نوع مقاله : پژوهشی
1 دانشیار دانشکدۀ جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار
2 دانشیار دانشکدۀ علوم و فنون نوین دانشگاه تهران
3 دانشجوی دکتری اقلیمشناسی شهری، دانشکدۀ جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار
4 دانشجوی دکتری تغییر اقلیم، دانشکدۀ جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار
5 دانشجوی کارشناسی ارشد اکوهیدرولوژی، دانشکدۀ علوم و فنون نوین، دانشگاه تهران
عنوان مقاله [English]
The climate of the land has always been changing over time but the current trend in climate change has become important due to the role of humans which has been unprecedented over the past years. In this study, The LARS WG and SDSM models are used to evaluate the climate change at Bojnourd station for downscaling climate and both models evaluate A and B scenarios but also used RCP new scenario in SDSM. The result of the status of the station showed temperature mean station is 13.52 c but after 2000, the temperature of the station is higher than its long-term average. The results of the LARS WG model show that precipitation will increase at the station in year 2050. In scenario B1, the amount of precipitation increase almost 16 mm more than base precipitation to 2050 and all season’s precipitation are increasing in this scenario. In scenario A2, the increase in this scenario reaches almost 256mm. In total, the SDSM model downloading to increase precipitation and this increase showed A1 and B2 scenarios more than RCP scenarios, therefore In the scenario A2 value is 289mm, the scenario B2 is 294 mm, the scenario RCP 2.6 is 264 mm, the scenario 4.5 RCP this is 273 mm and in the RCP scenario of 264 mm. In the case of temperature in the LARS WG model, the minimum temperature increases in both cases. The maximum winter temperature rise is 0.8 c in the A2 scenario. In both scenarios, the maximum temperature in March, April and May is the highest increase compared to other months. The SDSM model shows that the maximum and minimum annual temperatures of this model are lower temperatures than the base period. But the monthly increase in the months of March, April and May is the minimum and maximum increase, which indicates the matching of the results of both models.