Synoptic Climatology of Weather Parameters Associated With Tropical Cyclone Events In The Coastal Areas of Bay of Bengal

doi:10.21203/rs.3.rs-954033/v1

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Synoptic Climatology of Weather Parameters Associated With Tropical Cyclone Events In The Coastal Areas of Bay of Bengal

https://doi.org/10.21203/rs.3.rs-954033/v1

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Tropical cyclones (TCs) are the most devastating weather phenomena that trigger massive loss of property and life in the coastal areas of the Bay of Bengal (BoB). Scientific understanding of TCs occurrence can aid policy-makers and residents in coastal areas to take necessary actions and appropriate planning in advance. In this study, we aimed to examine the possible linkage of weather parameters with deadly 22 TCs events in the BoB from 1975 to 2014 using principal component analysis, K-mean clustering and General circulation model (GCMs). Results showed that among 22 TCs, cluster 1 belongs to 12 TCs which occurred under the same atmospheric situation when the sea level pressure (SLP) was below 990hPa, and the temperature ranged from 300C to 390C. A deep negative anomaly of SLP and temperature was observed up to 500hPa levels. In contrast, a negative depression was found at 300hPa geopotential height (GPH) over the study area. Cluster 2 consisted of 9 TCs when SLP was below 1000hPa, and the average temperature was 33.50C. A strong negative anomaly was noticed when surface level up to 500 hPa GPH, but dramatically this depression was completely absent at 300hPa geopotential height over the BoB and entire coastal region. Cluster 3 contained only 1 TCs when the atmospheric circumstance was completely diverse, and the SLP was above 1000hPa. The results of the GCM model revealed that the SLP was lower, and the temperature was higher over BoB compared to the North Indian Ocean. We identified the larger depression of SLP and unpredictable temperature anomalies at the upper atmosphere that can trigger an enormous unpredictability throughout the atmospheric level, leading to severe TCs. The outcomes of this study can improve our understanding of weather variables in the upper atmospheric column for forecasting the TCs system more accurately in the future.

Atmospheric Sciences

Sea level pressure

severe cyclone

Geopotential height

PCA

K-mean Clustering

Bangladesh

Tropical cyclones (TCs) are the utmost damaging and potentially extreme life-threatening weather events in the tropical coastal regions, including Bangladesh (Alam et al. 2003; Parker et al. 2017; Wahiduzzaman et al. 2021a). The TCs are the weather associated disaster over the Bay of Bengal (BoB), which trigger enormous damage to properties and lives in these coastal regions (Paliwal and Patwardhan 2013; Vissa et al. 2013; Balaguru et al. 2014; Rajasekhar et al. 2014; Mohapatra et al. 2014). TCs can cause hazardous effects via heavy winds and floods triggered by related storm surges and heavy precipitation over the BoB (Wahiduzzaman et al., 2020a; b). Although small amount (7%) of the global TCs frequency occurred in the North Indian Ocean such as Bangladesh, India and Myanmar, the social and economic effects of TCs in the vital region is much higher than those in other TCs region (Wahiduzzaman et al., 2020a). For instance, in 2008, severe cyclone Nargis, more than 38,000 people casualties and a US$10 billion loss occurred in Bangladesh (Webster 2008). Apart from the deadly costs to human life, TCs pose a considerable monetary risk to the nearby coastal inhabitants (Rumpf et al., 2007). The high fatality in the BoB coastline area is due to the low-lying deltaic topography, landfall, shallow bathymetry level of the continental shelf zone and funnel-shaped shoreline, which creates the region highly susceptible to storm surge induced flooding and direct wind speed (Wahiduzzaman et al. 2021b). Moreover, the residents' high population growth and poor social and economic situation along the BoB coastline also found driving factors behind the damages generated by TCs. Thus, a thorough understanding of synoptic climatology of weather parameters associated with TCs over the BoB is highly important (Singh et al., 2012).

