L.LM. / Climate change is change in the world’s temperatures, precipitation and wind that differ significantly from previous conditions and are seen to induce or bring about a change in the ecosystem and socio-economic activities. The UNFCCC defines climate change as “change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods”.The international concerns are that increased concentrations of greenhouse gas emissions such as carbon dioxide are changing climate in a way that is detrimental to our social and economic well being. Human activities have increased greenhouse gas emissions drastically since the industrial revolution by 31%. The impact this would have on the environment would include a rise in sea levels, causing loss of coastlines worldwide of which small island states are more at risk. Inhabitants of these coastline areas would in turn be vulnerable to floods and would eventually be forced to migrate creating yet another problem in international law what has come to be termed climate change refugees. The risk of flooding due to climate change is not limited to coastline areas alone incidents of flooding have been experienced on main lands in several states. According to UNEP half of the world’s population, i.e. 3billion, live in coastal areas. The catastrophic effects of climate change tend to have ripple effects. While climate change originated as an environmental problem it now impact on everyday aspects of human life with implications on international economy, public health, social issues such as migration and loss of livelihood and ultimately threatening peace and security.

This series of studies evaluates the ability of global climate models (GCMs) to simulate the observed relationship between the upper limit of tropical cyclone (TC) intensity and sea surface temperature (SST). Previous studies addressed whether GCMs are capable of reproducing observed TC frequency and intensity distributions. This research builds upon these earlier studies by examining how well GCMs capture physically relevant relationships that are important for understanding the impacts of climate change on TC intensity. The research presented here aims to 1) quantify differences between the observed and simulated sensitivity of TC limiting intensity to SST, and 2) explore possible explanations for any differences that exist. Observed TC data are compared to simulated TCs from four different GCMs---the FSU-COAPS, GFDL-HiRAM, MRI-AGCM, and NCAR-CAM. Model horizontal grid spacing ranges from ~100 km for the FSU-COAPS to ~20 km for the MRI-AGCM. An additional comparison is made for TCs generated through a statistical-deterministic downscaling technique. This research uses a spatial tessellation approach that spatially bins North Atlantic TC and SST data into equal-area hexagon regions. For each region, the statistical upper limit of observed and simulated TC intensity (i.e., limiting intensity) is estimated using extreme value theory. For comparison with the statistical limiting intensity, reanalysis and model field data are employed to approximate observed and simulated potential intensity, respectively. Results reveal that the current suite of GCMs do not capture the observed sensitivity of TC limiting intensity to SST. While a 1° C increase in SST corresponds to a 7.9 +/- 1.19 m/s increase in observed limiting intensity, the same 1° C increase in SST is not associated with a statistically significant increase in simulated TC limiting intensity. This is found to be true both for relatively coarse resolution GCMs that do not generate TCs with intensities exceeding 50 m/s as well as for higher resolution GCMs that are capable of simulating Category 5 hurricanes. Rather than SST, it is found that simulated TC limiting intensity is highly sensitive to 700--400 hPa relative humidity. Conversely, relative humidity does not describe any of the residual variance in observed TC limiting intensity. Therefore, this research suggests that even if a model is able to resolve realistically strong TCs, those simulated TCs may not be governed by the same thermodynamic principles as those that we observe. Although GCMs do not capture the observed sensitivity of limiting intensity to SST, it is shown that the FSU-COAPS model capably reproduces the observed sensitivity of potential intensity to SST. The model generates a thermodynamic environment suitable for the development of strong TCs over the correct portions of the basin, however strong simulated TCs do not develop. This result strongly supports the notion that direct simulation of TC eyewall convection is necessary to accurately represent TC intensity and intensification processes in climate models. / A Dissertation submitted to the Department of Geography in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester, 2015. / February 27, 2015. / Global climate models, Tropical cyclones / Includes bibliographical references. / James B. Elsner, Professor Directing Dissertation; Henry Fuelberg, University Representative; Chris Uejio, Committee Member; Tingting Zhao, Committee Member.

