Clouds have a large impact on the Earth’s radiation and energy budget and play consequently a crucial role in prediction of climate change. At the same time, they are highly variable in time and space. To study and distinguish the different influences of clouds on the climate system it is useful to separate clouds into individual cloud regimes. In this thesis a new cloud classification and their response to disturbed cloud droplet number concentration is presented. Liquid water clouds at cloud scale are separated on the basis of cloud properties derived from combined A-Train satellite measurements involving the MODIS measurements onboard Aqua, the CloudSat cloud radar and the CALIPSO cloud lidar. Using the combined MODIS and CALIPSO cloud-top phase discrimination, liquid water clouds are identified. The high resolved vertical measurements of the CALIPSO lidar provide three cloud base height classes and the spatial variability of cloud top height within a 20 km footprint as an inhomogeneity parameter from which two cloud inhomogeneity classes are defined resulting in a total of six liquid cloud classes. The classification smoothly disentangles marine and continental clouds as well as stratiform and cumuliform clouds in different latitudes at the high spatial resolution of about 20 km. Analyzing the cloud droplet effective radius reff , cloud optical thickness τc , adiabatic liquid water path Lad, adiabatic cloud droplet number concentration Nc,ad and cloud geometrical thickness Hthick,CC derived from collocated combined MODIS, CloudSat and CALIPSO measurements shows a useful discrimination between cloud regimes. Further separations between non precipitating and precipitating clouds using the CloudSat precipitation flag as well as between three classes of free tropospheric relative humidity from a meteorological reanalysis above cloud top are made to investigate adjustments to aerosol-cloud interactions for individual cloud regimes. For this, the cloud liquid water path response, cloud thickness response, and cloud fraction response to perturbed cloud droplet concentration is analyzed. All sensitivities depend on the chosen cloud or environmental parameter indicating the importance of analyzing aerosol-cloud interactions for particular cloud regimes since similar clouds with similar cloud parameter responses are grouped together helping to identify individual behavior of these cloud regimes to perturbations in cloud droplet number concentration.

Climate change is an environmental issue that has actual or potential strategic impacts on many companies. The research problem emanates from the scientific work on climate change and the vast health effects that would pose as implications within the healthcare industry. The aim of the research was to explore the strategic response of private healthcare funders in South Africa to global climate change. By means of a case-study based research design, the stimuli for strategic response, risks and opportunities related to global climate change and strategy and an overall strategic organisational posture under the RDAP continuum scale framework had evolved. Evidence from the results and analysis brings light to the fact that global climate change as a strategic concern to private healthcare funders remains a point of scepticism. Although some of the organisations from the sample have considered climate change as a strategic concern, there are others that do not. The study showed that global climate change continues to remain an issue of complexity and uncertainty in the external business environment such that strategy formulation and implementation and acting proactively on the matter remains complicated. Copyright / Dissertation (MBA)--University of Pretoria, 2010. / Gordon Institute of Business Science (GIBS) / unrestricted

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The estuarine sheepshead minnow (Cyprinodon variegatus) is the most eurythermic fish species, with a thermal tolerance window between 0.6°C and 45.1°C. However, little is known about the physiological mechanisms that allow this species to survive this temperature range. In order to understand how sheepshead minnow physiology is affected by temperature acclimation and acute changes in temperature, I conducted research on this species using a multi-level approach. I began at the organismal level, and examined the effects of these temperature changes on the sheepshead minnow's metabolic rate and swimming performance. The next chapter investigated the effects of changing temperatures on cardiac function (i.e., tissue/organ specific effects). In the final chapter, I conducted research at the sub-cellular level, and determined how mitochondrial bioenergetics / function is impacted by changing temperatures. This research shows that while sheepshead minnows are able to sustain heart function and mitochondrial respiration over a broad range of temperatures; they also display a plastic temperature response which is associated with the downregulation of standard metabolic rate and cardiac remodeling to maintain force generation. Collectively, these physiological responses may contribute to the sheepshead minnow's ability to maintain physiological and organismal function across a large temperature range.

