If you're not reading RealClimate, you should be. It's quickly becoming an excellent resource not just for understanding why climatologists argue that the Earth is warming and that human-introduced greenhouse gases are to blame, but for seeing how science works in general. Their recent examination of how we know that the measured increases in CO2 come from human activity was an excellent example of how breadth of evidence across disciplines can be applied to a given subject. Today, RealClimate contributor Gavin Schmidt takes a look at how climate models work, and asks "Is Climate Modeling Science?" Although the article focuses on climate models, it's a useful treatise on how sciences of all sorts use models to further understanding.
While Schmidt comes down in favor of using models (no real surprise), the article emphasizes how much work goes into checking the models, and how difficult getting them right can be:
Climate is complex. [...] One of the most important features of complex systems is that most of their interesting behaviour is emergent. It's often found that the large scale behaviour is not a priori predictable from the small scale interactions that make up the system. So it is with climate models. If a change is made to the cloud parameterisation, it is difficult to tell ahead of time what impact that will have on, for instance, the climate sensitivity. This is because the number of possible feedback pathways (both positive and negative) is literally uncountable. You just have to put it in, let it physics work itself out and see what the effect is.
This means that validating these models is quite difficult. (NB. I use the term validating not in the sense of 'proving true' (an impossibility), but in the sense of 'being good enough to be useful'. In essence, the validation must be done for the whole system if we are to have any confidence in the predictions about the whole system in the future. This validation is what most climate modellers spend almost all their time doing.
As scientists are continually learning more details about both past and present conditions, the models must be able to take the new inputs and still produce results which accurately reflect the historical record. This is as true for climate studies as it is for economics or cosmology or epidemiology. When the numbers don't match, both the data and the models are re-examined; over time, the models get more accurate.
Schmidt makes another point, almost as an aside, which has some WorldChanging relevance:
Since climatologists don't have access to hundreds of Earth's to observe and experiment with, they need virtual laboratories that allow ideas to be tested in a controlled manner.
We don't have hundreds of Earths, but we do have a gradually-growing body of knowledge about the atmospheric workings of other planets, and a need to learn more. And learning about other Earths may be on the horizon. The Terrestrial Planet Finder telescope (either in space or at the south pole) will let us make observations of planets much like our own in orbit around other stars.