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J. F. Scinocca, V. V. Kharin, Y. Jiao, M. W. Qian, M. Lazare, L. Solheim, G. M. Flato, S. Biner, M. Desgagne, and B. Dugas

1. Introduction At the Canadian Centre for Climate Modelling and Analysis (CCCma), a new regional climate model, the CCCma Regional Climate Model (CanRCM4), has been developed. CanRCM4’s novelty does not arise from the method of solution in its dynamical core or the climate-based physics package it employs. Both of these are well known and currently operational for global model applications. The novelty of CanRCM4 stems from a new philosophy of coordinating the development and application of

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Yongkang Xue, Ratko Vasic, Zavisa Janjic, Fedor Mesinger, and Kenneth E. Mitchell

1. Introduction Despite increases in computer power, most atmospheric general circulation model (GCM) simulations still use coarse resolution with the horizontal resolution being about 200–500 km. Therefore, they only produce large- and synoptic-scale atmospheric features. Because of the resolution limitation, which could be crucial for land–atmosphere interaction studies with highly heterogeneous land surface conditions and steep orography, regional climate models (RCMs) have been developed

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Christopher L. Castro, Roger A. Pielke Sr., Jimmy O. Adegoke, Siegfried D. Schubert, and Phillip J. Pegion

1. Introduction This paper is the second in a series investigating the summer climate of the contiguous United States and Mexico with a regional climate model (RCM). The previous paper ( Castro et al. 2007 , hereafter Part I ) described the climatological behavior of 53 years of summer simulations with the Regional Atmospheric Modeling System (RAMS) dynamically downscaling the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis ( Kalnay

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Mark A. Snyder and Lisa C. Sloan

on Climate Change (IPCC; Houghton et al. 2001 ) presented the most compelling and complete climate change research to date. However, a deficiency in our understanding is the nature of regional-scale climate changes within the broader context of global climate change. Coupled atmosphere–ocean general circulation models (AOGCMs) have been the primary tools for investigating future climate change through the end of this century in the IPCC TAR. AOGCMs are run with relatively coarse horizontal

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Tanya L. Spero, Christopher G. Nolte, Jared H. Bowden, Megan S. Mallard, and Jerold A. Herwehe

1. Introduction The Weather Research and Forecasting (WRF) Model ( Skamarock and Klemp 2008 ) has become one of the more commonly used models for dynamical downscaling (e.g., Caldwell et al. 2009 ; Salathé et al. 2010 ; Racherla et al. 2012 ; Mearns et al. 2012 ; Gao et al. 2012 ; Vautard et al. 2013 ; Harkey and Holloway 2013 ). The goal of dynamical downscaling is to use a physics-based regional climate model to project regional- and local-scale climate features at finer spatial and

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Lucas M. Harris and Shian-Jiann Lin

1. Introduction Regional climate models (RCMs; Christensen et al. 2007 ) typically use a limited-area numerical model, such as the Weather Research and Forecasting model (WRF; Skamarock et al. 2005 ) or the Regional Climate Model (RegCM; Giorgi et al. 2012 ), with lateral boundary supplied by either a reanalysis or a global climate simulation. However, the boundary conditions are often available only at 6 h or longer intervals. The model used to produce the boundary conditions may be very

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Wanli Wu, Amanda H. Lynch, Sheldon Drobot, James Maslanik, A. David McGuire, and Ute Herzfeld

because of technical and environmental limitations. It has been suggested that an alternative to estimating terrestrial water and energy cycles is to use land surface models (LSMs; Bonan 2002 ) or regional climate models (RCMs; Wu and Lynch 2000 ; Wu et al. 2005 ). The models close the water and energy budget by design. Thus, if the large-scale forcing data, which drive LSMs and RCMs, are accurate, and if model biases are small, these modeled water and energy fluxes might be used in lieu of

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Xin-Zhong Liang, Min Xu, Xing Yuan, Tiejun Ling, Hyun I. Choi, Feng Zhang, Ligang Chen, Shuyan Liu, Shenjian Su, Fengxue Qiao, Yuxiang He, Julian X. L. Wang, Kenneth E. Kunkel, Wei Gao, Everette Joseph, Vernon Morris, Tsann-Wang Yu, Jimy Dudhia, and John Michalakes

Liang 2010 ; Yuan and Liang 2011a ). The WRF was designed originally for short-range NWP but not expressly for long-term climate simulation. There has been some success using WRF for regional climate downscaling with a continuous model integration of longer than a season ( Liang et al. 2002 ; Leung and Qian 2009 ; Evans and McCabe 2010 ; and all the following references cited in this paragraph). Such direct applications, however, also have encountered numerous problems. These include 1

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Robert Schoetter, Peter Hoffmann, Diana Rechid, and K. Heinke Schlünzen

al. 2010 ). Results from impact studies can be very sensitive to the meteorological data used. For this reason, evaluation of regional climate model results is crucial for the interpretation of results from impact studies (e.g., Dibike et al. 2008 ). Biases in regional climate model simulation results can arise from shortcomings of the regional climate models themselves, but also from erroneous forcing data. Regional climate model (RCM) evaluations are mostly done for RCMs forced with reanalysis

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G. T. Narisma and A. J. Pitman

Australian environment. 2. Methodology 2.1. Model configuration We used the Regional Atmospheric Modeling System (RAMS; Pielke et al. 1992 ; Liston and Pielke 2001 ) developed by the Colorado State University coupled to the General Energy and Mass Transport Model (GEMTM; Chen and Coughenor 1994 ; Eastman et al. 2001 ). RAMS is a flexible meteorological modeling system that has been extensively used to study the impact of LCC on weather and climate (see Pielke et al. 1998 ). More importantly, RAMS

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