Sima Rahimi Bondarabadi; Saeed Jahanbakhsh; Behrooz Sari Saraf
Abstract
Any change in the concentration of greenhouse gases will upset the balance between the components of the climate system. But, the change in the concentration of these gases and how they will affect in the future is unknown. To study the effects of climate change on different systems in the future, climate ...
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Any change in the concentration of greenhouse gases will upset the balance between the components of the climate system. But, the change in the concentration of these gases and how they will affect in the future is unknown. To study the effects of climate change on different systems in the future, climate variables must first be simulated under changes in greenhouse gases (climate scenarios). There are several ways to do this, the most reliable of which is the use of climatic models. AOGCMs can simulate climate parameters globally in large scale, while these may not be suitable for small scales. One of the most important downscaling methods is dynamic methods that are based on increasing the resolution and analysis of planetary climate models. Here, in this research, climate change status in Karkheh River Basin where a major basin for water and agricultural yields is studied. For this purpose, the PRECIS model was used. PRECIS is an exponential dynamics downscaling model used to estimate the temperature and precipitation rates for the period of 2070 to 2100 under A2 and B2 scenarios. According to the results of climate change assessment under scenario A2, precipitation would increase up to 11% and up to five degree centigrade would rise in average maximum and minimum temperature while concerning B2 scenario, an increase in precipitation up to 7% and a rise in temperature rise up to three degree centigrade are estimated. However, under both the scenarios, despite, the fall’s precipitation is higher than the winter’s precipitation.
Saeed Jahanbakhsh asl; Behroz Sari Sarraf; Tayeb Raziei; Akram Parandeh khouzani
Abstract
In order to monitor spatiotemporal variability of snow in mountainous areas such as Zagros in Iran, long-term records of snow observations with high spatial resolution are required. However, no such data are either observed or available for the stations of the Zagros region. Therefore, in this study, ...
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In order to monitor spatiotemporal variability of snow in mountainous areas such as Zagros in Iran, long-term records of snow observations with high spatial resolution are required. However, no such data are either observed or available for the stations of the Zagros region. Therefore, in this study, the Era-Interim/Land snow depth data for the period 1979-2010 were used in order to investigate the spatiotemporal variability of snow season length and the associated starting and ending dates in the Zagros region. To do so, for each hydrological year starting from October and ending in September, the first and last snow dates with snow depth equal to or greater than one centimeter were defined as the first and last day of observed snow on the ground and the time period between these two dates was considered as the snow season length. For each grid points over the study area, the time series of snow start and end dates, as well as the length of the snow season, were extracted and the rate of their temporal changes was estimated using the Sen Slope estimator and were examined using the Mann-Kendal trend test to test if they are statistically significant. Moreover, the considered time period was divided into three different sub-periods and the mean values of these parameters (i.e., first and last snow dates and snow season length) in the three sub-periods were also compared. The links between these parameters and the latitude, longitude, and altitude of the grid points were also examined. Results indicated that the spatial pattern of the first and last snow dates and snow season length fairly follow the geographical features of the study area and thus have a statistically significant relationship with the latitude, longitude, and altitude. Time variability of the considered parameters over all the studied grid points revealed that the date of the first snow in the most proportion of the study area retreated towards the late autumn and January and the date of the last snow also retreated towards March and February, thus, resulting in the shorter winter season in recent years. The observed statistically significant decreasing trend in the time series of the last snow dates towards March and February has the most contribution in shortening the length of the snow season.
sima rahimi bondarabadi
Abstract
The increase of greenhouse gases caused imbalance in the amount of air and water in the Earth, which called climate changed. Increasing the greenhouse gases not only impact on the weather parameters, but also impact on water resources, agriculture, environment, health and the economy as well. For the ...
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The increase of greenhouse gases caused imbalance in the amount of air and water in the Earth, which called climate changed. Increasing the greenhouse gases not only impact on the weather parameters, but also impact on water resources, agriculture, environment, health and the economy as well. For the effects of climate change on different systems in the future, first the climatic variables which are affected under the greenhouse gases should be simulated (different climate scenarios). There are several simulation methods where the climate model methods are most suitable. The AOGCM model is able to simulate global climate in large scale, while not suitable for small and regional scale. So, it is necessary to identify the variations (climate) in small scale. For this reasons it is necessary to use the downscaling methods such as dynamic methods which are based on high resolution and analysis of climate models. This method is suitable and appropriate for Iran since it suffers from lacks of observed data as well as lack of long term and enough stations in the country. In this study, PRECIS model (a dynamical downscaling climate model) was evaluated for simulation of precipitation and temperature. In general, the results of PRECIS model indicate this model can be a good estimate of temperature and precipitation in the region. Although for the rainfall in autumn and spring, due to the local nature of the precipitation, the model is not very strong. Also, comparison of spatial and point evaluation of the model showed that areal evaluation is appropriate as opposed to a point.
