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Process-level modeling at the farm scale provides a tool for evaluating strategies for both mitigating greenhouse gas emissions and adapting to climate change. The Integrated Farm System Model (IFSM) simulates representative crop, beef, or dairy farms over many years of weather to predict performance, economics, and environmental impacts including various emissions and a farm-gate life cycle assessment of carbon, energy, water, and reactive nitrogen footprints of the feed, meat, or milk produced. To illustrate use of the model, a representative dairy farm in central New York was simulated over 25 years of recent historical weather to determine the environmental benefits and economic costs of alternative manure handling strategies. Use of an enclosed manure storage with a flare to burn the methane produced decreased the farm-gate carbon footprint of the milk produced by 20% at an increased annual cost of $42 cow-1. Using an anaerobic digester to produce gas and electricity used on the farm reduced the carbon footprint by 19% and reduced profitability by $56 cow-1. The addition of subsurface injection of manure along with a reduction in N fertilizer use greatly reduced ammonia emission from the farm and increased annual profit by $9 cow-1. Climate change is projected to affect many aspects of dairy production, including growing season length, crop growth processes, harvest timing and losses, cattle performance, nutrient emissions and losses, and ultimately farm profitability. Climate projections for high and low emission scenarios were downscaled from nine general circulation models. IFSM was then used to simulate the same New York dairy farm over 25-year periods using recent, mid-century, and late century climate projected by each of the climate models. Simulations were done without and with adaptation through modified crop varieties and planting and harvest dates. Forage production normally increased with projected climate change and corn grain yields decreased, and together feed production was maintained. Warmer temperatures increased volatile loss of ammonia N, and changes in precipitation patterns increased nutrient runoff losses in surface water. The reactive N footprint of the milk produced was increased by 2% to 11% with the change in climate, but other environmental footprints were relatively unaffected. With appropriate adaptation to climate change, annual farm profitability increased by about $100 cow-1. However, for the high emission, late century projection, profit decreased by $10 cow-1 and the risk or annual variance in profit increased by 34%, reflecting greater annual variation in crop and animal productivity. Whole-farm and climate models provide useful tools for studying the benefits and costs of greenhouse gas mitigation and the adaptation of farms to changing climate.

Ice storms are extreme weather events that can have devastating implications for the sustainability of natural ecosystems as well as man made infrastructure. Ice storms are caused by a complex mix of atmospheric conditions and are among the least understood of severe weather events. Our ability to model ice storms and characterize storm features will go a long way towards both enabling support systems that offset storm impacts and increasing our understanding of ice storms. In this paper, we present a holistic computational framework to answer key questions of interest about ice storms. We model ice storms as a function of relevant surface and atmospheric variables. We learn these models by adapting and applying supervised and unsupervised machine learning algorithms on data with missing or incorrect labels. We also include a knowledge representation module that reasons with domain knowledge to revise the output of the learned models. Our models are trained using reanalysis data and historical records of storm events. We evaluate these models on reanalyis data as well as Global Climate Model (GCM) data for historical and future climate change scenarios. Furthermore, we discuss the use of appropriate bias correction approaches to run such modeling frameworks with GCM data., 7 pages including bibliography

The article of record as published may be found at https://doi.org/10.7930/

This research estimates the impact that eight alternative climate change scenarios are likely to have on agricultural returns and the useful life of the Ogallala aquifer in the Southern High Plains (SHP) over a 90-year planning horizon, relative to the situation where climate conditions are maintained at the historical average condition for 1960 to 2009. The empirical analysis is accomplished with the aid of an integrated water policy model that couples a dynamic economic optimization model to a detailed aquifer model of the Southern Ogallala Aquifer. The integrated model controls for the effects of spatial heterogeneity in land use practices and aquifer characteristics. For each climate scenario, changes in annual economic returns, irrigated acres, water use, and aquifer storage levels are measured relative to respective estimates derived from the historic no change climate scenario. The annual 90-year time path of economic returns, water use, and cropping patterns under the eight climate change scenarios significantly varies from the baseline forecast. Moreover, relative to a baseline condition that estimates significant annual decreases in economic returns due to continued groundwater mining, the climate change scenarios generally suggest climate change will mitigate the cost of increasing groundwater scarcity due to a complimentary effect between crop yields and the various climate change scenarios.

