Predictability of Weather and Climate

The topic of predictability of weather and climate has advanced significantly over recent years, both through an increased understanding of the phenomena that affect predictability, and through development of techniques used to forecast state-dependent predictability.

   This book brings together some of the world’s leading experts on predictability of weather and climate. It addresses predictability from the theoretical to the practical, on timescales from days to decades. Topics such as the predictability of weather phenomena, coupled ocean–atmosphere systems and anthropogenic climate change are among those included. Ensemble systems for forecasting predictability are discussed extensively. Ed Lorenz, father of chaos theory, makes a contribution to theoretical analysis with a previously unpublished paper.

   This well-balanced volume will be a valuable resource for many years. High-quality chapter authors and extensive subject coverage will make it appeal to people with an interest in weather and climate forecasting and environmental science, from graduate students to researchers.

TIM PALMER is Head of the Probability Forecasting and Diagnostics Division at the European Centre for Medium-Range Weather Forecasts (ECMWF). He has won awards including the American Meteorological Society Charney Award and the Royal Meteorological Society Buchan Award. He is a fellow of the Royal Society, and is co-chair of the Scientific Steering Group of the World Climate Research Programme’s Climate Variability and Predictability (CLIVAR) project. He was a lead author of the Intergovernmental Panel on Climate Change Third Assessment Report, and coordinator of two European Union climate prediction projects: PROVOST and DEMETER.

RENATE HAGEDORN is the education officer for the ECMWF research department. She gained her Ph.D. at the Institute for Marine Sciences in Kiel, Germany, where she developed a coupled atmosphere ocean model for the Baltic Sea catchment area. Upon joining the ECMWF she was part of the DEMETER team. More recently she has been working on improving the ECMWF ensemble prediction systems. In conjunction with Tim Palmer and other colleagues, she was awarded the Norbert Gerbier-Mumm International Award 2006 by the World Meteorological Organization.

Predictability of Weather and Climate

Edited by

Tim Palmer and Renate Hagedorn
European Centre for Medium-Range Weather Forecasts, Reading, UK

CAMBRIDGE UNIVERSITY PRESS
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© Cambridge University Press 2006

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First published 2006

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Contents

Contributors

Myles Allen Department of Atmospheric Physics, University of Oxford, Parks Road, Oxford OX1 3PU, UK

David Anderson ECMWF, Shinfield Park, Reading RG2 9AX, UK

Roberto Buizza ECMWF, Shinfield Park, Reading RG2 9AX, UK

Arun Chakraborty Department of Meteorology, Florida State University, Tallahassee, FL 32306-4520, USA

H.-R. Chang School of Earth and Atmospheric Sciences, Atlanta, GA, USA

Susanna Corti Institute of Atmospheric Sciences and Climate, ISAC-SNR, Bologna, Italy

Francisco J. Doblas-Reyes ECMWF, Shinfield Park, Reading RG2 9AX, UK

Martin Ehrendorfer Inst. für Meteorologie und Geophysik, Universität Innsbruck, Innrain 52, A-6020 Innsbruck, Austria

Brian Farrell Harvard University, Division of Engineering and Applied Sciences, Pierce Hall 107d, Oxford Street Mail Area H0162, Cambridge, MA 02138, USA

David Frame Department of Physics, University of Oxford, UK

R. Grossman Colorado Research Associates, Boulder, CO, USA

Renate Hagedorn ECMWF, Shinfield Park, Reading RG2 9AX, UK

Thomas Hamill Physical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO, USA

T. Hopson Program in Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA

Brian Hoskins Department of Meteorology, University of Reading, 2 Early Gate, Whiteknights, Reading RG6 6AH, UK

C. Hoyos School of Earth and Atmospheric Sciences, Atlanta, GA, USA

Brian Hunt Chaos Group, University of Maryland, College Park, MD, USA

Petros Ioannou Section of Astrophysics, Astronomy and Mechanics, Department of Physics, Panepistimiopolis, Zografos 15784, Athens, Greece

