THE PERFORMANCE SEA SURFACE TEMPERATURE ANOMALY OVER PACIFIC OCEAN AND NIÑO 3.4 AREA BY USING INTERMEDIATE COUPLED MODEL (ICM)

Abstract

In this research, climate variability is discussed as the main important problem in the world. Because the extreme event has impacted the naturally changed and the results of the naturally changed are many severe damages varying accordingly in a wide range of territory over the world. These damages include storms, sea-level rise, floods, and droughts. The El Nino-Southern Oscillation (ENSO) is one of the primaries of the climate variability under current concern. Therefore, the aim of this research is to study the mechanism of Sea Surface Temperature Anomaly (SSTA) during 2011–2020 and simulate SSTA over the Pacific Ocean and Nino 3.4 area in advance. The results of ICM are compared with the Extended Reconstructed Sea Surface Temperature (ERSST) observation dataset. Through time series analysis, the result from the ICM model can capture the highest value in 2016, similar to the ERSST observation. However, in the time series pattern, the results from Case I (simulation six months in advance) exhibited a good trend than Case II (simulation 12 months in advance). In statistical analysis, the values from statistical analysis, the statistical values (R, RMSE, ME, and MAE) of six months in advance revealed a good accuracy value from 12 months in advance. In conclusion, the ICM model showed high-performance results indicating simulation of SSTA over the Pacific Ocean and Nino 3.4 area, especially in the case of six months in advance.