Violent tropical cyclones (TCs) continue to inflict serious impacts on national economies and welfare, but how they are responding to global warming has not been fully clarified. Here I construct an empirical framework that shows the observations supporting a strong link between rising global ocean warmth and increasing trade-off between TC intensity and frequency in the western North Pacific. Thermodynamic structure of the tropical western North Pacific with high global ocean warmth is characterized by convectively more unstable lower troposphere with greater heat and moisture, but this instability is simultaneously accompanied by anomalous high pressure in the middle and upper troposphere over the same region. Increasing trade-off level between TC intensity and frequency in a warmer year proves that this environment further inhibits the TC occurrences over the region, but TCs that form tend to discharge stored energy to upper troposphere with stronger intensities. By increasing the intensity threshold at higher levels we confirmed that the TC climate connection with global ocean warmth occurs throughout the strongest portion of TCs, and the environmental connection of the TC climate is more conspicuous in the extreme portion of TCs. Intensities at the strongest 10~% of the western North Pacific TCs are comparable to super typhoons on average, the increasing trade-off magnitude clearly suggests that super typhoons in a warmer year gets stronger. Conclusively, the negative collinear feature of the thermodynamics influences the portion of TCs at the highest intensities, and super typhoons are likely to become stronger at the expense of overall TC frequencies in a warmer world. The consequence of this finding is that record-breaking TC intensities occur at the expense of overall TC frequencies under global warming. TC activity is understood as a variation which is independent of global warming, and could be assumed to be an internal variability having no trend. Frequency variation and super typhoon intensity variation are regarded as the addition of global warming influence on TC activity variation. The structure depicts how a previous intensity record is overtaken and frequency falls continuously in the global warming environment in a linear perspective. A peak TC activity year when global ocean warmth is the highest ever is likely to experience a record-breaking intensity. In the same way, the least number of annual TCs may appear when a lull of TC activity occurs in the warmest year. / A Dissertation submitted to the Geophysical Fluid Dynamics Institute in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Fall Semester, 2014. / August 25, 2014. / efficiency of intensity, global warming, super typhoon, Tropical cyclone climate / Includes bibliographical references. / James B. Elsner, Professor Directing Dissertation; Robert Hart, University Representative; Kevin Speer, Committee Member; Mark Bourassa, Committee Member.

