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Chapter - 1 Introduction
Chapter 1:
INTRODUCTION
1.1 Introduction
The word wave usually brings to mind a picture of undulations on the surface of
the sea or a lake. Ocean wave is a process where energy is transported through
water without any significant overall transport of the water mass. These waves
progress from a region of formation to a coast where they are generally dissipate
(Pond and Pickard, 1983; Brown et al., 1994). The dimensions of an idealized
water wave, and the terminology used to describe them is given in Figure 1.1.
Here wave height refers to the overall vertical change in height between the wave
crest and the wave trough. Wavelength is the distance between two successive
peaks. Steepness is defined as wave height divided by wave length. The time
interval between two successive peaks passing a fixed point is known as the
period, and is measured in seconds. The number of peaks which pass a fixed
point per second is known as the frequency.
Figure 1.1: Dimensions of an idealized ocean wave.
(Source: http://www.kirksville.k12.mo.us)
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Chapter - 1 Introduction
The real ocean waves are not pure sine waves but are a sum of sine waves with a
range of wavelengths, corresponding periods and amplitudes. It is common to
quote the mean height of the highest one-third of the waves called the Significant
Wave Height (SWH) as a descriptive characteristic. In general wind waves or,
more precisely, wind-generated waves are surface waves that occur on the free
surface of huge water bodies like oceans and seas. They usually result from the
wind blowing over a vast enough stretch of fluid surface. The duration of time that
the wind acts on water surface is called the ‘wind duration’. The area over which
the wind blows is called the ‘fetch’. The generation and growth of waves involve
the transfer of energy from wind to waves, resulting in wave growth is not
completely understood. Wind waves which are generated locally are commonly
known as ‘sea’ and they have a quite irregular surface with fairly wide range of
directions of propagation on the sea surface. ‘Swell’ is the term for wave which
has been generated elsewhere, travels in one direction and is much more regular.
Waves in the oceans travel thousands of kilometers before they break at shallow
water regions or coastlines. Wind waves range in size from small ripples to huge
waves. In all these surface waves, gravity is the primary restoring force, allowing
oscillations to occur. The classification of the waves with respect to
frequency/wavelength is given in the figure 1.2.
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Figure 1.2: Typical diagram showing qualitative power spectrum of various types of
waves.
(Source: http://www.co-ops.nos.noaa.gov)
Wind blowing over the sea surface generates wind waves. They develop with time
and space under the action of the wind and become huge waves called ocean
surface waves. This process can be described as follows: the wind blowing over
the water surface generates tiny wavelets which have a two-dimensional spectral
structure. The spectral components develop with time and through space by
absorbing the energy transferred from the wind. Non-linear energy transfer among
spectral components is also important in the development of spectrum. The high
frequency components then gradually saturate, losing the absorbed energy as the
waves break, while the low frequency components are still growing. In this way,
the spectral energy increases and the spectral peak shifts to the low frequency side.
It took a very long time to arrive at such a dynamical model of ocean surface
waves. This discussion on the development of theories focuses mainly on how we
reached our present understanding of the ocean surface waves.
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1.1.1 Theory of wind generated waves
Many theories have been proposed to explain the formation of deepwater waves in
terms of their growth, propagation and decay. These theories also addressed the
growth, height and period with respect to time and distance, due to forcing
mechanism. All the wave forecasting relationships are adjusted by use of actual
wave data. The wave forecasting theories are not completely theoretical but are
semi-theoretical or semi-empirical. They are used for forecasting purposes in
order to avoid the complex physical processes involved. A brief description of
various basic theories on wave generation and growth is given in the following.
Jeffreys (1924; 1925) explained the growth of the waves in his sheltering theory.
He considered that if the wind velocity is faster than the wave velocity, the air
flow over the wave separates at the wave crest and transfers the momentum to
surface waves through the form drag associated with flow separation.
Furthermore, based on a consideration of simple energy balance, in the process of
wave generation, he estimated the sheltering coefficient that can be used to
calculate the growth of waves due to the wind. Jeffrey proposed that eddies on the
leeward side of the waves resulted in reduction of normal pressure as compared
with the wind ward face and a consequent transfer of energy from wind to waves.
Figure 1.3 shows the typical concept of Jeffrey’s hypothesis. The rear face of the
wave against which the wind blows experiences a higher pressure than the front
face which is sheltered from the force of the wind. Air eddies are formed in front
of each wave, leading to differences in air pressure. The excesses and deficiencies
of pressure are shown by plus and minus signs respectively.
