneo. Over time, weather patterns that show the co-occurrence of the Borneo Vortex and cold surge have shown a declining trend, likely due to the northward shift of the Borneo Vortex, while other WTs including Borneo Vortex and cross-equatorial surge have been increasing. The study highlights how these weather patterns evolve, transition between each other, and contribute to extreme rainfall events, improving understanding of regional monsoon variability.

In conclusion, this study provides a significant contribution to the understanding of Malaysia's dominant synoptic circulation patterns and their role in shaping rainfall variability. The identification of these robust and stable WTs enhances our understanding of Northeast Monsoon dynamics and provides a framework for evaluating climate models. Amid Malaysia’s ongoing challenges arising from climate change, research like this is essential for developing strategies to manage the risks associated with extreme weather events. Furthermore, this study provides insights into the effectiveness of climate models in simulating synoptic circulation patterns in Malaysia. By comparing the observed frequency and structure of these WTs with model simulations, researchers can evaluate the accuracy of climate models in replicating real-world conditions, which is also important in many other sectors including agriculture, water resource management, and disaster risk reduction.

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Figures

Abstract

1. Introduction

2. Data and methods

2.1. Study area and data

2.2. Classification of weather types

2.3. Determination of the optimal number of clusters

3. Results and discussion

3.1. Identification and characteristics of WTs

3.2. Occurrence Frequency of WTs

3.3. Progression and persistence of WTs

3.4. Trends in WTs

3.5 Relationship between WTs and Precipitation

3.6. Relationships between WTs, ENSO, and IOD

4. Conclusion

References