Note: Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: Ecosystem Impacts -- 1. On Evaluation of Climate Models -- 1.1 Introduction -- 1.2 A brief tour of climate models -- 1.3 Evaluation of climate model outputs: summary measures -- 1.3.1 Simple summary measures -- 1.3.2 Evaluation by process isolation, instrument simulators, and initial value techniques -- 1.4 Ensemble-based approaches -- 1.4.1 Multimodel ensembles -- 1.4.2 Perturbation-parameter ensembles -- 1.4.3 Reliability ensemble averaging -- 1.4.4 Bayesian ensembles -- 1.4.5 Machine-learning ensemble approaches -- 1.5 Probabilistic model evaluation techniques -- 1.5.1 Model comparison by moving-block bootstrap -- 1.5.2 Evaluation using functional representations -- 1.6 Ensemble using empirical likelihood -- 1.7 Conclusions and future directions -- References -- 2. A Statistical Analysis of North Atlantic Tropical Cyclone Changes -- 2.1 Introduction -- 2.2 Data -- 2.3 Statistical methods -- 2.3.1 Penalized likelihood changepoint methods -- 2.3.2 Poisson counts -- 2.3.3 Correlated Gaussian data -- 2.4 Results -- 2.4.1 Total cyclone counts -- 2.4.2 Hurricanes and major storms -- 2.4.3 Analyses with segment-length restrictions -- 2.4.4 Accumulated cyclone energy -- 2.5 Comments and conclusions -- References -- 3. Fire-Weather Index and Climate Change -- 3.1 Introduction -- 3.2 Statistical modeling of the fire-weather index monthly maxima -- 3.2.1 Separate modeling -- 3.2.2 Spatial modeling -- 3.3 Summary and discussion -- References -- 4. Probabilistic Projections of High-Tide Flooding for the State of Maryland in the Twenty-First Century -- 4.1 Introduction -- 4.2 Methods -- 4.2.1 Regional ocean model -- 4.2.2 Design of numerical experiments -- 4.2.3 Inundation impact analysis -- 4.3 Results -- 4.3.1 Bay-wide response -- 4.3.2 Dorchester County. , 4.3.3 Annapolis and Baltimore -- 4.4 Conclusions -- References -- 5. Response of Benthic Biodiversity to Climate-Sensitive Regional and Local Conditions in a Complex Estuarine System -- 5.1 Introduction -- 5.2 Methods -- 5.2.1 Data sources -- 5.2.2 Biodiversity-climate modeling -- 5.3 Results -- 5.3.1 Benthic biodiversity patterns -- 5.3.2 Biodiversity-climate modeling results -- 5.3.3 Multivariate assemblage analysis -- 5.4 Discussion -- 5.4.1 Long-term trends in Chesapeake Bay benthic biodiversity -- 5.4.2 Climate drivers of benthic biodiversity -- 5.4.3 Regional climate outlook for Chesapeake Bay -- 5.4.4 Global outlook for estuarine communities in the face of climate forcing -- 5.5 Conclusions -- References -- 6. Using Structural Comparisons to Measure the Behavior of Complex Systems -- 6.1 Introduction -- 6.2 Data -- 6.3 Network alignment -- 6.4 Visualization -- 6.5 Example: the Chesapeake Bay -- 6.6 Critical considerations -- 6.7 Recipe -- 6.7.1 Ingredients -- 6.7.2 Step 1/3: Data -- 6.7.3 Step 2/3: Network alignment -- 6.7.4 Step 3/3: Visualization -- 6.8 Final thought -- References -- 7. Causality Analysis of Climate and Ecosystem Time Series -- 7.1 Introduction -- 7.2 Methods of causality detection -- 7.2.1 Granger causality -- 7.2.2 Nonlinear state space methods -- 7.2.3 Causal graphical models -- 7.3 Simulations -- 7.3.1 Simulated data -- 7.3.2 Arctic and the midlatitude jet stream -- 7.3.3 Sardine-anchovy and sea surface temperature -- 7.4 Conclusions -- References -- Part II: Socioeconomic Impacts -- 8. Statistical Issues in Detection of Trends in Losses from Extreme Weather and Climate Events -- 8.1 Introduction -- 8.2 Loss distribution -- 8.2.1 Overall distribution of losses -- 8.2.2 Distribution of extreme high losses -- 8.2.3 Reconciling implications for extremes -- 8.3 Bias, uncertainty, and variability in losses. , 8.3.1 Variability and uncertainty as sources of bias -- 8.3.2 Effects of adjustments -- 8.4 Detection and attribution of trends in losses -- 8.4.1 Random sum representation -- 8.4.2 Trend analyses -- 8.4.3 Issues in normalization of losses -- 8.5 Summary and discussion -- References -- 9. Event Attribution: Linking Specific Extreme Events to