In the MBH source code, they apply steps that purport to weight the temperature PCs in their regression calculations proportional to their eigenvalues. Comments on their code say:

c set specified weights on data

…

c weights on PCs are proportional to their singular values

This is one of two weighting procedures in MBH for the regression, the other being the weighting of the proxy series. Wahl and Ammann tout one of the key results of their study as showing that the MBH algorithm is “robust” against “important” simplifications and modifications. They claimed:

A second simplification eliminated the differential weights assigned to individual proxy series in MBH, after testing showed that the results are insensitive to such linear scale factors.

This claim is repeated in slightly different terms on several occasions:

Additionally, the reconstruction is robust in relation to two significant methodological simplifications-the calculation of instrumental PC series on the annual instrumental data, and the omission of weights imputed to the proxy series.

…

Our results show that the MBH climate reconstruction method applied to the original proxy data is not only reproducible, but also proves robust against important simplifications and modifications.

…

Indeed, our analyses act as an overall indication of the robustness of the MBH reconstruction to a variety of issues raised concerning its methods of assimilating proxy data, and also to two significant simplifications of the MBH method that we have introduced.

I’ll discuss this claim in respect to weights on proxies in detail on another occasion, but today will note that it can trivially be seen to be untrue, merely through considering the acknowledged impact of differing weights on the NOAMER PC4. Obviously results are not “insensitive” to the “linear scale factors” (weight) of the PC4. Low weights remove the HS-ness of the result and yield a low RE statistic. So this particular claim is patently false.

However, it is true that the MBH result is “robust” to the use or non-use of weight factors on the temperature PCs. This can be seen through some trivial (though long-winded) linear algebra as follows.

As noted in the previous post, the matrix of *unrescaled *reconstructed RPCs can be expressed as follows (using notation from prior post):

Mann re-scaled each such series so that its standard deviation (in the calibration period) individually matched the standard deviation of the corresponding temperature PC . Denoting the matrix of rescaled RPCs by , we then have:

for i=1,…,k

Or stacking:

where applied to a matrix is the square root of the diagonal of – which, when divided by yields the standard deviation and, for these ratio calculations, equivalent results.

The expression for can be used to expand as follows:

We obtain the following long (but easily calculable expression) for by substituting for :

where and

In the 1-dimensional case (one reconstructed temperature PC), these long matrix expressions reduce to a scalar and a very simple expression – as I discussed a long time ago. Even in the multiple PC cases, because the normalization is being done one by one and L is a diagonal matrix, one can apply the simple identity:

where is the diagonal matrix from the square root of the diagonal.

Thus all uses of the weight matrix L **cancel out** in the above expression yielding:

)

**Because** the matrix L cancels out because of the underlying linear algebra, one would expect that any Wahl and Ammann “experiments” with varying this procedure would be “robust” to this particular variation.

## 29 Comments

Am I correct in assuming that Mann provides a procedure that does nothing and that his colleagues do not understand this.

I’m also curious about how likely it is that Mann’s hockey team doesn’t even realize that this is a problem.

But I’m sure it doesn’t matter and everybody should just move on.

BTW, nice catch.

Closing parentheses on last equation?

Macbeth comes to mind:

“..it is a taleTold by an idiot, full of sound and fury,

Signifying nothing.”

..a poor player, who struts and frets his hour upon the stage, and then is heard no more.(From the same soliloquy, just before your quotation.)

Steve,

check your email

Anthony

Please, what must I do to be able to submit a comment?

Have tried with two longish contributions and one short one, but all failed :-((

Robin

However, a short attempt has just loaded successfully. Mysterious – must be that my others were too long.

Robin

OT: The Shakespeare quotation by Michael Jankowski and Pat Keating is from King Lear😉

9 Pedro

Wrong king. Macbeth, but he wasn’t yet king at the time he said it.

Re #10

Yes he was king, it was said when he received the news of his wife’s death as the English backed army attacked his castle at Dunsinane.

11

After I posted, I realized that I had confused that soliloquy with the “If ’tis done,..” soliloquy, so your post was not unexpected.

However, wasn’t it “My life has fallen into the sere, the yellow leaf” at the point you are talking about? I thought “Tomorrow” was earlier than Dunsinane.

at some point between the matrix algebra and the transition to the bard I got lost.

And I’m tangent man!

13 Steve M

Yes, you are. But we can try to compete….

I think the tele-connection is “tale told be an idiot”.

Re Robin, #7, your longish post is under the related “Squared Weights” thread: http://www.climateaudit.org/?p=2962#comment-232458“>.

It might be that I’m not reading the notation correctly, but it looks like the second to last equation equates a scalar and a matrix. It seems to me that diag^1/2^(LXL) a number, while L*diag^1/2^(X) is a matrix. I can’t claim to be well-versed in the terminology, so I’m not sure how this affects the math logic.

