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Evaluating Climate Change Impacts
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Evaluating Climate Change Impacts

Book Details

Format Paperback / Softback
Book Series Chapman & Hall/CRC Applied Environmental Statistics
ISBN-10 0367552132
ISBN-13 9780367552138
Publisher Taylor & Francis Ltd
Imprint Chapman & Hall/CRC
Country of Manufacture GB
Country of Publication GB
Publication Date Sep 25th, 2023
Print length 394 Pages
Weight 740 grams
Product Classification: Environmental factors Epidemiology & medical statistics Geophysics Geochemistry Climate change
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Presents the topic of assessing and quantifying the climate change and its impacts from a multi-faceted perspective of ecosystem, human health, and social and infrastructure resilience, given through a lens of statistical and data sciences.

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.

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