Sunspots and Sea Level

Guest Post by Willis Eschenbach

I came across a curious graph and claim today in a peer-reviewed scientific paper. Here’s the graph relating sunspots and the change in sea level:

And here is the claim about the graph:

Sea level change and solar activity

A stronger effect related to solar cycles is seen in Fig. 2, where the yearly averaged sunspot numbers are plotted together with the yearly change in coastal sea level (Holgate, 2007). The sea level rates are calculated from nine distributed tidal gauges with long records, which were compared with a larger set of data from 177 stations available in the last part of the century. In most of the century the sea level varied in phase with the solar activity, with the Sun leading the ocean, but in the beginning of the century they were in opposite phases, and during SC17 and 19 the sea level increased before the solar activity.

Let me see if I have this straight. At the start of the record, sunspots and sea level moved in opposite directions. Then for most of the time they were in phase. In both those cases, sunspots were leading sea level, suggesting the possibility that sunspots might affect sea level … except in opposite directions at different times. And in addition, in about 20% of the data, the sea level moved first, followed by the sunspots, suggesting the possibility that at times, the sea level might affect the number of sunspots …

Now, when I see a claim like that, after I get done laughing, I look around for some numerical measure of how similar the two series actually are. This is usually the “R2” (R squared) value, which varies from zero (no relationship) to 1 (they always move proportionately). Accompanying this R2 measure there is usually a “p-value”. The p-value measures how likely it is that we’re just seeing random variations. In other words, the p-value is the odds that the outcome has occurred by chance. A p-value of 0.05, for example, means that the odds are one in twenty that it’s a random occurrence.

So … what did the author of the paper put forwards as the R2 and p-value for this relationship?

Sad to relate, that part of the analysis seems to have slipped his mind. He doesn’t give us any guess as to how correlated the two series are, or whether we’re just looking at a random relationship.

So I thought, well, I’ll just get his data and measure the relationship myself. However, despite the journal’s policy requiring public archiving of the data necessary for replication, as is too common these days there was no public data, no code, and not even a Supplementary Online Information.

However, years of messing around with recalcitrant climate scientists has shown me that digitizing data is both fast and easy, so I simply digitized the graph of the data so I could analyze it. It’s quite accurate when done carefully.

And what did I find? Well, the R2 between sunspots and sea level is a mere 0.13, very little relationship. And even worse, the p-value of the relationship is 0.08 … sorry, no cigar. There is no statistically significant relationship between the two. In part this is because both datasets are so highly auto-correlated (~0.8 for both), and in part it’s because … well, it’s because as near as we can tell, sunspots [or whatever sunspots are a proxy for] don’t affect the sea level.

My conclusions from this, in no particular order, are:

• If this is the author’s “stronger effect related to solar cycles”, I’m not gonna worry about his weaker effect.

• This is not science in any sense of the word. There is no data. There is no code. There is no mathematical analysis of any kind, just bald assertions of a “stronger” relationship.

• Seems to me the idea that sunspots rule sea level would be pretty much scuttled by sunspot cycles 17 and 19 where the sea level moves first and sunspots follow … as well as by the phase reversal in the early data. At a minimum, you’d have to explain those large anomalies to make the case for a relationship. However, the author makes no effort to do so.

• The reviewers, as is far too often the case these days, were asleep at the switch. This study needs serious revision and buttressing to meet even the most minimal scientific standards.

• The editor bears responsibility as well, because the study is not replicable without the data as used, and the editor has not required the author to archive the data.

So … why am I bothering with a case of pseudo-science that is so easy to refute?

Because it is one of the papers in the Special Issue of the Copernicus journal, Pattern Recognition in Physics … and by no means the worst of the lot. There has been much disturbance in the farce lately regarding the journal being shut down, with many people saying that it was closed for political reasons. And perhaps that is the case.

However, if I ran Copernicus, I would have shut the journal down myself, but not for political reasons. I’d have closed it as soon as possible, for both scientific and business reasons.

I’d have shut it for scientific reasons because as we see in this example, peer-review was absent, the editorial actions were laughable, the authors reviewed each others papers, and the result was lots of handwaving and very little science.

And I’d have shut it for business reasons because Copernicus, as a publisher of scientific journals, cannot afford to become known as a place where reviewers don’t review and editors don’t edit. It would make them the laughing stock of the journal world, and being the butt of that kind of joke is something that no journal publisher can survive.

To me, it’s a huge tragedy, for two reasons. One is that I and other skeptical researchers get tarred with the same brush. The media commentary never says “a bunch of fringe pseudo-scientists” brought the journal down. No, it’s “climate skeptics” who get the blame, with no distinctions made despite the fact that we’ve falsified some of the claims of the Special Issue authors here on WUWT.

The other reason it’s a tragedy is that they were offered an unparalleled opportunity, the control of special issue of a reputable journal.  I would give much to have the chance that they had. And they simply threw that away with nepotistic reviewing, inept editorship, wildly overblown claims, and a wholesale lack of science.

It’s a tragedy because you can be sure that if I, or many other skeptical researchers, got the chance to shape such a special issue, we wouldn’t give the publisher any reason to be unhappy with the quality of the peer-review, the strength of the editorship, or the scientific quality of the papers. The Copernicus folks might not like the conclusions, but they would be well researched, cited, and supported, with all data and code made public.

Ah, well … sic transit gloria monday, it’s already tuesday, and the struggle continues …

w.

PS—Based on … well, I’m not exactly sure what he’s basing it on, but the author says in the abstract:

The recent global warming may be interpreted as a rising branch of a millennium cycle, identified in ice cores and sediments and also recorded in history. This cycle peaks in the second half of this century, and then a 500 yr cooling trend will start.

Glad that’s settled. I was concerned about the next half millennium … you see what I mean about the absence of science in the Special Edition.

