Sea Level and Eddy KineticEnergy Variability in the Bay

of Bengal Using SatelliteAltimetry

Palanik kumar #1

1. Institute of Remote sensing, Anna University, Chennai.

2. National Institute of Oceanography Goa

# Corresponding author contact: palani.kumar1986@gmail.com

Abstract-Using seventeen years

(1993 -2009) of merged altimetric

data, from different missions

(TOPEX/Poseidon, ERS1/2, Jason-1

and Envisat), EKE and SLA

variability in BoB. Overall

analysis shows that mean EKE

ranges from a minimum value of

45.55 (cm2/s2) to a maximum value

of 2780.6 (cm2/s2), with an average

value of 461.5 (cm2/s2). The

maximum eddy kinetic energy was

observed south east of Sri Lanka

and along the east coast of India

and the rest of the BoB exhibits

low EKE throughout the year. The

seasonal cycles is the dominant

signal controlling sea level and

EKE variability. According to

Sharma et al (2010), instability

in the monsoon currents, the

Rossby wave propagation from the

eastern BoB and wind stress curl

are the main causes for the EKE

variability in the BoB. 17 years

(1993-2009) of mean sea level

anomaly, this varies from a

maximum value of 4.953 cm to

minimum value of -2.59 cm with a

mean value of 1.0776 cm. The sea

level presents a trend about 2.85

cm for 17 years in BoB. The

monthly mean sea level anomaly is

maximum (9.8 cm) in December 2008

and minimum (-10.9 cm) in January

1998. In 2008, the BoB experienced

a number of intense cyclones and

the year 1997-98 is characterised

by an active El Nino event (Chen

et al 2009, Chellappan et al

2009). SLAs (both positive and

negative) during December of all

the years (1993-2009) show high

fluctuations associated with warm

and cold core eddies. The minimum

SLA was observed in March/April.

This annual cycle variation in sea

level is due to the steric-effect

- increase in the volume of ocean

without change in the mass

(Caballero et al., 2007). The

intense variability in sea level

along the east coast of India and

around Sri-Lankan coast is due to

the existence of western boundary

currents (WBC). Since the ocean

and the atmosphere are a coupled

system, variability of sea level

reflects changes in climate. One

of the most important aspects of

the climate in the bay is the

Indian monsoon, which possesses

large inter-annual variability.

This variability may cause marked

sea level change due to uctuationfl

of monsoon wind, precipitation

minus evaporation (P - E), heat

uxes, and river runoff into thefl

bay.

Keywords- sea level anomaly, eddy kinetic energy, El-Nino, Rossby waves, Bay of Bengal.

I. INTRODUCTION

Monitoring ocean eddies is vital

for a number of reasons; ocean

eddies affect climate,

transportation, recreation and the

food supply. Sensor technology has

made it easier to probe the ocean

and monitor its eddies, currents,

temperature, salinity, colour and

sea surface height. This

introduction part discusses remote

sensing of oceans by satellite

altimeter, its principle and

applications. Before satellite

altimeter missions, most knowledge

of the global ocean circulation

has been pieced together from

spatially and temporally scattered

observations, which are not

adequate

for a quantitative description of

either the mean or the time-

varying components of the global

ocean circulation. The only

viable approach to observe the

surface circulation of the global

ocean with sufficient resolution

and consistent sampling is the use

of a satellite radar altimeter to

measure the height of the sea

surface [Wunsch and Gaposchkin,

1980; Stewart, 1985; Wunsch,

1992]. Use of satellite radar

altimeters to measure global sea

surface height (SSH) has come a

long way since the brief Seasat

mission of 1978. Early missions

measured SSH with an accuracy of

tens of meters. Satellite

altimeter missions such as

TOPEX/Poseidon (launched August

1992) and Jason-1 (launched

December 2001) measure SSH to an

accuracy of a few centimetres.

These satellites were specifically

designed to measure SSH to the

highest possible accuracy.

1.1 Measuring the ocean from space

A moment of reflection will

remind us of the many

oceanographic insights which first

came from satellite images such as

ubiquity of mesoscale eddies in

the ocean, the existence of

phytoplankton blooms in global

oceans. The rapid development of

remote sensing techniques has

provided more and more multi-

source and large area ocean remote

sensing data. How to retrieve the

ocean eddy information from those

remote sensing products and

utilize them effectively in

scientific applications has became

an urgent priority. An examination

of global picture of satellite sea

level anomaly shows that along the

western boundaries of the ocean,

SLA tends to be heterogeneous,

consisting of positive as well as

negative anomaly indicating strong

eddy activity.

Fig. 1.1. Principle of Satellite Altimeter

(www.jason.noaa.gov.org)

1.1.1 Satellite altimeter and

principle of altimetry

Altimetry satellites determine the

height of the ocean surface with

respect to a reference such as

average global sea level (known as

the Earth’s “geoid”). Orbiting

altimeters make very precise

measurements of the ocean’s

surface topography to derive the

speed and direction of ocean

currents and eddies and to observe

tides and other features.

The primary function of satellite

altimeter is the determination of

satellite's height above the sea

surface. On the simplest level the

altimeter consists of a

transmitter that sends out the

sharp pulses towards the earth, a

receiver to record the pulse after

it is reflected from the surface

and a clock to measure the round

trip travel time of the pulse.

Because the velocity of radar

pulse is known these times can be

used to calculate the satellite’s

height above the surface (Stewart,

1985). As depicted in Fig. 1. 1 to

calculate SSH, several

measurements and calculations are

required along with altimetry

data, including precise

determination of the orbit and

highly accurate tide models. In

addition, because satellite

altimeters measure SSH relative to

the geoid (the sea surface at

resting state), accurate knowledge

of the geoid is required. Besides

surface height, by looking at the

return signal’s amplitude and

waveform, we can also measure wave

height and wind speed over the

oceans, and more generally,

backscatter coefficient and

surface roughness. If the

altimeter emits in two

frequencies, the comparison

between the signals, with respect

to the frequencies used can also

generate interesting results (rain

rate over the oceans, detection of

crevasses over ice shelves, etc).

1.1.2. Components of satellite

altimetry: The main components of

satellite altimetry systems are:

The radar altimeter and antenna,

which measure the sea surface

height , the radiometer, which

measures atmospheric disturbances,

the systems for determining the

satellite precise location in

orbit.

1.1.3 Sources of error in

satellite altimetry: The errors

which occur while measuring SSH

are described below. The

uncertainties of the sea-state

bias have become the leading

sources of error in satellite

altimetry. This error can be

reduced by using BM3 model and

non-parametric model [Gaspar,

1994, Labroue, 2004]. Instrumental

error, atmospheric refraction, and

the interaction between the radar

pulse and the air-sea interface

are the other errors. Instrumental

error consist of random and

systematic component (long-

wavelength). The random error

referred to as instrumental

precision. Uncertainty in SSH

introduced by atmospheric

refraction results from decrease

in the speed of light mainly due

to dry gases, water vapour, and

atmospheric free electrons (Lee et

al.,1988). The range error due to

refraction by dry gases is large,

but can be corrected with the

knowledge of sea level atmospheric

pressure obtained from the

meteorological models. Ionospheric

model (Bentmodel[ERS-1/2]),

(DORIS&TOPEX) dual-frequency[T/P]

by AVISO,1996 is used to correct

the ionospheric range error. ECMWF

model is used to reduce the error

by dry troposphere (Cabellero et

al., 2005)

1.2 Multi-mission mapping

In many ways, the orbit

design of an altimetry satellite

is a compromise. But one point

that deserves special attention is

to get the right balance between

spatial and temporal resolution. A

satellite that revisits the same

spot frequently covers fewer

points than a satellite with a

longer orbital cycle. One solution

to achieve high resolution

coverage is to operate several

satellites together. Merging

multisatellite data sets open a

door to resolve the main space

scales and time scales of the

ocean circulation, in particular,

the meso-scale. Combining

different altimetric missions is a

delicate task: since each mission

has different error budgets and

orbit differences, leading to

large-scale biases and requiring

trends to be corrected. It has

been shown that homogenous and

inter calibrated SSH data sets can

be obtained by using the most

precise mission (T/P and, later

on, Jason-l) as a reference for

the less precise missions (Le

Traon et al., 1995; Le Traon and

Ogor, 1998). Ducet and Le Traon

(2000) gave the first results of

the global mapping. Increasing the

spatial and temporal resolution

was only an idea until the launch

of T/P and ERS-1, which in fact

provided the first opportunity to

merge observations from two

altimeters. Most meso-scale

studies are now taking advantage

of the improved resolution derived

from the SSALTO/DUACS merged,

gridded data sets distributed by

AVISO (Le Traon et al., 1998).

Ducet et al. (2000) presented the

first global high- resolution maps

of mesoscale variability.

Comparing the SSH wavenumber

spectrum from the merged data to

that from the along-track data,

which was considered to represent

the intrinsic resolution of the

altimeter data, they estimated

the resolution of the merged data

to be about 150 km in wavelength.

A similar resolution of about

2° longitude by 2° latitude has

recently been inferred from

wavenumber spectral analysis of

the gridded SSH fields (Chelton

et al., 2009). For Gaussian-

shaped eddies, this wavelength

resolution is able to detect meso-

scale features with e-folding

scales of about 0.4°.

1.3. Ocean dynamic topography:

Ocean currents are mapped by

studying the peaks and trough in

maps of the height of the sea

surface relative to the geoid. The

height is called “dynamic

topography. The altimetric

observation of dynamic topography

η is described in Fig.1.2. Present

geoids are not generally accurate

enough to estimate globally the

absolute dynamic topography η

except at very long wavelengths.

