Accuracy Evaluation of the Outputs of Regional Climate Models in Iran

Document Type : Full length article


1 PhD Student in Climatology, Department of Physical Geography, Faculty of Geographical Sciences and Planning, University of Isfahan, Iran

2 Associate Professor of Climatology, Faculty of Geographical Sciences and Planning, University of Isfahan, Iran


All studies in the field of assessment of climate change impacts needs climate data with different spatial and temporal scales. The lack of temperature and precipitation data with high spatial resolution is a major limitation to analysis of future climate change. In addition, the output of the models has the error that needs to be corrected; otherwise, they will make a significant bias for assessing the effects of climate change. Therefore, identifying the best regional climate model for downscale the global climate models is essential to better understanding of climate conditions in the local and regional scale. In the last few years, use of various regional climate models for producing a multi-member set of the downscaled data in the CMIP5 project by World Climate Research Program (WCRP) in action with Coordinated Regional climate Downscaling Experiment (CORDEX) was established as an input to the researches about the impacts of climate change and adaptation ways. The main objective of this research is accuracy evaluation of different model outputs of the CORDEX project with different domain and resolution in Iran.
Materials and Methods
In the CORDEX project, there are two domains that covering Iran. These two domains are North Africa-Middle East (CORDEX-MNA) and South Asia (CORDEX-WAS). To do this research, daily output of precipitation, maximum and minimum temperatures in the period of 1990-2005 for three regional climate models with a special resolution of 0.22° and 0.44° are performed by three international meteorology institutes, available at ESGF web site (Table 1). Daily observation data recorded in 304 synoptic stations in Iran for the three variables were collected from Iran Meteorology Organization and transferred to a matrix with 3044×5844 dimensions. Then, several scripts were written in the MATLAB software to extract the model data in Iran and compare model output and observational data with two conditions. The first condition is in the output model resolution of 0.44° (spatiotemporal matrix with dimensions of 5844×740), the observation station should have a distance of less than 25 km, and the next condition is in the resolution of 0.22 ° (spatiotemporal matrix with dimensions of 5844×3218) should have a distance of less than 12 km. The difference between observation values and its corresponding estimated values were investigated with statistical method such as Mean Error (ME), Pearson Correlation Coefficient, Root Mean Square Error (RMSE) and Standard Deviation (SD). We also used Box-Whisker plots and Taylor Diagram to find the best regional climate model.  
Results and Discussion
The precipitation accuracy of regional climate models output presented by different meteorological institutes (Table 1) was evaluated by observational data in two domains, CORDEX-MENA and CORDEX-WAS, in Iran (Fig. 4). The calculation of the outputs mean error of different models showed that none of the models have a suitable estimation of precipitation values in research domain. The HadRM3P model shows the lowest RMSE relative to observational data for the maximum temperature across Iran except the central parts. However, for the minimum temperature RegCM4.1 model shows the lowest difference with comparison with observation data in most parts of the research domain. For annual precipitation using the Box-Whisker plot, we can compare the correlation coefficients between the observed data and the corresponding cells in the northern and southern parts of Iran. According to the results, none of the models have an accurate estimate of precipitation in Iran (Fig. 8a). This plot for different models showed that the outputs of the HadRM3P and RegCM4.1 models have more than 0.8 correlation coefficien for maximum and minimum temperatures in most cells, respectively, (Fig. 8b and c).
The correlation of rainfall data shows that most models in the central and mountainous regions of Iran do not have high correlation coefficient with observational data. Spatial distribution of correlation between maximum temperature model outputs and observational data in Iran shows that the two HadRM3P and RCA4-WAS0.44 models have a strong correlation coefficient. The results also show that changes in the correlation coefficient in the HadRM3P model are low in both the northern and the southern parts of Iran. The RegCM4.1 model had the stronger correlation in the northern half in comparison with the southern parts of Iran. Also, the mean difference of estimated model output with observation data of this variable in the whole of Iran is less than 1°C and this model is the most appropriate model among the available models for minimum temperature in Iran.


Main Subjects

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