PPS—The usual request. I can defend my own words. I can’t defend your interpretation of my words. If you disagree with something I or anyone has written, please quote the exact words that you object to, and then tell us your objections. It prevents a host of misunderstandings, and it makes it clear just what you think is wrong, and why.

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temp
January 21, 2014 10:30 pm

“Well, the R2 between sunspots and sea level is a mere 0.13, very little relationship. And even worse, the p-value of the relationship is 0.08 … sorry, no cigar. ”
On what years are you getting those values because from the paper they have a bunch of different cycles pointed out. I’m assuming your getting those values from the whole data set which both you and the paper point out starts with the data in question as an opposing force. Thus pointing out again through these values makes no sense since the paper clearly states as such. I only briefly skimmed the paper since its late but I can I can already see some problems with this whole line here assuming your using the whole data set.

Steve W.
January 21, 2014 10:43 pm

Thanks Willis,
I know you are talking about much more than this one paper, and I see that the correlations are poor, but seeing both of those lines having about the same frequency just makes me wonder what is going on! Why would the frequency of the two signals be about the same? What causes the signal in the sea level? Could there be a loose coupling between the two, like something poking at a pendulum? Why would you expect them to be perfectly in lock-step? Perhaps the sun is a factor, but there are multiple other cyclical drivers of sea level change? The sea level looks like the path of a drunk with several people (one visible, others unknown) trying to push him in the right direction. OK, maybe too much coffee…

Kuhnkat
January 21, 2014 11:04 pm

Willis,
“And I’d have shut it for business reasons because Copernicus, as a publisher of scientific journals, cannot afford to become known as a place where reviewers don’t review and editors don’t edit. It would make them the laughing stock of the journal world, and being the butt of that kind of joke is something that no journal publisher can survive.”
You mean like Nature and the rest of the Believers Climate papers??
HAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHA

kuhnkat
January 21, 2014 11:05 pm

OK, that is one paper. Gonna do the rest for them??

January 21, 2014 11:05 pm

Willis,
The next paragraph says:

The coastal sea level variation cannot be explained as due to expansion/contraction of the oceans due to heating/cooling during a solar cycle as proposed by Shaviv (2008) simply because, near the shore, the thermal expansion becomes zero since the expansion is proportional to the depth (Mörner,
2013b). The good correlation and nearly in-phase response between solar activity and sea level indicates that this is a direct mechanical response – and not a thermal response that needs time to heat up and cool, and therefore shows delayedresponse. This may be seen comparing Figs. 1 and 2.

The author appears to have used Fig 2 to illustrate the de-coupling between solar cativity and sea levels; specifically noted the phase inconsistency and then reasoned that oceanic thermal mass is the reason for phase shifting and a reduction in response for “weak” cycles.
I can’t see how you got the idea from the paper that the author was trying to indicate a simple relationship between solar activity and sea level. Admittedly, I haven’t read the paper in as much depth as you must have before draughting your article.

kuhnkat
January 21, 2014 11:08 pm

By the way Willis, by picking a weak paper to pick on you leave the implication that they are ALL weak. is this your intent or are you going to do the rest of the papers??

Eliza
January 21, 2014 11:15 pm

It’s called spurrious relationship/correlation geeses and babies etc////

January 21, 2014 11:31 pm

Sir James Jeans of the Royal Society posted a similar chart for Sir Richard Gregory in his book “Through Space and Time”. The chart shows the water line of Lake Victoria perfectly correlation to Sunspot Activity.
I was allowed by the publisher to repost that work in my papers along with Atlantic Ocean hurricane seasons Accumulated Cyclone Energy. You can do the same with Earthquake data.
Maybe with for different correlations along with temperatures on our sister planets maybe the EPA will toss the Hockey Stick and Puck back to Ice and quit their false science.
My work is posted at sunspotshurricanesandglaiers.com. Top section.
Sincerely,
Paul Pierett

January 21, 2014 11:42 pm

I would like to thank professor Jan-Erik Solheim, a specialist in astrophysics, for an interesting article.
Regards
Agust

January 21, 2014 11:42 pm

kuhnkat says:
January 21, 2014 at 11:08 pm
By the way Willis, by picking a weak paper to pick on you leave the implication that they are ALL weak. is this your intent or are you going to do the rest of the papers??
##########################################
Why should willis do what reviewers should have done.
In the first 3 papers I read ( this was one) I found similar claims of “relationship’ or correlation, but no numbers.
You CANNOT replicate a find of “good correlation’ unless the author actually tells you what he found.
After reading 3 papers I gave up.

January 22, 2014 12:36 am

Ok, Team WUWT, united really only by shared insomnia, go!
So Willis, Mosh, if all, or almost all of these papers similar in quality, would a better name for PRiP be CitC, (Castles in the Clouds)?

Maybe this could be a paper in the Journal?

Janice Moore
January 22, 2014 12:42 am

Okay! And YOU GO, CHARLES THE MODERATOR! #(:))
btw: no insomnia — just lack of self-discipline to get to bed.
(hope you don’t mind this totally OT post to just say, “Hi!”)
(yawn)…. bowl of cereal time…. then….. zzzzzzzzzzzz (after I kick my German Shepherd off the bed where he is now snoring away, heh)
LOLOLOL — it’s fun hanging out on WUWT after midnight!

Alex
January 22, 2014 1:07 am

I think you have completely misconstrued the argument into a strawman. Sunspots are an effect, not a cause. No one says ‘Sunspots caused this anomoly’. They are a just a proxy for sun’s magnetic activity and nothing else. And whatever magnetic event that might be affecting earth’s climate could easily precede the change in sunspots. It is absurd to try to disprove the paper based on a claim the authors weren’t even making.

Stephen Wilde
January 22, 2014 1:34 am

The Journal is about pattern recognition.
A pattern is being discussed in the paper.
The conclusion to be derived from any pattern and the question as to whether or not there is a real pattern are obviously open for reasonable discussion.
In this case the pattern appears to hold for a time but to shift in and out of phase.
Many things can cause real patterns to do that and the proper way forward is to investigate whether the pattern may or may not be real in the light of all the potentially confounding influences.
Seems a reasonable scientific endeavour to me.