The pressure
difference pushes the wave along. His results suggested that the wind could add
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energy to waves until the wind speed equals the wave celerity. When the wave
celerity became equal to the wind speed, the waves reached maximum height and
the sea attains the steady state. The critical wind speed suggested by Jeffreys was
of the order of 1.03 m/s.
Figure 1.3: Jeffrey’s ‘sheltering’ model of wave generation. Curved lines indicate
air flow; short straight arrows show water movement.
(Source: Waves, Tides and Shallow Water Processes, The Open University,
England)
The next advancement in the theory of wave generation was attempted by
Sverdrup and Munk (1947). Jefferys theory took into account only the transfer of
energy by normal stresses, whereas Sverdrup and Munk considered both normal
and tangential stresses (figure 1.4). The effect of normal stresses dominates for a
short time during the early stages of wave development. However, when the ratio
of wave celerity (C) to the wind speed (U) exceed 0.37 (i.e., C/U > 0.37), the
transmission by tangential stress is dominant. According to Sverdrup and Munk,
the fully developed sea occurs when C/U = 1.37 and gH/U2 = 0.26, when ‘g’ is
acceleration due to gravity and ‘H’ is wave height.
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Figure 1.4: The concept of wave generation and growth by Sverdrup and Munk. U wind speed, C - wave speed, RT - tangential stress and RN - normal stress.
(Source: Estuary and Coastline Hydrodynamics, A.T.Ippen)
Another theory that explains the generation of wave from an initially undisturbed
surface was proposed by Phillips (1957; 1966). He pointed out that the turbulent
air flow also result in pressure fluctuations along with the velocity fluctuations.
These pressure fluctuations may start wave motion that lead to a growth of wave
energy proportional to the time. Once the waves exit, they may modify the air
flow so that the growth rate becomes proportional to the wave amplitude and
hence exponential in time.
1.1.2 Wave characteristics
The phase speed or celerity ‘c’ of the wave is defined as
------- (1.1)
Here ‘g’ is acceleration due to gravity, ‘λ’ is wave length of the wave and ‘d’ is
depth of the water column (Pond and Pickard, 1983). In case of deep waves, λ is
very high compared to d (λ<2d).
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Chapter - 1 Introduction
Then tanh(2πd/ λ)= 1. Hence the equation 1.1 becomes
--------- (1.2)
This is the equation (Equation 1.2) for phase velocity of deep water waves. From
this equation, the celerity of the wave is proportional to the wave length of the
wave and independent of the depth of the water column. This shows that different
waves are having different velocities and is responsible for lateral diffraction of
wave energy from the localized source. This is one of the major factors for the
decay and almost occurs immediately after the waves leave the generating area.
There are other processes by which the wave energy decays directional spreading,
air resistance, wave-wave interaction and current-wave interaction etc.
The
typical figure of particle motion for deep water wave is shown in fig.1.5.
Figure 1.5: Deep water wave pattern
(Source: http://www.umt.edu)
When a wave propagates into shallow water, it undergoes number of
modifications due to refraction, diffraction, shoaling and other energy losses.
More often, as wave move into shallow water, all the properties of wave changes,
except their period. Hence, if a series of parallel-crested wave approaches at an
angle to a straight shoreline over a smooth sea bottom shoaling gradually, they
progressively change direction. The wave nearer to the shore slows down earlier
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than that farther away. As a result, the waves become more parallel to the shore
by the time they pile up as surf. The change in direction associated with the
change of speed is called ‘refraction’.
Another important phenomenon is
‘diffraction’ of wave at obstacles such as break water, jetty and groins etc. While
the wave is crossing the obstacle, some of the wave energy diffracts into the
geometrical shadow area behind the obstacle. The pattern of diffraction is
different for different dimensions of the obstacle.
In shallow water, the
wavelength is much larger than depth of the water column (i.e., λ > 20d).
Therefore the second term (tanh(2πd/ λ)) in Equation 1.1 becomes 2πd/ λ. Hence
the equation (1.1) results as follows.
------- (1.3)
Hence the celerity of the wave in shallow water is directly proportional to the
square root of the depth of the water. Figure 1.6 shows the pattern of particle
motion in shallow water wave.
Figure 1.6: Particle motion for shallow water wave.
(Source: http://www.umt.edu)
1.1.3 Measurement of waves
Among all the ocean state parameters, measurement of waves is an important as
well as difficult task due to their dynamic behavior. A number of methods are
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available for obtaining information on waves. Visual observation was one of the
oldest and simplest methods to make an estimate. However it needs lot of practice
to obtain reliable data. Visual estimate against a graduated vertical scale mounted
on fixed platform was another simplest method.
