ENSO IMPACT ON SST AND SLA VARIABILITY IN INDONESIA

ENSO IMPACT ON SST AND SLA VARIABILITY IN INDONESIA Bambang Sukresno 1 1 Institute for Marine Research and Observation e-mail : Bambang_sukresno@yahoo.com ABSTRACT The observation of El Nino Southern Oscillation (ENSO) impact on Sea Surface Temperature (SST) and Sea Level Anomaly (SLA) reveals some impacts on Indonesian water. The data used is SST of NOAA-Pathfinder satellite dataset while SLA derived from Jason-1 and Topex/POSEIDON satellite dataset. Correlation coefficient of ENSO impact is calculating from Pearson Equation using Multivariat ENSO Index. The SST and SLA in Indonesian water such as Arafura sea, Banda sea, Maluku sea, Java sea, Natuna sea and Makasar strait is mostly correlated to ENSO phenomenon. Keywords : ENSO, SST, SLA 1. Introduction El Nino is an oscillation of the oceanatmosphere system in the tropical Pacific, having important influence to the global weather. Normally, the sea surface height in Indonesia is about 0.5 meter higher rather than in Ecuador. While, the sea surface temperature is averagely 8 C higher in the west, with cool temperatures off South America, due to an upwelling of cold water from deeper levels, (NOAA,2009). NOAA CIRES Climate Diagnostics Center, Boulder CO, developed a Multivariate ENSO Index (MEI) based on six observed variables over the tropical Pacific: sea-level pressure, zonal wind, meridional wind, sea surface temperature, surface air temperature and total cloudiness, (Wolter, 2009). During the period of El Niño or La Niña, the changes of Pacific Ocean temperatures, influence the patterns of tropical rainfall from Indonesia to the west coast of South America, a distance covering approximately one-half way around the world. These changes in tropical rainfall correlate to weather patterns throughout the world, (NOAA, 2009).

During El Nino observation, SST is a main indicator, measured by time series satellite dataset. El Nino, a unique surface phenomenon of Pacific Ocean, give direct influence on Indonesian water characteristic, including SST, (Sukresno, 2008) Satellite technology offer the best platform to monitor the dynamic of ocean currents and heat storage by providing continuous and precise measurement of sea surface height. It s used to calculate the speed and direction of ocean currents and temperature-related changes in ocean volume. The overall shape of the oceans' "hills and valleys" is called ocean surface topography (OST). Precise knowledge of OST is essential to predict and mitigate the effects of catastrophic events such as El Niño, La Niña, and hurricanes, (NASA, 2008) The 1997/1998 El Niño s event was probably one of the most important ever observed. The whole planet recorded abnormal events that might be related to El Niño effects, (Cardon, 1999) The relationship between the tropical central Eastern Pacific SST and Asian monsoon is the most popular topic in SST monsoon studies because of the long history and nature of ocean atmosphere coupling associated with ENSO. (Wang, 2006) Suryachandra (2002), observed that significant interannual variability in the subsurface tropical Indian Ocean is associated with the Indian Ocean Dipole rather than with the most dominant interannual signal in the tropics, ENSO Rao et al. (2002) demonstrated that the subsurface interannual variability in the tropical Indian Ocean (TIO) is associated with the Indian Ocean Dipole (IOD) rather than El Niño and Southern Oscillation (ENSO). Their results are based on 17 years of model simulations and 7 years of sea level data. The strongest El Nino-Southern Oscillation (ENSO) event of the last century was accompanied by anomalous conditions in the Indian Ocean which had historic regional climatic impacts. The debate continues about whether these zonal modes in the Indian Ocean (IOZM) need an external trigger or can be initiated internally within the Indian Ocean. An ocean General Climate Model ( GCM) coupled to an advective atmospheric mixed layer model and forced with National Centers For Environmental Prediction ( NCEP) reanalyses winds for the period of 1949-2001 is employed to analyze each IOZM event to understand their preconditioning, onset, and growth phases with respect to ENSO events. The composite analyses of the weak, strong, and aborted IOZM events clearly demonstrate that the