Stephen Wilde
January 22, 2014 1:41 am

Note that the author actually draws attention to the problems in the analysis as a precursor to further investigation.
The author makes some attempt to suggest a possible explanation in the millennial solar cycle and oceanic thermal inertia.
That is a more scientifically valid approach than most of AGW theory which appears to try and hide or adjust inconvenient data.
I think Willis is being unfair here.

goldminor
January 22, 2014 1:50 am

Janice Moore says:
January 22, 2014 at 12:42 am
btw: no insomnia — just lack of self-discipline to get to bed.
Similar here, Janice. I could easily keep my thoughts here for the rest of my life.
I raised a G Shep Akita mix for 18 years and 3 generations. I still see them in my dreams at times. They were beautiful creatures.

goldminor
January 22, 2014 2:00 am

Willis,s argument about taking care to maintain the proper level of integrity in published research makes sense to me.
The graph does have interesting features. Could it be that the main story this graph is telling is that there is another modifier interacting with these systems?

January 22, 2014 2:01 am

No, Willis is not being unfair. Willis is pointing out that if you’re going to do peer-reviewed scientific analysis, then you’d better make sure that your peers are competent enough to critique the analysis.
If Willis can spot the obvious flaws in a paper with five seconds of inspection and a quick calculation, then its clear that the journal (and therefore the publisher) is heading for a pasting that is thoroughly deserved.
Pattern recognition is at the basis of human intelligence, but it is also the greatest threat to human progress if it is not constrained by proper analysis.

Paul Westhaver
January 22, 2014 2:05 am

Willis,
Notwithstanding your gripes about the overall paper and the journal review process, If someone placed the curves in front of me, I;d be very interested i knowing why they are related. They are related without a doubt. So why?
Is the sea level data repeatable? The sunspot data looks right.
I’d say there is something there.

Brian H
January 22, 2014 2:14 am

Stephen Wilde says:
January 22, 2014 at 1:41 am
Note that the author actually draws attention to the problems in the analysis as a precursor to further investigation.

Agree; this comes across as that preliminary pre-hypothesis stage called “speculation”. It’s not trying to claim to do more than point out something curious, suggestive. Demanding it meet inappropriate standards is churlish.

Siberian_Husky
January 22, 2014 2:29 am

Willis,
“The p-value measures how likely it is that we’re just seeing random variations. In other words, the p-value is the odds that the outcome has occurred by chance. A p-value of 0.05, for example, means that the odds are one in twenty that it’s a random occurrence.”
This is not correct. The pvalue is the probability of observing the given result or one more extreme, given that the null hypothesis is true. If you don’t understand the difference between what I have written and what you have said, you shouldn’t be attempting statistics. Period.
“And what did I find? Well, the R2 between sunspots and sea level is a mere 0.13, very little relationship. And even worse, the p-value of the relationship is 0.08 … sorry, no cigar. There is no statistically significant relationship between the two. In part this is because both datasets are so highly auto-correlated (~0.8 for both), and in part it’s because … well, it’s because as near as we can tell, sunspots don’t affect the sea level.”
So let me get this straight- you find very little relationship between the two, and yet you say that the two datasets are highly auto-correlated!!! What do you think autocorrelated means Willis??? I mean look at the two lines Willis- they are practically on top of one another!!! Maybe you didnt find any relationship between the two because you know squat about statistics?
This is why it is an utter waste of time reading blogs like these written by poorly trained pseudo-scientists. The peer review process is there so that crap like this doesnt see the light of day.
What an embarassment.

Greg Goodman
January 22, 2014 2:33 am

Steve Mosher: “Why should willis do what reviewers should have done.”
What? You mean READ the paper before trying to diss it? Don’t be silly.
Willis just picks out one graph and ignores the rest of the paper and then goes on to draw his own preconceived conclusions that have little “correlation” with the contents of the paper.
The rest of the paper shows a much deeper examination of the spectral content including a strong 9.1 year cycle that Scafetta, BEST and others have already documented.
Now if you superimpose a 9.1 year and a rather variable 10-11 year cycle you will get exactly the kind of phase shifts and decorrelation that Willis’ sharp eye spots in figure 2. if you only look at either one in isolation.
This is precisely the point I made in an article I submitted to Talkshop last year just before getting banned for daring to question with His Majesty.
My copy here:
http://climategrog.wordpress.com/2013/03/01/61/
Willis, if your are going to dismiss a paper at least have the good manners to read it first.

Agnostic
January 22, 2014 2:35 am

well, it’s because as near as we can tell, sunspots don’t affect the sea level.
I don’t offer any judgement on the worthiness of this paper, but this remark jumped out at me. Surely you are not suggesting that the authors think sunspots affect sea level? Maybe it ought to have been more explicit in the paper, I don’t know I haven’t read it, but the first thing I thought of looking at the graph you displayed is that they are using sunspots as an indicator of solar activity the consequence of which may affect temperatures which may affect sea level. Sunspots have been pretty reliably measured which is why I presume this graph was made.
My first thought to this was I didn’t think sea levels would be affected on these time scales because of thermal inertia, but maybe it has something to do with the top layer or something. But sea levels are notoriously difficult to measure reliably and I wondered about the level of error – i suspect they would be so large that any sort of wiggle would fit within them.
I think your skepticism is well-merited – the danger of “pattern recognition” is that the human mind contrives to find patterns where none exist, and this looks like a prime candidate. Yet if you are going to kick this one down the hall, at least don’t mischaracterise what they are saying. Or DID they say that sunspots cause sea level change explicitly? Because that would be nuts…

Stephen Wilde
January 22, 2014 2:35 am

“if you’re going to do peer-reviewed scientific analysis, then you’d better make sure that your peers are competent enough to critique the analysis.”
And if the analysis throws up new areas for investigation such that no peers are sufficiently competent ?
This paper does not purport to ‘prove’ anything. The criticisms are indeed churlish.

