PORSEC 2014 12th Biennial Conference of PAN OCEAN REMOTE SENSING CONFERENCE Ocean remote sensing for suatainable Resources.
12th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 2014)
04 – 07 November 2014, Bali-Indonesia
SEA SURFACE TEMPERATURE VARIABILITY IN THE
SOUTHERN PART OF JAVA ISLAND AND THE LESSER
SUNDA: CORRESPONDING TO THE INDIAN OCEAN
DIPOLE MODE (IODM)
I Gede Hendrawan1,*), I Wayan Gede Astawa Karang1), I Made Kertayasa2),
and I G.A. Diah Valentina Lestari2)
1)
Department of Marine Sciences, Udayana University, Kampus Bukit Jimbaran,
Badung, Bali, Indonesia 80361
2)
Department of Physics, Udayana University, Kampus Bukit Jimbaran,
Badung, Bali, Indonesia 80361
*)
E-mail: hendra_mil@yahoo.com
ABSTRACT
The impact of Indian Ocean Dipole Mode (IODM) for the sea surface temperature (SST)
variability in the Southern of Java and Lesser Sunda has been investigated. The Aqua MODIS
satellite data has been used to investigating the SST distribution both spatially and temporally.
The Dipole Mode Index (DMI) was calculated from 2003 until 2011 and found that 2010 has an
indication as an IOD (Indian Ocean Dipole) year. It was coincide with the spatial change of
SST distribution in the Southern of Java and Lesser Sunda. The temporal change has been
investigating by wavelet transform, and found that the high spectrum indicated in 2010. It was
clearly found that in 2010 the SST variability in the southern part of Java Island and the Lesser
Sunda has a strong relationship with the IODM. Those relationship was confirmed through the
spatial, temporal and wavelet analysis methods.
Keywords: IODM, MODIS, SST, wavelet
1. INTRODUCTION
El-Nino Southern Oscillation (ENSO)
andthe Indian Ocean Dipole Mode (IODM)
are the largest earth climate phenomenon that
has a connection with the sea surface
temperature (SST) anomaly. Several
investigation regarding to the influence of
ENSO in the Indonesia seas has been
conducted, such as: the influence of ENSO
for the chlorophyll-a variability in the
southern of Java (Susanto and Marra, 2005),
the influence of ENSO for the upwelling in
Java and Sumatra Sea (Susanto et al., 2001),
and the influence of ENSO for the SST in the
Indonesian
Seas
(Nicholl,
1983).
Furthermore, some researches has been done
regarding to the IODM phenomenon, such
as: the dipole mode in the tropical area of the
Indian Ocean (Saji et al., 1999), the structure
of SST variability and surface wind in the
Indian Ocean during the IODM (Saji,
Yamagata, 2003), the influence of IODM for
the rainfall in Indonesia (Hermawan, 2007),
and the influence of IODM for the SST and
salinity in the Western of Sumatra
(Holliludin, 2009).
The IODM has an impact for the rainfall
variability in some countries, such as Africa
and Asia (Hu, Nita, 1996; Behera et al.,
2006; Harou et al., 2006). The SST
variability in the southern of Java and the
western of Sumatra is one of the key factors
for the IODM phenomenon, which is also
occurred simultaneously with the changing
of Indonesia season (Qu et al., 2005). The
period of IODM is more than a year
(interannual) (Saji et al., 1999 and 2003; Rao
et al., 2002) that could be influencing the
climate in Indonesia.
The SST in Indonesia Seas is the most
important point to determine the regional and
583
12th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 2014)
04 – 07 November 2014, Bali-Indonesia
global climate. This is due to the complex
sea bottom topography of the Indonesian
seas, and also connecting the Pacific and the
Indian Ocean (Qu et al., 2005).From the
numerical model and the field observation
shows that a little change of SST in
Indonesia could give a high change on the
rainfall in the Indo-pacific (Miller et al.,
1992; McBride et al., 2003; Ashok et al.,
2001; Neale, Slingo, 2003). The SST
variability in Indonesia seas is also important
for the ecology point of view, since the
Indonesia Sea has a rich of the ocean
biodiversity. Therefore the investigation of
SST variability and its characteristics
become a substantial work. In this study, we
used the satellite data to make an
investigation of SST variability and its
characteristics in the southern of Java and the
lesser Sunda.
Remote sensing technology had been
widely used to observe the ocean resources.
The
Moderate
Resolution
Imaging
Spectroradiometer (MODIS) is one of the
satellite imaging that can be used easily to
make a periodic SST observation.
Furthermore, the temporal analysis of SST
data is done using wavelet transform, and
hence the period and the time of the
phenomenon can be analyzed.
1.1. Dipole Mode Index (DMI)
IODM signature are originally occurs in
the Indian Ocean. It could be due to an
increasing of SST in the western Indian
Ocean (50 W - 70 W and 10 S - 10 N), and
simultaneously decreasing of the SST in the
eastern part of Indian Ocean (90 W – 110 W
and 10 S - Equator) (Saji et al., 1999). The
IODM is recognized by determining the
Dipole Mode Index (DMI), which is the
difference of SST anomaly between the
western part and eastern part of Indian Ocean
(Saji et al., 1999). Saji, et al. (1999)
mentioned that the positive DMI (above 0.7)
is the indication of the positive IODM
phenomenon, whereas the negative of the
DMI (below -0.7) is indicating the negative
IODM phenomenon. The more positive the
IODM, the higher SST in the western Indian
Ocean will be. This makes the convection
increase around the western part of the
Indian Ocean. However, the eastern part of
Indian Ocean will experience drought
(including some areas in Indonesia). The
opposite phenomenon will occur during the
negative IODM.