Extreme weather events, i.e., severe TCs posed a challenging situation for the scientists, planners, and decision-makers as these TCs events affect society and the eco-environment (Seneviratne et al., 2012). A few studies have been reported that it is challenging to study and forecast these weather extremes because of their nature, rarity and severity; thus, the data are inadequate (Sillmann et al., 2017; Maw and Jinzhong, 2017; Islam et al., 2020; Wahiduzzaman and Yeasmin 2019). Sometimes less noteworthy coastal floods resulted in huge loss of lives and property even greater than extreme events like TCs landfall (Moftakhari et al. 2017). In this context, this study has motivated to address the inadequacy of the TC occurrence dataset of upper atmospheric zones in the BoB coastal region over the last 40 years. In addition to this, glacier and ice melting due to the increased temperature resulting from the mean sea level rising they were also restarting the occurrences of TCs events that claim enormous economic and social losses which were being unnoticed (Bamber et al. 2018; Cazenave et al. 2019; Spada 2017; Chen et al. 2017). It is proven that if the possible landfall of TCs can be identified at an earlier time, probable damage control would be possible (Paliwal and Patwardhan, 2013; Weinkle et al., 2012; Singh et al., 2012).

Synoptic climatology has been proven a potential field of research to identify regional and global climate patterns around the globe using statistical analysis (Barry and Carleton, 2001; Wahiduzzaman et al., 2020b; Wahiduzzaman et al., 2021a). Statistical analysis of weather variables associated with TCs activity can help policymakers, land planners, and coastal residents take proper action in advance. To linkage the statistical relationship between TCs and weather parameters, it is crucial to know the influential factors such as weather regimes, geopotential height, sea level pressure, wind flows, etc. that affect TCs genesis and landfall (Reinhold and Pierrehumbert, 1982; Michelangeli et al., 1995; Rudeva et al., 2019). A few cited works have explored the impact of upper atmospheric and oceanic conditions on the variation of TCs event in the BoB (Sengupta et al., 2007; Girishkumar and Ravichandran, 2012; Felton et al. 2013; Wahiduzzaman et al. 2017; Sattar and Cheung 2019). Gaona et al. (2018) and Zhou et al. (2018) studied TCs activity over the BoB, resulting in disastrous impact by heavy floods and winds with the association of enormous rainfall and surges. However, these earlier works have hardly examined the statistical association between synoptic climatology and TCs events over the BoB and the surrounding coastal region.

Multivariate statistical approaches like Principal Component Analysis (PCA) and clustering have been a potential tool that used in many fields ((i.e. Blasius and Greenacre, 2014; Bro and Smilde, 2014; Hair et al. 2006; Hou et al. 2015; Kline, 2014; Shlens, 2014; Duke et al. 1985). PCA has also been used by many scientists, i.e. Farukh and Yamada (2014), Farukh et al. (2014) and Islam et al. (2021), to identify severe snowfall, flood, tropical cyclone phenomena and its synoptic climatology. PCA has been proven effective in studying vulnerability assessment, health vulnerability and disaster risk reduction (Miller, 2014; Howe et al., 2013; Zhu et al. (2014); Fisher et al. (2015); Tasnuva et al., 2020; Siddique et al., 2021). On the other hand, the general circulation model (GCM) has been used to determine the effect of climate extremes on agriculture (Glibert et al., 2014; Iyalomhe et al., 2015; Rahman et al. 2017; Farukh et al. 2014; Das 2021), rising of sea level and coastal surges (Neumann et al. 2015) and so on. Ruane et al. (2013) found that increased emissions also affected coastal agriculture coupled with the changing climate. Some GCM models have been used to predict storm surge and atmospheric circulation patterns (Maw et al., 2017). Many scientists were successfully adopted GCM models in simulating the level of inundation but remain poorly understand the coastline structures with the definite progressions of wave circulation, breaking, and interface (Sielecki and Wurtele, 1970; Flather and Heaps, 1975). On the other hand, Ghosh et al. (1983), Flather and Khandaker (1987), Katsura et al. (1992) were able to develop some numeral models to simulate storm surges of BoB. Additionally, Esteban et al. (2005) applied both PCA and clustering in daily sea-level pressure rotations and found that snowfall was less than 30 cm per day patterns. However, due to a lack of direct observations and instruments, upper atmospheric conditions of climatic parameters triggering TCs events and possible future changes in their frequency and intensity caused by climate change is poorly understood in the BoB.

