Sudden stratospheric warmings (SSWs) are a key driver of winter climate variability in the Northern Hemisphere. SSWs are a disruption of the strong stratospheric westerlies over the winter pole in which the winds in the upper to middle stratosphere, from about 30 to 50 km above the surface, weaken and reverse and the polar cap temperatures increase by up to 50 K in only a few days. These events affect tropospheric conditions for the two months following, on average shifting the North Atlantic storm track equatorward and resulting in a negative Northern Annular Mode and North Atlantic Oscillation at the surface. These changes are associated with colder and drier than average conditions in Northern Europe and Eurasia and warmer and wetter than average conditions across Southern Europe, as well as high temperatures across North Africa, the Middle East, and Central Asia and increased cold air outbreaks in North America and Eurasia. This thesis examines this typical surface response to SSWs in several different contexts. We consider its relationship to other atmospheric phenomena and features, first quantifying its importance relative to the North Atlantic impacts of the El Niño-Southern Oscillation (ENSO) and then examining the role of ozone chemistry in modeling the surface response to SSWs. We also study the variability of the surface signature of SSWs, with the goal of understanding the uncertainty in magnitude and spatial pattern of surface climate patterns following SSWs and the relative roles of different sources of this uncertainty. After providing background and context in the first chapter, the second chapter studies interactions between SSWs and the El Niño phase of ENSO. El Niño affects climate in the North Atlantic and European regions, those most affected by SSWs, through tropospheric and stratospheric pathways. One of these pathways is increased SSW frequency. However, most SSWs (about 90\%) are unrelated to ENSO, and the importance for boreal winter surface climate of this frequency increase compared to other El Niño pathways remains to be quantified. We here contrast these two sources of variability using two 200-member ensembles of one-year integrations of the Whole Atmosphere Community Climate Model, one ensemble with prescribed El Niño sea surface temperatures (SSTs) and one with neutral-ENSO SSTs. We form composites of wintertime climate anomalies, with and without SSWs, in each ensemble and contrast them to a basic state represented by neutral-ENSO winters without SSWs. This approach allows us to isolate the distinct effects of ENSO and SSWs more clearly than was done in previous work. We find that El Niño and SSWs both result in negative North Atlantic Oscillation anomalies and have comparable impacts on European precipitation, but SSWs cause larger Eurasian cooling. These results indicate the potential impact of a strong El Niño on seasonal forecasting in the North Atlantic as well as the importance of resolving the stratosphere in subseasonal and seasonal forecast models to best capture stratospheric polar vortex variability. In the third chapter, we study the importance of interactive ozone chemistry in representing the stratospheric polar vortex and Northern Hemisphere winter surface climate variability. Modeling and observational studies have reported effects of stratospheric ozone extremes on Northern Hemisphere spring climate. Recent work has further suggested that the coupling of ozone chemistry and dynamics amplifies the surface response to midwinter SSWs. We contrast two 200-year simulations from the interactive and specified chemistry (and thus ozone) versions of the Whole Atmosphere Community Climate Model with constant year-2000 forcings. This experiment is thus designed to clearly isolate the impact of interactive ozone on polar vortex variability. In particular, we analyze the response with and without interactive chemistry to midwinter SSWs, March SSWs, and strong polar vortex events (SPVs). With interactive chemistry, the stratospheric polar vortex is stronger, and more SPVs occur, but we find little effect on the frequency of midwinter SSWs. At the surface, interactive chemistry results in a pattern resembling a more negative North Atlantic Oscillation following midwinter SSWs, but with little impact on the surface signatures of late winter SSWs and SPVs. These results suggest that including interactive ozone chemistry in model simulations is important for representing North Atlantic and European winter climate variability. In the fourth chapter, we turn from models to reanalysis and consider the uncertainty in the surface response to SSWs. While the qualitative features of the mean surface signature of SSWs in the North Atlantic and Europe are well-established, its uncertainties as well as other features of surface climate following SSWs are less well-understood. To address the question of robustness of the mean observed response to SSWs, we use bootstrapping with replacement to construct synthetic SSW composites from SSW events in reanalysis, creating an ensemble of composites comparable to the observed one. We then examine the differences across these synthetic composites. We find that the canonical responses of a negative North Atlantic Oscillation and associated temperature and precipitation anomalies in the North Atlantic and European regions in the months following SSWs are robust. However, the magnitude and spatial pattern of these anomalies vary considerably across the composites. We further find that this uncertainty is unrelated to vortex strength and is instead the result of unrelated tropospheric variability. These results have implications for evaluating the fidelity of forecast models in capturing the surface impact of SSWs, by comparing both the mean impact as well as the contribution from internal variability with observations. Overall, we demonstrate the complexity of interactions of sudden stratospheric warmings with other sources of variability in the Earth system. We find that the state of the polar vortex itself, the strength of downward propagation following the SSW, and the surface response can all be affected in important ways by these other components (e.g. tropospheric variability and Arctic ozone). We close by providing broader context for these results and looking towards continuing and future work in the field.

