The data I mean...
The job of the geophysicist, I was told yesterday at a seminar given by Mrinal Sen, is essentially impossible. It is, given data that is insufficient, inaccurate and inconsistent to come up with a useful model.
Interestingly, it is unlikely that the skeptics' squad will make much of this admission, since the utility of the models Sen is specifically interested in is whether they correctly inform placement of oil well drilling efforts.
Nevertheless wells do get drilled. In the past it was a matter of some combination of analysis and intuition. Nowadays, statistics works in there as well.
Does climatology partake of these fundamental problems? Not as much as seismology, really; our observations are relatively accurate and consistent compared to seismic data. They are far from sufficient for long time scale processes, and the formalism of use of paleodata still leaves much to be desired.
Nevertheless, the future will happen, and we will do something about it. The question at hand for climate modelers and their customers is to what extent the models ought to affect what we do.
Computational geosciences and computational biological sciences are very different from computational engineering in flavor. In engineering (aside from civil and architectural, which partakes slightly of our problems) the system is entirely controlled and hence the tradeoffs between model expense and model accuracy are well understood. By contrast, our problem is to establish the right model based on observational data and theory.
This is called "inverse modeling" in some circles. I dislike the name and regret the loss of the appropriate term "cybernetics" to Hollywood and to some rather ill-defined corners of computer science. I propose, therefore, to call what some computational sciences do "meta-modeling", wherein the model itself (and the nature of its relationship to observation and theory) is construed as a legitimate object of study.
It is interesting how well-established this modality of thought is in mineral exploration (where there is, after all, a bottom line to take care of) and how controversial it remains in climate science. I have some thoughts as to why this might be. Some of the problems are directly a consequence of the unfairness of the unfair criticisms to which we have been subjected; this makes a fair assessment of fair crticisms fraught with peril. Others emerge from the history of the field and the social structures in which the work is performed.
It seems obvious to me that if the computational resources applied to climate go up several orders of magnitude, the correct approach to this is not, or at the very least not solely, to build a small number of immensely complex modeling platforms which use all the available computational power. Rather, it is to apply the (expensive but powerful) inverse modeling methodology to explore the model space of simpler models; to improve the expressiveness of model description languages; and to use these tools to build a much larger and more formal ensemble of climate modeling tools.
I also have a question whether the most policy-relevant models and the most science-relevant models are necessarily the same. I suspect otherwise. Climate modelers are so defended against the accusation of heuristics that they fail to apply the heuristics that might apply in an applied science effort.
Sen presented an interesting taxonomy of inverse methods at his talk. Much more to follow on this subject, hopefully.