In the recent time with the
advent of electronic systems and pressure sensors it is possible to measure the
hydrostatic pressure below surface waves. The variation in hydrostatic pressure
below surface waves is proportional to the depth of water from the surface to the
sensor. Therefore continuous record of pressure against time will provide
information on surface shape. This method is well suitable for shallow water wave
measurements as the pressure variations due to waves decrease with increasing
water depth. However mounting of pressure sensor on fixed platform for deep
water measurements is a difficult task.
Figure 1.7 shows different types of
devices/techniques commonly use for wave measurement.
Another most successful method used during recent days is floating sphere shape
wave rider buoy. It is spherical in shape made up of stainless steel and floats on
water surface because of its buoyancy. The buoy contains heave-pitch-roll sensor,
three axis fluxgate compass, two fixed X and Y accelerometers and a temperature
sensor. All these three accelerations (vertical, north and west) are then digitally
integrated to displacements and for every half an hour. Total 256 data points are
added to get 6 degrees of freedom per frequency on 1600 seconds of data. This
method is well suitable for coastal as well as offshore applications.
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Chapter - 1 Introduction
Figure 1.7: Different types of wave measuring devices (a) Valeport directional wave
recorder (bottom mount pressure sensor), (b) Directional Wave Rider Buoy (floating
type) and (c) SARAL / AltiKa (remote sensing).
All the above mentioned techniques provide information at single location
limiting the information at synoptic scale. The wave information at the larger
spatial scale can be obtained by on board satellite sensors. The remote sensing
systems such as SEASAT, GEOSAT and ERS series, has capability of imaging
wave conditions at day and night through all weather conditions (Chelton, Hussey
and Parke, 1981; Douglas and Cheney, 1990; Bruning et al., 1993). However the
satellite acquiring data with a swath width upto 18 km and widely spaced ground
tracks (315 km at the equator) may result in non-homogeneity while presenting a
composite of larger area (Rosmorduc et al., 2011). Due to its low temporal
resolution (10 day ground track repeated cycle), it is not suitable for short time
scale event such as daily variations.
Also while using data from multiple
platforms it is require to normalize for analyzing long term variability.
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1.2 Literature Review
Several researchers have been carried out investigations on ocean wind waves.
The development of wave forecasting procedures was attempted by Sverdrup and
Munk (1947) from forecast winds, for beach landing during World War II. They
also made an attempt to track the storms by using forerunners of the swells.
Barber and Ursell (1948) measured frequency spectra of ocean waves in order to
develop a reliable method of predicting amplitude and period of wind waves and
swell from meteorological charts and forecasts. The propagation and detection of
swells over large distances was shown many years ago. Munk (1947) in his
studies detected swell waves at Guadalupe Island off the coast of Baja California.
These waves had travelled over 15,000 km from a storm in the Indian Ocean.
Subsequently, Snodgrass et al. (1966) focused on the evolution of the swell energy
along the propagation direction in North Atlantic Ocean. These early works
provided important insights on swell generation and propagation that have stood
the proof of time and are still valid paradigms today.
Studies conducted over the last few decades have expanded these initial insights,
revealing that the presence of swell affects several important processes at the air–
sea interface such as the modulation, blockage and suppression of short period
wind-generated waves. The waves were estimated by visual means before
commencement of automated measuring instruments. The visual wave
observations from selected ships were used for the analysis of sea state (Graauw,
1986; Badulin and Grigorieva, 2012). The coexistence of sea and swell can
significantly affect the sea-keeping safety, offshore structure designs, navigation
and surf forecasting (Earle, 1984). These mixed sea state also affect the dynamics
of near-surface processes such as air-sea momentum transfer (Dobson, Smith and
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Anderson, 1994; Donelan, Drennan and Katsaros, 1997; Mitsuyasu, 1991). The
new and challenging issues have renewed the interest of the scientific community,
motivating the publication of several papers on the topic of swell propagation
(Hasselmann, 1974; Hanson and Phillips, 1999).
The past 50 years has also brought enormous advances in the ability to measure
and predict the ocean wave field using automated in-situ instruments and satellite
based remote sensing techniques. Studies on spatial distribution of wave field
made possible in 1970s with the development of satellite altimeter systems such as
Skylab, GEOS-3 and Seasat (Chelton et al., 2001). The analysis of this growing
observational data base begun to yield the global wave climatology needed for
activities such as shipping and ocean engineering. The first quantitative estimate
of the global wave climate was presented by Young (1994) using GEOSAT
altimeter observations. The results showed that there was a marked difference in
wave climate of two hemispheres. The Southern Ocean having consistently high
sea state. Subsequently, the altimeters such as ERS series, Topex/Poseidon and
GFO were also provided reliable wave data. The present altimetry missions
currently in service are Jason-1, Jason-2, Envisat, Cryosat and HY-2.
