So not only are Wahl & Ammann not “independent” of Mann in any plausible sense but they are getting the professional credit from Mann including them on another paper’s co-authorship in the same time period.

Talk about ethically conflicted! In light of Mann’s upcoming talk for a Stephen Schneider memorial lecture at Stanford, it might be an ideal time for someone able to write up a critical review of the Mann-Wahl-Ammann and Schneider saga. Not trying to add to Steve’s load, really, maybe someone at WUWT or BH could take this on….. it might get attention at Stanford in time for Mann’s talk in a few weeks.

]]>So there I am, years after this post laughing at a computer screen. My wife thinks I’m nuts. 20 hours later, I find this post.

haha.

]]>Hansens statement

The records of these stations were compared with records of the nearest neighboring stations; if neighboring stations displayed similar features the records were retained.

allows for cherry picking as well as for defect rejection. It will be interesting to see what the impact was.

]]>Thanks for the clarification on the Gaussian filter!

Although IPCC AR4 WGI Chapter 6 generally uses a Gaussian filter, it’s curious that Mann (2004) instead uses the relatively arcane Butterworth filter. This is an electronic filter that has a very flat passband and a monotonic (albeit slow) stopband rolloff. With infinite order, it becomes an ideal “brickwall” lowpass filter, but since all realizeable circuits have some resistances that makes them non-ideal, electrial engineers have to settle for imperfect filters like the Butterworth. The likewise imperfect Chebyschev filter is generally preferred, since it has faster rolloff at the expense of some passband ripple. (See Wikipedia)

However, computer filtering of data is not subject to the same contraints as electrical engineering, and so one could just as well use the ideal filter, which in continuous time is the cardinal sine function sinc(x) = sin(x)/x, appropriately scaled and normalized. In unbounded discrete time there is presumably a discrete version of this. In bounded discrete time, one could simply truncate the weights at the boundaries and renormalize, reflect the data at the boundaries until the weights are imperceptible, or (more ambitiously) recompute optimal weights that kill, in expectation, an equally spaced set of frequencies out to the Nyquist frequency, taking the expectation over the random initial phase of the signal.

But if a precise bandwidth isn’t crucial, one could just use Gaussian, binomial, Kalman, or even plain old equal weights. Mann’s Butterworth filter serves no apparent purpose in this context whatsoever, other than to dazzle the rubes with some high-tech but inappropriate apparatus.

IPCC’s Chapter 3 Apprendix 3.A states that Chapter 3, if not Chapter 6, uses either the 5-weight filter (1/12)[1,3,4,3,1], or a likewise integer-based 13 weight filter. It’s not clear where these come from, since they are not quite binomial, but at least the short one does have the attenuations it claims, making it a clever way to achieve an almost 10-year half power bandwidth with only 5 points! They say the long one has a response function similar to the 21-term binomial filter used in TAR, despite using only 13 points.

— Hu McCulloch

Econ Dept.

Ohio State U.

mcculloch.2@osu.edu ]]>