We refer the interested reader to Betteridge’s Law. End of post.
Ok, I will add a little more. This is the title of our latest (last?) paper, which came out a couple of months ago. The work was a long time in progress, despite being in principle a fairly run-of-the-mill application of our previously-developed methods to a new time period. While the work was underway, we got hold of a new data set (and new collaborators) which meant doing it all over again, though that’s not sufficient to explain my overall slowness. Anyway it’s done now.
The mid-Pliocene Warm Period (which could perhaps more precisely be referred to as the mid-Piacenzian Warm Period, we had some discussion with reviewers about this but argued it wasn’t our responsibility to enforce the less-widely-used name on the community, especially as we were using outputs from the Pliocene Modelling Intercomparison Projects) is the most recent period when the climate was thought to be substantially warmer than the pre-industrial state, for a significant period of time. But it was more than 3 million years ago, so data are sparse and imprecise, and boundary conditions (such as atmospheric CO2 level) are also not that well known. Nevertheless, lots of modelling groups have performed simulations of this period, and others have collected proxy data pertaining to the same time.
We basically repeated our recent(ish) work on the Last Glacial Maximum, using the model simulations and proxy data…..and as part of this, compared results obtained with different types of proxy data. Unfortunately these disagreed substantially, which led us to conclude that we really can’t provide a very confident answer. And even if we assume that one data set is correct, we still have significant uncertainty over the result generated. Our (weakly) preferred number is 3.6 +- 1C warmer than pre-industrial, but I wouldn’t claim to be too confident about that. That’s pretty much it, really. “More work is necessary” is actually true in this instance.
We did produce a bunch of pictures, such as this one, which shows the central estimate of surface air temperature anomaly across the globe. But the regional detail of the patterns isn’t reliable (such as the occasional spots of cooling). It’s just…that’s what the algorithm churned out.