1.2. Wavelet Transform
The wavelet transform is useful to analyze
time series data that contain non-stationary
power at many different frequencies
(Foufoula-Georgiou and Kumar, 1995;
Daubechies, 1990). Torrence, and Compo
(1997) proposed a Morlet Wavelet that used
as the mother wavelet (Equation 1).
Ψ
= π
η
/
eω
η
e
(1)
/
η
Where
is the non-dimensional frequency
and was taken to be 6 in this study to satisfy
the admissibility condition (Farge, 1992).
This is known as the scaled wavelet, which is
defined by:
/
=
′
Ψ
′
Ψ
(2)
Where s is the dilation parameter used to
change the scale, and n is the translational
parameters used as time shifting. The s-1/2 is
a normalization factor to maintain the total
energy of the scaled wavelet constant.
The continuous wavelet transform (CWT)
of a discrete sequence xn is a convolution of
xn with the scaled wavelet functions and
translated from Ψ 0(η) (Torrence and Compo,
1997):
=
1
′
′
′=0
Ψ
(3)
It is possible to calculate the wavelet
transform using equation (3), but it would be
simpler and easier if it done in Fourier space.
Hence, by using the convolution theorem, the
wavelet transform is the inverse of Fourier
transform of the product.
584
12th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 2014)
04 – 07 November 2014, Bali-Indonesia
=
1
(4)
Ψ
=0
where the angular frequence is defined by:
=
>
(5)
Hence, the value of power spectum wavelet
|
| can be found from the wavelet
equation aboved.
2. Data and Method
The data used in this research is SST
derived from the Aqua MODIS satellite data.
This data can be downloaded from NASA
website (http://modis.gsfc.nasa.gov/) as a
level 3 satellite data. The eight years monthly
data from 2004 until 2011 were used for the
SST analysis.
SST Aqua MODIS satellite data in the
southern part of Java and Lesser Sunda are
averaged spatially. The average value of SST
in the western Indian Ocean and the eastern
Indian Ocean also calculated to determine
the DMI that will be used for IODM
analysis.
IODM is an interannual phenomenon
(Saji et al., 1999 and 2003, Rao et al., 2002,
etc.), while the period of SST in Indonesia is
less than one year. Therefore the monthly
SST data from Aqua MODIS is then filtered.
This should be done to remove the seasonal
changes of each variable in order to obtain a
more significant relationship between SST
and IODM.
After the seasonal effects of the SST had
been removed, the wavelet transform were
applied (Equation 1-5). The power spectrum
for each variable then used to determine the
relationship between IODM period and SST
variability in the study area.
3. RESULT AND DISCUSSION
3.1 Seasonal Characteristic ofSSTin the
Indonesia Seas
Seasonal characteristic of SST from 20032011 during rainy and dry season are shown
in the figure 1 and figure 2. The
characteristic of SST during rainy season
were determined by the averaged SST in
December-January-February (DJF) period.
While the June-July-August (JJA) data were
used to find the SST characteristic in dry
season. The SST in Indonesia during the
rainy season are shows warmer rather than
dry season. There is also a significant
difference along the southern of Java Island
until Arafuru and Banda Sea, which makes
the SST becomes colder in dry season. It
could be caused by the upwelling process
due to the monsoon (Wyrtki, 1961 and Qu,
2005). However, warmer SST during the
rainy season is caused by the downwelling
process.
2003-2004
2004-2005
2005-2006
2006-2007
2007-2008
2008-2009
2009-2010
2010-2011
Figure 1. SST Characteristics during rainy season
(December-January-February [DJF])
Beside the difference of SST condition,
the SST anomaly is occurred during the dry
seasons in 2007, 2008 and 2010 along the
southern of Java Island until Banda Sea
(Figure 1). It shows that the SST was
decreasing. However, the increasing of SST
was occurred during rainy season at 2005,
2009, and 2010 (Figure 2).
585
12th Biennial Conference of Pan Ocean Remote Sens
ensing Conference (PORSEC 2014)
04 – 07 November 2014, Bali-Indonesia
2004
2005
2006
2007
nd 22006, and also end of
end of both 2003 and
he filtered SST anomaly
2011. There is also the
nificant anomaly in the
data that show a signif
could be caused by a
middle of 2010. It cou
oscillation effect.
strong interannual oscill
SST (degree celcius)
2003
2008
32
31
30
29
28
27
26
25
Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11
Year
Figure 3. SST Variability in the Southern part of Java
Sunda (bold line), and 1
Island and the Lesserr S
ash line)
standard deviation (dash
2010
2011
3
SST Anomaly (degree celcius)
2009
2
1
0
Jan-03Jan-04Jan-05Jan-06Jan-07
Jan-07Jan-08Jan-09Jan-10Jan-11
-1
-2
-3
Figure 2. SST Characteristics
cs during Dry Season
(June-July-August [JJA])
SST Anomaly
Year
Anomali SPL
Figure 4. SST Anomaly in Southern part of Java
Sunda (black bold line),
Island and the Lesserr S
(black dash line), 1 standard
Filtered SST anomaly (bla
ine)
deviation (gray dash line)
dex (DMI)
3.3 Dipole Mode Inde
3.2 SST Variability alon
along the Southern
part of Java Island
and and the Lesser
Sunda
The temporal variabilit
bility of SST from
2003-2011 along the southe
southern part of Java
and the lesser Sunda are show
shows in the figure
3. During 2003 and 2004, tthe SST indicated
were less than 1 standard
rd deviation during
the rainy season (DJF per
period). Meanwhile,
the SST was greater tthan 1 standard
deviation in 2011 during the dry season (JJA
period).
