When scientists talk about models, they are not referring to gorgeous women, or small wooden planes (unfortunately). Models are used in science as a means of understanding complex ideas in simpler terms. While some models do involve the use of powerful super computers, other models can be relatively simple.
An example of a simple model is what we know as the ‘water cycle’. In school, we learn that the sun heats a body of water, which then evaporates, forming clouds, before raining back to Earth and finding its way back into a body of water. In reality, the water cycle is much more complex, involving all sorts of physical processes that this simple model does not account for. But that’s what makes a model useful. It takes a complex natural process and breaks it down into smaller, easily understood components.
At the other extreme end of modeling are climate models, for example. Climate models similarly are simplified understandings of the way the real world works. In the case of climate models, many more processes are accounted for and feedbacks between all of the different components of the model are examined. Because of the size of the model, it must be run using super computers. Even though this model is much more complex that the simple water cycle shown above, the same principals apply. Complex processes in nature are simplified into concepts we can understand, allowing the user of the model to get a better understanding of how everything fits together.
One important thing to note is that a model is not the same as reality. Since it is a simplified version of a complex system, some components that are either too complicated to be easily included, or that are simply not understood or known about, will inevitably be left out. It is because of this that there is uncertainties associated with the use of the model, which need to be understood in order to use the model properly.