نوع مقاله : مقاله تحقیقی (پژوهشی)
1 دانشگاه یزد
2 پردیس ابوریحان، دانشگاه تهران
عنوان مقاله [English]
Since human systems such as agriculture and industry, which depend on climatic elements, are designed and created based on compatibility and stability of climate, it is essential that the long-term changes of temperature and precipitation, which constitute the most important chanllenges in the environmental sciences, are identified and considered. In ordet to long-term forecast climatic elements for future periods, the use of Global Climate Models (GCMs) is inevitable. Typically, GCMs have a resolution of 150-300 km in each horizontal direction. Many impact applications require the equivalent of point-wise climate observations and are highly sensitive to fine-scale climate variations that are parameterized in coarse-scale models. Due to the coarse-resolution of the computational cell of GCMs, it is essential to use a downscaling procedure in order to convert large-scale data to regional/local-scales data. Downscaling aims to obtain fine-resolution climate or climate change information from relatively coarse-resolution GCMs. In general, downscaling is divided into dynamical and statistical categories. Dynamical downscaling fits output from GCMs into regional meteorological models such as Weather Research Forecasting (WRF) model. Thus, due to the fine-resolution (20-60 km) of the limited area models, it is possible to simulate some regional climatic features such as orographic precipitation, cloudiness, and some exetrem events. In climatological and meteorological researches using dynamic downscaling, a researcher can achieve both global-scale projections down to a regional/local-scale and the effect of global patterns on local weather conditions. The amount of computations involved in dynamical downscaling makes it computationally expensive to produce decades-long simulations with different GCMs or multiple emissions scenarios. The statistical downscaling method is created based on statistical relationships that link the large-scale atmospheric variables with local/regional climate variables. This method has many advantages such as being easy to apply, and computationally economical. As a result, in most regional/local researches, statistical downscaling is used to consider potential impacts on specific regions or stations. In this method using appropriate statistical relationships between predictor and predictand variables, it is possible to determine the relationships for future periods. In general, if the long-term data exist for the desired station, the best method is statistical downscaling. To determine the best statistical method for downscaling in each region, it is necessary to investigate the capabilities of various statistical methods. The aim of the present research is to investigate the capability of Statistical Downscaling Model (SDSM) in a hot and dry climate to downscale temperature and precipitation as output from Hadley Centre Coupled Model, version 3 (HadCM3) under scenario A2. Several modeling tools are employed in generating the sets of Intergovernmental Panel on Climate Change (IPCC) emission scenarios. The scenario A2 is one of the IPCC emission scenarios. This scenario is based on the following assumptions; a- relatively slow demographic transition and relatively slow convergence in regional fertility patterns, b- relatively slow convergence in inter-regional gross domestic product per capita differences, c- relatively slow end-use and supply-side energy efficiency improvements, d- delayed development of renewable energy, and e- no barriers to the use of nuclear energy. As mentioned earlier, characteristically dry and hot climate is considered to evaluate the performance of SDSM model. Therefore, daily NCEP/NCAR reanalysis and station data during the 1961-2001 period and output from HadCM3 under scenario A2 for 1961-2001 period containing temperature and precipitation for Yazd and Tabas synoptic stations are used. Comparing the results obtained from statistical analyses for observational and downscaled data indicates that the SDSM model can downscale correctly temperature output from HadCM3 in hot and dry climates. Daily precipitation resulted from downscaling using SDSM model has marked differences with observational precipitation in most of the statistical quantities used such as maximum and minimum precipitation. Only some statistical quantities such as the sum of the monthly precipitation and maximum consecutive dry days are consistent with the observed data.