شبیه‌سازی روند و فرین های دمای زنجان براساس سناریوهای اقلیمی و شگرد شبکه عصبی مصنوعی

تغییرات شدید آب و هوایی (و گرمایش کرۀ زمین) در سال های اخیر به تغییر الگوهای جوی و پدید آمدن ناهنجاری های اقلیمی در اغلب نقاط جهان منجر شده است. فرایند تغییر اقلیم به‌ویژه تغییرات دما از مهم‌ترین چالش ها در قلمرو علوم زمین و علوم محیطی است. هرگونه تغییر در مشخصه های دما به‌عنوان یکی از عناصر مهم اقلیمی هر منطقه موجب تغییر در ساختار اقلیمی آن منطقه می گردد. از این‌رو شناخت تغییرات و روند دما در برنامه ریزی های محیطی مبتنی بر دانسته های آب وهوایی هر نقطه و ناحیه امری ضروری به نظر می رسد. به همین جهت پژوهش حاضر به شبیه‌سازی دمای روزانۀ (کمینه، بیشینه و میانگین) شهر زنجان تا سال 2100 می‌پردازد. روش اجرای پژوهش از نوع توصیفی – تحلیلی و روش گردآوری داده‌ها کتابخانه‌ای (اسنادی) است. برای بررسی دمای شهر زنجان از داده‌های کمینه، بیشینه و میانگین روزانۀ دما از ایستگاه همدید شهر زنجان طی دوره 2021 1961 استفاده شد. داده‌های مدل گردش عمومی جوی جهت شبیه‌سازی متغیرهای اقلیمی (دمای کمینه، متوسط و بیشینه) با استفاده از شگرد شبکه عصبی مصنوعی و سناریوهای اقلیمی، در دوره‌های آتی مورد استفاده قرار گرفت. نتایج حاصل از شبیه‌سازی فرین‌های دمایی با استفاده از سناریوهای rcp2.6، rcp4.5 و rcp8.5، نشان داد که افزایش متوسط دمای روزانه، کمینه و بیشینه تحت تمامی سناریوها، به‌ترتیب 3/6، 3/3 و 2/7 درجۀ سلسیوس برای دورۀ 2022 2100 محتمل است. بررسی داده‌های ماهانۀ شبیه‌سازی شده تحت سناریوها و داده‌های مشاهده شدۀ نظیر نشان می‌دهد که احتمال دارد کمینه، میانگین و بیشینۀ دما در ماه‌های ژانویه و فوریه بیشترین افزایش را داشته باشند. در حالی‌که با توجه به 3 سناریو، احتمال دارد که میانگین کمینه در ماه اوت، متوسط دما در ماه آوریل و بیشینۀ دما در ماه اکتبر کم‌ترین افزایش را تجربه کنند. همچنین دمای فصلی شبیه‌سازی شده تحت سناریوها نشان می‌دهد همه فصل‌های سال به‌ویژه فصل‌های سرد سال، گرم‌تر خواهند شد. شمار رخداد فراوانی فرین‌ها نیز در هر سه مقیاس دمایی (کمینه، میانگین و بیشینه) برای چارک 25ام و75ام در هر سه سناریو افزایش خواهد یافت.  

simulation of zanjan temperature trends based on climate scenarios and artificial neural network method

simulation of zanjan temperature trends based on climate scenarios and artificial neural network methodabstractsevere climate changes (and global warming) in recent years have led to changes in weather patterns and the emergence of climate anomalies in most parts of the world. the process of climate change, especially temperature changes, is one of the most important challenges in the field of earth sciences and environmental sciences. any change in the temperature characteristics, as one of the important climatic elements of any region, causes a change in the climatic structure of that region. the summary of the investigated experimental models on climate change shows that if the concentration of greenhouse gases increases in the same way, the average temperature of the earth will increase dangerously in the near future. more than 70% of the world’s co2 emissions are attributed to cities. it is expected that with the continuation of the urbanization process, the amount of greenhouse gases will increase. according to the fifth report of the international panel on climate change, the average global temperature has increased by 0.85 degrees celsius during 1880 2012. therefore, knowing the temperature changes and trends in environmental planning based on the climate knowledge of each point and region seems essential. for this reason, the present study simulates the daily temperature (minimum, maximum and average) of zanjan until the year 2100.research methodsthe method of conducting the research is descriptive analytical and the method of collecting data is library (documents). to check the temperature of zanjan city, the minimum, maximum and average daily temperature data from hamdeed station of zanjan city during the period of 1961 2021 were used. the data of general atmospheric circulation model was used to simulate climate variables (minimum, average and maximum temperature) using artificial neural network and climate scenarios in future periods. the output variables in this study are minimum, maximum and average daily temperature. therefore, three neural network models were selected. for model simulation, model inputs (independent variables) need to be selected from among 26 atmospheric variables. therefore, two methods of progressive and step by step elimination were chosen to determine the inputs of the model. in these methods, climate variables that have the highest correlation with minimum, maximum and average daily temperature were selected. by using rcp2.6, rcp4.5 and rcp8.5 scenarios, variables were simulated until the year 2100. markov chain model was used to check the possibility of occurrence of extreme temperatures of the simulated values.resultsaccording to the rcp2.6, rcp4.5 and rcp8.5 scenarios and the simulation made by the neural network model, it is possible that on average the minimum temperature will be 3.6 degrees celsius, the average temperature will be 3.3 degrees celsius and the maximum temperature will be 2.7 degrees celsius. celsius will rise. the monthly review of the simulated data for all scenarios and the observed data of the studied variables shows that the average minimum, average and maximum temperatures in january and february, which are the coldest months of the year, will increase the most and become warmer. while the average minimum temperature in august, the average temperature in april and the maximum temperature in october will have the least increase. according to the simulated seasonal temperature table based on all scenarios, it was found that the average minimum, average and maximum temperature observed with the maximum simulated conditions were 6.9, 5.5 and 5.4 respectively in the winter season, and 3.3 in the spring season. 4, 2.3 and 3, in the summer season it increases by 3.3, 3.4 and 1.4 and in the autumn season it increases by 4.6, 4.5 and zero degrees. the frequency of extreme temperatures observed in all three variables of minimum, average and maximum temperature for the 25th and 75th quartiles is less than the number of occurrences of extreme temperatures simulated in all three scenarios. based on this, all three variables will increase and there will be fewer cold periods. an increase in night temperature and average temperature in winter season and maximum temperature in summer season will occur more than other seasons. the difference between day and night temperature will be less in autumn and summer. also, all seasons, especially the summer season, will be hotter and the occurrence of extreme temperatures is increasing for the coming years.keywords: climate scenarios, simulation, extreme temperatures, artificial neural network, zanjan