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This chapter offers results from an artificial simulation exercise that was designed to answer three fundamental questions that lie at the heart of anticipatory adaptation. First, how can confidence in projected vulnerabilities and impacts be greater than the confidence in attributing what has heretofore been observed? Second, are there characteristics of recent historical data series that do or do not portend our achieving high confidence in attribution to climate change in support of framing adaptation decisions in an uncertain future? And finally, what can analysis of confidence in attribution tell us about ranges of “not-implausible” extreme futures vis-à-vis projections based at least implicitly on an assumption that the climate system is static? An extension of the IPCC method of assessing our confidence in attribution to anthropogenic sources of detected warming presents an answer to the first question. It is also possible to identify characteristics that support an affirmative answer to the second. Finally, this chapter offer some insight into the significance of our attribution methodology in informing attempts to frame considerations of potential extremes and how to respond.

Analytical and semi-empirical models are inexpensive to run and can complement experimental and numerical simulations for risk analysis-related applications. Some models are developed by employing simplifying assumptions in the Navier-Stokes equations and searching for exact, but many times inviscid solutions occasionally complemented by boundary layer equations to take surface effects into account. Other use simple superposition of generic, canonical flows for which the individual solutions are known. These solutions are then ensembled together by empirical or semi-empirical fitting procedures. Few models address turbulent or fluctuating flow fields, and all models have a series of constants that are fitted against experiments or numerical simulations. This chapter presents the main models used to provide primarily mean flow solutions for tornadoes and downbursts. The models are organized based on the adopted solution techniques, with an emphasis on their assumptions and validity.

This chapter summarizes the book’s study on non-synoptic wind storms (NSWSs). The book covers aspects related the general vulnerability to NSWSs in terms of (1) incidence, including the flow field and intensity and the frequency and occurrence of these storms; and (2) exposure, including preparedness for NSWSs. In doing so, it presents the state of the art regarding full-scale data acquisition and analysis, mesoscale and microscale numerical modeling, physical simulations, structural analysis, risk modeling, building codes implementation, and insurance analysis. For each of these aspects, the presentation aims at being informative, reviewing a large palette of approaches and presenting their advantages and limitations. It also stresses the need for future research.

Decision making under questions of deep uncertainty can be vague or specific, open-ended or fixed, easy or hard. This chapter very briefly addresses issues and approaches to decision making on adaptation to climate change. Depending on the research question, a complicated set of multiple approaches and tools may be needed. To highlight the types of approaches, this chapter discusses a variety of decision making tools and relates them to a particular problem: a homeowner choosing whether to do nothing, buy insurance, or elevate their home. The chapter culminates in a table summarizing the pros and cons of a variety of approaches.

Some of the most profound impacts of climate variability and change are expected in the water sector. These include more frequent, severe, and persistent droughts; more frequent, widespread, and extreme floods; more episodic and harmful water pollution episodes. Coping with more variable water supplies alongside rising demand will involve institutional reform, new infrastructure, adjustments to operations, and water demand management. A smarter, decision-led approach to deploying climate information in water management will also be required. This chapter begins with an overview of analytical frameworks for assessing and adapting water resource systems to uncertain climate threats and opportunities. It then gives examples of the diverse sources of information that are being accessed by some water managers to establish plausible ranges of climate change as a basis for decision-making. Examples from Denver, Colorado, and Dublin, Republic of Ireland show how narratives of future system changes and historical data can help test the efficacy of decisions under uncertainty. These two case studies demonstrate how early dialogue and information exchange among practitioners and scientists are fundamental to adaptation planning. In both places, unconventional sources of climate risk information were used to more rigorously stress test water management and planning assumptions. The preferred adaptation decision frameworks were dynamic, iterative, and open-ended. The chapter closes by acknowledging that further development of the decision-making approaches described herein may be needed to evaluate mixtures of adaptation options across multiple sectors.

This chapter is an introduction to tornado storms from an engineering perspective. The material included here relates to warnings and subsequent response by people, the chance of tornado hazard at a location, tornado–structure interaction, and building design for tornadoes for life safety. Other chapters in this handbook, referenced here, give details on interrelated subjects, in this chapter, reader will gain an overview of the available knowledge on tornadoes from an engineering perspective. Other chapters of this handbook and the references at the end of this chapter can provide in-depth understanding of engineering other aspects of tornado.

