Satellite and In-Situ Measurement for Temporal Variability of Sea Temperature in

Talang Satang National Park

Nur Asilah binti Awang

Bachelor of Science with Honours

(Aquatic Resource Science and Management)

2014

Faculty of Resource Science and Technology

SATELLITE AND IN-SITU MEASUREMENT FOR TEMPORAL VARIABILITY OF

SEA TEMPERATURE IN TALANG SATANG NATIONAL PARK

NUR ASILAH BINTI AWANG

This project is submitted in partial fulfillment of the requirements for the degree of

Bachelor of Science with Honours

(Aquatic Resource Science and Management)

Department of Aquatic Science

Faculty of Resource Science and Technology

UNIVERSITI MALAYSIA SARAWAK

2014

II

Acknowledgement

Praise to the most merciful Allah, for giving me strength, courage, and wisdom to

write my final year thesis. Final semester has been tough to me personally, but after going

through it all, I believe I can look back at my achievement and be proud of myself for what

I have managed to do.

Much thanks to my supervisor, Dr Aazani Mujahid, for all the guidance,

encouragement, support and recommendation. Without you, completing this project would

have been impossible. You were always there when I need you, and nothing that I can do

will ever repay all the kindness that you have shown me. You are the best supervisor I

could ever ask for, and I am always grateful for you.

As for my beloved family, including my dearest friend, Mohd Efree Budiman (who

is like a family to me), you guys have always been the sources of my strength, you have

helped me a lot, so often that I lost count. Your supports and blessings are all I need in this

life, and I will always pray for all your healthy and success.

Last but not least, for all my lecturers, colleagues, postgraduate students, and lab

assistants; I thank you all for your support and any sorts of help you have given me, either

directly or indirectly. I apologize for not included your name here as they are too long for

me to list out.

All in all, I sincerely hope this thesis could be useful for anyone who wishes to seek

information from it. May God bless you all. Cheers.

III

Table of Contents

Content Page

Acknowledgement II

List of Abbreviations V

List of Figures VII

List of Tables IX

List of Appendices IX

Abstract X

1.0 Introduction 1

2.0 Literature Review 3

2.1. Remote Sensing 3

2.1.1. Remote Sensing and Sea Temperature 5

2.1.2. Satellite Sensors 7

2.1.2.1. Advance Very High Resolution Radiometer (AVHRR) 7

2.2. Data logger and Other In-Situ Instruments 8

2.3. South China Sea and Monsoon Climate 9

2.4. Ocean and Climate 10

2.4.1. El Niño Southern Oscillation (ENSO 10

3.0 Materials and Methods 12

3.1. Study Sites 12

3.2. Sea Temperature Datasets 13

3.2.1. In-Situ Sea Temperature Data 14

3.2.2. Satellites Sea Temperature Data 14

3.3. Data Analysis 15

4.0 Results 16

IV

5.0 Discussions 19

5.1. Comparison of Satellite and In-Situ Sea Temperature of

Talang Satang National Park (TSNP) 19

5.2. Sea Surface Temperature Variability 22

5.2.1. Seasonal Sea Surface Temperature Variability 22

5.2.2. Interannual Sea Surface Temperature Variability 29

5.3. ENSO Forecast 34

6.0 Conclusion 36

7.0 Recommendation and Future Study 37

References 38

Appendix 1 42

Appendix 2 44

V

List of Abbreviations

ANOVA Analysis of Variance

AVHRR Advance Very High Resolution Radiometer

ENSO El Niño Southern Oscillation

EA East Asia

GAC Global area coverage

GISS Goddard Institute For Space Satellite

HadCRUT3 global temperature series from Hadley Center

ITCZ Inter-Tropical Convergence Zone

LAC Local Area Coverage

MEI Multivariate ENSO Index

MCSST Multichannel Sea Surface Temperature

NASA National Aeronautics and Space Administration

NCDC National Climate Data Center

NE Northeast

NW Northwest

NH Northern Hemisphere

NOAA National Oceanic and Atmospheric Administration

ONI Oceanic Niño Index

SCS South China Sea

SE Southeast

SW Southwest

SH Southern Hemisphere

SEVIRI Spinning Enhanced Visible and Infrared Imager

VI

SST Sea Surface Temperature

SOI Southern Oscillation Index

TSNP Talang Satang National Park

VII

List of Figures

Figure Page

Figure 1 El Niño region based on the location in the equatorial Pacific.