Elevated and prolonged exposure to extreme heat is an important cause of excess summertime mortality and morbidity. To protect people from health threats, some governments are currently operating syndromic surveillance systems. However, a lack of resources to support time- and labor- intensive diagnostic and reporting processes make it difficult establishing region-specific surveillance systems. Big data created by social media and web search may improve upon the current syndromic surveillance systems by directly capturing people’s individual and subjective thoughts and feelings during heat waves. The primary objectives of the dissertation are to improve existing heat wave and health surveillance systems by testing current heat exposure metrics, checking system improvements with social media/web search data, and studying differential vulnerability to extreme heat exposure. In order to conduct the research, this dissertation employed two popular statistical techniques: time series and case-crossover analysis. Chapter 2 examines the relationship between the count of heat-related tweets and heat exposure. For this, I collected Twitter data focusing on six different heat-related themes (air conditioning, cooling center, dehydration, electrical outage, energy assistance, and heat) for 182 days from May 7 to November 3, 2014. First, exploratory linear regression associated outdoor heat exposure to the theme-specific tweet counts for five study cities (Los Angeles, New York, Chicago, Houston, and Atlanta). Next, autoregressive integrated moving average (ARIMA) time series models formally associated heat exposure to the combined count of heat and air conditioning tweets while controlling for temporal autocorrelation. Finally, I examined the spatial and temporal distribution of energy assistance and cooling center tweets. The result indicates that the number of tweets in most themes exhibited a significant positive relationship with maximum temperature. The ARIMA model results suggest that each city shows a slightly different relationship between heat exposure and the tweet count. A one-degree change in the temperature correspondingly increased the Box-Cox transformed tweets by 0.09 for Atlanta, 0.07 for Los Angeles, and 0.01 for New York City. The energy assistance and cooling center theme tweets suggest that only a few municipalities used Twitter for public service announcements. The timing of the energy assistance tweets also indicates that most jurisdictions provide heating instead of cooling energy assistance. Chapter 3 aims to investigate the relationship between heat-related web searches, social media messages, and heat-related health outcomes. I collected Twitter messages that mentioned “air conditioning (AC)” and “heat” and Google search data that included weather, medical, recreational, and adaptation information from May 7 to November 3, 2014, focusing on the state of Florida, U.S. I separately associated web data against two different sources of health outcomes (emergency department (ED) and hospital admissions) and five disease categories (cardiovascular disease, dehydration, heat-related illness, renal disease, and respiratory disease). Seasonal and subseasonal temporal cycles were controlled using autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) and generalized linear model (GLM). The results show that the number of heat-related illness and dehydration cases exhibited a significant positive relationship with web data. Specifically, heat-related illness cases showed positive associations with messages (heat, AC) and web searches (drink, heat stroke, park, swim, and tired). In addition, terms such as park, pool, swim, and water tended to show a consistent positive relationship with dehydration cases. However, I found inconsistent relationships between renal illness and web data. Web data also did not improve the models for cardiovascular and respiratory illness cases. These findings suggest web data created by social medias and search engines could improve the current syndromic surveillance systems. In particular, heat-related illness and dehydration cases were positively related with web data. This study also shows that activity patterns for reducing heat stress are associated with several health outcomes. Chapter 2 and chapter 3 suggest that web data could benefit both regions without the systems and persistently hot and humid climates where excess heat early warning systems may be less effective. Chapter 4 investigates whether there is a difference between five different types of heat sensitive health outcomes (cardiovascular disease, dehydration, heat-related illness, renal disease, and respiratory disease) between undocumented immigrants and US citizens. This study also examines if the impact of heat exposure on health by citizenship status is further modified by sex, age, or race/ethnicity. I conducted a case-crossover analysis to assess different heat-related health impact by citizenships, focusing on the warm season (May through September) from 2008 to 2012 in Florida. I reported separate case-crossover models for each health outcome and type of healthcare visit (emergency department, hospitalization). I stratified the data by immigration status and then added interaction terms to understand the impact of sex, age, or race/ethnicity. For both groups, higher temperature raised the risk of all heat-related health outcomes and healthcare visits. This analysis suggest undocumented people (ED: 1.127, 95 % CI: 1.056 ~ 1.204; hospitalization: 1.061, 95 % CI: 1.046 ~ 1.076) have moderately higher renal disease ORs than US citizens (ED: 1.069, 95 % CI: 1.059 ~ 1.078; hospitalization: 1.051, 95 % CI: 1.049 ~ 1.053). In addition, male US citizens had significantly higher ORs than female citizens for both ED (male: 1.080, 95 % CI: 1.076 ~ 1.085; female: 1.060, 95 % CI: 1.056 ~ 1.064) and hospitalization (male: 1.063, 95 % CI: 1.060 ~ 1.066; female: 1.054, 95 % CI: 1.052 ~ 1.057). This study documents some heat and health inequalities between US citizens and undocumented immigrants. / A Dissertation submitted to the Department of Geography in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / 2019 / November 1, 2019. / Google search, Health, Heat wave, Surveillance system, Twitter, Undocumented immigrants / Includes bibliographical references. / Christopher K. Uejio, Professor Directing Dissertation; Yiyuan She, University Representative; James B. Elsner, Committee Member; Sandy Wong, Committee Member.

No description available.

American Viticultural Areas (AVAs) are susceptible to small variations in climate and microclimates and are found within a narrow latitudinal range of prime climate conditions. These AVAs are geographically determined based on the best soil, climate, precipitation and temperature combinations for specific winegrape regions. As climate change continues to alter the local weather and the greater climate region of the Western United States, winegrape growing regions in Oregon are being affected. In an effort to determine what the pattern of change is, and compare previous studies of climate change using climate indices, a comparative study based in part on prior research was conducted. Using 800 meter resolution Parameter-elevation Regressions on Independent Slopes Model (PRISM) climate datasets, four individual climate indices were analyzed for statistical correlation with the climate data. These climate indices are: growing degree-days (GDD), the average growing season temperatures (GST), Huglin Index (HI) and the biologically effective degree-day (BEDD). Based on currently available data for this research, these climate indices were statistically analyzed during the years 2000 to 2010. A further avenue of research included a statistical analysis of the reported winegrape production, although this data was available only at an aggregated county-level. Results show that all four climate indices exhibit statistical significance, although the inclusion of the winegrape production data exhibited no statistical significance for many of the analyses, most likely due to subjective and aggregated data, few did result in significance with the climate indices. The research discussed here confirms the accuracy of the four climate indices and suggest that a longer time frame, coupled with less aggregated and subjective winegrape production data could produce interesting results in future research on the results of climate indices in winegrape growing regions.

