Things went well yesterday, but the painkillers are making my head a little fuzzy. As such, I figured it'd be a good time to write up something I probably should have written up a while back. You see, over a month ago Steve McIntyre wrote this about the recent Gergis et al paper:
Gergis et al 2016 stated that they screened proxies according to significance of the correlation to local gridcell temperature. Law Dome d18O not only had a significant correlation to local temperature, but had a higher t-statistic (and correlation) to local instrumental temperature than:
24 of the 28 proxies retained by Gergis in her screened network;
either of the other two long proxies (Mt Read, Oroko Swamp tree ring chronologies);
Nonetheless, the Law Dome d18O series was excluded from the Gergis et al network. Gergis effected her exclusion of Law Dome not because of deficient temperature correlation, but through an additional arbitrary screening criterion, which excluded Law Dome d18O, but no other proxy in the screened network.
This was a serious accusation he and I had actually discussed in e-mails before he wrote that post. As I told him in those e-mails, I couldn't find a way to replicate his results. I asked him to confirm the data he was using matched what I was using, but that didn't happen. When he wrote the post, I asked again. I asked again later via e-mail, again without success.
Mind you, McIntyre never said, "No," and I think he does intend to do this eventually. I tried to be patient, but given the seriousness of McIntyre's accusations and how they appear to be completely wrong, I think waiting over a month is more than sufficient.
I've owed you guys another post about the recent Gergis et al paper for a little while now. I've been held back by losing all my code written to examine to a power outage, and I'm going to be out of town for the weekend. Fortunately, there is an interesting issue I can write about today. It came to my attention due to the blogger Anders writing this comment at his site:
Hope this doesn’t the “clean exit”, but I thought I would post this figure from Gergis et als SI. It compares the main reconstruction (black) with one in which there was no screening and all 51 proxies were used (red dash) and one with no screening and using all the 36 proxies in the reconstruction domain (green dot). Doesn’t appear to be wild differences, but am not sure how the non-screening reonstructions would influence the 2SE.
I had seen this figure before, but I've been hung up on trying to replicate screening results for the paper (as well as Steve McIntyre's stated results for it) so I hadn't paid much attention to it. Anders drawing my attention to it led me down a windy and strange path.
Readers familiar with the 2012 Gergis et al paper will likely remember the paper was withdrawn after it had been accepted for publication (but prior to actually being published) because it turns out the authors did not do what they claimed to have done. Specifically, they claimed to use detrended series for their screening to try to avoid the "screening fallacy" when in reality they hadn't detrended anything. Today I'd like to show the authors have once again failed to do what they claim to have done.
For some background on this issue, there's a good post up at Climate Audit about this paper, and I'm going to try not to rehash the points it covers. There are also two posts I've previously written on the subject, here and here. Being familiar with these posts should be helpful but not necessary as the central problem I want to discuss is really as simple as, "They didn't do what they claim to have done."
As our last post discussed, one of the key issues in this whole Gergis et al affair is how one should screen proxies to decide which ones to use and which ones not to use. In 2012, Gergis et al claimed to have screened proxies because:
For predictor selection, both proxy climate and instrumental data were linearly detrended over the 1921–1990 period to avoid inflating the correlation coefficient due to the presence of the global warming signal present in the observed temperature record. Only records that were significantly (p<0.05) correlated with the detrended instrumental target over the 1921–1990 period were selected for analysis.
They wanted to take steps to avoid a problem commonly known as the Screening Fallacy. Of course, as it turned out they hadn't actually detrended their data like they claimed, and if they had, their results would have been very different. This gives rise to an important question, namely, why do they say this in their latest paper:
Our results also show that the differences between using detrended and raw correlations to screen the predictor networks, as well as between using field mean and local correlations, are minor (Figs. S1.3 and S1.4 in the supplemental material). Given this insensitivity, local detrended correlations were used to select our final set of temperature predictors (see theoretical discussion in section S1 in the supplemental material).
According to their newest paper, it makes very little difference whether one detrends or not. They also say it makes little difference whether you use "local" temperatures for correlation testing or temperatures of the region, but I'll discuss that in a little bit. Just understand the quotations marks I place around the word "local" are important.
For now, the main question is how do the authors conclude it doesn't matter whether or not you detrend your data before correlation testing? That's the exact opposite of what they concluded when they were forced to withdraw their 2012 paper.
As readers likely know, I have long followed The Hockey Stick Debate. It is, in fact, the reason I became interested in the global warming discussion. And while I've written about it a fair amount in the past, to the point of writing two (short) eBooks on the subject, I haven't discussed it much in some time. The reason is simple - the debate has largely died off.
I could go on and on about how climate science has largely abandoned the infamous Hockey Stick, claiming to support it while only publishing results very different from it, but the reality is climate scientists have largely tried to distance themselves from the subject. I suspect they have largely because the problems surrounding the subject became too much for them to bear, meaning a tactical retreat was in order.
Whatever the reason, the point is debates over paleoclimatic reconstructions have died down so much there's been little reason for me to write about them until today. Today, a new story has broken which will be a source of great interest. I came across the news because I saw a link to a news article whose headline says:
How a single word sparked a four-year saga of climate fact-checking and blog backlash
This caught my eye for a variety of reasons, including my interest in the rise in "fact-checking" by the media (which often isn't really fact-checking). Naturally, I clicked on it. I never anticipated it'd be about a subject I know well, a 2012 paper by Joelle Gergis and co-authors.