The variable part of dynamic

topography η′ = η -< η>or sea

level anomaly (SLA) is, however,

easily extracted since the geoid

is stationary on the time scale of

an altimetric mission. The most

commonly used method is the

repeat-track method (collinear

analysis). This method is suitable

for satellites whose orbits repeat

their ground tracks (to within ± 1

km) at regular intervals. For a

given track, the variable part of

the signal is obtained by removing

a mean profile (e.g., over the

mission duration), which contains

the geoid and the quasi-permanent

dynamic topography from each

profile.

Fig.1.2. Schematic diagram of dynamics inOcean. (Deborah Klatt 1987)

1.3.1. Sea level change and

variability

The measurement of long-term

changes in global mean sea level

can provide an important

corroboration of predictions by

Ho -- Height of thesatellite’s orbit

Hg -- Geoid height

Ha-- Height of thealtimeter above thesea surface

Hs-- Height of sea

climate models of global warming.

Coastal tide gauges have provided

the main technique by which sea

level change has been measured

during the past century. However,

tide gauges are usually restricted

to coastline and open sea island,

and therefore do not adequately

represent the global ocean.

Satellite altimetry provides a

means of overcoming the

limitations of tide gauge

measurements because the

measurements are truly global in

distribution and tied to the

earth’s center-of-mass in a well

defined reference frame. With over

a decade of precision sea level

measurements from satellite

altimetry in hand and with the

recent launch of new satellite

missions addressing different

aspects of sea level rising,

observationally, we have more

information on sea level change

than ever before. The latest

globally averaged sea level change

estimated from satellite altimeter

3.4 ± 0.4 mm/yr is significantly

larger than the historical rate of

sea level change measured by tide

gauges during the past decades (in

the range of 1–2 mm/yr)( (Ablain

et al.,2009). Moreover, satellite

altimetry has given, for the first

time, information about sea level

change in open oceans and shown

that sea level trends are not

geographically uniform some

regions exhibiting trends about 10

times the global mean (Cazenave

and Nerem, 2004). While the exact

causes of the observed patterns

are still unknown, such a result

is crucial when considering

coastal impacts of sea level rise,

some regions being clearly much

more vulnerable than others. The

rate inferred from altimeter could

still be influenced by decadal

variations of sea level unrelated

to long-term climate change, such

as the Pacific Decadal

Oscillation. It is still unclear

how much of the sea level rise

comes from long-term trend and how

much from decadal variability.

Thus, a longer time series is

needed.

1.4 Ocean currents and eddies

Fig.1.3. shows between spatial-

scale and time-scale shows the

requirement of different spatial-

scale and temporal-scale for

different ocean related studies.

In the ocean, spatial variability

occurs on a wide range of scales

from basin-scale gyres to internal

waves with wavelengths of 100 m,

and further down to molecular

scale. Theory and observations

have shown that the energy of the

ocean variability decreases as the

scales of motion decrease, with

maximum energy at the meso-scale

(from ~10-~100 km, dependent on

the location) of which meso-scale

eddies are very important part.

Fig.1.3. Time and spatial scales of Oceanic

features (LIU cui-hua et al, 2008)

1.4.1 Geostrophic currents

On scales of tens of kilometers

and larger, horizontal motions are

much larger than vertical motions,

and the ocean is approximately

hydrostatic (pressure is

determined by the height and

density of the water column). At

these scales, for approximately

steady motions, and away from

boundaries, the primary balance of

forces is between horizontal

pressure differences and the

Coriolis force, which drives

currents that follow lines of

constant pressure, known as

geostrophic currents. Because

pressure is related to sea surface

height (SSH), geostrophic currents

can be calculated using horizontal

gradients in SSH. The system of

altimeters today captures the

meso-scale, with real-time

estimation (less than a day) of

global SSH.

1.4.2. Meso-scale eddies

Eddies are an ubiquitous

phenomenon in the ocean and, like

weather systems in the atmosphere,

play a fundamental role in shaping

the ocean’s general circulation

and its transport properties.

However, observing eddy transport

properties based on traditional in

situ observing systems remains a

challenge, even with present

unprecedented observing

capabilities at hand. In this

context satellite measurements of

the ocean have proven to be

essential for providing unique

observations of the ocean meso-

scale eddy field. In particular,

altimetry is instrumental for

observing properties of the ocean

eddy field and more generally for

improving our understanding of

eddy dynamics. A detailed summary

of what has been learned from

altimetric sea surface height

(SSH) observations in terms of

large-scale and eddy dynamics is

summarized by Le Traon and Morrow

(2001). Ferrari and Wunsch (2009)

provide a more recent summary of

altimeter-based studies of the

energetics of the ocean

circulation.

Mesoscale eddies occur when there

is a balance of two major forces,

one is a horizontal pressure

gradient force arising from

differences in water density and

the other is an "apparent" force

associated with the Earth’s

rotation, called the Coriolis

force. The Coriolis effect causes

cold-core eddies in the southern

hemisphere to rotate clockwise

(cyclonic) and warm-core eddies

rotate counter clockwise (anti-

cyclonic).

Satellite altimetry has made a

unique contribution to observing

and understanding mesoscale

eddies. We now have more than 20

years of good mesoscale

variability measurements from

Geosat (1986-1989), ERS-1 and ERS-

2 (1991-2008), T/P (1992-2005),

and Jason-1 (2001-2008) to learn

more about the

seasonal/interannual variations in

meso-scale variability. Analyses

of altimeter data have produced

global, quantitative estimates of

eddy kinetic energy (EKE) with

high spatial resolution, revealing

details such as correlation of EKE

with the mean currents and the

role of topography and winds.

Energy of meso-scale eddies

generally exceeds the energy of

mean flow by an order of magnitude

or more (e.g, Wyrtki et al.,

1976; Richardson, 1983; Schmitz

and Luyten, 1991). Merged datasets

from multiple altimeters greatly

improved ability of resolving

mesoscale eddies (Chelton and

Schlax, 2003; Fu et al., 2003;

Pascual et al., 2006). The ability

to monitor them from space has

applications in navigation,

offshore operations, fisheries,

hurricane and climate forecasting.

1.4.3 .Eddy Kinetic Energy

The energy associated with the

turbulent part of the flow of a

fluid or kinetic energy of that

component of fluid flow represents

a departure from the average

kinetic energy of fluid, the mode

of averaging depends on the

particular problem. Also known as

turbulence energy. There are

several studies taken up by

various researchers on EKE of the

global oceans as well as regional

oceans. The satellite altimetry is

a powerful tool to observe the EKE

variability in oceans. The main

sources of EKE is major current

systems exist in the oceans.

Stammer (1997) showed that there

is a good correspondence between

EKE and mean kinetic energy (MKE),

which confirms that the currents

are the main sources of eddies. Xu

et al (2008) showed that Agulhas

region, Kuroshio, Gulf Stream and

ACC are having maximum EKE. They

also found that, ACC meso-scale

variability is found in regions of

abrupt changes of bottom

topography and do not appear to be

associated with strong mean

currents. This may simply imply

that in these areas, baroclinic

instability is unlikely to occur

and that other mechanisms are to

be sought (e.g., barotropic

instability, mean current/bottom

topography interaction). Sources

and sinks of eddy kinetic energy

are Wind-driven sources/sink of

kinetic energy, Sink of KE via

bottom friction,conversion of

potential energy into KE and

dissipation. The baroclinic

instability process depends on the

presence of vertical shear in the

mean flow, implying sloping

density gradients, and an

available store of potential

energy. Under certain conditions,

instabilities form, releasing this

potential energy and converting it

to eddy potential and kinetic

energies. If the mean flow has

significant horizontal shear as in

strong narrow jets, barotropic

instabilities can also occur. In

the real ocean, the mean flow has

both vertical and horizontal

shear, so potentially both

baroclinic and barotropic (mixed)

instabilities can coexist. Through

these instability-forcing

mechanisms, eddy activity is

maximum in regions of major

oceanic currents.

1.4.4. Application of eddy kinetic

energy (EKE)

These oceanic feature having main

applications such as

• Acoustic propagation,

• Optimum ship route planning

• Fishery

• Delineation of good/badmonsoon years

• Heat transport mechanisms.

1.4.5. Wind and its role on EKE

Wind stress is the vertical

transfer of horizontal momentum,

and this momentum is transferred

from the atmosphere to the ocean

by wind stress. Wind fluctuating

in the oceans may generate the EKE

[Stammer 2001]. Seasonal changes

in eddy energy imply that relative

importance of wind –generated eddy

energy is maximum at depth where

baroclinic variability level is

low. There significant correlation

between the seasonal cycle in the

variance of wind stress and the

seasonal cycle in eddy energy over

a substantially wider area than

near the surface. There is a close

correlation between sea level

anomaly and wind stress. Quilfen,

(2000) analysed El-Nino event

during 1997-1998, and showed that

correlation between SLA and wind

stress is 0.94 in the Pacific

region.

1.5. Challenges and perspectives

Better knowledge of ocean

circulation enable us to

understand and predict climate

variability. Altimetry is one of

the most important tools for

monitoring ocean dynamics, and as

such is a source of vital data to

include in forecasting models of

ocean-atmosphere coupled events

such as El Niño, monsoons, the

North Atlantic Oscillation or

decadal oscillations (Xu et al

2008).

However, challenges still exist in

monitoring the ocean variability

from satellite altimetry.