Energy intensity in the U.S. from 1780 to 2010 shows a declining trend when traditional energy is included, in contrast to the "inverted U-curve" seen when only commercial energy is considered. The analysis quantifies use of human and animal muscle power, wind and water power, biomass, harvested ice, fossil fuels, and nuclear power. Historical prices are provided for many energy resources. The analysis reaffirms the importance of innovation in conversion technologies in energy transitions. An increase in energy intensity in the early 20th century is explained by diminishing returns to pre-electric manufacturing systems, which produced a transformation in manufacturing. In comparison to similar studies for other countries, the U.S. has generally higher energy intensity. A population-weighted series of heating degree days and cooling degree days partially explains differences in energy intensity. Series are developed for 231 countries and territories with multiple reference temperatures, with a "wet-bulb" series accounting for the effects of humidity. Other variables considered include energy prices, income per capita, and governance indices. A panel regression of thirty-two countries from 1995 to 2010 establishes GDP per capita and share of primary energy as determinants of energy intensity, but fails to establish statistical significance of the climate variables. A group mean regression finds average heating and cooling degree days to be significant predictors of average energy intensity over the study period, increasing energy intensity by roughly 1.5 kJ per 2005 international dollar for each annual degree day. Group mean regression results explain differences in countries' average energy intensity, but not changes within a country over time. Energy Return on Investment (EROI) influences the economic competitiveness and environmental impacts of an energy resource and is one driver of energy transitions. The EROI of U.S. petroleum production has declined since 1972, with a partial rebound in the 1980s and 1990s. External Energy Return (EER), which excludes the consumption of energy from within the resource, falls by two-thirds from 1972 to 2007. A literature review finds the projected EROI of oil shale to be much lower than the EROI of U.S. petroleum production.

Previous research indicates a distinct seasonal pattern in mortality rates. Increases are prominent during the northern-hemispheric cold season. These patterns are seen in overall mortality, diabetes mellitus, circulatory, digestive, and respiratory diseases. A principal component analysis indicates that departure from normal temperature, minimum, maximum, and average daily temperature, and dew-point temperature are the primary atmospheric variables that influence mortality patterns. ANOVA and Kruskal-Wallis tests support findings of principal component analysis. Although a day-to-day relationship between mortality rates and atmospheric variables was noted in several instances, results suggest that the influence of the primary atmospheric variables on mortality rates is greatest when a three to five-day lag time is in place. Furthermore, results indicate that the combination of these variables in conjunction with frontal passage is linked to seasonal increases in mortality. A combination of atmospheric variables that influence mortality rates has been identified, however, their exact influence is still unclear.

Ten years of lightning data was used to examine the lightning climatology in the Mid-South and to create a model capable of predicting severe hail storms using CG lightning. Cloud to ground lightning peaked reached a maximum in July and a minimum in January. Positive CG accounted for 5.3% of all strikes. The percentage of positive strikes reached a maximum in December and a minimum in August. Artificial intelligence along with logistic regression models were used for hail prediction. The 95% confidence intervals of the contingency statistics were used to determine the performance of the models. The linear cost 100 model and logistic regression had the highest performance and were tested with an independent data set. The logistic regression model outperformed the linear cost 100 model. The performance by both models was under the median statistics but within the 95% confidence interval.

