An investigation on Copen’s Climate Classification in 1975 in Comparison with the Output of MIROC in the years 2030, 2050, 2080, and 2100 under Scenario A1B and A2

جغرافیا و مخاطرات محیطی

Article 3, Volume 6, Issue 2 - Serial Number 22, January 2018, Pages 59-72   

Document Type: Research Article

DOI: 10.22067/geo.v6i2.57155

Authors

Seyyed Hossein Mirmousavi; Hadis Kiani^*

Zanjan University

Abstract

. Introduction
Climate, is the general condition of the prevailing weather conditions in a specific location based on its long-term statistics. The diversity of climatic elements affects determining the climate of a region and forms a variety of climates. Comparing the characters of the recorded weather in different places of the earth reveals significant differences in the surface of this planet. Thus, every place has unique characteristics. Comprehending the weather and ultimately climate has been an important consideration for human beings since a long time ago and has a significant importance in today’s human life. Comprehending the natural characteristics of each region, particularly, climate, plays a major role in land use planning. In order to achieve this objective, climatic zoning to identify different areas of climate seems to be necessary. From a geographer's point of view, climatic zoning means organizing and categorizing the zones climatically in a way that each region has similar climatic characteristics. On the other hand, the increase in emissions of greenhouse gases and subsequently climate change and global warming have had many consequences for the planet. Particularly, the areas which are in the warm and dry belt, for the growth of the dry and semi-dry climate’s coverage is increasing. Climate change is a long-term and non-recoverable climatic condition which occurs over a period of tens or millions of years in an area's climate. The recorded data suggests that from 1901 to 2012, the temperature of the earth’s surface and the temperature of oceans have increased averagely 0.89 degrees Celsius. Therefore, changes in the climate of different parts of the world and the displacement of the existing boundaries of the climatic zones are expected. Due to the wide geographic latitudes and the existence of different local conditions in different regions, Iran has a wide climatic diversity and this issue has raised the importance of climatic zoning in this country. On the other hand, due to Iran’s presence in the warm and dry belt of the world, the country is affected by the global warming and climate change issues. Therefore, the study of new climate conditions in the country, as well as a survey on the possible movements of the existing boundaries of the climatic zones will be necessary.

2. Material and Methods
In this study, the information from Copen’s classified data for Iran, which are turned to maps, using the ArcGis software, using the year 1975 as a base year and the simulated data, using the atmosphere-ocean MIRCO-H model for simulation, which is presented in the fourth report IPCC (AR4), under two scenarios A1B and A2, has been used for the years 2030, 2050, 2080 and 2100. Then, Copen’s climate classification for Iran in 1975, is compared to the classification of atmosphere-ocean MIROC output for the years 2030, 2050, 2080 and 2100 under the two scenarios A1B and A2. This model, with the cooperation of the National Institute for Environmental Studies (NIES), the Oceanic-Pacific Institute of Oceanography (CCSR) Tokyo University and Japan Marine and Land-Based Science and Technology Agency for predicting the 21st century climate, is designed with a relatively high degree of complexity and Resolution. Compared with HadCM3 models (With an atmospheric resolution of 3.75 ° 2.5 ° and oceanic 1.25 ° x 1.25 °), it has a higher resolution. Predictive atmospheric variables in this model include temperature, north and east wind components, and surface pressure, and predictive oceanic variables include the velocity of orbital and meridian wind, temperature, salinity, and altitude.

