Abstract: The skill of six regional climate models (RCMs) in reproducing short-term (24-yr), observed, near-surface temperature trends when driven by reanalysis is examined. The RCMs are part of the North American Regional Climate Change Assessment Program (NARCCAP). If RCMs can reproduce observed temperature trends, then they are, in a way, demonstrating their ability to capture a type of climate change, which may be relevant to their ability to credibly simulate anthropogenic climate changes under future emission scenarios. This study finds that the NARCCAP RCMs can simulate some resolved-scale temperature trends, especially those seen recently in spring and, by and large, in winter. However, results in other seasons suggest that RCM performance in this measure may be dependent on the type and strength of the forcing behind the observed trends.
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: There is a growing interest in quantifying the health impacts of climate change. This paper examines the risks of future ozone levels on non-accidental mortality across 19 urban communities in Southeastern United States. We present a modeling framework that integrates data from climate model outputs, historical meteorology and ozone observations, and a health surveillance database. We first modeled present-day relationships between observed maximum daily 8-hour average ozone concentrations and meteorology measured during the year 2000. Future ozone concentrations for the period 2041 to 2050 were then projected using calibrated climate model output data from the North American Regional Climate Change Assessment Program. Daily community-level mortality counts for the period 1987 to 2000 were obtained from the National Mortality, Morbidity and Air Pollution Study. Controlling for temperature, dew-point temperature, and seasonality, relative risks associated with short-term expos ure to ambient ozone during the summer months were estimated using a multi-site time series design. We estimated an increase of 0.43 ppb (95% PI: 0.14–0.75) in average ozone concentration during the 2040s compared to 2000 due to climate change alone. This corresponds to a 0.01% increase in mortality rate and 45.2 (95% PI: 3.26–87.1) premature deaths in the study communities attributable to the increase in future ozone level.
Abstract: This study aims to analyse the interannual variability simulated by several regional climate models (RCMs), and its potential for disguising the effect of sessonal temperature increases due to greenhouse gases. In order to accomplish this, we used an ensemble of regional climate change projections over North America belonging to the North American Regional Climate Change Program, with an additional pair of 140-year continuous runs from the Canadian RCM. We find that RCM-simulated inter-annual variability shows important departures from observed one in some cases, and also from the driving models? variability, while the expected climate change signal coincides with estimations presented in previous studies. The continuous runs from the Canadian RCM were used to illustrate the effect of interannual variability in trend estimation for horizons of a decade or more. As expected, it can contribute to the existence of transitory cooling trends over a few decades, embedded within the expected long-term warming trends. A new index related to signal-to-noise ratio was developed to evaluate the expected number of years it takes for the warming trend to emerge from interannual variability. Our results suggest that detection of the climate change signal is expected to occur earlier in summer than in winter almost everywhere, despite the fact that winter temperature generally has a much stronger climate change signal. In particular, we find that the province of Quebec and northwestern Mexico may possibly feel climate change in winter earlier than elsewhere in North America. Finally, we show that the spatial and temporal scales of interest are fundamental for our capacity of discriminating climate change from interannual variability.
Abstract: In recent decades, the need of future climate information at local scales have pushed the climate modelling community to perform increasingly higher resolution simulations and to develop alternative approaches to obtain fine-scale climatic information. In this article, various nested regional climate model (RCM) simulations have been used to try to identify regions across North America where high-resolution downscaling generates fine-scale details in the climate projection derived using the "delta method". Two necessary conditions were identified for an RCM to produce added value (AV) over lower resolution atmosphere-ocean general circulation models in the fine-scale component of the climate change (CC) signal. First, the RCM-derived CC signal must contain some non-negligible fine-scale information—independently of the RCM ability to produce AV in the present climate. Second, the uncertainty related with the estimation of this fine-scale information should be relatively small compared with the information itself in order to suggest that RCMs are able to simulate robust fine-scale features in the CC signal. Clearly, considering necessary (but not sufficient) conditions means that we are studying the "potential" of RCMs to add value instead of the AV, which preempts and avoids any discussion of the actual skill and hence the need for hindcast comparisons. The analysis concentrates on the CC signal obtained from the seasonal-averaged temperature and precipitation fields and shows that the fine-scale variability of the CC signal is generally small compared to its large-scale component, suggesting that little AV can be expected for the time-averaged fields. For the temperature variable, the largest potential for fine-scale added value appears in coastal regions mainly related with differential warming in land and oceanic surfaces. Fine-scale features can account for nearly 60% of the total CC signal in some coastal regions although for most regions the fine scale contributions to the total CC signal are of around ~5%. For the precipitation variable, fine scales contribute to a change of generally less than 15% of the seasonal-averaged precipitation in present climate with a continental North American average of ~5% in both summer and winter seasons. In the case of precipitation, uncertainty due to sampling issues may further dilute the information present in the downscaled fine scales. These results suggest that users of RCM simulations for climate change studies in a delta method framework have little high-resolution information to gain from RCMs at least if they limit themselves to the study of first-order statistical moments. Other possible benefits arising from the use of RCMs—such as in the large scale of the downscaled fields—were not explored in this research.
