The North American Regional Climate Change Assessment Program (NARCCAP) is an international effort designed to investigate the uncertainties in regional-scale projections of future climate and produce highresolution climate change scenarios using multiple regional climate models (RCMs) nested within atmosphere–ocean general circulation models (AOGCMs) forced with the Special Report on Emission Scenarios (SRES) A2 scenario, with a common domain covering the conterminous United States, northern Mexico, and most of Canada. The program also includes an evaluation component (phase I) wherein the participating RCMs, with a grid spacing of 50 km, are nested within 25 years of National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) Reanalysis II. This paper provides an overview of evaluations of the phase I domain-wide simulations focusing on monthly and seasonal temperature and precipitation, as well as more detailed investigation of four subregions. The overall quality of the simulations is determined, comparing the model performances with each other as well as with other regional model evaluations over North America. The metrics used herein do differentiate among the models but, as found in previous studies, it is not possible to determine a “best” model among them. The ensemble average of the six models does not perform best for all measures, as has been reported in a number of global climate model studies. The subset ensemble of the two models using spectral nudging is more often successful for domain-wide root-mean-square error (RMSE), especially for temperature. This evaluation phase of NARCCAP will inform later program elements concerning differentially weighting the models for use in producing robust regional probabilities of future climate change.

We investigate major results of the NARCCAP multiple regional climate model (RCM) experiments driven by multiple global climate models (GCMs) regarding climate change for seasonal temperature and precipitation over North America. We focus on two major questions: How do the RCM simulated climate changes differ from those of the parent GCMs and thus affect our perception of climate change over North America, and how important are the relative contributions of RCMs and GCMs to the uncertainty (variance explained) for different seasons and variables? The RCMs tend to produce stronger climate changes for precipitation: larger increases in the northern part of the domain in winter and greater decreases across a swath of the central part in summer, compared to the four GCMs Climatic Change (2013) driving the regional models as well as to the full set of CMIP3 GCM results. We pose some possible process-level mechanisms for the difference in intensity of change, particularly for summer. Detailed process-level studies will be necessary to establish mechanisms and credibility of these results. The GCMs explain more variance for winter temperature and the RCMs for summer temperature. The same is true for precipitation patterns. Thus, we recommend that future RCM-GCM experiments over this region include a balanced number of GCMs and RCMs.

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An analysis of several multidecadal simulations of the present (1971–90) and future (2041–60) climate from the Canadian Regional Climate Model (CRCM) is presented. The effects on the CRCM climate of model domain size, internal variability of the general circulation model (GCM) used to provide boundary conditions, and modifications to the physical parameterizations used in the CRCM are investigated. The influence of boundary conditions is further investigated by comparing the GCM-driven simulations of the current climate with simulations performed using boundary conditions from meteorological reanalyses. The present climate of the model in these different configurations is assessed by comparing the seasonal averages and interannual variability of precipitation and surface air temperature with an observed climatology. Generally, small differences are found between the two simulations on different domains, though both domains are quite large as compared with previously reported results. Simulations driven by GCM output show a significant warm bias for wintertime surface air temperatures over northern regions. This warm bias is much reduced in the GCM-driven simulation when an updated set of physical parameterizations is used in the CRCM. The warm bias is also reduced for simulations with the standard set of physical parameterizations when the CRCM is driven with reanalysis data. However, use of the modified physics package for reanalysis-driven simulations results in surface air temperatures that are colder than the observations. Summertime precipitation in the model is much larger than observed, a bias that is present in both the GCM-driven and reanalysis-driven simulations. The bias in summertime precipitation is reduced for both types of driving data when the updated set of physical parameterizations is used. Model projections of climate change between the present and future periods are also presented and the sensitivity of these projections to many of the above-mentioned modifications is assessed. Changes in surface air temperature are predicted to be largest over northern regions in winter, with smaller changes over more southerly regions and in the summer season. Changes in seasonal average precipitation are projected to be in the range of ±10% of present-day amounts for most regions and seasons. The CRCM projections of surface air temperature changes are strongly affected by the internal variability of the driving GCM over high northern latitudes and to changes in the physical parameterizations over many regions for the summer season.

A new approach of coordinated global and regional climate modeling is presented. It is applied to the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) and its parent global climate model CanESM2. CanRCM4 was developed specifically to downscale climate predictions and climate projections made by its parent global model. The close association of a regional climate model (RCM) with a parent global climate model (GCM) offers novel avenues of model development and application that are not typically available to independent regional climate modeling centers. For example, when CanRCM4 is driven by its parent model, driving information for all of its prognostic variables is available (including aerosols and chemical species), significantly improving the quality of their simulation. Additionally, CanRCM4 can be driven by its parent model for all downscaling applications by employing a spectral nudging procedure in CanESM2 designed to constrain its evolution to follow any large-scale driving data. Coordination offers benefit to the development of physical parameterizations and provides an objective means to evaluate the scalability of such parameterizations across a range of spatial resolutions. Finally, coordinating regional and global modeling efforts helps to highlight the importance of assessing RCMs’ value added relative to their driving global models. As a first step in this direction, a framework for identifying appreciable differences in RCM versus GCM climate change results is proposed and applied to CanRCM4 and CanESM2.