Satellite altimeters are not able

to measure the time-mean

geostrophic currents due to large

uncertainty in geoid. This poses

challenges for deriving the

absolute geostrophic flow in

regions where bottom velocities

are non-zero since hydrographic

estimates of absolute dynamic

topography are unable to capture

the effects of the bottom. The

uncertainties of satellite

altimetry measurements have a high

geographic variability. The

existence of high frequency

energetic barotropic motions in

the ocean can lead to a large

aliasing error in satellite

altimetric observations. New

evidences show that the combined

aliasing from several neighbouring

and crossing tracks could produce

unreal meso-scale signals in

altimeter mapped product. Although

satellite altimetry has improved

our understanding of the climate

system dramatically, it is

important to keep in mind the

problems and new challenges.

1.6. Need for study

A key weakness of nearly all

global climate models is the

absence of an explicit

representation of ocean eddies,

thereby relying on

parameterization. The overall

effect of eddy structure is

important for climate; the

understanding of ocean circulation

is very important for diagnosing

and predicting climate changes and

their effects.Meso-scale eddy are

a critical element in the

establishment of ocean tracer

properties; Eddy provide an avenue

for affecting the ventilation of

heat, Carbon, and other tracers,

and they support rich levels of

biological activity.

A better understanding of large-

scale ocean circulation and long-

term climate variability requires

the knowledge of ocean eddy

kinetic energy.

1.7. Objectives of this study

to estimate the Eddy Kinetic

Energy using geostrophic velocity

components from 17 years of Merged

Sea Level Anomaly altimetry data.

To study the sea level and EKE

variability in the Bay of Bengal

at different temporal and spatial

scales. To infer the eddies,

seasonal currents and other

factors that affect the

variabilities.

2.1. Satellite altimetry and its applications for ocean studies:

The instrument precision of the

Skylab altimeter was inadequate to

detect unambiguously even the sea

level variability associated with

the Gulf Stream (Fu et al 1983).

The first oceanographically useful

altimeter measurements were made

by the Geos-3 Mission (April 1975-

December 1978), which gave

continuous coverage only in the

western North Atlantic. In spite

of crude precision and accuracy,

the Geos-3 data have been used

to produce a map of Gulf Stream

meso-scale variability that

compares well with historical

ship observations (Douglas et

al., 1983). Fu et al. (1987)

demonstrated that a seasonal

signal in the surface current of

the Gulf Stream could be detected

from the Geos-3 data.

The Seasat altimeter (July-October

1978) was the first designed for

oceanographic applications. An

on-board microwave radiometer was

used to estimate the water vapour

range correction. Precision

orbit determination has resulted

in a Seasat orbit accuracy of

better than 50cm (Marsh et

al., 1988). Seasat altimeter sea

level measurements have been shown

to agree with dynamic heights

computed from aircraft

expendable bathythermographs to

an accuracy of 10cm at scales

up to order 1000kin (Bemstein

et al., 1982). With the Seasat

data, oceanographers began to

develop confidence in the quality

and utility of altimeter

measurements. An important aspect

of the Seasat mission is the

availability of a nearly

continuous global data set for the

3.5-month mission, providing

the first global perspective of

sea surface topography and its

variability.

Using only data from the last

month, when Seasat was flown in a

3-day repeat orbit, Cheney et al.

(1983) produced a map showing the

global distribution of mesoscale

sea level variability. Mazzega

(1985) and Woodworth and

Cartwright (1986) pro- duced

altimetrically-determined global

charts of the M2 tide. Using a

mean sea surface computed from

the Seasat data, Tai and Wunsch

(1984) derived the first global

absolute dynamic topography,

showing many qualitatively

plausible patterns of the long

wavelength components of the

mean circulation of the world

ocean. Fu and Chelton (1985)

used Seasat altimeter data to

study the large-scale temporal

variability of the Antarctic

Circumpolar Current for the

period July-October 1978.

After launching of TOPEX/Poseidon

in 1992 there was great

improvement in ocean studies

especially in sea level and meso-

scale studies. There were many

studies taken up by various

researchers using single mission

data understand the dynamics of

ocean. A detailed analysis taken

by Leo Troan (1996, 1998, 2002,

2004) shows main contribution

about merging and analysig methods

of altimetry data to study

mesoscale variability and

estimation of EKE. The T/P+ERS

merged data provide a very good

representation of the meso-scale

variability, the remaining error

can be quite important especially

for correctly determining the

velocity field in coastal and

meso-scale situations (Greenslade

et al., 1997; Tai, 1998). Le Traon

and Dibarboure (2002) provided a

summary of the mapping

capabilities of the T/P + ERS

(Jason-1 + Envisat) configuration,

using simulations from a high-

resolution ocean general

circulation model. With two

altimeters in the T/P-ERS

configuration, they found that sea

level could be mapped with an

accuracy of better than 10% of

signal variance, while velocity

can be mapped with an accuracy of

20–40% of signal variance

(depending on latitude). The

mapping of the ocean signal was

done globally through an improved

objective analysis method that

takes long wavelength residual

errors into account and uses

realistic correlation scales of

the ocean circulation with a

global high-resolution of ¼ degree

every 10 days. To reduce

measurement noise, sea level

anomaly (SLA) is filtered and sub-

sampled. SSALTO/DUACS project

applied the sub-optimal

interpolation technique to

construct gridded data sets from

multi-mission altimeter data (Le

Traon and Dibarboure, 2002).

Before mapping, a crossover

analysis is applied to minimize

the errors between ground tracks,

including a correction for the

large-scale orbit errors. All of

the available altimetric data is

then mapped onto a regular 1/3°

grid every 7-10 days.

2.2 Eddy kinetic energy studies using altimetry

Holloway et al., (1986) provided

the first demonstration of using

satellite altimetry to estimate

the spatial pattern of eddy heat

transport. He applied geostrophic

turbulence theory to the standard

deviation of global SSH obtained

from Seasat altimeter data for

estimating eddy diffusivity, which

was then multiplied by the depth-

averaged climatological gradient

of temperature to compute eddy

heat transport in the North

Pacific Ocean. His results

revealed a great deal of

variability associated with the

Kuroshio Current. Shum et al.

(1990) carried out detailed

analysis of global distribution of

eddy kinetic energy synoptically

from the analysis of GEOSAT Exact

Repeat Mission altimeter data

collected from a 2-year period

from November 1986 November 1988.

The result shows that the maximum

eddy kinetic energy per unit mass

exceeds for most of western

boundary currents. More than 60%

of World Ocean has relatively low

variability with an eddy kinetic

energy less than 300 cm2/s2.

Karen J. Heywood et al. (1997) used

Sea level anomalies from ERS-1

altimetry data to calculate eddy

kinetic energy (EKE) in the South

Indian Ocean and they concluded

that the clearest annual signal is

in the Leeuwin Current, which

displays markedly higher eddy

energy in austral winter than in

austral summer. The South

Equatorial Current shows high

energy at 10-20° S, strongest in

winter. To the east of Madagascar,

low EKE is seen to the west of the

point at which the South

Equatorial Current branches: the

northern branch passing around the

northern tip of Madagascar, while

the southern branch becomes the

eddy rich east Madagascar Current,

which is markedly more variable in

winter. Stammer et al. (1997)

analyzed the correlation between

the T/P derived EKE (assuming

isotropy) and mean kinetic energy

(MKE) (0/1000 dbar geostrophic

current) as derived from Levitus

historical data. As expected,

there is a good correlation

between T/P EKE and MKE maxima, as

the currents are the main sources

of eddies. Qiu et al. (1999) has

made a detailed analysis of the

North Pacific Subtropical Counter

current (STCC) using more than 5

years of T/P data. They showed

that the seasonal modulation of

the STCC eddy field is related to

seasonal variations in the

intensity of baroclinic

instability. The seasonal

cooling/heating of the upper

thermocline modifies the vertical

velocity shear of the STCC/NEC and

the density difference between the

STCC and NEC layers. As a result,

the spring time condition is

considerably more favourable for

baroclinc instability than the

fall-time condition. The

theoretically predicted time scale

of the instability is 60 days and

matches the time lag between the

EKE maximum and the maximum shear

of STCC/NEC. Ducet et al. (2000)

provide a more recent estimation

of the EKE based on the

combination of 5 years of T/P and

values upto 4500 cm2/s2 has been

observed in the western boundary

currents.

Sharma et al. (1999) explained

that Eddy kinetic energy

variations in the Indian Ocean

using four years, 1993-1996 TOPEX

altimeter data. They showed that

along 5±N, EKE is high (>1,000

cm2/s2) toward the eastern side up

to 73±E due to the strong north

equatorial current also observed

in the current atlas. Lowest EKE

is present in the north-eastern

parts of the Arabian Sea and Bay

of Bengal. Almost the entire

Arabian Sea has EKE less than 300

cm2/s2, with a sharp gradient near

the Somali region. Along 50N, east-

to-west strong zonal currents are

observed in the current atlas in

January up to 650E, and is

strongest between 700 and 750E due

to the north equatorial current.

EKE is also more than 1,000

cm2 /s2at this location. The NEC is

more organized and extends beyond

650E toward the west in February

(Cutler and Swallow, 1984). They

noticed During January 1993, the

highest EKE of more than 1,000 cm2

/s2 is above the Sri Lanka coast

in the Bay of Bengal, which is not

present in the other years. They

found that in all the years, the

Arabian Sea experiences low EKE

compared to the Bay of Bengal,

which is in accordance with the

climatological current magnitudes.

Several studies have shown that

Eddy Kinetic Energy approximation

is largely underestimated when

only one satellite is used (Ducet

et al., 2000; Pascual et al.,

2005). Ducet et al.(2000) showed

that the merging of data from two

satellites (T/P and ERS 1/2)

yields EKE levels 30% higher than

EKE from one satellite. A study

by Pasual et al.( 2005) showed

that the EKE can be improved

considerably in areas with intense

vaiability by merging data from

four satellites rather than two.