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In this thesis I present the results of a comprehensive assessment of the Pacific-North American (PNA) teleconnection pattern in general circulation models (GCMs) and a regional climate model (RCM). The PNA teleconnection pattern is a quasi-stationary wave field over the North Pacific and North America that has long been recognized as a robust feature of Northern Hemisphere atmospheric circulation, and directly affects the interannual variability of North American temperature and precipitation. The teleconnection is evaluated under present (1950-2000) and future (2050-2100) climate in a coupled GCM (MPI/ECHAM5) and a high-resolution regional climate model (RegCM3). I further assess the PNA in 27 atmosphere-ocean GCMs and earth system models (ESMs) from the ongoing fifth phase of the Coupled Model Intercomparison Project (CMIP5). The National Centers for Environmental Prediction and Atmospheric Research (NCEP/NCAR) Reanalysis serves a quasi-observational baseline against which the models are evaluated. For each analysis, changes in the spatial and temporal patterns of the PNA spatial are assessed for both the present and future climates, and these changes are then related to changes in climate and surface hydrology in North America. Coupling the NCEP and ECHAM5 GCMs with RegCM3 is very successful in that the PNA is resolved in both models with little loss of information between the GCMs and RegCM3, thereby allowing an assessment of high-resolution climate with an inherent skill comparable to that of the global models. The value of the PNA index is generally independent of the method used to calculate it: three- and four-point modified linear pointwise calculations for both the RegCM3 and ECHAM5 model simulations produce very similar indices compared with each other, and compared with those extracted from a rotated principle component analysis (RPCA) which is also used to determine the PNA spatial pattern. The spatial pattern of the PNA teleconnection emerges as a leading mode of variability from the RPCA, although the strength of the teleconnections are consistently weaker than NCEP as defined by four main "centers of action". This discrepancy translates into the strength of the controls of the PNA on surface climate. Maps of the correlations between the GCM PNA indices and RCM surface climate variables are compared to the results from the NCEP/NCAR Reanalysis. I find that correlation patterns with temperature and precipitation are directly related to the positioning of the Aleutian low and Canadian high, the two main drivers of upper-atmospheric circulation in the PNA sector. The CMIP5 models vary significantly in their ability to simulate the quasi-observed features of the PNA teleconnections. The behavior of the models relative to NCEP is more definite than the trends within the models. Most models are unable to resolve the temporal variability of NCEP; however, on the other hand most of the models are able to capture the PNA as a low-frequency quasi-oscillation. Many of the models are unable to simulate the barotropic instability that initiates wave energy propagation through the 500-hPa geopotential height field, thereby leading to phase-locking and thus the positive and negative modes of PNA are indistinguishable. The behavior and the spatial patterns of the PNA throughout the 21st century are consistent with other projections of future climate change in that most models exhibit a lengthening of the eddy length scale and a poleward shift of the mid-latitude jet stream associated with polar amplification of greenhouse-gas driven global warming. Finally, my analyses underscore the robustness of multi-model means, suggesting that the cumulative results of multiple climate models outperform the results from individual models because ensemble means effectively cancel discrepancies and hereby expose only the most robust common features of the model runs. While ensembles provide better representation of the average climate, they potentially mask climate dynamics associated with inter-annual and longer time scales. Relying on ensemble means to limit model spread and uncertainties remains a necessity in using models to project future climate. / Graduation date: 2013

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Tree-ring series that record climatic variation have long been of interest for study of possible effects of solar variability on terrestrial phenomena. Spectral analysis, harmonic dial analysis, digital filtering, cross-correlation and principal component analysis were used separately and in combination in an attempt to detect relationships between the annual Wolf sunspot numbers and ring-width indices, primarily from western North America. The results show no evidence of significant, consistent relationships between tree-ring data and sunspot numbers.

This thesis examines the media coverage of anthropogenic climate change in the Russian Federation. It achieves this aim by testing Edward S. Herman and Noam Chomsky's Propaganda Model (1988) which argues that media coverage predominately stays within the boundaries defined by the 'elite's' interests. Through media analysis as well as elite interviews, this project has found that in the Russian case, regardless of the newspapers' ownership structure or dependence on advertising, there is little difference in quantity and quality of overall coverage on climate change. Most newspapers rely on Russian officials as information sources, almost none criticize or question Russian climate change policy and Russia's contribution to global levels of greenhouse gas emissions. This subordinate media policy is not the result of any purposeful and overt state censorship or management of media activity on the issue of climate change, but the product of the media's 'genuine' interest in the state elites as the 'main newsmakers' on the problem and a 'genuine' public lack of interest in climate change as an issue. Furthermore, the study concludes that in the Russian case the omission of climate change issues from media discussions is a greater problem than biased coverage as it prevents the issue from entering public debates. However, considering media interest in the state and the recent change in state climate policy (by becoming more tolerant towards climate change mitigation measures) it is argued that coverage of climate change in Russia will steadily increase and in this case, media 'consent' with the elites' interests will eventually benefit the development of public and official discourse on the problem.