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

Increasing the amount of resilient infrastructure investments in developing countries is key to achieving development goals. Two issues need to be addressed to better support investment decisions. First, analysts need to better integrate the social, economic, and environmental dimensions of investment decisions in their quantitative analyses, given the intertwined objectives of climate change adaptation and poverty reduction. Second, analysts and practitioners need to recognize that the future state of those three dimensions is deeply uncertain and that new techniques need to be used that look for robust investments—performing well under multiple future conditions—rather than an optimal solution under a single prediction of the future. Doing so can be achieved by beginning important decision processes with an integrated model representing technical and socioeconomic factors, and exploring various interventions under many possible futures.

This article focuses on techniques for eliciting expert judgement about complex uncertainties, and more specifically the habitat of the Australian brush-tailed rock-wallaby. Modelling wildlife habitat requirements is important for mapping the distribution of the rock-wallaby, a threatened species, and therefore informing conservation and management. The Bayesian statistical modelling framework provides a useful ‘bridge’, from purely expert-defined models, to statistical models allowing survey data and expert knowledge to be ‘viewed as complementary, rather than alternative or competing, information sources’. The article describes the use of a rigorously designed and implemented expert elicitation for multiple experts, as well as a software tool for streamlining, automating and facilitating an indirect approach to elicitation. This approach makes it possible to infer the relationship between probability of occurrence and the environmental variables and demonstrates how expert knowledge can contribute to habitat modelling.

This Oxford handbook on non-synoptic wind systems is an outlook of the state of knowledge of various aspects of these wind systems and their impacts on our natural and build environment. During the last two decades, it has become clear that these types of winds dominate in terms of damage in some geographical areas; at the same time, they are different from the large-scale synoptic winds for which the knowledge matured. As opposed to the synoptic winds, the non-synoptic ones are localized in both space and time, three dimensional in nature while having similar intensities. The handbook explores the particularities of this type of wind in terms of climatology, surface layer, and aerodynamic and structural impacts on buildings, structures, and natural habitat. It also addresses the implications on risk analysis, engineering guidelines and codes, socioeconomic aspects, and insurance policies. The handbook comes at the moment when the state of knowledge in this area has evolved but is not yet mature. Therefore, it provides the opportunity to inform and trigger debate.

This article examines the dynamics of the US economy over the last five decades using Bayesian analysis of dynamic stochastic general equilibrium (DSGE) models. It highlights an example application in what is commonly referred to as the new macroeconometrics, which combines macroeconomics with econometrics. The article describes a benchmark New Keynesian DSGE model that incorporates four types of agents: households that consume, save, and supply labour to a labour ‘packer’; a labour ‘packer’ that puts together the labour supplied by different households into an homogeneous labour unit; intermediate good producers, who produce goods using capital and aggregated labour; and a final good producer that mixes all the intermediate goods. It also considers the application of the model in policy analysis for public institutions such as central banks, along with private organizations and businesses. Finally, it discusses three avenues for further research in the estimation of DSGE models.

The study of wind effects on buildings and structures is primarily based on physical simulations of wind events. Synoptic, atmospheric boundary layer (ABL) winds have been simulated in boundary layer wind tunnels. Non-synoptic wind events such as tornadoes and downbursts are three-dimensional, dynamic, and non-stationary, and, as a result, a new generation of physical simulators have emerged in the past decades. Some of these simulators, their performances as well as their limitations, are reviewed in this chapter.

In comparison with atmospheric boundary layer winds, which are generally regarded as stationary, windstorms such as hurricanes, typhoons, and cyclones; thunderstorms and downbursts; and tornadoes generally exhibit non-stationary features characterized by changes in wind speed and direction. Due to these characteristics, it is usually challenging to model them in a simplistic format. To overcome this difficulty, a data-driven approach may be an alternative, one that has gained significant popularity in many fields mainly due to the rapid advance in measurement and monitoring systems that allows the collection of long-term massive datasets. This chapter reviews data-driven approaches employed in the fields of non-stationary non-synoptic winds from their characterization, modeling, and simulation perspectives.