Adapt from NCEP(2014).

11

Figure 2 Map shows the location of Talang Satang National Park (TSNP). 12

Figure 3 Flowchart for sea temperature datasets analysis 13

Figure 4 Partial gridded image indicating the pixel for location of TSNP.

Grid 2º x 2º = 400 km2. Red pixel: TSNP location; yellow pixel:

points for composite mean SST. These four points were averaged

for the SST of TSNP (Mean TSNP SST = [(SST 1 + SST 2 + SST

3 + SST 4) / 4].

15

Figure 5 Sea temperature in Talang Satang National Park (TSNP) recorded

by HOBO Pendant Data Logger. * Data is not available from

August 2012 until March 2013.

16

Figure 6 Variability of SST retrieved via satellite in TSNP. Trends were

almost similar throughout the year showing TSNP was

experiencing seasonal monsoon every year. * Mean SST is shown

in bold line, while mean SST ± standard deviation (dotted line).

17

Figure 7 Comparison of sea temperature between AVHRR and HOBO

Pendant Temperature/Light Data Logger. Red (black) line

represents the AVHRR (data logger) mean sea temperature. Most

of the time AVHRR reading intercepts with the data logger

reading and all of the readings are lower than the logger. *Data is

not available from September 2012 to March 2013.

20

Figure 8 Monsoon season with its corresponding months. Northeast

monsoon (November-March), Southwest monsoon (May-

September), and Inter-monsoon (April & October).

22

Figure 9 Seasons changing as the earth circling the sun, passing through the

orbit. The earth inclines 23.5º from its axis, causing unequal

distribution of solar radiation between the hemispheres. Modified

from Barry & Chorley, 1992).

24

Figure 10 Monsoon wind patterns. ITCZ shift southwards during boreal

winter (a) and northward during boreal summer (b). As the thermal

equilibrium (ITCZ) shifts, Easterlies Trade wind deflected when it

pass through the equator due to Coriolis Effect, thus forming

Northeast monsoon and Southwest monsoon.

24

VIII

Figure 11 Comparison of light and sea temperature recorded from HOBO

Pendant Light/Temperature Data Logger. * Data is not available

from September 2012 to March 2013.

25

Figure 12 Monthly sea surface temperature trends during monsoon season

cycle. Alphabets represent Regime A, Regime B, Regime c,

Regime D, Regime E, and Regime F.

26

Figure 13 Mean SST of TSNP for 10 years period (2004-2013). Graph shows

apparent seasonal cycle throughout the year. Red box in the figure

indicates significant different of SST (2008-2013) trends

compared to other years.

30

Figure 14 Comparison of SST anomalies (ºC) of El Nino event (2009 to

2010) with the anomalies of El Nino predicted (2013 to March

2014).

35

Figure 15 Diagram shows the process of transmitting electromagnetic

radiation from the ocean to satellite sensor within the IFOV. Adapt

from Robinson (2004).

42

Figure 16 Electromagnetic spectrum showing the variation with wavelength

of atmospheric transmission and the spectral windows used for

remote sensing. Adapt from Robinson and Guymer (2002).

42

IX

List of Tables

Table Page

Table 1 Regimes with their corresponding months and temperature trends. 26

Table 2 Historical El Niño and La Niña episode based on the ONI. Pacific

warm (red) and cold (blue) episodes based on a threshold +/- 0.5

ºC for the ONI. Threshold must be exceeded for a period of at

least 5 consecutive overlay 3 months seasons. ONI more than +/-

1.0 ºC is considered as strong ENSO event. (Adapted from NCEP,

2014)

32

Table 3 Months that is significant than other years (2004-2013) in Talang

Satang National Park. Highlighted box indicates warm (red)

months and cold (blue) months.

