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Title: Seasonal status of Tropical Cyclone Frequency (TCF) and Sea Surface Temperature

Anomalies (SSTA) over the Bay of Bengal (BOB)

Authors: 1. R. Guha

2. R. Bhattacharya

Institution: Department of Environmental Science, University of Kalyani,

Kalyani741235, West Bengal, India

Corresponding Author: R. Guha

Department of EnvironmentalScience

University of Kalyani

Kalyani741235

Email :rishiparna@live.com

mailto:rishiparna@live.commailto:rishiparna@live.commailto:rishiparna@live.commailto:rishiparna@live.com

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Abstract:

Frequencies of tropical cyclonic storms are studied seasonally from 119 years data over the

Bay of Bengal (BOB). Three major classes of these bay-storms (excluding those occur over

land and Arabian Sea) are explored viz. Cyclonic Depression (CD; 17Wind Speed

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1. Introduction

Tropical Cyclones (TCs) are undoubtedly one of the most devastative weather phenomena

around the world (Gray, 1988). Their potential of destruction certainly carries the

importance to study their spatial and temporal variability (Emanuel, 1987; Cyclone

Mannual, IMD, 2003; Emanuel, 2005). Recent studies reveal that TC frequency (TCF) over

northern hemisphere has registered a decrement of 33% accompanied by 40% decrease in

global number of storm days (Webster et al, 2005; Maue, 2010). However, in the global

platform of TCF analysis Northern Indian Ocean (NIO) is often deprived from detailed

study for its relatively less contribution to global TC number (Chan, 2006; Klotzbach,

2006; Maue, 2010; Klotzbach, 2010). But some studies made in the last decade bring about

some remarkable climatological features of the TC status over this active cyclonic basin.

About 80% of the TCs over NIO (Latitude: 5 N to 20 N, Longitude: 55 E to 90 E) occur

in the Bay of Bengal (BOB) (Singh et al, 2001; Niyas et al, 2009). A typical MAPVIEW

(5 5) of the annual TCF over NIO for the period of 1891-2007 is given in Fig.1. The

grids with TCF > 300 are highlighted and they all belong to BOB. Fluctuation in TC

occurrences over NIO is thus mainly dependent on the TCF over BOB since TCs over

Arabian Sea (AS) are less prone to alteration. BOB and AS, together project a negative

trend of -0.8 per year (Singh et al, 2000). But monthly TCF profiles revealed positive

trends for May and November with a value of 0.67 per years for the later. These two

months are abundant with intense TC formations over BOB and this increasing trend is

observed to be doubled in the period of 1877 to 1999 (Singh et al, 2000; Singh et al, 2001).

On the contrary, a drastic decrease in TCF has been observed in monsoon months (June-

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July-August-September) after 1980. The subsiding trend for annual TCF is attributed for

this radical monsoonal decrement (Mandke and Bhide, 2003). This TCF reduction in

monsoon is also found to be associated with an inverse behavior in Sea surface

Temperature (SST) over BOB. A positive shift is also found in SST anomalies (SSTA) over

the bay after the same crucial year and similar changes are also observed for pre-monsoon

and post-monsoon (Mandke and Bhide, 2003; Bhattacharya et al, 2011). The relationship of

co-variability between TCF and SST over BOB is an important concern to emphasize

because SST is one of the major criteria for TC genesis (Gray, 1988, Chu, 2002). Average

SST over BOB and AS is higher than any other active cyclonic basin in northern

hemisphere and projects an increasing trend as revealed from satellite data since 1970

(Webster et al, 2005). Available global SST consensus accounts for 0.5 degree rise and

subsequently suggests probable modifications of present climate and inevitably the

climatology of severe weather events (Sellers et al, 1998; Pielke et al, 2005). Again

monsoon circulations also exert a powerful interplay in modulating the TCF status over

BOB. Delay or advancement in the progression of monsoon decides seasonal TCF status

over this bay (Rajeevan et al, 2000; Bhanu Kumar et al, 2010). Thus seasonal studies on

the modulation of enhanced SST environment on TCF over the bay is essential (Kumar and

Sankar, 2010). In this context decreased TCF along with rising SST together provide an

interesting issue to study their variability. In the present study seasonal TCF status over

BOB is detailed in terms of trend identification and strength classification. Synchronization

of simultaneous variations in TCF and SST is explored in the observed SST environment.