With the development of state of art of numerical modelling, a new era has
dawned in the study of wave generation and its propagation. The first generation
wave models, developed between 1960s and 1970s, assumed that the wave
components suddenly stopped growing as soon as they reached a universal
saturation level (Phillips, 1958). The second generation wave models were
developed by considering the importance of nonlinear transfer of energy and
dependence of high frequency region of the spectrum on low frequencies
(Hasselmann et al., 1976; Hasselmann et al., 1985). To overcome the limitations
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in first and second generation wave models the WAM group was established. The
main task of the WAM group was to develop a third generation wave model in
which the wave spectrum was computed by integration of the energy balance
equation, without any prior restriction on the spectral shape (WAMDI, 1988).
Deepwater waves can be well modelled with third-generation wave models which
are driven by predicted wind fields, and based on physical processes rather than
empirical formulations (WAMDI, 1988). Hanson and Phillips (1999) investigated
the wind sea growth and dissipation in a swell-dominated, open ocean
environment. Later, an automated swell tracking and storm identification system
was developed using the wave partitioning method (Hanson and Phillips, 2001).
Ardhuin et al. (2009) provided an accurate estimation of the dissipation rates of
swell energy. All these studies essentially followed swells along a great circle and
showed that it is possible to forecast swell heights at great distances fairly
accurately. Ardhuin (2012) suggested a parameterization based on saturationbased dissipation for the improvement of present wave models. The National
Oceanographic Partnership Program (NOPP) by United States (US) focussing on
improving operational wind wave forecasting in deep water and continental shelf
areas (Tolman, 2012). The present status of wave forecasting at ECMWF and
other research work related to ocean wave observation and modelling was
presented by several authors in ECMWF Workshop on Ocean Waves during June
2012 (Bidlot, 2012).
1.3 Indian Ocean characteristics
The Indian Ocean is the third largest of the world’s oceans and it is characterized
by unique geomorphology. The region is closed at the northern boundary unlike
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Atlantic and Pacific. The area is also under the influence of seasonally reversing
winds (monsoon). Further, among all the oceans, Indian Ocean remained the least
explored. The following section briefly describes characteristic features of Indian
Ocean that influences the wave climate.
The geographical extent of the Indian Ocean lies under tropical region for major
part and has an extension on south up to Antarctic Ocean. The topography of the
Indian Ocean obtained from ETOPO1 of National Oceanic and Atmospheric
Administration (NOAA) is shown in figure 1.8. It is bounded by Africa at the
west, Australia at the east, Asia at North and Antarctica at south.
It has
connectivity with the Atlantic Ocean at the southern tip of Africa at Cape
Agulhas, along the 20˚E longitude. It borders with Pacific Ocean from the
southeast cape on the island of Tasmania, along the 147˚E longitude. Together
with the area of the marginal seas, the Indian Ocean covers an area of
81,602,000 km2.
Figure 1.8: The topography of the Indian Ocean
(Source: http://www.ngdc.noaa.gov/mgg/bathymetry/relief.html)
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The average depth of this ocean is 4,280 m, while a maximum depth of 7450 m is
recorded in the Java Trench south of Java. The ocean contains a volume of
349,600,000 km3 of water (Groves and Hunt, 1980). The Indian Ocean has less
number of marginal seas as compared with to Pacific and Atlantic oceans. The
two important of these are Arabian Sea (AS) and Bay of Bengal (BoB). The
Arabian Sea lies off the curve formed by India, Pakistan and Africa, where as the
Bay of Bengal lies off the east coast of India. The existence of numerous ridges
and plateaus influences ocean currents (Tomczak and Godfrey, 2002) and
propagation of long planetary waves (Wang, Koblinsky and Howden, 2001;
Killworth and Blundell, 2003a; Killworth and Blundell, 2003b; Killworth and
Blundell, 2003c; Tailleux, 2003).
1.3.1 Surface wind
The surface wind field over the ocean determines the sea surface roughness and
wave climate and there by play significant role in the energy exchange at the air
sea interface. Winds over the Indian Ocean have a number of unique regional
features. The major feature among them is the seasonally reversing monsoon
wind, particularly prominent in the northern part of the ocean. The second major
feature is the absence of sustained easterly winds along the equator. Instead, there
is a tendency for westerly wind-bursts two times a year during monsoon transition
periods and as a result a weak westerly annual mean (Schott, Xie and McCreary,
2009). Another feature is the Somali jet which is the narrow southwesterly
surface wind with speed greater than 12 m/s during summer monsoon season
(Wooster, Schaefer and Robinson, 1967; Halpern and Woiceshyn, 1999).
