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In this paper, wintertime precipitation from a variety of observational datasets, regional climate models (RCMs), and general circulation models (GCMs) is averaged over the state of California (CA) and compared. Several averaging methodologies are considered and all are found to give similar values when model grid spacing is less than 3°. This suggests that CA is a reasonable size for regional intercomparisons using modern GCMs.

Results show that reanalysis-forced RCMs tend to significantly overpredict CA precipitation. This appears to be due mainly to overprediction of extreme events; RCM precipitation frequency is generally underpredicted. Overprediction is also reflected in wintertime precipitation variability, which tends to be too high for RCMs on both daily and interannual scales.

Wintertime precipitation in most (but not all) GCMs is underestimated. This is in contrast to previous studies based on global blended gauge/satellite observations which are shown here to underestimate precipitation relative to higher-resolution gauge-only datasets. Several GCMs provide reasonable daily precipitation distributions, a trait which doesn't seem tied to model resolution. GCM daily and interannual variability is generally underpredicted.

Abstract: Regional Climate Models (RCMs) constitute the most often used method to perform affordable highresolution regional climate simulations. The key issue in the evaluation of nested regional models is to determine whether RCM simulations improve the representation of climatic statistics compared to the driving data, that is, whether RCMs add value. In this study we examine a necessary condition that some climate statistics derived from the precipitation field must satisfy in order that the RCM technique can generate some added value: we focus on whether the climate statistics of interest contain some fine spatial-scale variability that would be absent on a coarser grid. The presence and magnitude of fine-scale precipitation variance required to adequately describe a given climate statistics will then be used to quantify the potential added value (PAV) of RCMs. Our results show that the PAV of RCMs is much higher for short temporal scales (e.g., 3-hourly data) than for long temporal scales (16-day average data) due to the filtering resulting from the time-averaging process. PAV is higher in warm season compared to cold season due to the higher proportion of precipitation falling from small-scale weather systems in the warm season. In regions of complex topography, theorographic forcing induces an extra component of PAV, no matter the season or the temporal scale considered. The PAV is also estimated using high-resolution datasets based on observations allowing the evaluation of the sensitivity of changing resolution in the real climate system. The results show that RCMs tend to reproduce relatively well the PAV compared to observations although showing an overestimation of the PAV in warm season and mountainous regions.

Abstract: We use Regional Climate Model (RCM) simulations from the North American Regional Climate Change Assessment Program (NARCCAP) to evaluate implications of climate change for the discharge of the Colorado River in the mid-21st century. We compare historical RCM simulations and simulations from their host global General Circulation Models (GCMs) to 1/8-degree gridded observations of precipitation, surface air temperature, and runoff (generated by the Variable Infiltration Capacity (VIC) land surface model forced with gridded observations) for the historical period 1970-1999. The RCMs capture the primary features of observations better than their host GCMs in part because of their ability to better represent strong upward lift in topographically complex regions. Although the RCMs do not significantly improve the simulation of precipitation, their ability to better represent surface temperature in mountainous regions has important effects on simulations of evapotranspiration, snowpack, and runoff. In the Colorado River basin, projected mid-21st century runoff changes are mostly impacted by the combination of snow cover change in winter, temperature change in spring, and precipitation change in summer. In particular, the response of cold-season temperatures in headwater streams is key to determining the basin's susceptibility to a warming climate. Due to the cooler temperature and higher snow line in RCMs relative to GCMs, the RCMs project less warming in the spring and thus generate smaller decreases in runoff, both during spring and annually, as compared with GCMs. Changes in surface air temperature, runoff, and snow water equivalent at high elevations all indicate that headwater streams of the Colorado River are less susceptible to a warming climate in climate change simulations that use RCMs than in simulations that use GCMs. Nonetheless, the 50-km NARCCAP grid resolution has some limitations in resolving orographic effects, which suggests that there may remain residual biases in the climatic sensitivity of the RCM simulations.

Abstract: There are two main uncertainties in determining future climate: the trajectories of future emissions of greenhouse gases and aerosols, and the response of the global climate system to any given set of future emissions [Meehl et al., 2007]. These uncertainties normally are elucidated via application of global climate models, which provide information at relatively coarse spatial resolutions. Greater interest in, and concern about, the details of climate change at regional scales has provided the motivation for the application of regional climate models, which introduces additional uncertainty [Christensen et al., 2007a]. These uncertainties in fine-scale regional climate responses, in contrast to uncertainties of coarser spatial resolution global models in which regional models are nested, now have been documented in numerous contexts [Christensen et al., 2007a] and have been found to extend to uncertainties in climate impacts [Wood et al., 2004; Oleson et al., 2007]. While European research in future climate projections has moved forward systematically to examine combined uncertainties from global and regional models [Christensen et al., 2007b], North American climate programs have lagged behind.

