Guest Post by Willis Eschenbach
People sometimes ask why I don’t publish in the so-called scientific journals. Here’s a little story about that. Back in 2004, Michael Mann wrote a mathematically naive piece about how to smooth the ends of time series. It was called “On smoothing potentially non-stationary climate time series“, and it was published in Geophysical Research Letters in April of 2004. When I read it, I couldn’t believe how bad it was. Here is his figure illustrating the problem:
Figure 1a. [ORIGINAL CAPTION] Figure 1. Annual mean NH series. (blue) shown along with (a) 40 year smooths of series based on alternative boundary constraints (1) – (3). Associated MSE scores favor use of the ‘minimum roughness’ constraint.
Note the different colored lines showing different estimates of what the final averaged value will be, based on different methods of calculating the ends of the averages. The problem is how to pick the best method.
I was pretty naive back then. I was living in Fiji for one thing, and hadn’t had much contact with scientific journals and their curious ways. So I innocently thought I should write a piece pointing out Mann’s errors, and suggesting a better method. I append the piece I wrote back nearly a decade ago. It was called “A closer look at smoothing potentially non-stationary time series.”
My main insight in my paper was that I could actually test the different averaging methods against the dataset by truncating the data at various points. By doing that you can calculate what you would have predicted using a certain method, and compare it to what the true average actually turned out to be.
And that means that you can calculate the error for any given method experimentally. You don’t have to guess at which one is best. You can measure which one is best. And not just in general. You can measure which one is best for that particular dataset. That was the insight that I thought made my work worth publishing.
Now, here comes the story.
I wrote this, and I submitted it to Geophysical Research Letters at the end of 2005. After the usual long delays, they said I was being too hard on poor Michael Mann, so they wouldn’t even consider it … and perhaps they were right, although it seemed pretty vanilla to me. In any case, I could see which way the wind was blowing. I was pointing out the feet of clay, not allowed.
I commented about my lack of success on the web. I described my findings over at Climate Audit, saying:
Posted Oct 24, 2006 at 2:09 PM
…
[Mann] recommends using the “minimum roughness” constraint … apparently without noticing that it pins the endpoints.
I wrote a reply to GRL pointing this out, and advocating another method than one of those three, but they declined to publish it. I’m resubmitting it.
w.
So, I pulled out everything but the direct citations to Mann’s paper and resubmitted it basically in the form appended below. But in the event, I got no joy on my second pass at publishing it either. They said no thanks, not interested, so I gave up. I posted it on my server at the time (long dead), put a link up on Climate Audit, and let it go. I was just a guy living in Fiji and working a day job, what did I know?
Then a year later, in 2007 Steve McIntyre posted a piece called “Mannomatic Smoothing and Pinned End-points“. In that post, he also discussed the end point problem.
And now, with all of that as prologue, here’s the best part.
In 2008, after I’d foolishly sent my manuscript entitled “A closer look at smoothing potentially non-stationary time series” to people who turned out to be friends of Michael Mann, Dr. Mann published a brand new paper in GRL. And here’s the title of his study …
“Smoothing of climate time series revisited”
I cracked up when I saw the title. Yeah, he better revisit it, I thought at the time, because the result of his first visit was Swiss cheese.
And what was Michael Mann’s main insight in his new 2008 paper? What method did he propose?
“In such cases, the true smoothed behavior of the time series at the termination date is known, because that date is far enough into the interior of the full series that its smooth at that point is largely insensitive to the constraint on the upper boundary. The relative skill of the different methods can then be measured by the misfit between the estimated and true smooths of the truncated series.”
In other words, his insight is that if you truncate the data, you can calculate the error for each method experimentally … curious how that happens to be exactly the insight I wasted my time trying to publish.
Ooooh, dear friends, I’d laughed at his title, but when I first read that analysis of “his” back in 2008, I must admit that I waxed nuclear and unleashed the awesome power that comes from splitting the infinitive. The house smelled for days from the sulfur fumes emitted by my unabashed expletives … not a pretty picture at all, I’m ashamed to say.
