Publications

Exploring Linkages between Consumer Food Co-operatives and Domestic Fair Trade in the United States

Leslie Duram — Tue, 15 Jan 2013 12:14:31 PST

Consumer Food Co-operatives (co-ops) have provided consumers an alternative to corporate supermarkets and big-box stores since the 1960s. Producers seeking broader marketing opportunities often turn to co-ops. This study examines how, within Alternative Food Networks (AFNs), co-ops play a role in the emergence of the Domestic Fair Trade movement in the US. The Domestic Fair Trade (DFT) movement is based on the idea that family farms and small- to mid-sized farms in the global north are facing many of the same pressures as producers in the global south. The Domestic Fair Trade Association (DFTA) in the United States is the umbrella organization for a variety of stakeholders. The DFTA seeks to "support family-scale farming, to reinforce farmer-led initiatives such as farmer co-operatives, and to bring these groups together with mission-based traders, retailers and concerned consumers to contribute to the movement for sustainable agriculture in North America" (1). This study assessed five co-ops (through interviews and document analysis) to determine their experiences with integrating DFT into their business practices. The research reveals that DFT concepts are important to co-operative decision-makers, but they are faced with challenges when it comes to actually integrating DFT into their business model. Insight into stakeholder perceptions and professional level DFT activities, indicates that co-ops will be a key factor is whether the DFT movement will succeed in the US.

Information Dissemination in Alternative Agricultural Research: An Analysis of Researchers in the North Central Region

K L. Larson et al. — Thu, 25 Oct 2012 09:39:56 PDT

Agricultural research and education significantly influence the direction of U.S. agriculture by improving the practices available to farmers and by decreasing uncertainties associated with adopting new farming practices. Because sustainable agriculture is management-intensive, access to information is particularly important in adopting and implementing sustainable farming practices. Given that relatively little funding is allocated to sustainable agriculture research by the federal government, successful dissemination of these research results is critical. This paper presents an analysis of the dissemination efforts of 42 researchers funded through the USDA's North Central Region Sustainable Agriculture Research and Education (SARE) program. Results show that these SARE researchers purposefully consider the effectiveness of various dissemination methods in reaching targeted audiences and attempt to involve farmers in their dissemination efforts. Overall, researchers note that information dissemination is limited by farmer interest. Additional barriers exist, most notably insufficient resources and institutional biases. In the future, the ways in which information is compiled and made available must be improved, and responsibility for farmer outreach should be better coordinated.

Factors in Organic Farmers' Decisionmaking: Diversity, Challenge, and Obstacles

Leslie Duram — Thu, 25 Oct 2012 09:39:55 PDT

This research promotes our understanding of organic farmers' decisionmaking through individual farmers' experiences. A twofold survey was conducted to investigate characteristics of certified organic farmers in Colorado. Data from a mail survey (26 responses to 49 surveys sent) reveal patterns of farm operations and attitudes among this group of farmers. These questionnaires focused on land use, land tenure, operational change, and personal characteristics. In-depth interviews of five case study farmers provide additional insight into farmers' agricultural decisionmaking. These interviews were conversations that the farmers guided toward topics of relevance to them. Taken together, the mail and interview surveys provided information about on-farm operational factors and personal characteristics. Quantitative analysis and qualitative data reduction techniques were used to identify factors in organic farmer decisionmaking. The following eight factors help us understand organic agriculture in this region: diversity, challenge, change, businesslike approach, no formal agricultural education, love of the land, anti-”radical environmentalist,” and obstacles.

Taking a Pragmatic Behavioral Approach to Alternative Agriculture Research

Leslie Duram — Thu, 25 Oct 2012 09:39:53 PDT

This article seeks to stimulate thought on the philosophy behind agricultural research. Pragmatism is identified as a philosophical basis for studying environmental issues that focus on human behavior. The ways in which this approach is applicable to the study of alternative agriculture are illuminated. “Behavioral pragmatists” differ from “behavioral positivists” in their aim, focus, process, and approach to research. I describe the main goals of the pragmatic behavioral approach: accepting a systems approach to study the interrelationships between humans and the environment; gaining understanding through human experiences; viewing problems as whole complex “problematic situations”; and promoting social activism and appropriate policy formulation. Combining qualitative and quantitative methods is often most effective. Pragmatism allows for holistic analysis that incorporates numerous factors that influence human uses of the environment. A specific example shows how behavioral pragmatism is effective in research on alternative agriculture.

