Simulatiing ExtremeTemperature Indicators Based on RCP Scenarios: The Case of Khuzestan Province

جغرافیا و مخاطرات محیطی

Article 5, Volume 4, Issue 4 - Serial Number 16, January 2016, Pages 105-124   

Document Type: Research Article

DOI: 10.22067/geo.v4i4.47304

Authors

Amena Dashtbozorgi^{* 1}; Bohloul Alijani²; Zeinolabedin Jafarpour³; Alireza Shakiba⁴

¹Azad University, Science and Research Branch, Tehran

²Kharazmi University

³Kharazmi University, Tehran, IRAN

⁴Shahid Beheshti University

Abstract

1. Introduction
One of the six great challenges recognized by the World Climate Researches Program (WCRP) is the prediction and the characteristics of extreme events. The outcomes of various sources indicate that the extreme climate events have significantly increased over the last decades. Trend analysis of extreme temperature indicators is of crucial importance in estimating the trend of global warming. Temperature rise plays a decisive role in drought intensity leading to a more frequent occurrence of extreme drought events. Temperature rise also results in desertification, decline of water resources, and a decrease in agricultural products following the loss and decay of the products. Following the establishment of the Intergovernmental Panel on Climate Change (IPCC) in 1988 and the extensive research on it at a global scale, researchers began to realize that the average monthly temperature measure cannot indicate the trend changes of global warming. The results of the analysis of minimum and maximum average temperatures in the early 90's show that the minimum average temperatures have been rapidly increasing in relation to minimum average temperatures.
At the beginning of the 2000s, IPCC published a special report on emissions scenarios (SRES) in the Third Assessment Report (TAR) and used them for the Assessment Report Four (AR4). With the beginning of the 2010s, the Coupled Model Intercomparison Project Phase 5 (CMIP5) suggested new scenarios called Representative Concentration Pathways (RCP), identified with values of 2.6, 4.5, 6.0, and 8.5 W/m2. The 4.5, 6.0 and 8.5 RCP scenarios roughly correspond to B1, A1B, and A2 scenarios. After the development of RCP scenarios, some researchers studied the effects of such scenarios on diverse issues such as water resources, agriculture, and so forth through the temperature change trend analysis.

2. Study Area
This research is an attempt to analyze the future climate conditions of Khuzestan Province till the 2050s according to the RCP scenarios. Located in the southwest of Iran, Khuzestan plays a vital role in Iran’s economy in terms of agriculture and industry.

3. Material and methods
The method of the study on Khuzestan’s future climate conditions is based on RCP scenarios, dealing with both current and future conditions different stages. To analyze the future climate conditions, the following seven indices were selected out of the indices introduced by the Expert Team on Climate Change Detection and Indices ETCCDI: DTR (diurnal temperature range), TMAX mean (mean maximum temperature), T MIN mean (mean minimum temperature), TN10p (cold nights), TX10p (cold days), TN90p (warm nights), and TX90p (warm days to analyze the future conditions from 2013 to 2050, the 2.6, 4.5, and 6.0 RCP scenarios were chosen through the four proposed scenarios of the fifth assessment report of IPCC. The goal of all these scenarios is to predict the highest, lowest, and the mid-range of future climate changes.
MarkSim model(Jones, 2012) was selected as the downscaled model of the study.First, to assess the accuracy of the simulation model of regional temperature changes, the following models were selected for the years from 2013 to 2015: GCM, HadGEM2-ES (1.2414× 1.875), MIROC5 (1.4063×1.4063) and MRI-CGCM3 (1.125×1.125). Then, downscaling and temperature indices modeling were performed and the output of different models was compared. Regarding the variance analysis, the model of MIROC5 was considered as an appropriate model for the study and the level of significance for temperature indices trend was analyzed (p=0.05).

4. Results and Discussion
In this paper, the spatial change pattern of temperature extreme indices of Khuzestan was compared and analyzed within the two statistical periods of 1983-2012 and 2013-2050 using RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 scenarios. The results indicate that under different scenarios, maximum and minimum temperature indices, particularly those in percentile would depict different trends in different parts of Khuzestan. On the basis of the present situation, the northern, central and southern parts of Khuzestan represent a significantly increasing trend, whereas other areas of the province have just an increasing trend. The time series of the maximum and minimum temperature means in the present situation indicate that in reference to the present situation, the temperature minimums (+2.72°C) are increasing ata roughly more rapid pace in relation to temperature maximums (+1.2°C), which explains the increase in DTR index. In relation to other scenarios, on average the TMIN mean index will show 1°C increase under RCP 4.5 scenario which will be witnessed in most parts of Khuzestan. In relation to other scenarios, the trend of this index under RCP 6.0 is more homogenous throughout the province and barely southern parts of Khuzestan will show an increase of trend in relation to other areas. The TMAX mean index under RCP 2.6 will show a more increasing trend as compared with the two other scenarios. The highest increase occurs in western parts of Khuzestan while other parts represent a similar trend.
In like manners, the analysis of percentile-based temperature indices such as the indices of cold events (TX10p, TN10p) and warm events (TN90p, TX90p) under miscellaneous scenarios indicate that the cold events indices depict a declining trend, whereas their warm equivalents depict an increasing trend. Of all the future scenarios, the RCP 4.5 and RCP 6.0 scenarios represent the highest spatial changes of the TN10p and TN90p indices, respectively. Under different scenarios, the TN90p index represents an increasing trend with almost the same linear slope that cold nights index curve declines. Under the RCP 2/6 scenario, the TN90p index indicates that if appropriate policies are taken to adjust to climate changes and bring them under control, the trend of warm nights will be controlled.

5. Conclusion
With a spatial-temporal analysis of temperature extreme indices under the present situation (from 1982-2012) and the RCP 2.6, RCP 4.5 and RCP 6.0 scenarios (2013-2050), This article simulates the temperature changes of Khuzestan based on the obtained data, GCM and MIROC5 models. However, the results indicate that in the present situation, the temperature minimums are roughly increasing more rapidly than the temperature maximums to such an extent that they lead to the declining trend of the DTR index. The simulation of climate change trend based on the RCPs suggests that the increase in temperature trend is likely to maintain in the future. All in all, the results show that, the trend of cold and warm nights indices (TN90p, TN10p) are compatible with the TMIN mean index trend as well as the compatibility of the trend of cold and warm days indices (TX90p, TX10p) and the TMAX mean index trend in different parts of Khuzestan. In this sense, the indices of the cold period (cold days and nights) and the indices of the warm period (warm days and nights) will represent a declining and an increasing trend, respectively.

Keywords

Temperature extreme indicators; Khuzestan; RCP scenarios

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