Posted by Jeff Condon on October 28, 2009
I’ve got plenty of posts right now to work on but today Steve McIntyre called our attention to a couple of acerbic replies from Keith Briffa to Steve’s discovery that the Briffa Yamal temperature data which has a HUGE hockey stick blade was actually the EXACT same data as the orignal H&S Yamal which shows NONE. The difference is in the RCS “standardization” method (AKA – hockeysickization) used to correct for tree ring widths. If I don’t miss my guess, we’ll be seeing more of RCS with improper exponentialesque curve standardizations as in my opinion it’s a near guarantee of a hockey stick blade and after Mann and now Briffa I don’t trust these paloclimate ‘scientists’ any farther than I can throw them.
I’ve got to say again Briffa’s original Yamal is a disgusting piece of garbage work and the sooner paleo’s drop the P.O.S. the better. It’s got an unreasonable blade created from RCS with NO science or verification to prefer the ‘accidentally’ chosen exponential curve that is ENTIRELY RESPONSIBLE for the big evil bullcrap blade. See one of my posts on this HERE.
Well I’m not happy with Briffa or anyone who chooses to defend this garbage work so I’ve decided to dig through his reply to Steve a bit and make a little trouble. In particular, I want to focus on this little gem by Briffa himself.
We would never select or manipulate data in order to arrive at some preconceived or regionally unrepresentative result. However, as we will show here, the fact that we did not incorporate the KHAD data has no serious implications for the general validity of our published work.
My emphasis. But those of us who follow climate know that is not the case. So I went looking for a few examples from his own PUBLISHED work. Here is an article from Briffa. although Osbourne was the lead author. I assure you this is standard in paleo science where proxy’s are often assumed to be temperature without any verification other than mathematical correlation. There are many papers which have similar statements, Mann08 is no exception. I could write a whole post on the differences between clear and obvious selection or the ‘partial selection’ created by multivariate methods where non-correlating proxies are deweighted rather than deleted resulting in a less transparent selection of data based on preconceived conclusions.
The 387 density chronologies were screened according to their correlation with the “local” grid-box April-to-September temperature, already computed by Briffa et al. (2002a; their Figure 5a) over the period 1881–1994. The actual period over which these correlations were computed is often shorter than 1881–1994, because either some temperature observations are missing (see section 2.2) or the trees were sampled before 1994 (Figure 2a). Almost half of the chronologies exhibit a correlation of 0.5 or above, and almost 90% exhibit a correlation of 0.22 or above (the latter threshold indicates that we can be 95% confident of a true association between MXD and April-to-September temperature, given a sample of 81 years, which is the average overlap between chronology and temperature data). The 46 chronologies that do not meet this criterion were excluded from further use;
Oh yeah, Steve McIntyre is the bad guy. They would NEVER select data to arrive at some preconceived result. No chance. IT’S RIGHT THERE IN PRINT!!
A reasoned mind might ask the obvious question which dendroclimatology has never answered clearly. – What about the fact that trees might not ACTUALLY be good linear thermometers?
Briffa et al. (2002a) have already identified many of the climate signals recorded in the treering data, and concluded that the tree-ring density variability is dominated by the temperature during the growing season.
Well that about settles it then. Briffa has determined that MXD (latewood density) data IS temperature so we don’t have to WORRY OUR PRETTY LITTLE HEADS about that anymore. But wait, in the same PEERREVIEWEDLITERACHURE there’s this.
A number of factors were taken into account when selecting the most appropriate periods for calibration and verification of the gridded density data against observed temperatures. The most important factor is the identification by Briffa et al. (1998a) of a recent downward trend in the highlatitude tree-ring density data, relative to (and apparently unrelated to) warm-season temperature. This density decline becomes large enough to impair the calibration after about 1960. For this reason, both Briffa et al. (2001) and Briffa et al. (2002a) used only pre-1961 data for calibration of their subcontinental, regional temperature reconstructions. This is a reasonable choice, provided that it is explicitly stated that this approach assumes the apparent recent density decline is due to some anthropogenic factor and that similar behaviour is assumed, therefore, not to have occurred earlier in the reconstruction period – which would otherwise introduce bias in the reconstructed temperatures. At present, no satisfactory explanation of the relative MXD decline has been identified, and further work must dictate whether this assumption will be supported or rejected (Briffa et al., 1998a, 2003, and Vaganov et al., 1999, discuss and investigate possible causes).
MY GOD, it’s time for dendros to stop the charade. I know they’ve spent years trying to extract temperature from noisy data but scrapping data for PRECONCEIVED RESULTS is REGULARLY done!! Equally invalid are reweighting methods that reduce the importance of lower correlation data- its the same damn thing in a more confusing approach.
But wait, why is Jeff so wound up today?!! Because Briffa responded specifically to the criticisms of too few proxies ignoring the criticisms of the method and repeated the same MESS with RCS and more trees. See the new improved YAMALINATOR at the link in the quote below!
We have now undertaken a more extensive sensitivity test, using the RCS approach, to examine the relative growth rates of trees at each of the 3 original locations removed by McIntyre, as well as the KHAD site. We have also taken the opportunity to acquire and incorporate additional data from the 3 original sites, in this analysis.
We don’t have the actual math used for this yet but if you see the RCS curves they came up with, they are functionally no different than the original exponential curves of Briffa Yamal the original.
Next are these new datasets with Yamal included.
For some reason EVERY RCS CORRECTION Briffa can conceive of refuses to turn upward to fit the ACTUAL data. This lack of flexibility in the RCS curves is what creates the HS. We ARE going to see more of this, mark my words! Below is what the Yamal original data looked like when averaged per year.
Old trees grow faster in their end years, the RCS curves used by Briffa in the new improved Yamal don’t match the data again!! It’s the same exact thing as the original Briffa Yamal.
I’m tired again. The insanity of paleoclimatology is getting to be too much. Mann’s doubletalk on flipping proxies upside down, RCS creating hockey sticks out of nothing. People creating one obviously bad result after another. Comments by people on the internet which are confused by this ignorant crap. There is just too much bad work for my head to absorb. WTF, none of this is any better than a simple mean. NONE!! Data IS thrown away for predetermined conclusions I’m SICK TO DEATH of the lying and posturing. NONE of this is reasonable.
I have email conversations with Dr. Christy which do nothing but make sense and then am assaulted with this IDOCY.
Understanding Keith Briffa will make you dumber!!! It will suck the brains right out of your ears – don’t read it…. ever!!
In my opinion, this is not honest science.