A Framework to Assess State Support of Certified Organic Farming

Shauna M. Bloom et al. — Thu, 25 Oct 2012 09:39:52 PDT

Support for organic farming varies from state to state, and there have been few attempts to document what types of support currently exists. This research assesses regionally specific and relevant support available to organic farmers at the state level. This exploratory study develops a framework of ten key categories of organic agricultural support: leadership, policy, research, technical support, financial support, marketing and promotion, education and information, consumer issues, inter-agency activities, and future developments. Data from state departments of agriculture, land grant universities, extension services, and other state-level agencies provide the basis for a numerical assessment of support in each category. State assessments are based on the number of activities, availability of information, and attention from personnel for each of the ten categories. A pilot study of Minnesota and Illinois was conducted to verify the utility of the framework and to explore the variation of support available within a region. This assessment framework is a valuable tool for farmers, researchers, state agencies, and citizen groups seeking to document existing types of organic agricultural support and discover topics that need more attention.

Assessing the U.S. Watershed Management Movement: National Trends and an Illinois Case Study

Leslie Duram et al. — Thu, 25 Oct 2012 09:39:51 PDT

Local watershed planning is an increasingly important component of environmental management. This article provides an overview of local watershed management efforts occurring across the US and then focuses on one case study from southern Illinois. First, analysis of 1145 local watershed groups shows that government agencies, farmers, and rural residents are key stakeholders in local groups that primarily deal with regional environmental stressors in the form of soil erosion, nutrients, and agrichemicals. Second, the role of watershed partnerships in mediating the complex interactions among stakeholders and local water resources are investigated through a case study of one watershed group. The Cache River is located in the far-southern tip of Illinois and a watershed planning process was initiated there in the 1990s. An in-depth assessment of this watershed planning process was accomplished through participant interviews, stakeholder focus groups, and a regional telephone survey. This case study illuminates how different stakeholder groups have varying perceptions as to the efficacy and success of watershed management plans.

Agents’ Perceptions Of Structure: How Illinois Organic Farmers View Political, Economic, Social, and Ecological Factors

Leslie Duram — Thu, 25 Oct 2012 09:39:50 PDT

Various structural factors influence organic farmer decision-making. Analyses that combine structure and agency provide an opportunity for understanding farmers' perceptions of the political, economic, and social "world" in which they operate. Rich conversational interviews, conducted with twenty certified organic farmers in Illinois and analyzed with multiple qualitative methods, show how farmers mediate structural concerns. In addition to political, economic, and social structures, a fourth structure is needed. Indeed these organic farmers emphasize the importance of ecological factors in their decision-making. Within the perceived economic, political, social, and ecological structures, numerous topics (i.e., marketing, policy, family, ecosystems) and subtopics (i.e., diversification, farm programs, traditions, soils) exist. Farmers' quotations provide detailed information of how they view and mediate structures in their daily on-farm decision-making.

Insights and Applications Assessing Public Participation in U.S. Watershed Planning Initiatives

Leslie Duram et al. — Thu, 25 Oct 2012 09:39:49 PDT

A mail survey of 126 federally funded watershed planning initiatives yielded valid responses from 64 watershed contacts. Quantitative analysis revealed wide variation among watershed initiatives in terms of population size and land area encompassed. Likewise, watershed organization and participation characteristics ( agencies involved, frequency of meetings, and number of active participants) vary greatly. Qualitative analysis delineated the key issues of concern to watershed contacts: agricultural land use, stakeholder awareness, and interaction between local and federal entities. While specific situations vary by watershed, results indicate that door-to-door contact, public meetings, and information programs are the most useful methods for soliciting participation. Participation was perceived to be most helpful in the planning stages of outreach, identifying issues, and prioritizing issues. The perceived effects of participatory watershed planning include increasing awareness of watershed conditions, heightening interagency coordination, reaching consensus on resource management plans, and lending legitimacy to final plans.

