Thirteenth International Conference on Climate Change: Impacts & Responses

Abstract

Identification of trend of hydrologic climatic variables such as temperature and precipitation plays a significant role in climate change studies. It aids in future predictions about possible consequences on the urban environment, agriculture, water availability, etc. Variation of extreme temperature intensity are observed in many regions all over the globe as an outcome of abrupt changes in climate. In present study, the variability in time series of extreme daily temperature has been analyzed using Generalized Extreme Value (GEV) distribution (Type I) and its suitability has been examined by superimposing on to Gringorten plotting positions. Singular Spectrum Analysis (SSA), is carried out on the extreme daily temperature of Abu Dhabi City station, UAE, to detect the trend and variability of the time series. 36 years of temperature data have been used for the present analysis. This method decomposes the time series into its component parts, and reconstructs the series by leaving the random (noise) component behind. No statistical assumptions such as stationarity of the series or normality of the residuals are required. These extracted components are subjected to forecast by SSA algorithm. SSA has the ability to depict the slowly varying component (i.e., trend) and oscillatory components from the extreme temperature time series. SSA is also capable of forecasting the extracted components of these time series.