This work fills to close this knowledge gap in the existing literature. The study's primary objective is to examine synoptic climatology of weather parameters associated with extreme TCs events in the BoB and to explore the reasons behind severe TCs formation in the nearby coastal region. This study adopted PCA and clustering methods to identify the most responsible variables (i.e. Sea level pressure, temperature) that play a pivotal role in forming severe TCs events. The GCM model was also used to visualize the instability of those weather variables (sea level pressure and temperature) in the total atmospheric column on the TCs occurring days.

2.1 Study region description

The southern coastal region of Bangladesh, which lies within 21 23″N and 89 93″E were chosen as the study areas where the occurrence of monsoon cyclones is frequent that affect the current agriculturally based economy (WARPO, 2006) (Fig. 1). The average temperature of these areas ranges from 18⁰C to 28 ⁰C where the average maximum has found in May, and the minimum was seen in January (Rahman and Ferdousi, 2011; Das and Islam 2021).

From 1975 to 2014, lots of TCs hit on Bangladesh. For example, some of the disastrous cyclones were reported in 1985, 1988, 1991, 1994, 1995, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2007, 2009 and 2013 (Table S1) Among these, cyclone SIDR in 2007 and cyclone AILA in 2009 was most destructive in terms of damage of properties and fatalities (BBS, 2014). Most importantly, all the cyclones stroke in the southern part as the area is a coastal area of Bangladesh (Table S2). That is why this area has been selected for the synoptic study of cyclone events in the past 40 years. Datasets were collected from Satkhira, Khulna, Mongla, Khepupara, Barishal, Bhola, Patuakhali, Hatiya, Chandpur, Feni's meteorological station Sandwip, Sitakunda, Chittagong, Kutubdia, Cox's Bazar and Teknaf (Fig. 1).

2.2 Data sources and quality check

We collected data on 22 TCs that hit the coastal region from 1975-2014 and caused severe destructions. Weather variables, i.e. sea level pressure (SLP) and Temperature data, were collected from the BMD of the study areas. Three hourly data of every parameter were collected and then compiled, tabulated into daily data to avoid any deviance and then analyzed according to the study's objectives.

Historical records of TCs that attacked Bangladesh were collected from the Disaster Preparedness Center, Asian Institute of Technology (AIT) and Bangladesh Bureau of Statistics (BBS). These data were then plotted following the arrangement of day/month/year. We collected air pressure data between 120N ¬to 320N latitude and 780E to 1020E longitudes from NCEP (National Centers for Environmental Prediction) Reanalysis of NOAA (National Oceanic and Atmospheric Administration) / ESRL (Earth System Research Laboratory), Physical Science Division, USA (United States of America). NCEP (National Centers for Environmental Prediction) global Circulation modelling was applied to develop a map and analyze air pressure. We also concentrated on the possible association of TCs events with the synoptic climatology of SLP and air temperature. The BMD staff was initially the checked quality control of the dataset. A two-sample Student's t-test was performed to assess the dataset’s statistical significance where the outcomes are significant at a 95% confidence level for all TCs occurrence days.

2.3 Principal Component Analysis (PCA)

We used PCA to understand the core affiliation among the allied variables through a small set of factors. PCA is used to convert variables that are correlated in any way into uncorrelated observations that are linear calling principal components. Lastly, PCA produced a set of variables of vectors that were not associated in orthogonal basis (Zhu et al., 2014; Jolliffe, 2002). Yarnal (1993) used S mode (grid points as variables and days as observations) centred data matrix PCA, which used the standardized spatial SLP data, which also maintained the original characteristics such as original daily temporal scale (Esteban et al., 2005). Barry and Carleton (2001) showed that the correlation matrix provides the best proficient demonstration. Lastly, we used the varimax procedure, which assists the spatial analysis of principal components used by Yarnal (1993). We used the scree test to define the factors which describe the special ratio of the total variance by Cattel, 1966. The KMO values range from 0 to 1, where the value between 0.8 and 1 indicates that the number of samples is adequate, where the value less than 0.6 means that the sampling is not sufficient, and possible remedial measures should be taken. The scree test results showed in our study that it was acceptable according to the range of the KMO values.

