According to the recently released National Climate Assessment (NCA), climate change will disproportionally impact the health of the most vulnerable communities in Central Texas (Melillo, 2014). Exactly how climate change will impact these populations is unclear (Measham, 2011; Martens, 2014). Nationwide, there are few examples of cities looking at the impacts of climate change on existing public health issues and vulnerable communities. The NCA, Austin/Travis County Community Health Assessment (CHA) and Community Health Improvement Plan (CHIP), broadly identifies vulnerable communities as children, the elderly, the sick, the poor, and some communities of color (Melillo, 2014: Luber, 2009). The 2014 release of the NCA, in addition to the 2013 completion of the CHA and CHIP, provides an opportunity to compare current public health issues with projected changes in climate. The deductive process starts with a review of the CHA and CHIP to identify issues that are directly impacted by hotter and longer heat waves including a lack of physical activity, a decrease in mobility, and greater social isolation. These issues are then compared to likely climate scenarios for Austin in the coming century. For Austin, climate scientists project longer and hotter heat waves and higher overnight average temperatures. The results of the process are a hypothetical framework and specific actions to incorporate increasing temperatures into short-term and long-term health improvement planning. Comparing the NCA and CHA/CHIP reveals that an increase in intensity and duration of heat waves will make it especially dangerous for vulnerable communities who already struggle with health issues sensitive to heat such as obesity, respiratory ailments, and social isolation (Martens, 2014). Further analysis finds that the health implications of climate change come down to three broad topics: outdoor physical activities, lack of access to healthcare facilities, and isolation. Austin’s increasing temperatures and growing population means that more resources and efforts are needed to ensure the safety of all Austin residents. In this thesis, I put forth a hypothetical decision-making framework that prioritizes the allocation of resources to advance Austin’s pathway to climate resiliency. In addition, tools and actions are proposed to increase the climate resilience of the most vulnerable community members in Austin. / text

The purpose of this report was to investigate how municipals in the county of Västra Götaland work with climate adaptation of storm water management and to identify problems that occur in the work. As with the rest of the world, Sweden will be affected by expected climate changes and it is therefore important that Swedish municipalities work with climate adaptation and to help them make the work manageable. The method used was semi-structural qualitative interviews where 13 municipalities were interviewed in April 2016. The result of the interviews shows that there is an ambition to work with climate adaptation of storm water management but that there are different problems associated with the work that have made it difficult to start. 69% of the municipalities include climate adaptation to some extent when they work with master plans and 5 out of 8 municipalities are or will be including climate adaptation strategies in their storm water management document. A majority of the municipalities don’t prioritize climate adaptation and could be doing more to include climate adaptation in their work. The most common problems reported by the municipalities were too little resources/lack of finance, undecided responsibility and not clear enough laws regarding the subject.

Understanding of climate change and policy responses thereto rely on accurate measurements as well as models of both socio-economic and physical processes. However, data to assess impacts and establish historical climate records are non-stationary: distributions shift over time due to shocks, measurement changes, and stochastic trends - all of which invalidate standard statistical inference. This thesis establishes econometric methods to model non-stationary climate data consistent with known physical laws, enabling joint estimation and testing, develops techniques for the automatic detection of structural breaks, and evaluates socio-economic scenarios used in long-run climate projections. Econometric cointegration analysis can be used to overcome inferential difficulties stemming from stochastic trends in time series, however, cointegration has been criticised in climate research for lacking a physical justification for its use. I show that physical two-component energy balance models of global mean climate can be mapped to a cointegrated system, making them directly testable, and thereby provide a physical justification for econometric methods in climate research. Automatic model selection with more variables than observations is introduced in modelling concentrations of atmospheric CO₂, while controlling for outliers and breaks at any point in the sample using impulse indicator saturation. Without imposing the inclusion of variables a-priori, model selection results find that vegetation, temperature and other natural factors alone cannot explain the trend or the variation in CO₂ growth. Industrial production components, driven by business cycles and economic shocks, are highly significant contributors. Generalizing the principle of indicator saturation, I present a methodology to detect structural breaks at any point in a time series using designed functions. Selecting over these break functions at every point in time using a general-to-specific algorithm, yields unbiased estimates of the break date and magnitude. Analytical derivations for the split-sample approach are provided under the null of no breaks and the alternative of one or more breaks. The methodology is demonstrated by detecting volcanic eruptions in a time series of Northern Hemisphere mean temperature derived from a coupled climate simulation spanning close to 1200 years. All climate models require socio-economic projections to make statements about future climate change. The large span of projected temperature changes then originates predominantly from the wide range of scenarios, rather than uncertainty in climate models themselves. For the first time, observations over two decades are available against which the first sets of socio-economic scenarios used in the Intergovernmental Panel on Climate Change reports can be assessed. The results show that the growth rate in fossil fuel CO₂ emission intensity (fossil fuel CO2 emissions per GDP) over the 2000s exceeds all main scenario values, with the discrepancy being driven by underprediction of high growth rates in Asia. This underestimation of emission intensity raises concerns about achieving a world of economic prosperity in an environmentally sustainable fashion.