Over the years, global climate modelling has advanced, aiming for realistic and precise models by increasing their complexity. An integral component of climate models, the physics parameterizations, are a major limitation, but are required due to limited computational power. Grid adaptivity is an avenue that is being explored to mitigate these challenges, but comes with its own difficulties. For example, the question of whether the physics should be ``scale-aware’’, by adjusting according to the resolution and the fact that parameterizations are optimized for specific grid ranges. To research these challenges, test cases that work in both the adaptive and non-adaptive cases are required. This thesis concentrates on physics parameterizations of Atmospheric Global Climate Models (AGCMs) presenting the current hierarchy of idealized physics parameterizations found in the literature. It focuses on and provides a comprehensive explanation of a simplified dry physics model for AGCMs, exploring where it is situated in the current hierarchy and its steady states in the uncoupled case. A coupling of the physics model to the adaptive dynamical core wavetrisk is explained and explored. This includes characterizing the results in the non-adaptive case for time convergence, grid convergence, and the effects of the soil, while also benchmarking the climatology of the coupling. The simplified dry physics model introduces another level of complexity in the current dry physics hierarchy and is stable in the coupled and uncoupled cases. A decreasing temperature trend with height is observed, however warmer surface temperatures and cooler upper atmosphere temperatures, than that of Earth, are produced in the steady states. Additionally a linear rate of convergence in space is noted and an improvement in parallel efficiency with resolution is required. Overall these results can be used as a benchmark for future coupling in the adaptive case. / Thesis / Master of Science (MSc)

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A mesoscale evaluation is performed to determine the impacts of climate change on terrestrial hydrological components and the Net Irrigation Water Requirement (NIWR) throughout the Chesapeake Bay watershed in the mid-Atlantic region of the United States. The Noah-MP land surface model is calibrated and evaluated against the observed datasets of United States Geological Survey (USGS) streamflow gages, actual evapotranspiration from USGS Simplified Surface Energy Balance (SSEBop) Model and soil moisture from Soil Analysis Climate Network (SCAN). Six best performing Global Climate Models (GCM) based on Multivariate Adaptive Constructed Analogs (MACA) scheme are included for two future scenarios (RCP 4.5 and RCP 8.5), to assess the change in water balance components, change in NIWR for two dominant crops (corn and soybeans) and uncertainty in GCM projections. Using these long-term simulations, the flood inundation maps are developed for future scenarios along the Susquehanna River including the City of Harrisburg in Pennsylvania. The HEC-RAS 2D model is calibrated and evaluated against the high-water marks from major historical flood events and the stage-discharge relationship of the available USGS streamgages. Finally, the impacts of climate change are assessed on flood inundation depth and extent by comparing a 30-yr and 100-yr flood event based on the historical and future (scenario-based) peak discharge estimates at the USGS streamgages. Interestingly, flood inundation extent and severity predicted by the model along the Susquehanna River near Harrisburg is expected to rise in the future climate scenarios due to the greater frequency of extreme events increasing total precipitation. / Master of Science / Climate change is inevitable due to increased greenhouse gas emissions, with impacts varying in space and time significantly throughout the globe. The impacts are strongly driven by the change in precipitation and temperature which affect the control of the movement of water on the surface of the Earth. These changes in the water cycle require an understanding of hydrological components like streamflow, soil moisture, and evapotranspiration. Development of long-term climate models and computational hydrological models (based on mathematical equations and governed by laws of physics) has helped us in understanding this climate variability in space and time. This study performs a long-term simulation using the datasets from six different climate models to analyze the change in terrestrial hydrological components for the entire Chesapeake Bay watershed in the mid-Atlantic region of the United States. The simulations provide an understanding of the interplay between various land surface processes due to climate change and can help determine future water availability and consumption. To illustrate the usefulness of such long-term simulations, the crop water requirement is quantified for the dominant crops in Chesapeake Bay watershed to project water availability and support the development of mitigation strategies. Flood inundation maps are also developed for a section of Susquehanna River near the City of Harrisburg in south-central Pennsylvania using the streamflow from long-term simulations. The flood inundation depth and extent for major flood events such as Tropical Storm Agnes (1972) and Tropical Storm Lee (2011) are compared along the Susquehanna River, which can aid in managing flood operations, reduce the future flood damages and prioritize the mitigation efforts for endangered communities near the City of Harrisburg.