Steve:EVerything is a matrix in this expression. The notation is a little idiosyncratic as diag^{1/2}(X) is the diagonal matrix consisting of the square roots of diag(X). EVerything in this line is a diagonal matrix.Re #12

No, it’s Act V, scene v in Dunsinane:

” Seyton The Queen, my lord, is dead.

Macbeth She should have died hereafter;

There would have been a time for such a word.

To-morrow, and to-morrow, and to-morrow,

Creeps in this petty pace from day to day

To the last syllable of recorded time;

And all our yesterdays have lighted fools

The way to dusty death. Out, out, brief candle!

Life’s but a walking shadow, a poor player

That struts and frets his hour upon the stage

And then is heard no more. It is a tale

Told by an idiot, full of sound and fury,

Signifying nothing.”

The line about the “sere, the yellow leaf” comes 2 scenes earlier.

Re #16:

Didn’t notice the update. It’s not the second to last equation anymore; it is the second to last before the note.

Steve:I’m tidying the nomenclature a little offline.Thanks for the notes; I understand now (I think).

Steve, you seem to be implying that the product of symmetric matrices must be symmetric. Not so, I’m afraid – but I haven’t delved far enough into this piece to see if it affects the result.

e.g. A = {{1,3},{3,2}}, B={{7,4},{4,9}} AB = {{19,29},{31,30}}

Thanks. Brain cramp on my part. I was using diagonal matrices here and the results rely on properties of diagonal matrices. I’ve corrected the text accordingly.Steve:

Anybody, When GISS does their monthly update to the historical reports, do they update the entire history or do the just tack on the latest month’s result to the previous month’s record?

Thanks

wkkruse:

When I visited the monthly data in march, several previous monthly values had changed. So, at least occasionally, they do more than just tack on the new month’s value. I think some HadCrut numbers also change. But I’ve only checked once, so it’s possible that the change at HadCrut was me looking at different files.

In any case, if you are using this data, it seems wise to check for the latest data fairly regularly. Don’t assume you can just go in, add the most recent value, and use that only.

re 22,21

In any professionally managed engineering program, no previous version of data would just be discarded or overwritten. The older version would be archived and a change notice issued detailing the reason(s) for the update. This change notice would have to be approved by an authorized official before any modification to the current data could be done.

Perhaps GISS is doing this and has the older versions safely archived with their change notices.

“

It might be that I’m not reading the notation correctly, but it looks like the second to last equation equates a scalar and a matrix. It seems to me that diag^1/2^(LXL) a number, while L*diag^1/2^(X) is a matrix.”I have absolutely NO idea what you just said. You are taking this entire controversy to a place outside of my areas of expertise, and thereby marginalizing the worth of my contributions to the discussion. As I am a person of worth and intellect and moral correctness, to exclude my input in favor of a technocrat’s formulas is entirely wrong, and works to show only that you have valued and weighted the wrong inputs and influences in your results-driven propogandizing.

Clearly, your ad hominum, math-centric attacks on Mr. Mann’s work cannot detract from the essential TRUTH of what he tells us. Mann shows us our evil, and your attempt to use these outlandish, inscrutable MATH problems to discredit him fails to address, much less impeach, the core of his work.Mann is post-math!

I have the Jan GHCN+ERSST numbers, and the ones now up for Apr. The anomaly for a number of months in 2007 are different.

Jan version: 2007 87 63 59 66 55 53 51 56 50 55 46 39 Year 57

Apr version: 2007 86 63 60 64 55 53 51 56 50 55 49 40 Year 57

I didn’t check other years.

Rather odd; the first quarter is unaffected, the second quarter .02 lower (average 0 for the quarter) the third quarter unaffected, and the fourth quarter .04 warmer (average .1 for the quarter) and the year unaffected.

Why bother; The climate only matters on 30 year time scales.

Somebody ought to tell the people working with the monthly and yearly anomalies to not bother with this weather stuff.

OOOps, I meant .01 for the quarter 3rd.

re 27. you made a mistake. you made a mistake. you made a mistake.

Its my tamino impression.

A reader posted this nice characterization of this particular MBH “robustness” at his blog.

“3x/x” captures the situation exactly. To be fair, Mann himself did not claim that the changes were “robust” to weighting factors; his claims were to things like all dendroclimatic indicators; he himself never mentioned that he used weighting factors. This particular issue was raised by Wahl and Ammann. Again to be precise, they didn’t make the claim of robustness about the weighting factor

L(using the nomenclature of the post) where the claim is trivial but true, but about the weighting factorPwhere the claim is false. This is the Team after all.## One Trackback

[…] To MBH’s credit, they at least employed a form of the CCE calibration approach, rather than LNA’s direct regression of temperature on proxies. There are, nevertheless, serious problems with their use (or non-use) of the covariance matrix of the residuals. See, eg, “Squared Weights in MBH98″> and “An Example of MBH ‘Robustness’”.> […]