Volume 3 • Issue 2 • 1000117 J Earth Sci Climate Change ISSN:2157-7617 JESCC, an open access journal Research Article Open Access Ullah et al., J Earth Sci Climate Change 2012, 3:2 http://dx.doi.org/10.4172/2157-7617.1000117 Research Article Open Access Earth Science & Climatic Change Keywords: Climate change; Delta change; GCM; Downscaling Introduction Te rising concentration of greenhouse gases in the atmosphere due to human activities such as land use changes and dependence upon fossil fuels has led to global warming and global energy unbalance [1- 4]. Fourth Assessment Report (4AR) of the Intergovernmental Panel on Climate Change (IPCC) had reported a 0.74°C increase in the global mean surface temperature during the last hundred years (1906- 2005). In the last 50 years a signifcant increase has been reported with an increasing rate of 0.13°C every 10 years. Te global mean surface temperature is projected to increase approximately from 1.1 to 6.4°C during the 21st century [5]. Tis increased global warming can afect the hydrological cycle, water resources, public health, industrial and municipal water demands, water energy exploitation and the ecosystem [2,6]. It is widely consented that General Circulation Models (GCMs) are physically based means for formulating climate scenarios [7]. At the global and continental scale the climate prediction is good [8], but GCMs may not provide the required climate features in many of the regional- and local-scale processes that are needed for climate change impact studies [9,10]. Tis is due to the coarse spatial resolution of GCMs which limits their efectiveness at smaller scales [9]. Another reason for this gap is the inadequate parameterization of various processes (e.g., evapo-transpiration, soil moisture) concerning cloud formation and land surface interactions with the atmosphere [7]. Terefore, some type of downscaling, such as statistical downscaling, dynamic downscaling, or the delta change method is required to develop scenarios of higher temporal and/or spatial resolutions than presently produced by GCMs [10]. However each of these downscaling techniques has its advantages and limitations. Dynamic downscaling embeds Regional Climate Models (RCMs) within large-scale GCMs. RCMs interpret atmospheric processes running at small scales for GCMs, such as orographic rainfall and surface–atmosphere interactions [11,12]. However, RCMs involve a huge number of thermodynamics equations and therefore RCMs are computationally expensive and complex [10,12]. Statistical downscaling presumes that large-scale atmospheric processes and regional forces afect regional climates [12-14]. To predict regional-scale climate changes the statistical relationships between large scale climate variables and local variable, are found out from observed data and then applied to the GCM simulations [12]. Since the relationships between local and large-scale variables difer as a function of a land-space, therefore most of the statistical downscaling studies are restricted to individual regions [15,16]. Another problem with this technique is that it presumes that the relationships between large-scale climate variables and local variables always remain the same [12]. Tere are many studies where the inter-comparison of diferent downscaling techniques has been made (e.g. [17-20]). Te fastest technique used for global-scale downscaling of a large number of climate simulations is the change factor method [12,13,21-23], ofen named as “delta change”. Tis approach has been widely used in climate change research e.g., reproducing future climate scenarios for high-resolution modeling of fora and bionomical sensitivity to climate change in United States [12]. As described above GCM data is widely used for the analysis of climate change impacts on agriculture and biodiversity. For diferent GCMs the data is freely available [24], but in many cases the data cannot be used in its existing format for further processing. Sofware tools (e.g. Climate Date Operators (CDO), PANOPLY) are required to convert that data into ASCII or Excel format [25]. In this study a tool *Corresponding author: Kifayat Ullah, Computer Science and Information Management, Asian Institute of Technology, Thailand, Tel: +66836975337; E-mail: st105999@ait.ac.th, Kifayat821@gmail.com Received August 03, 2012; Accepted September 26, 2012; Published September 28, 2012 Citation: Ullah K, Shrestha, S, Guha S (2012) A Decision Support Tool for Selection of Suitable General Circulation Model and Future Climate Assessment. J Earth Sci Climate Change 3:117. doi:10.4172/2157-7617.1000117 Copyright: © 2012 Ullah K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract It is widely accepted that General Circulation Models (GCMs) are physically based means for formulating climate scenarios. At the global and continental scale the climate prediction is good but GCMs may not provide the required climate features, in many of the regional and local-scale processes, which are needed for climate change impact studies. Therefore, downscaling is required to develop climate scenarios of higher spatial resolutions. Also the simulated data for different GCMs is freely available at IPCC data distribution Centre, but to extract the data for a particular region and convert it to a user readable format, different software packages are required. This study presents a decision support tool to select a suitable GCM for any specifc region, based on statistical parameters such as coeffcient of determination (R 2 ) and Root Mean Square Error (RMSE). The tool also provides the facility to obtain the simulated data for different GCMs in the excel format. Delta change method is incorporated as a downscaling technique to project future climate scenarios. To demonstrate the application of the tool, future air surface temperature and precipitation scenarios are projected for 2021-2050, using data for four meteorological stations from upper Jhelum basin, Pakistan. A Decision Support Tool for Selection of Suitable General Circulation Model and Future Climate Assessment Kifayat Ullah 1 *, Sangam Shrestha 2 and Sumanta Guha 1 1 Computer Science and Information Management, Asian Institute of Technology, Thailand 2 Water Engineering and Management, Asian Institute of Technology, Thailand