But before long, sanity prevailed, and I came to realize that I’d have been a fool to expect anything else. I had revealed a huge, gaping hole in Mann’s math to people who were obviously his friends … and while for me it was an interesting scientific exercise, for him it represented much, much more. He could not afford to leave the hole unplugged or have me plug it.
And since I had kindly told him how to plug the hole, he’d have been crazy to try something else. Why? Because my method worked … hard to argue with success.
The outcome also proved to me once again that I could accomplish most anything if I didn’t care who got the credit.
Because in this case, the sting in the tale is that at the end of the day, my insights on how to deal with the problem did get published in GRL. Not only that, they got published by the guy who would have most opposed their publication under my name. I gotta say, whoever is directing this crazy goat-roping contest we call life has the most outré, wildest sense of humor imaginable …
Anyhow, that’s why I’ve never pushed too hard to try to publish my work in what used to be scientific journals, but now are perhaps better described as popular science magazines. Last time I tried, I got bit … so now, I mostly just skip getting gnawed on by the middleman and put my ideas up on the web directly.
And if someone wants to borrow or steal or plagiarise my scientific ideas and words and images, I say more power to them, take all you want. I cast my scientific ideas on the electronic winds in the hope that they will take root, and I can only wish that, just like Michael Mann did, people will adopt my ideas as their own. There’s much more chance they’ll survive that way.
Sure, I’d prefer to get credit—I’m as human as anyone, or at least I keep telling myself that. So an acknowledgement is always appreciated.
But if you just want to just take some idea of mine and run, sell it under another brand name, I say go for it, take all you want, because I’ve learned my lesson. The very best way to keep people from stealing my ideas is to give them away … and that’s the end of my story.
As always, my best wishes for each of you … and at this moment my best wish is that you follow your dream, you know the one I mean, the dream you keep putting off again and again. I wish you follow that dream because the night is coming and no one knows what time it really is …
w.
[UPDATE] In my above-mentioned comment on Steve McIntyre’s blog, I mentioned the analysis of Mannian smoothing by Willie Soon, David Legates, and Sallie Baliunas, entitled Estimation and representation of long-term (>40 year) trends of Northern-Hemisphere-gridded surface temperature: A note of caution.
Dr. Soon has been kind enough to send me a copy of that study, which I have posted up here. My thanks to him, it’s an interesting paper.
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APPENDIX: Paper submitted to GRL, slightly formatted for the web.
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A closer look at smoothing potentially non-stationary time series
Willis W. Eschenbach
No Affiliation
[1] An experimental method is presented to determine the optimal choice among several alternative smoothing methods and boundary constraints based on their behavior at the end of the data series. This method is applied to the smoothing of the instrumental Northern Hemisphere (NH) annual mean, yielding the best choice of these methods and constraints.
1. Introduction
[2] Michael Mann has given us an analysis of various ways of smoothing the data at the beginning and the end of a time series of data (Mann 2004, Geophysical Research Letters, hereinafter M2004).
These involve minimizing different boundary conditions at those boundaries, and are called the “minimum norm”, “minimum slope”, and “minimum roughness” methods. These methods minimize, in order, the zeroth, first, and second derivatives of the smoothed average. M2004 describes the methods as follows:
“To approximate the ‘minimum norm’ constraint, one pads the series with the long-term mean beyond the boundaries (up to at least one filter width) prior to smoothing.
To approximate the ‘minimum slope’ constraint, one pads the series with the values within one filter width of the boundary reflected about the time boundary. This leads the smooth towards zero slope as it approaches the boundary.
Finally, to approximate the ‘minimum roughness’ constraint, one pads the series with the values within one filter width of the boundary reflected about the time boundary, and reflected vertically (i.e., about the ‘‘y’’ axis) relative to the final value. This tends to impose a point of inflection at the boundary, and leads the smooth towards the boundary with constant slope.” (M2004)
[3] He then goes on to say that the best choice among these methods is the one that minimizes the mean square error (MSE) between the smoothed data and the data itself:
“That constraint providing the minimum MSE is arguably the optimal constraint among the three tested.” (M2004)
2. Method
[4] However, there is a better and more reliable way to choose among these three constraints. This is to minimize the error of the final smoothed data point in relation, not to the data itself, but to the actual final smoothed average (which will only be obtainable in the future). The minimum MSE used in M2004 minimizes the squared error between the estimate and the data points. But this is not what we want. We are interested in the minimum mean squared error between the estimate and the final smoothed curve obtained from the chosen smoothing method. In other words, we want the minimum error between the smoothed average at the end of the data and the smoothed average that will actually be obtained in the future, when we have enough additional data to determine the smoothed average exactly.
