Climate variability and changes in local climate.

Trend analysis is an ideal way of investigating first‐hand information about climate change; climate variables exhibit trends on variety of scales, and an understanding of these trends can be used to make estimates about the future of the climate. The objective of this paper is to examine temporal variations in climate by analysing climatic variables. Angul is one of the hottest districts in India, with a maximum summer temperature of 50°C recorded in 2005. Climate data for the period 1901 to 2015 have been statistically analysed to investigate variations in climate in the region. The nonparametric Mann–Kendall rank test, Pearson's correlation, Kendall's rank correlation, analysis of variance (ANOVA) and the autoregressive integrated moving average (ARIMA) model were used to identify diurnal, monthly, seasonal, and annual changes in temperature, precipitation, vapour pressure, potential evapotranspiration and crop evapotranspiration. The results of these analyses reveal the presence of both increasing and decreasing trends for a number of climate variables. Temperature has increased drastically over the last three decades. The average minimum and maximum temperatures for 1901 were 20 and 31°C, respectively; these values increased to 23 and 33°C in 2015. The Auto‐Regressive Integrated Moving Average model projected a significant rise in seasonal temperature and a decreasing trend in rainfall; these results are considered to be an early warning sign for future extreme climate events. Significant changes in climate have been identified through statistical analysis of climate data from 1901 to 2015. A statistically significant positive trend has been identified in the temperature data: the average minimum and maximum temperatures for 1901 are 20 and 31°C, increasing to 23 and 33°C in 2015. Correlation techniques found a negative correlation between rainfall and temperature. Projections made using the ARIMA model predicted significant changes in temperature and rainfall, indicating the possibility of the continued occurrence of extreme climate events such as heatwaves and drought. [ABSTRACT FROM AUTHOR]