Analysis of Stochastic Patterns of Daily Minimum Extreme Temperature of Karachi in Global Climate Change Perspective

Abstract

Effects of climate change are a critical and globally accepted phenomenon and gradually becoming inevitable and catching the attention of policymakers around the world. Temperature is a principal climatic factor and is defined as the degree or intensity of heat causing huge consequences on human beings’ lives. This paper suggests some stochastic approaches to do an analysis of the Karachi region’s daily minimum extreme temperature from Jan 1, 2010, to Dec 31, 2014. It is observed that the average daily minimum temperature fits the Markov chain and its limiting probability has reached steady-state conditions after 20 to 87 steps or transitions. The results indicate that after 20 to 87 days the distribution becomes stationary. The smaller steady-state time represents the stationary of the data series, whereas long-term behavior shows non-stationarity in trend behavior in the respective seasonal time series. Furthermore, the overall annual dormancy of 24 o C to 31o C daily minimum temperature was analyzed early part of the summer season. This study can be useful for weather variability forecasting.