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Though a small fraction of the US citizenry, agricultural producers are directly responsible for the stewardship of almost half of the country's land. This group is therefore an especially important one to understand from the standpoint of how they process and respond to science as it relates to agroecological phenomena. Data from a sample (n = 111) of farmers and ranchers located in the US state of Colorado are used to expand our understanding of how food producers process scientific claims. These insights, I argue, help us think through public understandings of science more generally. Using semi-structured interviews, the paper unpacks an identified asymmetry in how respondents perceive climate science and the science associated with genetically modified food and seed. These tensions are interrogated with the help of a novel methodological design that generated data converted to shading matrices—also known as heat maps. The heat maps illuminate certain cultural values among respondents, which were reinforced by motivated reasoning. This allows for an interrogation of tensions and inconsistencies in respondents' remarks about a variety of scientific claims. The heat maps, coupled with the qualitative data, allow for an exploration into how respondents perceive certain salient socio-technical issues. [ABSTRACT FROM AUTHOR]

Average cloud-to-ground (CG) lightning flash density values for Colorado are analyzed for the 21-yr period 1996-2016. An annual mean map and monthly mean maps of flash density provide insight into the thunderstorm/lightning climatology over the complex physical landscapes of Colorado. Findings include that 1) the Denver convergence/vorticity zone regional circulation influences the CG lightning distribution across the northeastern Colorado region; 2) moisture associated with the North American monsoon increases CG lightning over the entire state, focusing along the southern exposures of the San Juan Mountains; and 3) the highest concentrations of CG lightning occur where moisture, lift, and instability are maximized. [ABSTRACT FROM AUTHOR]

Managers are increasingly being asked to integrate climate change adaptation into public land management. The literature discusses a range of adaptation approaches, including managing for resistance, resilience, and transformation; but many strategies have not yet been widely tested. This study employed in-depth interviews and scenario-based focus groups in the Upper Gunnison Basin in Colorado to learn how public land managers envision future ecosystem change, and how they plan to utilize different management approaches in the context of climate adaptation. While many managers evoked the past in thinking about projected climate impacts and potential responses, most managers in this study acknowledged and even embraced (if reluctantly) that many ecosystems will experience regime shifts in the face of climate change. However, accepting that future ecosystems will be different from past ecosystems led managers in different directions regarding how to respond and the appropriate role of management intervention. Some felt management actions should assist and even guide ecosystems toward future conditions. Others were less confident in projections and argued against transformation. Finally, some suggested that resilience could provide a middle path, allowing managers to help ecosystems adapt to change without predicting future ecosystem states. Scalar challenges and institutional constraints also influenced how managers thought about adaptation. Lack of institutional capacity was believed to constrain adaptation at larger scales. Resistance, in particular, was considered impractical at almost any scale due to institutional constraints. Managers negotiated scalar challenges and institutional constraints by nesting different approaches both spatially and temporally. [ABSTRACT FROM AUTHOR]

Seasonal streamflow forecast bias, changes in climate, snowpack, and land cover, and the effects of these changes on relations between basin‐wide snowpack, SNOw TELemetry (SNOTEL) station snowpack, and seasonal streamflow were evaluated in the headwaters of the Rio Grande, Colorado. Results indicate that shifts in the seasonality of precipitation and changing climatology are consistent with periods of overprediction and underprediction in streamflow forecasts. Multiple linear regression of SNOTEL data, postcedent precipitation, and land‐cover changes explained 2%–18% more variability in streamflow prediction than using SNOTEL station data alone. Simulated basin‐wide snowpack from a physically based model had significant negative trends in snow water equivalent (−4.33 mm/yr) and snow‐covered area (−0.05%/yr) during the melt period April–June. Simulated streamflow from a precipitation‐runoff model increased an average 5% when the effects of bark beetle‐induced tree mortality were compared to a baseline simulation with static vegetation. The effects of a 2013 wildfire increased simulated seasonal streamflow an average 35% for 1–4 years postfire. The combined effects of climate and land‐cover changes on snowpack‐streamflow relations highlight the difficulty in seasonal streamflow forecasting, which has important implications for water‐resource management. Research Impact Statement: Changing relations between snow, precipitation, and seasonal runoff were evaluated in the Rio Grande Headwaters basin using statistical and physical modeling techniques. [ABSTRACT FROM AUTHOR]