A Pragmatic Assessment of Government Support for Organic Agriculture in Ireland

Leslie Duram — Thu, 25 Oct 2012 09:39:47 PDT

Drawing on a pragmatic approach, this paper provides an analysis of governmental support for organic farming in Ireland. There are varying levels of encouragement and programmes provided to farmers in their conversion from conventional to organic production, and in their maintenance of organic production. Support policies vary across regions and are linked to European Union legislation, thus it is challenging to document the many types of support in place. This research investigates relevant technical, financial, and policy support available to organic farmers in Ireland. This exploratory study develops an assessment of Ireland within eight key categories of organic agricultural support: leadership, policy, research, technical support, financial support, marketing and promotion, education and information, and future developments. Information and data from the Irish Department of Agriculture, Fisheries and Food (DAFF), the Irish Agriculture and Food Development Authority (Teagasc), and other governmental and semi-governmental agencies were utilized to assess the level of support in each category. Following the pragmatic approach, this assessment provides key findings which allow policymakers, organizations and citizens to better understand the current situation and set a path for the future development of organic farming in Ireland.

A Geographic Approach to Place and Natural Resource Use in Local Food Systems

Leslie Duram et al. — Tue, 09 Oct 2012 08:53:30 PDT

This article illuminates the geographic concept of ‘place’ in local foods. Because the social aspects of local food have been more fully addressed in previous literature, this review focuses instead on the ecological aspects of farming and food. First, the literature on natural resource use in agriculture provides contextual understanding of water use, biodiversity, soils and agro-ecological methods. The complex relationship between climate change and agriculture is described and models assessing the impacts of climate change on agriculture are detailed. The geography of local food is specifically addressed by describing methods for assessing natural resource use in local food, including food miles, consumer transportation, scale and community, agricultural methods and diet. Finally, future research paths are suggested to provide a comprehensive evaluation of the environmental impact of local food. Such research would encompass the geography of local food through development of broader, more inclusive strategy, including the concept of the ‘ecological appetite’ of crops and foods, the union of both social and ecological aspects of resource use, the linkages between rural and urban producers and consumers and the inclusion of farmers’ ecological knowledge. Overall, the geography of local food seeks to assess the where of food production and consumption, while incorporating key issues of how (agro-ecological methods benefiting the community) and what (locally appropriate crops).

Irish Chefs and Restaurants in the Geography of “Local” Food Value Chains

Leslie Duram et al. — Tue, 25 Sep 2012 09:30:36 PDT

Restaurant chefs and owners have a potentially influential role to play in promoting the use of local foods and supporting producers. Indeed chefs are taste makers, and their conceptualization of local food can influence consumers’ perceptions, knowledge and geographical awareness. Yet their role is not well documented in research literature. In-depth interviews were conducted with seven purposively selected chefs in Galway City, Ireland, who are seeking to develop a local cuisine. Menus, websites, and statements of philosophy were also analyzed. Attention focused on the definition of local food, sources of supply, how local food is used in cuisine, and how it is presented on menus. Common themes among restaurants include the geographic stretching of local food to include artisan products, issues of seasonality and variability in supply, and questions about the authenticity of promoting the use of local foods. There are opportunities for building linkages between chefs and local producers, to promote authentic use of locally sourced foods. Recommendations are made for five strategies to build a sustainable local food geography, based on strengthening the farmer- to-chef shortened value chain.

Changes in the Seasonality of Precipitation over the Contiguous USA

S C. Pryor et al. — Mon, 20 Sep 2010 11:54:43 PDT

Consequences of possible changes in annual total precipitation are dictated, in part, by the timing of precipitation events and changes therein. Herein, we investigated historical changes in precipitation seasonality over the US using observed station precipitation records to compute a standard seasonality index (SI) and the day of year on which certain percentiles of the annual total precipitation were achieved (percentile day of year). The mean SI from the majority of stations exhibited no difference in 1971–2000 relative to 30-year periods earlier in the century. However, analysis of the day of year on which certain percentiles of annual total precipitation were achieved indicated spatially coherent patterns of change. In some regions, the mean day of the year on which the 50th percentile of annual precipitation was achieved differed by 20–30 days between 1971–2000 and both 1911–1940 and 1941–1970. Output from the 10-Atmosphere-Ocean General Circulation Models (AOGCM) simulations of 1971–2000, 2046–2065, and 2081–2100 was used to determine whether AOGCMs are capable of representing the seasonal distribution of precipitation and to examine possible future changes. Many of the AOGCMs qualitatively captured spatial patterns of seasonality during 1971–2000, but there was considerable divergence between AOGCMs in terms of future changes. In both the west and southeast, 7 of 10 AOGCMs indicated later attainment of the 50th percentile accumulation in 2047–2065, implying a possible reversal of the twentieth-century tendency toward relative increases in precipitation receipt during winter and early spring over the southeast. However, this is also a region characterized by considerable interannual variability in the percentile day of year during the historical period.