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The mean annual surface wind field obtained from Comprehensive Ocean
Atmosphere Data Set (COADS) climatology is shown in figure 1.9. Winds at
equator change direction, but are generally weak (speed < 2 m/s during MarchApril and November-December). The southeast trades are strong, compared to
others in the oceans and extend from about 5˚ to 20˚ S with speed more than 6 m/s
during July-August (Gangadhara Rao and Shree Ram, 2005).
The annual
variation of wind speed over AS and BoB shows the prevalence of weaker (4 m/s)
winds in the equatorial zones (5˚N - 5˚S) throughout the year. The latitudinal
variation of wind speed show the zone of higher wind speed occupies the zonal
belt 5˚ – 25˚ N/S on either side of the equator with maximum winds centred at 15˚
N/S. The higher wind speeds (13 m/s) in the northern belt corresponds to the
south west monsoon. In the southern belt, highest winds occur almost throughout
the year (Murthy and Murthy, 2001). However, one can notice relatively weaker
winds in the BoB during the southwest monsoon compared to those in the AS,
where there is no appreciable east-west variation in the wind speed south of the
equator.
Figure 1.9: Annual mean surface wind over the Indian Ocean from COADS
climatology.
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An EOF analysis applied by (Breidenbach, 1990) on pseudo wind stress over
Indian Ocean reveals that the seasonal and interannual variability is dominated by
seasonal reversal of winds. However, southeast trades show strong interannual
variability over south Indian Ocean.
A similar type of variability was also
observed in wind stress curl fields of Indian Ocean (Rao, 2002) which explains the
variability of upper ocean circulation.
1.3.2 Wind generated waves
There is a wide spectrum of wind generated waves in the Indian Ocean having
very high magnitude over south Indian Ocean to small and moderate wave heights
over north Indian Ocean (Chandramohan, Sanil Kumar and Nayak, 1991;
Vethamony et al., 2000). In the north Indian Ocean during summer monsoon
about 66% of the waves have heights greater than 1 m, during winter monsoon
about 95% of the waves have heights not exceeding than 2 m and during transition
periods about 73-79% of the time the waves are not exceeding 1 m in height
(Reddy, 2001). The north Indian Ocean was characterized by the quietest sea state
conditions during transition periods and very high sea state conditions during
southwest monsoon. In the south Indian Ocean, the region between 30˚S to 60˚S
experiences high waves all along the year particularly during southwest monsoon.
During northeast monsoon the region between equator and 20˚S was the quietest
region due to the interaction of northeast and southeast trades (Reddy, 2001).
In various studies, data from altimeters were validated against in-situ observations
from buoys (Alves and Young, 2004; Vethamony et al., 2006; Suchandra et al.,
2009). A study conducted by Vethamony et al. (2000) using GEOSAT altimeter
data showed that the SWH during February was less compared to other months.
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Moreover, presently the satellite data are used for model validation and
assimilation because of the accuracy and its global coverage. The wave
characteristics in the Indian Ocean region have been studied by different
researchers by using models and observational data. Sudheesh et al. (2004) and
Vethamony et al. (2006) used spectral wave model to generate the offshore waves
by using National Centre for Medium Range Weather Forecasting (NCMRWF)
winds. In the Indian Ocean region, the impact of Southern Ocean swell was
studied by using WAM model (Sulagna, Rajkumar and Abhijit, 2006). In this
study they made an attempt to compare model derived and observed wave heights
at a point location. Their study showed that high swell waves from the Southern
Ocean propagate towards the Bay of Bengal (BoB) and Arabian Sea (AS). A study
using in-situ and model along Indian coast revealed the occurrence of ‘Shamal’
swells along west coast of India (Aboobacker, 2010). An experiment conducted
by Sabique et al. (2012) using third generation wave model and MIKE 21 revealed
that the swell from Southern Ocean play an important role in determining the
North Indian Ocean wave climate.