Greg Goodman
January 22, 2014 2:42 am

John A : “If Willis can spot the obvious flaws in a paper with five seconds of inspection and a quick calculation, then its clear that the journal (and therefore the publisher) is heading for a pasting that is thoroughly deserved.
Pattern recognition is at the basis of human intelligence, but it is also the greatest threat to human progress if it is not constrained by proper analysis.”
So rather then read the paper and find out that it does contain a detailed “proper analysis” , you just pile on, on the basis of Willis’ inconvenienced comments, which do indeed seem to be based on ” five seconds of inspection “.
Mindlessly repeating what you read in a blog post , without checking the facts, is “the greatest threat to human progress”.
Now go away, read the paper then come back and tell what you think.

January 22, 2014 3:04 am

Hi Willis
First of all I am recognised as a ‘sceptic pseudo-scientist’, although I give primate to my engineering degree to the higher science degree and often say ‘I am an engineer not a scientist’.
Of the ‘oscillating’ things going in and out of phase:
few years ago I looked at the AMO (9yr) and SSN (11yr), a perfectly normal thing:
http://www.vukcevic.talktalk.net/AMO-SSN.htm
Hey, ‘pattern recognition’ not Copernicus but ‘vuk’ style had me occasionally speculate: is there anything more to it, and speculation lead to a highly speculative outcome as registered here , where everything fell neatly into phase.
Sometimes in the remotely distant future we might exchange my speculation and your knowledge and experience of the amazing Solomon Islands.

johnmarshall
January 22, 2014 3:25 am

Whilst sunspots are an easy thing to count sea level is not. In fact sea level can change in different ocean basins in different ways so we need to know how this metric was arrived at.

tallbloke
January 22, 2014 3:41 am

While I agree with Greg Goodmans points to Willis I’ll just say that like Willis, he wasn’t banned from the talkshop for his science.
Goodman teaches Eschenbach manners – more popcorn please.

January 22, 2014 3:55 am

Siberian_Husky says:
This is why it is an utter waste of time reading blogs like these written by poorly trained pseudo-scientists. The peer review process is there so that crap like this doesnt see the light of day. What an embarassment.
Who are you to judge? You are just trolling, anonymously. Post your name, and readers will see if you have the credentials to pass judgement like that.
You would love it if you could censor articles like this. That’s what you wrote here, isn’t it? FYI, this site does the job that your pal reviewed ‘science’ shirks.

Chris Wright
January 22, 2014 4:03 am

I’m a huge Willis fan, but on this issue I’m a bit – shall we say – sceptical.
The first problem is obvious: by simply eyeballing the graph you can see a striking correlation. Of course, the eye can be easily fooled. One test would be to create a set of similar but random graphs and to estimate what proportion seem to have the same correlation.
The sea level graph has 10 positive peaks. The sun spots very accurately match 5 of the peaks, particularly in term of phase (date of the peak). Several other peaks match fairly well. Strangely, the two worst phase matches are at the start and end of the graph. Coincidence?
There is a dramatic phase mismatch right at the start of the graph. There are two possible explanations for these mismatches:
1. Sunspot data should be accurate (easy to measure) but sea level data is much harder. My guess is that, if Holgate had used a different (but still good quality set), his graph would have been similar overall, but in detail somewhat different. Remember that Holgate’s data set was quite small. A different data set might well change the phase matches.
2. The thing (if it exists e.g. solar wind) that affected the sea level is probably complex. And the relationship between that thing and sunspots is also probably complex.
I think R2 tests may be fairly misleading in some cases. Any test has to do what the human eyeball is very good at: noticing correlations between specific features rather than an overall trend.
As I said, some kind of Monte Carlo test could possibly prove whether the apparent correlation is real or just a trick of random chance: create a set of similar randomly generated graphs and ask this question: what proportion of randomly generated graphs appear to have the same correlation?
Ironically, true believers would kill to be able to show a graph with similar correlation between CO2 and global temperature (obviously, with CO2 leading and not lagging the temperature).
Finally, there is an abundance of evidence that solar activity has a dramatic effect on river flows and lake levels. If solar activity had literally a zero effect on sea levels, that would be quite surprising.
Chris

Greg Goodman
January 22, 2014 4:21 am

tallbloke says:
“While I agree with Greg Goodmans points to Willis I’ll just say that like Willis, he wasn’t banned from the talkshop for his science”.
No. I was banned because you are unable to accept criticism and have a tantrum if anyone disagrees with you on your blog. You control your blog space like a petulant child and don’t apply your own blog rules.
Rule (1) There are no rules.
Rule (2) See rule (1)
Rule (3) See rule (2)
http://climategrog.wordpress.com/2013/03/05/talkshop-immoderation/
I would have thought you would have the sense to keep schtum about that outside of a space where you have admin rights. You end up looking about as objective as Grant “Tamino” Forster and Real Climate crew.

Agnostic
January 22, 2014 4:37 am

@johnmarshall
Whilst sunspots are an easy thing to count sea level is not. In fact sea level can change in different ocean basins in different ways so we need to know how this metric was arrived at.
Agreed – this is the reason I am skeptical of drawing a link between sunspots as a metric for solar activity and sea level rise. I don’t think it is necessary to go as far as an R^2 – I’d have questioned the sea level data. And R^2 of harmonically related trends don’t automatically discount a relationship. If you were to describe π in binary you wouldn’t get a statistically significant result but it doesn’t mean that it wasn’t meaningful.
I think the reasoning behind the graph is reasonable, but I doubt there is sufficient accuracy in the sea level data to draw much in the way of conclusion from it.