2.4 K-mean clustering method

We used the nonhierarchical K-means method to clustering the remarks (Hair et al., 1998). Cluster analysis is used to grouping a set of data that have the very homogeneous characteristics of algorithms. Many scientists have used clustering in their study to separate homogenous data into a group (Anderberg, 2014; Duran & Odell, 2013; Tan et al., 2013). In this study, we also used this wide-ranging method to identify the groups of cyclones with similar characteristics, i.e. temperature and SLP. The number of groups and centroids were decided according to the PCA results, which establish the spatial variation patterns. Centroids and groups were created using the Birkeland et al. (2001) and Tait and Fitzharris (1998) method. Using K-means was to classify the distribution of Temperature and SLP values with a similar characteristic. For Temperature and SLP, atmospheric circulation clusters were formed by using the synoptic map.

2.5 General Circulation Model (GCM)

The GCM is the most complicated climatic model developed by Viner (2000), which displays the three-dimensional climatic scenario of the components used to represent the components. We followed the method applied by Onogi et al. (2007), the data of deadly 22 TCs landfalling periods, i.e. with synoptic weather parameters like SLP and temperature were obtained from Japanese 25-year reanalysis project (JRA-25) by the Japan Meteorological Agency (JMA) with a 1.125° spatial resolution encircling by 12°N–32°N and 78°E–102°E. A detailed description of the GCM model can be found in Onogi et al. (2007).

3.1 Clustering analysis

Figure 2 represents the clustering of 22 cyclones from 1975-2014. From figure 2, it is clearly understood that the most important factors, i.e. SLP and Temperature responsible for the formation of 22 TCs, have been grouped into 3 clusters (centroid). The cyclonic factors which characteristics are similar are in the same cluster as we can see from the figure that circle with green colour is considered cluster 1 where the temperature was 41.70C, and sea level pressure was 995.7hPa.

So, cluster 1 indicates that 41.70C temperature was responsible for one cyclone out of 22 TCs from 1975-2014. In cluster 2, 9 circles with red colour have presented, which have considered the same characteristics for occurring nine cyclones in 40 years we studied. The temperature triggered behind the formation of 9 TCs was from 300C to 36.2 0C where the sea level pressure was from 991.8hPa to 999.7hPa. The centroid two is pointing at 33.50C, which is the average of these nine red circles. So, these results indicate that temperature above 300C was mostly responsible for low atmospheric pressure, which turns into cyclonic activity. The rest of the 12 black circles have been considered under the third centroid, where the circles are in the same characteristics for the formation of the rest of 12 TCs from 1975-2014. The SLP behind these TCs formation was below 990hPa, and temperature also ranged from 300C to 390C.

3.2 Sea level pressure anomaly and severe TCs

This section described the scenario of the different atmospheric pressure levels and temperatures of the 22 TCs occurring days.

3.2.1 Cluster 1

Figures 3 represented the composite geographical distribution of SLP anomalies compared with the climatology from 1975 to 2014 of cluster 1 for surface level, 850hPa, 700hPa, 500hPa and 300hPa. Figure 3 illustrates SLP (hPa) from 120N to 320N latitude and 780E to 1020E longitude derived from NCEP, NOAA/ESRL Physical Science Division. In the clustering part, we have seen that only one cyclone has passed in the last 40 years have followed different characteristics among 22 TCs.

It is indicated that a tough negative anomaly was present over the BB and the southern part of Bangladesh (Fig. 3a). Deep depression of SLP was observed over the south-eastern and southern coastal regions. A slightly lighter depression was observed throughout the whole of Bangladesh except the easternmost part of the country.

At 850hPa, a dice-shaped strong negative anomaly of SLP was observed over Myanmar. A slightly lower depression was observed near the eastern and southwestern parts of the country. A lighter depression was observed throughout the whole country (Fig. 3b).

Complete desertion of adverse abnormality was observed at above 700hPa level (Fig. 3c). This positive anomaly was expanded all over Bangladesh and over BB. Deep negative depression of SLP was observed in the southwestern part of Myanmar that also can cause a very unstable condition in the upper atmosphere. At 500hPa height, a strong negative anomaly was observed over India in the north-eastern part of Bangladesh (Fig. 3d). This oval-shaped deep depression was as large as Bangladesh.

A relatively low depression could observe all over Bangladesh in those cyclones occurring days. In figure 3e, it is seen that at 300hPa height, a deep depression was observed in the Northern part of Bangladesh over India. The atmosphere over Bangladesh was very clear, and a positive anomaly was observed. This positive anomaly at this higher atmosphere could create a very unstable weather condition at this height of the atmosphere.

