Thesis (Ph. D.)--University of Wisconsin--Madison, 1970. / Typescript. Vita. Description based on print version record. Includes bibliographical references.

Thesis (M.A.)--University of Wisconsin--Madison, 1964. / eContent provider-neutral record in process. Description based on print version record. Bibliography: l. 62.

Thesis (M.S.)--University of Wisconsin--Madison, 1965. / eContent provider-neutral record in process. Description based on print version record. Bibliography: l. 76-80.

Thesis (Ph. D.)--University of Washington, 2005. / Vita. Includes bibliographical references (p. 89-92).

Mountain snowline is important as it is an easily identifiable measure of the phase state of water in the landscape. However, frequent observation of the snowline in Scotland is difficult as reduced visibility is common, obscuring ground based and remotely sensed methods. Changes in seasonal snowline elevation can indicate long-term climate trends. Snow cover influences local flora and fauna, and knowledge of snowline can inform management of water and associated risks. Complete Scottish Snow Survey of Great Britain (SSGB) records were transcribed and form the primary snow cover dataset used for this work. Voluntary observers collected the SSGB between 1945 and 2007. Other snow cover data used includes remotely sensed (Moderate-resolution Imaging Spectroradiometer: MODIS) and Met Office station observations (as point observations and interpolated to form UK Climate Projections 2009, UKCP09). I present a link between the North Atlantic Oscillation (NAO) index and days of snow cover in Scotland between winters from 1875 to 2013. Broad (5 km resolution) scale datasets (e.g. UKCP09) are used to extract nationwide patterns, supporting these findings using SSGB hillslope scale data. The strongest correlations between the NAO index and snow cover are found in eastern and southern Scotland; these results are supported by both SSGB and UKCP09 data. Correlations between NAO index and snow cover are negative with the strongest relationships found for elevations below 750 m. A degree-day snow model was developed using daily precipitation and temperature data to derive snow cover and melt. This model was run between 1960 and 2011 using point data from five Met Office stations and data on a 5 km grid (UKCP09 temperature and CEH GEAR precipitation) across Scotland. Due to CEH GEAR data underestimating precipitation at higher elevations, absolute values of melt are uncertain. However, relative correlations are apparent, e.g. the proportion of precipitation as melt and number of days with snow cover each year are generally decreasing through time, except around Ben Nevis. Notably, this increase correlates with positive NAO, and it is thought Ben Nevis remains cold enough to accumulate lying snow in the face of a warming climate. Snowmelt rates were found to annually exceed the maximum snowmelt rate used for fluvial impoundment structure design, but this was only at the highest elevations in areas like the Cairngorms.

