A graph that is fairly commonly promoted to – apparently – illustrate that models and observations have diverged, is one produced by John Christy which compares models to satellite/balloon data for the troposphere. Ignoring all the potential issues with this graph, one problem is simply that it has never really been explained fully; until now that is. Gavin Schmidt has just completed a post on Realclimate called Comparing models to the satellite datasets.I don’t really need to say much more, because you should really read Gavin’s post. What I did want to say is that it is a masterclass in how to present and discuss scientific evidence. It steps through all the different choices one can make when doing something like comparing model results and observations. I was, however, wanting to just highlight the figure on the right. Essentially you need to decide how to align your different model runs; do you normalise them with respect to a single year, some average over a number of years, or with respect to the trends. The figure on the right shows what happens if your baseline is 1 year (1979), 4 years (1979-1983), 10 years (1979-1988), or if you force the trend lines to all pass through the same point (the x-axis in 1979).
As you can see, there is quite a difference, both in terms of the apparent mean trend and also the 95% spread. Gavin’s argument (which makes sense to me) is that if you want to enhance the forced trend, you should average over a reasonable time interval so as to smooth out – as much as possible – the impact of internal variability. That would mean using the 1979-1988 baseline in the figure (pink). I’ll leave you to guess what John Christy chose for his graph. I’ll also stop there and encourage you to read thoroughly Gavin’s post