Abstract: Regional Climate Models (RCMs) have been developed in the last two decades in order to produce high-resolution climate information by downscaling Atmosphere-Ocean General Circulation Models (AOGCMs) simulations or analyses of observed data. A crucial evaluation of RCMs worth is given by the assessment of the value added compared to the driving data. This evaluation is usually very complex due to the manifold circumstances that can preclude a fair assessment. In order to circumvent these issues, here we limit ourselves to estimating the potential of RCMs to add value over coarse-resolution data. We do this by quantifying the importance of fine-scale RCM-resolved features in the near-surface temperature, but disregarding their skill. The Reynolds decomposition technique is used to separate the variance of the time-varying RCM-simulated temperature field according to the contribution of large and small spatial scales and of stationary and transient processes. The temperature variance is then approximated by the contribution of four terms, two of them associated with coarse-scales (e.g., corresponding to the scales that can be simulated by AOGCMs) and two of them describing the original contribution of RCM simulations. Results show that the potential added value (PAV) emerges almost exclusively in regions characterised by important surface forcings either due to the presence of fine-scale topography or land-water contrasts. Moreover, some of the processes leading to small-scale variability appear to be related with relatively simple mechanisms such as the distinct physical properties of the Earth surface and the general variation of temperature with altitude in the Earth atmosphere. Finally, the article includes some results of the application of the PAV framework to the future temperature change signal due to anthropogenic greenhouse gasses. Here, contrary to previous studies centred on precipitation, findings suggest for surface temperature a relatively low potential of RCMs to add value over coarser resolution models, with the greatest potential located in coastline regions due to the differential warming occurring in land and water surfaces.
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: This study presents a performance-based comprehensive weighting factor that accounts for the skill of different regional climate models (RCMs), including the effect of the driving lateral boundary condition coming from either atmosphere-ocean global climate models (AOGCMs) or reanalyses. A differential evolution algorithm is employed to identify the optimal relative importance of five performance metrics, and corresponding weighting factors, that include the relative absolute mean error (RAME), annual cycle, spatial pattern, extremes and multi-decadal trend. Based on cumulative density functions built by weighting factors of various RCMs/AOGCMs ensemble simulations, current and future climate projections were then generated to identify the level of uncertainty in the climate scenarios. This study selected the areas of southern Ontario and Quebec in Canada as a case study. The main conclusions are as follows: (1) Three performance metrics were found essential, having the great er relative importance: the RAME, annual variability and multi-decadal trend. (2) The choice of driving conditions from the AOGCM had impacts on the comprehensive weighting factor, particularly for the winter season. (3) Combining climate projections based on the weighting factors significantly increased the consistency and reduced the spread among models in the future climate changes. These results imply that the weighting factors play a more important role in reducing the effects of outliers on plausible future climate conditions in regions where there is a higher level of variability in RCM/AOGCM simulations. As a result of weighting, substantial increases in the projected warming were found in the southern part of the study area during summer, and the whole region during winter, compared to the simple equal weighting scheme from RCM runs. This study is an initial step toward developing a likelihood procedure for climate scenarios on a regional scale using equal or differ ent probabilities for all models.
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: The water cycle of the southwestern United States (SW) is dominated by winter storms that maintain a positive annual net precipitation. Analysis of the control and future climate from four pairs of regional and global climate models (RCMs and GCMs) shows that the RCMs simulate a higher fraction of transient eddy moisture fluxes because the hydrodynamic instabilities associated with flow over complex terrain are better resolved. Under global warming, this enables the RCMs to capture the response of transient eddies to increased atmospheric stability that allows more moisture to converge on the windward side of the mountains by blocking. As a result, RCMs simulate enhanced transient eddy moisture convergence in the SW compared to GCMs, although both robustly simulate drying due to enhanced moisture divergence by the divergent mean flow in a warmer climate. This enhanced convergence leads to reduced susceptibility to hydrological change in the RCMs compared to GCMs.