Further analysis of SST
T aanomaly is shown
tive anomaly were
in figure 4. The positive
occurred in the middle of 2005, early 2007
and end of 2010. Howeve
ever, the negative
anomalies were shown in the middle until
ode Index) is an IODM
DMI (Dipole Mode
from SST data (Saji et
index that calculatedd fro
ta used were 9 years
al., 1999). The data
monthly SST data dderived from Aqua
MODIS. Figure 5 iss show the DMI value
nd has an indication of
during 2003-2011 and
high DMI at end of 2004 until early of 2005,
end of 2006 and 2007, and also middle of
which are more than 1
2010 and end of 2011,, w
0.6oC). Figure 5 is also
standard deviation (0.6
ive IODM occurred in
show that the negative
2004- 2007 and 2010, while the positive
IODM occurred inn 2006, 2007 and 2011.
586
12th Biennial Conference of Pan Ocean Remote Sens
ensing Conference (PORSEC 2014)
04 – 07 November 2014, Bali-Indonesia
-1,5
Year
-2,5
Figure 5. Dipole Mode Index (DM
(DMI) (black bold line),
and 1 standard deviation (dash
ash line)
3.4 Wavelet Transform
(ms-1)
The power spectrum ((PS) of DMIwith
95% confidence level (bold
old countur line) are
founded at midle of 2009
09 until 2011. The
period isaround 1 until 2 year (Figure 6a).
The IODM is clearly occur
curred in 2010 with
the period of 2 years.
ars. However, the
variability of SST shownn tthat the minimum
temperature is higher than
han normal condition
in the same year (Figure 3)
3). The SST in the
a) Standardize rainfall (monthly)
4
study
area is spatially incr
ncreasein 2009 and
the2 highest is occurred inn 2010 (Figure 1 gh).0 And the global wavelet
let spectrum (GWS)
-2
shown
that the IODM
DM wereoccurred
-4
periodically
with2006 time
period
eriod
around
12011until2012
2003
2004
2005
2007
2008
2009
2010
Time (year)
5 years globally (Figure
6b)
6b).
Period (years)
DMI
SPL JAWA-BALI-NT
2
4
8
16
32
2003
2006
2007
Time (year)
2008
2009
2010
2011
2012 0
2009
2010
2011
2012
(a)
SPL JAWA-BALI-NT
0.15 8
0.1 7
0.05 6
0 5
2003
4
2004
2005
2006
c) DMI Global Wavelet Spectrum
2007
Time (year)
2008
3
1
4
0
1
2
4
period (year)
8
16
32
(b
(b)
2004
2005
2006
2007
2008
Time (year)
2009
2010
2011
2012 0
(a)
d) 2-7 yr rainfall Scale-average Time Series
0.2
0.15
0.1
0.05
0
2003
2005
d) 2-7 yr rainfall Scale-average Time Series
2
32
2003
2004
0.2
2
8
2004
2005
2006
2007
2008
Time (year)
2009
2010
2011
2012
(b)
Figure 6. a. Power Spectrum
m of DMI, b. Global
Wavelet Spectrum (GWS) for DMI
Figure 7 shows the po
power spectrum of
SST in the southern partt of Java Island and
c) Global Wavelet JAWA-BA
1
1
16
2s-2)
Avg variance (m
(ms -1)
-0,5 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11
Period (years)
0,5
2)
Avg variance (C
1,5
Lesser Sunda. The period of SST variability
with 95% confidencee llevel are founded at
ime period of 1.5 year.
2003 to 2006 with tim
period in 2009 to 2011
Meanwhile, the time pe
ever the SST variability
is 1 to 4 years. Howeve
power spectrum for 2003
that shown by the powe
nciding with the DMI
to 2006 is not coinci
ight be caused by the
power spectrum. It might
annual phenomenon of the Pacific Ocean
oscillation-ENSO).
Beside
(El-nino southern oscill
a) Standardize SST (monthly)
of 4 that, the variabilityy oof SST has the similar
2
pattern
with the DMII in 2010. SST in the
va Island and Lesser
southern
part of Java
0
periodicity
with the DMI
Sunda
has
the
same
peri
-2
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
as shown in the GWS gr
graph.
Time (year)
Power (C2)
DMI (degree celcius)
2,5
Figure 7. Wavelet transform
rm in southern part of Java
1
2
3
4
5
Island
and
Lesser
Sunda
nda,
a) Power Spectrum of
2 -2
Power (m s )
SST, b) Global Wavelet
let S
Spectrum (GWS) for SST
In order to determ
rmine the relationship
bility and IODM in the
between SST variabilit
va Island and Lesser
southern part of Java
on coefficient between
Sunda, the correlation
the power spectrum of SST and DMI had
ure 8). The dot line in
been calculated (Figure
the figure 8 refers to the 95% confidence
correlation coefficient
level. Hence, the cor
above the confidencee llevel concluded as a
on. There is a positive
significant correlation.
correlation above the 95% confidence level,
significant correlation
which is show a si
ST in the southern part
between DMI and SST
nd Lesser Sunda. The
of Java Island and
587
2
4
Power (C2)
6
12th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 2014)
04 – 07 November 2014, Bali-Indonesia
Correlation Coefficient
significant positive correlations occurred in
the 1.5 to 1.7 years, 2.2 to 2.8 years, and 4.7
to 5 years’ time period. The result states that
SST in the southern of Java Island and
Lesser Sunda got a lot of impact from the
IODM during those periods. While a strong
negative correlation may indicate that the
annual variability of SST is caused by other
phenomenon.