Greg Goodman
January 22, 2014 4:49 am

I don’t think the paper is particularly strong , it is mainly recapitulation of other work (mainly Scafetta it would seem), however, I have not seen this particular tide series before.
Some of it seems of dubious value, eg:
“The GISP2 may have a timing error of decades and/or
show temperatures out of phase with the global temperature
variation. In Fig. 9 we compare the simulation determined
from the GISP2 data with the HadCRUT4 global tempera-
ture series, and find a good fit if we introduce a shift of 85 yr,
which means the response in the ice core as shown in Fig. 8
is delayed 85 yr compared with the instrumental temperature
record. ”
One half wiggle looks similar to another one if we shift it by an arbitrary 85 years. Hmm.
Comparing frequency spectra in fig. 3a and 3b:
“The dominance of a 22 yr period compared with a 10–12 yr
period can be explained by GCR variations. The 22 yr Hale
period is the Sun’s magnetic period, and represents a polarity
change in the two hemispheres of the Sun. This is observed in
the GCR variations as shown in Fig. 5. ”
This is more like it. What is measured in SSN is a “rectified” version of solar variations.
Climate is very complex and it is not possible to just overlay two time series or do trivial linear regressions. No understanding will be gained by such trivial analysis, nor can the presence of a signal be refuted over simplified correlation tests and statistical significance.
This is the main problem with current climate science. There is a resounding need to apply the wealth of existing knowledge in systems analysis to climate system.
It seems these papers suffered from the rather incestuous circle of authors, editors and reviewers and could have been strengthened by applying the publishers rules. There is plenty of expertise in relevant techniques that can be called on, even if its application to climate is lacking.

E.M.Smith
Editor
January 22, 2014 4:59 am

@Vukcevic:
Nice graph. Nine years is one of the lunar cycyles. I would look into lunar tidal cycles and orbital resonance periods with the gas giant planets for the “connection”. Both for your work and for the interesting graph in the posting.
@Willis:
Your analysis is statistically sound for two matched signals, but will fail on two resonant signals of common origin but different harmonics… A 3:2 frequncy ratio will give shifting phase, yet is a relationship. It speaks to something deeper to find.
Is the article trash? Depends on the authors’ claims in the text, not just the stats on the graph. Taking your word for it that strong correlation was claimed without the math: if so, that is very weak. Ought to have shown the math basis for the claim. IMHO, and also shown the correlation stats for harmonic relations that do match, if any.
But in any case, to look at tides and NOT look at orbital periods of the moon and sun is a big weakness. There are 3, 9, and 18 year lunar orbital cycles, and more, and an 11 year Jovian jiggle. Ignoring them in a lunar solar cycle paper is silly.

January 22, 2014 5:11 am

Is this just coincidence?

>NASA Finds Sun-Climate Connection in Old Nile RecordsIs solar variability reflected in the Nile River?<
Alexander Ruzmaikin, Joan Feynman, and Yuk L. Yung
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 111, D21114, doi:10.1029/2006JD007462, 2006
http://trs-new.jpl.nasa.gov/dspace/bitstream/2014/39770/1/06-1256.pdf
http://onlinelibrary.wiley.com/doi/10.1029/2006JD007462/abstract
http://trs-new.jpl.nasa.gov/dspace/handle/2014/40231

By the way, Joan Feynman is Richard's Feynman sister.
http://en.wikipedia.org/wiki/Joan_Feynman

January 22, 2014 5:13 am

NASA Finds Sun-Climate Connection in Old Nile Records
http://www.jpl.nasa.gov/news/news.php?feature=1319

Greg Goodman
January 22, 2014 5:21 am

Hi Chiefio, this look at lunar cycles in Arctic ice data may interest you.
http://climategrog.wordpress.com/?attachment_id=756
It’s also a good example of the detailed analysis needed to detect signals in spectral data.
Many things in climate lead to one effect modulating another, this leads to splitting of peaks into triplets where the central value may be nearly non existent. Failure to understand how this shows up in spectral data will lead the false conclusion that a particular frequency is not present.
I’d also been surprised by the lack of a clear 29.5 day signal in this data until recently though there was a broad spread in that part of the spectrum there was nothing relating to the usual figure of 29.53 days as synodic lunar period.
I recently discovered (thanks to a link of cheifio’s site) that there is quite a spread in length of the visible lunar cycle. It turned out that the extreme values are clearly present whereas the average is not.
None of this is simple and trivial analysis is not sufficient to either detect or refute the presence of natural cycles.

Greg Goodman
January 22, 2014 5:31 am

Similar processing finds a notable anomalistic month signal (distance of moon) . Equally looking for the signal directly you would conclude there is nothing at 27.5545d , however, the combined energy of the triplet makes it clearly significant against the background noise.
http://climategrog.wordpress.com/?attachment_id=757
I also have to say hats off to NOAA for the quality of this data. It really says something for the signal to noise ratio when you can pull out this kind of detail.

Rathnakumar
January 22, 2014 5:42 am

Well done again, sir! It never ceases to fascinate me to think how easy it is to fool oneself.

Charlie K
January 22, 2014 5:45 am

@Siberian_Husky

So let me get this straight- you find very little relationship between the two, and yet you say that the two datasets are highly auto-correlated!!! What do you think autocorrelated means Willis???

You should do a little research before you imply that someone is a fool. Especially when your accusation exposes your glaring lack of understanding of simple math terms. Autocorrelation means that a signal correlates to itself (auto = self, correlation = the state of being correlated) . The two datasets are indeed highly autocorrelated since they are not only cyclical, but also quite periodic. Here is the Wikipedia article regarding autocorrelation.
The Merriam-Webster definition of autocorrelation is:

: the correlation between paired values of a function of a mathematical or statistical variable taken at usually constant intervals that indicates the degree of periodicity of the function

In conclusion, it would appear that autocorrelation does not mean what you seem to think it means.

