Stochastic Physics and Climate Modelling

The first book to promote the use of stochastic, or random, processes to understand, model and predict our climate system.

Tim Palmer (Edited by), Paul Williams (Edited by)

9781108446990, Cambridge University Press

Paperback / softback, published 1 March 2018

496 pages, 142 b/w illus. 3 tables
24.5 x 17 x 2.5 cm, 0.85 kg

'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

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.

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 Lévy processes Cécile 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.

Subject Areas: Mechanics of fluids [TGMF], Mechanics of solids [TGMD], Meteorology & climatology [RBP], Earth sciences, geography, environment, planning [R]