Pujol et al. (2005) analysed the

eddy kinetic energy (EKE)

variability of the Mediterranean

Sea over eleven years (1993–2003)

was studied using merged (T/P,

Jason, ERS and Envisat) altimetric

data. They showed that the mean

EKE structure is the consequence

of the superposition of different

variability components and meso-

scale, seasonal and inter-annual

components play a key role in this

variability, but the contribution

of long-term decadal signals, as

well as sporadic events, are also

important.

2.3. Methods of estimating EKE

The first estimation of EKE is

provided by Wyrtki et al (1976 )

estimated the mean flow kinetic

energy and from that deviation

energy (EKE ) was calculated using

velocity components derived from

surface drift currents. They are

computed kinetic energy per unit

mass, Emean = ½(u2+v2), of the mean

velocity field on square averages.

Then they computed EKE using

velocity deviations ,

and the quantities

u'u', u'v', We consider these

quantities as estimates of the

horizontal components of the

Reynolds stress tensor of the

large-scale oceanic turbulence

with time scales larger than 1

day and length scales

2.4. Role of different components for generation of EKE

Gill et al. (1974) studied the

conditions for the baroclinic

instability of the oceanic

interior circulation, and showed

that regions such as the Atlantic

North Equatorial Current are

potentially unstable, and, in

principle, would provide eddy

source terms in the interior

oceans. A field study (Fu et al.,

1982), however, failed to confirm

the hypothesis. Later observations

also failed to produce unequivocal

evidence for any simple baroclinic

instability hypothesis in the open

sea (e.g., Mercier and Colin de

Verdiere, 1985). Wyrtki et al

(1976) studied the eddy energy in

the ocean. They observed the

surface currents using merchant

ships to calculate the MKE as well

EKE. Stammer et al. (1999)

investigated relationship between

temporal changes of the eddy field

and local wind stress forcing, no

simple conclusions was arrived and

negatively correlated. The eddy

source terms are associated with

the ocean flow field (baroclinic

and barotropic instability) and

most of the seasonal and secular

changes of the eddy energy

variability are associated with

similar fluctuations in the

strength and stability properties

of these currents, and in the

strength of the interactions with

local bottom topography.

Exceptions are a few places with

high wind energy, notably in the

North Pacific and in the northern

North Atlantic, where a

significant correlation of

altimetric eddy kinetic energy

with the NCEP wind stress fields

can be found both on annual and

inter-annual time scale. Carsten

et al. (2002) analysed the sources

of Eddy Kinetic Energy in the

Labrador Sea. They concluded that

main source of EKE in the Labrador

Sea is an energy transfer due to

Reynolds interaction work

(barotropic instability) in a

con ned region. Brandt et al.fi

(2004) investigated the seasonal

to interannual variability of the

eddy field in the Labrador Sea.

The mean EKE shows strong

seasonality, and the inter-annual

variability shows distinct

regional differences. Pujol et al

(2005) showed a strong positive

EKE trend, coupled with an

increase in the seasonal cycle

amplitude. They found that high

correlation of wind stress

variations and high-frequency EKE

in the Ionian Sea, from this they

conclude that seasonal variations

are partly due to wind-induced

meso-scale activity. Wu et al.

(1999) and Wang, Y. et al. (2006)

suggested that the anomalous wind

stress curl is responsible for the

inter-annual variability of the

SCS circulation. The wind stress

curl over the SCS is closely

correlated with ENSO at inter-

annual scale (Wang, B. et al.,

2000; Fang et al., 2006; Wang, Y.

et al., 2006). The wind stress in

boreal winter exhibits an anti

cyclonic (cyclonic) anomaly during

El Niño (La Niña) events and a

negative (positive) wind stress

curl anomaly occupies the central

basin during El Niño (La Niña)

events. By contrast, the coastal

area has a positive (negative)

wind stress curl anomaly in boreal

winter during El Niño (La Niña)

events. As the climatologically

mean current field in boreal

winter is cyclonic, a weakened

(strengthened) boreal winter

monsoon during El Niño (La Niña)

events is associated with a spin

down (spin up) of the SCS upper-

layer circulation (Liu et al.,

2004; Wang, Y. et al.,

2006).Caballero et al. (2007) used

twelve years (1993–2005) of

altimetric data, combining

different missions (ERS-1/2,

TOPEX/Poseidon, Jason-1 and

Envisat), to analyse sea level and

Eddy Kinetic Energy variability in

the Bay of Biscay at different

time-scales. They showed that the

seasonal cycle is the main time-

scale affecting the sea level and

Eddy Kinetic Energy variability.

Some areas of the ocean basin are

also characterised by intense

variability, due to the presence

of eddies.

Xiaoming et al. (2008) analysed the

seasonal variability in Gulf

Stream region. They found that

eddy kinetic energy (EKE) peaks in

summer while, as measured by the

baroclinic eddy growth time scale,

the ocean is most baroclinically

unstable in late winter. They

argued that the seasonally-varying

Ekman pumping is unlikely to be

responsible for the seasonal

variation in growth time, and that

the summer peak in EKE results

from a reduction in dissipation in

summer compared to winter. They

also note that the ocean surface

velocity dependence of the wind

stress leads to a direct

mechanical damping of eddies

( Duhaut and Straub, 2006; Zhai

and Greatbatch, 2007). The damping

depends on wind speed and implies

stronger damping in winter than in

summer. Fifteen years of merged

altimetric data to analyze the

seasonal to inter-annual

variations of eddy kinetic energy

in South China Sea (SCS). Their

results show the maximum eddy

kinetic energy in the SCS occurs

in August-February, while it peaks

in September-December. Besides,

the seasonal variation, the EKE

also shows strong inter-annual

variation, which has a negative

anomaly in boreal winter during

El-Nino events. The inter-annual

variation of local wind stress

curl associated with El-Nino

Southern Oscillation events may be

cause of inter-annual variation of

eddy kinetic energy in the SCS

(Xuhua et al. 2009).

2.6. Sea level anomaly and EKE

Sea-surface height anomalies

(SSHA) from TOPEX/Poseidon

altimeter from January1993 to

December 1997 were analysed

seasonal, intra-seasonal and

inter-annual variability of

surface circulation in the Bay of

Bengal by Somayajulu et al.

(2002). The western bay between

121 and 161N exhibited large

variability on all time scales. A

strong cyclonic gyre prevailed in

this region from autumn through

winter season and was replaced by

an anti-cyclonic gyre in spring.

In 1993 and 1997, the intense

winter cyclonic gyre was embedded

as cyclonic eddy between two anti-

cyclonic cells in the following

spring. During summer (southwest

monsoon period), alternate

cyclonic and anti-cyclonic

circulation cells prevailed in the

western bay. On inter-annual time

scales, the surface circulation of

the bay was closely linked to the

El Nino Southern Oscillation

(ENSO) and the bay circulation

responded to all the phases of

ENSO events. The SSHA and the sea-

surface temperature anomaly (SSTA)

off Sumatra and in the bay were

negatively correlated to the SSTA

in the Western Equatorial Paci cfi

both during El Ni * no and La Ni *

na. The coastal trapped Kelvin

waves and radiated Rossby waves

provide the oceanic link between

the equatorial region and the bay

for the observed variability in

SSHA. Eigenheer et al. (2000)

analysed the circulation in the

interior of the Bay of Bengal

and of its western boundary

current, the East Indian

Coastal Current, is inferred

from historical ship drift data

and from TOPEX/Poseidon

altimeter data. The boundary

current shows a strong seasonal

variability with reversals twice

per year that lead the

reversal of the local monsoon

wind field by several months.

They concluded that some unusual

behaviour of BoB can be because of

influence of remotely forced

planetary waves.

2.7 Forcing Mechanisms in BoB:

Vinayachandran et al.1997 studied

the monsoon response of sea around

Sri-Lanka. The study provides

further insight into the structure

of the SMC. As in MKM, part of the

SMC turns north-eastward east of

Sri Lanka and ows into the Bay offl

Bengal. This turning, the shallow

nature of the SMC, and its

termination in the east are

attributed to Rossby wave

propagation. The Rossby wave

associated with the spring Wyrtki

(1973) jet has dominant velocity

toward the northeast. A second

Rossby wave, generated by

weakening of the spring Wyrtki jet

and wind stress curl in the

eastern Bay of Bengal, has

dominant velocity toward the

southwest. We note that, while

both Rossby waves can cause the

migration of the SMC toward the

coast of Sri Lanka and maintain

the northeastward branch that owsfl

into the Bay of Bengal (Schott et

al. 1994). Vinayachandran et al,

1998 studies suggest that there

are two important mechanisms that

drive the summer monsoon

circulation in the interior of the

BoB. The first one is due to the

Ekman pumping by the winds over

the BoB. The winds over the BoB

are south-westerly’s during

summer and they possess

positive (cyclonic) curl in the

western part of the Bay of

Bengal and negative

(anticyclonic) curl in the

eastern part. In particular,

the curl of the wind stress

has large positive value

around Sri Lanka. This large

cyclonic curl drives a cyclonic

circulation east of Sri Lanka

leading to the formation of the

Sri-Lanka Dome(SLD). The second

mechanism is due to the Rossby

wave radiated from the eastern

boundary, primarily by the

reflection of the eastward

spring Wyrtki jet in the

equatorial Indian Ocean. During

the transition between monsoons

(i.e., during April- May and

October-November) the westerly

winds along the equatorial

Indian Ocean drive intense

eastward jets (Wyrtki, 1973). The

Kelvin wave associated with these

equatorial jets propagates

eastward, and on meeting the

eastern boundary, one part of

the energy reflects as a Rossby

wave and the other propagates

poleward as a coastal Kelvin

wave (Moore and Philander,

1977). The presence of these

waves in climatological

hydrographic data and numerical

models has been confirmed

(e.g., Yamagata et al., 1996),

and their implications to the

circulation in the BoB has been

studied in detail (Po- temra et

al., 1991; Yu et al., 1991;

