Apparently, Michael Mann is yet again attempting a repackaging of his hockey stick work. The question is, has he re-worked his methodologies to overcome the many statistical issues third parties have had with his work, or is this more like AirTran changing its name from ValuJet to escape association in people’s mind with its 1996 plane crash?
Well, Steve McIntyre is on the case, and from first glance, the new Mann work seems to be the same old mish-mash of cherry-picked proxies, bizarre statistical methods, and manual tweaking of key proxies to make them look the way Mann wants them to look. One thing I had never done was look at all the component proxies of the temperature reconstructions all in one place. At the link above, Steve has all the longer ones in a animated GIF. It is really striking how a) almost none of them have a hockey stick shape and b) even the few that do have HS shapes typically show the warming trend beginning in 1800, not in the late 19th century CO2 period.
If you would like to eyeball all 1209 of the proxies Mann begins with (before he starts cherry picking), they are linked here. I really encourage you to click through to one of the five animations, just to get a feel for it. As someone who has done a lot of data analysis, it is just staggering that he can get a hockey stick out of these and claim that it is in some way statistically significant. It is roughly equivalent to watching every one of your baseball team’s games, seeing them lose each one, and then being told that they have the best record in the league. It makes no sense.
The cherry-picking is just staggering, though you have to read the McIntyre articles as a sort of 2-3 year serial to really get the feel of it. However, this post gives one a feel of how Mann puts a thin statistical-sounding veneer to cover his cherry-picking, but at the end of the day, he has basically invented a process that takes about a thousand proxy series and kicks out all but the 484 that will generate a hockey stick.
Update: William Briggs finds other problems with Mann’s new analysis:
The various black lines are the actual data! The red-line is a 10-year running mean smoother! I will call the black data the real data, and I will call the smoothed data the fictional data. Mann used a “low pass filter” different than the running mean to produce his fictional data, but a smoother is a smoother and what I’m about to say changes not one whit depending on what smoother you use.
Now I’m going to tell you the great truth of time series analysis. Ready? Unless the data is measured with error, you never, ever, for no reason, under no threat, SMOOTH the series! And if for some bizarre reason you do smooth it, you absolutely on pain of death do NOT use the smoothed series as input for other analyses! If the data is measured with error, you might attempt to model it (which means smooth it) in an attempt to estimate the measurement error, but even in these rare cases you have to have an outside (the learned word is “exogenous”) estimate of that error, that is, one not based on your current data.
If, in a moment of insanity, you do smooth time series data and you do use it as input to other analyses, you dramatically increase the probability of fooling yourself! This is because smoothing induces spurious signals—signals that look real to other analytical methods. No matter what you will be too certain of your final results! Mann et al. first dramatically smoothed their series, then analyzed them separately. Regardless of whether their thesis is true—whether there really is a dramatic increase in temperature lately—it is guaranteed that they are now too certain of their conclusion.
The corollary to this truth is the data in a time series analysis is the data. This tautology is there to make you think. The data is the data! The data is not some model of it. The real, actual data is the real, actual data. There is no secret, hidden “underlying process” that you can tease out with some statistical method, and which will show you the “genuine data”. We already know the data and there it is. We do not smooth it to tell us what it “really is” because we already know what it “really is.”
Update: I presume it is obvious, but the commenter "mcIntyre" has no relation that I know of to the "mcintyre" quoted and referred to in the post. As a reminder of my comment policy, 1) I don’t ban or delete anything other than outright spam and 2) I strongly encourage everyone who agrees with me to remain measured and civil in your tone — everyone else is welcome to make as big of an ass out of him or herself as they wish.
By the way, to the commenter named "mcintyre," I have never ever seen the other McIntyre (quoted in this post) argue that CO2 does not act as a greenhouse gas. He spends most of his time arguing that the statistical methods used in certain historic temperature reconstructions (e.g. Mann’s hockey stick but also 20th century instrument rollup’s like the GISS global temperature anamoly) are flawed. I have read his blog for 3 years now and can honestly say I don’t know what his position on the magnitude of future anthropogenic warming is. Mr. McIntyre is apparenlty not alone — Ian Jolliffe holds the opinion that the reputation of climate science is being hurt by the statistical sloppiness in certain corners of dendro-climatology.