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Chapter 12 - Predictability of seasonal climate variations: a pedagogical review

Published online by Cambridge University Press:  03 December 2009

By
J. Shukla  and
J. L. Kinter III
Edited by
Tim Palmer  and
Renate Hagedorn
J. Shukla
Affiliation:
Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland
J. L. Kinter III
Affiliation:
Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland
Tim Palmer
Affiliation:
European Centre for Medium-Range Weather Forecasts
Renate Hagedorn
Affiliation:
European Centre for Medium-Range Weather Forecasts
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Summary

Introduction

It is well known that the day-to-day changes in the large-scale atmospheric circulation are not predictable beyond two weeks. The small-scale rainfall patterns associated with the large-scale circulation patterns may not be predictable beyond even a few days. However, the space–time averages of certain atmospheric and oceanic variables are predictable for months to seasons. This chapter gives a pedagogical review of the ideas and the results that have led to our current understanding and the status of the predictability of seasonal climate variations.

We first review the current status of the understanding of the limits of the predictability of weather. We adopt Lorenz' classical definition of the predictability of weather as the range at which the difference between forecasts from two nearly identical initial conditions is as large in a statistical sense as the difference between two randomly chosen atmospheric states. With this definition of predictability, it is implied that the upper limit of predictability depends on the saturation value of the maximum possible error, which, in turn, is determined by the climatological variance. Lorenz provided a simple conceptual model in which the upper limit of weather prediction skill is described by three fundamental quantities: the size of the initial error, the growth rate of the error and the saturation value of the error. This simple model is able to explain the current status of the seasonal, regional and hemispheric variations of numerical weather prediction (NWP) skill.

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Predictability of Weather and Climate , pp. 306 - 341
Publisher: Cambridge University Press
Print publication year: 2006

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