TY - JOUR ID - 77988 TI - Projection the Long-Term Outlook Iran Future Temperature Based on the Output of The coupled model intercomparison project phase 6 (CMIP6) JO - Journal of the Earth and Space Physics JA - JESPHYS LA - en SN - 2538-371X AU - Zarrin, Azar AU - Dadashi Roudbari, Abbas Ali AD - Assistant Professor, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran AD - Post-Doc Researcher, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran Y1 - 2020 PY - 2020 VL - 46 IS - 3 SP - 583 EP - 602 KW - CMIP6 Models KW - SSP Scenarios KW - DCF KW - Temperature trend KW - Iran DO - 10.22059/jesphys.2020.304870.1007226 N2 - The Intergovernmental Panel on Climate Change (IPCC) has stated that climate change is undoubtedly real and greenhouse gas emissions continue to heat up all components of the climate system (IPCC, 2013). In this study, we have addressed two main concerns:  First, we assessed the performance of temperature simulations of the available CMIP6 and next, we projected future temperature in Iran by underlying socioeconomic scenarios by the late 21th century. In this study, the average annual temperature data of 43 synoptic stations were obtained for the period of 1980-2018. Also, the latest Coupled Model Intercomparison Project phase 6 (CMIP6) dataset was analyzed to examine the projected changes in temperature over Iran during the twenty-first century. Three available CMIP6 models used in this study including BCC-CSM2-MR, CAMS-CSM1-0, and MRI-ESM2-0. To project the temperature anomaly and the monthly temperature trend of Iran to the end of 21st century, data from the CMIP6 model set under two SSP2.4-5 scenarios (Consistent with the RCP4.5 scenario of the CMIP5 modes) and SSP5.8-5 (Consistent with RCP8.5 scenario of the CMIP5 modes) was used for the period (2020-2100) with a horizontal resolution of 100 km. The Delta change factor (DCF) method was used to correct the bias of the data and to test the trend analysis in the long-term data series of the Man-Kendall nonparametric test (MK). Sen’s Slope Estimator nonparametric method was also used to estimate the actual slope of the trend in the time series. The minimum monthly temperature in Iran was investigated based on observational data with 5.64 degrees Celsius in January and the maximum temperature of 29.21 degrees Celsius in July. The same is true for future data; the minimum monthly temperature of Iran during the projected future (2020-2100) under the scenario of SSP245 is 7.14 degrees Celsius and under the scenario of SSP585 is 33.8. The maximum temperature, like the observational period in the future projected period in July, for SSP2.4-5 and SSP5.8-5 scenarios, 31.38 and 32.76 degrees Celsius was calculated, respectively. The average temperature anomaly, according to the SSP2.4-5 scenario, is more than 2 degrees Celsius in 9 months of the year, and less than 2 degrees Celsius in January, November and December, which are considered the coldest months of the year. Investigating the temperature trends for over 80 years, it is found that the overall trend of increase in the amount of the increase in intensity in the scenario of SSP5.8-5 under the 0.1 level in all months is significant. The maximum trend intensity was calculated with a Z score of 10.60 in September and the minimum trend intensity was calculated as 2.77 in January. The average temperature trend based on the non-parametric Man-Kendall (M-K) test in Iran is increasing in all months in both of the studied scenarios. This value is statistically significant at the alpha level of 0.1. The trend slope was also measured using the nonparametric Sen’s Slope Estimator (SSE); Under the SSP2.4-5 scenario, the average annual Celsius will be 0.02 year-1 degrees Celsius, and according to the SSP5.8-5 scenario, the average -0.05 year-1 degrees Celsius will increase. Locally, the maximum monthly temperature variability was observed in the mountainous areas of the Zagros and Alborz highlands, respectively. In other words, the highest monthly temperature change rate was observed in the cold period of the year in the mountainous regions. One of the reasons for this high variability can be due to the existence of different weather conditions in the cold season of the year to Iran. UR - https://jesphys.ut.ac.ir/article_77988.html L1 - https://jesphys.ut.ac.ir/article_77988_687ae3eca55adc807ae16871e73faf94.pdf ER -