The variability of Indian Ocean has been studied by several authors in the past in
terms of atmospheric circulation, air-sea interaction processes and upper ocean
processes (Breidenbach, 1990; Allan et al., 2001; Bhatt et al., 2003; Mohanty et
al., 2002; Gangadhara Rao and Shree Ram, 2005). The Indian Ocean experiences
semi-annual and annual processes and they have been subjected to numerous
studies (Clarke and Liu, 1993; Yamagata, Mizuno and Masumoto, 1996;
Masumoto and Meyers, 1998; Schott and McCreary, 2001; Wang, Koblinsky and
Howden, 2001; Yuan, 2005). The inter-annual variability of Indian Ocean was
also dominated by the variability of tropical Pacific (Latif and Barnett, 1995;
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Tourre and White, 1997; Venzke, Latif and Villwock, 2000). In the present study
an attempt has been made to study the variability of Tropical North Indian Ocean
(TNIO) in terms Significant Wave Height (SWH) and influence of synoptic
phenomena on SWH variability.
1.4 Synoptic phenomena influencing surface wind forcing
The variability of SWH is primarily depends on the variability of surface wind
forcing. To understand the spatial and temporal variability of SWH over TIO, the
knowledge about the influence of synoptic phenomena on surface wind forcing is
essential. The major synoptic phenomena which influence the surface wind over
Tropical Indian Ocean (TIO) were monsoon, Indian Ocean Dipole (IOD) and El
Nino Southern Oscillation (ENSO). A detailed discussion on these phenomena
was given in the following sections.
1.4.1 Indian Monsoon
Monsoon was traditionally defined as a seasonal reversing wind accompanied by
corresponding changes in precipitation. The monsoon systems are planetary scale
seasonal cycles in atmospheric circulation with ocean-continent thermal contrasts
and typical movements of inter tropical convergence with its characteristic band
of convection (Webster, 1987). Indian monsoon is the most prominent of the
world’s monsoon systems. The winds blow from the northeast during cooler
months and reverses direction to blow from the southwest during warmest months
of the year.
This phenomenon of seasonal reversal of wind is particularly
prominent in the northern part of the Indian Ocean. The southwest monsoon
circulation pattern is fundamentally due to the land-sea pressure gradient caused
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by the heating of Asia and the Tibetan Plateau during summer (Halley, 1686).
The elevated east African coastline intensifies the wind near the surface and
directs it parallel to the coasts of Somalia, Yeman, and Oman. This strong flow,
embedded within the broad southwest flow appears as a low level atmospheric jet
known as the Findlater jet.
The Findlater jet, which is remarkable for its
steadiness of direction and strength crosses the Indian Ocean equator and blows
over the Arabian Sea parallel to the Omani coastline in a northeast direction
(Findlater, 1974).
There is an alternative hypothesis in which the monsoon is considered as a
manifestation of seasonal migration of the Inter Tropical Convergence Zone
(ITCZ) (Charney, 1969) or the equatorial trough (Riehl, 1954; Riehl, 1979), in
response to the seasonal variation of the latitude of maximum incoming solar
radiation. It is important to note that whereas the first hypothesis associates the
monsoon with a system special to the monsoonal region, in the second, the system
responsible is the planetary scale system associated with the major tropical rain
belt (ITCZ/equatorial trough) and the monsoonal regions differ from other tropical
regions only in the amplitude of the seasonal migration of the basic system. The
two hypotheses have very different implications for the variability of the
monsoon. For example, in the first case we expect the intensity of the monsoon to
be directly related to the land-ocean temperature contrast. The winter monsoon is
characterized by high pressure over the Asian land mass; the winds are northeasterly, away from the Asian continent, causing north-easterly wind stresses
over the Arabian Sea and Bay of Bengal.
Another feature of the Indian Ocean is the absence of sustained easterly
winds along the equator which are present in other oceans. Instead, there is a
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tendency for westerly wind-bursts two times a year during monsoon transition
periods and as a result a weak westerly annual mean (Schott, Xie and McCreary,
2009).
1.4.2 Indian Ocean Dipole
The IOD is a coupled ocean-atmosphere phenomenon in the Indian Ocean
characterized by an anomalous cold SST in the south-eastern equatorial
Indian Ocean and anomalous warming of the western equatorial Indian Ocean.
This anomalous pattern was first described as a “dipole” or “zonal” mode
(Webster et al., 1999; Saji et al., 1999). Both studies suggested that IOD is a
native mode of the Indian Ocean that exists independently from the Pacific. The
term IOD itself was introduced by Saji et al., (1999). It reflects a zonal structure
of the phenomena with two maxima of different “polarity”. This anomaly can be
found not only in SST but also in other oceanic and atmosphere fields over the
Indian Ocean, such as sea surface heights (SSH), wind, pressure, rainfall, and
outgoing long wave radiation.