1
0,5
0
-0,5 1,5
2,5
-1
3,5
4,5
Time Scale
Figure 8. Power Spectrum Correlation
4. CONCLUSION
The relationship between SST variability
and the IODM in the southern part of Java
Island and Lesser Sunda can be confirmed by
using the Aqua Modis Satellite data. It is
clearly shown in 2010 that SST in those
regions has a strong relationship with IODM
phenomenon.
This relationship is well
confirmed by spatial, temporal and even by
the wavelet method.
For further study, the numerical
simulation will be useful to find an impact
for the ecology and climate condition in
Indonesia, weather by the assimilation of
satellite data or the in situ data.
Acknowledgments
The authors are grateful to the Udayana
University who was supported under the
scheme of “Hibah Penelitian Unggulan
Universitas Udayana” with contract number:
21.20/UN14/LPPM/2012.
References
Ashok, K., Guan Z. and Yamagata T., 2001.
Impact of the Indian Ocean Dipole on
the relationship between Indian Ocean
monsoon
rainfall
and
ENSO,
Geophysical Research Letters, 28:4, pp.
499–4,502.
Behera, S. K., Luo J.-J., Masson S., Rao
S.A., Sakuma H. and Yamagata T.,
2006. A CGCM study on the Interaction
between IOD and ENSO, Journal of
Climate, Vol. 19, pp. 1688-1705.
Harou, A.P., Lajoie, Robert F., Kniveton,
Dominic R., Frogley and Michael R.,
2006. The influence of the Indian Ocean
Dipole Mode on Precipitation over the
Seychelles, Int. J. Climatology, Vol. 26,
pp. 45-54.
Hermawan, E., 2007. Analisis Hubungan
Antara Propagasi dan Struktur Vertikal
MJO Dengan Perilaku Curah Hujan
yang Terjadi di atas Kototahang dan
Kawasan Sekitarnya Berbasis Analisis
Data, EAR, BLR, Radiosonde dan
NCEP/NCAR Re-analysis, Bandung:
LAPAN.
Holiludin, 2009. Variabilitas Suhu dan
Salinitas di Perairan Barat Sumatra A
dan Hubunganya dengan Angin Muson
dan IODM
(Indian Ocean Dipole
Mode), Bogor : Institut Pertanian Bogor
Hu, Z.Z., Nitta T., 1996. Wavelet analysis of
summer rainfall over North China and
India and SOI using 1891-1992 data,
Journal of the Meteorological Society of
Japan, Vol. 74, pp. 833-844.
McBride, J.L., Haylock, M. R., and Nichols,
N., 2003. Relationships between the
maritime continent heat source and the
El
Niño–Southern
Oscillation
phenomenon, Journal of Climate, 16:2,
pp. 905–2,914.
Miller, M.J., Beljaars A.C.M. and Palmer
T.N., 1992. The sensitivity of the
ECMWF model to the parameterization
of evaporation from the tropical oceans,
Journal of Climate, 5: pp. 418–434.
Neale, R.B., Slingo J.M., 2003. The
Maritime Continent and its role in the
global climate: A GCM study, Journal
of Climate, 16: pp. 834–848.
Nicholls, N., 1983. The Southern Oscillation
and
Indonesian
Sea
Surface
Temperature, Melbourne: Australian
588
12th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 2014)
04 – 07 November 2014, Bali-Indonesia
Numerical
Meteorology
Research
Centre.
Rao, S.A., Behera S.K, Masumoto Y. and
Yamagata T.,
2002.
Interannual
Subsurface Variability in the Tropical
Indian Ocean with Special Emphasis on
the Indian Ocean Dipole, Deep-Sea Res.
II, 49, pp. 1549-1572.
Saji, N.H., Goswami B.N., Chandran P.N.V.
and Yamagata T., 1999, A Dipole Mode
in the Tropical Indian Ocean. Nature,
401: pp. 360 – 363.
Saji, N.H., Yamagata T., 2003. Possible
Impacts of Indian Ocean Dipole Mode
Events on Global Climate, Clim. Res,
25: pp. 151 – 169.
Susanto, D., Gordon A.L. and Zheng Q.,
2001. Upwelling along the Coast of Java
and Sumatra and Its Relation to ENSO,
Geophysical Research Letters, vol.28
no.8.
Susanto, D., Marra J., 2005. Effect of the
1997/98 El Niño on Chlorophyll a
Variability along the Southern Coasts of
Java and Sumatra, Oceanography, vol.18
no.4.
Torren C, Compo G.P., 1998. A Practical
Guide to Wavelet Analysis, USA:
Bulletin of American Meteorological
Society.
Qu Tangdong, Du Yan J., Strachan G., 2005.
Sea surface temperature and its
variability in the Indonesian region,
Oceanography, Vol. 18, No. 4.