Thesis advisor: Jeremy Shakun / As recently measured amounts of global atmospheric carbon dioxide concentrations have risen 40% from pre-Industrial levels and will likely reach double by mid-century, climate scientists have expressed concern over the future state of the climate system, and have attempted to gauge the consequences of such a large forcing. The principal parameter for climate scientists is equilibrium climate sensitivity, which is the change in temperature following a doubling of atmospheric CO2 concentrations. Current estimates of climate sensitivity span too expansive of a range to provide a clear understanding of the magnitude of temperature changes one can expect. Therefore, I conduct many individual multivariate analyses as a means of narrowing these ranges of sensitivity and to investigate geographical distributions of sensitivity, at the very least. To do so, I analyze four major climate forcings: greenhouse gas, atmospheric dust, ice volume, and insolation. Using several multiple linear regressions, I calculate the relative weighting of each forcing in driving the temperature signal in 47 local temperature proxy records. The paleoclimate proxy records chosen span glacial cycles over the past 800 kyr. These results provide insight into the geographical distributions of the relative influences of each of the forcings, while working to constrain the range of sensitivity estimates through the weighting of the greenhouse gas forcing. Separating out the individual climate inputs allows me to conclude what percentage of climate change was caused by CO2 in the past, and by implication how much warming might be expected due to GHG forcing in the future. / Thesis (BS) — Boston College, 2015. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Earth and Environmental Sciences.

This document is a summary of research conducted to develop and apply climate analysis tools toward a better understanding of the past and future of hydroclimate variability in the state of Utah. Two pilot studies developed data management and climate analysis tools subsequently applied to our region of interest. The first investigated the role of natural atmospheric forcing in the inter-annual variability of precipitation of the Sahel region in Africa, and found a previously undocumented link with the East Atlantic mode, which explains 29% of variance in regional precipitation. An analysis of output from an operational seasonal climate forecast model revealed a failure in the model to reproduce this linkage, thus highlighting a shortcoming in model performance. The second pilot study studied long-term trends in the strength of the Great Plains low-level jet, an driver of storm development in the region’s wet spring season. Our analysis showed that since 1979 the low-level jet has strengthened as shifted the timing of peak activity, resulting in shifts both in time and location for peak precipitation, possibly the result of anthropogenic forcing. Our third study used a unique tree-ring dataset to create a reconstruction of April 1 snow water equivalent, an important measure of water supply in the Intermountain West, for the state of Utah to 1850. Analysis of the reconstruction shows the majority of snowpack variability occurs monotonically over the whole state at decadal to multidecadal frequencies. The final study evaluated decadal prediction performance of climate models participating in the Coupled Model Intercomparison Project 5. We found that the analyzed models exhibit modest skill in prediction of the Pacific Decadal Oscillation and better skill in prediction of global temperature trends post 1960.

Cultural heritage and human comfort: The issue of indoor climate in historic buildings in the twentieth centuryI

This PhD project explores the political influence of four sets of non-governmental actors upon the international politics of global warming. The forms of influence attributable to Working Group 1 of the Intergovernmental Panel on Climate Change (the body set up to advise governments on the science of climate change), the mass media's coverage of global warming, and the political involvement of both the fossil fuel lobbies and environmental groups in the policy debate on climate change, are understood via use of literatures on the dimensions of power appropriate to understanding the significance of non-state actors. The project stems from a widely acknowledged absence of a detailed understanding of the role of non-governmental organisations in international environmental politics, which extends to the international politics of global warming. The influence of each group of actors is conceptualised in different ways, so that the forms of power used to describe the various groups are not compared. Rather, the aim of the thesis is to assess what a less state-centred reading of the international politics of global warming, derived from a discussion of the role of the above actors, has to offer existing explanations. The analysis of these groups of actors sheds light on different aspects of the way the issue of climate change has been addressed at the international level. The conclusions drawn about the influence of these actors are used to critique the popular use of Regime accounts in international environmental politics that focus upon the process of institutional bargaining between states, which are argued to provide an inadequate basis for explanation of the global politics of climate change.

Changes in the physical properties of the atmosphere are caused by geographic influences and dynamic effects. The nature of the surface of the earth over which the air passes is usually the main factor in modifying the air in the lower levels of the troposphere. The ocean area of Hudson Bay in northeastern Canada presents an extensive surface for modification of air. Continental polar air crossing Hudson Bay and continuing into eastern Canada is modified not only by the underlying surface but also by the dynamic effects occurring in the atmosphere during its trajectory over these regions. The amount of modification is of major importance in the climate of the surrounding area, and in the meteorology of North American air masses. It was the purpose of this study: 1. To compute the changes in the physical properties that occur in continental polar air over Hudson Bay, Consequently, a project was undertaken to determine the amount of ice that forms on Hudson Bay in winter. The effect of the formation of ice in changing the modification of air was then studied. [...]