Paul Vaughan
January 22, 2014 5:46 am

A recurring problem with “open review” is when the reviewers reach beyond their own grasp (and refuse to admit it). See figure 1 — when there is coupling, linear correlation is for political distortion artists:

Greg Goodman
January 22, 2014 7:28 am

http://climategrog.wordpress.com/?attachment_id=755
The “9.1” year peak found by N Scafetta (as referenced in the Solheim paper) and in the recent BEST land temp study is also found in cross-correlation of N. Pacific and N. Atlantic SST.
Closer inspection reveals that it is likely to be 9.05 , the result of interference between lunar declination by lunar perigee (tidal force) .
The super-position of the two close cycles results in 9.05 year cycle modulated by 356 years. Only the 9.05 is readily detectable in available records due to limited data length. This kind of analysis can reveal longer periods.

tallbloke
January 22, 2014 7:34 am

Data disclosure and personal statement on peer review by Professor Jan-Erik Solheim:
In my paper “The sunspot cycle length – modulated by planets?” the section 2. Data and methods, contain links to the data:
http://www.ngdc.noaa.gov/stp/space-weather/solar-data/solar-indices/sunspot-numbers/cycle-data/table_cycle-dates_maximum-minimum.txt.
(here I made contact with the person responsible – who promised to keep this address alive)
and to the program package I use for analysis:
http://www.astro.univie.ac.at/dsn/dsn/Period04/.
This is a thrusted period search code used for variable stars – based on Discrete Fourier Analysis
For my second paper “Signals from the planets, via the Sun to the Earth” the same period search code is used
Regarding peer review:
I have done most of my research in the field of close binary stars and pulsating white dwarfs. Our communities are quite small, so we prefer to be anonymous in our referee work. I think this is the best practice.
Normally there is only one referee in Astronomy and Astrophysics publications. Even if I had my last publication in that field in 2010, I still occationally referee for Monthly Notices of the Royal Astronomical Society and The Astrophysical Journal.
The last years I have had a number of referee tasks for Climate research journals.
Regards
Jan-Erik

January 22, 2014 7:43 am

[snip – you are welcome to resumbit without the ad homs – mod]

January 22, 2014 8:04 am

I am resubmitting with some editing.
Willis criticizes without reading a paper first.
The same graph discussed above by Willis was discussed with Anthony’s great approval here:
http://wattsupwiththat.com/2009/04/15/the-oceans-as-a-calorimeter/
and here
http://wattsupwiththat.com/2009/04/07/archibald-on-sea-level-rise-and-solar-cycles/
The graph is essentially taken from Shaviv (2008):
Shaviv, N. J.: Using the oceans as a calorimeter to quantify the solar radiative forcing,
J. Geophys. Res., 113, A11101, doi:1029/2007JA012989, 2008.
which has been properly referenced by Solheim and where a lot of details are present. Solheim does not need to repeat the details given the fact that those are in the referenced work.
The uncertainty noted by some above is also discussed in Solheim’s paper
http://www.pattern-recogn-phys.net/1/177/2013/prp-1-177-2013.pdf
and it is due to the fact that the decadal climatic cycle may be due to a soli-lunar oscillation at about 9.1 year and to the 10-12 year solar cycle. This is clearly show in numerous figures of the paper such as figure 7 which is taken from one of my papers, which are these
Scafetta, N.: Solar and Planetary Oscillation control on climate change hind-cast, forecast and an comparison with the CMIP5 GCMs, Energ. Environ., 42, 455–496, 2013a.
Scafetta, N.: Discussion on climate oscillations: CMIP5 general circulation models versus a semi-empirical harmonic model based on astronomical cycles, Earth-Sci. Rev., 126, 321–357, 2013b.
Willis, unfortunately, does not seem to me to know how to read a scientific paper.

January 22, 2014 8:13 am

Willis, sounds like you are arguing the “a trend is not a prediction” story. The article is pointing out a trend, from which one can reasonably conclude that somewhere in the background there might be at least a co-determinant.
Kicking a dog when its down doesn’t make you a great hunter.

tallbloke
January 22, 2014 8:31 am

Nicola Scafetta says:
January 22, 2014 at 7:43 am
[snip – you are welcome to resumbit without the ad homs – mod]

Yes Nicola, behave yourself on his Nibs thread. Here’s an example of the sort of thing you can’t say:
“Copernicus, as a publisher of scientific journals, cannot afford to become known as a place where reviewers don’t review and editors don’t edit”
REPLY: That’s not ad ad hom, it isn’t directed at a specific person. You really don’t have much integrity to stand on here Mr. Tattersall, since you don’t allow Willis to comment at your own blog. And then there’s that other incident where you violated my trust. – Anthony

January 22, 2014 8:50 am
dikranmarsupial
January 22, 2014 8:54 am

“A p-value of 0.05, for example, means that the odds are one in twenty that it’s a random occurrence.”
no, that is the “p-value fallacy”, the p-value is the probability of observing a result as extreme as that observed IF it ocurred due to random chance. This is not the same thing as the probability that it ocurred DUE to random chance.

tallbloke
January 22, 2014 8:56 am

REPLY: That’s not ad ad hom, it isn’t directed at a specific person. You really don’t have much integrity to stand on her Mr. Tattersall, since you don’t allow Willis to comment at your own blog. And then there’s that other incident where you violated my trust. – Anthony
That violation of trust was over Willis’ ad hom attacks too, remember?
Here’s Niklas Morner’s statement on the way we conducted peer review:
(2) “the editors selected the referees on a nepotistic basis”
Nepotism is to favor friends and relatives without respects to qualifications. We did the opposite; the reviewer chosen were all specialists on the topics in question.
It is true that they primarily were chosen among the authors of the special issue with some additional from outside. This does not mean “pal-reviewing”, but serious colleague reviewing. Most members of the author-team only new each other superficially or as authors. It is common practice when printing proceedings or collective volumes to seek the reviewers within the group, not in order to make the reviewing process less serious, but because those persons are the true experts within the field.
And almost always they do a tremendously good job to improve the papers in constructive ways. So also in our case: our reviewing was simply excellent, which I am sure all persons involved would happily testify. This includes strong points and forces for relevant changes and updating.
And what we achieved was a wonderful collection of papers that together make a very strong impact of elevating an old hypothesis into a firm theory saying that the solar variability is, indeed, driven by the planetary beat.