McCreary et al., 1993, 1996;

Kumar and Unnikrishnan, 1995;

Shankar et al., 1996;

Vinayachandran et al., 1996)

3. MATERIALS AND METHODS

3.1 Study region:

Figure 3.1 Bay of Bengal

The Bay of Bengal located (50

N to 250 N, 75 0 E to 100 0 N) in

north-eastern part of Indian

Ocean. The Bay of Bengal (BOB) is

one of the least studied basins of

the world ocean. On an average BOB

is warmer and less saline than

Arabian Sea. It is the largest bay

in the world. This region is land

locked on three sides undergoing

intense air-sea interaction

processes. It is also dominated by

upwelling-derived plumes, wedges

of cold waters, and oceanic

eddies. The Bay of Bengal is

forced locally by seasonally

reversing monsoon winds and

remotely by the winds in the

equatorial Indian Ocean [McCreary

et al., 1993]. In addition, the

Bay receives a large quantity of

freshwater from both rainfall and

river runoff from the bordering

countries. The circulation along

the western boundary of the Bay of

Bengal consists of the East India

Coastal Current (EICC) that

reverses its direction with

seasons. The Bay of Bengal is

characteristically different from

the other tropical basins of the

world. This is partly due to its

limited northern extent and the

monsoonal wind forcing which

reverses semi- annually. Although

the geographical setting of the

Bay of Bengal is very similar to

the Arabian Sea, the Bay appears

to be vastly different from the

Arabian Sea in physical, chemical

and biological features. The prime

reason for this is the immense

quantities of fresh water runoff

(~1.5 x 1012 m3 per year) and the

associated sediment load (billions

of tonnes) it brings in to the

basin. There are two monsoons,

northeast and southwest monsoon

well dominate in the bay and

highly modulated in EKE because

this monsoon. During monsoon

seasons the bay receives more rain

particularly in northeast monsoon,

the areas, Sri-Lanka and Tamil-

Nadu Andhra coast.

3.2 Data and Methodology

In order to understand the

characteristics of the Sea Level

and EKE and their variability in

the BOB and address their origin

and evolution, a suite of data

sets of remote sensing were

utilized. The spatial structure

and the variability of eddies

were examined by analyzing using

weekly merged sea level anomalies

data and geostrophic velocity

components of Topex-

Posiedon,ERS1/2 ,Envisat,Jason-

1,and Ocean Surface Topography

Mission (OSTM)/Jason-2 satellites

obtained from AVISO ftp

ftp.aviso.oceanobs.com . 17 years

of merged altimetry data were

selected for this study extending

from October- 1992 March-2010 with

weekly interval on 1/3 degree

Mercator grid.

Merged product were obtained:

From October 1992 to August

2002 : Topex/Poseidon + ERS-

1 or ERS-2.

From August 2002 to June

2003 : jason-1 + ERS-2.

Topex/Poseidon was replaced

by Jason – 1 in August 2003

after it orbit change.

From June 2003 to January

2004 : Jason – 1 + Envisat.

ERS – 2 is no longer used

since the loss of its Low

Bit Recorder (LBR) in June

2003.

From January 2009 :

OSTM/Jason – 2 + Envisat. Jason –

2 was replaced by OSTM/Jason – 2

in January 2009 after its orbit

change (ground track interlaced

with Jason-1’s and with a time lag

of approximately 5 days between

both) Several studies have shown

that Eddy Kinetic Energy

approximation is largely

underestimated when only one

satellite is used (Ducet et al.,

2000; Pascual et al., 2005). Ducet

et al.(2000) showed that the

merging of data from two

satellites (T/P and ERS 1/2)

yields EKE levels 30% higher than

EKE from one satellite. A study

by Pasual et al.( 2005) showed

that the EKE can be improved

considerably in areas with intense

vaiability by merging data from

four satellites rather than two.

However very, few studies have

been carried out to understand the

impact of different altimeter

configuration on the Eddy Kinetic

Energy level in the BoB. The data

continues to be used to map the

sea surface height, geostrophic

velocity and significant wave

height over the global ocean. In

combination with other data

streams such as ocean colour,

winds, sea surface temperature,

gravity and ocean profiling

floats, scientific researchers are

discovering a new ways to view the

ocean processes and are

increasingly able to discern more

mesoscale structures. The data has

proved to be a key to understand

the Earth’s delicate climate

balance, and is a critical

component of global climate

studies, research on El Nino and

La Nino, and longer term climate

events, and studies of Sea Level

Rise

3.2.1 MSLA delayed Time Products:

The Delayed Time component of

SSALTO/DUACS system is responsible

for the production of processed

Jason-1, Jason-2, T/P, ENVISAT,

GFO, ERS1/2 and even Geosat data

in order to provide a

homogeneous, inter-calibrated and

highly accurate long time series

of SLA and MSLA altimeter data.

DT products are more precise than

NRT products. Two reasons explain

this quality difference. The first

one is the better intrinsic

quality of the POE orbit used in

the GDR processing. The second

reason is that in the DT DUACS

processing, both SLA and MSLA

products can be computed optimally

with a centred computation time

window for OER, LWE and mapping

processes (6 weeks before and

after the date). The nomenclature

used for the along-track delayed

time Maps of Sea level Anomaly

data product is:

delay_range_zone_mission_product_variable_d

atebegin_date end_dateprod.nc

Eg:dt_upd_global_merged_msla_h_199

30414_19930414_20100503.nc

3.3 Computation of Sea level anomaly and EKE

The energy can be calculated

from sea level anomaly and

geostrophic velocity components.

The energy associated with

turbulent part of the fluid in

oceanic motion. This parameter is

very important to study the

circulation pattern and

variability in oceans. The

satellite altimeter will give the

sea level anomaly computed as

mentioned below. From the sea

level anomaly using geostrophic

balance equations the zonal and

meridional velocities are

computed. If Ho, the height of

satellite orbit above a standard

reference ellipsoid is also

accurately known, the height of

the sea surface referenced to the

same ellipsoid given by

Hs(x,t )= Ho(x,t) - Ha(x,t)

Where Ha(x,t) is the altimeter

measurement of its height above

the sea surface. If the ocean were

at rest, the sea surface would

vary only because of variations in

gravity due to irregular

distribution of the earth’s mass.

This surface of constant

geopotential, which varies

worldwide by approximately 100 m

(Born et al.,1982) is called geoid

Because the ocean is not at rest,

however ,there is a displacement η

of the surface relative to geoid

called dynamic topography.

According to Stewart (1985) causes

of this departure includes tides,

winds, atmospheric pressure and

current systems with associated

mesoscale feature. Combining the

definition of η

η(x,t )= Hs(x,t )- Hg(x)

the η(x,t ) is called sea level

anomaly. and from there the zonal

and meridonial components of the

geostrophic velocity anomalies are

derived

Ug = -g/f* η /y

Vg = -g/f* η /x

Where f and g are the Coriolis

parameter and gravity,

respectively. The variance of

these velocity anomalies is

considered as EKE, which is

representative of mesoscale

vaiablity,

EKE=1/2(Ug2+Vg

2)

This is a proven tool in studying

the mesoscale features as well as

seasonal variation of the ocean.

3.4 Software used:

3.4.1 Basic Radar Altimetry

Toolbox (BRAT):

It is a collection of tools

to use the radar altimetry data

from all altimeter-satellite. Such

a tool is an integrated approach

and view is vital not only for

assessing the current status of

what altimeter products but also

show a systematic and consistency

with past. It is able to read most

distributed radar altimetry data,

from ERS-1/2, Topex/Poseidon,

Geosat Follow-on,

Jason-1/2/Envisat, and Cryosat

missions, and also perform data

processing, editing and providing

results statistics and

visualization the results. As a

part of the toolbox, a Radar

Altimetry Tutorial gives general

information about altimetry, the

technique involved and its

applications, as well as an

overview of past, present and

future missions, including

information on how to access data

and additional software and

documentation. It also presents a

series of data use cases, covering

all uses of altimetry over ocean,

cryosphere and land, showing the

basic methods for some of the most

frequent manners of using

altimetry data. BRAT is being

developed under contract with ESA

and CNES.

Figure3.2: Snapshot of GUI in BRAT

3.5 Ferret: Ferret is an

interactive computer visualization

and analysis environment designed

to meet the needs of

oceanographers and meteorologists

analyzing large and complex

gridded data sets. Ferret’s

gridded variables can be one to

four dimensions-usually longitude,

latitude, depth, and time. The

coordinates along each axis may be

regularly or irregularly spaced.

Ferret offers the ability to

define new variables interactively

as mathematical expressions

involving dataset variables and

abstract coordinates.

3.6 Flow chart explaining the work flow:

Figure 3.3 Methodology

RESULTS AND DISCUSSIONS:

4.1 Climatological mean EKE distribution

Figure 4a shows the

climatologically mean EKE

distribution derived from merged

data during 1993-2009. For the

first time, a 17-year series of

altimetric data has made it

possible to produce a relatively

realistic view of the main

characteristics of the mean EKE in

the BoB (75°–100°E, 5°–25°N).