Abstract: This paper analyzes the ability of the North American Regional Climate Change Assessment Program (NARCCAP) ensemble of regional climate models to simulate extreme monthly precipitation and its supporting circulation for regions of North America, comparing 18 years of simulations driven by the National Centers for Environmental Prediction (NCEP)-Department of Energy (DOE) reanalysis with observations. The analysis focuses on the wettest 10% of months during the cold half of the year (October-March), when it is assumed that resolved synoptic circulation governs precipitation. For a coastal California region where the precipitation is largely topographic, the models individually and collectively replicate well the monthly frequency of extremes, the amount of extreme precipitation, and the 500-hPa circulation anomaly associated with the extremes. The models also replicate very well the statistics of the interannual variability of occurrences of extremes. For an interior region containing the upper Mississippi River basin, where precipitation is more dependent on internally generated storms, the models agree with observations in both monthly frequency and magnitude, although not as closely as for coastal California. In addition, simulated circulation anomalies for extreme months are similar to those in observations. Each region has important seasonally varying precipitation processes that govern the occurrence of extremes in the observations, and the models appear to replicate well those variations.
Abstract: An appropriate, rapid and effective response to extreme precipitation and any potential flood disaster is essential. Providing an accurate estimate of future changes to such extreme events due to climate change are crucial for responsible decision making in flood risk management given the predictive uncertainties. The objective of this article is to provide a comparison of dynamically downscaled climate models simulations from multiple model including 12 different combinations of General Circulation Model (GCM)–regional climate model (RCM), which offers an abundance of additional data sets. The three major aspects of this study include the bias correction of RCM scenarios, the application of a newly developed performance metric and the extreme value analysis of future precipitation. The dynamically downscaled data sets reveal a positive overall bias that is removed through quantile mapping bias correction method. The added value index was calculated to evaluate the models' simulations. Results from this metric reveal that not all of the RCMs outperform their host GCMs in terms of correlation skill. Extreme value theory was applied to both historic, 1980–1998, and future, 2038–2069, daily data sets to provide estimates of changes to 2– and 25–year return level precipitation events. The generalized Pareto distribution was used for this purpose. The Willamette River basin was selected as the study region for analysis because of its topographical variability and tendency for significant precipitation. The extreme value analysis results showed significant differences between model runs for both historical and future periods with considerable spatial variability in precipitation extremes.
Abstract: The elevated risk of collision while driving during precipitation has been well documented by the road safety community, with heavy rainfall events of particular concern. As the climate warms in the coming century, altered precipitation patterns are likely. The current study builds on the extensive literature on weather-related driving risks and draws on the climate change impact literature in order to explore the implications of climate change for road safety. It presents both an approach for conducting such analyses, as well as empirical estimates of the direction and magnitude of change in road safety for the highly urbanized Greater Vancouver metropolitan region on Canada's west coast. The signal that emerges from the analysis is that projections of greater rainfall frequency are expected to translate into higher collision counts by the mid 2050s. The greatest adverse safety impact is likely to be concentrated on moderate to heavy rainfall days (≥ 10 mm), which are associated with more highly elevated risks today. This suggests that particular attention should be paid to future changes in the frequency and intensity of extreme rainfall events.
Abstract: The authors analyze the ability of the North American Regional Climate Change Assessment Program's ensemble of climate models to simulate very heavy daily precipitation and its supporting processes, comparing simulations that used observation-based boundary conditions with observations. The analysis includes regional climate models and a time-slice global climate model that all used approximately half-degree resolution. Analysis focuses on an upper Mississippi River region for winter (December-February), when it is assumed that resolved synoptic circulation governs precipitation. All models generally reproduce the precipitation-versus-intensity spectrum seen in observations well, with a small tendency toward producing overly strong precipitation at high-intensity thresholds, such as the 95th, 99th, and 99.5th percentiles. Further analysis focuses on precipitation events exceeding the 99.5th percentile that occur simultaneously at several points in the region, yielding so-called "widespread events". Examination of additional fields shows that the models produce very heavy precipitation events for the same physical conditions seen in the observations.
Abstract: Annual maxima (AM) series of precipitation from 15 simulations of the North American Regional Climate Change Assessment Program (NARCCAP) have been analysed for gridpoints covering Canada and the northern part of United States. The NARCCAP Regional Climate Models' simulations have been classified into the following three groups based on the driving data used at the RCMs boundaries: (1) NCEP (6 simulations); (2) GCM-historical (5 simulations); and (3) GCM-future (4 simulations). Historical simulations are representative of the 1968-2000 period while future simulations cover the 2041-2070 period. A reference common grid has been defined to ease the comparison. Multi-model average intensities of AM precipitation of 6-, 12-, 24-, 72-, and 120-h for 2-, 5-, 10-, and 20-year return periods have been estimated for each simulation group. Comparison of results from NCEP and GCM-historical groups shows good overall agreement in terms of spatial distribution of AM intensities. Comparison of GCM-future and GCM-historical groups clearly shows widespread increases with median relative changes across all gridpoints ranging from 12 to 18% depending on durations and return periods. Fourteen Canadian climatic regions have been used to define regional projections and average regional changes in intense precipitation have been estimated for each duration and return period. Uncertainties on these regional values, resulting from inter-model variability, were also estimated. Results suggest that inland regions (e.g. Ontario and more specifically Southern Ontario, the Prairies, Southern Quebec) will experience the largest relative increases in AM intensities while coastal regions (e.g. Atlantic Provinces and the West Coast) will experience the smallest ones. These projections are most valuable inputs for the assessment of future impact of climate change on water infrastructures and the development of more efficient adaptation strategies.
