A changing climate may portend increasing disaster risk across many countries and business enterprises. While many aspects of the hazards, exposure and vulnerability that constitute disaster risk have been well studied, several challenges remain. A critical aspect that needs to be addressed is the rapid response and recovery from a climate-induced disaster. Often, governments need to allocate funds or design financial instruments that can be activated rapidly to mobilize response and recovery. The proposed research addresses this general problem, focusing on a few selected issues. First, there is the question of how to rapidly detect and index a climate hazard, such as a flood, given proxy remote sensing data on attributes that may be closely related to the hazard. The second is the need to robustly estimate the return periods of extreme climate hazards, and the temporal changes in their projected frequency of occurrence using multi-century climate proxies. The third is the need to assess the potential losses from the event, including the disruption of services, and cascading failure of interlinked infrastructure elements. The fourth is the impact on global and regional supply chains that are induced by the event, and the associated financial impact. For each of these cases, it is useful to ground an analysis and the development of an approach around real world examples, which can then collectively inform a strategy for emergency response. Here, this will be pursued through an analysis of flooding in the Philippines, livestock mortality induced by drought and freezing winter in Mongolia, Hurricane Sandy impacts in New York, supply chain impacts in Thailand, and an end to end analysis of the potential process using data from Thailand and Bangladesh. Collectively, these analyses are expected to inform climate hazard planning and securitization processes with broad applicability at a regional to national level.

In response to increased greenhouse gases and global temperatures, changes to the hydrologic cycle are projected to occur and new precipitation characteristics are expected to emerge. The study of these characteristics is facilitated by common indices to measure precipitation and temperature developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). These indices can be used to describe the likely consequences of climate change such as increased daily precipitation intensity (SDII) and heavier rainfall events (R95p). This study calculates a subset of these indices from observed and modelled precipitation data in Portland, Oregon. Five rainfall gages from a high resolution rain gage network and projections from three downscaled global climate models including CanESM2, CESM1, CNRM-CM5 are used to calculate precipitation indices. Mann-Kendall's tau is used to detect monotonic trends in indices. The observational record is compared with models for the historic period (1977-2005) and these past trends are compared with projected future trends (2006-2100). The influence of study unit on trend detection is analyzed by computing trends at the annual and monthly scale. Study unit is shown to be important for trend detection. When the annual study unit is used, projected future trends towards increased precipitation intensity and event volumes are not observed in the historic data. However, when analyzed with a monthly study unit, trends towards increased precipitation intensity and event volumes are observed in the historic data. These trends are shown to be important for Portland area flooding, as precipitation indices are shown to significantly correlate with 40 maximum peak flow events that occurred during the period of study.

Flood events have caused detrimental impacts to humans' lives and anthropogenic climate change is anticipated to exacerbate the impact. It has been recognized that a long-term planning through risk-based optimization of flood defence will lead to a cost-effective solution for managing flood risk, but the prevailing assumption of stationarity may lead to an erroneous solution. In attempt to investigate the potential impact of the uncertain underlying statistical characteristics of extreme flow series to flood protection decisions, this research explores risk-based flood protection decisions in the context of climatic variability and change. In particular, the implications of persistence series and nonstationarity were investigated through hypothetical and real case studies. Monte Carlo simulation approach was adopted to capture the uncertainty due to the natural variability. For persistence model, AR(1) was integrated with the GEV model to simulate extreme flow series with persistence. To test the effects of nonstationary, GEV models with a linear location parameter and time as covariate were adopted. Rational decision makers' behaviours were simulated through a designed decision analysis framework. One of the main findings from the research is that the traditional stationary assumption should remain the basic assumption due to insignificant difference of the decisions' economic performance. However, exploration of the nonstationarity assumption enabled identification of options that are robust to climate uncertainties. It is also found that optimized protection of combined measures of flood defence and property-level protection may provide a cost-effective solution for local flood protection. Overall, the simulation and case studies enlighten practitioners and decision makers with new evidence, and may guide to practical enhancement of long term flood risk management decision making.

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.

This study investigates the extent to which South African short-term insurance companies manage climate change risk, as evidenced in their annual and sustainability reporting. The study context takes into account the fact that the world’s climate has been changing at a more accelerated rate since the early 1970s, causing disasters that have negatively affected world economies in the last ten years. Insurers, due to their huge financial resource base, long history of spurring innovation around risk and encouraging loss-reducing behaviour as well as high levels of vulnerability, have been identified as one industry that could lead societies in finding solutions to climate change risk. A key element of such a corporate resolve involves taking a leadership position which makes business sense for insurers. As such, this research analyses how innovative solutions to change-related problems could result in reduced exposure to climate change in line with corporate triple bottom line objectives. Based on a purposive sampling of short-term insurance companies operating in the South African market during the 2007 financial year, the study uses the companies’ annual and sustainability reports in order to critically assess evidence of climate change-related performance. The assessment is undertaken against the best practice indicators of climate change risk management, as defined by Ceres – a global researcher on climate change management in the business context. The data analysis is largely qualitative, consisting of a narrative presentation of the results and a conceptual application of the results to the triple bottom line which forms the theoretical framework of this study. The study finds that the South African short-term insurers were generally not living up to the climate change management ideals, in comparison to their multinational counterparts. For the South African short-term insurers, corporate strategic product innovation and planning was insignificant. Also negligible was board involvement, as well as CEO involvement, though in at least one case of the 4 local short-term insurance, there was evidence of extensive CEO involvement in climate change risk management. On the whole, these findings represent a lapse in corporate governance inasmuch as climate change risk management is concerned. Local short-term insurers generally performed well in the area of public disclosure, with their scores ranging from insignificant to extensive. In contrast, multinational short-term insurers’ performance with regard to climate change risk intervention ranged from insignificant tointegrated, across the five governance areas of board oversight, management execution, public disclosure, emissions accounting and strategic planning. As such, the study broadly recommends that short-term insurers in South Africa should make climate change part of their overall risk management strategies in order for them to remain competitive in an environment of increased climate change-related risk. More specifically, the research project recommends that the local insurers should proactively lead climate change mitigation measures through, for instance, investing in clean energy projects and incentivising their clients’ participation in the carbon market to prepare themselves for possible regulatory restrictions after the Copenhagen climate change conference planned for December 2009. This study also challenges insurers to help communities and as well as other businesses in their value chain to reduce their negative impacts on the world’s climate and to be more resilient against disasters which may arise from the high levels of greenhouse gases already in the atmosphere. Further, it recommends that insurers should create internal board and executive level climate change-related structures, as these will facilitate the integration of the proposed initiatives into their overall sustainability strategies. Above all, the study recommends that insurers should enhance the reporting of their climate change-related risk, opportunities and initiatives to improve their integrity.