Amen to that Niklas, you did 100 hours a week for several months to make this happen, and I’m with you all the way. The 580+ peer reviewed papers you have to your name in the geophysics and oceanology fields enabled you to get some superb outside reviewers such as the very eminent Physicist Prof. Giovanni P. Gregori, who has written a brilliant letter to Martin Rasmussen. Anthony should read it.
REPLY: So because you hate what Willis has to say, along with the maths to back it up (you’ve presented none), it was OK to violate my trust? Interesting. – Anthony

January 22, 2014 8:59 am

Dear Anthony,
******
As for Anthony Watts, this reminds me of the old advice: when you circle the wagons to fend off an attack by wild Indians, direct your fire outward, not inward.
******
Don’t you think that would be better for you to close this post by Willis (it has been fully disproved by me above), and open a new post where you reproduce Giovanni de Gregori letter?
You may also make two posts by coping and past my articles on notrikzone:
(2) http://notrickszone.com/2014/01/19/scientists-react-sharply-to-copernicus-publishing-censorship-of-alternative-scientific-explanations-do-you-realize-what-you-have-done/

dikranmarsupial
January 22, 2014 9:02 am

Should add, it is better to perform a statistical test and not describe it completely accurately than not to perform the test at all! It is a common error even made by scientists on a regular basis.
Stephen Wilde says:
January 22, 2014 at 1:34 am
The Journal is about pattern recognition.
A pattern is being discussed in the paper.
=========================================
That is not what is meant by Pattern Recognition in science, to get a good idea of what it ought to be about, see the content of the journal “Pattern Recognition” http://www.journals.elsevier.com/pattern-recognition/
REPLY: So the Copernicus “Pattern Recognition in Physics” was nothing more than a ripoff of the Elsevier journal, right down to the orange cover?

– Anthony

dikranmarsupial
January 22, 2014 9:26 am

Anthony, I think that may be another spurious correlation! ;o)
It is a pity really as PRIP was a really good idea for a journal, there are lots of good applications for pattern recognition in phsyics and the sciences, if it was the sort of pattern recognition methods published in Patter Recognition (which is a very good journal).

January 22, 2014 9:38 am

@ Dikran Like you, I think the idea of such a journal was a good one, though the titles and covers are a bit too close for copyright comfort. Unfortunately, the people that ran the special edition bollixed the opportunity handed to them in the most self destructive way possible. It was a classic own-goal. As I pointed out previously Copernicus made equally bad errors. They should have spotted and fixed the problem before it exploded. – Anthony

dikranmarsupial
January 22, 2014 9:44 am

Yes, I agree with much of that. The cover is probably just a coincidence and I don’t think the name is to big a deal either, it is quite common to see a journal titled X and then another International Journal of X and then a Journal of Applied X etc. It is difficult to find a good name for a journal after a while. Journals that have the most obvious short name for the topic are ususally good ones as it is an indication that they got there first and have survived for a while!

dikranmarsupial
January 22, 2014 9:46 am

Also these days we mostly access the journals electronically and rarely see the cover, I’d forgotten PRs was even orange!

January 22, 2014 9:52 am

E.M.Smith says:
January 22, 2014 at 4:59 am
…..
Hi E.M
Thanks. The graph is based on the interaction of two magnetic fields. The Earth’s field apparently is not immune to tidal forces acting on the liquid core where it is generated. Andy Jackson, who is a world recognised authority on the mater, has publicly acknowledged (in a speech to some august science organisation) that they have found frequencies that are coincidental with certain E-M orbital numbers.

G. Karst
January 22, 2014 9:57 am

There is nothing gained, other than disappointment, concerning this affair. An extraordinary opportunity has been fumbled. Hindsight and finger pointing after the fact… brings no relief.
Personally, I always try to fail safe: If anything must be hidden, or secret… can that thing be good? The answer to that question, has kept me mostly out of trouble. GK

January 22, 2014 10:06 am

Anthony
Thats not the first time Rog has violated trust. Recall gavin’s letter and Lisbon.

January 22, 2014 10:16 am

Wilde
‘This paper does not purport to ‘prove’ anything.”
then it’s not even wrong.
Novels and poems dont purport to prove anything either.

J. Casey
January 22, 2014 10:21 am

It is interesting that the two cycles appear to have similar frequencies…but a simple relation is agreed to not fit due to phase shifts. Think of an electrical analog — You can have a sinusoidal voltage source driving current in a resistor, and the measured current would be in phase. You could parallel that resistor with a capacitor, and as you varied the value of the capacitor, you would phase shift the current w.r.t. the drive. Pretty simple differential equations. Clearly, if there is a correlation & causation by solar forcing, than a complete model has to look into what other effects might be varying the energy storage. PDO? Something else? This is an intriguing pattern, but there is a lot to figure out here.

January 22, 2014 10:34 am

vukcevic says:
January 22, 2014 at 9:52 am
Thanks. The graph is based on the interaction of two magnetic fields.
No, just poorly done curve-fitting.

January 22, 2014 10:58 am

Thanks Willis.

