3.1.2 Types of models
Simplifications are unavoidable when designing a climate model as the processes that should be taken into account range from the scale of centimetres (for instance for atmospheric turbulence) to that of the Earth itself. The involved time scales also vary widely from the order of seconds for some waves, to billions of years when analysing the evolution of the climate since the formation of Earth. It is thus an important skill for a modeller to be able to select the processes that must be explicitly included compared to those that can be neglected or represented in a simplified way. This choice is of course based on the scientific goal of the study. However, it also depends on technical issues since the most sophisticated models require a lot of computational power: even on the largest computer presently available, the models cannot be routinely used for periods longer than a few centuries to millennia. On longer time scales, or when quite a large number of experiments are needed, it is thus necessary to user simpler and faster models. Furthermore, it is often very illuminating to deliberately design a model that includes only the most important properties, so as to understand in depth the nature of a feedback or the complex interaction between the various components of the system. This is also the reason why simple models are often used to analyse the results of more complex models in which the fundamental characteristics of the system could be hidden by the number of processes represented and the details provided.
Modellers have first to decide the variables or processes to be taken into account and those that will be taken as constants. This provides a method of classifying the models as a function of the components that are represented interactively. In the majority of climate studies, at least the physical behaviour of the atmosphere, ocean and sea ice must be represented. In addition, the terrestrial and marine carbon cycles, the dynamic vegetation and the ice sheet components are more and more regularly included, leading to what are called Earth-system models.
A second way of differentiating between models is related to the complexity of the processes that are included (Fig. 3.2). At one end of the spectrum, General Circulation Models (GCMs) try to account for all the important properties of the system at the highest affordable resolution. The term GCM was introduced because one of the first goals of these models is to simulate the three dimensional structure of winds and currents realistically. They have classically been divided into Atmospheric General Circulation Models (AGCMs) and Ocean General Circulation Models (OGCMs). For climate studies using interactive atmospheric and oceanic components, the acronyms AOGCM (Atmosphere Ocean General Circulation Model) and the broader CGCM (Coupled General Circulation Model) are generally chosen.
At the other end of the spectrum, simple climate models (such as the Energy Balance Models, or EBMs, see section 3.2.1) propose a highly simplified version of the dynamic of the climate system. The variables are averaged over large regions, sometimes over the whole Earth, and many processes are not represented or accounted for by the parameterizations. EBMs thus include a relatively small number of degree of freedom.
EMICs (Earth Models of Intermediate Complexity) are located between those two extremes. They are based on a more complex representation of the system than EBMs but include simplifications and parameterisations for some processes that are explicitly accounted for in GCMs. Actually, the EMICs form the broader category of models. Some of them are relatively close to simple models, while others could be considered as slightly degraded GCMs.
When employed correctly, all the model types can produce useful information on the behaviour of the climate system. There is no perfect model, suitable for all purposes. This is why a wide range of climate models exists, forming what is called the spectrum or the hierarchy of models that will be described in section 3.2. Depending on the objective or the question, one type of models could be selected. The best type of model to use depends on the objective or the question. On the other hand, combining the results from various types of models is often the best way to gain a deep understanding of the dominant processes in action.