A comparison of amalgamated geographical dispersal of synoptic temperature from the specified level has shown in Fig. 4. Fig. 4a showed the surface air temperature anomaly, which is undoubtedly displayed from the Figure. There’s a progressive external air temperature irregularity region above the eastern and southeastern part of Bangladesh. This positive anomaly is prominent over the whole coastal part of Bangladesh, which is noticeable over West Bengal of Indian territory. This positive anomaly zone was also available on the eastern border of Bangladesh, covering India and some parts of Myanmar also. A negative anomaly zone has been indicated in the southern-eastern part of Myanmar and the northern-western part of Thailand and Laos. From the results, we can say that outward level air temperature in Bangladesh and India was somewhat warmer than in Myanmar, Thailand and Laos, where the air is much cooler on this cyclone occurring day.

Figure 4b showed a tough, positive air temperature irregularity region at 850hPa level over southwestern Bangladesh. The strong positive zone anomaly covers the part of Bangladesh from 24.50N to the south of its area. The alteration region between warmer and cooler anomalies was occurred over the central part of Myanmar, keeping one strong positive zone over southeastern India and the central-western portion of Myanmar.

But at around 3,000m above (~700hPa) from outward, there was the complete vanishing of warmer zones (Fig. 4c). The appearance of some part of the negative zone was found in the southeastern part of Bangladesh and in from central to the southern part of Myanmar. Thus, this cooler zone at a relatively upper atmosphere would not affect creating an unstable atmosphere.

The temperature distribution map showed that the temperature anomaly at the most influential weather zone of the upper atmosphere was at 500hPa level (Fig. 4d). A strong negative area at 500hPa level surrounded the southwestern part of Bangladesh, and BB was noticed. This situation implies the formation of a cooler region at around 6,000m (~500hPa) over the surface. At 300hPa level (~10,000 m above), occurrences were observed where a wide-ranging area was covered by warmer air from Tibet to NIO and the eastern part of India (Fig. 4e), where the regions of Bangladesh were subjugated by a deep warmer area which can be resulted in a severe instability throughout the whole atmospheric column.

3.2.2 Cluster 2

In the clustering part, we have seen that nine TCs passed in the last 40 years following the same characteristics among the 22 TCs. These conditions, which have played a big role in forming those cyclones, have fallen under cluster 2.

The climatological comparison of combined geographical dispersal of SLP variances from 1975 to 2014 of cluster 2 for surface level, 850hPa, 700hPa, 500hPa and 300hPa can be seen in Figure 5(a), 5(b), 5(c), 5(d) and 5(e).

From Fig. 5a, it can be seen that a strong negative depression of SLP was prominent in the southerly region of Bangladesh and above the BB. This oval-shaped depression was as large as the size of Bangladesh. In contrast, the remaining BoB portion has a relatively low depression of SLP, except for the north-eastern and north-western parts of the country.

Figure 5(b) shows that at 850hPa, a strong negative anomaly existed over the BoB, and it was prominent toward the Bangladesh coastal and hilly region. The rest of the parts had a normal SLP at this height. These two states of weather conditions at this height can cause a very unstable condition resulting in very extreme cyclonic events.

At 700hPa, about 3000m above, we can observe a relatively large shaped negative depression of SLP above BB and the south-easterly region of Bangladesh, which was larger than Bangladesh in diameter. In the rest of the country's area, the weather condition was very normal (Fig. 5c). These two different conditions can result in a very unstable weather event. At 500hPa, from Fig. 4d, a very strong negative anomaly was observed over BoB and the south-eastern part of Bangladesh and India.

At the 300hPa (~10,000 m above) height from the surface (Fig. 5e) there a complete disappearance of negative anomaly zones was observed. These two different zones of negative and positive anomaly can result in a serious weather-related extreme event.

Evaluation of combined geographical dispersal of synoptic temperature from the specified level can be seen in Fig. 6. The warmer outer air temperature irregularity region is prominent above the easterly and south-easterly portions of Bangladesh. The day the cyclone hit; a cooler zone was expanding from the Indian terrain towards the south-westerly landscape of Bangladesh. At the same time, a strong cooler zone is also seen in the southern coastal region of Bangladesh.