As water systems are likely to experience mounting challenges managing for climate variability and extremes as well as a changing climate, there is increasing interest in what motivates systems to implement adaptive measures. While extreme events have been hypothesized to stimulate organization change and act as "windows of opportunity" and "pacemakers" driving toward adaptation, they do not always seem to do so. We therefore sought to understand the responses and motivations for organizational behavior in the wake of two significant droughts across five smaller water systems in Western Colorado, USA. We conducted interviews and focus groups across these systems to understand whether and why significant droughts in 2002 and 2012 prompted adaptive change. Results indicate that systems did not uniformly decide to change their policies in the wake of drought, and even well-prepared systems were driven to change policies by other pressures, such as peer-system pressure and political pressure from residents. We find that organizational worldviews were important mediators of how the experience of drought manifest, or not, in organizational changes. These findings have implications for assumptions about what might drive organizational learning and change among water managers for climate adaptation in the future. [ABSTRACT FROM AUTHOR]

Summary: Global change forecasts in ecosystems require knowledge of within‐species diversity, particularly of dominant species within communities. We assessed site‐level diversity and capacity for adaptation in Bouteloua gracilis, the dominant species in the Central US shortgrass steppe biome.We quantified genetic diversity from 17 sites across regional scales, north to south from New Mexico to South Dakota, and local scales in northern Colorado. We also quantified phenotype and plasticity within and among sites and determined the extent to which phenotypic diversity in B. gracilis was correlated with climate.Genome sequencing indicated pronounced population structure at the regional scale, and local differences indicated that gene flow and/or dispersal may also be limited. Within a common environment, we found evidence of genetic divergence in biomass‐related phenotypes, plasticity, and phenotypic variance, indicating functional divergence and different adaptive potential. Phenotypes were differentiated according to climate, chiefly median Palmer Hydrological Drought Index and other aridity metrics.Our results indicate conclusive differences in genetic variation, phenotype, and plasticity in this species and suggest a mechanism explaining variation in shortgrass steppe community responses to global change. This analysis of B. gracilis intraspecific diversity across spatial scales will improve conservation and management of the shortgrass steppe ecosystem in the future. [ABSTRACT FROM AUTHOR]

Downscaled high-resolution climate simulations were used to provide inputs to the physics-based Distributed Hydrology Soil VegetationModel (DHSVM), which accounts for the combined effects of snowmelt and rainfall processes, to determine spatially distributed available water for runoff (AWR). After quasi-stationary time windows were identified based onmodel outputs extracted for two differentmountainous field sites in Colorado and California, intensity--duration--frequency (IDF) curves for precipitation and AWR were generated and evaluated at each numerical grid to provide guidance on hydrological infrastructure design. Impacts of snowmelt are found to be spatially variable due to spatial heterogeneity associated with topography according to geostatistical analyses. AWR extremes have stronger spatial continuity compared to precipitation. Snowmelt impacts on AWR are more pronounced at the wet California site than at the semiarid Colorado site. The sensitivities of AWR and precipitation IDFs to increasing greenhouse gas emissions are found to be localized and spatially variable. In subregions with significant snowfall, snowmelt can result in an AWR (e.g., 6-h 100-yr events) that is 70%higher than precipitation. For comparison, future greenhouse gas emissions may increase 6-h 100-yr precipitation and AWR by up to 50% and 80%, respectively, toward the end of this century. [ABSTRACT FROM AUTHOR]