Overall analysis of BoB shows that

mean EKE ranges from a minimum

value of 45.55 (cm2/s2) to a

altimetry data products

geostrophic velocity (u,v)

computation of eddy kinetic energy, sla

basic radar altimetry tool box, ferret

analyzing the eke and sea level in spatial and time series in bob

monthly variability

seasonal variabilit

y

interannual variability

sea level anomaly (h)

maximum value of 2780.6 (cm2/s2),

with an average value of 461.5

(cm2/s2). The maximum eddy kinetic

energy was observed south east of

Sri Lanka, along the east coast of

India and the rest of the BoB

exhibits low EKE throughout the

year. The spatial distribution of

EKE is found to be higher when

compared to previous estimation

made by Sharma et al., (1999)

using four years of TOPEX/Poseidon

altimeter data(1993-1996). This

region is modified by seasonally

reversing current pattern during

northeast as well as southwest

monsoons, Shankar et.al (2002).

The main sources of EKE is the

East Indian Coastal Current (EICC)

instability which changes its

direction twice a year. During

southwest monsoon it flows

poleward and flows towards equator

during winter monsoon. Average EKE

of BoB was high compared to

Arabian sea and whole Indian

Ocean. BoB is prone to intense

tropical cyclones throughout the

year. It is also remotely forced

by equatorial process such as IOD

Vinayachandran et al., (1997) and

El-Nino and La-Nino. 1984). During

November1998, the highest mean EKE

of more than 700 cm2/s2 is noticed

above the Sri Lanka coast in the

Bay of Bengal, which is not

present in the other years except

the June same year and December

2005. The dominant influence of

warm El-Nino event, particularly

off east coast of India, off

Sumatra and equatorial region,

south of Sri-Lanka has been

reported by Somayajulu et.al,

(2003). The monsoonal wind

develops wind stress curl near

Sri-Lanka. This negative and

positive wind stress curl may

cause the high EKE. In the east

coast the currents produce

instability

(barotrophic/baroclinic) which

causes the high EKE. The baroclinc

instability process depends on the

presence of vertical shear in the

mean flow, implying sloping

density gradients, and an

available store of potential

energy. Under certain conditions,

instabilities form, releasing this

potential energy and converting it

to eddy potential and kinetic

energies. If the mean flow has

significant horizontal shear as in

strong narrow jets, barotropic

instabilities can also occur. In

the real ocean, the mean flow has

both vertical and horizontal

shear, so potentially both

baroclinic and barotropic (mixed)

instabilities can coexist. Through

these instability-forcing

mechanisms, eddy activity is

maximum in regions of major

oceanic currents.

Figure 4a: Mean EKE in BoB (1993-2009)

The mean EKE distribution in Fig

4a, represents the mean state of

the BoB over 17 years. The EKE

structure in the BoB is a

consequence of superimposition of

various variability components.

The features leaving their

signature in the global mean EKE

are ranging from few weeks to

months in the entire period

studied. Seasonal eddies and wind

stress by monsoonal changes may

give such variability in BoB

throughout the study period. The

EKE also strongly correlated to

El-Nino La-Nino events. Merging of

altimetry products will refine the

spatial distribution of EKE in

BoB. This merging of more

satellite altimetry data will

bring the 30 % of increase in

energy level compared to the use

of only one satellite altimeter

[Pascual et.al.(2005)]. Though EKE

is higher at the locations of

eddies, it is also present near

the regions of strong currents.

Computation of EKE will be helpful

in understanding the role of

oceans in air–sea interaction

processes.

4.2 Annual cycle of EKE in BoB

The figure 4b shows the mean

EKE of annual cycle from 1993-

2009. The EKE peaks (554.71

cm2/s2) in November with a

secondary peak in June (487.32

cm2/s2) for the entire BoB. The

minimum EKE (409.01 cm2/s2) was

observed in March. The EKE

observed in western BoB is very

high compared to entire BoB. The

maximum EKE observed in November

with an value of 998.46 cm2/s2

western BoB in November and

minimum in February(485.53 cm2/s2).

Fig 4b Annual cycle of EKE averaged 17 yrsfor BoB (50N to 250N and 750E to 1000E) andwestern BoB (50N to 200N and 800E to 850E)

The western BoB experiences more

EKE in November, with highest

values in 1998 and followed by

2005. The highest values are due

to western boundary currents which

intensify by during northeast

monsoon until December. The

presence of maximum EKE during

November, June and December along

the western Bay of Bengal is

attributed to the combined effect

of interior Ekman pumping, remote

along-shore winds, and equatorial

forcing (McCreary et al., 1996).

4.3 Monthly variability of EKE:

Figure 4 c Monthly evolution of EKE

The figure 4c shows the monthly

variability of EKE in BoB from

1993 to 2009. The highest eddy

kinetic energy was observed in

June 1998 and November 1998 with a

values of 732.98 cm2/s2, 744.92

cm2/s2. The lowest eddy kinetic

energy was observed in April 2000

and April 1995 with values of

298.49 cm2/s2, 298.98 cm2/s2. The

high EKE in BoB in June 1998 must

be the impact of El Nino event

during December 1997-January 1998.

The next highest value of EKE are

found during December 2001 with an

average value of 730.08 cm2/s2. As

expected, the highest EKE values

are found in areas with higher

SLA variability (Caballero et al

2005). High variability associated

with the existence of monsoonal

currents such as summer monsoon

current, winter monsoon current

and eddies. These areas are

characterised by seasonal changes

in all year. EICC will changes its

direction twice a year due to

monsoons. The eddies are generated

from the EICC and remotely forced

by equatorial process. In all the

years, Bay of Bengal experiences

high EKE compared to the Arabian

Sea, which is in accordance with

the climatological current

magnitudes. This trend continues

until May. In 1994 and 1996, there

is a patch of high energy near Sri

Lanka. The strong EKE in this

region could be due to the

striking of the reflected Rossby

waves at the Sri Lankacoast.

However, the explanation of this

feature needs more detailed

investigation. Except east coast

of India and Around Sri-Lanka more

than 50% of study region shows low

EKE (<400 cm2/s2) over the entire

study period. EKE and SLA is not

always correlated because some

time for example during December

1997 high variability in SLA, that

time EKE was normal. But after

three months there was high

variability in EKE that is during

June1998 as well as November 1998.

There is a phase-lag between EKE

and SLA was found in BoB.

4.4 Inter-annual variability of EKE

Figure 4 d Annual variations of EKE in BoBfrom 1993-2009

Figure 4d shows the year-by-year

mean EKE in the BoB, and renders

evidence of strong year by year

differences. The peak EKE is

observed in the BoB in 1998 with a

value of 567.08 cm2/s2. The minimum

value 386.07 cm2/s2 of EKE is found

in 2008, which is 32% less than

maximal one in 1998. Higher EKE

years are 1997,1998,2005,2002,

which corresponds to both El-Nino

and La-Nino events. There was

strong El-Nino event in 1997-1998

which affect the EKE variability

in BoB. The temporal variations of

EKE is shown in figure 4. Here we

computed the mean EKE trend

starting from January 1993 to

December 2009. The studies of Liu

et al. (2004) and Xie et al.

(2003) show that ENSO is a major

forcing for regional climate

variability, as reflected in

monsoon and SST variations. We

found that EKE is high in 1998

because of La-Nino event. In 2000

EKE was very low, because the

ENSO event not occurred with a

normal SST distribution. In 2008

also low eddy kinetic energy was

found. SLA was found to be always

positive in the entire BoB

throughout the year 2008, having

EKE negatively correlated with

SLA. Figure 4e shows the spatial

distribution of EKE from 1993 to

2009. Off southeast of Sri-Lanka,

EKE remains high in all year. This

is be because of the monsoonal

winds, westward propagation of

Rossby waves (vinayachandran et al

2005) and bottom topography of

Sri-Lanka. Pujol et al. (2005)

also showed that recurrent

structures contribute

significantly to EKE annual

variations. Second, Beckmann et

al. (1994) and Stammer (1998)

found that the eddy kinetic energy

distributions we closely

associated with the (local) mean

baroclinic flow. Detlef et al.

(1999) pointed out that mean flow

instabilities were the major

source of eddy energy over most

areas of the mid-latitude oceans.

However, the strong western

boundary current also exists in

winter (Li et al., 2000) when the

EKE is relatively weak, indicating

that mean flow baroclinic

instabilities are not the major

source of the EKE seasonal

variability in this area. Fang et

al. (2002) and Wu et al. (1998)

noticed wind stress curl was the

major factor in generating gyres

in the BoB. Detlef et al. (1999)

found a strong correlation with

time-varying altimetric eddy

kinetic energy (in the

northeastern Pacific, etc.) over

annual or longer periods where

wind stress forcing was found. The

figure 4e shows spatial

distribution of EKE from 1993-2009

in BoB. The figure shows that EKE

varies with annually particularly

in 1996,1997,1998 shows strong

variations in EKE compare to rest

of the years. Every year high EKE

is found in southeast of Sri-

Lanka. The EKE was also seasonally

modulated in the east coast of

India particularly in the Tamil

Nadu and Andhra coastal regions.

These are coastal upwelling

regions which causes the formation

of eddies.

Figure 4e Spatial distribution of Inter-

annual EKE in BoB.

Intra-annual variability:

Figure 4f Intra –annual variability of EKE

Figure 4g Spatial distribution of average

17year EKE in every month

4.5 Seasonal variability of EKE:

The figure 4h shows the seasonal

variability of 17 years mean EKE

in BoB. The seasonal scale is

summer monsoon (southwest) four

month periods (June to September)

and winter monsoon (northeast)

preceding year of October-

December and current year of

January. The non-monsoon periods

are taken as February to May every

year. During southwest monsoon the

maximum EKE (638.12 cm2/s2) found

to be in 1998 and minimum value

(377.33 cm2/s2) found to be in

2008, average value of 475.12

cm2/s2. In northeast monsoon the

maximum (643.67 cm2/s2) EKE found

to be in 1998 and minimum value

(394.67 cm2/s2) found to be in

2009, average value of 499.5

cm2/s2. As expected lowest EKE is

found in non-monsoon season with

maximum value (527.38 cm2/s2)

occurred in 2005 and minimum value

(348.54 cm2/s2) in 2001, average

value is 430.86 cm2/s2. The spatial

distribution of EKE for two

monsoon and one non monsoon shows

during monsoon period variability

of EKE was very high. During

northeast monsoon the currents are

flowing towards equator which

makes high EKE along the east

coast of India especially in north

of Sri-Lanka and Tamil Nadu coast.