3. Results and Discussion
Iran’s climate in 1975 contains three primary groups, namely, B (dry and semi-dry with a lack of precipitation), C (temperate with mild winters) and D (cold, cold winter, the coldest month temperature is lower than 3 ° C) and sub-categories of the three groups. As can be seen in Figure 2, climate group B is covering most of the country’s area. Bwh (T≥18˚c) climate, which represents the warm and dry climate, covers the widest area of the country (641043.7 km 2) and includes large parts of central Iran, South, South East, and South West of the country, which is about 40% of the country’s area. Moreover, regarding the Bsk climate, which is semi-dry and cold and indicates the extent of 22% in the North East, the eastern slopes of the Zagros and Alborz and Bwk climate, (cold and dry climate) 4.15 percent of the central country’s margin areas and parts of East and Southeast have been placed in next categories. In our country temperate climate from group C mostly appears in the Csa group form which represents the Mediterranean climate in our country, covering about 6 percent of the country’s area, including regions of North, Northwest, West and the western foothills of the Zagros. The cold climates Group D, appears in the mountainous regions of the West and North West of the country which covers 12% of the country’s area. Considering the climate change and global warming issues, which have been effected in the recent years, many models and methods have been used to rebuild and predict the global climate for the coming years. Although these models still have weaknesses and shortcomings to predict the future of the earth accurately, they also have much strength that makes them credible to predict and understand the future climate of the planet. The use of the output of atmosphere-ocean that coupled general circulation models are increasing as the most credible tool in climate change research. Various versions of the (AOGCM) models have been presented, including the 4th International Climate Change Board Assessment Report (AR4) (2007).
The classifications based on the model’s output indicates a growing trend in group B, which represents the dry and semi-dry, in Copen’s classification. Considering the outspread area covered by this type of climate over the coming years, climate Bwh (hot and dry climate) is increasing, such an increase that in 2100, more than 95 percent of the country is covered by this type of climate based on both scenarios. While temperate climate group c, which represents the Mediterranean climate in Iran, just appears in parts of coastal North and North West of the country, group D includes very narrow cold climate areas of the mountainous areas of the North West. In general, the results from the model’s output, reveals a gradual increase in the size of the area covered by the warm and dry climate and a decrease of the cold and temperate climate in Iran over the coming years until 2100. Thus, we can see the adverse effects of climate change and global warming and the shifting borders of the Copen’ climate categories in the country.

Keywords

Climate change; Climate classification; Global warming; The model MIROC

References

بحری، معصومه، دستورانی، محمد تقی و مسعود گودرزی؛ 1394. بررسی خشکسالی‌های دهه 2011- 2020 تحت اثر تغییر اقلیم، مطالعه موردی: حوزه آبخیز اسکندری، استان اصفهان. نشریه علمی-پژوهشی مهندسی و مدیریت آبخیز. شماره 2. جلد 7. ص 171-157.

پورمحمدی، سمانه؛ ملکی نژاد، حسین؛ 1391. طبقه‌بندی مناطق همگن اقلیمی کشور ایران تحت تأثیر تغییر اقلیم و سناریوهای انتشار گازهای گلخانه‌ای با استفاده از تکنیک گشتاور خطی. پژوهشنامه مدیریت حوزه آبخیز. سال چهارم. شماره 8. ص 76-58.

جعفر پور، خسرو؛ کارشناس، محمود؛ 1378. طبقه‌بندی اقلیمی – تابشی برای ‌ایران، دومین کنفرانس منطقه‌ای تغییر اقلیم- سازمان هواشناسی کشور.

حیدری، حسن؛ علیجانی، بهلول؛ 1378. طبقه‌بندی اقلیمی‌ایران با استفاده از تکنیک‌های آماری چند متغیره. پژوهش‌های جغرافیایی. شماره 37. ص 74-57.

صالح‌پور‌جم، امین؛ محسنی ساروی، محسن؛ بذر افشان، جواد و شهرام خلیقی سیگارودی؛ 1392. بررسی اثر تغییر اقلیم بر ویژگی‌های خشکسالی دورة آتی با کاربرد مدل گردش عمومی جو HadCM (مطالعة موردی: شمال غربی‌ایران). مجله منابع طبیعی‌ایران، دوره 67، شماره 4، ص 537-548.

طائی سمیرمی، سیاوش؛ مرادی، حمیدرضا و مرتضی خداقلی؛ 1394. پیش‌بینی تغییرات برخی از متغیرهای اقلیمی با استفاده از مدل ریز مقیاس سازی LARS-WG و خروجی‌های مدل HADCM3 تحت سناریوهای مختلف. نشریه علمی-پژوهشی مهندسی و مدیریت آبخیز، جلد 7. شماره 2. صفحات 411 .