Abstract: The energy sector comprises approximately two-thirds of global total greenhouse gas emissions. For this and other reasons, renewable energy resources including wind power are being increasingly harnessed to provide electricity generation potential with negligible emissions of carbon dioxide. The wind energy resource is naturally a function of the climate system because the ?fuel? is the incident wind speed and thus is determined by the atmospheric circulation. Some recent articles have reported historical declines in measured near-surface wind speeds, leading some to question the continued viability of the wind energy industry. Here we briefly articulate the challenges inherent in accurately quantifying and attributing historical tendencies and making robust projections of likely future wind resources. We then analyze simulations from the current generation of regional climate models and show, at least for the next 50 years, the wind resource in the regions of greatest wind e nergy penetration will not move beyond the historical envelope of variability. Thus this work suggests that the wind energy industry can, and will, continue to make a contribution to electricity provision in these regions for at least the next several decades.

Abstract: This study analyzes mid-21st century projections of daily surface air minimum (Tmin) and maximum (Tmax) temperatures, by season and elevation, over the southern range of the Colorado Rocky Mountains. The projections are from four regional climate models (RCMs) that are part of the North American Regional Climate Change Assessment Program (NARCCAP). All four RCMs project 2C or higher increases in Tmin and Tmax for all seasons. However, there are much greater (>3C) increases in Tmax during summer at higher elevations and in Tmin during winter at lower elevations. Tmax increases during summer are associated with drying conditions. The models simulate large reductions in latent heat fluxes and increases in sensible heat fluxes that are, in part, caused by decreases in precipitation and soil moisture. Tmin increases during winter are found to be associated with decreases in surface snow cover, and increases in soil moisture and atmospheric water vapor. The increased moistening of the soil and atmosphere facilitates a greater diurnal retention of the daytime solar energy in the land surface and amplifies the longwave heating of the land surface at night. We hypothesize that the presence of significant surface moisture fluxes can modify the effects of snow-albedo feedback and results in greater wintertime warming at night than during the day.

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Future climate change is expected to alter the spatial and temporal distribution of surface wind speeds (SWS), with associated impacts on electricity generation from wind energy. However, the predictions for the direction and magnitude of these changes hinge critically on the assessment methods used. Many climate change impact analyses, including those focused on wind energy, use individual climate models and/or statistical downscaling methods rooted in historical observations. Such studies may individually suggest an unrealistically high level of scientific certainty due to the absence of competing projections (over the same region, time period, etc). A new public data archive, the North American Regional Climate Change Assessment Program (NARCCAP), allows for a more comprehensive perspective on regional climate change impacts, here applied to three wind farm sites in California.

We employ NARCCAP regional climate model data to estimate changes in SWS expected to occur in the mid-21st century at three wind farm regions: Altamont Pass, San Gorgonio Pass, and Tehachapi Pass. We examined trends in SWS magnitude and frequency using three different global/regional model pairs, focused on model evaluation, seasonal cycle, and long-term trends. Our results, while specific to California, highlight the opportunities and limitations in NARCCAP and other publicly available meteorological data sets for energy analysis, and the importance of using multiple models for climate change impact assessment. Although spatial patterns in current wind conditions agree fairly well among models and with NARR (North American Regional Reanalysis) data, results vary widely at our three sites of interest. This poor performance and model disagreement may be explained by complex topography, limited model resolution, and differences in model physics. Spatial trends and site-specific estimates of annual average changes (1980-2000 versus 2051-71) also differed widely across models. All models predicted changes of <2% at each site, but the direction of the change varies. However, decreases of <2% in resources at Altamont Pass are agreed upon by each NARCCAP model used. This lack of model agreement suggests uncertainty in future changes, and a potentially high degree of risk for future investors in wind-generated electricity. More broadly, our study highlights the need for multiple calculation approaches to help distinguish between robust and method-dependent results.

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The U.S. Fish and Wildlife Service is conducting a 12-month status review of the American pika (Ochotona princeps) in response to an initial review of a petition (CBO, 2008) seeking to protect the American pika under the Endangered Species Act (ESA) (see http://www.fws.gov/mountain-prairie/pressrel/09-34.html). The petition asserted that climate change is an important threat for the species. This report provides a rapid-response assessment of climate observations and projections of change in pika habitat, focusing on mountainous regions of the western United States. We summarize findings from peerreviewed studies, interpret downscaled climate projections, and present new graphics and data summaries derived from existing datasets. Knowledge about climate variability and change is rapidly evolving, so this report is a snapshot of the best available science as of mid-2009. The report provides a climatological context for the status review. Some of the results have not been published elsewhere, and further analysis is recommended. However, in the expert judgment of the authors, the major conclusions of this report are expected to be robust because of the large spatial scale of the observed and projected warming.