589
12th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 2014)
04 – 07 November 2014, Bali-Indonesia
590
04 – 07 November 2014, Bali-Indonesia
SEA SURFACE TEMPERATURE VARIABILITY IN THE
SOUTHERN PART OF JAVA ISLAND AND THE LESSER
SUNDA: CORRESPONDING TO THE INDIAN OCEAN
DIPOLE MODE (IODM)
I Gede Hendrawan1,*), I Wayan Gede Astawa Karang1), I Made Kertayasa2),
and I G.A. Diah Valentina Lestari2)
1)
Department of Marine Sciences, Udayana University, Kampus Bukit Jimbaran,
Badung, Bali, Indonesia 80361
2)
Department of Physics, Udayana University, Kampus Bukit Jimbaran,
Badung, Bali, Indonesia 80361
*)
E-mail: hendra_mil@yahoo.com
ABSTRACT
The impact of Indian Ocean Dipole Mode (IODM) for the sea surface temperature (SST)
variability in the Southern of Java and Lesser Sunda has been investigated. The Aqua MODIS
satellite data has been used to investigating the SST distribution both spatially and temporally.
The Dipole Mode Index (DMI) was calculated from 2003 until 2011 and found that 2010 has an
indication as an IOD (Indian Ocean Dipole) year. It was coincide with the spatial change of
SST distribution in the Southern of Java and Lesser Sunda. The temporal change has been
investigating by wavelet transform, and found that the high spectrum indicated in 2010. It was
clearly found that in 2010 the SST variability in the southern part of Java Island and the Lesser
Sunda has a strong relationship with the IODM. Those relationship was confirmed through the
spatial, temporal and wavelet analysis methods.
Keywords: IODM, MODIS, SST, wavelet
1. INTRODUCTION
El-Nino Southern Oscillation (ENSO)
andthe Indian Ocean Dipole Mode (IODM)
are the largest earth climate phenomenon that
has a connection with the sea surface
temperature (SST) anomaly. Several
investigation regarding to the influence of
ENSO in the Indonesia seas has been
conducted, such as: the influence of ENSO
for the chlorophyll-a variability in the
southern of Java (Susanto and Marra, 2005),
the influence of ENSO for the upwelling in
Java and Sumatra Sea (Susanto et al., 2001),
and the influence of ENSO for the SST in the
Indonesian
Seas
(Nicholl,
1983).
Furthermore, some researches has been done
regarding to the IODM phenomenon, such
as: the dipole mode in the tropical area of the
Indian Ocean (Saji et al., 1999), the structure
of SST variability and surface wind in the
Indian Ocean during the IODM (Saji,
Yamagata, 2003), the influence of IODM for
the rainfall in Indonesia (Hermawan, 2007),
and the influence of IODM for the SST and
salinity in the Western of Sumatra
(Holliludin, 2009).
The IODM has an impact for the rainfall
variability in some countries, such as Africa
and Asia (Hu, Nita, 1996; Behera et al.,
2006; Harou et al., 2006). The SST
variability in the southern of Java and the
western of Sumatra is one of the key factors
for the IODM phenomenon, which is also
occurred simultaneously with the changing
of Indonesia season (Qu et al., 2005). The
period of IODM is more than a year
(interannual) (Saji et al., 1999 and 2003; Rao
et al., 2002) that could be influencing the
climate in Indonesia.
The SST in Indonesia Seas is the most
important point to determine the regional and
583
12th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 2014)
04 – 07 November 2014, Bali-Indonesia
global climate. This is due to the complex
sea bottom topography of the Indonesian
seas, and also connecting the Pacific and the
Indian Ocean (Qu et al., 2005).From the
numerical model and the field observation
shows that a little change of SST in
Indonesia could give a high change on the
rainfall in the Indo-pacific (Miller et al.,
1992; McBride et al., 2003; Ashok et al.,
2001; Neale, Slingo, 2003). The SST
variability in Indonesia seas is also important
for the ecology point of view, since the
Indonesia Sea has a rich of the ocean
biodiversity. Therefore the investigation of
SST variability and its characteristics
become a substantial work. In this study, we
used the satellite data to make an
investigation of SST variability and its
characteristics in the southern of Java and the
lesser Sunda.
Remote sensing technology had been
widely used to observe the ocean resources.
The
Moderate
Resolution
Imaging
Spectroradiometer (MODIS) is one of the
satellite imaging that can be used easily to
make a periodic SST observation.
Furthermore, the temporal analysis of SST
data is done using wavelet transform, and
hence the period and the time of the
phenomenon can be analyzed.
1.1. Dipole Mode Index (DMI)
IODM signature are originally occurs in
the Indian Ocean. It could be due to an
increasing of SST in the western Indian
Ocean (50 W - 70 W and 10 S - 10 N), and
simultaneously decreasing of the SST in the
eastern part of Indian Ocean (90 W – 110 W
and 10 S - Equator) (Saji et al., 1999). The
IODM is recognized by determining the
Dipole Mode Index (DMI), which is the
difference of SST anomaly between the
western part and eastern part of Indian Ocean
(Saji et al., 1999). Saji, et al. (1999)
mentioned that the positive DMI (above 0.7)
is the indication of the positive IODM
phenomenon, whereas the negative of the
DMI (below -0.7) is indicating the negative
IODM phenomenon. The more positive the
IODM, the higher SST in the western Indian
Ocean will be. This makes the convection
increase around the western part of the
Indian Ocean. However, the eastern part of
Indian Ocean will experience drought
(including some areas in Indonesia). The
opposite phenomenon will occur during the
negative IODM.
