Climate Time Series Analysis Classical Statistical and Bootstrap Met
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
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Atmospheric and Oceanographic Sciences Library Volume 42
Editors Lawrence A. Mysak, Department of Atmospheric and Oceanographic Sciences, McGill University, Montreal, Canada Kevin Hamilton, International Pacific Research Center, University of Hawaii, Honolulu, HI, U.S.A. Editorial Advisory Board A. Berger Université Catholique, Louvain, Belgium J.R. Garratt CSIRO, Aspendale, Victoria, Australia J. Hansen MIT, Cambridge, MA, U.S.A. M. Hantel Universität Wien, Austria H. Kelder KNMI (Royal Netherlands Meteorological Institute), De Bilt, The Netherlands T.N. Krishnamurti The Florida State University, Tallahassee, FL, U.S.A. P. Lemke Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany A. Robock Rutgers University, New Brunswick, NJ, U.S.A. S.H. Schneider † Stanford University, CA, U.S.A. G.E. Swaters University of Alberta, Edmonton, Canada J.C. Wyngaard Pennsylvania State University, University Park, PA, U.S.A.
For other titles published in this series, go to www.springer.com/series/5669
Manfred Mudelsee
Climate Time Series Analysis Classical Statistical and Bootstrap Methods
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Dr. Manfred Mudelsee Climate Risk Analysis Schneiderberg 26 30167 Hannover Germany Alfred Wegener Institute for Polar and Marine Research Bussestrasse 24 27570 Bremerhaven Germany [email protected]
ISSN 1383-8601 ISBN 978-90-481-9481-0 e-ISBN 978-90-481-9482-7 DOI 10.1007/978-90-481-9482-7 Springer Dordrecht Heidelberg London New York Library of Congress Control Number: 2010930656 c Springer Science+Business Media B.V. 2010 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Cover illustration: Wave@2009 JupiterImages Corporation Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
To my parents, Anna-Luise Mudelsee, n´ ee Widmann and Richard Mudelsee
Preface
Climate is a paradigm of a complex system. Analysing climate data is an exciting challenge. Analysis connects the two other fields where climate scientists work, measurements and models. Climate time series analysis uses statistical methods to learn about the time evolution of climate. The most important word in this book is “estimation.” We wish to know the truth about the climate evolution but have only a limited amount of data (a time series) influenced by various sources of error (noise). We cannot expect our guess (estimate), based on data, to equal the truth. However, we can determine the typical size of that deviation (error bar). Related concepts are confidence intervals or bias. Error bars help to critically assess estimation results, they prevent us from making overstatements, they guide us on our way to enhance the knowledge about the cl
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