[5] This choice can be determined experimentally, by realizing that the potential error increases as we approach the final data point. This is because as we approach the final data point, we have less and less data to work with, and so the potential for error grows. Accordingly, we can look to see what the error is with each method in the final piece of data. This will be the maximum expected error for each method. While we cannot determine this for any data nearer to the boundary than half the width of the smoothing filter, we can do so for all of the rest of the data. It is done by truncating the data at each data point along the way, calculating the estimated value of the final point in this truncated dataset using the minimum norm, slope, and roughness methods, and seeing how far they are from the actual value obtained from the full data set.
[6] In doing this, a curious fact emerges — if we calculate the average using the “minimum roughness” method outlined above, the “minimum roughness” average at the final data point is just the final data point itself. This is true regardless of the averaging method used. If we reflect data around both the time axis and the y-axis at the final value, the data will be symmetrical around the final value in both the “x” and “y” directions. Thus the average will be just the final data point, no matter what smoothing method is used. This can be seen in Fig. 1a of M2004:
ORIGINAL CAPTION: Figure 1. Annual mean NH series. (blue) shown along with (a) 40 year smooths of series based on alternative boundary constraints (1)–(3). Associated MSE scores favor use of the ‘minimum roughness’ constraint. (Mann 2004)
[7] Note that the minimum roughness method (red line) goes through the final data point. But this is clearly not what we want to do. Looking at Fig. 1, imagine a “smoothed average” which, for a data set truncated at any given year, must end up at the final data point. In many cases, this will yield wildly inaccurate results. If this method were applied to the data truncated at the high temperature peak just before 1880, for example, or the low temperature point just before that, the “average” would be heading out of the page. This is not at all what we are looking for, so the choice that minimizes the MSE between the data and the average (the “minimum roughness” choice) should not be used.
[8] Since the minimum roughness method leads to obvious errors, this leaves us a choice between the minimum norm and minimum slope methods. Fig. 2 shows the same data set with the point-by-point errors from the three methods (minimum norm, minimum slope, and minimum roughness) calculated for all possible points. (The error for the minimum roughness method, as mentioned, is identical to the data set itself.)
[9] To determine these errors, I truncated the data set at each year, starting with the year that is half the filter width after the start of the start of the dataset. Then I calculated the value for the final year of the truncated data set using each of the different methods, and compared it to the actual average for that year obtained from the full data set. I am using a 41-year Gaussian average as my averaging method, but the underlying procedure and its results are applicable to any other smoothing method. I have used the same dataset as Mann, the Northern Hemisphere mean annual surface temperature time series of the Climatic Research Unit (CRU) of the University of East Anglia [Jones et al., 1999], available at http://www.cru.uea.ac.uk/ftpdata/tavenh2v.dat.
Figure 2. Errors in the final data point resulting from different methods of treating the end conditions. The “minimum roughness” method error for the dataset truncated at any given year is the same as the data point for that year.
3. Applications
[10] The size of the errors of the three methods relative to the smoothed line can be seen in the graph, and the minimum slope method is clearly superior for this data set. This is verified by taking the standard deviation of each method’s point-by-point distance from the actual average. Minimum roughness has the greatest deviation from the average, a standard deviation of 0.110 degrees. The minimum norm method has a standard deviation of 0.065 degrees from the actual average, while the minimum slope’s standard deviation is the smallest at 0.048.
[11] Knowing how far the last point in the average of the truncated data wanders from the actual average allows us to put an error bar on the final point of our average. Here are the three methods, each with their associated error bar (all error bars in this paper show 3 standard deviations, and are slightly offset horizontally from the final data point for clarity).
