Time-Series Modeling and Short Term Prediction of Annual Temperature Trend on Coast Libya Using the Box-Jenkins ARIMA Model

Abstract

Aims: In this study a time series modeling was developed to predict the annual warming trend at coast Libya in the second decade of the 21st century using ARIMA model, and performing an evaluation for the results significance. Study Design: Utilizing Box-Jenkins method through, the stage of identification, parameter estimation and diagnosis, finally, a forecast of the annual surface temperature trend on Libya in the second decade of the 21st century was assembled, together with an evaluation of the significance of the predicted warming trend. Place and Duration of Study: Annual surface absolute temperature (ASAT) from 16 stations belonging to the coast of Libya during the period of (1892-2010) was used. Results: The most optimum two prediction models obtained for the above data, are non-seasonal linear trend model ARIMA (3-1-2) and quadratic trend model ARIMA (3-2-3). We found that the forecasted values followed the upward trend present in the data and the pattern of results almost followed the pattern predicted with a correlation value of approximately 80% for both models. According to linear Trend model, an increase in temperature of 0.12°C/decade and according to quadratic model, an increase of 0.53°C/decade had been predicted until the year 2020. This increase in temperature is the same as what was predicted by the United Nations (from 1.3°C to 5.8°C between the year 1990 and 2100). Conclusion: The two models, individually, produced the best overall performance in making short-term (∼10-year) predictions of annual surface absolute temperature in Libya. It can be used as a supplemental tool for environmental planning and decision making concerned with other environmental models.