Document Type : Original Article
Ph.D. Candidate, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran.
Associate Professor, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran.
Professor, School of Engineering, University of Guelph, Guelph, Ontario, Canada.
Climate change impacts on climate variables are among the challenges of large cities. In this regard, General Circulation Models (GCMs) are among the most reliable tools for assessment of the future climate variables. In 5th assessment report on climate change, the Intergovernmental Panel on Climate Change (IPCC) has applied the Coupled Model Intercomparison Project, Phase 5 (CMIP5) models. These models use scenarios called Representative Concentration Pathway (RCP). In current study, the assessment of the climate variables of Tehran, Iran, under climate change impacts was addressed. The preceding studies for Tehran show they need to be updated with newer scenarios and more downscaling models. Furthermore, there is merit in using more synoptic stations due to the vastness of Tehran. Given these findings, the current study focuses on two main objectives. The first objective is to assess the climate variables under the RCP scenarios in Tehran for 2021-2040. To this end, the eight CMIP5 models under RCP2.6, RCP4.5 and RCP8.5 were used. Accordingly, seven climate variables including mean Temperature (Tmean), maximum Temperature (Tmax), minimum Temperature (Tmin), precipitation, relative humidity, mean Wind speed (Wmean) and the sunshine hours were used and simulated for baseline period (1989-2018) and then assessed for future period. In the Second objective, for downscaling the CMIP5s, in addition to use of the Statistical DownScaling Model (SDSM), Fuzzy logic was also applied for downscaling. Accordingly, the Fuzzy DownScaling Model (FDSM) was generated and the performances of FDSM and SDSM were analyzed.
MATERIALS AND METHODS
In this study, to assess the Tehran climate variables under RCP scenarios, the multi-model ensemble were applied to reduce the CMIP5’s uncertainties. Accordingly, the eight CMIP5s including CanESM2, CNRM-CM5, CSIRO-Mk3.6, FGOALS-g2, GFDL-CM3, HadGEM2-ES, MIROC-ESM-CHEM and MPI-ESM-MR were used. Given the uncertainty caused by the different outputs of the eight CMIP5s, the weighted means of the models’ outputs were used to calculate the daily climate variables for future (according to the ability of the models in simulating the baseline period). For this purpose, first, the CMIP5s were downscaled. In this context, the SDSM software (version 5.3.5) was used and also FDSM was generated. Then the performances of FDSM and SDSM were analyzed. On this basis, the superior downscaling models were selected using the comparison of simulation results and the statistical indicators of R2, RMSE, NSE and MAE. Accordingly, the CMIP5’s outputs were downscaled using the superior downscaling models and then the daily values of each climate variable were calculated. In the calibration and validation of the downscaling models at baseline period, the predictors were selected from the daily data of the National Center for Environmental Prediction (NCEP) using correlation test in SDSM software. Furthermore, in developing the FDSM, the Fuzzy C-Means Clustering process was applied, to determine the Fuzzy Membership Functions and the relevant Fuzzy Rules. By using the structure obtained by clustering, the FDSM was built as a Mamdani Fuzzy Inference System. In this context, the FDSM was developed in MATLAB software using the trial and error process.
RESULTS AND DISCUSSION
By correlation test in SDSM software, the predictors were selected for the SDSM and FDSM models. Accordingly, the SDSM and FDSM were developed using the daily climate variable and the selected predictors. The performance analysis of both downscaling models (based on the statistical indicators of R2, RMSE, NSE and MAE and the comparison of simulation results in baseline period) demonstrate very good quality and performance for all the daily Tehran climate variables. Therefore, the Fuzzy approach has an appropriate capability in simulating and downscaling the climate variables. In addition, neither model has absolute superiority over the other in downscaling. However, it appears that with a slight margin, the FDSM had a better performance for Tmean, Tmax and Tmin, and SDSM had a better performance for precipitation, relative humidity, Wmean and the sunshine. Accordingly these models were chosen as the superior downscaling models. The results of future period show the increasing trend of annual changes in Tmean and Tmax, precipitation and the Wmean. The maximum increase of annual average in Tmean and Tmax and the Wmean among all scenarios will be in the order of 1.29oC, 1.57oC and 0.8m/s (for RCP8.5) and also the maximum increases of annual average precipitation will be 10mm (for RCP2.6). Furthermore, the month long-term averages of Tmean and Tmax in all three scenarios show significant increases in summer. For precipitation, relative stability in summer, and increases in winter and early spring are projected, but the changes in Tmin, relative humidity and sunshine indicate relative stability.
In this study, two main objectives including the assessment of the climate variables under the RCP scenarios in Tehran for 2021-2040 and also the performance analysis of Fuzzy logic in downscaling were addressed. The performance analysis of FDSM and SDSM demonstrated the high performance of both models and the appropriate ability of the Fuzzy approach in downscaling the Tehran climate variables. Therefore, taking the Fuzzy approach for downscaling has a technical justification. In this context, the application of the Mamdani Fuzzy Inference System and the Fuzzy C-Means Clustering increases the accuracy and quality of the results at different conditions. According to the results, the annual changes in Tmean, Tmax and the Wmean at all the three RCP scenarios will have an increasing trend, while precipitation will also (marginally) increase. However, the other variables will have a relative stability. From the point of view of monthly changes, there were noticeable increases in the long-term means of Tmean and Tmax in the future period during the months of July, August and September (i.e. summer season). As regards to precipitation, a relatively stable trend was observed in comparison with the baseline during the warm months of future period, but during winter and in particular at the beginning of the spring of the 2021–2040 period, there will be more precipitation at different months of the year than the 1989–2018 period.