32

Table 4 General features of ENSO (warm and cold phase) with different

strength in Talang Satang National Park

34

Table 5 Specification of HOBO Pendant Temperature/Light Data Logger 43

Table 6 Descriptive statistics for sea temperature from HOBO Pendant

Light/Temperature Data Logger

44

Table 7 Descriptive statistics for SST retrieved from AVHRR satellite

sensor (2004-2013)

45

Table 8 Pearson Correlation (r) between years (2004-2013) for SST

retrieved via satellite (r = 1 being the highest correlation). **.

Correlation is significant at the 0.01 level (2-tailed).

45

List of Appendices

Appendix Page

Appendix 1 Figures and Tables from Literature Review 42

Appendix 2 Figures and Tables from Results and Discussion 44

X

Satellite and In-Situ Measurement for Temporal Variability of Sea Temperature in

Talang Satang National Park

Nur Asilah binti Awang

Aquatic Resource Science and Management Faculty of Resource Science and Technology

Universiti Malaysia Sarawak

ABSTRACT

Remote sensing is one of the important tools used to measure sea surface temperature (SST). The main

purpose of this study is to determine sea temperature in Talang Satang National Park (TSNP) and to relate it

with El Niño Southern Oscillation (ENSO). Sea temperature in TSNP is measured using HOBO

Light/Temperature Data Logger and AVHRR satellite sensor. The mean sea temperature from data logger is

30.14 ± 0.74 ºC, while the sea temperature from the satellite is 29.52 ± 0.71 ºC. The reliability of SST

retrieved from the satellite in TSNP is questioned. Consecutively, the satellite datasets for SST in TSNP is

compared with data logger. There is high correlation between these two instruments (r = 0.792). Satellite managed to record the trends of SST, but not providing the absolute value. From the recorded trends, it

shows TSNP is influenced by seasonal monsoon; namely the Northeast (NE) monsoon (November - March)

and Southwest (SW) monsoon (May – September). Monsoon caused the variability of SST. High SST

recorded during SW monsoon and the transitional months, but low SST during the NE monsoon. There were

double maximum peak of SST every year; highest in June, followed by October. The SST in the year 2008 to

2011 were significantly different than others due to strong ENSO event. Strong ENSO event gives impact to

SW monsoon, which causes significant difference (p < 0.05) in SST during these periods. Forecasting ENSO

event is possible only by several months ahead. SST anomalies from previous strong El Niño episode (2009)

are compared with the prediction of El Niño (summer 2014). High correlation (r = 0.614) between these

anomalies indicates the incoming El Niño may affect the SST in TSNP.

Keywords: Sea surface temperature, satellite reliability, monsoon, ENSO

ABSTRAK

Penderiaan jarak jauh merupakan salah satu alat penting untuk megukur suhu permukaan air (SPA). Tujuan

utama kajian ini adalah untuk mengetahui suhu air di Taman Negara Talang Satang (TNTS) dan untuk menhubungkaikannya dengan “El Niño Southern Oscillation” (ENSO). Suhu air di TNTS diukur

menggunakan pengesan alat satelit AVHRR dan “HOBO Light/Temperature Data Logger”. Purata suhu air

daripada data logger ialah 30.14 ± 0.74 ºC, manakala suhu air daripada satelit ialah 29.52 ± 0.71 ºC.