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2. Data and Methodology

TCF data has been obtained from Cyclone e-Atlas (Version 1.0) outputs and Best-track

data archive, IMD for the period 1891-2009. Three seasons viz. pre-monsoon (March-April-

May: MAM), monsoon (June-July-August-September: JJAS) and post-monsoon (October-

November-December: OND) are considered for the study. Here winter season has been

ignored since it is less productive in terms of TC genesis over NIO (Niyas et al, 2009). The

statistical analysis has been carried out using MATLAB (Version 7.0.4) and MINITAB

(Version 16.0). Re-curvature probability of the TCs is generated by Cyclone e-Atlas

software itself. SSTA time-series data has been obtained from NCEP re-analysis database

for the period 1951-2010 for nullified trend at 1000 hPa pressure level. The SSTA values

are analyzed for the basin grid between latitude 6N to 20N and longitude 81E to 96E.

Forty point locations are selected to study the change in SSTA for each pentad segment (Pi,

where i=1 to 12) of the study period taken into consideration. This dataset is particularly

useful for climatological studies and available from 1948 to present. The geographical

positions of the locations (GP) chosen are given in table 1.

3. Results

a. Seasonal trend estimation

TCF time series data set is statistically analyzed and parameters shown in table 2. It is

evident that the values of co-efficient of variation are comparatively higher for MAM

whereas for other parameters it shows least values. Thus it suggests high dispersion in TCF

during pre-monsoon. More than 50% of the annual numbers of storms occur during the

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monsoon months according to values of f. These monsoon-storms usually belong to

depression category, relatively weak and embedded inside the monsoon system.

Figure 2 shows seasonal TCF variation utilizing a Box-Whiskers plot. Identification of the

median at 95 % confidence level allows the distribution pattern to emerge out. Interestingly

dispersive pattern is only reflected during MAM and OND with less extent for the latter

and outlier occurrences (marked by asterisk *) are observed during JJAS. Figure 3 shows

the fifth year moving averages of annual TCF according to their strength classification for

the concerned three seasons (a, b and c) and annual (d) period. The frequency of weak

storms i.e. Cyclonic Depressions (CD; 17Wind Speed

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b. Variation of SSTA and storm track recurvature

Variation of SSTA is studied over 40 locations given in table 1 for the period 1951-2010 on

every fifth year mean. A positive shift has been identified after 1980 in the SSTA values for

all three seasons for each location. This shift is more profound in case of OND and more

prominent in case of JJAS. Paired t test has been performed for each consecutive pentad

pair and the difference between the means of each pentad is investigated. Six out of eleven

cases (54.54%) are found to be significant for MAM and OND. In eight cases (72.72%),

mean of the presiding pentad are found to be less than just latter pentad (P imean< Pi+1mean) at

0.05 significance level with 90% confidence. To avoid congestion of data, varying pattern

of cumulative SSTA (CSSTA= ) along the study period is shown (Fig. 5)

instead of SSTA for individual locations. The probability of recurvature of these storms

over BOB is also evaluated 1951 onwards up to 2005 (P1 to P11). P12 could not be

included due to unavailability of finalized 2010 TC tracks. Probability of first, second and

third kind (PFK, PSK and PTK) values are shown in Fig. 6 for each pentad of a particular

season. These indices represent the tendencies (%) of a depression (D) system or CS to

intensify into higher category storms viz. SCS. It is evident that intensification tendency is

more during pre-monsoon and least during monsoon months. Positive trend is observed for

PFK (0.875) and PTK (1.54) during OND. The higher probability of recurvature during

pre-monsoon goes along with the idea of change of curvature which is often decided by the

onset of monsoon. However no apparent co-relation is found between the CSSTA and

recurvature probability of TCs.

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4. Discussions

Basin-respective variability is the main feature of trend identification for TCF. Different

basins have native diving forces for intensifying these tropical storms (Benstad, 2009). NIO

has its unique parameters governing the variability of TCF over BOB (Klotzbach, 2010).

In this study seasonal status of TC activity has been analyzed along with strength

classification based on wind speed. Seasonal periodicity of TCF is often not portrayed due

to flattening of the accumulation in TC number and in most of the studies TCF of peak

activity months are emphasized. Present approach reflects decreasing trend in total number

of TCs occurring over the basin. In spite of this decrement SCS frequency show positive

trend and this increment is evident in the TCF during post-monsoon months (OND) also.

Pre-monsoon (MAM) months TCF are found to be attributed with more characteristic

features comparatively than monsoon and post-monsoon seasons viz. high co-efficient of

variation and higher probability of recurvature.