Dynamically and Statistically Downscaled Seasonal Temperature and Precipitation Hindcast Ensembles for the Southeastern USA

Justin T. Schoof et al. — Mon, 14 Dec 2009 11:53:05 PST

We present results from a 15-year 10-member warm season (March–September) hindcast ensemble of maximum and minimum surface air temperatures and precipitation in southeast USA. The hindcasts are derived from the Florida State University/Center for Ocean-Atmospheric Prediction Studies Global Spectral Model (FSU/COAPS GSM) and downscaled using both the FSU/COAPS Nested Regional Spectral Model (NRSM) and a statistical downscaling method based on stochastic weather generator techniques. We additionally consider statistical bias correction of the dynamical model output. Basic descriptive statistics indicate that the bias-corrected and statistically downscaled data reduce the FSU/COAPS GSM bias considerably in terms of basic climatology. Statistics describing the daily precipitation process are improved by both downscaling techniques relative to the bias-corrected GSM. Improvement in monthly and seasonal hindcasts relative to FSU/COAPS GSM is spatially and temporally varying. Precipitation hindcasts are generally less skillful than those for temperature, although useful precipitation predictability exists at many locations. Hindcast improvements due to downscaling are greatest over peninsular Florida. The smallest root mean square errors (RMSE) for temperature hindcasts are found in the southern part of the study region during the spring months of March, April and May (MAM) for maximum surface air temperature, and in the summer, June, July and August (JJA), for minimum surface air temperature. Overall, there is no indication that either downscaling method has a direct advantage over the other.

Empirical Downscaling of Wind Speed Probability Distributions

S C. Pryor et al. — Wed, 01 Oct 2008 07:16:26 PDT

This paper presents a novel approach to developing empirically downscaled estimates of near-surface wind speed and energy density and results from application of the technique to multiple stations in northern Europe. The downscaling takes a probabilistic approach in that it uses the mean and standard deviation of relative vorticity at 500 hPa and mean sea level pressure gradients computed using output from the ECHAM4/OPYC3 atmosphere-ocean general circulation model as the predictors and parameters of the wind speed probability distribution at surface stations as the predictands.We demonstrate that this approach generates accurate depictions of the wind climate during the conditioning period and then apply the downscaling technique to examine changes between 1961–1990 and 2071–2100, which are compared to the results of dynamical downscaling. The empirically downscaled results for 1961–1990 and 2071–2100 show some evidence for small decreases in mean wind speed, 90th percentile wind speed, and energy density in 2071–2100 relative to 1961–1990. The projected changes are larger than the mean errors in the training period but smaller than current interannual variability. Rossby Centre regional climate model (RCM)–derived grid cell mean wind speeds exhibit a high degree of agreement with the empirically downscaled station wind speeds. However, in contrast to the empirical downscaling, simulations conducted using the Rossby Centre RCM indicate evidence for a small increase in the annual wind energy resource over northern Europe between the end of the 20th century and the end of the 21st century.

Downscaling Temperature and Precipitation: A Comparison of Regression-Based Methods and Artificial Neural Networks

Justin T. Schoof et al. — Wed, 01 Oct 2008 07:16:22 PDT

A comparison of two statistical downscaling methods for daily maximum and minimum surface air temperature, total daily precipitation and total monthly precipitation at Indianapolis, IN, USA, is presented. The analysis is conducted for two seasons, the growing season and the non-growing season, defined based on variability of surface air temperature. The predictors used in the downscaling are indices of the synoptic scale circulation derived from rotated principal components analysis (PCA) and cluster analysis of variables extracted from an 18-year record from seven rawinsonde stations in the Midwest region of the United States. PCA yielded seven significant components for the growing season and five significant components for the non-growing season. These PCs explained 86% and 83% of the original rawinsonde data for the growing and non-growing seasons, respectively. Cluster analysis of the PC scores using the average linkage method resulted in eight growing season synoptic types and twelve non-growing synoptic types. The downscaling of temperature and precipitation is conducted using PC scores and cluster frequencies in regression models and artificial neural networks (ANNs).