Shaped by countless influences from the atmosphere, biosphere, hydrosphere, and anthroposphere acting across a wide spectrum of spatiotemporal scales, spatial variations in climate are ubiquitous. Meanwhile, the warming signal from anthropogenically elevated greenhouse-gas concentrations is emerging as an overriding determinant for more and more aspects of the climate system, extreme heat among them. In this dissertation, I explore the interaction of these two effects, and the implications of the patterns they create. A key finding is that rapid increases in extreme heat are already occurring, by some metrics having already doubled in the past 40 years, and further nonlinear increases are expected. Another is the strong dependence of extreme heat-humidity combinations on atmospheric moisture, creating subseasonal and interannual patterns dictated by the principal source of regional warm-season moisture — pre-monsoonal advection in some cases, local evapotranspiration in others. These relationships lead to the demonstrated potential for improvements in predictive power, on the basis of sea-surface temperatures and other canonical modes of large-scale climate variability. In contrast to this overall confidence in current temporal patterns and long-term projections, I show that extreme heat at small spatial scales is much more poorly characterized in gridded products, and that these biases are especially acute along coastlines. While summer daytime temperature differences between the shoreline of the Northeast U.S. and locations 60 km inland are often 5°C or more, I find that recent high-resolution downscaled Earth-system models typically represent no more than 25% of this difference. Across the globe, ERA-Interim reanalysis similarly underestimates extreme humid heat by >3°C, a highly significant margin given the large sensitivity of health and economic impacts to marginal changes in the most extreme conditions. I find that these biases propagate into projections, and their importance is also amplified by the large populations living in the affected areas. Rapid mean warming is pushing the climate system to more and more frequently include extreme heat-humidity combinations beyond that which the human species has likely ever experienced. Such conditions, which had not been previously reported in weather-station data, are described in detail and some of the associated characteristics examined. Several channels of analysis highlight that these events are driven primarily by rising sea-surface temperatures in shallow subtropical gulfs, and the subsequent impingement of marine air on the coastline. Given the severity of potential impacts on infrastructure and agriculture, and the size of the populations exposed, this result underscores that major research and adaptation efforts are needed to avoid calamitous outcomes from the emergence of extreme heat-humidity combinations too severe to tolerate in the absence of artificial cooling. This dissertation discusses strategies for advancing knowledge of extreme heat’s natural variations and its behavior under climate change, in order to design metrics, models, methodologies, and presentation types such that essential findings are translated into tangible action in the most effective way possible. Sustained and integrated efforts are necessary to transition to a climate-system management style encompassing more foresight than the effectively unplanned experiment which has been pursued so far, and which has already exacerbated extreme heat events so much.

Thesis (M.A.)--University of Canterbury, 2008. / Typescript (photocopy). Includes bibliographical references (leaves 79-91). Also available via the World Wide Web.

Thesis (M.S. - Hydrology and Water Resources)--University of Arizona, 1984. / Includes bibliographical references (leaves 71-75).

