Projection of important climate variables in large cities under the CMIP5–RCP scenarios using SDSM and fuzzy downscaling models

Abstract

Climate change impacts are among the many challenges facing management of large cities. This study assesses the important climate variables under climate change impacts in Tehran, Iran, for 2021–2040. Eight Coupled Model Intercomparison Project, Phase 5 (CMIP5) models under the scenarios of Representative Concentration Pathway 2.6 (RCP2.6), RCP4.5, and RCP8.5 were used, and seven climate variables were projected utilizing the Fuzzy DownScaling Model (FDSM) and the Statistical DownScaling Model (SDSM). The FDSM and SDSM results underline the high performance of both models and the important capability of the FDSM, showing the increasing trend of annual changes in mean temperature (Tmean) and maximum temperature (Tmax), precipitation, and the mean wind speed (Wmean). The maximum increase of annual average in Tmean and Tmax and the Wmean among all scenarios will be in the order of 1.29 °C, 1.57 °C, and 0.8 m/s (for RCP8.5), and also the maximum increases of annual average precipitation will be 10 mm (for RCP2.6). Furthermore, the monthly 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 predicted, but the changes in minimum temperature, relative humidity, and sunshine hours indicate relative stability.

Application of MK trend and test of Sen's slope estimator to measure impact of climate change on the adoption of conservation agriculture in Ethiopia

“…The statistics for checking the performance of each of the models were the R2 and RMSE (Hassan & Hashim 2021). During the calibration, the downscaled parameters from the GCM which acted as the predictors were used iteratively to select the best predictor variables for the temperature (Shakeri et al 2022).…”