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.

Transferability intercomparisons provide a new approach for advancing the science of modeling the water cycle and energy budget on regional to global scales by using multiple limited-area models applied to multiple domains. The water and associated energy cycles introduce exponential, episodic, and other nonlinear processes that create difficulties for observing, simulating, and predicting climate variations. The water cycle both creates and responds to spatial heterogeneities that feed back strongly on the energy budget and circulation system. These feedback processes represent some of the largest uncertainties in our ability to simulate future scenarios of Earths climate, especially scenarios that suggest warming beyond the temperature bounds of recent interglacial conditions and hence for which we have no previous observations for comparison. Water cycle processes also occur on a wide range of spatial and temporal scales, many being far too small to either be globally observed and or simulated by global climate and weather forecast models.Transferability intercomparions represent a new approach for understanding the water cycle and energy budget on regional to global scales. This new class of intercomparisons applies multiple regional climate models to a prescribed collection of domains where enhanced observations are conducted and results are archived in a coordinated manner. The primary goals of the transferability intercomparisons are to understand the complex interactions forming the water cycle and evaluate our ability to simulate these processes. The transferability framework goes

Regional‐level recurring spatial patterns of yield variability are important for commercial activities, strategic agricultural planning, and public policy, but little is known about the factors contributing to their formation. An important step to improve our understanding is recognizing regional spatial patterns of yield variability in association with regional environmental characteristics. We examined the spatial distribution of county‐level mean yields and CVs of mean yields of four functionally different crops—corn (Zea mays L.), soybean [Glycine max (L.) Merr.], alfalfa (Medicago sativa), and oat (Avena sativa L.)—in Iowa using Moran's Index of spatial autocorrelation. Patterns of association with 12 county‐level climatic, edaphic, and topographic environmental characteristics were examined using partial least squares regression. Two distinct geographic provinces of yield stability were identified: one in the northern two‐thirds of the state characterized by high mean yields and high yield constancy, and one in the southern third of the state characterized by low mean yields and low yield constancy. Among eight partial least squares regression models, which explained 50 to 81% of variation of mean yields and yield CVs, mean organic matter and mean depth to seasonally high water table had greatest relative importance to mean yields of grass crops and legume crops, respectively. Among the CV models, variables describing water availability were of greatest relative importance, with less distinct differences between grass and legume crops. Partial least squares regression is a potentially powerful tool for understanding regional yield variability.

Hydrologists are increasingly using numerical weather forecasting products as an input to their hydrological models. These products are often generated on relatively coarse scales compared with hydrologically relevant basin units and suffer systematic biases that may have considerable impact when passed through the nonlinear hydrological filters. Therefore, the data need processing before they can be used in hydrological applications. This manuscript summarises discussions and recommendations of the first workshop on Postprocessing and Downscaling Atmospheric Forecasts for Hydrologic Applications held at Meteo France, Toulouse, France, 15-18 June 2008. The recommendations were developed by work groups that considered the following three areas of ensemble prediction: (1) short range (0-2 days), (2) medium range (3 days to 2 weeks), and (3) sub-seasonal and seasonal (beyond 2 weeks).