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: 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 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.
Abstract: 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.
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.
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.
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: Understanding future changes in the frequency, intensity and duration of extreme events in response to increased greenhouse gas forcing is important for formulating adaptation and mitigation strategies that minimize damages to natural and human systems. We quantify transient changes in daily-scale seasonal extreme precipitation events over the U.S. using a 5-member ensemble of nested, high-resolution climate model simulations covering the 21st century in the IPCC SRES A1B scenario. We find a strong drying trend in annual and seasonal precipitation over the Southwest in autumn, winter and spring, and over the central U.S. in summer. These changes are accompanied by statistically significant increases in dry day frequency and dry spell lengths. Our results also show substantial increases in the frequency of extreme wet events over the northwestern U.S. in autumn, winter and spring, and the eastern U.S. in spring and summer. In addition, the average precipitation intensity incre ases relative to the extreme precipitation intensity in all seasons and most regions, with the exception of the Southeast. Therefore, most regions receive a greater fraction of total seasonal precipitation from extreme events. These results imply fewer but heavier precipitation events in the future, leading to more frequent wet and dry extremes in most regions of the U.S. Our simulations suggest that many of these changes are likely to become statistically significant by the mid-21st century. Given current vulnerabilities, such changes in extreme precipitation could be expected to increase stress on water resources in many areas of the U.S., including during the near-term decades.
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: Seasonal extreme daily precipitation is analyzed in the ensemble of NARCAPP regional climate models. Significant variation in these models' abilities to reproduce observed precipitation extremes over the contiguous United States is found. Model performance metrics are introduced to characterize overall biases, seasonality, spatial extent and the shape of the precipitation distribution. Comparison of the models to gridded observations that include an elevation correction is found to be better than to gridded observations without this correction. A complicated model weighting scheme based on model performance in simulating observations is found to cause significant improvements in ensemble mean skill only if some of the models are poorly performing outliers. The effect of lateral boundary conditions are explored by comparing the integrations driven by reanalysis to those driven by global climate models. Projected mid-century future changes in seasonal precipitation means and extremes are presented and discussions of the sources of uncertainty and the mechanisms causing these changes are presented.
Abstract: Decision support tools for agriculture often require meteorological data as inputs, but data availability and quality are often problematic. Difficulties arise with daily solar radiation (SRAD) because the instruments require electronic integrators, accurate sensors are expensive, and calibration standards are seldom available. NASA s Prediction of Worldwide Energy Resources (NASA/POWER; power.larc.nasa.gov) project estimates SRAD based on satellite observations and atmospheric parameters obtained from satellite observations and assimilation models. These data are available for a global 1° x 1° coordinate grid. The SRAD can also be generated from atmospheric attenuation of extraterrestrial radiation (Q0). We compared daily solar radiation data from NASA/POWER (SRADNP) with instrument readings from 295 stations (observed values of daily solar radiation, SRADOB) and values estimated by Weather Generator for Solar Radiation (WGENR) generator. Two sources of air temperature and precipitation records provided inputs to WGENR: the stations reporting solar data and the NOAA Cooperative Observer Program (COOP) stations. The resulting data were identified as solar radiation valaues obtained using the Weather Generator for Solar Radiation software in conjunction with daily weather data from the stations providing values of observed values of daily solar radiation (SRADWG) and solar radiation values obtained using the Weather Generator for Solar Radiation software in conjunction with daily weather data from NOAA COOP stations (SRADCO), respectively. Values of SRADNP for individual grid cells consistently showed higher correlations (typically 0.85 0.95) with SRADOB than did SRADWG or SRADCO. Mean values of SRADOB, SRADWG, and SRADNP for a grid cell usually were within 1 MJ m-2 d-1 of each other, but NASA/POWER values averaged 1.1 MJ m-2 d-1 lower than SRADOB. This bias increased at lower latitudes and during summer months and is partially explained by assumptions about ambient aerosol properties. The NASA/POWER solar data are a promising resource for studies requiring realistic accounting of historic variation.
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.