These two coexistences of the negative zone over the Indian Territory and the southern part of Myanmar possessed the moving area over the south part of Bangladesh to the BoB. The results suggest domination of comparatively colder temperatures in the southerly region not much above the exterior level, especially on cyclone incidence. The area was mostly cooler in the southern-western part than the southern-central and southern-eastern region of Bangladesh on the cyclone befalling days at surface level.

An irregular warmer air temperature area above the north-easterly region of Bangladesh was seen at 850 hPa level. Two negative and positive anomaly zones above south-eastern India and southern Myanmar and BoB created a transitional site (Fig. 6b). But nearly 3000m (700hPa) above the sea level (Fig. 6c). This warmer area surrounded the northeast, south-east and above BB. An unstable atmospheric situation might have resulted through this unstable situation which could interact with the surface.

A tough warmer area surrounding the south-westerly region of Bangladesh and above the surface level of BoB pointed to a progression of positive part nearly over 6000m (5000hPa) (Fig. 6d). An intense occurrence at 300hPa (~10,000 m above) level was seen by Fig. 6e, where an expanded area was concealed by progressive irregularity encompassing Tibet to North Indian Ocean (NIO) via eastern Indian Territory. Bangladesh was subjugated by an advanced irregularity zone forming a profound warmer area above these zones. A large unstable zone can result through the atmospheric column.

3.2.3 Cluster 3

The conditions that have played a pivotal role in forming the rest 12 TCs have fallen under cluster 3. Fig. 7 represents the merged geographical dispersal of SLP irregularities paralleled with the climatology from 1975 to 2014 of cluster 3 for surface level, 850hPa and 700hPa, 500hPa and 300hPa. From Fig. 7a, it is clear that a strong negative anomaly existed over BoB. This negative anomaly was prominent in Bangladesh.

From Fig. 7b, at 850hPa from the surface, the same situation was observed over the BoB, and it was also prominent towards Bangladesh. The same situation was observed at the height of 700hPa, from Fig. 7c, but the negative anomaly was relatively bigger than the other level. This strong negative anomaly over BB also covers the southern lower part of the Bangladesh coastal region. But a slightly lighter negative anomaly was observed throughout Bangladesh. But noticeably, a huge area from Myanmar to India was covered by a positive anomaly.

At the 500hPa height, deep depression of SLP is observed over BoB and India. This circular shape deep depression also covered the southern part of the coastal region and a small part of the south-eastern part of Bangladesh. The perimeter of this depression is almost equal to the size of Bangladesh. In these cyclonic days, a slightly lower depression was observed over BB and India, prominent towards the NIO.

The most dramatic situation was observed at 300hPa height in these cyclones occurring days (Fig. 7b). A positive anomaly throughout Bangladesh is observed. Though, a slightly negative anomaly was observed over BB. A deep negative anomaly was observed over NIO and south-eastern India. The positive anomaly at this height and negative anomaly below this height could result in serious instability throughout the whole atmosphere.

A comparison of the composite geographical distribution of synoptic temperature from the specified level has shown in Fig. 8. Fig. 8a shows the surface-level air temperature anomaly. It was clearly shown that the positive surface air temperature anomaly region was almost disappeared from the map except for some small areas of NIO though they are not strong enough. The day when cyclones befall, the robust adverse irregularity region from Indian Territory extended toward all over Bangladesh and expanded to the whole of south-east Asia. A transition zone over the Bay of Bengal to the NIO Territory was observed since the positive zone and negative zone coexists over the Indian Territory and the whole part of Myanmar. On the day of occurrence, relatively cooler air temperature dominance was observed in the southerly region, especially near the surface. The area is mostly cooler on the southern-western and central-western part than the southern-central and southern-eastern part of Bangladesh on the cyclone occurring at surface level.

Fig. 8b represented a serious negative air temperature irregularity region over the southern-western area of Bangladesh at 850hPa level. A transitional zone above BB with a double negative region above south-westerly India and southerly Myanmar was observed. Around 3000m (700hPa), a disappearance of this zone was noticed over Bangladesh. A warmer zone existed from the northeast to the southern parts of Bangladesh, and it extended up to BoB. This warmer region in a comparatively upper atmosphere might result in an unbalanced atmosphere over the thermal uncertainty relating to surface level.