Kesahihan pengesan alat satelit AVHRR di TNTS dipersoalkan. Oleh itu, data SPA di TNTS yang diambil

daripada satelit dibandingkan dengan data daripada data logger. Terdapat korilasi yang tinggi antara alat

pengukur ini (r = 0.792).Satelit berjaya merekod trend SPA, tetapi bukan nilai sebenar. TNTS diepengaruhi

oleh musim monsoon, iaitu monsun Timur Laut (TL) (November – Mac) dan monsun Barat Daya (BD) (Mei –

September). Monsun mengakibatkan perubahan SPA. SPA tinggi pada monsun BD dan bulan perantaraan,

tetapi SPA rendah pada monsun TL. Terdapat juga dua puncak maksima pada trend SPA setiap tahun;

paling tinggi pada bulan Jun, dan diikuti bulan Oktober. Perbezaan ketara pada SPA telah dikesan pada

tahun 2008-2011 berbanding tahun-tahun yang lain. Peristiwa ENSO yang kuat mampu memberi kesan pada

monsun BD dan seterusnya menyebabkan perbezaan yang ketara (p < 0.05) pada SPA pada tahun-tahun tersebut. Ramalan peristiwa ENSO hanya boleh dilakukan beberapa bulan sebelumnya. Anomali SPA

daripada episode El Niño (2009) telah dibandingkan dengan EL Niño (musim panas 2014). Korilasi yang

tinggi antara anomali tersebut membuktikan El Niño yang akan datang ini berkemungkinan besar member

impak pada SPA di TNTS.

Kata kunci: Suhu permukaan air, kesahihan satelit, monsun, ENSO

1

1.0 Introduction

The ocean covers 98% out of the 70% of water in the earth. This large body of water has

high specific heat that makes it able to absorb and store heat during the day and release it at

night, causing global cooling and warming effect (Richard et al., 2007), thus stabilizing

our climate system (Wells et al., 2002). In order to study and understand the oceans that

we depend for our continued existence, one of the technologies invented by scientist and

engineers that are widely used throughout the world is remote sensing.

Remote sensing has provided spatially and temporally detailed measurements that

concern the global oceanic and atmospheric environment. Nowadays, many researchers

rely on this technology to observe the ocean. However, remote sensing such as satellite

sensors and ocean colour are subjected to backscatter of radiation (Martin, 2004), depth

limitation (Griffiths & Thorpe, 2002) and weather interference (Jensen, 2005). Scientists

are forced to depend on other conventional methods for deeper water measurement of

selected parameters (Griffiths & Thorpe, 2002). Hence, for research using satellites that

involves waters deeper than a metre or so, it is necessary to clarify the difference between

the satellite-retrieval data with the in-situ data.

In response to global warming, the study of sea temperature has become essential

for experts to determine the climate change of the planet. It is crucial to ascertain the value

of sea temperature as it affects earth weather, ocean’s dynamic and also the biochemical

processes of living organisms, specifically aquatic organisms.

Numerous studies regarding to this has been recorded such as related to El-Niño

Southern Oscillation (ENSO) (Dewitte et al., 2012; Simard et al., 1985; Tarakanov &

Borisova, 2013), primary productivity (Feng & Zhu, 2012), coral studies (Putnam &

2

Edmunds, 2011) and many others. This confirms that the sea temperature is highly

influence and may gives impact to the environment and ecosystem.

Measurement of temperature using satellite is one of the data accessible since 50

years ago, but it is not available for all places in the world, thus restricted the ability to

direct measure of climate scale changes in the ocean (Wells et al., 2002). This study

provides the variability of sea temperature mainly in Talang-Satang National Park (TNSP),

where it is located in the Southern of South China Sea. Seeing as there were not many

research have been conducted in TSNP related to sea water temperature, this research may

add new information for future study.

TSNP is influenced by seasonal wind (monsoon). Temperature is one of the

parameters that are highly affected by monsoon (Liu et al., 2002; Tang et al., 2003). Ng

(2012) has conducted a study in obtaining temporal variability of sea temperature using

data logger in the same region as this study. In continuing this previous research, temporal

variability of sea temperature will be analysed using both data logger and remote sensing.