1.2. Wavelet Transform
The wavelet transform is useful to analyze
time series data that contain non-stationary
power at many different frequencies
(Foufoula-Georgiou and Kumar, 1995;
Daubechies, 1990). Torrence, and Compo
(1997) proposed a Morlet Wavelet that used
as the mother wavelet (Equation 1).
Ψ
= π
η
/
eω
η
e
(1)
/
η
Where
is the non-dimensional frequency
and was taken to be 6 in this study to satisfy
the admissibility condition (Farge, 1992).
This is known as the scaled wavelet, which is
defined by:
/
=
′
Ψ
′
Ψ
(2)
Where s is the dilation parameter used to
change the scale, and n is the translational
parameters used as time shifting. The s-1/2 is
a normalization factor to maintain the total
energy of the scaled wavelet constant.
The continuous wavelet transform (CWT)
of a discrete sequence xn is a convolution of
xn with the scaled wavelet functions and
translated from Ψ 0(η) (Torrence and Compo,
1997):
=
1
′
′
′=0
Ψ
(3)
It is possible to calculate the wavelet
transform using equation (3), but it would be
simpler and easier if it done in Fourier space.
Hence, by using the convolution theorem, the
wavelet transform is the inverse of Fourier
transform of the product.
584
12th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 2014)
04 – 07 November 2014, Bali-Indonesia
=
1
(4)
Ψ
=0
where the angular frequence is defined by:
=
>
(5)
Hence, the value of power spectum wavelet
|
| can be found from the wavelet
equation aboved.
2. Data and Method
The data used in this research is SST
derived from the Aqua MODIS satellite data.
This data can be downloaded from NASA
website (http://modis.gsfc.nasa.gov/) as a
level 3 satellite data. The eight years monthly
data from 2004 until 2011 were used for the
SST analysis.
SST Aqua MODIS satellite data in the
southern part of Java and Lesser Sunda are
averaged spatially. The average value of SST
in the western Indian Ocean and the eastern
Indian Ocean also calculated to determine
the DMI that will be used for IODM
analysis.
IODM is an interannual phenomenon
(Saji et al., 1999 and 2003, Rao et al., 2002,
etc.), while the period of SST in Indonesia is
less than one year. Therefore the monthly
SST data from Aqua MODIS is then filtered.
This should be done to remove the seasonal
changes of each variable in order to obtain a
more significant relationship between SST
and IODM.
After the seasonal effects of the SST had
been removed, the wavelet transform were
applied (Equation 1-5). The power spectrum
for each variable then used to determine the
relationship between IODM period and SST
variability in the study area.
3. RESULT AND DISCUSSION
3.1 Seasonal Characteristic ofSSTin the
Indonesia Seas
Seasonal characteristic of SST from 20032011 during rainy and dry season are shown
in the figure 1 and figure 2. The
characteristic of SST during rainy season
were determined by the averaged SST in
December-January-February (DJF) period.
While the June-July-August (JJA) data were
used to find the SST characteristic in dry
season. The SST in Indonesia during the
rainy season are shows warmer rather than
dry season. There is also a significant
difference along the southern of Java Island
until Arafuru and Banda Sea, which makes
the SST becomes colder in dry season. It
could be caused by the upwelling process
due to the monsoon (Wyrtki, 1961 and Qu,
2005). However, warmer SST during the
rainy season is caused by the downwelling
process.
2003-2004
2004-2005
2005-2006
2006-2007
2007-2008
2008-2009
2009-2010
2010-2011
Figure 1. SST Characteristics during rainy season
(December-January-February [DJF])
Beside the difference of SST condition,
the SST anomaly is occurred during the dry
seasons in 2007, 2008 and 2010 along the
southern of Java Island until Banda Sea
(Figure 1). It shows that the SST was
decreasing. However, the increasing of SST
was occurred during rainy season at 2005,
2009, and 2010 (Figure 2).
585
12th Biennial Conference of Pan Ocean Remote Sens
ensing Conference (PORSEC 2014)
04 – 07 November 2014, Bali-Indonesia
2004
2005
2006
2007
nd 22006, and also end of
end of both 2003 and
he filtered SST anomaly
2011. There is also the
nificant anomaly in the
data that show a signif
could be caused by a
middle of 2010. It cou
oscillation effect.
strong interannual oscill
SST (degree celcius)
2003
2008
32
31
30
29
28
27
26
25
Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11
Year
Figure 3. SST Variability in the Southern part of Java
Sunda (bold line), and 1
Island and the Lesserr S
ash line)
standard deviation (dash
2010
2011
3
SST Anomaly (degree celcius)
2009
2
1
0
Jan-03Jan-04Jan-05Jan-06Jan-07
Jan-07Jan-08Jan-09Jan-10Jan-11
-1
-2
-3
Figure 2. SST Characteristics
cs during Dry Season
(June-July-August [JJA])
SST Anomaly
Year
Anomali SPL
Figure 4. SST Anomaly in Southern part of Java
Sunda (black bold line),
Island and the Lesserr S
(black dash line), 1 standard
Filtered SST anomaly (bla
ine)
deviation (gray dash line)
dex (DMI)
3.3 Dipole Mode Inde
3.2 SST Variability alon
along the Southern
part of Java Island
and and the Lesser
Sunda
The temporal variabilit
bility of SST from
2003-2011 along the southe
southern part of Java
and the lesser Sunda are show
shows in the figure
3. During 2003 and 2004, tthe SST indicated
were less than 1 standard
rd deviation during
the rainy season (DJF per
period). Meanwhile,
the SST was greater tthan 1 standard
deviation in 2011 during the dry season (JJA
period).
