بکارگیری مدلSdsm جهت ریزمقیاس نمائی داده های Gcm بارش و دما مطالعه موردی: پیش‌بینی‌های اقلیمی ایستگاهی در ایران

بکارگیری روش‌های جدید در حل معادلات جوی و در اختیار داشتن پیش بینی های اقلیمی با توجه به ماهیت بازه زمانی طولانی مدت آنها، نقش بسیار ارزنده‌ای در مدیریت‌های کلان ایفا می‌نماید. ولیکن در بازه زمانی دراز مدت بدلیل محدودیت جدی در قدرت تفکیک مکانی، قادر به پیش‌بینی آب و هوای واقعی در مقیاس ایستگاهی و خرد مقیاس نمی‌باشد. لذا جهت بکارگیری خروجی مدل‌های اقلیمی تمام کره‌ای و دستیابی به قدرت تفکیک فضائی کم، روش ریز مقیاس نمائی مورد استفاده قرار گرفته که به دو دسته آماری و دینامیکی و گاها تلفیقی از آن دو تقسیم‌بندی می‌گردند. در این میان صحت سنجی داده‌های ریزمقیاس شده جهت تحلیل پیش‌بینی‌های درازمدت بعنوان یکی از پارامترهای اساسی در کسب دقت این گونه مدل‌ها از اهمیت ویژه‌ای برخوردار است. در این تحقیق سعی گردیده تا با انتخاب  کل منطقه کشور و ابتدا برای 41 ایستگاه منتخب کشور که دارای آمار اقلیمی 41 ساله (2001-1961 میلادی) می‌باشند، خروجی مدل اقلیمیhadcm3 تحت سناریوی اقلیمی a2 که یکی از محتمل‌ترین سناریوهای انتشار می‌باشد، توسط مدلsdsm که قادر است خروجی مدل‌های گردش عمومی جو را به مقیاس ایستگاهی تبدیل نماید، ریزمقیاس گردد.  سپس با استفاده از روش‌های آماری و بدست آوردن ضرائب وزنی داده‌‌های ریزمقیاس شده و داده‌های پایه را مورد تجزیه و تحلیل قرار داده و واسنجی مناسبی از آنها ارائه گردد. نتایج بیانگر آنست که بین مقادیر ریزمقیاس شده بارش، دمای حداقل و حداکثر و مقادیر واقعی آنها تفاوت معنا داری با خطای بحرانی 0/05 وجود ندارد و بازه اطمینان داده‌ها مشتمل برمقدار صفر است. لذا بکارگیری داده‌های ریزمقیاس‌شده مدل جهت بهینه‌سازی داده‌های آینده در مقیاس ایستگاهی می‌تواند بصورت قابل قبول مورد استفاده قرار گیرد.

Using SDSM Model to Downscaling Precipitation and Temperature GCM Data for Study Station Climate Predictions over Iran

Introduction:Iran is located in the southwest of Asia and is in the arid belt of the world and about 60% of the extent of the country is mountainous and the remaining part (1/3) is desert and arid lands. The climate of the country can be divided into three main categories: Warm temperate, rainy with dry summer in a narrow strip in the north, Dry, hot desert in the central plateau, Dry, hot steppe covering the rest of the country. So, it could be so difficult to predict climate change over whole of country. In this case, using new methods for solving weather equations and having climate prediction because of its long term temporal has so many important rolls for massive management.In climate change studies, the global circulation models (GCMs) are usually used to simulate the past and future global climate. Unfortunately, despite the advancement in GCM research and modern computing technology, the most recent generation of general circulation models still have serious problems due to their low spatial resolutions (with the field variables being represented on grid points 300 km apart). So, because of its serious limitation and resolution, using them in long term forecast couldn’t predict actual weather in station scale or small scale and it is important to assess the accuracy and uncertainty of GCMs in various climatic and geographical regions.Methodology:To employ output of Global Climate Models and accesses to good resolution, Downscaling Methods are used that are divided in two dynamical and statistical groups and some when syncretistic of them. A thorough evaluation of the current generation of GCMs has only started recently and the evaluation of a rich spectrum of indices on extremes is new. In this, calibrating downscaled data is very important as a main parameter to reach best resolution and for analyzing long term forecast. Two different approaches to downscaling have been employed. It has adopted a methodology that exploits mean inter station correlations to correct the statistics of gridbox means. The method, closely related to blockkriging, is demonstrated to remove the sle size sensitivity of statistics in daily gridpoint precipitation. It has adopted a direct downscaling by distance and direction weighted average of point observations. At this filed, SDSM is a Statistical down scaling model that distributed both of these aggregation techniques to the consortium. Several data of selected stations have been started applying to a dataset and coding study area. These datasets will provide a valuable reference for model evaluation simulate predictor variables across selected region. The SDSM model run on selected period and reached amount of precipitation, minimum and Maximum of temperature and their standard deviations. By using statistical methods we could evaluate SDSM outputs to reach the best conclusion and selecting best data. With acceptable results, we could use them for climate prediction over region.Materials:In this paper, at the first we tried to select 41 synoptic stations that have 41 years climate data (19612001). These stations distributed to whole country with several climates. These data applied our observation dada. At this method we used third version of the coupled global Hadley Centre Climate Model (HadCM3) Outputs as predictor of method and A2 scenario that is one of the most probable emission scenarios. Then we down scaled them by using SDSM model version4.2 that could downscale general circulation models to station scales. Then by using statistical methods and reaching differential coefficients could analyses downscaled data by base data and present suitable correlation of them.Results and Discussion:Results was shown, there is no significant deference with 0.5 critical errors and correlation of data and accepted at 0.01 significant levels. And there is a good accepted correlation between modeled data and observing minimum and maximum temperature and precipitation data.Conclusion:So, using Downscaled data is acceptable with suitable efficiency to correct future data at station scale. This study should help to fill in the knowledge gap in GCM downscaling research of climate and add an important piece in the global climatic assessment jigsaw puzzle.