500hPa level is considered the most influential upper atmospheric weather zone (Fig. 8d). A positive anomaly zone circling above the south-westerly region of Bangladesh and above BB dictates the progress of the warmer region at the height of 6000m near the surface. At 300hPa level (~10,000m above), a larger area covering by positive irregularity region expanded from Tibet to NIO via eastern Indian territory caused the major instanced occurrence (Fig. 8e). The total area of Bangladesh was subjugated by a positive irregularity zone, which indicates a huge warmer region over these areas. The development of a larger unstable zone through the whole atmosphere could result from this bigger warm air mass.

In this paper, we define the extreme TCs events as those events that were the most destructive and claimed the loss of hundreds of lives and millions of properties. The climate of the North Indian Ocean region is highly affected by oceanic variability on different spatiotemporal levels (Schott and McCreary 2001). Many scientists studied these extreme events because of their paucity and nature of occurrence (Ali, 1999; Webster, 2008; Mohapatra et al., 2014; Wahiduzzaman et al., 2017; Wahiduzzaman et al., 2020a, Islam et al., 2020; Wahiduzzaman et al., 2021a). This study intends to examine the association of the atmospheric condition responsible for the TCs occurring days for 22 deadliest TCs from 1975-2014. It was found that among the 22 TCs, we studied 12 TCs happened under the same atmospheric situation when the SLP was below 990hPa, and the temperature ranged from 30.90C to 390C. On these cyclone ensuing days, a serious negative anomaly of SLP was observed at surface level, 850hPa, 700hpa and 500hPa level whereas, a complete absence of negative depression was observed at 300hPa GPH over the BoB and southern part of the country.

On the other hand, a positive temperature was observed in the surface level and 850hPa and 700hpa levels. On the contrary, the complete disappearance of the positive anomaly zone was prominent at 500hPa. Again, a positive anomaly zone at the 300hPa was the most dramatic situation at this GPH, resulting in the severe instability of the atmospheric zone, causing deadly cyclones. Farukh and Yamada (2014) were found a negative anomaly that triggered the extreme snowfall event at 300hPa GPH. Moreover, it also found that the consequence of SLP negative anomaly occurred in the equatorial eastern pacific El-Nino and Central pacific El-Nino (Sun et al., 2013; Maw and Jinzhong 2017; Wahiduzzaman et al., 2021b). A recent study was done by Gaona et al. (2018), and Zhou et al. (2018) using synoptic climatology showed that tremendous rainfall and surges occurred, which was the consequence of heavy floods and winds that caused by extreme TCs place over the BoB, resulting in huge damages to the society and economy. This also supports that synoptic study of extreme events is essential for advancing the field of planning, forecasting and management.

Nine TCs were formed where the SLP was below 1000hPa, and the temperature ranged from 300C to 36.20C. On these TCs occurring days, a strong negative anomaly of SLP was observed at surface level, 850hPa and 700hPa level over BB. The depression turned larger at 500hPa level height, but dramatically this depression was completely disappeared at 300hPa GPH over the BoB and the entire coastal region of Bangladesh. On the other hand, a cooler air temperature was observed at the surface level, where a complete departure was followed at 850hPa and 700hPa levels. This co-existence of positive and negative temperature zones in the immediate atmospheric level could be triggering the most destructive atmospheric instability at these levels. This kind of phenomenon was observed for causing many unusual atmospheric phenomena in many parts of the world, i.e. negative anomalies at the 700hPa height over the eastern North Pacific Ocean and the western USA also affect above-average snowfall accumulation (McCabe and Legates, 1995). During the pre-monsoon season, an elevated thermal unstable region and a vertical wind shear in the higher atmospheric zone was observed by Yamane and Hayashi (2006). Our research also indicates that negative anomaly in different atmospheric levels triggers deadly TCs events over the BoB. A study done by Farukh et al. (2019) showed that a higher temperature at 850hPa to 300hPa levels could be followed by extreme weather phenomena like TCs when agricultural production was seriously damaged. Several scientists also reported a similar result (e.g., Glibert et al. 2014 and Iyalomhe et al. 2015; Islam et al., 2020). Ruane et al. (2013) showed that coastal agriculture is being affected by higher emissions of pollutants under the climate change scenario.