Therefore, the objectives of this study are:

1. To determine sea temperature in TSNP using satellite and data logger.

2. To compare satellite-retrieved temperature with temperature logged by data logger.

3. To determine the seasonal variability of sea temperature from satellite for the past

10 years until present (2004-2013).

4. To determine the interannual variability of sea temperature from the satellite for the

past 10 years until present (2004-2013).

5. To investigate the possibility of year 2014 being El Niño using sea surface

temperature anomalies.

3

2.0 Literature Review

2.1 Remote Sensing

Remote sensing is the application of electromagnetic radiation to gain information about

the ocean, land and atmosphere without being in physical contact with it (Martin, 2004).

The sensors can range from camera to multispectral satellite scanner. The use of satellites

has provides information that describes many global phenomena through the understanding

of regional variability and global climate changes. For instance, consistent global maps of

the SST distribution will show seasonal variations and patterns of global climate. Thus

having long-term consistent data records is important to understand changes in marine

ecosystem (Feng & Zhu, 2012)

Measuring the ocean from space is a complex method. Point of measurement of the

ocean is recorded by sensors corresponding to an average over the instantaneous field of

view (IFOV), which the area of IFOV depends on the sensor capability (Robinson &

Guymer, 2002). All objects emit and reflect electromagnetic radiation where the

electromagnetic energy is generated by several mechanisms such as changes in the energy

levels of the electron and thermal motion of atoms and molecules (Campbell & Wynne,

2011). Appendix 1: Figure 15 shows the satellite sensor first received the electromagnetic

radiation from the ocean passing through the atmosphere within the IFOV, then it will

process the signal before sending it to the ground station for other application (Robinson,

2004).

Born and Wolf (1999) stated electromagnetic radiation behaves as electromagnetic

wave. Appendix 1: Figure 16 shows the spectrum of electromagnetic wave and it’s

wavelength, which depends on the observing band or window.

4

The mainly used electromagnetic bands to measure the oceans are visible, infrared

and microwave band (Martin, 2004). Visible band (0.4 - 0.7 nm) depends on the daylight

and it measures for as far as the light can penetrate into the ocean, which could exceed

more than 10 m depth; while infrared band (0.7 – 20 µm) is independent of daylight and it

measures blackbody radiation that emit from the subsurface to the top of the sea surface;

and unlike microwave, visible and infrared bands are restricted to cloud-free period

(Martin, 2004). Microwave radiation below the frequency of 12 GHz has the probability to

eliminate atmospheric contamination problem as the clouds and aerosols are transparent to

it, thus providing a more reliable data (Chelton & Wentz, 2005). Unfortunately, measuring

the ocean using microwave is usually specialized on surface roughness (Robinson &

Guymer, 2002), which it will not be covered in this study.

Atmosphere including air, water vapour and aerosols absorb most radiation at

wavelength less than 350 µm (Robinson, 2004). Atmospheric particles and gases also

altered the intensity and wavelength of the sun’s energy, thus influencing the accuracy of

interpreting satellite data (Campbell & Wynne, 2011). However, there are few bands where

the atmosphere is fairly transparent. These bands are known as atmospheric window

(Campbell & Wynne, 2011). The spectral window wavebands are visible (0.4 – 0.7 nm),

part of infrared (3.7 and region between 10 – 13 µm) and microwave (10mm) (Robinson &

Guymer, 2002).

5

2.1.1 Remote Sensing and Sea Temperature

Water temperature is the measure of energy due to the motion of molecules, which are

caused by solar heating, radiating and evaporating cooling of the sea water, wind and wave

mixing (Martin, 2004). Sea surface temperature (SST) is directly related to the exchanges

of heat, momentum and gases between the ocean and the atmosphere (Emery et al., 2003).

It affects the atmosphere through its sensible and latent heat fluxed across air-sea interface,

thus making it essential for climate research (Chelton & Wentz, 2005). Daily or seasonal

heating or cooling may give impact to the sea temperature (Wells et al., 2002).