Human-Caused Climate Change -- 9.1 Why is this chapter in this book? -- 9.2 Background on event attribution -- 9.3 Event attribution methodologies -- 9.4 Impact attribution -- 9.5 FAR -- or "Not possible without climate change" -- 9.6 Communicating event attribution studies -- 9.7 Summary -- References -- 10. Financing Weather and Climate Risks in the United States -- 10.1 Disasters in the United States-the recent record -- 10.2 Climate and extremes -- 10.3 Assessing economic impacts -- 10.4 Insurance and risk financing -- 10.5 Data and analytical challenges -- 10.6 Implementation challenges -- 10.7 Financing mitigation and resilience -- 10.8 Pathways and conclusion -- References -- 11. Extreme Events, Population, and Risk: An Integrated Modeling Approach -- 11.1 Introduction -- 11.2 Conceptual framework for risk modeling -- 11.3 Applications of the conceptual framework -- 11.2.1 Hazard, exposure, vulnerability, and risk -- 11.3.1 An example considering hazard counts -- 11.3.2 An example considering hazard space-time fields -- 11.4 Discussion, conclusions, and future work -- References -- 12. Aspects of Climate-Induced Risk in Property Insurance -- 12.1 Introduction -- 12.2 The role of statistics in assessing insurance climate risk -- 12.3 Water damage to properties in Norway -- 12.4 The Gjensidige case study -- 12.4.1 Data -- 12.4.2 Modeling -- 12.4.3 Claim predictions -- 12.4.4 Extensions -- 12.5 Climate change and property insurance interactions -- 12.6 Conclusions -- References. , 13. Climate Change Impacts on the Nation's Electricity Sector -- 13.1 Introduction -- 13.2 Climate impacts and implications for the electricity sector -- 13.2.1 Specific extreme weather hazards and impacts to the electricity sector -- 13.3 Resilience approaches and options -- 13.4 Analytical approaches for assessing costs and benefits of resilience investments -- 13.4.1 Consolidated Edison of New York (Con Edison) case study: risk prioritization model -- 13.4.2 Public Service Electric & -- Gas (PSE& -- G) case study: break-even analysis -- 13.4.3 Entergy's case study: building a resilient Gulf Coast -- 13.5 Gaps and opportunities for improvement in resilience planning -- References -- 14. Impacts of Inclement Weather on Traffic Accidents in Mexico City -- 14.1 Introduction -- 14.2 Data description -- 14.3 Methods -- 14.4 Results -- 14.5 Conclusions -- References -- 15. Statistical Modeling of Dynamic Greenhouse Gas Emissions -- 15.1 Overview -- 15.2 Background -- 15.3 Introduction -- 15.4 Statistical framework -- 15.5 Ecosystem dynamical optimization -- 15.6 Numerical results -- 15.7 Summary -- 15.8 Appendix: model parameters and variables -- References -- 16. Agricultural Climate Risk Management and Global Food Security: Recent Progress in Southeast Asia -- 16.1 Climate risks management in agriculture-use of climate prediction in crop models -- 16.2 Current approaches integrating SCFs and crop simulation models applied in Southeast Asia -- 16.3 Examples of integrated SCF-crop modeling approach for climate risk management in Southeast Asia -- 16.3.1 The climate-agriculture-modeling and decision tool (CAMDT) -- 16.3.2 The integrated seasonal climate-crop yield forecasting system for robusta coffee (ICCFS-Robusta) -- 16.4 Challenges for operationalizing seasonal climate-crop modeling frameworks in Southeast Asia -- 16.4.1 Data scarcity. , 16.4.2 Assessing the skills of seasonal climate forecasts -- 16.4.3 Communicating SCF outputs to farmers -- 16.4.4 Extending the range of applications of integrated SCF-crop modeling systems -- 16.5 Improved climate risk management in Southeast Asia-the way forward -- References -- 17. Poppy Cultivation and Eradication in Mexico, 2000-2018: The Effects of Climate -- 17.1 Introduction -- 17.2 Context -- 17.3 Methodology -- 17.3.1 Overview of index construction -- 17.3.2 Statistical weighting of the index components, using the Shapley decomposition -- 17.3.3 Components of the poppy eradication index -- 17.4 Results -- 17.5 Discussion -- 17.6 Conclusions -- References -- Index.