Although 484 (~40%) pass the temperature screening process over the full (1850–1995) calibration interval, one would expect that no more than ~150 (13%) of the proxy series would pass the screening procedure described above by chance alone.
Reader DC said:
Of the 484 proxies passing the 1850-1995 significance test, 342 also passed both sub-period tests (with 341 having r values with matching sign). 111 passed only one of the sub-period tests, and 31 failed both sub-periods.
Let’s think about this a little in terms of statistics. If a “proxy” is a proxy, then it is a proxy regardless of the subperiod. It is not enough to have a “significant” relationship in the 1850-1995 period, it should also have “significant” relationship in the 1850-1949 and 1896-1995 periods (Mann’s late-miss and early-miss periods.)
DC remarked above, in effect, that nearly 30% of the 484 “passing” proxies failed this elementary precaution. I checked this calculation and can confirm it. This can be done as follows.
342 out of 1209 is only 29% (as opposed to Mann’s stated 13% by chance). As observed in September, Mann’s chance benchmark is wrong because his pick two-daily keno method inflates the odds. [As a reader noted, Mann’s 13% is based on the 1850-1995 period and the yield for passing 1850-1995, 1850-1949 and 1896-1995 would necessarily be lower. This goes the other way from pick two daily keno. Autocorrelation is a third benchmarking issue and it doesn;t look to me like Mann’s benchmarks adequately allow for observed autocorrelation.]
I don’t want readers to place any weight on any benchmarks right now other than indicatively, as today I want to look at a different issue: how different proxy classes stand up to this undemanding test. In a given proxy class (ice cores, dendros, speleos, whatever), which proxy classes outperform random picking?
The “best” performer are the Luterbacher series – series which have no business whatever being in a “proxy data sets”. 71 out of 71 Luterbacher series pass the above test. This is not much of an accomplishment since Luterbacher uses instrumental data in his “proxies”. That instrumental data has a high correlation with instrumental data means precisely nothing. You’d think that someone in the climate science “community” would object to this, but seemingly not. The inclusion of these series obviously inflates the count. Without these absurd inclusions, we have 24% of the proxies passing elementary screening ( (342-71)/(1209-71).
“Low-frequency” make up 51 of the 1209 series. Of these 51 series, only 8 series pass the above elementary screening (15.8%). One of these series (Socotra O18- which is non-incidental in M08 reconstructions BTW), fails an additional undemanding test that “significance” have a consistent sign. This leaves 7/51 (13.7%) as being “significant”.
Code 9000 dendro proxies make up over 927 of 1209 M08 proxies. Only 143 pass the above simple test ( 15.4%).
c(sum(dendro),sum(temp&dendro)) # 927 143
On the other hand, Briffa MXD proxies (code 7500) have a totally different response: 93 out of 105 (88%) pass M08 screening. This is such a phenomenonal difference from run-of-mill dendro proxies that one’s eyebrows arch a little. Now these aren’t ordinary Briffa MXD proxies. These series were produced in part by Rutherford (Mann) et al 2005 performing RegEM on Briffa MXD data; then M08 truncated the Rutherford Mann MXD versions in 1960 because of the “divergence” problem and replaced actual data from 1960 to 1990 by infilled data, all prior to calculating the above correlation. I haven’t parsed every little turn of Mannian adjustments, but you will understand if I view the statistical performance of this data for now as a little suspect. None of this data is earlier than AD1400 in any event.
I’ll look at the other classes of data (only 55 series left) tomorrow.