Intensity of the IOD is represented by anomalous SST gradient between the
western equatorial Indian Ocean (50˚E-70˚E and 10˚S-10˚N) and the south eastern
equatorial Indian Ocean (90˚E-110˚E and 10˚S-0˚). This gradient is named as
Dipole Mode Index (DMI). When the DMI is positive then, the phenomenon is
refereed as the positive IOD and when it is negative, it is refereed as negative
IOD. Since, IOD is a coupled ocean-atmosphere phenomenon it can also be
represented by any other atmospheric (pressure, Outgoing Long Radiation) or
oceanographic (sea surface height) as well.
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Chapter - 1 Introduction
Figure 1.10: Schematic diagrams of a positive IOD and negative IOD events
representing SST anomalies are shaded (red color is for warm anomalies and blue is
for cold). White patches indicate increased convective activities and arrows indicate
anomalous wind directions during IOD events.
(Source: http://www.jamstec.go.jp)
The positive and negative IOD events are shown in Figure 1.10. IOD as any
tropical phenomena is strongly locked to the annual cycle, reaching a peak during
boreal autumn in September-October. The pattern has positive and negative
phases. The positive IOD is characterised by the anomalously cold SST in the east
and warm in the west; the equatorial wind is anomalous easterlies, coupled with
anomalous SST and blowing from east to west towards warmer waters (Fig. 1.10,
left panel), causing excessive rain at the eastern African coast and drought in
Australia. The negative IOD has an opposite pattern (Fig. 1.10, right panel).
During the negative phase of the IOD, “there are warmer than average
SST's near Indonesia and cooler than average SST's in the western Indian
Ocean, resulting in more westerly winds across the Indian Ocean, greater
convection near Australia and enhanced rainfall in the Australian region”.
1.4.3 El Nino Southern Oscillation (ENSO)
ENSO refers to the coupled ocean atmosphere phenomena in which El Nino refers
to the oceanic component of the El Nino/Southern Oscillation system, the
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Chapter - 1 Introduction
Southern Oscillation to the atmospheric component.
Figure 1.11 shows the
typical pattern of SST during El Nino and La Nina events. In practice, El Nino is
sometimes used to refer to the entire system. El Nino and La Nina events tend to
develop during the period Apr-Jun and they tend to reach their maximum strength
during Dec-Feb, typically persist for 9-12 months, though occasionally persist up
to 2 years and typically recur every 2 to 7 years (Enfield and Allen, 1980; Chelton
and Davis, 1982; Wooster and Fluharty, 1985; Simpson, 1992; Lynn, Schwing
and Hayward, 1995; Lynn et al., 1998). The figure 1.12 shows the historical sea
surface temperature index for NINO3.4 region.
A major advance in our understanding of the interannual variation of the monsoon
occurred in the 1980's with the discovery (or rediscovery) of a strong link with El
Nino and Southern Oscillation (ENSO) (Sikka, 1980; Pant, 1981; Rasmusson and
Carpenter, 1983). Recent studies (Gadgil, 2003; Gadgil et al., 2004; Ihara et al.,
2007) have revealed that one more mode plays an important role in the interannual
variation of the monsoon viz. the Equatorial Indian Ocean Oscillation
(EQUINOO). The nature of these teleconnections is discussed by Gadgil et al.
(2007) and Rajeevan (2011).
The situation in the southern hemisphere is dominated by the pressure gradient
between the tropical low and the subtropical high pressure belts. The axis of low
pressure in the tropics is near 10°S. The Southeast Trades persist in the Indian
Ocean throughout the year south of 10°S, with some shift northward (southward)
of their northern edge during northern summer and fall (winter and spring)
(Schott, Xie and McCreary, 2009).
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Chapter - 1 Introduction
Figure 1.11: Typical pattern of SST during (a). El Nino (1998), and (b). La Nina
(1989) and (c), (d) temperatures departures from climatology,
(Source: http://www.cpc.ncep.noaa.gov)
Aside from the seasonal cycle, interannual fluctuations—associated most notably
with tropical El Nino and La Nina events—are the strongest and most familiar
signals of natural global variability.
Figure 1.12: Historical sea surface temperature index for the region NINO3.4.
(Source: http://www.esrl.noaa.gov).
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Chapter - 1 Introduction
Despite our long-standing awareness of these events and their remote impacts, our
understanding of the processes that initiate and terminate them is incomplete.