This research estimates the impact that eight alternative climate change scenarios are likely to have on agricultural returns and the useful life of the Ogallala aquifer in the Southern High Plains (SHP) over a 90-year planning horizon, relative to the situation where climate conditions are maintained at the historical average condition for 1960 to 2009. The empirical analysis is accomplished with the aid of an integrated water policy model that couples a dynamic economic optimization model to a detailed aquifer model of the Southern Ogallala Aquifer. The integrated model controls for the effects of spatial heterogeneity in land use practices and aquifer characteristics. For each climate scenario, changes in annual economic returns, irrigated acres, water use, and aquifer storage levels are measured relative to respective estimates derived from the historic no change climate scenario. The annual 90-year time path of economic returns, water use, and cropping patterns under the eight climate change scenarios significantly varies from the baseline forecast. Moreover, relative to a baseline condition that estimates significant annual decreases in economic returns due to continued groundwater mining, the climate change scenarios generally suggest climate change will mitigate the cost of increasing groundwater scarcity due to a complimentary effect between crop yields and the various climate change scenarios.

Texas comprises the eastern portion of the Southwest region, where the convergence of climatological and geopolitical forces has the potential to put extreme stress on water resources. Geologic records indicate that Texas experienced large climate changes on millennial time scales in the past, and over the last thousand years, tree-ring records indicate that there were significant periods of drought in Texas. These droughts were of longer duration than the 1950s “drought of record” that is commonly used in planning, and they occurred independently of human-induced global climate change. Although there has been a negligible net temperature increase in Texas over the past century, temperatures have increased more significantly over the past three decades. Under essentially all climate model projections, Texas is susceptible to significant climate change in the future. Most projections for the 21st century show that with increasing atmospheric greenhouse gas concentrations, there will be an increase in temperatures across Texas and a shift to a more arid average climate. Studies agree that Texas will likely become significantly warmer and drier, yet the magnitude, timing, and regional distribution of these changes are uncertain. There is a large uncertainty in the projected changes in precipitation for Texas for the 21st century. In contrast, the more robust projected increase in temperature with its effect on evaporation, which is a dominant component in the region’s hydrologic cycle, is consistent with model projections of frequent and extended droughts throughout the state. For these reasons, we recommend that Texas invest resources to investigate and anticipate the impacts of climate change on Texas’ water resources, with the goal of providing data to inform resource planning. This investment should support development of 1) research programs that provide policy-relevant science; 2) education programs to engage future researchers and policy-makers; and 3) connections between policy-makers, scientists, water resource managers, and other stakeholders. It is proposed that these goals may be achieved through the establishment of a Texas Climate Consortium, consisting of representatives from academia, industry, government agencies, water authorities, and other stakeholders. The mission of this consortium would be to develop the capacity to provide decision makers with the information needed to develop adaptation strategies in the face of future climate change and uncertainty., Texas Water Journal, Vol. 1 No. 1 (2010)

