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Effectiveness of the MPI-ESM-LR Model in forecasting the Monthly Temperature of Iran under Representative Concentration Pathway (RCP) Scenarios

Document Type : Research Paper

Authors

1 PhD student, Department of Physical Geography, University of Mohaghegh Ardabili

2 University of Mohaghegh Ardabili

3 Postdoctoral Research Associate, University of Mohaghegh Ardabili

Abstract

Climate change is a key factor in most weather-related disasters worldwide. Regarding its distinctive geographical location and diverse climate, Iran has the most variable climate in the world. The present study aims to investigate the effectiveness of the MPI-ESM-LR model from the CMIP5 model series in predicting the monthly temperature of Iran under representative concentration pathway scenarios (RCPs) with the CORDEX-WAS project. In this research, for the historical period of 1980-2005, the daily air temperature data of 49 synoptic stations of the country and the MPI-ESM-LR model under the CORDEX project were used. Likewise, for the future period, from the predicted temperature data of RCP 8.5, RCP 4.5, and RCP 2.6 scenarios of the mentioned model in three periods of the near-future (2021-2050), mid-future (2051-2075) and far-future (2076-2100) was used. Validation of the model was done with three statistical indices: r, RMSE, and MBE. The results revealed that the model has a good performance. The slope of the temperature trend in station data and model data has been increasing in the historical period and the future period in RCP8.5 and RCP4.5 in all months, the temperature trend slope has been observed in every decade. In all months, the maximum anomaly of temperature under the scenarios studied in all three future periods can be seen in the northwest and western highlands. The eastern and southeastern regions of Iran have indicated minimum temperature anomalies, except in RCP 2.6 and RCP 8.5, respectively, the southern coasts and the northeastern heights of the country also show minimum temperature anomalies. In the cold half of the year, the minimum area of temperature anomaly has been extended to the north-western heights and low-altitude interior regions of the country.

Highlights

Climate change is a key factor in most weather-related disasters worldwide. Regarding its distinctive geographical location and diverse climate, Iran has the most variable climate in the world. The present study aims to investigate the effectiveness of the MPI-ESM-LR model from the CMIP5 model series in predicting the monthly temperature of Iran under representative concentration pathway scenarios (RCPs) with the CORDEX-WAS project. In this research, for the historical period of 1980-2005, the daily air temperature data of 49 synoptic stations of the country and the MPI-ESM-LR model under the CORDEX project were used. Likewise, for the future period, from the predicted temperature data of RCP 8.5, RCP 4.5, and RCP 2.6 scenarios of the mentioned model in three periods of the near-future (2021-2050), mid-future (2051-2075) and far-future (2076-2100) was used. Validation of the model was done with three statistical indices: r, RMSE, and MBE. The results revealed that the model has a good performance. The slope of the temperature trend in station data and model data has been increasing in the historical period and the future period in RCP8.5 and RCP4.5 in all months, the temperature trend slope has been observed in every decade. In all months, the maximum anomaly of temperature under the scenarios studied in all three future periods can be seen in the northwest and western highlands. The eastern and southeastern regions of Iran have indicated minimum temperature anomalies, except in RCP 2.6 and RCP 8.5, respectively, the southern coasts and the northeastern heights of the country also show minimum temperature anomalies. In the cold half of the year, the minimum area of temperature anomaly has been extended to the north-western heights and low-altitude interior regions of the country.

Keywords

Main Subjects

References
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Journal of Geography and Planning
Volume 28, Issue 88 - Serial Number 89
March 2024
Pages 377-355
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