Re: If it's "not ready for prime time" ...
This isn't doubt about the science.
This is not even doubt about the models.
What this is, is doubt about whether, EVEN IF THE MODELS ARE TOTALLY CORRECT, AND THE SCIENCE IS SPOT ON, any meaningful predictions can be made about the future.
The example I always use is balancing a pencil on the sharp end, and predicting which way it will fall.
There is nothing to dispute about the science or the mathematics or the modelling of such and exercise, but in the limit, it doesn't allow you predict the right answer.
Running a model of that on various different machines may well give you a range of completely different answers. That doesn't tell you the models or the science are wrong, merely that the problem you are trying to solve has a very large range of possible solutions, and which one actually happens is probably beyond your power to predict.
I.e. in this case, its may well be that the science and the models are perfectly correct accurate and good. But absolutely no use whatsoever in determining the actual course of future events.
That is in the nature of 'chaotic' systems.
If you are lucky, you will have an attractor, which broadly says that the answer will be inside some bounded set of conditions. (the car that runs off the road bounces and ends up in a field). If you are unlucky it may mean that you have no idea what the final outcome will be (the car that hits a tree, and ends up tumbling across the landscape to end up a twisted lump of metal whose exact shape and location are the result of some extremely fine data on exactly what tree it hit, where it hit it, what the ground was made of at a micro scale, and several other factors you would normally ignore).
What you don't seem to understand is that mathematics science and indeed philosophy have, during the 20th century, arrived at an understanding of the nature of problems which are by its methodology, insoluble, and worse, one of those problems turns out to be even understanding which problems are in fact insoluble.
Science and models are in an IT sense, COMPRESSED forms of the real world data. Sometimes the real world is very highly compressible. The data turns out to actually contain very little ultimate information.
But this is not always the case, and attempting to apply compression techniques that work well on one data set, to another, results in abject failure to deduce any meaningful predictive power whatsoever.
When we do science, what we are doing, is guessing at what compression algorithms we can apply to real world data, and, insofar as it is successful, the algorithm we use is held to be 'not refuted' (in the Popperian sense). What the Great Unwashed erroneously call 'scientific truth'.
However, such models are not th actual data sets themselves. And re-expanding them to data sets that predicts the future is only valid if both the algorithm is correct, and the expansion process itself is not subject to data sensitivity of such magnitude as to make the result meaningless.
You can, in theory, average out a huge bitmaps of a detailed picture into half a dozen bytes of information. But you cannot - unless you have accurately detected a deep pattern in the original bitmap - reassemble it from those half dozen bytes.
And that is the problem with climate forecasting. Whether the science is settled or not (and I would say its very far from settled) the models that the science leads to, do not it seems produce any reliable forecasting whatsoever.
Biting the hand that feeds IT