Possible future climate changes in California are investigated from a varied set of climate change model simulations. These simulations, conducted by three state-of-the-art global climate models, provide trajectories from three greenhouse gas (GHG) emission scenarios. These scenarios and the resulting climate simulations are not “predictions,” but rather are a limited sample from among the many plausible pathways that may affect California’s climate. Future GHG concentrations are uncertain because they depend on future social, political, and technological pathways, and thus the IPCC has produced four “families” of emission scenarios. To explore some of these uncertainties, emissions scenarios A2 (a medium-high emissions) and B1 (low emissions) were selected from the current IPCC Fourth climate assessment, which provides several recent model simulations driven by A2 and B1 emissions. The global climate model simulations addressed here were from PCM1, the Parallel Climate Model from the National Center for Atmospheric Research (NCAR) and U.S. Department of Energy (DOE) group, and CM2.1 from the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluids Dynamics Laboratory (GFDL). As part of the scenarios assessment, a statistical technique using properties of historical weather data was employed to correct model biases and “downscale” the global-model simulation of future climates to a finer level of detail, onto a grid of approximately 7 miles (12 kilometers), which is more suitable for impact studies at the scales needed by California decision makers. In current climate-change simulations, temperatures over California warm significantly during the twenty-first century, with temperature increases from approximately +3ºF (1.5ºC) in the lower emissions scenario (B1) within the less responsive model (PCM1) to +8ºF (4.5ºC) in the higher emissions scenario (A2) within the more responsive model (CM2.1). Three of the simulations (all except the low-emission scenario run of the low-response model) exhibit more warming in summer than in winter. In all of the simulations, most precipitation continues to occur in winter, with virtually all derived from North Pacific winter storms. Relatively little change in overall precipitation is projected. Climate warming has a profound influence in diminishing snow accumulations, because there is more rain and less snow, and earlier snowmelt. These snow losses increase as the warming increases, so that they are most severe under climate changes projected by the more sensitive model with the higher GHG emissions.

This white paper focuses on how climate change-induced effects on weather will translate into changes in wildland fire severity and outcomes, particularly on the effectiveness of initial attack at limiting the area burned in contained fires and the number of fires that escape initial attack. Prior research has indicated that there is a potential for significant increases in the number of fires escaping initial attack, particularly in areas in which the fuel matrix is dominated by grass and brush. These results were driven primarily by predicted increases in wind speeds. Those findings, however, were derived using less sophisticated models of initial attack than currently available. The results of this study, using more sophisticated models and climate projections, indicate that subtle shifts in fire behavior of the sort that might be induced by the climate changes anticipated for the next century are of sufficient magnitude to generate an appreciable increase in the number of fires that escape initial attack, at least for areas where brush fuels dominate. Such escapes of considerable importance in wildland fire protection planning, given the high cost to society of a catastrophic escape like those experienced in recent decades in the BerkeleyOakland, Santa Barbara, San Diego, or Los Angeles areas. However, at least for the limited region in the Sierra Nevada considered in this study, it would appear that relatively modest augmentations to existing firefighting resources would be sufficient to compensate for changeinduced changes in wildland fire outcomes. Generalizing our findings with respect to wildland fire intensity and outcomes for the AmadorEl Dorado Unit to other private lands in the state will require both further analysis using the data for that CDF unit, and replication of this analysis using data for several other units and for the state’s federal forest lands. No attempt is made in this paper to extrapolate the results beyond the boundaries of the study area, in part because of necessary work that was identified with respect to further validation of this study’s modeling approach.

Climate change is a global issue that affects regional and local scales, which can have significant psychological effects on all people. Science teachers, who deal with this information themselves, also must inspire and motivate young people to confront this information. Without understanding the foundations of how and why people learn new scientific information, teachers feel helpless teaching students how to respond to information related to climate change. The literature presents complicated relationships among people’s ideology, education, age, sense of control, their concern for the environment and how each of these factors influence pro-environmental behavior and climate change mitigation behavior. Explaining this relationship should prove helpful in the creation of climate change curricula. The purpose of this study was to analyze demographic and scientific attitudinal factors on climate change perceptions and behavior. The research questions were these: 1. How do teachers’ political ideologies, environmental concern and climate change efficacy influence their climate change mitigation intention and behavior? 2. How do teachers’ perceptions of subjective norms influence their science teaching efficacy and climate change efficacy? 3. How do teachers’ perceptions of control and climate change efficacy influence their climate change mitigation intentions and behaviors? 4. Does the Climate Change Efficacy Framework predict teacher intentions and behaviors? An exploratory analysis using axis factoring was implemented to discover the true dimensions of the items used in this study. This was done using factor extraction and rotation. A total number of seven factors emerged from the 47 items: (1) Science Teacher Efficacy; (2) Environmental Concern; (3) Climate Mitigating Behavioral Intention; (4) Climate Change Efficacy; (5) Subjective Norms; (6) Perceived Behavioral Control (7) Climate Mitigating Behavior. Reliability analyses were conducted and showed good internal consistency for most constructs. The results of the study provide the basis for understanding the socio-cognitive dimensions of science teachers in the United States. Implications of the study and future directions are presented.

