Authors
Abstract
Introduction
Iran has very complicated topography and climate including two mountain chains of Zagros and Elborz, two wide deserts of Kevir-e-lut and Dasht-e-kevir, forest lands and three large water bodies of Caspian Sea, Persian Gulf and Oman Sea. Climatically, Iran has a variable climate. Winters are cold with heavy snowfall in the northwest and below freezing temperatures during December and January. Spring and fall are relatively mild, while summers are dry and hot. In the south, winters are mild and the summers are very hot. On the Khuzestan plain, summer heat is accompanied by high humidity (Alijani, 2003). The intergovernmental Panel on Climate Change (IPCC) reported that the global mean temperature has been increased 0.6o C during 20th century while the atmospheric concentration of carbon dioxide also increased from 280ppm to 370 ppm in third Assessment Report (TAR) published in 2001(Kwon, 2005). Validation of PRECIS regional climate model in Bangladesh is performed with the surface observational data of rainfall and temperature (maximum and minimum) at 26 observational sites throughout the country from 1961-1990. It is found that regional analysis provides overestimation of PRECIS values in Bangladesh whereas data extracted at some particular locations provide better performance of PRECIS. For baseline, the performance of PRECIS is about 90% for rainfall.PRECIS can detect about 96% and 100.3% of maximum and minimum temperature respectively (Islam et. al., 2005).
Climate change in the past decade in Jianghuni valley is studied by using statistical techniques. Both frequency and strength of extreme climate events such as hot weather, droughts and floods have increased remarkably since 1990s. Also, the regional climate model of PRECIS is used to provide a prediction of future climate in the valley. The results give an average surface warming of 2.9oC under the SRES B2 emission scenario by the end of this century (2071-2100). Precipitation may increase on the same period (Tian, et. al., 2006).
Methodology
Under Article of the United Nation Framework Convention on Climate Change (UNFCC), all Parties must study the impact of climate change in their countries using regional climate models. To do this, a PC-based regional climate model named PRECIS has been developed at the Hadley Center of United Kingdom Meteorology Office. PRECIS is based on the atmospheric component of the HadCM3 general circulation model. The atmospheric dynamics module of PRECIS is a hydrostatic version of the full `primitive equations and uses a regular longitude-latitude grid in the horizontal and a hybrid vertical coordinate. In this research monthly to seasonal precipitation of Iran has been modeled using PRECIS regional climate model with HadAM3P boundary condition data. PRECIS has a horizontal resolution of 50 km with 19 levels in the atmosphere (from the surface to 30 km in the stratosphere) and four levels in the soil. The present version of PRECIS has the option to downscale to 25 km horizontal resolution. In addition to a comprehensive representation of the physical processes in the atmosphere and land-surface, it also includes the sulphur cycle. The validation of model has been done by comparing observation data and model output data with two different methods of region to region and station to station.
For this study, the PRECIS model domain has been set up with a horizontal resolution of 50 x 50 km. The domain is roughly stretched over the latitude 23 to 45?N and longitude 43 to 68?E. The HadAM3P global data set is used to drive the PRECIS model. The horizontal resolution of the HadAM3P boundary data is 150 km and for the present and future climate, it covers the period 1960-1990 and 2070-2100 respectively (Wilson et al., 2005). For the future climate, both SRES A2 and B2 greenhouse gas emission scenario is selected.
Results and Discussion
Seasonal to annual error and bias of the model outputs have been calculated using to different approach of region to region and station to station methods. We considered Masoudian's zoning (Masoudian, 1387) approach of the precipitation of Iran for computing regional error and bias of the model. In this approach precipitation region of Iran have been categorized in 12 regimes of South Central, Farsi, Kordi, Sistani, West Khorasan, East-Khorasan, North Central, Khuzi, Hormozi, Azari, Baluchi and Khazari.
It is found that overall error of model is 5.3%. Maximum error has occurred over Farsi, Hormozi, Khuzi and Khazari regions with errors of 24.9, 16.9, 12.2 and -10.2 respectively. Minimum errors occurred over Kordi, Azari, and Northern central and eastern Khorasan regions. Maximum monthly errors occurred in September, the transition month between summer and autumn and minimum monthly precipitation has happened in May. It seems that wet bias of simulations in Southern regions can be due to transferring of high amount of humidity from large-scale GCM’s cells into RCM’s fine cells. Also dry bias of simulations in Caspian region is because of low ability of PRECIS in parameterizations of convective precipitations.
Results show the regional errors are found in Farsi and Hormozi regions and minimum errors are in Kordi and North-Central regions. Maximum and minimum monthly errors are found in September and December, respectively.
Conclusion
As a main result, PRECIS skill in modeling regional precipitation, especially over the regions with high amount of convective and local precipitation is low, but it can model well the total precipitation of Iran. Average error of modeling over the country is less than 2%, but maximum regional error of modeling is 10% in Farsi region. Maximum precipitation errors are found in transition months. We found that PRECIS can model overall precipitation of Iran well, but it has some deficiency in modeling convective precipitation in Caspian region and southern part of Iran. There is no significant difference between PRECIS-modeled data and actual data retrieved from weather stations. So, as a powerful regional model, PRECIS can be used for regional climate modeling over Iran and future climate change projections.
Keywords
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