Figure 3. Potential errors at the end of the dataset resulting from different methods of treating the end conditions. Error bars represent 3 standard deviations. The minimum slope constraint yields the smallest error for this dataset.
[12] Note that these error bars are not centered vertically on the final data point of each of the series. This is because, in addition to knowing the standard deviation of the error of each end condition, we also know the average of each error. Looking at Fig. 2, for example, we can see that the minimum norm end condition on average runs lower than the true Gaussian average. Knowing this, we can improve our estimate of the error of the final point. In this dataset, the centre of the confidence limits for the minimum norm will be higher than the final point by the amount of the average error.
3.1 Loess and Lowess Smoothing
[13] This dataset is regular, with a data point for each year in the series. When data is not regular but has gaps, loess or lowess smoothing is often used. These are similar to Gaussian smoothing, but use a window that encompasses a certain number of data points, rather than a certain number of years.
[14] When the data is evenly spaced, both lowess and loess smoothing yield very similar results to Gaussian smoothing. However, the treatment of the final data points is different from the method used in Gaussian smoothing. With loess and lowess smoothing, rather than using less and less data as in Gaussian smoothing, the filter window stays the same width (in this case 41 years). However, the shape of the curve of the weights changes as the data nears the end.
[15] The errors of the loess and lowess averaging can be calculated in the same way as before, by truncating the dataset at each year of the data and plotting the value of the final data point. Fig. 4 shows the errors of the two methods.
Figure 4. Lowess and loess smoothing along with their associated end condition errors.
[16] The end condition errors for lowess and loess are quite different, but the average size of the errors is quite similar. Lowess has a standard deviation of .062 from the lowess smoothed data, and loess has a standard deviation of .061 from the loess smoothed data. Fig 5 shows the Gaussian minimum slope (the least error of the three M2004 end conditions), and the lowess and loess smoothings, with their associated error bars.
Figure 5. Gaussian, lowess and loess smoothing along with their associated error bars. Both lowess and loess have larger errors than the Gaussian minimum slope error.
[17] Of the methods tested so far, the error results are as follows:
METHOD Standard Deviation of Error Gaussian Minimum Roughness 0.111 Gaussian Minimum Norm 0.065 Lowess 0.062 Loess 0.061Gaussian Minimum Slope 0.048
[18] Experimentally, therefore, we have determined that of these methods, for this data set, the Gaussian minimum slope method gives us the best estimate of the smoothed curve which we will find once we have enough additional years of data to determine the actual shape of the curve for the final years of data.
3.2 Improved and Alternate Methods
[19] At least one better method of dealing with the end conditions exists. I call it the “minimum assumptions” method, as it makes no assumptions about the future state of the data. It simply increases the result of the Gaussian smoothing by an amount equal to the weight of the missing data. Gaussian smoothing works by multiplying each data point within the filter width by a Gaussian weight. This weight is greatest for the central point of the filter. From there it decreases in a Gaussian “bell-shaped” curve for points further and further away from the central point. The weights are chosen so that the total of the weights summed across the width of the filter adds up to 1.
[20] Let us suppose that as the center of the filter approaches the end of the dataset, the final two weights do not have data associated with them because they are beyond the end of the dataset. The Gaussian average is calculated in the usual manner, by multiplying each data point with its associated weight and summing the weighted data. The final two points, of course, do not contribute to the total, as they have no data associated with them.
[21] However, we know the total of the weights for the other data points. Normally, all of the weights would add up to 1, but as we approach the end of the data there are missing data points within the filter width. Their total of the existing data points might only be say 0.95, instead of 1. Knowing that we only have 95% of the correct weight, we can approximate the correct total by dividing the sum of the existing weighted data points by 0.95. The net effect of this is a shifted weighting which, as the final data point is approached, shifts the center of the weighting function further and further forwards toward the final data point.
[22] The standard deviation of the error of the minimum slope method, calculated earlier, was 0.048. The standard deviation of the error of the minimum assumptions method is 0.046. This makes it, for this data set, the most accurate of the methods tested. Fig. 6 shows the difference between these two methods at the end of the data set.
