Investigation and detection effects of global warming on soil temperature trends and its estimated regression correlation

Global warming, due to greenhouse gas increase in the earth atmosphere, caused an increase of about 0.8 oC in global mean air temperature since the early 20th century. The long-term heat energy deposition, both in atmosphere and in soil depths, affect physical and biological processes. Climate change detection statistically is a process which could reveal significant changes that might not be related to natural variations. Long-term thermal energy deposition in soil depths could play an important role in detecting climate change. Thus, the aims of this study were to fill the gaps in soil temperature data and determination of the effect of global warming on long term soil temperature trend.
Kerman synoptic station was chosen to collect data for 1951-200 interval. This station is located at 30o 15’ north latitude and 56o58’ east longitude with 1754 m above Sea level. The long-term annual mean temperature and mean annual total precipitation are 15.8 oC and 150 mm, respectively. For detection of climate change the long-term annual rainfall and cloudiness, daily air temperatures for 1951-2009 interval and daily soil temperatures at 3, 9 and 15 GMT at different depths (5, 10, 20, 30, 50 and 100 cm) for 1992-2009 interval were collected. The parametric methods of Pearson and regression techniques, along with nonparametric techniques of Spearman and Mann-Kendall were employed to detect temperature trends.
The results of these study indicated that Mann-Kendall test could reveal soil and air temperature trends more accurately. 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 significant negative trends in both cloudiness and rainfall (P≤0.05).
It is concluded that there is a pronounced similarity in Kerman air and soil temperature trends with global temperature trend and soil temperature could be used to detect climate change.