Investigation and Detection Effects Of Global Warming On Soil Temperature Trends And Its Estimated Regression Correlation (Case Study:Kerman)

Climate change detection statistically is a process which could reveal significant changes that are not related natural variations. Thermal energy deposition in soil depths could play an important role in tdetecting climate change. Thus, the aims of this study are to fill the gap in soil temperature data and determination of the effect of global warming on long term soil temperature trends. For this purpose long term annual rainfall and cloudiness, and mean annual air and soil depths temperature (5, 10, 20, 30, 50 and 100 cm) data were collected from Kerman synoptic station. The parametric methods of Pearson and regression techniques, and nonparametric techniques of Spearman and Mann-Kendall were employed to detect temperature trends. The results of these tests indicated that Mann-Kendall could more accurately reveal soil and air temperature trends. The mean annual, spring and summer air temperature trends significantly increased (p ≤ 0.01). The mean annual, summer and autumn soil temperatures also had increasing and significant trends (p ≤ 0.01). These findings also show a significant negative trend in cloudiness and rainfall (p ≤ 0.05). It is concluded that global warming affected air and soil temperatures of Kerman synoptic station.