Evaluating the effects of climate change on the climatic classification in Iran

Document Type : Research/Original/Regular Article

Authors

1 M.Sc. Student, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

2 Professor, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

3 Assistant Professor, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

Abstract

Introduction
The average weather condition in a specific region is defined as climate. The diversity of climatic variables is effective in determining the climate of a region and causes the formation of diverse and different climates. One of the effects of climate change is that causes an increase or decrease in a climate zone and, as a result, a shift in climate zones. Climate classification is an attempt to identify and recognize the differences and similarities of climate in different regions and to discover the relationships between different components of the climate system. Climate classification indicators are used to visualize current climate and quantify future changes in climate types as predicted by climate models. The studies conducted on these methods show that climatic variables affecting experimental methods such as temperature and precipitation should be considered effective variables in determining climatic boundaries in a new way. The De Martonne aridity index is an empirical index for climate classification based on two components, precipitation and temperature. Due to its high accuracy, and the use of variables that are more accessible and can be measured at most meteorological stations, De Martonne’s index has received more attention from researchers and has been used in many studies of climate change. Therefore, the purpose of this research is to evaluate the effects of climate change on the climatic classification of Iran.
 
Materials and Methods
To investigate the effects of climate change on the climatic classification of Iran, the De Martonne aridity index has been used. To show the effects of climate change in the past and the future on Iran's climate, data from 120 meteorological stations of Iran, which are distributed in different locations with different climates, were collected and analyzed in the statistical period of 1933-2022. The climatic condition of Iran in the base period was determined according to the De Martonne aridity index. In addition, to investigate the effects of climate change in the coming periods on the climatic classification of Iran, the data related to the output of the CanESM2 model, which is one of the CMIP5 models that is hybridized by the Canadian Center for Climate Modeling and Analysis (CCCMA) by combining CanCM4 and CTEM models, were used. To examine the changes in climatic classes of Iran under different scenarios and conditions, the output of two release scenarios, RCP2.6 and RCP8.5, were utilized. Due to the large-scale output of General Circulation Models (GCM), the output of this model was downscaled using the LARS-WG model. The LARS-WG model, which is considered one of the most famous and widely used models for downscaling weather data, was used to generate precipitation values, minimum and maximum temperatures, as well as daily radiation, under base and future climate conditions.
 
Results and Discussion
According to the results, the majority of Iran (90.49%) has an arid and semi-arid climate. The percentage of arid climate is 68.82%, while that of semi-arid climate is 21.97%. Therefore, Iran should be called an arid and semi-arid country in terms of climate. By analysis of the effects of climate change indicates that in future periods, the precipitation and average temperature will increase. This increase will be greater under the RCP8.5 scenario than the RCP2.6 scenario. The study of the climatic classification of Iran in the coming periods indicates that the majority of the country will continue to experience arid and semi-arid climates. The sum of arid and semi-arid climates will reach its lowest level in the period of 2020-2041. This is following the RCP2.6 scenario, after which these climates are expected to expand once more. According to the RCP8.5 scenario, during the periods of 2021-2040, 2041-2060, and 2061-2080, the total area of arid and semi-arid climates will decrease. However, from 2081 to 2100, this trend will be reversed, increasing in these climates. According to the results of this research and according to the forecast, although according to different release scenarios, the difference in the area of different classes can be seen, in the future, arid and semi-arid climatic zones will still form the majority of Iran.
 
Conclusion
In this research, by using the latest available data, Iran's climate is classified by the De Martonne aridity index, and then the changes in Iran's climate classes under the effects of climate change in the future periods, according to the output of the CanESM2 model from the CMIP5 modes, which is downscaled using the LARS-WG model. It has been investigated according to two emission scenarios, RCP2.6 and RCP8.5. The results indicated that the arid climate with 68.82% and the semi-arid climate with 21.97% constitute the largest area of Iran. The remaining climatic classes collectively comprise less than 10% of Iran's area. Therefore, Iran should be called an arid and semi-arid country in terms of climate. Investigating the effects of climate change on precipitation and temperature showed that both precipitation and average temperature will increase in future periods. However, the increase in both variables will be greater under the RCP8.5 scenario. The study of the climatic classification of Iran in the coming periods indicates that the majority of the country will continue to experience arid and semi-arid climates. The findings of this study indicate the necessity of addressing the issue of climate change and the importance of involving experts and macro planners in the analysis of the effects of climate change. It is suggested to use the output of other GCM models in future research due to the uncertainty of climate scenarios. Also, the use of diverse climate classification methods that incorporate other variables is suggested for more precise identification of climate characteristics