There is strong scientific consensus on the evidence of anthropogenic climate change which will increasingly present social, economic and institutional challenges. The Fifth Assessment report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) established that ‘human influence on the climate system is clear’ and that ‘changes in many extreme weather and climate events have been observed since about 1950’ (IPCC 2014a). Associated impacts include sea level rise and increased likelihood of extreme weather worldwide such extreme rainfall, heat waves, hurricanes and tornados (IPCC 2014a; Klijn et al. 2015). Climate change adaptation is the adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects in order to minimise the impacts and to take advantage of new opportunities (IPCC 2007). Many vulnerable countries, regions and cities have accepted that some form of adaptation is inevitable (Swart et al. 2014). This thesis contributes to the research on decision-making for climate change adaptation in order to reduce vulnerability. Both bottom-up and top-down analyses are applied to complement one another with an application to flooding. Flood risk is expected to increase in the UK under climate change (Alfieri et al. 2016; Scottish Government 2016) associated significant economic damage (CEA 2007). From a top-down perspective, the thesis explores how to enhance economic decision-making under climate change uncertainty. In a situation of uncertainty the costs may be clear and immediate whereas the benefits are uncertain and often only realised in the distant future. This impedes the use of standard decision-making tools such as cost-benefit analysis that rely on the quantification of (expected) costs and benefits. The thesis begins on the macro scale with a taxonomy of economic decision-making tools for climate change adaptation, discusses the sector level and subsequently proceeds to the case study micro-scale with applications of adaptation decision-making. First, the potential of alternative decision-making tools, so-called robust decision-making approaches, is examined. The strengths and weaknesses of these tools relative to traditional decision-making processes such as CBA are explored and their future potential in the adaptation process evaluated. It is found that robust decision-making tools under uncertainty provide performance across a range of climate change scenarios, but they may yield lower overall performance if compared with the alternative strategy under the actual climate outturn. Furthermore, they are resource intense and decision makers need to balance the resources required for employing the methods with the added value they can offer. A flow-chart is developed to provide guidance on which decision-making tool should be applied depending on the scale and type of adaptation project. On the sector level, the economic appraisal of adaptation options for agriculture is explored. Agriculture is particularly vulnerable to climate change due to the direct impacts of weather and climate on agricultural output and the sector plays an indispensable role in providing (and improving) food security as well as creating employment. Many of the adaptation options in agriculture involve short-term managerial changes and can be appraised with standard economic decision-making and the options can be carried out after the climate signal has been observed. For those adaptations that do require a longer time to take effect or are long-lived and are (partly) irreversible in nature, robust approaches have a valuable role to play in decision-making. Suggestions are made regarding how robust decisionmaking tools under uncertainty can be practically applied to adaptations in agriculture, outlining the data needs and the steps of the data analysis for three different applications. On the micro level, for a case study in the Eddleston Water catchment in the Scottish borders, UK, two different economic appraisal tools are applied. These include a cost-benefit analysis of afforestation as a flood management measure under different climate change scenarios which can provide important insights for adaptation decisions when robust decision-making tools under uncertainty are not feasible due to resource constraints. It is found that the flood risk under climate change increases substantially in the case study area which needs to be taken into consideration for economic appraisal. The results of the CBA reveal that all modelled scenarios of afforestation have positive NPVs which are driven by further eco-system services (including climate regulation, water quality and recreation) rather than flood regulation benefits. It is concluded that eco-system services beyond flood regulation should be considered for the appraisal of NFM to enable policy-makers to make informed decisions. Second, the Expected values can be used in situations of quantifiable uncertainty, i.e risk. But for climate change we do not have a strong methodology to assess these subjective probabilities. They cannot be fully based on the past, because climate change is a new process for which we have no historical equivalent. Models share common flaws in their assumptions and their dispersion in results cannot be used to assess the real uncertainty (Hallegatte, 2012). The term deep uncertainty (Lempert et al., 2003) or severe uncertainty is used (Ben-Haim, 2006) in these contexts. Such uncertainty is characterised as a condition where decision makers do not know or cannot agree upon a model that adequately describes cause and effect or its key parameters (Walker et al., 2012). This leads to a situation where it is not possible to say with confidence whether one future state of the world is more plausible than another. The robust decision-making tool under uncertainty real option analysis is applied to the same case study to allow for adjusting adaptation options over time by integrating lessons learned about climate change in the appraisal process. A simplified ROA is presented to minimise the life cycle cost of a system that aims to prevent flooding of a return period of 1/20 using tools which should be available to most public authorities. This includes the use of UKCP09 climate data, analysis of changes of peak flow under the measure implemented, cost structures for the measure and damage cost under different outcomes. The analysis can be carried out in an excel spread sheet with the aforementioned types of input. The results of the analysis demonstrate that the obtained strategy is significantly cheaper than planting for the worst case scenario and presents the potential for learning under climate change uncertainty as a way to allocate resources in a more efficient way. The complementing bottom up approach investigates behavioural barriers to decisionmaking for adaptation. Standard economic theory tells us that self-interest will motivate most actors to engage in efficient private adaptation as long as the costs do not exceed the benefits. Thus, we would expect households at flood risk to invest in flood adaptation measures. However, it has been observed that households do not necessarily take action to protect themselves and their assets from flooding. In a study carried out in co-operation with 36 communities around Scotland, protection motivation theory is used to explain the uptake of household flood protection and whether community led flood action groups can increase uptake. It is found that flood action groups directly and indirectly influence the uptake of some flood protection measures positively in particular if tailored information is provided. Overall, it is concluded that both top-down and bottom-up approaches play an important role to move towards an economically efficient adaptation in the context of flooding. / From a top-down perspective, uncertainty should be explicitly acknowledged and included in economic decision-making for adaptation (to flooding) to make an informed decision. The type of analysis will depend on the adaptation project and resources at hand. Developing and fostering bottom-up tools such as flood action groups to increase the uptake of the type of household flood protection with a benefit-cost ratio above 1 may also contribute towards the more efficient allocation of resources.