Figure 6. Gaussian minimum slope and minimum assumptions error bars. The minimum assumptions method provides the better estimate of the future smoothed curve.
[23] We can also improve upon an existing method. The obvious candidate for improvement is the minimum norm method. It has been calculated by padding the data with the average of the full dataset, from the start to the end of the data. However, we can choose an alternate interval on which to take our average. We can calculate (over most of the dataset) the error resulting from any given choice of interval. This allows us to choose the particular interval that will minimize the error. For the dataset in question, this turns out to be padding the end of the dataset with the average of the previous 5 years of data. Fig 7 shows the individual errors from this method, compared with the minimum assumptions method. Since the results from the two very different methods are quite similar, this increases confidence in the conclusion that these are the best of the alternatives.
Figure 7. Smoothed data (red), minimum assumptions errors (green), tuned minimum norm (previous 5-year average) errors (blue)
[24] The standard deviation of the error from the minimum norm with a 5-year average is slightly smaller than from the minimum assumptions method, 0.045 versus 0.046.
4. Discussion
[25] I have presented a method for experimentally determining which of a number of methods yields the closest approximation to a given smoothing of a dataset at the ends of the dataset. The method can be used with most smoothing filters (Gaussian, loess, low-pass, Butterworth, or other filter). The method also experimentally determines the average error and the standard deviation of the error of the last point of the dataset. Although the Tuned Minimum Norm method yields the best results for this dataset, this does not mean that it will give the best results for other datasets. It also does not mean that the Tuned Minimum Norm method is the best smoothing method possible; there may be other smoothing methods out there, known or unknown, which will give a better result on a given dataset.
[26] The method for experimentally determining the smoothing method with the smallest end-point error is as follows:
1) For each data point for which all of the data is available to determine the exact smoothed average, determine the smoothed result that would be obtained by each candidate method if that data point were the final point of the data. (While this can be done by truncating the data at each point, padding the data if required, and calculating the result, it is much quicker to use a modified smoothing function which simply treats each data point as if it were the last point of the dataset and applies the required padding.)
2) For each of these data points, subtract the actual smoothed result of the given filter at that point from the smoothed result of treating that point as if it were the final point. This gives the error of the smoothing method for the series if it were truncated at that data point.
3) Take the average and the standard deviation of all of the errors obtained by this analysis.
4) Use the standard deviation of these errors to determine the best smoothing method.
5) Use the average and the standard deviation of these errors to establish confidence limits at the final point of the smoothed data.
5. Conclusions
1) The Minimum Roughness method will always yield the largest standard deviation of the endpoint error in relation to the smoothed data and is thus the worst method to choose.
2) For any given data set, the best method can be chosen by selecting the method with the smallest standard deviation of error as measured on the dataset itself.
3) The use of an error bar at the end of the smoothed average allows us to gauge the reliability of the smoothed average as it reaches the end of the data set.
References
Mann, M., 2004, On smoothing potentially non-stationary climate time series, Geophysical Research Letters, Vol. 31, 15 April 2004
Willis, your experience is par for the course with skeptics coming into this debate when trying to publish in the scientific literature. While posting to the web is useful it will simply be used as an excuse to dismiss your work as “not scientific” or “irrelevant” (do not underestimate this).
I recommend the following;
1. Always publish your scientific work on an open access site like arxiv.org, even while you are trying to get it in a journal, since certain scientific indexes will still link it to any work you are citing and researchers are more likely to take it seriously (every skeptic having trouble getting published needs to do this). I highly encourage you to do so with this paper regardless of it’s age.
2. Always seek out alternative journals if rejected by your first choice, such as the open review ones Pielke Sr. suggested. By controlling key editorial boards alarmists have been able to keep most skeptics from publishing in the most prominent journals.
They want you to be discouraged and not try to publish again. If they have achieved this, they have won. While I am sure everyone appreciates your time devoted to posting here, a reduction in these posts in exchange for a published paper every 4-6 months would not only be welcome but likely have a greater scientific impact.
