A good example is related to clouds. Obviously, in an actual cloud, the relative humidity is close to 100%, but at a grid box scale of 100's of km, the mean humidity - even if there are quite a few clouds - will be substantially less. Thus a parameterisation is needed that relates the large scale mean values, to actual distribution of clouds in a grid box that one would expect. There are of course many different ways to do that, and the many modelling groups (in the US, Europe, Japan, Australia etc.) may each make different assumptions and come up with slightly different results.
In short, the model does not simulate cloud formation; it just substitutes an input variable (= parameter) designed to represent average humidity over the grid square. A simplifying assumption. Perhaps an accurate one; perhaps a vastly inaccurate one. Who knows?
Climate modelling, like any other kind, needs to be verified against empirical observation, and for multiple cycles of whatever you are trying to model. This is an inherent problem when you are trying to model 100 year shifts in a non-linear, tightly coupled, chaotic system. Saying, as your link does, that gee, we can confidently predict that winter is colder than summer, so we can too predict climate, is nothing short of absurd. |
