Assessing the Seasonal Anomaly of Iran Temperature under Representative Concentration Pathways (RCPs) Scenarios: A Study on the Efficiency of the MPI-ESM-LR Model

It is essential to disclose or evaluate the impacts of global warming and climate change on temperature in order to account for the climate variations in each given area, such as Iran. This research aimed to assess the effectiveness of the MPI-ESM-LR model, a part of the CMIP5 model series, in evaluating the seasonal anomaly of Iran's temperature. It addressed the lack of comprehensive studies on modeling and forecasting the effects of global warming on temperature events in Iran. This study focused on different Representative Concentration Pathways (RCPs) to provide a comprehensive analysis. It examined two time periods, including the historical eras (1980–2005) and the future (2021–2100). The analysis was done based on the CORDEX-WAS dynamical downscaling approach at the station size. The present study aimed to provide solutions to the following two questions: Is the described model, which utilizes the CORDEX dynamical downscaling approach, capable of accurately forecasting seasonal temperature fluctuations in Iran with little error? Will there be a rise in air temperature conditions on a seasonal scale in the future?

Eight models, namely CanESM2, CSIRO-Mk3-6-0, GFDL-ESM2M, ICHEC-EC-EARTH, IPSL-CM5A-MR, MIROC-ESM, MPI-ESM-LR, and NorESM1-M, were examined to determine the optimum model for forecasting Iran's seasonal temperature. The verification findings of the preceding models indicate that the MPI-ESM-LR model exhibits the least bias, making it the most accurate model for forecasting Iran's seasonal temperature in future periods. To verify the accuracy of the air temperature data obtained from the MPI-ESM-LR model, a total of 49 synoptic stations were chosen across the country. This selection was made based on the historical data provided by the CORDEX project, covering the statistical period from 1980 to 2005. This study examined the effectiveness of the MPI-ESM-LR model, a collection of CMIP5 models, in assessing seasonal air temperature in Iran. It focused on three scenarios: RCP 2.6 (optimistic), RCP 4.5 (moderate), and RCP 8.5 (pessimistic). The research analyzed the seasonal temperature anomaly in Iran for three time periods: the near future (2021-2050), the middle future (2051-2075), and the distant future (2076-2100). The time frame spanning from 1980 to 2005 was designated as the historical period for comparing the data collected from synoptic stations with the simulations generated by CMIP5 models. The validity of observational and modeled data from 1980 to 2005 has been assessed using statistical measures such as the mean bias error (MBE), root mean square error (RMSE), and Pearson correlation coefficient (r). The non-parametric sense approach was employed to calculate the slope of the data trend in the time series. The present study utilized CORDEX-WAS dataset with a spatial resolution of 0.44 arc degrees. The dynamical downscaling was performed using the RCA4 model for regional climate modeling (RCM) and the r1i1p1 ensemble.

The findings indicated that the MPI-ESM-LR model, when implemented with the CORDEX dynamical downscaling approach, demonstrates a high level of accuracy in forecasting seasonal temperature extremes in Iran. This is evident from its average correlation coefficient of 0.99, average RMSE index of 0.55° c, and MBE of -0.3° c. The majority of stations have mean square error values that are below 1 ° c. The greatest bias was found in Bandar Lengeh, with a deviation of 1.6° c, while the smallest bias was found in Sanandaj, with a deviation of 0.06° c. The average error deviation index reveals that Khorram Abad had the highest positive skewness, measuring 0.81° c, whereas Dushan Tappeh displayed the highest negative skewness, measuring -1.8°c. Throughout the year, the lowest rise in air temperature under the 2.6 RCP scenario in the distant future is 7.1°, 21.5°, 25.7°, and 10.8° c. The highest temperature increase in the pessimistic scenario is 8.5 RCP in the distant future. The expected temperatures for winter, spring, summer, and autumn are 10.5°, 25.7°, 30.3°, and 14.9° c, respectively. During the historical period, the mean air temperature in winter, spring, summer, and autumn was 6.3°, 20.3°, 24.8°, and 9.9° c, respectively. In RCP 2.6, the spring, summer, and autumn seasons exhibit a declining trend in temperature. Specifically, the temperature decreases by -0.037°, -0.075°, and -0.037 ° c every decade, respectively. However, in other times and scenarios, the model and station data from the historical period consistently demonstrate a rising temperature trend throughout all seasons. During the winter season, the highest deviation from the average temperature occurs in the Zagros, Sablan, and Sahand Mountains in the western and northwestern regions of the country. Conversely, the lowest deviation from the average temperature is observed in the low-altitude parts of the desert plain and the eastern slopes of Shirkouh. During the spring season, the northwest and Zagros mountains have the most deviation from the average maximum temperature, while the coastal parts of the Caspian and the eastern part of the country see the lowest deviation from the average minimum temperature. During the summer season, the northwest and western regions of the country have had the highest temperature anomalies in most times and scenarios. Conversely, the southeast regions of Jask and Chabahar have shown the lowest temperature anomalies. During the autumn season, the eastern half of the country, particularly the southeastern region and the Lut plain, experience the most deviation in temperature. The lowest temperature anomaly occurs mostly in the elevated regions of Talesh and Sabalan and sometimes on the western slopes of the Zagros Mountains.

Regarding the projected increase in air temperature in the upcoming decades and the concern of water stress, it is recommended to carefully strategize future water management, particularly in arid and semi-arid areas. This planning should take into account population growth as well as the development of industrial and agricultural sectors. Additionally, the impact of climate change on water demand and water resources must be taken into consideration.