چشم‌انداز تغييرات دماهاي حداكثر اروميه با استفاده از ريزگرداني آماري خروجي مدل CanESM2

Future projection of Maximum Temperature in Urmia through Downscaling output of CanESM2 Model

افزايش دما و گرمايش جهاني از بزرگ­ترين چالش ­هايي است كه بشر در قرن بيست و يكم با آن مواجه است. تغييرات در پارامترهاي اقليمي به ويژه دما و بارش به عنوان مهم­ترين پارامترهاي اقليمي مي­تواند فرايندهاي هيدرولوژيكي، كشاورزي، محيط زيست، بهداشت، صنعت و اقتصاد را تحت تاثير قرار دهد. بنابراين چشم انداز تغييرات آن، كمك فراواني به چالش‌هاي مديران و برنامه ­ريزان محيطي خواهد نمود. لذا در اين پژوهش به منظور بررسي روند تغييرات دماهاي حداكثر در طول دوره آماري پايه (2005-1961) و همچنين چشم انداز تغييرات آتي دماهاي حداكثر در يك دوره­ ي 30 ساله (2051-2021) در ايستگاه­ سينوپتيك اروميه با كاربست مدل ريز مقياس گرداني SDSM پرداخته شد. بدين منظور ابتدا با استفاده آزمون ناپارامتري من- كندال و تخمين­ گر شيب سن، روند تغييرات اين پارامتر بررسي شد. سپس ميزان كار آيي مدل SDSM با استفاده از داده ­هاي مشاهداتي­ و مدل شده مركز ملي پيش ­بيني محيطي (NCEP) و شاخص­ هاي MSE، RMSE، MAE و همچنين ضريب تعيين و همبستگي مورد ارزيابي قرار گرفت. بعد از اطمينان از دقت مدل، با استفاده از مدل گردش عمومي CanESM2 تحت سه سناريوي RCP2.6 و RCP4.5 و RCP8.5 چشم انداز آتي دماهاي حداكثر و حدي در دوره (2051-2021) بررسي گرديد. نتايج نشان داد كه در طول دوره آماري پايه، دما داراي روند افزايشي است اما معني ­داري روند مورد تأييد قرار نگرفت. نتايج حاصل ارزيابي مدل SDSM نيز نشان داد كه مدل مذكور توانايي لازم جهت مدل­سازي دماي حداكثر را دارد. بر اساس نتايج بدست آمده از داده ­هاي مدل CanESM2 ميزان دماهاي حداكثر افزايش خواهد يافت كه اين ميزان برابر با 7/0 درجه سلسيوس نسبت به دوره­ي پايه مي ­باشد. از نظر فصلي نيز بيشترين و كمترين تغييرات مربوط به تابستان با 1/6 درجه سلسيوس و فصل زمستان با 0/1 درجه سلسيوس مي­ باشد.

With the seriousness of the climate change debate in the world, the study of parameters and elements of the climate has been widely considered. With changes in climate patterns and changes in temperature and precipitation patterns, other components such as runoff and soil moisture, which are important for natural and human systems, will undergo metamorphosis. Therefore, long-term prediction of climatic variables has been considered by many scientific communities worldwide in order to know about their changes and considering the necessary measures to moderate the adverse effects of climate change. The phenomenon of climate change is of increasing importance due to its scientific and practical dimensions, since human systems dependent on climatic elements such as agriculture, industry and the like are designed and operated on the basis of the stability and stability of the climate. Accordingly, general circulation models (GCMs) have been developed. Although these models represent significant results on the atmospheric and continental spatial scales, they combine a large part of the complexity of the planet's system, but they are inherently unable to control the dynamics and forms with a fine grid Local scalability. Therefore, an assessment of the effect of climate change on a local scale requires an interim and spatial gap between large-scale climatic variables and meteorological variables with local scale, in which case the main approach is the same downscaling models. The SDSM model is one of the most widely used statistical microscopic instruments, which has many uses in meteorological, hydrological, geographic and environmental studies. Because in this method, large-scale daily circulation patterns are used on a stationary scale; and when used for the rapid and cost-effective estimation of climate change, and for randomized meteorological generators and modified functions, have given acceptable results. Given that global models have generally simulated climatic elements until the year 2100, it is possible to use global model data to simulate the desired variables such as precipitation and temperature on a station scale. The Intergovernmental Panel on Climate Change (IPCC) has used its latest assessment report (AR5) on new scenarios for the RCP as representatives of different levels of greenhouse gas emissions. The new emission scenarios have four key paths RCP2.6, RCP4.5, RCP6 and RCP8.5, which are named after their radiation in 2100, Future Perspective. The variation of the maximum temperatures of the synoptic station of Urmia during the period (2021-2050) of the CanESM2 global model has been used under three scenarios RCP2.6, RCP4.5 and RCP8.5.