Fei-Fei Jin Department of Meteorology, Florida State University, Tallahassee, FL 32306-4520, USA

Eugenia Kalnay Department of Meteorology, University of Maryland, 3431 Computer and Space Sciences Building, College Park, MD 20742-2425, USA

Jamie Kettleborough Space Science and Technology Department, Rutherford Appleton Laboratory, Didcot, Oxon, UK

J. L. Kinter III Center for Ocean-Land-Atmosphere Studies, Calverton, MD, USA

Tiruvalam Krishnamurti Department of Meteorology, Florida State University, Tallahassee, FL 32306-4520, USA

T. S. V. Vijaya Kumar Department of Meteorology, Florida State University, Tallahassee, FL 32306-4520, USA

Fred Kucharski Abdus Salam International Centre for Theoretical Physics, Trieste

François Lalaurette Ecole Nationale de la Météorologie, Av. G. Coriolis, 31057 Toulouse, France

Mojib Latif IfM-Geomar, Düsternbrooker Weg 20, 24105 Kiel, Germany

Edward Lorenz Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA

Chiara Marsigli ARPA-SIM, Bologna, Italy

Franco Molteni Physics of Weather and Climate Dynamics, The Abdus Salam International Centre for Theoretical Physics, PO Box 586, I-34100 Trieste, Italy

Andrea Montani ARPA-SIM, Bologna, Italy

Ken Mylne Met Office, FitzRoy Road, Exeter EX1 3PB, UK

Fabrizio Nerozzi ARPA-SIM, Bologna, Italy

Edward Ott Chaos Group, University of Maryland, College Park, MD, USA

Tiziana Paccagnella ARPA-SIM, Bologna, Italy

Tim Palmer ECMWF, Shinfield Park, Reading RG2 9AX, UK

Wonsum Park Max-Planck-Institut für Météorologie, Hamburg, Germany

Goòrún Nína Petersen University of Reading, UK

Holger Pohlmann Max-Planck-Institut für Meteorologie, Hamburg, Germany

David Richardson ECMWF, Shinfield Park, Reading RG2 9AX, UK

Jagadish Shukla George Mason University, Institute of Global Environment and Society Inc, 4041 Powder Mill Road, Suite 302 Calverton, MD 20705-3106, USA

Adrian Simmons ECMWF, Shinfield Park, Reading RG2 9AX, UK

Leonard Smith OCIAM Mathematical Institute, 24–29 St Giles’, Oxford OXl 3LB, UK

David Stainforth Department of Physics, University of Oxford, UK

Lydia Stefanova Department of Meteorology, Florida State University, Tallahassee, FL 32306-4520, USA

A. Subbiah Asian Disaster Preparedness Centre, Bangkok, Thailand

Istvan Szunyogh University of Maryland, College Park, MD, USA

Olivier Talagrand Laboratoire de Météorologie Dynamique, Paris, France

Alan J. Thorpe Department of Meteorology, University of Reading, 2 Early Gate, Whiteknights, Reading RG6 6AH, UK

Stefano Tibaldi Servizio Meteorologico Regionale, ARPA Emilia-Romagna, Viale Silvani, 640122 Bologna, Italy

Axel Timmermann Department of Oceanography, University of Hawaii at Manoa, 1000 Pope Road, Marine Sciences Building, Honolulu, HI 96822, USA

Zoltan Toth Environmental Modeling Center, NCEP, NWS/NOAA, Washington DC 20233, USA

Gerald van der Grijn ECMWF, Shinfield Park, Reading RG2 9AX, UK

Duane Waliser Jet Propulsion Laboratory, MS 183-505, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA

Peter Webster School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332-0340, USA

Won-Tae Yun Department of Meteorology, Florida State University, Tallahassee, FL 32306-4520, USA

Yuejian Zhu National Centers for Environmental Prediction, Washington DC, USA

Preface

In his biography of the great twentieth-century theoretical physicist Richard Feynman, Gleick (1993) writes: ‘He (Feynman) believed in the primacy of doubt, not as a blemish on our ability to know, but as the essence of knowing’. Feynman’s philosophy applies as much to weather and climate forecasting as to fundamental physics, as made explicit by Tennekes et al. (1987) when they wrote: ‘no forecast is complete without a forecast of forecast skill’.

