Detecting the Phenomena of Sea Surface Temperature Anomaly by Vector Autoregressive

Abstract

Abstract Climate change is one of the issues and a considerable threat to the environment in the future. These changes tend to fluctuate and vary significantly with time. Mostly, climate change issues are related to global temperature. The Earth’s climate system is effected by many parameters. Sea Surface Temperature Anomaly (SSTA) is one of those parameters. The phenomena of SSTA is a necessary indicator for understanding of climate change variability. Aceh province as to the west of Indonesia has location face directly to the Indian Ocean, especially in western region, southwest, and southern. Vector Autoregressive (VAR) approach has shown that SSTA dataset has stationer and non-cointegration properties in the period 2006-2017. Based on this research, it can be concluded that the best model for SSTA with climate parameters (air temperature, rainfall, relative humidity, wind speed and short-wave radiation) is VAR with the 4th optimal lag or VAR(4). The Impulse Response Function (IRF) analysis based on VAR(4) model, which is formed to look at the phenomena of SSTA to climate parameters, shows that wind speed, rainfall and short-wave radiation have a similar pattern of detection on the equilibrium line due to shock from SSTA. It takes around 5 days for the three variables to reach the equilibrium line. Whereas the air temperature and relative humidity variables have no significant effects of shocks that occur in the sea surface temperature anomaly.