atmospheric circulation Changes associated with the onset of ENSO events in the Pacific are crucial for triggering the initial anomalous cooling off Java after which the coupled IOZM events can grow. The Madden Julian Oscillation ( MJO) activity and the Indonesian throughflow also play crucial roles, not only in preconditioning the Indian Ocean but also in the growth phase. The ENSO IOZM interactions underwent interdecadal changes centered around 1976, the well known climate shift. The details of the intercomparisons of the IOZM events and the mechanism of the ENSO trigger for each event is presented including the role of the 1976, (Murtugudde, 2002). A nonlinear aspect of the El Niño Southern Oscillation (ENSO) is described. In particular, it is shown that ENSO acts as a basin-scale heat mixer that prevents any significant increase from occurring in the timemean difference between the warm-pool SST (Tw) and the temperature of the thermocline water (Tc). When this temperature contrast is forced to increase, the amplitude of ENSO increases El Niño becomes warmer and La Niña becomes colder, (Sun, 2007). The recharge oscillator paradigm for ENSO is further investigated by using a simple coupled model, which externally includes the equatorial wave dynamics represented by the Kelvin and gravest symmetric Rossby waves. To investigate the role of eddies in the Pacific basin wide adjustment to the wind forcing, particularly at the western and eastern boundaries, the zonal mean and eddy parts are treated separately in the current model, (Soon, 2000) Using observed sea surface temperature data from 1871-1998, and observed wind data from 1958-1998, it is confirmed that the recently discovered Indian Ocean Dipole (IOD) is a physical entity. Many IOD events are shown to occur independently of the El Niño. By estimating the contribution from an appropriate El Niño index based on sea surface temperature anomaly in the eastern Pacific, it is shown that the major fraction of the IOD Mode Index is due to the regional processes within the Indian Ocean. Our circulation analysis shows that the Walker circulation during the pure IOD events over the Indian/ Pacific Ocean is distinctly different from that during the El Niño events. Our power spectrum analysis, and wavelet power spectrum analysis show that the periodicities of El Niño and IOD events are different. The results from the wavelet coherence analysis show that, during the periods when strong and frequent IOD events occurred, the Indian Ocean Dipole Mode Index is significantly coherent with the equatorial

zonal winds in the central Indian Ocean, suggesting that these events are well coupled, (Karumuri, 2003). NASA. (2010) revealed the changes in sea surface height were computed from TOPEX/ Poseidon altimeter data. The observation shows the influence of El Nino and La Nina from 1997 through 1998. This observation is aimed to measure the impact of ENSO on variability of SST and SLA in Indonesian water. SST = c 1 + c 2 *T 31 + c 3 * T 3132 + c 4 *( sec(θ) -1)* T 3132 (1) where T 31 = brightness temperature (BT) band 31 T 3132 = BT difference (band 32 band 31) θ = satellite zenith angle COEFISIENT T30 - T31 <= 0.7 T30 - T31 > 0.7 C 1 1.228552 1.692521 C 2 0.9576555 0.9558419 C 3 0.1182196 0.0873754 C 4 1.774631 1.199584 2. Method This study is performed in Indonesian water by using satellite dataset as follows : SST derived from NOAA-Pathfinder satellite dataset SLA derived from Jason-1 and Topex/POSEIDON satellite dataset. ENSO represented by Multivariat ENSO Index, retrieved from URL : http://www.esrl.noaa.gov/psd/people/kla us.wolter/mei/table.html The SST data is calculated from NOAA pathfinder satellite data, by applying Miami Pathfinder SST algorithm as follow : While, SLA is derived from level 2 of satellite dataset. Groundtract of level 2 is then interpolated to be able to display as 2 dimension image using Inverse Distance Weighted interpolation. Influence of ENSO on variability of SST and SLA is analyzed by using Pearson equation. 3. Results and Discussion Based on result of processed NOAA- Pathfinder satellite dataset, the variability of SST in Indonesia during 1992 to 2008 display in figure 1, where SST during northwest monsoon represented by data in February, while SST during southeast monsoon represented by data in August.

Feb 1992 Feb 1995 Ags 2000 Feb 2000 Ags 2005 Feb 2005 Ags 2008 Feb 2008 Ags 2009 Feb 2009 22 C 31 C Figure 1. Average of SST in Indonesian water Ags 1992 Ags 1995 Figure 1 reveal that SST in Indonesia during northwest monsoon is warmer than SST during south east monsoon. In February SST is increase to 31 C, while in August SST is decrease to 24 C.

Jason-1 satellite dataset processed to derive SLA. Variability of SLA in Indonesia during 1992 to 2008 is displayed in figure 2, where SLA during northwest monsoon represented by data in February, while SLA during southeast monsoon represented by data in August. Ags 1997 Feb 1992 Ags 2002 Feb 1997 Ags 2008 Feb 2002 Feb 2008 Meter Figure 2. Average of SLA in Indonesian water Sea level anomaly in Indonesia varies between -0.4 meter to 0.4 meter as seen in figure 2. Generally SLA in February (northwest monsoon) relativelly high compare to SLA in August (southeast monsoon). Ags 1992

