Scientific American: Are climate change models becoming more accurate and less reliable?
One of the perpetual challenges in my career as a modeler of biochemical systems has been the need to balance accuracy with reliability. This paradox is not as strange as it seems. Typically when you build a model you include a lot of approximations supposed to make the modeling process easier; ideally you want a model to be as simple as possible and contain as few parameters as possible. But this strategy does not work all the time since sometimes it turns out that in your drive for simplicity you have left a crucial factor out. So now you include this crucial factor, only to find that the uncertainties in your model go through the roof. What’s happening in such unfortunate cases is that along with including the signal from the previously excluded factors, you have also inevitably included a large amount of noise. This noise can typically result from an incomplete knowledge of the factor, either from calculation or from measurement. Modelers of every stripe thus have to tread a fine balance between including as much of reality as possibility as possible and making the model accurate enough for quantitative explanation and prediction.
It seems that this is exactly the problem that has started bedeviling climate change models. A recent issue of Nature had a very interesting article on what seems to be a wholly paradoxical feature of models used in climate science; as the models are becoming increasingly realistic, they are also becoming less accurate and predictive because of growing uncertainties. I can only imagine this to be an excruciatingly painful fact for climate modelers who seem to be facing the equivalent of the Heisenberg uncertainty principle for their field. It’s an especially worrisome time to deal with such issues since the modelers need to include their predictions in the next IPCC report on climate change which is due to be published this year. ...
A very interesting piece showing the challenges of forecasting the future with mathematical models. I liked this paragraph.
... But the lesson to take away from this dilemma is that crude models sometimes work better than more realistic ones. My favorite quote about models comes from the statistician George Box who said that “all models are wrong, but some are useful”. It is a worthy endeavor to try to make models more realistic, but it is even more important to make them useful.