Stochastic Physics and Climate Modelling [Paperback]

The first book to promote the use of stochastic, or random, processes to understand, model and predict our climate system.This is the first book promoting the use of stochastic, or random, processes to understand, model and predict our climate system. It shows how such methods improve climate simulation and prediction, compared with more conventional bulk-formula parameterization procedures, and is invaluable to graduates and researchers working on climate models.This is the first book promoting the use of stochastic, or random, processes to understand, model and predict our climate system. It shows how such methods improve climate simulation and prediction, compared with more conventional bulk-formula parameterization procedures, and is invaluable to graduates and researchers working on climate models.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 modeled. 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 modeling, and climate change.Preface Tim Palmer and Paul Williams; Introduction: stochastic physics and climate modelling Tim Palmer and Paul Williams; 1. Mechanisms l#i