However, few studies have investigated the influence of the monsoon and the
Indian Ocean on the Pacific Ocean. Barnett (1983) found that the observed
monsoon and Pacific trade-wind systems interact strongly on interannual timescales and create eastward propagation of disturbances from the Indian Ocean to
the Pacific Ocean preceding El Nino events. Observations indeed reveal the
strongest negative correlation between the Indian monsoon rainfall and the eastern
Pacific SST when the SST lags by about six months (Goswami, Krishnamurthy
and Annamalai, 1999; Kirtman and Shukla, 2000). Hastenrath et al. (1993) found
a link between climate anomalies in the western equatorial Indian Ocean and the
Southern Oscillation through ocean–atmosphere interactions that are most
effective during October–November. There is also a supposition of a global ENSO
signal that propagates eastward from the Indian Ocean to the Pacific Ocean
(Tourre and White, 1995; Tourre and White, 1997). Huang and Kinter (2001)
showed that the dominant mode of the tropical Indian Ocean variability has a
period of 2–5 years in the anomalies of upper ocean heat content, SST and wind
stress. They further demonstrated that the Indian Ocean variability is associated
with the ENSO variability in the Pacific, evolving nearly simultaneously and
involving a global shift of the Walker circulation. Model studies have been
attempted to explain the dynamical link in the effect of the monsoon on ENSO. A
diagnostic analysis by Nigam (1994) with a linear model showed that the
monsoon rainfall anomalies over Asia and the Indian Ocean force modest nearsurface wind anomalies over the tropical Pacific Ocean contribute to the
development of ongoing El Nino events.
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Chapter - 1 Introduction
1.5 Southern Ocean Winds
The southern ocean hosts the strongest surface winds of any open oceanic area,
fostering strong heat, moisture and momentum exchanges between ocean and
atmosphere. The mean zonal winds are the strongest in the world and are also
extremely variable (Gille, 2005). Wind forcing of the Southern Hemisphere
oceans is dominated by large scale, low frequency variability (Large and
vanLoon, 1988). However a study conducted by Thompson and Wallace revealed
that the Southern Ocean winds vary over a broad range of frequencies ranging
from interannual variability to super-inertial fluctuations (Thompson and Wallace,
2000). However, the southern ocean is the least explored by traditional observing
methods due to the remoteness of the area and rough environment, causing the
largest data gap of global oceans. With the commencement of remote sensing
techniques it is now possible to monitor such remote regions by using satellites.
Gille (2005) and Risien et al. (2006) studied winds over Southern Ocean using
QuikScat data. These studies show that winds were varied more in meridional
direction than in zonal direction.
The persistent, strong, periodical winds over the southern oceans generate high
waves that travel thousands of kilometers to the Indian Ocean as large swell
component (Rajkumar et al., 2009; Sabique et al., 2012). This emphasizes the fact
that, for accurate wave prediction over the Indian Ocean, swell part of the waves
should be crucially taken into account.
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Chapter - 1 Introduction
1.6 Scope of the present study
Studies on waves over Indian Ocean was limited due sparse coverage of in-situ
observations, particularly over deep Ocean it was still under-explored. With
advent of satellite remote sensing and numerical modelling it is now possible to
investigate waves with good spatial and temporal resolution. So far the works
carried out over India Ocean covered wave statistics of near shore waves using insitu measurements and spatial distribution of deep ocean waves using satellite
remote sensing (Vijayarajan et al., 1978; Chandramohan, Sanil Kumar and Nayak,
1991; Vethamony et al., 2000).
Extensive model studies were also done to
simulate and predict Indian Ocean waves (Vethamony et al., 2000; Vethamony et
al., 2006). In the present study, an attempt was made to understand the spatial and
temporal variability of Significant Wave Height (SWH) over Indian Ocean and
investigated the possible synoptic forcing mechanism for the variability using 13
years of Wavewatch III model data. Due to the sparse coverage of in-situ buoy
measurements and limited life time of satellite sensors inhomogeneity exists in the
longterm database.
The reanalysis wave data provided by Environmental
Modeling Center, NOAA using third generation wave model Wavewatch III was
found to be well suitable for such long term studies. Initially an investigation was
done to understand the significance of swell waves over Tropical North Indian
Ocean (TNIO) using in-situ wave spectrum measurements. Later the hindcasted
model data was analysed using different statistical techniques such as Fast Fourier
Transformation (FFT) and Empirical Orthogonal Function (EOF) analysis to
obtain meaningful results for the understanding of responsible forcing phenomena
to SWH variability over Tropical Indian Ocean.
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Chapter - 1 Introduction
Objectives:

Understanding the influence of swell off East Coast of India by analysing
the spectral characteristics measured using wave rider buoy.

Validation of model data with in-situ measurements of the Indian Ocean
region.

Generation of Significant Wave Height (SWH) climatology using 13 years
of Wavewatch III model data.

Investigation of forcing mechanisms responsible for the variability of
Significant Wave height (SWH) over Tropical Indian Ocean (TIO) using
different statistical techniques.
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