Stochastic physics and climate modelling

著者

    • Palmer, Tim
    • Williams, Paul

書誌事項

Stochastic physics and climate modelling

Tim Palmer, Paul Williams

Cambridge University Press, 2010

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注記

"Originating from a theme issue published in Philosophical transactions of the Royal Society A: mathematical, physical & engineering sciences." --T.p.

Includes bibliographical references and index

内容説明・目次

内容説明

This 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. The book 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, the book 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, 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.

目次

  • 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
  • Index.

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