Of these 22 TCs, only one event was formed when the atmospheric circumstance was completely different, and the SLP was above 1000hPa, and the temperature was 41.70C. In this TCs occurring day, the total coastal area was subjugated by a negative anomaly zone up to 300hPa level indicates the development of a huge depression region over this zone. Many researchers have also found that the larger depression area at the upper atmosphere develops bi vertical wind shear perpendicular wind shear through the total atmospheric column, resulting in severe weather events. Yamane et al. (2012) also have noticed a trough at 550hPa on severe local convective storm days.

Synoptic climatology is an emerging field in scientific research for planning, forecasting, managing disaster damages, and reducing disaster risk. Scientists have identified that tremendous economic and social losses were being unnoticed caused by extreme cyclone events boosting sea level rising due to the upward of temperature (Spada 2017; Chen et al. 2017; Bamber et al. 2018; Cazenave et al. 2019). If the possible landfall of TCs can be forecasted earlier, the damage control also would be significant (Paliwal and Patwardhan, 2013; Weinkle et al., 2012; Singh et al., 2012; Maw et al., 2017). However, the limitation of the study cannot be overlooked. Although unbiased correction was performed in this work, some missing datasets were found from the BMD. We removed the missing datasets before tabulating the TCs event. There was the unavailability of recent weather variables and TCs datasets. The more updated current datasets can be used in this research to obtain better results. These deserve further examination.

This paper explores the association between the weather parameters and TCs events and discusses TC genesis using multivariate statistics and the GCMs model. In this study, we found that the atmospheric instability in different atmospheric zones of the upper ambience triggered many disastrous TCs in the coastal regions and the whole country in the last four decades. From the outcomes of the GCMs model, it is observed that TC events and SLP might be very promising variables for disaster scientists to aid the early forecasting of deadly TCs. We identified a large depression of SLP and erratic temperature anomalies at the upper atmosphere that can cause an unstable atmospheric level that leads to extreme TCs events. To understand the upper atmospheric situation in TC occurring days, this research's outcomes will help establish a policy and preparedness plan for TC prediction. For planning and disaster management, forecast, intensification, and the movement of the TCs, this study will be helpful to measure the vulnerability due to climate change with a special emphasis on extreme TCs. Composite mapping of climate change indices using GCM technology is a strong tool to analyze extreme weather events, especially TCs. Determination of synoptic climatology is very useful for policymaking, planning, disaster mitigation, and saving local inhabitants and the agricultural sector from cyclone hazards. So, the present work will assist the understanding the synoptic climatology associated with SLP and temperature to identify the reasons behind extreme TCs formation in the BoB.

Acknowledgement

The authors would like to express their gratefulness to the Bangladesh Meteorological Department (BMD) and the NOAA for sharing dataset for this research.

Ethical approval

Not applicable

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Data availability

Data are available upon request on the corresponding author

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Author contributions

M.A.B and M.A.M.H, designed, planned, conceptualized, drafted the original manuscript, and M.A.B, and M.A.M.H were involved in statistical analysis, interpretation; M.A.B and M.A.K., contributed instrumental setup, data analysis, validation; A.R.M.T., M.A. B and M.A.M.H., contributed to editing the manuscript, literature review, proofreading; M.A.K. M.A.B and A.R.M. T.I., were involved in software, mapping, and proofreading during the manuscript drafting stage.

Conflict of interest

There is no conflict of interest to publish this work.

Funding statement

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You are reading this latest preprint version

Synoptic Climatology of Weather Parameters Associated With Tropical Cyclone Events In The Coastal Areas of Bay of Bengal

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Abstract

Figures

1. Introduction

2. Data And Methods

2.1 Study region description

2.2 Data sources and quality check

2.3 Principal Component Analysis (PCA)

2.4 K-mean clustering method

2.5 General Circulation Model (GCM)

3. Results

3.1 Clustering analysis

3.2 Sea level pressure anomaly and severe TCs

3.2.1 Cluster 1

3.2.2 Cluster 2

3.2.3 Cluster 3

4. Discussion

5. Conclusion

Declarations

References

Supplementary Files

Status:

Version 1