PORSEC 2014 12th Biennial Conference of PAN OCEAN REMOTE SENSING CONFERENCE Ocean remote sensing for suatainable Resources.

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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: [email protected]

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.

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12thBiennial 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 η = π / eω η e η / (1)

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:

parameters used as time shifting. The s-1/2is

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,

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= Ψ

1

= 0

(4)

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 2003-2011 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.

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).

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Figure 2. SST Characteristics (June-July-August [JJA])

3.2 SST Variability alon part of Java Island Sunda

The temporal variabilit 2003-2011 along the southe and the lesser Sunda are show 3. During 2003 and 2004, t were less than 1 standard the rainy season (DJF per

the SST was greater t

deviation in 2011 during the period).

Further analysis of SST a in figure 4. The positive

12thBiennial Conference of Pan Ocean Remote Sens 04–07

cs during Dry Season

along the Southern and and the Lesser

bility of SST from southern part of Java shows in the figure 2004, the SST indicated rd deviation during period). Meanwhile, than 1 standard the dry season (JJA

T anomaly is shown tive anomaly were of 2005, early 2007 ever, the negative in the middle until

end of both 2003 and 2 2011. There is also the data that show a signif middle of 2010. It cou strong interannual oscill

Figure 3. SST Variability in Island and the Lesser S standard deviation (dash

Figure 4. SST Anomaly in Island and the Lesser S Filtered SST anomaly (bla deviation (gray dash line)

3.3 Dipole Mode Inde

DMI (Dipole Mode index that calculated fro

al., 1999). The data

monthly SST data d MODIS. Figure 5 is show during 2003-2011 and high DMI at end of 2004 end of 2006 and 2007, 2010 and end of 2011, w standard deviation (0.6 show that the negative 2004- 2007 and 2010, IODM occurred in 2006,

25

Jan-03 Jan-04 Jan-05 Jan-06 Jan-07

SS

ensing Conference (PORSEC 2014) 07 November 2014, Bali-Indonesia

nd 2006, and also end of he filtered SST anomaly nificant anomaly in the could be caused by a oscillation effect.

in the Southern part of Java r Sunda (bold line), and 1 ash line)

in Southern part of Java r Sunda (black bold line), (black dash line), 1 standard ine) 2004 until early of 2005, 2007, and also middle of , which are more than 1

0.6oC). Figure 5 is also

ive IODM occurred in 2010, while the positive n 2006, 2007 and 2011.

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Figure 5. Dipole Mode Index (DM and 1 standard deviation (dash

3.4 Wavelet Transform

The power spectrum ( 95% confidence level (bold founded at midle of 2009 period isaround 1 until 2 The IODM is clearly occur the period of 2 years. variability of SST shown t temperature is higher than in the same year (Figure 3) study area is spatially incr the highest is occurred in 2010 h). And the global wavelet

shown that the IODM

periodically with time period 5 years globally (Figure 6b)

Figure 6. a. Power Spectrum Wavelet Spectrum (GWS) for

Figure 7 shows the po SST in the southern part of

Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08

DM

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 -4

a) Standardize rainfall (monthly)

Time (year)

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 1

c) DMI Global Wavelet Spectrum

20030 2004 2005 2006 2007 2008 2009 2010 2011 2012 0.05

d) 2-7 yr rainfall Scale-average Time Series

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04–07

(DMI) (black bold line), ash line)

(PS) of DMIwith old countur line) are 09 until 2011. The 2 year (Figure 6a). curred in 2010 with ars. However, the n that the minimum han normal condition 3). The SST in the ncreasein 2009 and n 2010 (Figure 1

g-power spectrum of t of Java Island and

Lesser Sunda. The period with 95% confidence l 2003 to 2006 with tim Meanwhile, the time pe is 1 to 4 years. Howeve that shown by the powe to 2006 is not coinci power spectrum. It might annual phenomenon of (El-nino southern oscill of that, the variability o pattern with the DMI southern part of Java Sunda has the same peri as shown in the GWS gr

Figure 7. Wavelet transform Island and Lesser Sunda SST, b) Global Wavelet S

In order to determ between SST variabilit southern part of Java Sunda, the correlation the power spectrum of been calculated (Figure the figure 8 refers to level. Hence, the cor above the confidence l significant correlation. correlation above the 95% which is show a si between DMI and SST of Java Island and

Jan-08 Jan-09 Jan-10 Jan-11

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 -4

a) Standardize rainfall (monthly)

Time (year)

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 1

c) DMI Global Wavelet Spectrum

20030 2004 2005 2006 2007 2008 2009 2010 2011 2012 0.05

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 -2

a) Standardize SST (monthly)

Time (year)

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 1

c) Global Wavelet JAWA-BALI-NT

20030 2004 2005 2006 2007 2008 2009 2010 2011 2012 0.05

1 2 4 8 16 32

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period of SST variability e level are founded at ime period of 1.5 year. period in 2009 to 2011 ever the SST variability power spectrum for 2003 nciding with the DMI ight be caused by the of the Pacific Ocean oscillation-ENSO). Beside y of SST has the similar I in 2010. SST in the va Island and Lesser periodicity with the DMI

graph.

rm in southern part of Java nda, a) Power Spectrum of let Spectrum (GWS) for SST

rmine the relationship bility and IODM in the va Island and Lesser on coefficient between of SST and DMI had ure 8). The dot line in to the 95% confidence correlation coefficient e level concluded as a on. There is a positive 95% confidence level, significant correlation ST in the southern part nd Lesser Sunda. The 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 -2

a) Standardize SST (monthly)

Time (year)

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 1

c) Global Wavelet JAWA-BALI-NT

20030 2004 2005 2006 2007 2008 2009 2010 2011 2012 0.05

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significant positive correlations occurred in the 1.5 to 1.7 years, 2.2 to 2.8 years, and 4.7 to 5years’ 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.

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.

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