Further analysis of SST
T aanomaly is shown
tive anomaly were
in figure 4. The positive
occurred in the middle of 2005, early 2007
and end of 2010. Howeve
ever, the negative
anomalies were shown in the middle until
ode Index) is an IODM
DMI (Dipole Mode
from SST data (Saji et
index that calculatedd fro
ta used were 9 years
al., 1999). The data
monthly SST data dderived from Aqua
MODIS. Figure 5 iss show the DMI value
nd has an indication of
during 2003-2011 and
high DMI at end of 2004 until early of 2005,
end of 2006 and 2007, and also middle of
which are more than 1
2010 and end of 2011,, w
0.6oC). Figure 5 is also
standard deviation (0.6
ive IODM occurred in
show that the negative
2004- 2007 and 2010, while the positive
IODM occurred inn 2006, 2007 and 2011.
586
12th Biennial Conference of Pan Ocean Remote Sens
ensing Conference (PORSEC 2014)
04 – 07 November 2014, Bali-Indonesia
-1,5
Year
-2,5
Figure 5. Dipole Mode Index (DM
(DMI) (black bold line),
and 1 standard deviation (dash
ash line)
3.4 Wavelet Transform
(ms-1)
The power spectrum ((PS) of DMIwith
95% confidence level (bold
old countur line) are
founded at midle of 2009
09 until 2011. The
period isaround 1 until 2 year (Figure 6a).
The IODM is clearly occur
curred in 2010 with
the period of 2 years.
ars. However, the
variability of SST shownn tthat the minimum
temperature is higher than
han normal condition
in the same year (Figure 3)
3). The SST in the
a) Standardize rainfall (monthly)
4
study
area is spatially incr
ncreasein 2009 and
the2 highest is occurred inn 2010 (Figure 1 gh).0 And the global wavelet
let spectrum (GWS)
-2
shown
that the IODM
DM wereoccurred
-4
periodically
with2006 time
period
eriod
around
12011until2012
2003
2004
2005
2007
2008
2009
2010
Time (year)
5 years globally (Figure
6b)
6b).
Period (years)
DMI
SPL JAWA-BALI-NT
2
4
8
16
32
2003
2006
2007
Time (year)
2008
2009
2010
2011
2012 0
2009
2010
2011
2012
(a)
SPL JAWA-BALI-NT
0.15 8
0.1 7
0.05 6
0 5
2003
4
2004
2005
2006
c) DMI Global Wavelet Spectrum
2007
Time (year)
2008
3
1
4
0
1
2
4
period (year)
8
16
32
(b
(b)
2004
2005
2006
2007
2008
Time (year)
2009
2010
2011
2012 0
(a)
d) 2-7 yr rainfall Scale-average Time Series
0.2
0.15
0.1
0.05
0
2003
2005
d) 2-7 yr rainfall Scale-average Time Series
2
32
2003
2004
0.2
2
8
2004
2005
2006
2007
2008
Time (year)
2009
2010
2011
2012
(b)
Figure 6. a. Power Spectrum
m of DMI, b. Global
Wavelet Spectrum (GWS) for DMI
Figure 7 shows the po
power spectrum of
SST in the southern partt of Java Island and
c) Global Wavelet JAWA-BA
1
1
16
2s-2)
Avg variance (m
(ms -1)
-0,5 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11
Period (years)
0,5
2)
Avg variance (C
1,5
Lesser Sunda. The period of SST variability
with 95% confidencee llevel are founded at
ime period of 1.5 year.
2003 to 2006 with tim
period in 2009 to 2011
Meanwhile, the time pe
ever the SST variability
is 1 to 4 years. Howeve
power spectrum for 2003
that shown by the powe
nciding with the DMI
to 2006 is not coinci
ight be caused by the
power spectrum. It might
annual phenomenon of the Pacific Ocean
oscillation-ENSO).
Beside
(El-nino southern oscill
a) Standardize SST (monthly)
of 4 that, the variabilityy oof SST has the similar
2
pattern
with the DMII in 2010. SST in the
va Island and Lesser
southern
part of Java
0
periodicity
with the DMI
Sunda
has
the
same
peri
-2
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
as shown in the GWS gr
graph.
Time (year)
Power (C2)
DMI (degree celcius)
2,5
Figure 7. Wavelet transform
rm in southern part of Java
1
2
3
4
5
Island
and
Lesser
Sunda
nda,
a) Power Spectrum of
2 -2
Power (m s )
SST, b) Global Wavelet
let S
Spectrum (GWS) for SST
In order to determ
rmine the relationship
bility and IODM in the
between SST variabilit
va Island and Lesser
southern part of Java
on coefficient between
Sunda, the correlation
the power spectrum of SST and DMI had
ure 8). The dot line in
been calculated (Figure
the figure 8 refers to the 95% confidence
correlation coefficient
level. Hence, the cor
above the confidencee llevel concluded as a
on. There is a positive
significant correlation.
correlation above the 95% confidence level,
significant correlation
which is show a si
ST in the southern part
between DMI and SST
nd Lesser Sunda. The
of Java Island and
587
2
4
Power (C2)
6
12th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 2014)
04 – 07 November 2014, Bali-Indonesia
Correlation Coefficient
significant positive correlations occurred in
the 1.5 to 1.7 years, 2.2 to 2.8 years, and 4.7
to 5 years’ time period. The result states that
SST in the southern of Java Island and
Lesser Sunda got a lot of impact from the
IODM during those periods. While a strong
negative correlation may indicate that the
annual variability of SST is caused by other
phenomenon.