Multivariat ENSO Index (MEI) during 1992 to 2008 shows in Table 1.Strongest El Nino occurs in 1997 represent by MEI magnitude 2.882 in August, than followed by La Nina event in 1998 represent by MEI magnitude 1.11 in February. La nina also occurs during 2008 as seen in february with magnitude 1.359. Average of SST in Indonesia can be seen in Figure 3 and Figure 4, represent region in Indonesia based on map of Fisheries Management of Indonesia. While average of SLA in Indonesia can be seen in Figure 5 and Figure 6. Table 1. Multivariat ENSO Index YEAR February August 1992 1.849 0.589 1993 0.941 1.054 1994 0.191 0.632 1995 0.883 0.065 1996-0.62-0.233 1997-0.488 2.882 1998 2.663-0.172 1999-1.11-0.737 2000-1.186-0.145 2001-0.682 0.321 2002-0.175 0.886 2003 0.909 0.277 2004 0.361 0.622 2005 0.742 0.432 2006-0.464 0.752 2007 0.491-0.411 2008-1.359-0.174 2009-0.725 0.978 http://www.esrl.noaa.gov/psd/people/klaus. wolter/mei/table.html

32 31 30 Average of SST in Indonesian water during February Arafuru sea Banda sea Maluku sea Sulawesi sea SST 'C 29 28 Makasar strait Java sea Natuna sea 27 26 1992 1993 1994 1995 1996 9708 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Indian Ocean (Sumatera) Indian Ocean (Java) Figure. 3 Average of SST in Indonesian water during February SST 'C 31 30 29 28 27 26 25 24 Average of SST in Indonesian water during August 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Arafuru sea Banda sea Maluku sea Sulawesi sea Makasar strait Java sea Natuna sea Indian Ocean (Sumatra) Indian Ocean (Java) Figure. 4 Average of SST in Indonesian water during August

Average of SLA in Indonesian water during February SLA (meter) 0.25 0.2 0.15 0.1 0.05 0-0.05-0.1-0.15 Figure. 5 Average of SLA in Indonesian water during February 1993 1994 1995 1996 9708 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Arafuru sea Banda sea Maluku sea Sulawesi sea Makasar strait Java sea Natuna sea Indian Ocean (Sumatera) Indian Ocean (Java) Figure. 5 Average of SLA in Indonesian water during February SLA (meter) 0.15 0.1 0.05 0-0.05 Average of SLA in Indonesian water during August Arafuru sea Banda sea Maluku sea Sulawesi sea Makasar strait Java sea Natuna sea -0.1-0.15 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Indian Ocean (Sumatra) Indian Ocean (Java) Figure. 6 Average of SLA in Indonesian water during August

Table 2. Correlation Coefficient of SST with ENSO No Region Correlation 1 Arafura sea -0.48 2 Banda sea -0.49 3 Maluku sea -0.67 4 Sulawesi sea -0.24 5 Makasar strait -0.15 6 Java sea -0.43 7 Natuna sea -0.25 8 Indian ocean (west of Sumatera) -0.25 9 Indian ocean (south of Java) -0.32 Table 3. Correlation Coefficient of SLA with ENSO No Region Correlation 1 Arafura sea -0.54 2 Banda sea -0.30 3 Maluku sea -0.46 4 Sulawesi sea -0.33 5 Makasar strait -0.53 6 Java sea -0.32 7 Natuna sea -0.47 8 Indian ocean (west of Sumatera) -0.62 9 Indian ocean (south of Java) -0.47 ENSO influences variability of SST in most of Indonesian region as displayed in table 2. Highest correlation coefficient of SST with MEI observed in Maluku sea with coefficient about -0.67, it is mean that during ENSO event ( positive value of MEI ) SST in Maluku sea will decrease. High correlation also observed in Arafura sea, Banda sea and Java sea. During ENSO event 1997, Indonesian SST in August drop down under average of SST each region. Java sea during that event was 27.73 C under the average of SST 28.18 C. Maluku was 26.89 C under the average of SST 27.75 C. while Sulawesi sea and Makasar strait didn t much different with SST average of those region. As shown in table 3, SLA in Indonesia affected by ENSO. Indian ocean (west of Sumatera) is the most correlated with ENSO represented by coefficient about -0.62. correlation with ENSO also observed in Arafura sea, Maluku sea, Makasar strait, Natuna sea and Indian ocean (south of Java). 4. Conclusion ENSO influences variability of SST in most of Indonesian region. During ENSO period, SST in Indonesia is decrease. with highest coefficient about -0.67 in Maluku sea. SLA in Indonesia is affected by ENSO. During ENSO period, SLA in Indonesia is decrease. Indian ocean (west of Sumatera) was the most correlated with ENSO represented by coefficient about -0.62. Acknowledgement

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