During southwest monsoon the high

energy centre was found in south

east of Sri-Lanka apart from this

rest of BoB shows low kinetic

energy was found. In non monsoon

season the EKE was very low

compared to monsoon periods.

During this period the EICC

changes its direction towards

equator. This time the EKE maximumis found in 180 N and 850 E. The

developing of positive and

negative wind stress curl and

westward propagation of EKE may be

the reason for EKE. Another

possible mechanism for generating

EKE during northeast monsoon are

coastal Kelvin waves propagating

northward along the eastern

boundary of the Bay excite

westward propagating Rossby waves

into the interior of the Bay.

These northward-propagating Kelvin

waves are excited by the

reflection of equatorial Kelvin

waves at the eastern boundary of

the basin. Kelvin waves, which

after travelling along the

northern boundary of the Bay and

on reaching the east coast of

India in March generate a polewardcurrent[Shankar et al.1996]

Figure 4h: Seasonal variations of EKE:northeast, southwest, Non-monsoon in BoB

Table 4a: Seasonal variations of EKE BoB

The tabulated values show the

variation of EKE seasonally from

1993 to 2009. The table summarise

that BoB strongly modulated by

seasonally in all years. For

example during 1998 seasons, the

northeast monsoon highly

dominated, but the same year in

non-monsoon time the EKE was 30 %

less than the non-monsoon season.

Year

EKE(cm2/s2)duringN-Emonsoon

EKE(cm2/s2)duringS-Wmonsoon

EKE(cm2/s

2)duringNon-

monsoon

1993

479.24

486.9

434.59

1994

471.73

450.21

364.99

1995

528.36

404.66

375.93

1996

547.93

469.28

490.98

1997

448.52

573.84

509.88

1998

643.67

638.12

438.02

1999

543.25

424.8

439.44

2000

434.83

404.51

367.47

2001

617.28

407.3

348.54

2002

465.86

490.67

477.27

2003

472.89

473.47

453.15

2004

443.17

490.86

437.92

2005

499.68

523.49

527.38

2006

448.21

485.09

419.62

2007

459.34

405.15

409.03

2008

448.08

377.33

355.99

2009

394.37

406.24

377.99

But some reverse trend observed in

2005 seasons. During non-monsoon

was high compare to the other two

seasons. Except 2005 rest of the

year non-monsoon seasons show low

value of EKE than other two

monsoon. Strong El-Nino event

observed in December of 1997 and

January of 1998 in Pacific region,

in BoB during 1997 northeast

monsoon EKE was very low which

consists of November ,December

periods of El-Nino event. This El-

Nino event may causes the low EKE

in northeast monsoon during 1997.

But 1998 is a La-Nino year, during

that time high monsoonal EKE was

observed in two seasons. The

regionally averaged picture

confirms that EKE is higher in

northeast monsoon, especially in

east coast of India (100N to 180

N). During summer the EKE also

relatively high, especially in

south east of Sri-Lanka (800 E to

860 E). During non-monsoon the EICC

changes its direction towards

equator. During that time high EKE

found in 180 N.

Figure 4i: EKE variations during monsoonperiods top 54 seasons from 1993 southwestmonsoon to non-monsoon, below shows thetemporal evolution of EKE every year all

monsoon and non-monsoon

The temporal evolution of EKE

during season time for entire BoB

figured out. The EKE was very low

during non-monsoon periods on

because of currents are stable and

not fluctuated by wind, but during

seasons time the wind highly

fluctuating the currents speed and

its direction. Xiaoming Zhai et

al, 2008 reported that Subtropical

Countercurrent is subject to

strong baroclinic instability in

spring, but is only weakly

unstable in fall, due to seasonal

variation of the background

stratification. The dissipation of

EKE might also vary seasonally.

Alexander and Deser [1995] have

discussed how sea surface

temperature anomalies can

disappear beneath the summer

thermocline only to re-emerge the

following winter and we suggest

that eddies can be similarly

shielded from interaction with the

atmosphere during summer, leading

to less damping at that time of

year. Likewise, direct mechanical

damping of eddies [Duhaut and

Straub, 2006] depends on wind

speed, which implies less damping

in summer than in winter. Heresome consequences in BoB leading

to during winter seasons also the

EKE was highly variable because

wind speed was more during winter

time.

4.6 EKE variations with Latitude

and Longitude:

Figure 4j: EKE variations with latitude for17 year (top), with longitude (below)

The figure 4j shows the EKE

variability with respect to

latitude and longitude. The EKE

varies strongly with latitude,

especially at 60 latitude with a

value of 663.5cm2/s2 in south of

Sri-Lanka. This place is

frequently undergoing various

processes such as seasonal wind,

westward propagation of Rossby

waves and seasonal eddies. The

next high peak was found in 160 N

with a value 548.46 cm2/s2.This

regions are east coast of India

currents flow regions throughout

the year. These places also

frequent upwelling regions. The

EKE also varies with longitude,

which is very high in 830 E east of

Sri-Lanka with a value of 754.45

cm2/s2. Except east coast of India

rest of the BoB exhibits low EKE

in all longitude. But near Myanmar

coast at there will high upwelling

in all year. This will dominate

the high EKE at 990 E.

4.7.1Climatologically mean of Sea

level anomaly distribution (1993-

2009):

Figure 4k shows the 17 years

(1993-2009) of mean sea level

anomaly, which varies from a

maximum value of 4.953 cm to

minimum value of -2.59 cm with a

mean value of 1.0776 cm. The

highest SLA was found in near

Myanmar coast in eastern BoB and

northern BoB. The lowest values are

found to be north-western side of

BoB located exactly at 170 N and 870

E. Other than that east coast of

India also experience a negative

and positive SLA during 1993-2009.

This SLA evolution was not giving

the exact distribution of SLA

because it undergoes various

changes such seasonal annual as

well as mesoscale. However, the

17-year mean annual circulation

showed the dominant in uence offl

intense warm El Nino event,

particularly off the eastern BoB,

Myanmar coast and equatorial region

(Somayajulu et al. 2003).

Figure 4k mean SLA in BoB (1993-2009)

4.7.2 Monthly variability of SLA

Figure 4l monthly variability of SLA from 1993 to 2009

The figure 4l shows the SLA

variability in BoB from 1993 to

2009. The highest mean value of SLA

9.8 cm is found in December 2008.

The minimum mean negative value of

SLA -10.93 cm is found in December

1998 which corresponds to strong

El-Nino event (December1997-

January-1998).The month of

November, December 1997 and January

of 1998 shows high negative SLA in

BoB. The sea level trend in BoB is

found 2.85 cm in 17 years, where

increase in sea level is found to

be 1.67 mm/year. During El-Nino

years the negative SLA was observed

in BoB and during La-Nino years

positive SLA was found. The monthly

SLA shows there is increase in

trend of sea level in BoB, which is

2.85 cm in BoB for 17 years.

Another interesting thing is in

2008 the sea level anomalies are

always positive which corresponds

to La-Nino event 2007-2008. This

year BoB experiences more cyclones

(ref:IMD). The sea level is a good

indicator for changes that occur in

the upper ocean water properties as

well as in the oceanic circulation;

thus, it is particularly important

in the sea level variation studies.

In recent decades, considerable

efforts have been devoted to

estimate long-term sea level trends

(e.g. Douglas, 1991; Cabanes et

al.,2001; Church et al., 2001;

White et al., 2005). It is commonly

accepted that global sea level has

been rising at a rate of 1.5±0.5

mm/yr during the last century

(Church et al., 2001) and

satellite-based estimation suggest

that sea level rise has increased

to a even faster rate of 2.8±0.4

mm/yr during the last decade

(Cazenave and Nerem, 2004).

However, mean rise in sea level is

only part of the story. Many

studies have indicated that the

spatial distribution of sea level

rise presents regional differences

and sea level instead fell in some

regions (e.g. Douglas, 2001;

Cazenave et al., 2002; Cazenave and

Nerem, 2004; Carton et al., 2005).

There are larger sea level

variations in the western tropical

Pacific, eastern Indian and

Southern Ocean during the past

decade (Carton et al., 2005). Some

studies on marginal sea level

variations suggested an even faster

changing rate in those areas during

the last decade (Cazenave et al.,

2002; Li et al., 2002). There are

two reasons to be consider increase

in sea level, the first process

involved an increase in mass of

water in the ocean (eustatic

component ) derived largely from

the melting of ice on land and an

increase of the volume of ocean

without change in mass (steric

effect) largely caused by thermal

expansion of ocean water.

4.7.3 Inter-Annual Variability of

Sea level anomaly in BoB:

Figure 4m Inter-Annual variations of SLA in

BoB from 1993-2009

The figure 4m shows the inter-

annual variability of SLA from 1993

to 2009.The lowest mean value of

SLA (-2.345 cm) was observed in

1997 and highest value (5.085 cm)

found in 2008.The rest of the year

shows a normal trend. During 1997

El-Nino event the BoB experiences

very low sea level anomaly all

through the months which is

reflected in annual. During El-Nino

years the SLA is positive and La-

Nino years it is negative. In 2008

SLA is positive which year a normal

La-Nino year.

