Advertisement

Stochastic Climate Models

  • Peter Imkeller
  • Jin-Song von Storch

Part of the Progress in Probability book series (PRPR, volume 49)

Table of contents

  1. Front Matter
    Pages i-xxvii
  2. The Hierarchy of Climate Models

    1. Front Matter
      Pages 1-1
    2. Klaus Fraedrich
      Pages 65-100
    3. Roger Temam
      Pages 117-138
  3. The Emergence of Randomness: Chaos, Averaging, Limit Theorems

    1. Front Matter
      Pages 139-139
    2. Manfred Denker, Marc Kesseböhmer
      Pages 159-169
    3. Yuri Kifer
      Pages 171-188
  4. Tools and Methods: SDE, Dynamical Systems, SPDE, Multiscale Techniques

  5. Reduced Stochastic Models and Particular Techniques

    1. Front Matter
      Pages 297-297
    2. Joseph Egger
      Pages 299-308
    3. Jan A. Freund, Alexander Neiman, Lutz Schimansky-Geier
      Pages 309-323
    4. Adam Hugh Monahan, Lionel Pandolfo, Peter Imkeller
      Pages 325-343
    5. Prashant Sardeshmukh, Cécile Penland, Matthew Newman
      Pages 369-384
    6. W. A. Woyczyński
      Pages 385-398

About these proceedings

Introduction

The proceedings of the summer 1999 Chorin workshop on stochastic climate models captures well the spirit of enthusiasm of the workshop participants engaged in research in this exciting field. It is amazing that nearly 25 years after the formal theory of natural climate variability generated by quasi-white-noise weather forcing was developed, and almost 35 years after J . M. Mitchell first suggested this mechanism as the origin of sea-surf ace-temperature fluctuations and climate variability, there have arisen so many fresh perspectives and new applications of the theory. The workshop has succeeded admirably in high­ lighting these new aspects while clarifying the position of stochastic climate modelling within the general framework of climate research and mathematical modelling. The organizers can be congratulated in bringing together leading researchers covering a wide range of scientific expertise, from mathematicians concerned with the derivation of stochastic models from first principles, to app­ lied climate modellers trying to understand the dynamics of the complex climate system. Following the first burst of stochastic modelling papers in the decade from the mid-seventies to the mid-eighties, in which the viability of the concept was demonstrated using relatively simple conceptual models, there was a lull of work in this field. One awaited the development of more sophisticated climate models with which one could carry out realistic quantitative analyses of the implications of stochastic forcing for the global climate system. Now that these models have become widely available, it is natural that one is witnessing a resurgence of stochastic modelling investigations.

Keywords

Differentialgleichungen Scale climate modelling digital elevation model globale Analysis random dynamical system stochastic process

Editors and affiliations

  • Peter Imkeller
    • 1
  • Jin-Song von Storch
    • 2
  1. 1.Institut für MathematikHumboldt-Universität zu BerlinBerlinGermany
  2. 2.Institut für MeteorologieUniversität HamburgHamburgGermany

Bibliographic information