I helped organize one of the events in the Edinburgh International Science Festival – a talk by Prof. Simon Tett entitled A gentle introduction to climate modelling. As one of the hosts of the event, I had dinner with the speaker and had the opportunity to discuss his views regarding what is hard about modelling these types of processes.
Clearly, the weather seems rather unpredictable and complex. So, what does it take to understand it well enough to be able to do long term predictions – such as for climate change related questions. Is data really the bottleneck as many people believe?
Simon pointed that most of these models are based on a somewhat coarse discretization of the underlying process.For instance, fluid and heat transfer models might be based on cells that are something like 100 square miles across. Clearly, this is going to be ignoring a significant amount of fine structure – some part of which will undoubtedly have larger scale implications. Moreover, the simple solution of just throwing more processors at the problem doesn’t suffice because the problem scales poorly and there is significant sequential structure to these simulations. So, in his view, the hard questions all have to do with structuring models so that both the coarse and fine dynamics can be reasonably captured concisely. Clearly, uncertainty and probabilities play a key role. However, this is quite different from the naive approach of just collecting more and more data in order to refine the underlying distributions. The trick is to first structure the model well enough that as more and more data comes in we really do get a sequentially improved idea of what we really want to understand – the dynamical behaviour of this large system.