Keywords


Abbasi, F., Bazgeer, S., Kalehbasti, P.R., Oskoue, E.A., Haghighat, M., & Kalehbasti, P.R. (2022). New climatic zones in Iran: A comparative study of different empirical methods and clustering technique. Theoretical and Applied Climatology, 147(1), 47-61. doi:10.1007/s00704-021-03847-y
Allahverdipour, P., & Sattari, M.T. (2023). Comparing the performance of the multiple linear regression classic method and modern data mining methods in annual rainfall modeling (Case study: Ahvaz city). Water and Soil Management and Modeling, 3(2), 125-142. doi:10.22098/mmws.2022.11337.1120. [In Persian]
Azizi, H.R., nejatian, N., Athari, M.A., & hashemi, S.S. (2021). The effects of climate change on the drought trend of Varamin plain using De-Martonne index. Nivar, 45(112-113), 67-76. doi: 10.30467/nivar.2021.266357.1177
Bagherabadi, R. (2022). Investigation of climate change on the Kermanshah City using the de martoune, ambrothermic and embereger in 1991-2021. Geography and Human Relationships4(4), 173-185. dor:20.1001.1.26453851.1401.4.4.12.4. [In Persian]
Casanueva, A., Herrera, S., Fernández, J., & Gutiérrez, J.M. (2016). Towards a fair comparison of statistical and dynamical downscaling in the framework of the EURO-CORDEX initiative. Climatic Change, 137, 411-426. doi:10.1007/s10584-016-1683-4
Chong-Hai, X.U, & Ying, X. (2012). The projection of temperature and precipitation over China under RCP scenarios using a CMIP5 multi-model ensemble. Atmospheric and Oceanic Science Letters, 5(6), 527-533. doi:10.1080/16742834.2012.11447042
Chylek, P., Li, J., Dubey, M.K., Wang, M., & Lesins, G.J.A.C. (2011). Observed and model simulated 20th century Arctic temperature variability: Canadian earth system model CanESM2. Atmospheric Chemistry and Physics Discussions, 11(8), 22893-22907. doi:10.5194/acpd-11-22893-2011
De Martonne, E. (1941). Traite de Geographie Physique: 3 tomes, Paris. Flocas AA. 1994. Courses of 28 Meteorology and Climatology. Ziti Publications: Thessaloniki.
Fathizad, H., Tavakoli, M., Hakimzadeh Ardakani, M.A., TaghizadehMehrjardi, R., & Sodaiezadeh, H. (2021). Evaluation of the effects of climate change on meteorological parameters under different scenarios in Yazd meteorological station. Journal of Water and Soil Science, 24(4), 1-19 doi:10.47176/jwss.24.4.42131. [In Persian]
Feddema, J.J. (2005). A revised Thornthwaite-type global climate classification. Physical Geography, 26(6), 442-466. doi:10.2747/0272-3646.26.6.442
Gavrilov, M.B., Radaković, M.G., Sipos, G., Mezősi, G., Gavrilov, G., Lukić, T., Basarin, B., Benyhe, B., Fiala, K., Kozák, P., & Perić, Z.M. (2020). Aridity in the central and southern Pannonian basin. Atmosphere, 11(12), 1269. doi:10.3390/atmos11121269
Hajam, S., Khoush Khou, Y., & Shams Aldin Vandi, R. (2008). Annual and seasonal precipitation trend analysis of some selective meteorological stations in central region of Iran Using non-poarametric methods. Geographical Research Quarterly, 40(64), 157-168. https://jphgr.ut.ac.ir/article_26912.html?lang=en [In Persian]
Hedayati Dezfuli, A., & Kakavand, R. (2012). Climatic zoning of Qazvin Province. Nivar, 36(77-76), 59-66. [In Persian]
Jafary Godeneh, M., Salajeghe, A., & Haghighi, P. (2020). Forecast comparative of rainfall and temperature in Kerman County using LARS-WG6 models. Iranian Journal of Ecohydrology7(2), 529-538. doi:10.22059/ije.2020.298577.1294. [In Persian]
Jahangir, M.H. & Mohammadi, A. (2018). Climatic zoning of East Azerbaijan by LARS-WG down scaling model for 2011-2065. Geography (Regional Planning), 8(2), 119-130. dor: 20.1001.1.22286462.1397.8.2.8.7. [In Persian]
Kazemi, R., & Sharifi, F. (2018). Investigation and analysis of factors affecting base flow in different climates of Iran. Watershed Engineerin