Infrared satellites are sensors used to determine the SST (Robinson &Guymer,

2002). A lot of research has been conducted regarding SST variability using various types

of sensors. Luis and Kawamura (2003) studied seasonal variability of SST in West India

shelf using Advance Very High Resolution Radiometer (AVHRR) at 9km spatial

resolution. They also did monthly calibration to determine cloud contamination and quality

level of SST retrieval. In 2011, Wang et al. re-evaluate the reformation of global mean

temperature series through combination of land surface temperature and SST observation

by introducing three series from global temperature series from Hadley Center

(HadCRUT3), National Climate Data Center (NCDC) and Goddard Institute for Space

Studies (GISS).

Sea water temperature may give impact to the ecosystem either directly or

indirectly. For instance, McClanahan et al. (2007) acquire direct temperature reading from

National Oceanic and Atmospheric Administration (NOAA) satellite images to investigate

potential connection between the observed spatial temperature variations with coral

mortality for post 1998 El Niño Southern Oscillation (ENSO) event in the East African

Coastal Current System as coral mortality is influenced by sea temperature. Besides that,

6

diurnal SST due to day (warming) and night (cooling) influence carbon dioxide fluxes in

the ocean, and Kettle et al. (2009) proved this through their research using SST obtained

from Spinning Enhanced Visible and Infrared Imager (SEVIRI).

Research comparing satellite with in-situ data was also performed to test the

difference with the satellite-derived data. In 1999, Kumar et al. Concluded that data from

Multichannel Sea Surface Temperature (MCSST) algorithm from NOAA agrees with the

in-situ data from previous research of SST variability in Tropical Indian Ocean. Hughes et

al. (2009), compared in-situ with gridded sea temperature datasets which they obtained

from the combination of three different interpolated SST products. They used gridded SST

to identify mean temperature at a specific position, and they found out that the SST

products matched with the observed in-situ variability especially for a study with a time

scale more than three years. As for Yu and Emery (1996), they validated satellite data

obtained from AVHRR at 1.1 km spatial resolution with moored buoys data to determine

the variability of SST in West Tropical Ocean. They stated cloud cover is a major problem

in retrieving data form satellite. Therefore they did cloud filter methods to reduce the

problem, which are (1) dynamic threshold method to remove most cloudy data, and (2)

meridional threshold method to remove the rest of cloudy pixel.

Based on a research conducted by Emery et al. (2003), there are a few things that

need to be considered in comparing the satellite-derived data with the in-situ. One of them

is the difference in depth for measuring temperature between these two approaches. Most

comparison used moored buoys and ships, where they measured bulk SST (depths from 0.5

to 5 m below sea surface), while in contrast satellite only measures skin SST (depths of

approximately 10 µm from sea surface). The lack of in-situ skin SST measurements caused

uncertainty in satellite SST estimation as the satellite SSTs were adjusted to match a

7

selection of buoys SSTs, causing it to ignore the physics that connect the skin and bulk

SSTs during calibration. Therefore, they concluded to have a better estimation of satellite

SST, comprehensive validation program for in-situ skin SST measurement is important.

2.1.2 Satellite Sensors

Most previous study conducted used sensors from NOAA and National Aeronautics and

Space Administration (NASA) satellites, thus confirming that their datasets are reliable to

use for this study.

2.1.2.1 Advanced Very High Resolution Radiometer (AVHRR)

AVHRR is an infrared satellite sensor that is sun-synchronized, polar orbiting satellite, a

cross track scanning system with 2700km swath width at nadir and different spatial

resolution depending on the area coverage (Martin, 2004). Local area coverage (LAC)

provides full resolution AVHRR data at 1.1 x 1.1 km, and this data may be resample to 4 x

4 km global area coverage (GAC) (Jensen, 2005). AVHRR detect blackbody radiation

(Robinson, 2004), do mapping of both daytime and night-time clouds, snow, ice and SST,

and it used band 4 (10.3 to 11.3 µm) and band 5 (11.2 to 12.5 µm) spectral resolution

specifically for SST mapping (Jensen, 2005). AVHRR is an operational system that gathers

data frequently (Cracknell, 1997) as it complete global coverage within 24 hours and it

orbits the earth 14.1 times daily (Campbell & Wynne, 2011). This sensor is widely utilized

to study SST as its thermal bands are less noisy than some other instruments, and the data

can be received in real time and processed within a very short time providing us near real-

time SST maps (Cracknell, 1997). These have made AVHRR as a very informative

instrument for researcher to obtain data for SST studies.

