Climate Time Series Analysis:
Classical Statistical and Bootstrap Methods
2nd edition 2014

Mudelsee M (2014) Climate Time Series Analysis: Classical Statistical and Bootstrap Methods. Second edition. Springer, Cham Heidelberg New York Dordrecht London. [ISBN: 978-3-319-04449-1, e-ISBN: 978-3-319-04450-7, DOI: 10.1007/978-3-319-04450-7; xxxii + 454 pp; Atmospheric and Oceanographic Sciences Library, Vol. 51]

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.

Contains 29 algorithms, 101 figures, 1288 references and 46 tables.

An excerpt from the preface of the first edition can be found at Climate Risk Analysis.

A large sample part (PDF) of the first edition is here: Climate Time Series Analysis.

A small sample part of the first edition is at Google Books.

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Springer

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Errata

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From the preface of the first edition: "As the person responsible for the content, I offer my apologies in advance of the discovered errors, and I thank you for informing me. My email address is mudelsee@climate-risk-analysis.com".

Reviews (previous edition)

"... comprehensive mathematical and statistical summary of time-series analysis techniques geared towards climate applications ... accessible to readers with knowledge of college-level calculus and statistics." (Computers and Geosciences)

"A key part of the book that separates it from other time series works is the explicit discussion of time uncertainty ... a very useful text for those wishing to understand how to analyse climate time series." (Journal of Time Series Analysis)

"... outstanding. One of the best books on advanced practical time series analysis I have seen." (David J. Hand, Past-President Royal Statistical Society)

Climate Time Series Analysis:
Classical Statistical and Bootstrap Methods
1st edition 2010

Mudelsee M (2010) Climate Time Series Analysis: Classical Statistical and Bootstrap Methods. Springer, Dordrecht Heidelberg London New York. [ISBN-13: 978-90-481-9481-0, ISBN-10: 90-481-9481-4, e-ISBN: 978-90-481-9482-7, DOI: 10.1007/978-90-481-9482-7; xxxiv + 474 pp; Atmospheric and Oceanographic Sciences Library, Vol. 42]

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.

Contains 29 algorithms, 99 figures, 1135 references and 47 tables.

An excerpt from the Preface can be found at Climate Risk Analysis.

A large sample part (PDF) is here: Climate Time Series Analysis.

A small sample part is at Google Books.

Buy

Springer

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TOC (Level 1)

Preface Acknowledgements List of Algorithms List of Figures List of Tables Part I Fundamental Concepts 1. Introduction 1.1 Climate archives, variables and dating 1.2 Noise and statistical distribution 1.3 Persistence 1.4 Spacing 1.5 Aim and structure of this book 1.6 Background material 2. Persistence Models 2.1 First-order autoregressive model 2.2 Second-order autoregressive model 2.3 Mixed autoregressive moving average model 2.4 Other models 2.5 Climate theory 2.6 Background material 2.7 Technical issues 3. Bootstrap Confidence Intervals 3.1 Error bars and confidence intervals 3.2 Bootstrap principle 3.3 Bootstrap resampling 3.4 Bootstrap confidence intervals 3.5 Examples 3.6 Bootstrap hypothesis tests 3.7 Notation 3.8 Background material 3.9 Technical issues Part II Univariate Time Series 4. Regression I 4.1 Linear regression 4.2 Nonlinear regression 4.3 Nonparametric regression or smoothing 4.4 Background material 4.5 Technical issues 5. Spectral Analysis 5.1 Spectrum 5.2 Spectral estimation 5.3 Background material 5.4 Technical issues 6. Extreme Value Time Series 6.1 Data types 6.2 Stationary models 6.3 Nonstationary models 6.4 Sampling and time spacing 6.5 Background material 6.6 Technical issues Part III Bivariate Time Series 7. Correlation 7.1 Pearson's correlation coefficient 7.2 Spearman's rank correlation coefficient 7.3 Monte Carlo experiments 7.4 Example: Elbe runoff variations 7.5 Unequal timescales 7.6 Background material 7.7 Technical issues 8. Regression II 8.1 Linear regression 8.2 Bootstrap confidence intervals 8.3 Monte Carlo experiments 8.4 Example: climate sensitivity 8.5 Prediction 8.6 Lagged regression 8.7 Background material 8.8 Technical issues Part IV Outlook 9. Future Directions 9.1 Timescale modelling 9.2 Novel estimation problems 9.3 Higher dimensions 9.4 Climate models 9.5 Optimal estimation References Subject Index Author Index

Data

1-1 1-2 1-3a 1-3b 1-5a 1-5b 1-5c 1-5d 1-6 1-7 1-8 1-9a 1-9b 1-10a 1-10b 2-1 2-12 4-9-d18o 4-9-sedrate 4-13 5-4a 6-3a 7-1a 7-1b 7-1c 7-10a-cyclelength 7-10a-temp 8-6a-y 8-6a-sy 8-6b-x 8-6b-sx 8-8a 8-8b

Entries refer to figures. Data for Fig. 1-4 embargoed until paper accepted for publication.

Software

2. Persistence Models
Ox

3. Bootstrap Confidence Intervals
2SAMPLES DOS and Excel resampling programs Matlab/R (Politis and White 2004) Resampling Stats Shazam SPSS

4. Regression I
LAPACK SiZer strucchange Gasser-Müller kernel CLIM-X-DETECT agedepth_1.0.zip WinGeol Lamination Tool Autocomp/Match XCM BCal OxCal Isoplot

5. Spectral Analysis
dpss (Bell et al. 1993) Lees and Park (1995) Multitaper.zip mwlib SSA-MTM Toolkit REDFIT/RED2CON/SPECTRUM REDFIT ENVELOPE AutoSignal MC-CLEAN REDFITmc2

6. Extreme Value Time Series
MLEGEV WAFO ismev evd EVIM Dataplot Extremes GEVFIT Declustering (Fawcett and Walshaw 2006) VGAM Statistics of Extremes (Beirlant et al. 2004) Caliza

7. Correlation
PearsonT

8. Regression II
LEIV1 LEIV3 Stata (Carroll et al. 2006)

Manfred Mudelsee

Climate Risk Analysis