Some inspiration;
http://www.americanthinker.com/2009/12/a_climatology_conspiracy.html
http://www.cato.org/sites/cato.org/files/serials/files/regulation/2007/7/v30n2-1.pdf
http://scienceandpublicpolicy.org/images/stories/papers/reprint/Circling_the_Bandwagons_Correcting_the_IPCC.pdf
Severian says:
March 30, 2013 at 3:13 pm
I slaved over an analysis to the ballistic models of a system, made some huge improvements in accuracy with minimal mods to the code, and watched as it got called the Joe Blow algorithm.
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The solution is to feed Joe Blow a couple of good ones, let him take the credit, then slip him a real zinnger of a bone head. Done right he gets the door and you get his job. From then on no one will mess with you.
george e. smith says:
March 30, 2013 at 3:05 pm
“I also would make the comment, that ….Hoser….’s proposition of using Fourier transform filtering, is one that I find highly meritorious. If you are going to remove information, what better way is there to know exactly what it is you are removing.
Fourier transform filtering is widely used in image processing, and there you do like to know what you are throwing away.”
That can be done in the time domain just as easily, by subtracting the averaged signal from the original signal.
Willis,
You’re suffering from a severe case of spite deficiency. The cure is to eat some greens. A Mann salad would be ideal.
But you’ll need a proper smoothing technique to reduce the gristly lumps. I suggest you put the lot through a wood chipper. Then when you’ve done that feed the mush to a sty full of pigs — let them do the eating so you don’t have to chomp all that shit yourself.
See? Everything’s simple when you look at it logically.
I hope you get that slimy bastard.
Hey Willis, nice article, smart method. Congrats. I sympathize with how you feel, and I am really sorry about it. It happened to me in my field (unrelated to climate science) a number of times that reviewers rejected my work and then published it themselves. It sucks. If you can prove you wrote that stuff up and sumbitted it, and can show that it is plausible that Mann saw that, you can do all of us a great favor and sue the bejesus out of him.
Good luck!
JP
Roy says:
March 30, 2013 at 1:19 pm
[…] to be scrupulously fair, shouldn’t Mann be offered to opportunity to explain in this blog (or anywhere else if he would prefer it) where exactly he got the idea from?
——————————————————————————————————————-
I would have thought the offer of that would be implicit here. Unlike on Warmist blogs, where anything sceptical is censored instantly, I’m sure Anthony would be more than happy to allow a post by Mr Mann on the subject if he wished to submit one.
rogerknights says:
March 30, 2013 at 12:51 pm
Thanks, Roger, and I couldn’t disagree more strongly. Why would I want to indulge in pig-wrestling? What purpose would it serve? In any case the traditional “scientific journal” paradigm is evaporating, or perhaps more accurately transforming and shifting to the web and open access. I have no interest in beating a dying horse.
Next, I don’t like dealing with the journals at all, because of my unpleasant experiences. Why start now? Those jokers have a lawyer on staff, Mann has lawyers at his beck and call. Why on earth would I step into that buzz saw? In the 1800’s in the age of newspapers, Mark Twain once said, never argue with a man who buys ink by the barrelful. For me in the 21st century, the same applies to arguing with a man who has a lawyer on speed dial.
Also, as I noted in the head post, I posted a link to my paper on ClimateAudit before Mann’s second paper. He certainly could have read it there, whether he got the paper from the reviewers or not. I have no evidence that he got my paper from one of the reviewers, although I’ve since learned that (unfortunately) that is not uncommon. But for all I know, he could have been one of the reviewers. That also happens, I’ve since found out.
My only evidence for the fact that it was pal review rather than peer review is that they said it was too hard on Mann. Perhaps so, but to reject the paper entirely on that basis without comment on the mathematics said pal review to me.
Finally, as I said, I’m happy with the result. My idea about how to approach the problem, using data truncation to allow experimental determination of the error for different methods on a given dataset. did get published. Sure, I’d love for the world to be perfect and the good guys to always win. But that doesn’t make me want to go back and re-fight old battles. The new ones are so much more interesting … plus I published this here to show that yeah, I did think of it and publish it first. That’s plenty for me.
