Analysis of Satellite-Derived Sea Surface Temperature Data for South China Sea and Java Sea

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Analysis of Satellite Derived Sea Surface Temperature Data for South China Sea and Java Sea

Md. Monirul Islam*, Kimiteru Sado* and Chan Eng Soon**
*Dept. of Civil Engineering, Kitami Institute of Technology, 165 Koen-cho, Kitami 090-8507, Japan e-mail:islam-m@mail.kitami-it.ac.jp, sado@mail.kitami-it.ac.jp **Director, Tropical Marine Science Institute, National University of Singapore, 14 Kent Ridge Road Singapore 119223

e-mail:tmsdir@nus.edu.sg

Abstract
Monthly time-series (1985 to 2001) of sea surface temperature (SST) data were analyzed for South China Sea (SCS) and Java Sea (Lat: 9oS~24oN, Lon: 99oE~121oE). Monthly mean SST anomalies (SSTA) and synoptic anomalies were produced for the observation of SST variability. Comparison between two strong El Niño and La Niña events of 1997-1998 and 1988-1989 were performed by using synoptic SSTA. We attempted to find out seasonal and monthly mean variation of SST and standard deviation, and monthly mean SSTA and their standard deviation. Statistical model was used to find out the best probability distribution function (PDF) for the selected study area. We therefore discussed the warmer or cooler trends of SST and proposed the PDF of SST as the best-fitted curve for selected study areas. From changes of SST trends, it is deduced that the Java Sea and SCS is getting warmer, while Java Sea is getting warmer than SCS.

1. Introduction

SST and its long-term variability are important because SSTs serve as important environmental indicators providing information about ocean current flow, probable distribution of sea life, global energy budget, and weather and climatological trends. The use of satellite estimated SST has provided an enormous leap in our ability to view the spatial and temporal variation in SST. In contrast, a ship traveling at 10 knots would require 10 years to cover the same area, a satellite covers in two minutes (NOAA, 2001). Scientists have long yearned to decipher all the physical process occurring in the ocean. The complexities of the numerous marine systems have often eluded researchers. Over the past century, researchers concerned themselves with global process. It is only with the recent advances in technology, especially that of satellite measuring equipment with the increased accuracy, researchers have been able to study more process, over greater time period with ease in measuring and persistent sampling. Therefore so many researchers have paid their attention for satellite derived SST (Barton, 1995; Donlon and Robson, 1997; Donol et al., 1999; Schluessel et al., 1987) and also many scientists have been using SST data for SST variability anomalies for Ocean (Caron and O’Brien, 1998; Chelton and Davis, 1982; Smith et al., 1996; Chu et al., 2000). In this study we used monthly mean long-term satellite derived SST data from 1985 to 2001 for SCS and Java Sea to truly understand the annual, seasonal variability in SCS by applying temperature anomalies and statistical model for PDF. Interannual variability in SST has mainly been attributed to local thermodynamic interactions between the atmosphere and Upper Ocean (Gill and Niiler, 1973; Frankignoul and Reynolds, 1983; Frankignoul, 1985; Battisti et al., 1995, Delworth, 1996). Once created, ocean temperature anomalies in the surface mixed layer (~20-500 m) can be sustained for several months due to the large heat capacity of sea water. Alexander and Penland (1996), and Hall and Manabe (1997) showed that away from regions with strong currents much of the variability in mid-latitude SSTs on monthly and longer timescales can result from the ocean mixed layer being forced by surface heat fluxes associated with storms. The SST anomalies which develop are damped by a negative linear feedback which represents the enhanced (reduced) loss of heat from anomalous warm (cold) waters. However, much of the heat associated with anomalous sea-toair fluxes remains in the...
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