Re: Test Your Model
Backwards prediction is all fine and dandy, but what happens is that after a while you start tweaking your model to actually give good backwards prediction results. Or stated alternatively, you're effectively running a genetic algorithm where only models that do give good backward prediction survive. This is another form of "curve fitting". Thus by continuously using the same "validation" data, you're effectively using fitting the model to that data.
That still doesn't mean that they'll be any good with predicting was is going to happen. Interpolation is good, extrapolation is evil. Effectively fitting observed data to a model to obtain the "actual" forcing values becomes hazardous the more model parameters there are. The problem being that there may not be a unique solution and you can't determine beforehand whether the solution, "fitting", you've found is the correct one. (Most likely it isn't.)
Another thing to bear in mind is that many of the "constants" used in the model were obtained by fitting historic data to a model of some sort, the more data, the better the fit. Therefore models should be able to "predict" the past quite well since they are based on the historic data.
Unfortunately, only time will tell whether the models were/are correct/incorrect and by that time it may be too late.