Variation of TCFA over the period of 1951-2005 reveals a transition period of TCFA from

positive to negative phase. Monsoon months show prominent decrease in this very period

but fluctuation resides in case of other two seasons. This period renders the initiation phase

of decremented TCF at P6 (1976-1980) to P7 (1981-1985) as shown in Fig. 4. This decade

(two pentads) are also found to be influential in case of CSSTA as well but with inverse

effects. This kind of SSTA variation is also reported over western north Pacific Ocean with

different transition period on annual basis (GuangHua, 2009). Since monsoon TCs are less

intense and are of depression category their intensification is more or less invisible from the

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recurvature point of view. It can be acquainted that the association of enhanced SSTA is

more pronounced on the weaker storms or depression events. The initiation conditions are

inhibited by existing environment over BOB but the intensification conditions are favored

so that the number of intense storms is increasing. Although NIO is a data-sparse region,

extensive study has been carried out on the TCF variation by both monthly and seasonal

scale to reveal the pattern for occurrences of storms over this region. Several earlier studies

established this kind of SST modulation by Intra-seasonal oscillations (ISOs) over NIO

(Singh, 2008; Sugi et al, 2009). SST has always been considered as one of the most

important parameter to decide the fate of the TCs occurring over active cyclonic basins

around the world (Gray, 1988; Mehta, 1998). These types of modulations are also

interlinked with TCF by altering the location of monsoon trough. Present study mirrors the

impeding effect on relatively weak storms over the bay. More detailed study on the

influential parameters can uncover the potency to explicate the actual governing criteria for

the variations of these storms. At the verge of climate-change and global warming, the issue

of predictability of the tropical storms in terms of abundance and strength are crucial

(Sellers et al, 1998; Pielke et al, 2005; Sugi et al, 2009). Other active cyclonic basins of

northern hemisphere are enriched with direct and indirect measurements for both

climatological and dynamical criteria of TC genesis and maturation and even about their

direction of movement. NIO suffers from constraints in this regard in manifold. Further

studies on this arena will enlighten the actual variability of TCF on more insightful state.

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Acknowledgements

Authors are thankful to India Meteorological Department (IMD) and National Oceanic and

Atmospheric Administration (NOAA) for the relevant data. R. Guha is thankful to

University Grants Commission, India for financial assistance.

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Table 1. Geographical positions of the selected locations.

GP Lat Long GP Lat Long GP Lat Long GP Lat Long

L1 10N 81E L11 6N 87E L21 10N 90E L31 14N 93E

L2 12N 81E L12 8N 87E L22 12N 90E L32 16N 93E

L3 14N 81E L13 10N 87E L23 14N 90E L33 18N 93E

L4 6N 84E L14 12N 87E L24 16N 90E L34 20N 93E

L5 8N 84E L15 14N 87E L25 18N 90E L35 6N 96E

L6 10N 84E L16 16N 87E L26 20N 90E L36 8N 96E

L7 12N 84E L17 18N 87E L27 6N 93E L37 10N 96E

L8 14N 84E L18 20N 87E L28 8N 93E L38 12N 96E

L9 16N 84E L19 6N 90E L29 10N 93E L39 14N 96E

L10 18N 84E L20 8N 90E L30 12N 93E L40 16N 96E

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Table 2. Descriptive Statistics of the TCF time-series.

Period

Parameter

MAM JJAS OND Annual

Years 119

Mean 1.050 5.0 3.51 9.748

Std.dev. 0.7903 2.244 1.534 3.101

Variance 0.6246 5.034 2.354 9.614

Co-efficient of variation 75.24 44.87 43.68 31.81

Skewness 0.43 -0.12 -0.01 0.03

Kurtosis -0.15 -0.48 -0.37 -0.52

Seasonal to Annual ratio (f) 0.107 0.512 0.36

Table 3. Seasonal trend status of TCF.

MAM JJAS OND Annual

CD 0.009 -0.026 0.006 -0.02

CS -0.0002 -0.02 0.0015 -0.018

SCS 0.0016 -0.003 0.006 0.0046

CD+CS+SCS 0.0026 -0.059 0.006 -0.034

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Figure Captions

Fig.1 . Annual TCF over NIO from 1891-2007.

Fig.2. Seasonal TCF variation.

Fig.3. Five year moving averages of TCF for (a) MAM, (b) JJAS, (c) OND and (d)

Annual with varied storm strength according to wind speed (kt).

Fig.4.TCFA based pentad average of storm frequencies for (a) CD (17WS

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Fig. 1.

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Fig. 2.

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Fig.4.

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Fig.5.

-20

-15

-10

-5

0

5

10

15

20

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12CSSTA

MAM JJAS OND

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Fig. 6.