The first part of this thesis is an assessment of the ability of global climate models to reproduce observed features of the leading Empirical Orthogonal Function (EOF) mode of North Pacific sea surface temperature (SST) anomalies known as the Pacific Decadal Oscillation (PDO). The simulations from 13 global climate models I am analyzing were performed under phase 3 of the coupled model intercomparison project (CMIP3). In particular, I am investigating whether these climate models capture tropical influences on the PDO, and the influences of the PDO on North American surface temperature and precipitation. My results are that 1) the models as group produce a realistic pattern of the PDO. The simulated variance of the PDO index is overestimated by roughly 30%. 2) The tropical influence on North Pacific SSTs is biased systematically in these models. The simulated response to El Niño-Southern Oscillation (ENSO) forcing is delayed compared to the observed response. This tendency is consistent with model biases toward deeper oceanic mixed layers in winter and spring and weaker air-sea feedbacks in the winter half-year. Model biases in mixed layer depths and air-sea feedbacks are also associated with a model mean ENSO-related signal in the North Pacific whose amplitude is overestimated by roughly 30%. Finally, model power spectra of the PDO signal and its ENSO-forced component are “redder” than observed due to errors originating in the tropics and extratropics. 3) The models are quite successful at capturing the influence of both the tropical Pacific related and the extratropical part of the PDO on North American surface temperature. 4) The models capture some of the influence of the PDO on North American precipitation mainly due to its tropical Pacific related part. In the second part of this thesis, I investigate the ability of one such coupled ocean- atmosphere climate model, carefully initialized with observations, to dynamically predict the future evolution of the PDO on seasonal to decadal time scales. I am using forecasts produced by the Canadian climate data assimilation and prediction system employing the Canadian climate model CanCM3 for seasonal (CHFP2) and CanCM4 for decadal (DHFP1) predictions. The skill of this system in predicting the future evolution of the PDO index is then inferred from a set of historical “forecasts” called hindcasts. In this manner, hindcasts are issued over the past 30 years (seasonal), or over the past 50 years (decadal) when they can be verified against the observed historical evolution of the PDO index. I find that 1) CHFP2 is successful at predicting the PDO at the seasonal time scale measured by mean-square skill score and correlation skill. Weather “noise” unpredictable at the seasonal time scale generated by substantial North Pacific storm track activity that coincides with a shallow oceanic mixed layer in May and June appear to pose a prediction barrier for the PDO. PDO skill therefore depends on the start season of the forecast. PDO skill also varies as a function of the target month. Variations in North Pacific storminess appear to impact PDO skill by means of a lagged response of the ocean mixed layer to weather “noise”. In CHFP2, times of increasing North Pacific storm track activity are followed by times of reduced PDO skill, while the North Pacific midwinter suppression of storm track activity with decreasing storminess is followed by a substantial recovery in PDO skill. 2) This system is capable of forecasting the leading 14 EOF modes of North Pacific SST departures, that explain roughly three quarters of the total SST variance. CHFP2 is less successful at predicting North Pacific SSTs, i.e., the combination of all the EOF modes, at the seasonal time scale. 3) Besides the skill in Pacific SST, CHFP2 skillfully predicts indices that measure the atmospheric circulation regime over the North Pacific and North America such as the Pacific/North American pattern (PNA) (skillful for three out of four start seasons) and the North Pacific index (NPI) (skillful for all four start seasons). 4) CHFP2 is successful at forecasting part of the influence of Pacific SST on North American climate at the seasonal time scale. Measured by 12-month average anomaly correlation skill, in this system the PDO is a better predictor for North American precipitation (skillful for all four start seasons) than temperature (skillful for one out of four start seasons). In CHFP2, ENSO is a better predictor for North American temperature (skillful for all four start seasons) than the PDO. Both ENSO and the PDO are, however, good predictors for North American precipitation (skillful for all four start seasons). Finally, DHFP1 is less successful at forecasting the PDO at the decadal time scale. Ten-year forecasts of the PDO index exhibit significantly positive correlation skill exclusively in the first year of the forecast. When the correlation skill of the predicted index averaged over lead years is considered, the PDO skill in this system stays significantly positive during the first three years of the decadal forecast. In other words, this climate data assimilation and prediction system is expected to skillfully predict the future three year averaged evolution of the PDO index, but not the evolution of the index in each year individually. / Graduate

Thesis (doctoral)--University of Tartu, 2005. / Vita. Includes bibliographical references.