The effects of climate change on precipitation and temperature using SSP scenarios (case study: Fars province)

Introduction

Climate is one of the most important ecological factors, and its changes are currently the most important threat to sustainable development. The phenomenon of climate change causes different processes in the atmosphere and the earth. Phenomena such as rising sea levels, changes in meteorological variables such as temperature and precipitation, impact on surface currents, occurrence of floods and droughts, and changes in air currents and storms are only part of the effects of climate change. Therefore, it is necessary to model the future conditions of the climate to know the future conditions. There are various methods for simulating and predicting climate variables in future periods under the influence of climate change, the most reliable of which is the use of General Circulation Model (GCM) data. GCM models are only able to simulate the data of the atmospheric general circulation model at large levels. Even if global climate models are set up with high technical power to predict the future, the need to downscale the results of these models at station scales is felt. Therefore, in this research, the effects of climate change on the threshold values of precipitation and temperature have been evaluated using SSP scenarios.

Materials and Methods

General circulation models (GCMs) can provide the best information about the response of the atmosphere to increasing greenhouse gas concentrations. In this research, the climatic data of three synoptic stations of Abadeh, Shiraz and Lar, related to Fars province, were used. The data from three models ACCESS-ESM1-5, CNRM-CM6-1, and MRI-ESM2-0 are used from the general rotation models of the sixth report. Daily precipitation and maximum temperature data from 1990 to 2017 were used. Using the statistical model LARS-WG and three scenarios SSP126, SSP245 and SSP585, precipitation and maximum temperature have been downscaling. In this model, the process of generating artificial weather data is done in three parts: model calibration, model validation, and weather data generation. To evaluate the LARS-WG model, coefficient of determination (R2), root mean square error (RMSE) test statistics have been used. To investigate the relationship between precipitation and maximum temperature with different return periods, Gumbel distribution was used. The appropriate distribution for maximum precipitation, temperature, and flood data is Gumble's method; In this study, the distribution of precipitation and maximum temperature for different return periods is presented. In this method, the mean value and standard deviation of the data and the length of the data return period are considered to be the most important effective factors in estimating the maximum values.

Results and Discussion

Validation of the LARS-WG model was done by comparison between observation data and generated data. To evaluate the efficiency of the model, error test criteria have been used. The results show that the LARS-WG model was able to estimate the maximum temperature and precipitation. The accuracy of the modeling in the maximum temperature parameter has been more appropriate than the precipitation ratio. The monthly precipitation changes of the near future period (2021-2040) compared to the base period (1990-2017) of the three ACCESS-ESM1-5, CNRM-CM6-1 and MRI-ESM2-0 models of Abadeh synoptic station showed the amount of precipitation in April, May, June, August, and September has had a decreasing trend compared to the base period. The amount of precipitation in January, February, and December has also increased compared to the base period. At Abadeh station, it shows an increase in temperature under all three models and scenarios in the near future. At the Shiraz synoptic station, precipitation in April, July, and September has decreased compared to the base period. The amount of precipitation in January, February, and March has also increased compared to the base period. The maximum temperature has also increased. At the Lar synoptic station, the precipitation in April, September, and October has decreased compared to the base period. The amount of precipitation in January, February, and March has also increased compared to the base period. The maximum temperature has also increased. The Gumbel distribution output also showed that in all three stations, in a specific return period, precipitation and maximum temperature will increase compared to the base period. Examining the Gumbel distribution of precipitation values also shows an increase in precipitation in the specified return period in the ACCESS-ESM1-5 model.

Conclusion

The changes in the maximum temperature of the near future period (2021-2040) compared to the base period (1990-2017) were incremental in three stations and three models. In Abadeh synoptic station, the maximum temperature changes show an increase in the maximum temperature in the three scenarios SSP126, SSP245, and SSP585, respectively 1.57, 1.59, and 1.63 °C, and the amount of precipitation in the spring and summer seasons is decreasing and Winter precipitation is estimated to be increasing compared to the base period. In the Shiraz synoptic station, the maximum temperature shows an increase in the maximum temperature in the three scenarios SSP126, SSP245, and SSP585, 1.37, 1.50, and 1.48 °C, respectively, and in the ACCESS-ESM1-5 model, in all three scenarios, the amount It is estimated that the winter precipitation is decreasing and the amount of spring precipitation is increasing. The changes in the maximum temperature of Lar synoptic station show an increase in the maximum temperature in the three scenarios SSP126, SSP245, and SSP585, respectively 1.23, 1.37, and 1.28 °C. In the CNRM-CM6-1 model, the winter precipitation of this station is estimated to be a decreasing trend. Fall precipitation is also estimated in the MRI-ESM2-0 model in two scenarios, SSP126 and SSP585, but the ACCESS-ESM1-5 model has estimated an increase in the amount of precipitation in the Lar synoptic station in all seasons and scenarios. The Gumbel distribution output also showed that in all three stations, in a specific return period, precipitation and maximum temperature will increase compared to the base period. Therefore, extreme and heavy precipitation and the increase in the frequency of extreme events related to it, such as floods and droughts, are among the results of global warming.