My best to you,
w.
Roy says:
March 30, 2013 at 1:19 pm
Michael Mann is more than welcome to post his explanation right here on WUWT. I strongly suspect Anthony would be happy to give him a guest post spot, although I can’t speak for Anthony. Alternatively, he has his own tame and well-censored blog, Real Climate, where he is perfectly free to post anything that fits his fancy.
So your wish is granted, Roy, he does have and has had the opportunity to explain … now lets see if he does so. I suspect Mann has larger fish to fry, but I’m always glad to be surprised.
w.
Jack says:
March 30, 2013 at 2:46 pm
With the advantage of hindsight there is no scientist that I am aware of who had been remembered for being wrong. But there are times when a man’s name becomes an historical artifact: think Benedict Arnold, think Quisling. I am sure there are others. Names that come to symbolize a pejorative noun rather than an honored individual.
Lamarck comes to mind, and more notoriously, Lysenko, then there’s ‘cold fusion’, Pons and Fleischman. Ussher though not a scientist is only known for his erroneous calculation of the age of the Earth.
I’ve noticed that alarmist climate heroes have to be “infallible”. Alarmists will not admit a single mistake. Because doubting their message is impermissible.
Willis,
I think Mann’s method is useful in its place, but is probably not original. Statisticians have been looking at these issues for a long time. The idea of padding with some sort of reflection is a device for being able to then use a symmetric smoother, which avoids getting a lagged estimate. And I think it goes back at least to DeForest in 1884.
Choice of method does depend on what you are looking for. You used as your aim to get the best estimate of the final point. Mann doesn’t help you there, because the final point is unchanged.
But often you want an estimate of final trend. Minimum slope is useless for that, because it tries to set the final trend to zero. Minimum norm is worse.
The merit of Mann’s method is that, unlike the other two, it leaves a straight line unchanged. That’s the minimum requirement for getting the trend right.
Sean says:
March 30, 2013 at 1:44 pm
I have no such evidence. I believe it to be the case, but he could have just randomly decided to go back and review his previous work … although I know of no other time he’s done that.
And I published my work on the web before he wrote his piece. He could have read it there. I know everyone read Climate Audit then as now, especially Mann and the other un-indicted co-conspirators.
So what do we actually know? That there was a curious coincidence, that Mann and I came up with the same idea independently, even though I published first, on the web to be sure, but published first … sorry, I’m not willing to re-fight that battle. Life is too short for that.
w.
BarryW says:
March 30, 2013 at 2:27 pm
Barry, if you have a “serious problem” with what I’ve done then you should seriously read my paper from end to end and seriously give me the paragraph number(s) where you think I went wrong along with your reasoning about why I was wrong. Plus which, it’s an interesting analysis.
Alternatively, if you truly are “too lazy to look at it” … then why are you commenting?
Regards,
w.
Re:
Roy says:
March 30, 2013 at 1:19 pm
**From what Willis wrote it does not seem that Mann independently hit on the same idea but, to be scrupulously fair, shouldn’t Mann be offered to opportunity to explain in this blog (or anywhere else if he would prefer it) where exactly he got the idea from?**
We know where he got it from – he thought of it himself while watching the Superbowl.
Look, I know most of us here are not climate scientists, but what I want to know is this: Is Michael Mann a Climate Scientist?
[*NOTE Warmists insist on telling me that geologists are not climate scientists.]
I have looked long and hard and all I get is somebody who studies wood, is a physics & math guy and a geologist. Heck, I get similar results for James Hansen the astronomer.
rogerknights says:
March 30, 2013 at 2:43 pm
No, no, no. I have absolutely no interest in that. Pig-wrestling is a sport with absolutely no appeal to me. Mann’s position in history is quite assured. There’s no need to “nail” him, it’s much better to laugh at him.
w.
I forgot, here is Mann’s bios
http://ploneprod.met.psu.edu/people/mem45
http://www.realclimate.org/index.php/archives/2004/12/michael-mann/
Willis, kudos to you for doing the honorable thing and staying clear of arguments with journals. As the saying goes, if you wrestle with a pig, you get covered in mud and the pig loves it.
