1st Edition
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
- A Statistical Analysis of North Atlantic Tropical Cyclone Changes
- Fire Weather Index and Climate Change
- Probabilistic Projections of High Tide Flooding for the State of Maryland in the st Century
- Response of Benthic Biodiversity to Climate-Sensitive Regional and Local Conditions in a Complex Estuarine System
- Using Structural Comparisons to Measure the Behavior of Complex Systems
- Causality Analysis of Climate and Ecosystem Time Series
- Statistical Issues in Detection of Trends in Losses from Extreme
- Event Attribution: Linking Specific Extreme Events to Human-Caused Climate Change
- Financing Weather and Climate Risks in the United States
- Extreme Events, Population, and Risk: an Integrated Modeling Approach
- Aspects of Climate-Induced Risk in Property Insurance
- Climate Change Impacts on the Nation’s Electricity Sector
- Impacts of InclementWeather on Traffic Accidents in Mexico City
- Statistical Modeling of Dynamic Greenhouse Gas Emissions
- Agricultural Climate Risk Management and Global Food Security: Recent Progress in South-East Asia
- Poppy Cultivation and Eradication in Mexico : the Effects of Climate
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
Thomas J Fisher, Robert Lund, and Michael W Robbins
Introduction
Data
Statistical methods
Results
Comments and conclusions
Zuzana Hubnerova, Sylvia Esterby, and Steve Taylor
Introduction
Statistical modeling of the fire weather index monthly maxima
Summary and discussion
References
Ming Li, Fan Zhang, Yijun Guo and Xiaohong Wang
Introduction
Methods
Results
Ryan J Woodland and Jeremy M Testa
Introduction
Methods
Results
Discussion
Conclusions
Ryan E Langendorf
Introduction
Data
Network alignment
Visualization
Example: the Chesapeake Bay
Critical considerations
Recipe
Final thought
Mohammad Gorji Sefidmazgi and Ali Gorji Sefidmazgi
Introduction
Methods of causality detection
Simulations
Conclusions
II Socio-economic Impacts
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
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
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
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
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
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
Sophie Bailey, S Marcelo Olivera-Villarroel, and Vyacheslav Lyubchich
Introduction
Data description
Methods
Results
Nathaniel K Newlands
Overview
Background
Introduction
Statistical framework
Ecosystem dynamical optimization
Numerical results
Summary
Appendix: model parameters and variables
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
S Marcelo Olivera-Villarroel and Maria del Pilar Fuerte Celis
Introduction
Context
Methodology
Results
Discussion
Biography
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).
