We know that complex models, especially for the atmosphere, are likely to be subject to the butterfly effect, meaning that small variations in initial conditions may result in very different states in the long term evolution of the model.
How, then, should we treat models that are intended to be long term, but still exhibit non-linear and possibly chaotic behaviors? For example, what about climate models that aim at predicting for 50 years down the road? Or ocean and ice sheet models that look for the year 2100? Why would they be reliable?
My guess is that the reliability of the long-term models stems from the fact that the time step over which they are computed is much bigger, maybe in the range of months, compared to short term weather forecast, in which the time step is of minutes. This would imply that the chaotic behavior is there even in long-term models, and that it only takes longer for it to become evident. But I am not sure this is what is going on.