   The estimation of uncertainty in weather and climate prediction is encapsulated in the word ‘predictability’. If something is said to be predictable, then presumably it can be predicted! However, initial conditions are never perfect and neither are the models used to make these predictions. Hence, the predictability of the forecast is a measure of how these inevitable imperfections leave their imprint on the forecast. By virtue of the non-linearity of the climate, this imprint varies from day to day, just as the weather itself varies; predictability is as much a climatic variable as rainfall, temperature or wind.

   Of course, it is one thing to talk about predictability as if it were just another climatic variable; it is another thing to estimate it quantitatively. The predictability of a system is determined by its instabilities and non-linearities, and by the structure of the imperfections. Estimating these instabilities, non-linearities and structures provides a set of tough problems, and real progress requires sophisticated mathematical analysis on both idealised and realistic models.

   However, the big world out there demands forecasts of the weather and the climate: is it going to rain tomorrow, will the Arctic ice cap melt by the end of the century? The man in the street wanting to know whether to bring his umbrella to work, or the politician looking for advice on formulating her country’s strategy on climate change, cannot wait for the analysis on existence or otherwise of heteroclinic state-space orbits to be finalised! The difference between the real world of prediction, and the more aesthetic world of predictability has been perfectly encapsulated by one of the pioneers of the subject, Kiku Miyakoda, who said: ‘Predictability is to prediction as romance is to sex!’. Oscar Wilde, who wrote: ‘The very essence of romance is uncertainty!’, might well have approved.

   However, as we enter the twenty-first century, is this still a fair characterisation? We would argue not! In the last decade, the romantic world of predictability has collided head-on with the practical world of prediction. No longer do operational centres make forecasts without also estimating forecast skill – whether for predictions one hour ahead or one century ahead. This change has come in the last few years through the development of ensemble forecast techniques made practical by mind-boggling developments in high-performance computer technology.

   In late 2002, the European Centre for Medium-Range Weather Forecasts (ECMWF) held a week-long seminar on the topic of Predictability of Weather and Climate. A subtheme, borrowing from Kiku Miyakoda’s aphorism, was to celebrate the ‘reconciliation of romance and sex’! World leaders in the field of predictability of weather and climate gave pedagogical presentations covering the whole range of theoretical and practical aspects on weather and climate timescales, i.e. from a few hours to a century. It was decided, as this was a sufficiently landmark meeting and the presentations sufficiently comprehensive, that it was worth publishing the proceedings for the benefit of the larger scientific community. During 2004 and 2005 authors were asked to expand and update their presentations.

   In fact there is one exception to this strategy. One of the greatest pioneers of the subject is Ed Lorenz – his prototypical model of chaos spawned a revolution, not only in meteorology, but in mathematics and physics in general. Ed was unable to come to the 2002 meeting, but a few years earlier had given a presentation at ECMWF on what has become known as the Lorenz-1996 model. This paper is widely cited, but has never been published externally. We decided it would be proper to acknowledge Ed’s unrivalled contribution to the field of weather and climate predictability by publishing his 1996 paper in this volume.

   Lorenz’s contribution is one of the introductory chapters on predictability where both general and specific theoretical/mathematical aspects of predictability theory are discussed. These chapters are followed by contributions on data assimilation methods. The next chapters represent a journey through the predictability of different timescales and different phenomena. The link to real-world applications is made by discussing important developments in operational forecast systems, presenting methods to diagnose and improve forecast systems, and finally giving examples utilising predictability in decision-making processes.

   We would like to acknowledge the help of Anabel Bowen, Rob Hine, Els Kooij-Connally, and Matt Lloyd during all stages of the production of this book. Last but not least, we would like to thank ECMWF for initiating and supporting the seminar on which the contributions of this book are based.