Climate change is impacting the intensity, duration, and frequency of climatic events. With climate change comes a multitude of adverse conditions, including extreme heat events, changes in disease patterns, and increased likelihood and frequency of natural disasters, including in places previously not exposed to such conditions. Human health has foundations in the environment; therefore, these adverse climatic conditions are directly linked to human health. Rural communities in Appalachia are likely to experience negative consequences of climate change more severely due to unique geomorphology and sociopolitical realities of the region. Non-governmental organizations (NGOs) throughout the Appalachian region are currently working to build resilience and prepare for potential adverse effects from climate change. To aid in this process, projections of future climate scenarios are needed to understand possible situations and adequately prepare. In partnership with Ohio University and West Virginia University, this study aims to characterize potential future climatic scenarios from publicly-available global climate models (GCMs) and prepare information to share with Appalachian communities. Climate model information for this analysis was obtained from NASA's Coupled Model Intercomparison Project (CMIP6). All code for data processing and analysis was prepared using the open-source R programming language to support reproducibility. To confirm that models can accurately simulate Appalachian climatic conditions, CMIP6 hindcast simulations for precipitation and maximum temperature were compared to observed weather records from NOAA. Climate models over and underestimated average precipitation values depending on location, while models consistently underestimated extreme precipitation values, simulated by total five-day precipitation. For temperature, climate models consistently underestimated average and extreme high temperature indicators. For Appalachian region projections, three towns of interest (one for each state involved in the study: Virginia, West Virginia, and Ohio) were selected based on current community resilience efforts. In these locations, mid-century (2040 – 2064) and end-of-century (2075 – 2099) projections for precipitation and temperature were summarized under a low emissions scenario and a high emissions scenario. Increases in precipitation and temperature were observed under average and extreme scenarios; these increases were noticeably more extreme under higher emissions scenarios. These trends are consistent with other studies and climate science consensus. When compared to hindcast values, observed average precipitation values were overestimated and underestimated, while observed extreme precipitation indices, average temperatures, and heat wave indices were underestimated by GCMs. Context with observed data is important to understanding model accuracy for the Appalachian region. GCMs are a useful tool to project potential future climate scenarios at specific locations in the Appalachian region, though model data is best used to communicate general trends rather than as inputs for other physical models. / Master of Science / Climate change is driving previously unseen changes in many aspects of the environment. Among these aspects, and of particular concern, are increased precipitation and increased high temperatures, which have direct negative outcomes on human health. Climate change can impact human health in a variety of ways, such as increasing instances of heat-related illnesses like heatstroke, changing insect-carried diseases patterns (i.e. Lyme disease, malaria), worsening preexisting conditions like asthma, and increasing the likelihood of natural disasters like flooding. Climate change also impacts mental health, especially increasing instances of anxiety and post-traumatic stress disorder from disasters. Rural communities like Appalachia are more likely to experience severe negative outcomes due to lack of resources, remote location, and economies historically based on resource extraction. Appalachia specifically also faces unique challenges with flooding, as many towns are situated in valleys with streams or rivers running through the center of town. To address and prepare for possible climate change outcomes, community-based planning is required to build resiliency. Throughout many areas, but specifically in Appalachia, many community-based organizations are already working to strengthen their communities by providing stable housing, addressing flooding, and preparing emergency response teams. To aid in these efforts, information about potential future climate is beneficial to these organizations to understand and prepare for potential conditions. This study aims to use publicly-available climate models to generate information about possible future climate conditions to be shared with community organizations. Additionally, this project's datasets and procedures are publicly available, so this analysis can be performed by communities anywhere in the world given they have adequate computing power. To check that models are a good indicator of previous climate conditions, and therefore would be useful for future projections, historic projected climate model outputs were compared to observed weather data. After confirming that the models used were fairly consistent with observed data, projected values for midcentury (2040 – 2064) and end-of-century (2075 – 2099) were gathered for Appalachian towns with interested community organizations. Projected values show increases in high temperatures and precipitation throughout the Appalachian region, including in short-term event scenarios, which is consistent with other climate science. Higher emissions scenarios result in greater increases in average and extreme temperature and precipitation values. Climate models can be a useful tool in understanding potential general climatic trends for a specific location and can support climate science communication.