Figure 4n Inter-annual variability of SLAfrom1993-2009

The figure 4n shows the mean SLA

variations in BoB from 1993-2009.

The positive and negative cells

describe the anti-cyclonic

(clockwise) and cyclonic (anti-

clockwise) circulation,

respectively (with reference to

the Northern Hemisphere). The

location and spatial extent of

these cells, however, showed

considerable annual variation

positive anomaly cells in one year

were replaced by negative anomaly

cells in another year, and vice

versa. This suggests the

occurrence of prominent inter-

annual variability in the large-

scale surface circulation in the

BoB. Maps for the years 2000,

2001,2005,2008,2009 showed a

predominant positive anomaly in

eastern BoB, while maps for 1994,

1997, 2002, and 2006 revealed

intense negative anomaly at the

same region. These changes eastern

BoB could be attributed to the

ENSO (El-Nino, La-Nino) events.

During El-Nino event 1997 the east

coast of India experiences two

large patch of positive anomaly

followed by adjacent side negative

anomaly.

4.7.4 Seasonal variability of SLA

The figure 4o shows the

seasonal changes of sea level

anomaly for 17 years. During south

west monsoon the SLA varies from

maximum value of 20.64 cm to

minimum value of -18.2 cm with an

average value of 1.3751 cm. During

northeast monsoon the maximum

values are found to be 18.62 cm

and minimum value of -10.615 cm

with a average value of 1.637 cm.

But during northeast monsoon the

SLA maxima and minima value found

to be very low, which is 13.162 cm

and -13.567 cm with an average of

0.1918 cm. Low sea level anomaly

during southwest monsoon formation

of cyclonic eddies in BoB.

Figure 4o: Seasonal changes of SLA in BoB

for 17 years

Many aspects of seasonal

variability depend closely on

baroclinic Rossby waves which

together with coastally trapped

Kelvin waves and advective

processes are the principal means

by which the ocean adjusts to

uctuations at the surfacefl

boundary. Using T/P altimetric

data in the southern Indian Ocean,

Somayajulu and Heywood (1996) and

Heywood and Somayajulu (1997)

investigated the westward-

propagating Rossby waves. In the

tropical ocean (between 101S and

101N), the dominant contribution

to SSH from near-annual

Rossbywaves accounts for more than

50% of SSH variance (Chelton and

Schlax, 1996). During the

northeast monsoon, the north-

easterly wind adjacent to the east

coast of India produces a coastal

down welling, raising the sea

level by 2–6 cm along the east

Indian coast (January panel). At

the same time and in the same

region, freshwater from the rivers

increases the sea level by 2–4 cm,

owing to the river water being

advected southward by the East

India Coastal Current (EICC) as a

coastal plume (Shetye 1993;Han et

al. 2001). The negative SLA in theeastern and northern bay is caused

by coastal Kelvin waves associated

with a low sea level, which is

originated from the eastern

equatorial ocean because the

equatorial easterly wind during

winter generates an equatorial

divergence and shallows the

thermocline, and therefore

decreases the sea level.

Intra-annual variability of SLA

Figure 4p Spatial Distribution of EKE in

all months(average 17 year)

From February to May, negative

wind curl in the central and

western bay produces a high sea

level, especially in the

southwestern bay The negative SLA

in the eastern and northern bay

strengthens and extends farther

along the coast and to the bay

interior due to coastal Kelvin

waves and westward propagating

Rossby waves. In May, a high sea

level pattern presents in the

eastern equatorial ocean due to

the eastward propagation of

equatorial Kelvin waves associated

with a high sea level. The strong

equatorial westerly winds during

April and May cause equatorial

convergence, deepen the

thermocline, and increase the sea

level. In addition, the spring

Wyrtki jet acts to pile up water

in the eastern basin, contributing

to the sea level increase. This

high sea level signal with an

amplitude of 2–4 cm propagates

into the eastern and northern bay

as coastal Kelvin waves, and it

also radiates westward from the

eastern boundary into the bay

interior as Rossby waves, raising

the sea level in the eastern,

northern, and central bay from May

to July. During the same period

the bay rivers have signi cantfi

discharges, increasing the sea

level by 2–4 cm in some regions of

the bay. From July to September,

the southwest monsoon wind

generates coastal upwelling,

causing a low sea level along the

east Indian coast. At the same

time, sea level in the eastern

equatorial ocean decreases, due to

the relaxation of equatorial

westerly wind. As in during

November is caused by coastal

Kelvin waves originated from the

eastern equatorial ocean, where

the strong equatorial westerly

wind during fall and the fall

Wyrtki jet produce a high sea

level.(Han et al., 2000)

Figure 4q Annuaal cycle of SLA (17 years)

in BoB

The annual cycle shows that SLA is

highly modulated in all months. In

may SLA is very high compared to

other months. During this time

eastward propagation of Kelvin

waves associated with high sea

level. This high sea level signal

with an amplitude of 2–6 cm

propagates into the eastern and

northern bay as coastal Kelvin

waves, and it also radiates

westward from the eastern boundary

into the bay interior as Rossby

waves, raising the sea level in

the eastern, northern, and central

bay from May to July.

Understanding inter-annual

variability of sea level in the

Bay of Bengal, therefore, may be

an important step toward

predicting ooding.fl

Relation between EKE and SLA:

Figure 4r1 and 4r2 Intra-annual and annual

variability of SLA and EKE in 17 years

The figure 4r1 shows the

intra-annual varibility of SLA and

EKE in BoB. The fluctuations in

the SLA is reflected in the EKE as

well with a phase lag of two

months almost.However it clearly

gives the idea that SLA is not

only correlated with EKE but it is

also affected by some other

parameters. During El-Nino year

1997 entire BoB experiences the

low SLA. But 1998 after March the

SLA became normal but the EKE

trend to changes dramatically

increases. The figure 4r2 shows

the annual variability of SLA and

EKE in BoB. As expected the

highest EKE values are found in

areas with high sea level

varibility. In 1997 there was very

low SLA in BoB, which is refleted

in the EKE as well. In 2008 there

is a high postive anomlay in SLA

as we expect there will be high in

EKE. But it is not so, that time

we found very low kinetic energy.

This shows that the EKE not only

varied with SLA but there were

some other parameters such as wind

stress,remote forcing may causes

the high in EKE.

Figure 4s SLA and geostrophic currentsduring December2008 and January1998

Figure 4t :EKE and geostrophic currentsduring June1998 and November1998

SUMMARY AND CONCLUSIONS:

Using seventeen years (1993 -2009)

of merged altimetric data, from

different missions

(TOPEX/Poseidon, ERS1/2, Jason-1

and Envisat), EKE and SLA

variability in BoB. The eddy

variability in the oceans is not

always straightforward to

interpret. Overall analysis shows

that mean EKE ranges from a

minimum value of 45.55 (cm2/s2) to

a maximum value of 2780.6 (cm2/s2),

with an average value of 461.5

(cm2/s2). The maximum eddy kinetic

energy was observed south east of

Sri Lanka and along the east coast

of India and the rest of the BoB

exhibits low EKE throughout the

year. 17 year period 1993–2009

revealed large seasonal, intra-

seasonal and inter-annual

variability in the meso-scale

circulation in the BoB. The

contribution, at different time-

scales, to sea level and EKE

variability in the BoB has been

assessed; likewise, the oceanic

processes that give rise to this

variability have been inferred.

The seasonal cycles is the

dominant signal controlling sea

level and EKE variability.

According to Sharma et al (2010),

instability in the monsoon

currents, the Rossby wave

propagation from the eastern BoB

and wind stress curl are the main

causes for the EKE variability

in the BoB. We have further

analysed SST and wind stress data

to substantiate our study on the

sea level and EKE variability in

BoB. 17 years (1993-2009) of mean

sea level anomaly, this varies

from a maximum value of 4.953 cm

to minimum value of -2.59 cm with

a mean value of 1.0776 cm. The

highest SLA was found in near

Myanmar coast in eastern BoB and

northern BoB. The sea level

presents a trend about 2.85 cm for

17 years in BoB. We find that the

sea level rise in BoB is about

1.67 mm/year, which is within the

averaged values of sea level

rise for the global ocean (Xuhua

et al, 2007, Ablain et al, 2009).

The monthly mean sea level anomaly

is maximum (9.8 cm) in December

2008 and minimum (-10.9 cm) in

January 1998. In 2008, the BoB

experienced a number of intense

cyclones and the year 1997-98 is

characterised by an active El Nino

event (Chen et al 2009, Chellappan

et al 2009). SLAs (both positive

and negative) during December of

all the years (1993-2009) show

high fluctuations associated with

warm and cold core eddies. The

minimum SLA was observed in

March/April. This annual cycle

variation in sea level is due to

the steric-effect - increase in

the volume of ocean without change

in the mass (Caballero et al.,

2007). The intense variability in

sea level along the east coast of

India and around Sri-Lankan coast

is due to the existence of western

boundary currents (WBC). Since the

ocean and the atmosphere are a

coupled system, variability of sea

level reflects changes in climate.

One of the most important aspects

of the climate in the bay is the

Indian monsoon, which possesses

large inter-annual variability.

This variability may cause marked

sea level change due to uctuationfl

of monsoon wind, precipitation

minus evaporation (P - E), heat

uxes, and river runoff into thefl

bay. There are also potential

remote in uences. Sea levelfl

signals driven by the equatorial

wind can propagate into the bay as

coastal Kelvin waves and westward

radiating Rossby waves, each of

which has a sea level signature

that may impact the sea level in

the BoB.

Acknowledgement:

Authors express gratitude to the

NIO for giving such opportunity

for dissertation work. Thanks to

AVISO for providing data and other

valuable information. Finally

authors thanks to P.vethamony for

giving valuable guidance.

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