Like all tools you have to aware of its limitations, but I think shading can be a fantastic way to visualize uncertainty.
For example, the Bank of England occasionally uses it in plots of economic forecasts, where time is on the x-axis and things like GDP might be on the y-axis, eg. here http://www.bankofengland.co.uk/publications/Documents/inflat.... The fading out of intensity over time is a great visual reminder that predicting the future is hard.
It is much better when your chart is supposed to be targeted at the general public, because the "smearing out" of the data is very hard to misunderstand, unlike confidence intervals.
From the first few graphs I've seen in the links, the shading is discrete and not continuous (e.g. p 40). Discrete shading does address my first point, and it can help somewhat with communication too. Personally, I prefer simulating from the implicit model and plotting, say, 1000 hypothetical sample paths; but I agree that discrete shading can be effective too.
For example, the Bank of England occasionally uses it in plots of economic forecasts, where time is on the x-axis and things like GDP might be on the y-axis, eg. here http://www.bankofengland.co.uk/publications/Documents/inflat.... The fading out of intensity over time is a great visual reminder that predicting the future is hard.
It is much better when your chart is supposed to be targeted at the general public, because the "smearing out" of the data is very hard to misunderstand, unlike confidence intervals.