Assessing and managing risks posed by climate variability and change is challenging in the tropics, from both a scientific and a socio-economic perspective. While our understanding of the tropical land rainfall variability and its future projection is highly uncertain, most of the vulnerable countries with a limited adaptation capability are within the tropical band. This dissertation combines a process-based physical understanding with observational analysis to characterize the spatio-temporal changes in the tropical land rainfall during a transient El Niño evolution, with an emphasis on the risk management of the dry and wet extremes. The broad objectives are two-fold: 1) To make better sense of the higher uncertainty in the tropical rainfall response to warming and 2) to improve climate risk management strategies in the tropical developing countries. An ENSO teleconnection mechanism, referred to as the tropical tropospheric temperature or TTT mechanism provides a theoretical framework to study the remote tropical land rainfall behavior during a transient El Niño warming. The TTT mechanism postulates that the tropic-wide free tropospheric warming interacts locally with the deep convection to modulate remote tropical climate. During the growth phase, anomalous free tropospheric temperature causes direct and fast atmospheric adjustments leading to tropospheric stability to deep moist convection and a drier response. Subsequently, during mature phase, a recovery of the initial rainfall deficit follows due to indirect and slower adjustments in surface temperature and humidity fields. In chapter 2 and 3 of this dissertation, the changes in the observed tropical land rainfall characteristics and other climate fields conditional on the growth and mature phase of El Niño warming are investigated and the role of dynamical and thermodynamic mechanisms as hypothesized by the TTT mechanism are elucidated. In chapter 4, an El Niño forecast based early action investment strategy is developed to reduce the socio-economic impacts of rainfall extremes at sub-seasonal to inter-annual lead time scales. In the part I (chapter 2), the analysis is conducted at a regional scale over the tropical Africa. Using the TTT mechanism, a physical explanation is provided for the contrasting rainfall response over the Western Sahel and tropical Eastern Africa during an El Niño. The study finds that the Western Sahel’s main rainy season (July-September) is affected by the growth phase of El Niño through (i) a lack of neighboring North Atlantic sea surface warming, (ii) an absence of an atmospheric column water vapor anomaly over the North Atlantic and Western Sahel, and (iii) higher atmospheric vertical stability over the Western Sahel, resulting in the suppression of mean seasonal rainfall as well as number of wet days. In contrast, the short rainy season (October-December) of tropical Eastern Africa is impacted by the mature phase of El Niño through (i) neighboring Indian Ocean sea surface warming, (ii) positive column water vapor anomalies over the Indian Ocean and tropical Eastern Africa, and (iii) higher atmospheric vertical instability over tropical Eastern Africa, leading to an increase in mean seasonal rainfall as well as in the number of wet days. While the modulation of the frequency of wet days and seasonal mean accumulation is statistically significant, daily rainfall intensity (for days with rainfall >1 mm/day), whether mean, median, or extreme, does not show a significant response in either region. Hence, the variability in seasonal mean rainfall that can be attributed to the El Niño–Southern Oscillation phenomenon in both regions is likely due to changes in the frequency of rainfall. These observed changes agree with the predictions of the TTT mechanism. In the part II (chapter 3), a global scale analysis is performed to more generally characterize the spatio-temporal differences in remote tropical land rainfall response to El Niño warming. The principal conclusions are: 1) during the El Niño growth phase relative to the neutral phase, rainfall decreases. A significant decrease in mean accumulation can be attributed to a significant increase in proportion of dry days and decrease in median and extreme intensity. A significant descent anomaly confirms the vertical stabilization and dominance of dynamical processes. 2) During the mature phase relative to the growth phase, rainfall increases, signifying a recovery from the suppression of deep moist convection. A significant increase in mean accumulation is accompanied by a decrease in proportion of dry days and by an increase in median and extreme intensity characteristics. The significant rise in the moisture field corroborates the dominance of thermodynamic processes. These findings are expected from the TTT mechanism and generalizes the findings of part I to the global scale. In the part III (chapter 4), an El Niño forecast based index insurance policy is developed that can be used as an early action investment instrument. The forecast insurance (FI) design framework is illustrated with an application to El Niño associated flood hazard during the January-February-March-April (JFMA) season over Piura region of Peru. In order to determine the economic utility of the system, a simple cost-loss decision model, incorporating the insurance cost, is developed. The main conclusion is that the proposed El Niño forecast insurance policy with the pre-event Niño1.2 index based trigger has significant reliability and substantial utility for a wide range of policy parameters considered. Relative to a no early action strategy, the advantage of the system generally increases with i) shortening in the lead time from 9 to 1 month, ii) increase in El Niño severity level from 10 to 50 year return period and iii) increase in avoidable loss to cost ratio (LCR) ratio from 1 to 1000. These results and the forecast insurance modeling and utility evaluation frameworks have implications for designing optimal contingent financial instruments for disaster risk reduction and climate change adaptation.