طاووسی، تقی؛ دل آرا، قدیر؛ 1389. پهنه‌بندی آب‌وهوایی استان اردبیل. مجله علمی و فنی نیوار. شماره 71-70.

علیجانی، بهلول؛ کاویانی، محمد رضا؛ 1371) . مبانی آب‌وهوا شناسی. انتشارات سمت.

محمدی پور، سمانه؛ ملکی نژاد، حسین؛ 1392. طبقه بندی مناطق همگن اقلیمی کشور‌ایران تحت تأثیر تغییر اقلیم و سناریوهای انتشار گازهای گلخانه‌ای با استفاده از تکنیک گشتاور خطی. پژوهشنامه مدیریت حوزه آبخیز. سال چهارم. شماره 8.

منتظری، مجید؛ بای، ناصر؛ 1391. پهنه‌بندی اقلیم ناحیه خزری با استفاده از روش‌های آماری چند متغیره . فصلنامه تحقیقات جغرافیایی. سال 27. شماره 2.

مهدویان، عبدالرضا؛ بداق جمالی، جواد؛ موقر مفدم، حسین و سارا خجسته؛ 1381. بررسی روش‌های مختلف پهنه‌بندی اقلیمی استان سیستان و بلوچستان و ارتباط آن با خشکسالی. اولین کنفرانس ملی بررسی راهکارهای مقابله با بحران آب.

هدایتی دزفولی، اکرم؛ کاکاوند، رضا؛ 1391. پهنه‌بندی اقلیمی استان قزوین. مجله علمی و فنی نیوار. شماره 76-77.

Shokoohi, A., Raziei, T., & Daneshkar Arasteh, P. (2014). On the effects of climate change and global warming on water resources in Iran. International Bulletin of Water Resources and Development, 2(4), 1 -9.

Binita, K. C., Marshall Shepherd, J., & Johnson Gaither, C. (2015). Climate change vulnerability assessment in Georgia. Applied Geography, 62, 62-74.

Chen, D., & Chen, H. (2013). Using the Köppen classification to quantify climate variation and change: An example for 1901–2010. Environmental Development, 6, 69–79.

Crosbie, R. S., Pollock, D. W., Mpelasoka, F. S., Barron, O. V., Charles, S. P., & Donn, M. J. (2012). Changes in K¨oppen-Geiger climate types under a future climate for Australia: Hydrological implications. Hydrology and Earth System Sciences, 16, 3341–3349.

Wayne Erskine, M. J. Saynor & J. Lowry, (2006), Classification of Australian tropical rivers to predict climate change impacts. 9th International River Symposium, September 2006 New Delhi, India.

Belda, M., Holtanova, E., Halenka, T., & Kalvova, J. (2014). Climate classification revisited: From Köppen to Trewartha. Climate Research, 59, 1–13.

De Castro, M., Gallardo, C., Jylha, K., & Tuomenvirta, H. (2007). The use of a climate-type classification for assessing climate change effects in Europe from an ensemble of nine regional climate models. Climatic Change, 81, 329-341.

Beck, C., Grieser, J., Kottek, M., Rubel, F., & Rudolf, B. (2005). Characterizing global climate change by means of Köppen climate classification. Klimastatusbericht, 51, 139-149.

Herrera-Pantoja, M., & Hiscock, K. M. (2015). Projected impacts of climate change on water availability indicators in a semi-arid region of central Mexico. Environmental Science and Policy, 54, 81–89.

http://www.ipcc-data.org/sim/gcm_monthly/SRES_AR4, 2017.5.9

SPM, I. S. (2000). Summary for policymakers, emissions scenarios: a special report of IPCC Working Group III, IPCC. ISBN, 92(9169), 113.

http://www.ipcc-data.org/ar4/model-NIES-MIROC3_2-HI-change.html), 2017,5,9