Cinema has long been both a reflection of society and a powerful influence on how people think about and interact with it. Many filmmakers use this power to portray issues of injustice faced in the real world, regardless of how fantastic or otherworldly the setting may be. This paper attempts to draw connections between what we see on the big screen and how that can affect the way we behave, both politically and personally. I discuss a set of twelve films that each send a message related to a social justice issue, evaluating their potential to influence a behavior change in their audience based on how effectively that message was communicated. The resulting case study determines that effectiveness based on a typology which gauges the level of emotional engagement a film creates for its audience, leaving us with two final lists: films that hit the mark and films that fall short.

This study attempts to quantify the impacts of coastal flooding on land area, population and urban infrastructure that would result from an extreme tropical storm occurring over a range of for future sea level rise scenarios. This case study follows a storm named Xangsane, that occurred in Thua Thien Hue, a central province in Vietnam, in 2006. It compares the impacts caused by different scenarios of storm surges and sea levels simulated on two different datasets, a national one and a global one. A 2-dimentional hydrodynamic model is used to simulate the storm surge and coastal flooding. The numerical simulation is coupled with ArcGIS tools to analyze the level and extent of inundation areas as well as their influences on population and urban infrastructure (transportation system, schools, hospitals, and historical heritage sites). Projection indicates that the sea level rise (SLR) of 1 m and 2 m can cause huge impacts for transportation infrastructure and buildings. The impact increase on transportation infrastructure is considerably significant, from 200% to 400% increase on all types of road across different storm surge scenarios. The impact on railroads is the least, about 32 km to 50 km in SLR of 1 m and 2 m, but its impact percentage is the highest among all other categories. The maximum railroad impact increases nearly 2000% for SLR of 1 m and 3500% for SLR of 2 m with or without storm surge. For land area, building (school and hospital), and special infrastructure, the impacts increase up to 450% in SLR of 1 m and 2 m simulated on both datasets (the national and the global). The risk of flooding is closely related to the SLR more than storm surges. The impact of projected sea levels increases the need for flood control measures in Thua Thien Hue province.

