Modeling and predicting the trend of temperature changes in Hamedan County - Zohreh Maryanji; fatemeh sotoudeh; meysam toulabi nejad; Ziba Zarrin (تحقیقات کاربردی علوم جغرافیایی)
Understanding and predicting future climatic conditions and characteristics is crucial due to their implications for various aspects of life. This research aims to forecast trends in extreme temperatures in the Hamedan region by employing statistical downscaling of general circulation model data. The LARS statistical downscaling model has been utilized to downscale data from the HadGEM2-ES general circulation model and the coupled CMIP5 model under three emission scenarios (RCP2.5, RCP4.5, RCP8.5). Correlation estimates between the simulated and observed data indicate values exceeding 0.95 for all months. Additionally, the p-values derived from statistical tests based on the model outputs demonstrate an acceptable level of performance in data generation and simulation. Consequently, data from 2011 to 2050 were extracted and analyzed for trends. To elucidate changes in trends, the data were examined across three distinct time intervals. The results indicate that in the optimistic scenario (RCP2.5), no significant trend is observed in the average and minimum temperatures. In contrast, significant trends in temperature data are evident under the RCP4.5 and RCP8.5 scenarios, suggesting that the increase in average minimum temperatures reflects severe climatic changes, particularly affecting precipitation patterns during the cold season. Furthermore, the analysis of the trend data reveals a significant increase in average maximum temperatures on both annual and monthly scales across all three examined scenarios, indicating an imminent environmental crisis.