Abstract: Tropospheric ozone is one of the six criteria pollutants regulated by the US EPA under the Clean Air Act and has been linked with several adverse health effects, including mortality. Due to the strong dependence on weather conditions, ozone may be sensitive to climate change and there is great interest in studying the potential effect of climate change on ozone, and how this change may affect public health. In this paper we develop a Bayesian spatial model to predict ozone under different meteorological conditions, and use this model to study spatial and temporal trends and to forecast ozone concentrations under different climate scenarios. We develop a spatial quantile regression model that does not assume normality and allows the covariates to affect the entire conditional distribution, rather than just the mean. The conditional distribution is allowed to vary from site-to-site and is smoothed with a spatial prior. For extremely large data sets our model is computationally infeasible, and we develop an approximate method. We apply the approximate version of our model to summer ozone from 1997-2005 in the Eastern US, and use deterministic climate models to project ozone under future climate conditions. Our analysis suggests that holding all other factors fixed, an increase in daily average temperature will lead to the largest increase in ozone in the Industrial Midwest and Northeast.

Abstract: The hydrologic regime of the Lake Winnipeg watershed (LWW), Canada, is dominated by spring snowmelt runoff, often occurring over frozen ground. Analyses of regional climate models (RCMs) based on future climate projections presented in a companion paper of this special issue (Dibike et al., 2011) show future increases in annual precipitation and temperature in various seasons and regions of this catchment. Such changes are expected to influence the volume of snow accumulation and melt, as well as the timing and intensity of runoff. This paper presents results of modelling climate-induced hydrologic changes in two representative sub-catchments of the Red and Assiniboine basins in the LWW. The hydrologic model, Soil and Water Assessment Tool (SWAT), was employed to simulate a 21-year baseline (1980–2000) and future (2042–2062) climate based on climate forcings derived from 3 RCMs. The effects of future changes in climatic variables, specifically precipitation and temperature, are clearly evident in the resulting snowmelt and runoff regimes. The most significant changes include higher total runoff, and earlier snowmelt and discharge peaks. Some of the results also revealed increases in peak discharge intensities. Such changes will have significant implications for water availability and nutrient transport regimes in the LWW.

Abstract: Ground level ozone concentrations ([O3]) typically show a direct linear relationship with surface air temperature. Three decades of California measurements provide evidence of a statistically significant change in the ozone-temperature slope (ΔmO3-T ) under extremely high temperatures (>312 K). This ΔmO3-T leads to a plateau or decrease in [O3], reflecting the diminished role of nitrogen oxide sequestration by peroxyacetyl nitrates and reduced biogenic isoprene emissions at high temperatures. Despite inclusion of these processes in global and regional chemistry-climate models, a statistically significant change in ΔmO3-T has not been noted in prior studies. Future climate projections suggest a more frequent and spatially widespread occurrence of this ΔmO3-T response, confounding predictions of extreme ozone events based on the historically observed linear relationship.

Abstract: We evaluated the precipitation climatology of the Intermountain Region (IR) as generated by the six regional climate models of the North American Regional Climate Change Assessment Program (NARCCAP). A complex combination of the precipitation annual and semiannual cycles with their different phases form four major climate regimes over the IR. Each model produces systematic biases in the central IR where these different climate regimes meet. The simulated annual cycles are universally too strong, and the winter precipitation is too large. On the other hand, the semiannual cycles are relatively well produced. The strong annual cycles and the excess winter precipitation obscure the signals of spring/summer precipitation and may have led to false signals of the El Niño-Southern Oscillation (ENSO) found in the central IR. Therefore, caution is advised when interpreting the simulated NARCCAP precipitation for the IR.

Abstract: The potential expansion of biofuel production raises food, energy, and environmental challenges that require careful assessment of the impact of biofuel production on greenhouse gas (GHG) emissions, soil erosion, nutrient loading, and water quality. In this study, we describe a spatially-explicit integrative modeling framework (SEIMF) to understand and quantify the environmental impacts of different biomass cropping systems. This SEIMF consists of three major components: 1) a geographic information system (GIS)-based data analysis system to define spatial modeling units with resolution of 56 m to address spatial variability, 2) the biophysical and biogeochemical model EPIC (Environmental Policy Integrated Climate) applied in a spatially-explicit way to predict biomass yield, GHG emissions, and other environmental impacts of different biofuel crops production systems, and 3) an evolutionary multi-objective optimization algorithm for exploring the trade-offs between biofuel energy production and unintended ecosystem-service responses. Simple examples illustrate the major functions of the SEIMF when applied to a 9-county Regional Intensive Modeling Area (RIMA) in SW Michigan to 1) simulate biofuel crop production, 2) compare impacts of management practices and local ecosystem settings, and 3) optimize the spatial configuration of different biofuel production systems by balancing energy production and other ecosystem-service variables. Potential applications of the SEIMF to support life cycle analysis and provide information on biodiversity evaluation and marginal-land identification are also discussed. The SEIMF developed in this study is expected to provide a useful tool for scientists and decision makers to understand sustainability issues associated with the production of biofuels at local, regional, and national scales.

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