On-road transportation policies could play a key role in reducing greenhouse gas (GHG) and air pollutant emissions via a wide range of strategies that can be classified into three groups: reduce, avoid, and replace. The objective of this dissertation is to prioritize on-road emission mitigation strategies for policy -level transportation funding allocation and to quantify the environmental impacts of first, High Occupancy Vehicle (HOV) lanes and carpooling as a realization of avoid strategies; and second, Plug-in Electric Vehicles (PEVs) as an example of replace strategies. The resulting models have a wide range of applications in evaluating the effectiveness of each strategy, including the potential to assist policymakers, and transportation planners in optimizing infrastructural investments by identifying regions where the response to a specific policy would be maximized. In chapter II, reduce, avoid, and replace strategies were prioritized based on transportation and climate science professionals’ opinions through Analytical Hierarchy Process (AHP), applied to the cities of Dallas and Lubbock in Texas. The results indicated that reduce strategies had the highest preference score of 40%, followed by avoid strategies with 36% and replace strategies with 24%. In chapter III, we developed a statistical model to relate HOV lanes and other potential factors to carpooling propensity in all 50 U.S. states and the District of Columbia. At the state level, we found HOV lane-kilometers together with higher-than-average gasoline prices to be effective in promoting carpooling. An in-depth analysis of 58 counties in California found that HOV lane-kilometers also positively impact carpooling rates for individual counties. For a hypothetical scenario where existing HOV lane-kilometers in each state are expanded by 0.5 meters for every hour of total daily travel time to work, we found this strategy has the greatest potential to reduce annual carbon dioxide equivalent (CO2e) in the District of Columbia, by 4.5%, followed by Hawaii and New York, and New Jersey. The smallest potential is found in North Dakota. Nationally, 1.83 MMT of CO2e or 0.16% of light duty vehicle emissions would be reduced under this scenario In chapter IV, we attributed PEV adoption rate in 58 California Counties to charging station infrastructure and other potential factors using statistical models. We found charging station per capita to be effective in promoting PEV adoption, particularly among male buyers in households with less number of vehicles available. For a hypothetical scenario where existing charging station infrastructures in each county are expanded by 2 charging station for every one million total daily miles travelled, we found this strategy has the greatest potential to reduce petroleum use, GHG and criteria air pollutant emissions in Modoc County, by 0.06%-0.03%, followed by Sierra, and Mono counties. For 20 counties, including Butte, San Joaquin, and San Francisco counties, the benefit to cost ratio is below one, indicating the incompatibility of the strategy in these counties.

Institutions of Higher Education (IHEs) are viewed as analogous to small cities because of similarities in organizational structure and operational procedures. The existing literature on sustainability in higher education lacks a cross-national empirically tested study revealing the characteristics of IHEs most successful at producing high per capita recycling yields. As such, this dissertation employs a mixed-method research design to answer the question: Which variables create an opportunity for institutions of higher education to be successful at recycling? This study advances our understanding of the factors contributing to successful recycling in higher education by employing three research approaches to the analysis. First, the 2014 RecycleMania yield data is evaluated based on institutional characteristics and recycling behavior variables. Next, surveys were conducted amongst 2,114 participants across eleven IHEs to reveal the recycling outlook scores of the students, staff, and faculty at IHEs. Finally, IHEs characteristics are again evaluated by institution size while recycling programs and behaviors are discussed indepth based on developed case studies at eleven IHEs. The findings of this research illustrate that when considering all of the IHEs participating in the 2014 RecycleMania competition, the characteristics of IHEs including private status, highly residential, with mature recycling programs, and continual participation in the competition produce higher per capita recycling yields. Additionally, when evaluating recycling outlook scores based on survey data, those with higher scores are female, politically liberal, and upperclassmen. Ultimately, a conflict in existing literature and findings discussed in Chapter Four of this dissertation led to a reevaluation of the significant variables based on institution size. Re-testing the variables revealed varied conditions in per capita recycling yields for IHEs at small, medium, and large institutions. These differences and case studies of recycling programs and their RecycleMania efforts at eleven IHEs are discussed. This dissertation concludes with a discussion of policy diffusion between IHEs and Municipalities. This possibility creates a call for future research projects.

Despite its prominence in public relations literature, little research has examined the effects of dialogic communication. This project tested the effects of dialogue within a science context through two experiments. The impact of both observed and active online dialogue was tested in relation to organization-public relationship (OPR) qualities, trust in science, perceived issue importance, and supportive behavioral intentions. In order to examine the influence of political polarization, the effects of dialogue were tested for the polarized issue of climate change and the non-polarized issue of space exploration. Study 1 revealed dialogue significantly and positively affected OPR qualities and supportive behavioral intentions for space exploration, but not climate change. Study 2 found little difference between observing dialogue and actively engaging in it. These findings suggest that engaging in dialogue about non-polarized topics can help organizations build relationships with publics, which in turn help organizations accomplish other strategic goals. Results fill a void within the public relations literature, serve to integrate public relations theory and science communication scholarship, and inform public relations best practices. Specifically, this project has implications for how organizations utilize online platforms to engage with publics and when engagement is most likely to be effective.