<p> The climate over Ecuador is complex due to several interacting factors, such as its location at the equator, the Andean topography, and several modes of internal variability, including the El Ni&ntilde;o&ndash;Southern Oscillation (ENSO), affecting the region. In addition, the rapid increase in greenhouse gas concentrations will continue to affect both the mean state and climate variability in Ecuador over the coming decades. Hence, a thorough understanding of both natural and anthropogenic forcings and how they combine to influence Ecuadorian climate is a necessity for decision-making and implementation of adequate adaptation measures. However, the lack of observational data, both in space and time, severely limits our ability to study climate changes that affect Ecuador today. Employing a high-resolution regional climate model (RCM) can help to better diagnose the mechanisms and feedbacks that lead to climate changes and how they differ in space and time, as long as the model is able to adequately reproduce what is observed in the limited observational data. </p><p> With the purpose of contributing to a better understanding of how and why Ecuador&rsquo;s climate will change in the coming decades, three topics of specific relevance for this country are addressed in this dissertation: a) how well can a RCM simulate the mean climate state and its variability over a region of complex topography such as Ecuador under different parameterization schemes? b) what feedbacks are involved in producing elevation-dependent warming (EDW) in the Ecuadorian Andes? And c) how are the characteristics of climate extreme events (CEEs) over Ecuador projected to change by the middle of the 21st century? These three questions are addressed by use of observations and simulations using the Weather Research and Forecasting Model (WRF) configured as a RCM with a high-resolution of 10 km horizontal grid spacing and 51 vertical levels. </p><p> Sensitivity test runs were performed to choose a proper combination of parameterization schemes for conducting four WRF simulations comprising the territory of Ecuador and spanning 30 years. The first simulation was driven by the Climate Forecast System Reanalysis (CFSR) for the period 1980&ndash;2010 and used to evaluate the model&rsquo;s ability to realistically portray present-day climate over the region. The other three simulations used the output from the Community Climate System Model version 4 (CCSM4) as the boundary conditions to produce a baseline simulation (1976&ndash;2005) and two future simulations (2041&ndash; 2070) following the moderate-emissions scenario RCP 4.5 and the high-emissions scenario RCP 8.5. </p><p> EDW over the Ecuadorian Andes is studied by analyzing observations and the present-day WRF-simulation, while the future simulations were used to test the contribution to this effect caused by future changes in feedback mechanisms. Surface net radiation changes due to future changes in cloudiness were identified as the most important mechanisms leading to EDW over the Ecuadorian Andes, with future reductions in cloudiness dominating at high elevations. The model results also indicate different future warming signals on both sides of the Andes, with higher warming rates at the high elevations of the western Andes, likely due to enhanced subsidence and adiabatic warming in the mid-troposphere. </p><p> CEEs are analyzed by using annual climatic indices. First the present-day relationship between CEEs and Pacific (ENSO) and Atlantic modes of variability are investigated in both models and observations. Results confirm the dominant role played by ENSO in governing the occurrence of many CCEs over Ecuador, while calling for more studies on the potential influence of Atlantic modes over Ecuador&rsquo;s CEEs. The model projections suggest significant future changes in CEEs, with large increases in warm and wet extremes over most regions, but the simulations also highlight significant spatial heterogeneity, which suggests that it is important to study changes in extreme events using high-spatial resolution data.</p><p>

<p> Unforced internal variability abounds in the climate system and often confounds the identification of climate change due to external forcings. Given that greenhouse gas concentrations are projected to increase for the foreseeable future, separating forced climate change from internal variability is a key concern with important implications. Here, we leverage a 40-member ensemble, the Community Earth System Model Large Ensemble (CESM-LE) to investigate the influence of internal variability on the detection of forced changes in two climate phenomena. First, using cyclone identification and compositing techniques within the CESM-LE, we investigate precipitation changes in extratropical cyclones under greenhouse gas forcing and the effect of internal variability on the detection of these changes. We find that the ensemble projects increased cyclone precipitation under twenty-first century business-as-usual greenhouse gas forcing and this response exceeds internal variability in both near- and far- futures. Further, we find that these changes are almost entirely driven by increases in cyclone moisture. Next, we explore the role of internal variability in projections of the annual cycle of surface temperature over Northern Hemisphere land. Internal variability strongly confounds forced changes in the annual cycle over many regions of the Northern Hemisphere. Changes over Europe, North Africa and Siberia, however, are large and easily detectable and further, are remarkably robust across model ensembles from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive. Using a simple energy balance model, we find that changes in the annual cycle over the three regions are mostly driven by changes in surface heat fluxes. </p><p> The thesis also presents a novel ensemble-based framework for diagnosing forced changes in regional climate variability. Changes in climate variability are commonly assessed in terms of changes in the variances of climate variables. The covariance response has received much less attention, despite the existence of large-scale modes of variability that induce covariations in climate variables over a wide range of spatial scales. Addressing this, the framework facilitiates a unified assessment of forced changes in the regional variances and covariances of climate variables.</p><p>

Thesis (Ph.D.)--Boston University / The Antarctic Plateau is defined on the basis of elevation and slope, being above 2000 meters and with less than a one-degree slope in East Antarctica and being above 1500 meters with less than a one-degree slope in West Antarctica. This region is presented as a high latitude, high elevation, cold desert. It is shown to be a near homogenous geographical region, with a uniform snow surfaace, relatively little local relief, and great depths of snow. Its climate is controlled to a large degree by its geographical location. Elevation, slope, and distance from the coast are presented as the three most important geographical elements [TRUNCATED]

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