عنوان مقاله [English]
Greenhouse gases emission cause the rising average temperature of the Earth and has disturbed the global and local water cycle (IPCC, 2007). Precipitation is one of the most important climatic variables, which has been affected spatiotemporally by climate change. Its effects are not uniformly influenced the terrsitrial areas. Changes in the number of rainy days, extreme statitics of precipitation (and their variation of mean and standard deviation), etc. are those reported consequences of climate change over the world. The aim of this study is not to analyze the significances of climate change on the precipitation patterns and its extreme behavior, and which stations would be behaved in the projected future climate change scenarios farther from their historical pattern. The implemented methods are briefly explained in the following.
To assess the effects of climate change on distribution of precipitation in Iran, the downscaled precipitation over the network of 288 rain gauge stations have been adopted from Pahlavan et al. (2018), which are scattered over different areas/provinces on Iran. They used CanESM2 GCM model and statistically downscaled the precipitation values to project future climate with three different scenarios according to the various GHGs emission levels. The outputs of the current research are used to investigate the effects of climate change on precipitation distributions and their extreme values. To achieve the goal, three different steps have been performed. In the first step, the stationarity of precipitation was examined both in the historical and projected future scenarios via the Mann-Kendal test (Kendall, 1948; Mann, 1945). In the second step, the deviation of precipitation distributions in each scenario from their historical periods was determined. To assess the issue, three divergence methods were performed which are known in the literature as Energy Distance (Székely & Rizzo, 2013), Kolmogorov Smirnov test (Massey, 1951), and Jenson-Shannon divergence (Fuglede & Topsoe, 2004). Finally, the annual maximum precipitation values (in each period and scenarios) have analyzed via GEV distribution to examine how would be distributions of the extreme values of climate change scenarios in Iran. In the follow, the results are described in brief.
The results of stationarity analysis showed that in the historical period, there are some stations (5.5% of them) with non-stationary behavior. As reported in the previous report (Kottek et al., 2006), these stations are located in warm and dry areas, as well. The stationarity tests of the projected future scenarios show the share of non-stationary stations increases as well as the RCPs. According to the results, the portion of non-stationarity stations of the projected climate change scenarios (2.6, 4.5, and 8.5 RCPs) are increased up to 13%, 22%, and 56% of the whole stations, respectively.
In the next step, three divergence metrics have been used to evaluate the future climate scenarios, and the results showed that the stations in the northeast and southwest of Zagros Mountains are more diverged from their historical distributions. Comparing the historical and future scenarios of climate change, this is worthwhile to mention that with increasing the GHGs level, the deviations of precipitation patterns grow up. Calibrating the GEV distribution over the historical and evaluation of different return period values, positive trends of precipitation statistics (mean, mode, and range of extreme values) are obviously detected.
In this study, the patterns of precipitation distributions over Iran both in historical and future climate change scenarios have been analyzed. The results of trend analysis via Mann-Kendal test showed the same increasing trends of precipitation and GHGs level. So, the divergence methods were implemented to analyze the distance between rainfall distributions. The results showed some stations are more sensitive than the others and have more divergence from historical distributions. The extreme values of the recorded precipitation also analyzed using GEV distribution showed for a certain return period, the carbon emission level is directly correlated with the means and standard deviations of the extreme values. In conclusion, the results of this study showed that the increasing the level of emitted GHGs forces the statistical distribution to behaved more chaotic than the historical period. This makes the extreme values higher and more frequent than before. To further investigations, detection and attribution on Iran can be used to reflect the reality of national climate change and variabities. Also, considering the potential of climate change and climate classes, future (and probable) climate change patterns can be examined using the results of decreasing temperature and evaporation scales.