بررسی و پیش‌ بینی تغییرات اقلیمی با استفاده از رویکرد مدل ‌های گردش عمومی جو در استان ‌های غربی کشور

امروزه از مهم‌ترین ‌چالش ‌های جوامع انسانی، تغییرات اقلیمی است و بررسی این تغییرات به‌دلیل تاثیر گستردۀ آن بر موجودات زنده از اهمیت بسزایی برخوردار است. در این پژوهش به پیش‌بینی و بررسی پارامترهای دمای حداکثر، بارش و دمای میانگین تحت سه سناریوی rcp2.6، rcp4.5 و rcp8.5و کاربست مدل sdsm در 11 استان غربی کشور در دو دورۀ 20212036 و 20362100 نسبت به دورۀ پایۀ 19902020 مورد بررسی قرار گرفت. نتایج مدل نشان داد که بارش در بازۀ زمانی 20212035 نسبت به دورۀ پایه از 2.50 تا 3.86 درصد و در بازۀ زمانی 20362100 نیز 10.02 تا 15.73 درصد تغییر نسبت به دورۀ پایه خواهد داشت و بیشترین تغییر در میزان بارش در استان‌های آذربایجان شرقی و غربی رخ خواهد داد. بررسی دمای حداکثر در بازۀ زمانی 20212035 نسبت به دورۀ پایه از 0.2 تا 0.89 درجۀ سانتی‌گراد افزایش بیشینۀ دما در سطح منطقۀ مورد مطالعه اتفاق خواهد افتاد و در بازۀ زمانی 20362100 نیز بیشینۀ دما از 0.86 تا 0.89 درجۀ سانتی‌گراد نسبت به داده‌های دورۀ پایه افزایش خواهد یافت و بیشترین تغییر در میزان دمای حداکثر در جنوب استان فارس و مناطق مرزی استان کرمانشاه رخ خواهد داد و بررسی دمای میانگین هم در بازۀ زمانی اول از 0.05 تا 5.07 درصد افزایش دما را نشان می‌دهد و در بازۀ زمانی دوم نیز 4.47 تا 5.05 درصد افزایش میانگین دما نسبت به داده‌های پایه خواهیم داشت. کمترین تغییرات در پارامترهای مورد مطالعه مربوط به سناریو rcp2.6 و بیشترین تغییرات مربوط به سناریو rcp8.5 به دست آمد.

investigation and prediction of climate changes using the approach of general circulation models (gcms) in the western provinces of iran

introduction: considering its pervasive influence on live creatures, climate change is currently considered one of the most crucial challenges facing human societies, the investigation of which is of great significance. moreover, climate change exerts an adverse influence on biological resources, the natural environment, and water sources, causing various environmental, social, and economic consequences. therefore, this study sought to investigate and predict the parameters involved in climate change, including maximum temperature, precipitation, and average temperature under the rcp2.6, rcp4.5, and rcp8.5 scenarios.furthermore, the applicability of the sdsm model was tested on eleven western provinces of iran. to this end, first, the predictive variables such as temperature and precipitation were downscaled using combined regression techniques and a small-scale stochastic weather generator, and the required data were collected from the site of the monitoring station. then, variations in maximum temperature, average temperature, and precipitation for the 2021-2036 and 2036-2100 periods were compared to those of the baseline period (1990-2020). the sdsm model was then validated and its accuracy was assessed using metrics such as mse, mape, rmse, and mae. materials and methods: on the other hand, to verify the accuracy, the data collected for the 1990-2020 period were taken as the actual and observed values, followed by the performance of some simulations with the same data to gauge and measure the accuracy of the extracted data for the 2021-2035 and 2036-2100 periods and compare it with the baseline period (1990-2005). finally, after confirming the collected data against the actual data of the respective years and determining the accuracy of the four validation methods, the verification process was extended to cover the  1990-2020, 2021-2035, and 2036-2100 periods.result: the results of the study indicated that the application of the sdsm model led to a reduction in the required accuracy needed for investigating and simulating climate change. accordingly, the highest  mse, mape, rmse, and mae values were found under the second scenario (rcp4.5) in jolfa station (in terms of maximum temperature), urmia station (in terms of precipitation), nahavand station (in terms of precipitation), and zarrineh station (in terms of precipitation), whose reported values were 0.01, 16.90, 0.10, and 0.10, respectively. on the other hand, the lowest values belonged to the second scenario, whose values were reported to be 0.00, 0.41, 0.00, and 0.00, respectively.moreover, the results obtained from the application of the sdsm downscaling model to the investigation of precipitation under rcp2.6, rcp4.5, and rcp8.5 scenarios revealed that compared to the other two scenarios, the first scenario (rcp2.6) had a higher level of accuracy in predicting precipitation for 2021-2035 and 2036-2100 periods with a small margin of error, simulating precipitation more closely to the observed data in most cases. furthermore, the model’s predictive outcomes suggested that compared to the baseline period, precipitation would undergo changes throughout the 2021-2035 period, ranging from 2.50% to 3.86%.