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