Climatology is, to a large degree, the study of the statistics of our climate. The powerful tools of mathematical statistics therefore find wide application in climatological research. The purpose of this book is to help the climatologist understand the basic precepts of the statistician's art and to provide some of the background needed to apply statistical methodology correctly and usefully. The book is self contained: introductory material, standard advanced techniques, and the specialised techniques used specifically by climatologists are all contained within this one source. There are a wealth of real-world examples drawn from the climate literature to demonstrate the need, power and pitfalls of statistical analysis in climate research. Suitable for graduate courses on statistics for climatic, atmospheric and oceanic science, this book will also be valuable as a reference source for researchers in climatology, meteorology, atmospheric science, and oceanography.

The risks posed by climate change and its effect on climate extremes are an increasingly pressing societal problem. This book provides an accessible overview of the statistical analysis methods which can be used to investigate climate extremes and analyse potential risk. The statistical analysis methods are illustrated with case studies on extremes in the three major climate variables: temperature, precipitation, and wind speed. The book also provides datasets and access to appropriate analysis software, allowing the reader to replicate the case study calculations. Providing the necessary tools to analyse climate risk, this book is invaluable for students and researchers working in the climate sciences, as well as risk analysts interested in climate extremes.

An accessible introduction to statistical methods for students in the climate sciences.

The book presents the application of statistical methods to climatological data on temperature and precipitation. It provides specific techniques for treating series of yearly, monthly and daily records. The results’ potential relevance in the climate context is discussed. The methodical tools are taken from time series analysis, from periodogram and wavelet analysis, from correlation and principal component analysis, and from categorical data and event-time analysis. The applied models are - among others - the ARIMA and GARCH model, and inhomogeneous Poisson processes. Further, we deal with a number of special statistical topics, e.g. the problem of trend-, season- and autocorrelation-adjustment, and with simultaneous statistical inference. Programs in R and data sets on climate series, provided at the author’s homepage, enable readers (statisticians, meteorologists, other natural scientists) to perform their own exercises and discover their own applications.

EUROPEAN SCHOOl OF CLiMATOlOGY AND NATURAL HAZARDS The training of scientific and technical personnel and the development of highly qualified scientists are, and have always been, among the important concerns of the European Commission. Advanced training is an important requirement for the implementation of a common EU policy in science and technology. The European School of Climatology and Natural Hazards was started as apart of the training and education activities of the European Programme on Climatology and Natural Hazards (EPOCH), and is continued under the subsequent research programme (ENVIRONMENT 1990-1994). The school consists of annual courses on specialised subjects within re search in climatology and natural hazards, and is open to graduating, grad uate and post graduate students in these fields. Each of the courses is organized in cooperation with a European Institu tion involved in the current research programme, and is aimed at giving to the students formal lectures and participation in informal discussions with leading researchers. The present volume is based on the lectures given at the course held on the island of Elba from the 30th October to the 6th of November 1993 on Statistical Analysis of Climate Variability. It features selected and extended presentations, and represents an important contribution to advanced studies in climate statistical analysis, supplementing more traditional texts. I trust that all those involved in research related to climate change and climate variability will appreciate this work and will benefit from the com prehensive and state-of-the-art information it provides.

Climate is a paradigm of a complex system. Analysing climate data is an exciting challenge, which is increased by non-normal distributional shape, serial dependence, uneven spacing and timescale uncertainties. This book presents bootstrap resampling as a computing-intensive method able to meet the challenge. It shows the bootstrap to perform reliably in the most important statistical estimation techniques: regression, spectral analysis, extreme values and correlation. This book is written for climatologists and applied statisticians. It explains step by step the bootstrap algorithms (including novel adaptions) and methods for confidence interval construction. It tests the accuracy of the algorithms by means of Monte Carlo experiments. It analyses a large array of climate time series, giving a detailed account on the data and the associated climatological questions. This makes the book self-contained for graduate students and researchers.

A comprehensive and practical guide, providing technical background and user context for researchers, graduate students, practitioners and decision makers. This book presents the main approaches and describes their underlying assumptions, skill and limitations. Guidelines for the application of downscaling and the use of downscaled information in practice complete the volume.