This chapter surveys how the state of knowledge about the physical processes that cause extreme heat and the societal factors that determine its impacts can be used to better predict these aspects of future climate change. Covering global projections; event attribution; atmospheric dynamics; regional and local effects; and impacts on health, agriculture, and the economy, this chapter aims to provide a guide to the rapidly growing body of literature on extreme heat and its impacts, as well as to highlight where there remain significant areas in need of further research.

High-intensity wind events such as tornadoes and downbursts can be very destructive to structures and infrastructure systems. In the present chapter, an overview of the assessment of the wind hazard due to tornadoes and downbursts for Canadian sites is provided. Available tornado occurrence information available in Canada that can be used as the basis to develop a tornado occurrence model is discussed. The chapter presents an overall framework to develop tornado wind-velocity hazard maps for Canada. It also explores the use of simple equivalent along height wind profile that could be used to evaluate tornadic wind loading for line-like structures and of a practical procedure to evaluate the failure probability of structures subjected to high-intensity wind events. It is indicated that for a certain class of prismatic structures, the use of nonlinear static pushover analysis can be adequate to evaluate the capacity curve of the structure subjected to downburst wind loading. A probabilistic model of the capacity curve obtained in such a manner can then be used to evaluate the structural reliability by incorporating the assessed wind-velocity hazard map and equivalent wind profile.

The mechanics associated with thunderstorm outflows differ significantly from traditional turbulence in boundary layer winds both in its kinematics and dynamics. The key distinguishing attributes are the contrasting velocity profile with height, a rapid increase in speed, and the statistical features of the energetic gusts in the wind field, exhibiting a strong non-stationarity. This raises serious questions regarding the applicability of conventional stationary process-based theories, thus calling for a paradigm shift. This chapter reviews popular approaches concerning the structural analysis of non-stationary thunderstorm outflows, such as evolutionary power spectrum-based analysis, wavelet-based analysis, thunderstorm response spectrum technique involving the equivalent wind spectrum, and hybrid simulation-based analysis in the time domain. Finally, some preliminary comparisons between the results obtained using these different methods are presented.

Non-synoptic winds often exhibit rapid changes during a short period, which may be accompanied by changes in direction. This introduces non-stationarity both in the mean and the standard deviation of wind fluctuations. Thus, design loads in non-synoptic non-stationary winds obtained from conventional analysis frameworks included in codes and standards, such as the gust loading factor approach, may not be appropriate, thus calling for a careful examination of traditional design procedures. This chapter reviews a proposed design procedure for non-synoptic non-stationary winds. In particular, a codification of gust front winds originating from thunderstorms and downbursts is discussed because the event occurs frequently and is well-known to exhibit significant non-stationary characteristics. Two major frameworks reported in the past literature, such as the gust front factor and the thunderstorm response spectrum technique, are examined as a step toward the codification of gust front winds. In addition, a comparison is made between the two frameworks to assess their performance. Finally, a living codification concept through learning and updating invoking the emerging “design thinking” approach is discussed.

The unique coping capacities and other attributes that Pacific island nations have been developing for centuries have sustained them in the face of an enormous range of local and global challenges. These include climate change-related hazards, and especially tropical cyclones and high-wave incidents that notably generate landslides and river and coastal flooding; droughts; heat waves; and ocean warming. Such hazards place resources, people, and assets at serious risk, as reflected by their vulnerability. However, measuring climate change vulnerability is problematic since climate hazards combine with anthropogenic and other physical drivers to influence the nature, levels, and variability of vulnerability. The few longitudinal studies that have been undertaken for the Pacific island countries show high and increasing vulnerabilities, despite considerable investment of money and other resources at community, island, sector, and national levels.Considering the elements of risk (hazard, exposure, vulnerability, and capacity to adapt), this chapter critically reviews the approaches used in the Pacific to assess vulnerability, analyzes recent changes in the vulnerability of island nations, and lays the foundation for some new thinking on island habitability and futures. It uses lessons learned, as well as success stories and success factors, to present priorities related to the assessment of climate change vulnerabilities, risks, and possible adaptation interventions in the Pacific islands region. These underpin a series of principles aimed at harmonizing understanding and action. Notably, the chapter concludes that transformational resilient development can provide a more effective response to increasingly unprecedented risks and higher vulnerabilities, for both high and low islands, including atolls.