环境地球化学国家重点实验室知识库

Climate change is a global issue that's affecting food security. An increase and decrease in temperature due to climate change is expected across many regions of the world. Analysis of 39 weather stations (Pakistan) trend for maximum and minimum temperatures was done on monthly, seasonal and annual observations. Two statistical tests (Sen's slope and Mann-Kendall) were applied to find out the slopes and magnitude of climate change trend. This statistical analysis was carried out to study the possible variations for maximum and minimum temperature trend. A statistical downscaling climate projection model (SimCLIM) was used to predict magnitude of maximum and minimum temperature for 2030 and 2060. Ensemble of 40 General Circulation Models (GCMs) was used with median Representative Concentration Pathway (RCP-6.0) for future projections in SimCLIM. This study showed more number of positive trends for maximum temperature over all the weather stations. Significantly positive temperature trend was observed in February and March for maximum temperature for all sites ranges from 0.06 to 0.51 degrees C. Mostly, statistically significant negative trend (-0.06 to -0.30 degrees C) was found in Balochistan province and northern areas of Pakistan. In future, minimum temperature projected by model showed negative trends for 60% of weather sites for December where, the negative trend also increased for monthly and seasonal analysis. Minimum temperature trend reveal that December has large number of sites with negative trends with high magnitude, which further decreased for annual followed by seasonal analysis. Minimum temperature projections showed similar trends with past December results but negative trends decreased for seasonal and annual resolution. Future projections also reveal that annual maximum and minimum temperature will be increased for 2060 as compared to 2030. These results may have significant effect on agriculture of northern and high mountain areas of Pakistan, which could be managed by sustainable agricultural activities.