1
0,5
0
-0,5 1,5
2,5
-1
3,5
4,5
Time Scale
Figure 8. Power Spectrum Correlation
4. CONCLUSION
The relationship between SST variability
and the IODM in the southern part of Java
Island and Lesser Sunda can be confirmed by
using the Aqua Modis Satellite data. It is
clearly shown in 2010 that SST in those
regions has a strong relationship with IODM
phenomenon.
This relationship is well
confirmed by spatial, temporal and even by
the wavelet method.
For further study, the numerical
simulation will be useful to find an impact
for the ecology and climate condition in
Indonesia, weather by the assimilation of
satellite data or the in situ data.
Acknowledgments
The authors are grateful to the Udayana
University who was supported under the
scheme of “Hibah Penelitian Unggulan
Universitas Udayana” with contract number:
21.20/UN14/LPPM/2012.
References
Ashok, K., Guan Z. and Yamagata T., 2001.
Impact of the Indian Ocean Dipole on
the relationship between Indian Ocean
monsoon
rainfall
and
ENSO,
Geophysical Research Letters, 28:4, pp.
499–4,502.
Behera, S. K., Luo J.-J., Masson S., Rao
S.A., Sakuma H. and Yamagata T.,
2006. A CGCM study on the Interaction
between IOD and ENSO, Journal of
Climate, Vol. 19, pp. 1688-1705.
Harou, A.P., Lajoie, Robert F., Kniveton,
Dominic R., Frogley and Michael R.,
2006. The influence of the Indian Ocean
Dipole Mode on Precipitation over the
Seychelles, Int. J. Climatology, Vol. 26,
pp. 45-54.
Hermawan, E., 2007. Analisis Hubungan
Antara Propagasi dan Struktur Vertikal
MJO Dengan Perilaku Curah Hujan
yang Terjadi di atas Kototahang dan
Kawasan Sekitarnya Berbasis Analisis
Data, EAR, BLR, Radiosonde dan
NCEP/NCAR Re-analysis, Bandung:
LAPAN.
Holiludin, 2009. Variabilitas Suhu dan
Salinitas di Perairan Barat Sumatra A
dan Hubunganya dengan Angin Muson
dan IODM
(Indian Ocean Dipole
Mode), Bogor : Institut Pertanian Bogor
Hu, Z.Z., Nitta T., 1996. Wavelet analysis of
summer rainfall over North China and
India and SOI using 1891-1992 data,
Journal of the Meteorological Society of
Japan, Vol. 74, pp. 833-844.
McBride, J.L., Haylock, M. R., and Nichols,
N., 2003. Relationships between the
maritime continent heat source and the
El
Niño–Southern
Oscillation
phenomenon, Journal of Climate, 16:2,
pp. 905–2,914.
Miller, M.J., Beljaars A.C.M. and Palmer
T.N., 1992. The sensitivity of the
ECMWF model to the parameterization
of evaporation from the tropical oceans,
Journal of Climate, 5: pp. 418–434.
Neale, R.B., Slingo J.M., 2003. The
Maritime Continent and its role in the
global climate: A GCM study, Journal
of Climate, 16: pp. 834–848.
Nicholls, N., 1983. The Southern Oscillation
and
Indonesian
Sea
Surface
Temperature, Melbourne: Australian
588
12th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 2014)
04 – 07 November 2014, Bali-Indonesia
Numerical
Meteorology
Research
Centre.
Rao, S.A., Behera S.K, Masumoto Y. and
Yamagata T.,
2002.
Interannual
Subsurface Variability in the Tropical
Indian Ocean with Special Emphasis on
the Indian Ocean Dipole, Deep-Sea Res.
II, 49, pp. 1549-1572.
Saji, N.H., Goswami B.N., Chandran P.N.V.
and Yamagata T., 1999, A Dipole Mode
in the Tropical Indian Ocean. Nature,
401: pp. 360 – 363.
Saji, N.H., Yamagata T., 2003. Possible
Impacts of Indian Ocean Dipole Mode
Events on Global Climate, Clim. Res,
25: pp. 151 – 169.
Susanto, D., Gordon A.L. and Zheng Q.,
2001. Upwelling along the Coast of Java
and Sumatra and Its Relation to ENSO,
Geophysical Research Letters, vol.28
no.8.
Susanto, D., Marra J., 2005. Effect of the
1997/98 El Niño on Chlorophyll a
Variability along the Southern Coasts of
Java and Sumatra, Oceanography, vol.18
no.4.
Torren C, Compo G.P., 1998. A Practical
Guide to Wavelet Analysis, USA:
Bulletin of American Meteorological
Society.
Qu Tangdong, Du Yan J., Strachan G., 2005.
Sea surface temperature and its
variability in the Indonesian region,
Oceanography, Vol. 18, No. 4.
589
12th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 2014)
04 – 07 November 2014, Bali-Indonesia
590