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).