Lightning flashes during snowstorms occur infrequently compared to warm-season convection. The rarity of such thundersnow events poses an additional hazard because the lightning is unexpected. Because cloud electrification in thundersnow storms leads to relatively few lightning discharges, studying thundersnow events may offer insights into mechanisms for charging and possible thresholds required for lightning discharges. Observations of four northern Colorado thundersnow events that occurred during the 2012/13 winter are presented. Four thundersnow events in one season strongly disagrees with previous climatologies that used surface reports, implying thundersnow may be more common than previously thought. Total lightning information from the Colorado Lightning Mapping Array and data from conterminous United States lightning detection networks are examined to investigate the snowstorms' electrical properties and to compare them to typical warm-season thunderstorms. Data from polarimetric WSR-88Ds near Denver, Colorado, and Cheyenne, Wyoming, are used to reveal the storms' microphysical structure and determine operationally relevant signatures related to storm electrification. Most lightning occurred within convective cells containing graupel and pristine ice. However, one flash occurred in a stratiform snowband, apparently triggered by a tower. Depolarization streaks were observed in the radar data prior to the flash, indicating electric fields strong enough to orient pristine ice crystals. Direct comparisons of similar lightning- and nonlightning-producing convective cells reveal that though both cells likely produced graupel, the lightning-producing cell had larger values of specific differential phase and polarimetric radar-derived ice mass. Compared to warm-season thunderstorms, the analyzed thundersnow storms had similar electrical properties but lower flash rates and smaller vertical depths, suggesting they are weaker, ordinary thunderstorms lacking any warm (>0°C) cloud depth. [ABSTRACT FROM AUTHOR]

The climatology of Colorado's historical extreme precipitation events shows a remarkable degree of seasonal and regional variability. Analysis of the largest historical daily precipitation totals at COOP stations across Colorado by season indicates that the largest recorded daily precipitation totals have ranged from less than 60 mm day−1 in some areas to more than 250 mm day−1 in others. East of the Continental Divide, winter events are rarely among the top 10 events at a given site, but spring events dominate in and near the foothills; summer events are most common across the lower-elevation eastern plains, while fall events are most typical for the lower elevations west of the Divide. The seasonal signal in Colorado's central mountains is complex; high-elevation intense precipitation events have occurred in all months of the year, including summer, when precipitation is more likely to be liquid (as opposed to snow), which poses more of an instantaneous flood risk. Notably, the historic Colorado Front Range daily rainfall totals that contributed to the damaging floods in September 2013 occurred outside of that region's typical season for most extreme precipitation (spring-summer). That event and many others highlight the fact that extreme precipitation in Colorado has occurred historically during all seasons and at all elevations, emphasizing a year-round statewide risk. [ABSTRACT FROM AUTHOR]

Stretching along the border of North Dakota and Minnesota, The Red River Valley (RRV) of the North has the highest frequency of reported blizzards within the contiguous United States. Despite the numerous impacts these events have, few systematic studies exist that discuss the meteorological properties of blizzards. As a result, forecasting these events and lesser blowing snow events is an ongoing challenge. This study presents a climatology of atmospheric patterns associated with RRV blizzards for the winter seasons of 1979–1980 and 2017–2018. Patterns were identified using subjective and objective techniques using meteorological fields from the North American Regional Re-analysis (NARR). The RRV experiences, on average, 2.6 events per year. Blizzard frequency is bimodal, with peaks occurring in December and March. The events can largely be typed into four meteorological categories dependent on the forcing that drives the blizzard: Alberta Clippers, Arctic Fronts, Colorado Lows, and Hybrids. The objective classification of these blizzards using a competitive neural network known as the Self-Organizing Map (SOM) demonstrates that gross segregation of the events can be achieved with a small (eight-class) map. This implies that objective analysis techniques can be used to identify these events in weather and climate model output that may aid future forecasting and risk assessment projects. [ABSTRACT FROM AUTHOR]