8

2.2 Data Logger and Other In-Situ Instrumentations

Data logger has depth limitation and the limitation depends on the type of logger used. For

example, HOBO Pendant Temperature/Light Data Logger has depth limit to 30 m in -20ºC

to 20ºC (Appendix 1: Table 5). Due to this, it is not listed among popular equipments used

to study the sea temperature. Nonetheless, if the area of interest is situated in shallow

water, such as the coastal area, subsequently data logger may be one of the best options we

have for its high resolution in measuring the sea temperature. Furthermore, it is small and

light weighted, making it easily installed anywhere.

In spite of this, not much study has been conducted using data logger as an

instrument to record in-situ data for sea temperature. According to Emery et al. (2003),

measuring the SST was started off with the use of sailing vessel where the temperature was

measured with mercury in glass thermometer from a bucket of water that was collected

while the ship was underway. Then evolution brings out an alternative method which is by

mounting thermistors on the metal hull of the ship, followed by the use of remote sensing.

The use of thermistors has been applied by Malcolm et al. (2011), in studying the spatial

and temporal pattern of shallow-water sea temperature in Solitary Island. They also used it

to determine whether temperature pattern using thermistors correspond with spatial

distribution of the East Australia Current (EAC) examined from SST images obtained from

AVHRR at 1km spatial resolution.

Eventually, data logger became famous. One of the researches conducted was by

Putnam and Edmunds (2011), where they used HOBO logger for measuring sea

temperature at 10m depth to study the effects of fluctuating temperature on corals in Back

Reef of Moorea, French Polynesia. More advance research was carried out by McClanahan

et al. (2007), in which they combined sea temperature data recorded by HOBO

9

temperature logger with SST data retrieved via satellite to study sea temperature effects on

coral reefs in East African Coastal Current system.

2.3 South China Sea and Monsoon Climate

According to Liu et al. (2002), South China Sea (SCS) is the largest marginal sea with

wide continental shelves and received voluminous runoff from several large rivers. It is

situated at the centre of the Asian-Australian monsoon, where it joins four monsoon

subsystems; namely East Asia monsoon, Tropical Indian monsoon, Western North Pacific

monsoon and Australian monsoon (Wang et al., 2009).

The climatic variation in SCS is controlled by the East Asian monsoon (Wang et

al., 2009). Monsoon season is characterized by the high distribution of rainfall (Yen et al.,

2014). It happens when land heats more rapid than the ocean, the ocean blow cold air

toward the land, and continue heating caused the air to rise, forming clouds and eventually

rainfall (Garrison, 2002). The surface circulation in the SCS drastically changes in

response to the alternating monsoon. Summer monsoon takes place within late May to

September; winter monsoon is between November and March, while April and October are

the transitional months between these two monsoons (Yen et al., 2014). This seasonal

cycle and circulation are driven by the seasonal winds associated with monsoon which

influences both temporal and spatial distribution of SST (Liu et al. 2002; Palacios, 2004).

2.4 Ocean and Climate

According to Charnock (2002), ocean and atmosphere circulation is considered as a single

system, where the general circulation of this system is resolved by the distribution energy

that comes from the sun (in term of solar radiation). He stated almost half of the solar

radiation is absorbed by the earth’s surface, specifically by the ocean. To rationalized the

10

coupled system of ocean and atmosphere, it can be briefly explained by this; first most

energy radiates from the sun are absorbed by the earth surface (ocean), then this energy is

used to evaporates the water from the ocean to the atmosphere, and the latent heat released

from this process is used for the condensation of water to form cloud, and later leads to

precipitation (Barry & Chorley, 1992). The heat and water vapour is converted by complex

process into depression and cyclone or anti-cyclone; the winds of which that give energy to

generate ocean waves and driven ocean current (Charnock, 2002).