Regression models and ANNs yielded similar results, but the data for each regression model violated at least one of the assumptions of regression analysis. As expected, the accuracy of the downscaling models for temperature was superior to that for precipitation. The accuracy of all temperature models was improved by adding an autoregressive term, which also changed the relative importance of the dominant anomaly patterns as manifest in the PC scores. Application of the transfer functions to model daily maximum and minimum temperature data from an independent time series resulted in correlation coefficients of 0.34–0.89. In accord with previous studies, the precipitation models exhibited lesser predictive capabilities. The correlation coefficient for predicted versus observed daily precipitation totals was less than 0.5 for both seasons, while that for monthly total precipitation was below 0.65. The downscaling techniques are discussed in terms of model performance, comparison of techniques and possible model improvements.

An Evaluation of Two GCMs: Simulation of North American Teleconnection Indices and Synoptic Phenomena

Justin T. Schoof et al. — Tue, 30 Sep 2008 14:27:51 PDT

We evaluate the ability of two coupled atmospheric–oceanic GCMs – the Hadley Center’s third generation coupled climate model (HadCM3) and the Canadian Center for Climate Modeling and Analysis second-generation coupled model (CGCM2) – to simulate the North Atlantic Oscillation (NAO), the Pacific North American teleconnection pattern (PNA), and map patterns in the Midwest region of the United States, relative to NCEP/NCAR reanalysis (NNR) data. The observed (NNR-derived) and GCM-derived probability distributions and temporal behavior of the daily teleconnection indices exhibit agreement over the 1990–2001 reference period, and both GCMs successfully reproduce the range of 500-hPa map patterns over the study region. During the reference period, observed and modeled map patterns are similar in terms of frequency, coherence, persistence, and progression, although the most common map pattern occurs too often in HadCM3 relative to NNR and CGCM2-derived map patterns generally exhibit closer agreement with those derived from NNR data. Despite the relatively high degree of correspondence between the observed and simulated teleconnection indices and map patterns in the study area, differences between the GCM and NNR-derived map-pattern frequencies in the reference period are greater than either (1) recent historical changes in map-pattern frequencies or (2) changes in the mappattern frequencies as derived from twenty-first century GCM simulations, indicating that changes in these phenomena over recent and approaching decades are of insufficient magnitude relative to model uncertainty to be definitively identified.

The Impact of Non-Stationarities in the Climate System on the Definition of "A Normal Wind Year": A Case Study from the Baltic

S C. Pryor et al. — Tue, 30 Sep 2008 14:27:49 PDT

Wind speeds over the Baltic significantly increased over the second half of the 20th century (C20th), with the majority of the increase being focused on the upper quartile of the wind speed distribution and in the southwest of the region. These changes have potentially profound implications for the wind energy resource. For example, based on the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data it is shown that, owing to this non-stationarity, using the normalization period of 1987–98 to determine the wind resource (as in the Danish wind index) leads to overestimation of the wind energy index (and hence the wind energy resource) in western Denmark relative to 1958–2001 by approximately 10%. To address whether the increased prevalence of high wind speeds at the end of the C20th will be maintained in the future, we provide a first prognosis of annual wind indices from the HadCM3 coupled atmosphere–ocean general circulation model. The results suggest the 21st century (C21st) will be similar to the 1958–2001 period with respect to the wind energy density, but that the northeastern Baltic will exhibit slightly higher wind energy indices over the course of the C21st relative to the latter half of the C20th, whereas the southwest of the Baltic exhibits some evidence of declining wind indices towards the end of the C21st. These changes may indicate a tendency in HadCM3 towards more northerly tracking of mid-latitude cyclones in the future, possibly due to evolution of the North Atlantic oscillation. As a caveat to this finding, it should be noted that the NCEP–NCAR and European Centre for Medium-Range Weather Forecasts reanalysis data sets and HadCM3 simulations, although exhibiting commonalities during the period of overlap, differ quantitatively in terms of the spatial fields and empirical cumulative probability distributions at individual grid cells.

