نوع مقاله : مقاله پژوهشی
عنوان مقاله English
نویسندگان English
Reports by the Intergovernmental Panel on Climate Change (IPCC) have emphasized the increasing frequency and intensity of weather and extreme climate events under climate change. To have an outlook on future projections of climate extremes, the outputs derived from the ACCESS-CM2 model contributing to the sixth Assessment Report of the IPCC, AR6, under SSP4.5 and SSP585 scenarios have been downscaled using CMhyd during the period of 2026-2100 for Torbat Heydarieh and Kashmar stations.
1. Materials and Methods
For this study, daily observations of precipitation, minimum temperature and maximum temperature during 1989-2019 by two synoptic stations, including Torbat Heydarieh and Kashmar were used. The historical and future CMIP6 outputs have been downloaded from the Copernicus Climate Data Store (CDS). First, the precipitation and temperature time series of two stations were qualified. RHtests-dlyPrcp and RHtestsV4 packages in the R software environment were then used to test the homogeneity of the precipitation and temperature daily time series.
The general circulation models (GCMs) are the most important tool for projecting future climate change, which can reproduce important processes in the global and continental scale atmosphere and project future climate under different scenarios. The simulations of temperature and precipitation using GCMs often show significant biases due to systematic model errors or discretization and spatial averaging within grid cells, which hampers the use of simulated climate data as direct input data for climate change studies. Bias correction procedures are used to minimize the discrepancy between observed and simulated climate variables on a daily time step so that the corrected simulated climate data match simulations using observed climate data reasonably well. Many bias correction methods, ranging from simple scaling techniques to the rather more sophisticated distribution mapping techniques, have been developed to correct biased GCM outputs.
There are two sets of data in the CMIP6 simulations:
2-1. Historical data: Historical data of CMIP6 covers the period from 1850-2014, which can be used as a reference period to compare and verify the performance of each GCM.
2-2. Scenarios data: SSP scenarios provide different pathways for future climate forcing. They typically cover the period from 2015-2100.
Downsacling
Climate Model data for hydrologic modeling (CMhyd) is software that has been used to extract and bias-correct output of the selected global climate models using the statistical method i.e. delta change (DC). RMSE, R2 and Pearson correlation coefficient were used to evaluate the accuracy of the results of each model.
Extreme precipitation and temperature indices
Extreme events refer to rare events which, in the statistical view, the probability of those events is so low. For example, it can be defined as values between percentiles (95 and 5) (90 and 10) or with values above a threshold or with the continuing special conditions (Rahim Zadeh et al. 2009). A set of standard measurements of the extreme climate indices based on daily precipitation, and daily minimum temperature and maximum temperature were provided by the Expert Team on Climate Change Detection and Indices (ETCCDI). In this study, the extremes are described by twenty indices of ETCCDI.
2. Results and Discussion
In this study, daily precipitation and temperature time series from two synoptic stations i.e. Torbat Heydarieh and Kashmar were first used as primary quality control. Then the homogeneity of this data was tested. The large-scale data of daily precipitation and (maximum and minimum) temperatures of five CMIP6 models have been scaled down to the level of the stations using the statistical method, DC, and have been corrected for bias. Root-mean-square error (RMSE) and R2 were calculated to determine the accuracy and performance of each model. The results indicated that the ACCESS-CM2 model gives better results. Temporal changes of extreme temperature and precipitation indices from 2026 to 2100 show consistently drier conditions. In addition, extreme precipitation events are becoming more frequent and intense. The extreme temperature indices showed extreme temperature warming in these areas of Razavi Khorasan province under two SSP scenarios in the future.
2. Conclusion
The findings presented in this study suggests that, for the future under two SSP scenarios, Kashmar and Torbat Hyeidarie will experience rising temperatures, prolonged wet and dry periods, increased frequency of precipitation events with heavy to very heavy precipitation patterns, increasing heat durations, and decreasing cold durations.
Key Words: Extreme precipitation and temperature indices, Climate Change, Kashmar, Torbat Heydarieh, Cmhyd.
کلیدواژهها English