2.4.1 El Niño Southern Oscillation (ENSO)

One of the most vital climate studies is the El Niño Southern Oscillation (ENSO)

phenomenon. ENSO is known as a natural oscillation of the ocean-atmosphere system in

the tropical Pacific Ocean and the interaction between this system results in the changes of

SST (Wells et al., 2002).

ENSO is named due to the interactions of El Niño and Southern Oscillation

phenomenon (Chiew et al., 1998). Prior to El Niño, high and low pressure centers in the

equatorial Pacific Ocean fluctuates, and this atmospheric phenomenon is known as

Southern Oscillation (Piechota et al., 1997). Southern Oscillation term is used to portray

the rise and fall of the east-west ‘seesaw’ surface pressure in the southern Pacific (Hall et

al., 2001). As the trade winds deteriorate in central and western Pacific during El Niño, it

causes depression of thermocline in the eastern Pacific (Garrison, 2002). Subsequently,

warm water pool from the western Pacific Ocean moves toward eastern, specifically at the

coastal of Peru and Ecuador (Chiew et al., 1998).

ENSO cycles oscillate between warm and cold phase; namely El Niño and La Niña

respectively (Azmoodehfar & Azarmsa, 2013). This phenomenon usually implicates with

11

dramatic climate event such as annual fire occurrence and area burned (Simard et al.,

1985), unusual rainfall, drought, flood and streamflow in certain country (Chiew et al.,

1998), health impact (Zhang et al., 2007) and many others.

Forecasting ENSO phenomenon generally will use indices such as Southern

Oscillation Index (SOI), Oceanic Niño Index (ONI) and Multivariate ENSO Index (MEI).

SOI calculated from the difference in atmospheric pressure between Tahiti in the Pacific

and Darwin in Australia (Zhang et al., 2007), ONI is based on the 3 months running-mean

of SST departures in Niño 3.4 region (NCEP, 2014), while MEI is derived on sea level

pressure, zonal and meridional components of the surface wind, SST, surface air

temperature and total cloudiness fraction of the sky (Azmoodehfar & Azarmsa, 2013).

Figure 1 below shows the region of El Niño by the location in the equatorial Pacific Ocean.

Figure 1: El Niño region based on the location in the equatorial Pacific. Adapt from NCEP(2014).

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3.0 Materials and Methods

3.1 Study Sites

Study was conducted at Talang-Satang National Park (TSNP) which includes both Talang-

Talang Island (N 01º 54’ 57.4”, E 109º 46’ 27.9”) and Satang Island (N 01º 47’ 12.5”, E

110º 09’ 16.6”). TSNP is situated in the southern of South China Sea (SCS) and northern

of Sarawak, Malaysia (Figure 2). It is one of Marine Protected Area for conservation of

turtle and coral reefs. This study site was chosen based on the availability of existed

dataset.

Figure 2: Map shows the location of Talang Satang National Park (TSNP).

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3.2 Sea Temperature Datasets

Data for sea temperature was collected by using two different instruments; remote sensing

and in-situ. Figure 3 shows the flowchart for sea temperature analysis

Resolution

10 minutes Temporal 24 hours

< 10 cm Spatial 4 km x 4 km

Figure 3: Flowchart for sea temperature datasets analysis

Sea Temperature Data

Data Analysis

Averaged monthly means

Filtered by weekly and monthly

AVHRR Satellite Sensor

Monthly end user data

Data Logger

10 minute interval raw data

Descriptive Statistics

Mean, standard deviation, max/min value,

mean difference

Advance Statistics

Pearson correlation test

T-test

One-way Analysis of Variance (ANOVA)