Application of sequential mann-kendall test for detection of approximate significant change point in surface air temperature for kolkata weather observatory, West bengal, India

Large potential and scientific studies have been carried out up to date to detect and locate climate changes and trends in different parts of the world by examine and analyze various climate key factors such as; air temperature, rainfall records humidity concentration etc. Some of these studies deal with measurements of temperature time series for specific meteorological stations due to the importance of its long records of fluctuating temperature data, location and reliability. Pursuance on the recent studies, that have shown that the Earth’s surface air temperature has increased by 0.6˚C to 0.8˚C during the 20th century, along with the regional abrupt changes of lower atmospheric temperature. This environmental situation has drawn great interest to climate researchers leading to several studies on climate trend detection at various scales. This paper examines the long term modification of lower atmospheric temperature time series in Kolkata observatory. Time series of lower atmospheric temperature data for the period considering from 1941-2010 for Kolkata weather observation station was collected from the Indian Meteorological Department (IMD, Alipur). Fluctuations of trend and detection of abrupt change point of mean monthly temperature time series were examined. The nonparametric Mann-Kendall test was employed to detect trends and change points of the mean monthly temperature. Statistically approximate significant (p≤0.05) abrupt changes and trends have been detected. The plots of forward sequential values and backward sequential values intersect each others, than it has indicated change points in considered data set. According to this statistical test for Kolkata observatory, in 1986, 1973 and 1999, 1970, 1975, 1993, 1999, and 1958 are showing the significant potential change point in the temperature time series for March, April, May, June, July, November, December respectively.