Huge product rangeOver 140,000 books & equipment products
Rapid shippingUK & Worldwide
Pay in £, € or U.S.$By card, cheque, transfer, draft
Exceptional customer serviceGet specialist help and advice
Stochastic Physics and Climate Modelling is the first book to promote the use of stochastic, or random, processes to understand, model and predict our climate system. One of the most important applications of this technique is in the representation of comprehensive climate models of processes which, although crucial, are too small or fast to be explicitly modelled.
Stochastic Physics and Climate Modelling shows how stochastic methods can lead to improvements in climate simulation and prediction, compared with more conventional bulk-formula parameterization procedures. Beginning with expositions of the relevant mathematical theory, Stochastic Physics and Climate Modelling moves on to describe numerous practical applications. It covers the complete range of time scales of climate variability, from seasonal to decadal, centennial, and millennial.
With contributions from leading experts in climate physics, Stochastic Physics and Climate Modelling is invaluable to anyone working on climate models, including graduate students and researchers in the atmospheric and oceanic sciences, numerical weather forecasting, climate prediction, climate modelling, and climate change.
Preface Tim Palmer and Paul Williams
Introduction: stochastic physics and climate modelling Tim Palmer and Paul Williams
1. Mechanisms of climate variability from years to decades Geoffrey Vallis
2. Empirical model reduction and the modeling hierarchy in climate dynamics and the geosciences Sergey Kravtsov, Dmitri Kondrashov and Michael Ghil
3. An applied mathematics perspective on stochastic modelling for climate Andrew J. Majda, Christian Franzke and Boualem Khouider
4. Predictability in nonlinear dynamical systems with model uncertainty Jinqiao Duan
5. On modelling physical systems with stochastic models: diffusion versus Levy processes Cecile Penland and Brian D. Ewald
6. First passage time analysis for climate prediction Peter C. Chu
7. Effects of stochastic parametrization on conceptual climate models Daniel S. Wilks
8. Challenges in stochastic modelling of quasigeostrophic turbulence Timothy DelSole
9. Orientation of eddy fluxes in geostrophic turbulence Balasubramanya T. Nadiga
10. Stochastic theories for the irregularity of ENSO Richard Kleeman
11. Stochastic models of the meridional overturning circulation: time scales and patters of variability Adam H. Monahan, Julie Alexander and Andrew J. Weaver
12. A stochastic dynamical systems view of the Atlantic Multidecadal Oscillation Henk A. Dijkstra, Leela M. Frankcombe and Anna S. von der Heydt
13. Centennial-to-millennial-scale Holocene climate variability in the North Atlantic region induced by noise Matthias Prange, Jochen I. Jongma and Michael Schulz
14. Cloud radiative interactions and their uncertainty in climate models Adrian Tompkins and Francesca Di Giuseppe
15. Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model Judith Berner, Francisco Doblas-Reyes, Tim Palmer, Glenn J. Shutts and Antje Weisheimer
16. Rethinking convective quasi-equilibrium: observational constraints for stochastic convective schemes in climate models J. David Neelin, Ole Peters, Katrina Hales, Christopher E. Holloway and Johnny W. B. Lin
17. Comparison of stochastic parametrization approaches in a single-column model Michael A. W. Ball and Robert S. Plant
18. Stochastic parametrization of multiscale processes using a dual-grid approach Thomas Allen, Glenn J. Shutts and Judith Berner
Tim Palmer is Head of the Probability Forecasting and Diagnostics Division at the European Centre for Medium-Range Weather Forecasts (ECMWF). He has won the Royal Society Esso Energy Award, the Royal Meteorological Society Adrian Gill Prize, and the American Meteorological Society Jule Charney Award. He is a fellow of the Royal Society, the Royal Meteorological Society, the American Meteorological Society, and Academia Europaea. He is a lead author of the Intergovernmental Panel on Climate Change (IPCC), co-chair of the Scientific Steering Group of the UN World Meteorological Organisation's Climate Variability and Predictability (CLIVAR) project, and coordinator of two European Union climate prediction projects (PROVOST and DEMETER). He has had numerous appearances on radio and TV, in relation to weather, climate and chaos theory, and has co-edited another book with Cambridge University Press – Predictability of Weather and Climate – in 2006.
Paul Williams is a Research Fellow at the Department of Meteorology, University of Reading. He has won the Royal Astronomical Society Blackwell Prize (2004) and the Royal Meteorological Society Rupert Ford Award (2005), and has received a prestigious Crucible Fellowship from the National Endowment for Science, Technology and the Arts (2007). He was the lead author of a climate change report commissioned and published by the European Parliament (2004). He is a Fellow of the Royal Meteorological Society, the Institute of Physics, and the Royal Astronomical Society. His research findings have been reported widely in the media, including feature articles in New Scientist and the Financial Times, and a panel discussion on BBC Radio 4.
"With contributions from leading experts in climate physics, this book is invaluable to anyone working on climate models, including graduate students and researchers in the atmospheric and oceanic sciences, numerical weather forecasting, climate prediction, climate modelling and climate change."
– The Eggs EGU Newsletter (the-eggs.org)
"[This] book does a very good job of reviewing the state of the art of stochastic physics in climate modeling, and can be wholeheartedly recommended to any researcher seriously interested in that line of research."
– Philip Sura, Bulletin of the American Meteorological Society
"Stochastic Physics and Climate Modelling is a timely thought-provoking book on one of the most challenging and paradoxical scientific issues: stochastic physics may well be the key to substantial progress being made in climate change modelling and prediction, and to resolve the large uncertainties that exist. It is therefore a must for anyone having a keen interest in climate modelling, especially graduate students and researchers involved in climate studies."
– Nonlinear Processes in Geophysics