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Chapter 15 - Model error in weather and climate forecasting

Published online by Cambridge University Press:  03 December 2009

By
Myles Allen ,
David Frame ,
Jamie Kettleborough  and
David Stainforth
Edited by
Tim Palmer  and
Renate Hagedorn
Myles Allen
Affiliation:
Department of Physics, University of Oxford
David Frame
Affiliation:
Department of Physics, University of Oxford
Jamie Kettleborough
Affiliation:
Space Science and Technology Department, Rutherford Appleton Laboratory, Didcot
David Stainforth
Affiliation:
Department of Physics, University of Oxford
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

As if someone were to buy several copies of the morning newspaper to assure himself that what it said was true.

Ludwig Wittgenstein

Introduction

The phrase ‘model error’ means different things to different people, frequently arousing surprisingly passionate emotions. Everyone accepts that all models are wrong, but to some this is simply an annoying caveat on otherwise robust (albeit model-dependent) conclusions, while to others it means that no inference based on ‘electronic storytelling’ can be taken seriously at all. This chapter will focus on how to quantify and minimise the cumulative effect of model ‘imperfections’ (errors by any other name, but we are trying to avoid inflammatory language) that either have not been eliminated because of incomplete observations/understanding or cannot be eliminated because they are intrinsic to the model's structure. We will not provide a recipe for eliminating these imperfections, but rather some ideas on how to live with them. Live with them we must, because no matter how clever model developers, or how fast supercomputers, become, these imperfections will always be with us and represent the hardest source of uncertainty to quantify in a weather or climate forecast (Smith, this volume). This is not meant to underestimate the importance of identifying and improving representations of dynamics (see Hoskins, this volume) or parametrisations (see Palmer, this volume) or existing (and planned) ensemble-based forecast systems (Anderson, Buizza, this volume), merely to draw attention to the fact that our models will always be subject to error or inadequacy (Smith, this volume), and that this fact is especially chronic in those cases where we lack the ability to use conventional verification/falsification procedures (i.e. the climate forecasting problem).

Type
Chapter
Information
Predictability of Weather and Climate , pp. 391 - 427
Publisher: Cambridge University Press
Print publication year: 2006

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