Second, thanks for your wishes to all your readers. That was a lovely thought and a beautiful and succinct way to put it. Truly appreciated.
If you really want to know about Michael Mann see below. Michael Mann has been a useful idiot who is about to be thrown under the bus. The Mann has been a tool and does not even know it.
http://bishophill.squarespace.com/blog/2010/5/14/the-ascent-of-mann.html
Mr. Mann is a charlatan and as far as I am concerned he has been caught once again with his filthy little fat hands in the proverbial cookie jar.
Nice work Willis, even if you feel like letting the rotten scum slide into the shadow. I know it is never very rewarding to try and prosecute slime for stealing IP.
Clearly Mr. Mann must have failed his numerical methods for scientists & engineers class…or perhaps he never took one.
Willis E
No, no, no. I have absolutely no interest in that. Pig-wrestling is a sport with absolutely no appeal to me. Mann’s position in history is quite assured. There’s no need to “nail” him, it’s much better to laugh at him.
Old Confucius saying:
He who raughs rast, raughs the roudest!
🙂
Re: various comments about Fourier/frequency domain analysis above:
Fourier analysis does not “assume” sinusoidal components. It will detect them if they are there. White noise will be a “flat” spectrum with no prominent components. As an EE I do this all the time when looking at noisy signals- set my oscilloscope to FFT and see what’s happening in the frequency domain.
Yes- some kind of windowing is needed when transforming time-to-frequency or frequency-to-time to avoid artifacts (Gibbs phenomenon).
Gaussian averaging in the time domain- how is the Gaussian function truncated? It has a parameter alpha or equivalently standard deviation. Is this specified or standard in the climate change field? (excuse my ignorance)
Gaussian averaging in the time domain- the frequency response as a low pass filter is not that great. If one was trying to reject signals at or above certain frequencies, there are many other filters that could be used. Methinks these guys could use some help from signal processing experts as well as statisticians.
These “moving average” type filters are Finite Impulse Response filters and necessarily have a delay 1/2 the length. Infinite Impulse Response (IIR) filters can be designed for a desired frequency response with much less delay
Jack says:
March 30, 2013 at 2:46 pm
With the advantage of hindsight there is no scientist that I am aware of who had been remembered for being wrong. But there are times when a man’s name becomes an historical artifact: think Benedict Arnold, think Quisling. I am sure there are others. Names that come to symbolize a pejorative noun rather than an honored individual.
What will Mann’s be, I wonder? It seems to me that his strategy is to delay the inevitable disgrace until he retires.
The description you are looking for is already in use.
It is called Mannian Mathematics
Mails containing eschenbach
[1] “1022240460” “1071867706” “1077815565” “1080318686” “1089897080” “1093965453” “1173455973”
[8] “1175256080” “1175514499” “1175516878” “1175518086” “1175625460” “1176402767” “1176914054”
[15] “1176914058” “1176914115” “1176914126” “1176921225” “1177014768” “1177084894” “1177768674”
[22] “1177768680” “1177768685” “1177768691” “1177939146” “1179143741” “1179320358” “1179438052”
[29] “1179489629” “1179843250” “1188479890” “1191006119” “1228920976” “1228921629” “1228922050”
[36] “1232025011” “1242129707” “1242157611” “1242161854” “1250081982” “1250084021” “1250084210”
[43] “1252438429” “1252442034” “1252442659” “1252443088” “1252498172” “1254398790”
many repeats, nothing relevant to this discussion
Willis Sorry, I didn’t mean to offend. I have no problem with what you did (not that my opinion should matter to you). I didn’t mean you the person, I mean a generic “you” such as someone who attempts to pad the end of a series and guesstimates the endpoint based on data that doesn’t exist. Your minimum assumptions technique, for example, was similar to what I was trying to come up with with my own crude thinking on the matter though my only thought was using trailing averages. I keep thinking along the lines of dealing with real time data and estimating the data that will arrive in the future. Not really what you’re attempting. My apologies for annoying you or any insult you may have felt.