Evaluation of the NCEP/NCAR Reanalysis in Terms of Synoptic Scale Phenomea: A Case Study from the Midwestern USA

Justin T. Schoof et al. — Tue, 30 Sep 2008 14:27:46 PDT

We evaluate the ability of the National Centers for Environmental Prediction (NCEP)–National Center for Atmosphere Research (NCAR) reanalysis to represent the synoptic-scale climate of the Midwestern USA relative to radiosonde data. Independent, automated synoptic classifications, based on rotated principal component analysis (PCA) of 500 hPa geopotential heights, 850 hPa air temperatures, and 200 hPa wind speeds and a two-step clustering algorithm, result in a 15-type NCEP–NCAR synoptic classification and a 14-type radiosonde classification. The classifications are examined in terms of similarities and differences in the modes of variance manifest in the PCA solutions, the spatial patterns and variability of input variables within each weather type, and the temporal variability of the occurrence of each weather type. The classifications are then compared in terms of these characteristics and the degree of mutual class occupancy. Although the classifications identify a number of the same weather types (in terms of the input data, PCA solution, and mutual occupancy), the correspondence is imperfect. To assess whether the differences in the classifications are due to errant assignment of data to clusters or to differences in the fundamental modes present in the data sets as represented by the PC loadings and scores, a third targeted classification is undertaken that categorizes the NCEP–NCAR reanalysis data according to the radiosonde PCA solution. This classification exhibits a higher degree of similarity to that derived using the radiosonde data (in terms of both interpretability and mutual class occupancy), but the solutions still exhibit considerable differences. It is probable that the discrepancies are partly a function of the differing data structures and densities, but they may also reflect differences in the intensity of synoptic-scale phenomena as manifest in the data sets.

Dynamically and Statistically Downscaled Seasonal Simulations of Maximum Surface Air Temperature Over the Southeastern United States

Young-Kwon Lim et al. — Tue, 30 Sep 2008 14:01:29 PDT

Coarsely resolved surface air temperature (2 m height) seasonal integrations from the Florida State University/Center for Ocean-Atmospheric Prediction Studies Global Spectral Model (FSU/COAPS GSM) (~1.8º lon.-lat. (T63)) for the period of 1994 to 2002 (March through September each year) are downscaled to a fine spatial scale of ~20 km. Dynamical and statistical downscaling methods are applied for the southeastern United States region, covering Florida, Georgia, and Alabama. Dynamical downscaling is conducted by running the FSU/COAPS Nested Regional Spectral Model (NRSM), which is nested into the domain of the FSU/COAPS GSM. We additionally present a new statistical downscaling method. The rationale for the statistical approach is that clearer separation of prominent climate signals (e.g., seasonal cycle, intraseasonal, or interannual oscillations) in observation and GSM, respectively, over the training period can facilitate the identification of the statistical relationship in climate variability between two data sets. Cyclostationary Empirical Orthogonal Function (CSEOF) analysis and multiple regressions are trained with those data sets to extract their statistical relationship, which eventually leads to better prediction of regional climate from the large-scale simulations. Downscaled temperatures are compared with the FSU/COAPS GSM fields and observations. Downscaled seasonal anomalies exhibit strong agreement with observations and a reduction in bias relative to the direct GSM simulations. Interannual temperature change is also reasonably simulated at local grid points. A series of evaluations including mean absolute errors, anomaly correlations, frequency of extreme events, and categorical predictability reveal that both downscaling techniques can be reliably used for numerous seasonal climate applications.

Downscaling Daily Maximum and Minimum Air Temperature in the Midwestern USA: A Hybrid Empirical Approach

Justin T. Schoof et al. — Tue, 30 Sep 2008 13:29:45 PDT

A new hybrid empirical downscaling technique is presented and applied to assess 21st century projections of maximum and minimum daily surface air temperatures (Tmax, Tmin) over the Midwestern USA. Our approach uses multiple linear regression to downscale the seasonal variations of the mean and standard deviation of daily Tmax and Tmin and the lag-0 and lag-1 correlations between daily Tmax and Tmin based on GCM simulation of the large-scale climate. These downscaled parameters are then used as inputs to a stochastic weather generator to produce time series of the daily Tmax and Tmin at 26 surface stations, in three time periods (1990–2001, 2020–2029, and 2050–2059) based on output from two coupled GCMs (HadCM3 and CGCM2). The new technique is demonstrated to exhibit better agreement with surface observations than a transfer-function approach, particularly with respect to temperature variability. Relative to 1990–2001 values, downscaled temperature projections for 2020–2029 indicate increases that range (across stations) from 0.0 K to 1.7 K (Tmax) and 0.0 K to 1.5 K (Tmin), while increases for 2050–2059 relative to 1990–2001 range from 1.4 K to 2.4 K (Tmax) and 0.8 to 2.2K (Tmin). Although the differences between GCMs demonstrate the continuing uncertainty of GCM-based regional climate downscaling, the inclusion of weather-generator parameters represents an advancement in downscaling methodology.