Evaluating Climate Change Impacts

Evaluating Climate Change Impacts discusses assessing and quantifying climate change and its impacts from a multi-faceted perspective of ecosystem, social, and infrastructure resilience, given through a lens of statistics and data science. It provides a multi-disciplinary view on the implications of climate variability and shows how the new data science paradigm can help us to mitigate climate-induced risk and to enhance climate adaptation strategies. This book consists of chapters solicited from leading topical experts and presents their perspectives on climate change effects in two general areas: natural ecosystems and socio-economic impacts. The chapters unveil topics of atmospheric circulation, climate modeling, and long-term prediction; approach the problems of increasing frequency of extreme events, sea level rise, and forest fires, as well as economic losses, analysis of climate impacts for insurance, agriculture, fisheries, and electric and transport infrastructures. The reader will be exposed to the current research using a variety of methods from physical modeling, statistics, and machine learning, including the global circulation models (GCM) and ocean models, statistical generalized additive models (GAM) and generalized linear models (GLM), state space and graphical models, causality networks, Bayesian ensembles, a variety of index methods and statistical tests, and machine learning methods. The reader will learn about data from various sources, including GCM and ocean model outputs, satellite observations, and data collected by different agencies and research units. Many of the chapters provide references to open source software R and Python code that are available for implementing the methods.

I Ecosystem Impacts On Evaluation of Climate Models Kaibo Gong and Snigdhansu Chatterjee Introduction A brief tour of climate models Evaluation of climate model outputs: summary measures Ensemble-based approaches Probabilistic model evaluation techniques Ensemble using empirical likelihood Conclusions and future directions A Statistical Analysis of North Atlantic Tropical Cyclone Changes Thomas J Fisher, Robert Lund, and Michael W Robbins Introduction Data Statistical methods Results Comments and conclusions Fire Weather Index and Climate Change Zuzana Hubnerova, Sylvia Esterby, and Steve Taylor Introduction Statistical modeling of the fire weather index monthly maxima Summary and discussion References Probabilistic Projections of High Tide Flooding for the State of Maryland in the st Century Ming Li, Fan Zhang, Yijun Guo and Xiaohong Wang Introduction Methods Results Response of Benthic Biodiversity to Climate-Sensitive Regional and Local Conditions in a Complex Estuarine System Ryan J Woodland and Jeremy M Testa Introduction Methods Results Discussion Conclusions Using Structural Comparisons to Measure the Behavior of Complex Systems Ryan E Langendorf Introduction Data Network alignment Visualization Example: the Chesapeake Bay Critical considerations Recipe Final thought Causality Analysis of Climate and Ecosystem Time Series Mohammad Gorji Sefidmazgi and Ali Gorji Sefidmazgi Introduction Methods of causality detection Simulations Conclusions II Socio-economic Impacts Statistical Issues in Detection of Trends in Losses from Extreme Weather and Climate Events Richard W Katz Introduction Loss distribution Bias, uncertainty, and variability in losses Detection and attribution of trends in losses Summary and discussion Event Attribution: Linking Specific Extreme Events to Human-Caused Climate Change Stephanie Herring Why is this chapter in this book? Background on event attribution Event attribution methodologies Impact attribution FAR = or "Not possible without climate change" Communicating event attribution studies Financing Weather and Climate Risks in the United States Roger S Pulwarty, David R Easterling, Jeffery Adkins, and Adam B Smith Disasters in the US – the recent record Climate and extremes Assessing economic impacts Insurance and risk financing Data and analytical challenges Implementation challenges Financing mitigation and resilience Pathways and conclusion Extreme Events, Population, and Risk: an Integrated Modeling Approach Lelys Bravo de Guenni, Desireé Villalta, and Andrés Sajo-Castelli Introduction Conceptual framework for risk modeling Applications of the conceptual framework Discussion, conclusions, and future work Aspects of Climate-Induced Risk in Property Insurance Ola Haug Introduction The role of statistics in assessing insurance climate risk Water damage to properties in Norway The Gjensidige case study Climate change and property insurance interactions Climate Change Impacts on the Nation’s Electricity Sector Craig D Zamuda Introduction Climate impacts and implications for the electricity sector Resilience approaches and options Analytical approaches for assessing costs and benefits Gaps and opportunities for improvement in resilience planning Impacts of InclementWeather on Traffic Accidents in Mexico City Sophie Bailey, S Marcelo Olivera-Villarroel, and Vyacheslav Lyubchich Introduction Data description Methods Results Statistical Modeling of Dynamic Greenhouse Gas Emissions Nathaniel K Newlands Overview Background Introduction Statistical framework Ecosystem dynamical optimization Numerical results Summary Appendix: model parameters and variables Agricultural Climate Risk Management and Global Food Security: Recent Progress in South-East Asia Louis Kouadio and Eric Rahn Climate risks management in agriculture Current approaches integrating SCFs and crop simulation models Examples of integrated SCF-crop modeling approach Challenges for operationalizing seasonal climate-crop modeling Improved climate risk management in South-East Asia Poppy Cultivation and Eradication in Mexico : the Effects of Climate S Marcelo Olivera-Villarroel and Maria del Pilar Fuerte Celis Introduction Context Methodology Results Discussion

The book is an interdisciplinary initiative of statisticians, climatologists, ecologists and oceanographers whose research addresses development and implementation of analytical methodology for assessing climate change impacts. The team includes statisticians V. Lyubchich and Y. R. Gel (statistical and machine learning methods for quantification of the climate-induced risk), climatologist K. H. Kilbourne (paleoclimatology, geochemistry, assessment of the causes of climate variability), fisheries scientist T. J. Miller (effects of ocean acidification on blue crab, recruitment issues in menhaden and striped bass), disaster expert A. B. Smith (analysis of economic and societal impacts of extreme events and natural hazards), and research scientist in food-water-energy nexus N. K. Newlands (sustainability, precision agriculture and risk analysis using machine learning and integrated modeling).