Global warming is an important scientific issue. As the result of anthropogenic carbon emissions, global warming is already affecting life on this planet and will likely have even more significant impacts on economies, species, and human life in future decades.1 As potential solutions to global warming require economy-wide measures, global warming is also a prominent issue discussed in politics and the media. While there is not much debate regarding the fundamental science of global warming and the greenhouse effect among active, publishing researchers in the field,2 scientific misconceptions abound across broad age ranges and education levels, including politicians and journalists. These misconceptions give rise to a great deal of debate over issues such as the greenhouse effect that have been viewed as settled by the scientific community for decades and sometimes even centuries.3-17 The present science education research was designed with the specific goal of investigating the potential for increasing scientific understanding of a fundamental yet relatively basic physical phenomenon underlying global warming, namely the greenhouse effect. This research lies within the category of science learning in general, and conceptual change research in particular, as this is an area where significant misconceptions are documented to exist.4, 7, 11, 18 For example, a common misconception is that global warming is due to a hole in the ozone layer.4, 19 The Conceptual Change Hypothesis (CCH) holds that misconceptions interfere with the learning of correct concepts and that misconceptions are resistant to the traditional mode of correct instruction alone. According to the CCH, special instruction that effectively addresses learner misconceptions should be added. In the present study, this specialized form of instructional intervention will be called Misconceptions Instruction. The present study examines the relative effects of Traditional Instruction (TI) and Misconceptions Instruction (MI) on the learning of fundamental physics concepts related to the greenhouse effect. In the present study, approximately 200 students in intact course sections of a first-year Atmospheric Sciences course at Texas Tech University were quasi-randomly assigned to one of two instructional treatments: Traditional Instruction or Misconceptions Instruction. Both treatment groups received a pretest, an immediate posttest following treatment, and a delayed posttest approximately 14 days after the treatment. Raw scores were calculated, and mean scores for both groups were subjected to a repeated-measures ANOVA to determine if participants in each group improved over time and if there was a statistically significant difference in their assessment performances. The results of the repeated measures ANOVA reveal no improvement over time and no statistically significant difference between groups for performance on concept questions and on the assessment as a whole, although there was a statistically significant difference between groups for performance on the misconception questions only. The results carry significant implications for the quantity and type of instruction utilized in this study, as well as implications about the nature of the most common misconceptions about the greenhouse effect and global warming. For example, short reading passages about the greenhouse effect were found inadequate for bringing about conceptual change, in contrast with other studies that had longer exposure times and varied instructional methods.11, 20 Also, the nature of the most common misconceptions concerning the greenhouse effect was found to be categorical, meaning that students used the entirely wrong mental model as opposed to isolated misconceptions about the correct mental model, and thus refutational instructional methods were ineffective. These implications are discussed in light of previous findings related to science education research in general and to research on misconceptions-based instruction in specific. Appendix G: Global circulation models (GCMs) capture the large-scale trends of climate variables, but GCMs operate on a grid size that cannot account for local features which modify the climate and thus the global trend in a region. While the climate is warming globally, many effects are felt locally. Therefore, the GCM output needs to be downscaled to provide regional climate scenarios, and one approach for doing this is empirical statistical downscaling (ESD). There are a few different software packages and methods for performing ESD, one of which is SDSM 4.2, which computes the parameters of multiple linear regression optimized using ordinary least squares or dual simplex algorithms. In this study, the Statistical Downscaling Model software package (SDSM) was evaluated using data from 20 different weather stations and three different atmosphere-ocean general circulation models (AOGCMs) for two variables in order to determine SDSMs ability to meet modern downscaling needs. It was found that as a software package, SDSM was not suitable for projects involving the need to perform many runs of the system. With regard to performance, SDSM was found to perform worse than two of the newer methods against which it was compared.