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Hideki Kanamaru and Masao Kanamitsu

1. Introduction Regional climate models are commonly used to dynamically produce high-resolution analysis of atmosphere and land that global data assimilation systems cannot resolve. When the dynamical downscaling technique is used with reanalyses such as those from the National Centers for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF), all the local details are simulated by the regional model without an input of direct regional

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Ana M. B. Nunes and John O. Roads

1. Introduction During the past several years, reanalysis products have provided many large-scale meteorological fields that are not only useful for validating coarse-scale climate models, but can also be used as forcing for various regional application models (e.g., hydrologic models). These reanalysis products have been subject to much evaluation, and, unfortunately, precipitation and associated hydrologic products remain problematic. Reanalysis precipitation is classified as a C variable

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Tanya L. Otte, Christopher G. Nolte, Martin J. Otte, and Jared H. Bowden

may not accurately represent local changes in temperature and precipitation extremes ( Dulière et al. 2011 ; Werth and Garrett 2011 ). To predict the local effects of climate change, the GCM fields can be projected to local scales using a regional climate model (RCM) by applying dynamical downscaling techniques (e.g., Giorgi 1990 ). The RCM may then be used to inform problem-focused climate assessments that address community goals and values ( Tryhorn and DeGaetano 2011 ). To interpret climate

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Jinwon Kim, Duane E. Waliser, Chris A. Mattmann, Linda O. Mearns, Cameron E. Goodale, Andrew F. Hart, Dan J. Crichton, Seth McGinnis, Huikyo Lee, Paul C. Loikith, and Maziyar Boustani

impact on regional sectors on the basis of multiple GCMs and RCMs in conjunction with specific assessment models. It is important to recognize that the only quantifiable and objective information on future climate stems from projections by physically based, multicomponent numerical climate models, now often referred to as Earth system models (ESMs) (e.g., Abiodun et al. 2008 ; Pollard and Thompson 1992 ). These ESMs calculate the physical and dynamical processes and interactions within and between

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Jörg Winterfeldt and Ralf Weisse

variety of applications (e.g., the design and maintenance of offshore installations such as platforms and wind farms). However, for marine areas (e.g., the northeast Atlantic and the North Sea), long and homogeneous datasets are rare. Regional atmospheric hindcasts obtained from regional climate models (RCMs) driven by global reanalyses form an alternative that can be used either to analyze long-term changes and trends (e.g., Fowler and Kilsby 2007 ; Weisse et al. 2005 ) or as forcing for other (e

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Jared H. Bowden, Tanya L. Otte, Christopher G. Nolte, and Martin J. Otte

1. Introduction Regional climate models (RCMs) are beginning to evolve from atmospheric models into more complex regional earth system models that also include increasingly sophisticated representations of the ocean, cryosphere, land surface, and atmospheric chemistry ( Leung et al. 2006 ). The skill of regional climate change projections should increase because these earth system components modulate the regional-scale climate forcing. In particular, interactions due to chemistry

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Melissa S. Bukovsky

1. Introduction Regional climate models (RCMs) should be able to capture large-scale temperature trends when forcing for these trends is included in the driving boundary conditions. This is logical, but it is only recently that the RCM community has tested its models’ performances in this way, and examples are still not prevalent in the literature. Testing for skill in reproducing trends is a relatively recent phenomenon (e.g., Giorgi et al. 2004 ), while testing for general skill in regional

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Béatrice Morel, Benjamin Pohl, Yves Richard, Benjamin Bois, and Miloud Bessafi

of the island). One way for obtaining time–space-evolving information for rainfall at local scales is to apply dynamical downscaling with a high-resolution regional climate model (RCM) forced by global climate model boundary conditions and resolving the finescale surface processes (e.g., Wang et al. 2004 ). Over the neighboring regions of southern Africa and the SWIO, many studies documented the skill of current RCMs to simulate the regional climate mean state and variability. Investigated time

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Jiali Wang and Veerabhadra R. Kotamarthi

1. Introduction When considering the impacts of global climate change due to increases in the atmospheric concentration of carbon dioxide ( Houghton et al. 1990 , 1992 , 1996 ) and other trace gases, the focus is primarily on impacts at the local and regional scales resulting from large-scale changes (e.g., Wilby et al. 1998 ; Civerolo et al. 2008 ). Although general circulation models (GCMs) demonstrate significant skill at the continental and hemispheric spatial scales and incorporate a

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Valérie Dulière, Yongxin Zhang, and Eric P. Salathé Jr.

trends and scattered negative trends elsewhere ( DeGaetano 2009 ; Mass et al. 2011 ). These changes are broadly consistent with the anticipated effects of anthropogenic climate change ( Gutowski et al. 2008 ). It is unclear, however, whether local trends are discernible from the natural variability in the climate. We will address this problem in the current paper by comparing observed trends to trends simulated by global and regional climate models. The climate of the western United States is rather

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