January 22, 2014 11:04 am

I am not interested in reading the paper, however one can see from just looking at the graph that there is great correlation and that makes it fascinating. Sure it could just be a coincidence (which I assume is what you are essentially saying it is), but that seems extremely unlikely. There is ‘probably’ some reason for the close correlation.
It’s a similar situation with Milankovitch cycles. There is a great correlation. So great that people assume there is ‘something there’, they just aren’t sure exactly how the small-in-theory perturbation of the cycles translates into a huge difference in climate.
I won’t dispute your criticism of paper or journal, but I would like to point out your consistent closed-mindedness. There is an obvious correlation. Instead of being curious about why that is, you just choose to dismiss it, never to be thought about again as it doesn’t fit within your existing mental model. That’s poor science. Be curious! Being a criticizing curmudgeon is easy … anyone can do that.

Matthew R Marler
January 22, 2014 11:15 am

Thank you. Another good presentation.
I would not automatically disparage a model fit with an R^2 = 0.15 and p = 0.08. Granted, it is not very strong evidence against a null hypothesis, so I wouldn’t believe the result either, but for a field in which nearly all relationships have small p values it is probably reportable (unless it has been selected out of a large number of results, in which case the observed significance level is really greater than the reported p value.) On the other hand, it is more probable than not that the reported association is closer to R^2 = 0.15 than R^2 = 0.
What’s curious, as you noted, is how quickly the curves changed from out of phase to in phase.

Greg Goodman
January 22, 2014 11:44 am

Willis: “it’s a joke. The same is true of not requiring the “out of sample” testing of the Salvador model,”
I’ve already pointed out that this claim is incorrect. You ignore that I point it out and repeat it.
There is much to criticise in this paper and the whole review question. Repeating inaccurate claims and not reading the papers before trying to dismiss them is not part of it.
… color me unimpressed.

January 22, 2014 11:52 am

I am not a statistician, but it seems like R-Squared is the wrong measure for determining significance of periodic signals. Imagine you have a signal
y(t) = a Cos(w t) + b Cos(w2 t)
And you have a theory about some phenomenon that affects this signal has an output
o(t) = a(t) Cos(w t)
Now if b>a you do an R-squared on this you are going to get a low R. Low significance. But clearly this downplays the accuracy of o(t) in ‘partly’ explaining y(t).
As an experiment I would challenge you to do an R-squared of Milankovitch cycles versus temperature. I’m pretty sure the result will be terrible. Yet you wouldn’t simple go on to dismiss Milankovitch as irrelevant and coincidental would you? No, because the correlation is too high over too long a period and suggests cause and effect.

Greg Goodman
January 22, 2014 11:57 am

Matt: What’s curious, as you noted, is how quickly the curves changed from out of phase to in phase.
Willis: Causal relationships don’t do any of those things.
They do _exactly_ that kind of thing when there is an interference of two or more relatively close periodicities. I explained that in detail above and as usual you ignore what does not suit you.
The paper goes into much detail looking at what other periods are present and highlights the 9.1 year periodicity which would combine to produce something like the effects you noted. I explained that in detail above and as usual you ignore what does not suit you.
whether R^2 of 2 (or whatever) is significant needs to be assessed in relation to the number of data points in question and the kind of data, something which I don’t recall you ever having calculated despite your insistence of people calculating R^2 and publishing it , you seem to have some unstated idea of what is a “good” R2 value.
Calculating the R2 value and publishing it without any criterion to judge whether it is significant does not tell us much except that you think you know more than you do know.

tommoriarty
January 22, 2014 12:03 pm

I suspect the offset near 1990 would match up much better if the sea level data were corrected for the effect of the Mt. Pinatubo eruption.

Greg Goodman
January 22, 2014 12:19 pm

tommoriarty says: “I suspect the offset near 1990 would match up much better if the sea level data were corrected for the effect of the Mt. Pinatubo eruption.”
I suspect we’d do a lot better if we _stopped_ trying to “correct” data . See last graph in this post:
http://climategrog.wordpress.com/2013/03/01/61/
.. and here:

Alex
January 22, 2014 12:25 pm

[i]Causal relationships don’t do any of those things. When you walk out into the sun, you get warm. It doesn’t fade away and get replaced by warming from the stars. Nor does it change phase, so that next time you walk out into the sun it makes you cold. Nor does one sometimes lag and sometimes lead the other—I don’t get warm a few seconds before walking outside in some cases, and a few seconds after walking outside in other cases.[/i]
Please quote the line in the paper where they say sunspots cause changes in sea level. Again, they aren’t saying there is a causal relationship. The sunspots and the sea level are both effects. It is perfectly plausible for sunspots and sea level to lag behind a common root cause at varying times. This is a terrible failure of logic.

Curious George
January 22, 2014 12:26 pm

There are no error bars in the graph of sea level change. The sea level itself is difficult to measure, and its changes doubly so. I would like to see the uncertainties in that graph.

January 22, 2014 12:32 pm

One option seldom addressed in climate science is the possibility of synchronization. The idea that over time a system can be perturbed into synchronous behavior even with very weak coupling. Many chaotic systems are ‘accepting’ of synchronizing with a periodic driver even if the coupling is very small. These would seem to be a fascinating area to look into and I think should be considered more frequently. Sun activity and climate seems a ripe area for synchronization to occur without the activity having to be at direct forcing levels.

January 22, 2014 12:43 pm

What’s the R^2 of the rotation of the Earth with temperature of Edmonton. Probably not great if you don’t also take into account very important periodicity of Earth’s tilt (seasons) and random noise of ‘weather’ on top of it 😉

tommoriarty
January 22, 2014 1:00 pm

Greg Goodman said “I suspect we’d do a lot better if we _stopped_ trying to “correct” data.”
When data can be properly corrected it should be corrected.
The first link you provided is about smoothing techniques, but I did not suggest smoothing the data. However there are many occasions when smoothing is appropriate and useful.
The second link you provided concerns a specific data set – totally different from the sea level data that this post considers.
I think there is fairly broad agreement among alarmists and realists that the Mount Pinatubo eruption in 1991 reduced the sea level rise rate for several years. So why not correct for it in this context?
Consider…
Significant decadal-scale impact of volcanic eruptions on sea level and ocean heat content (Church, et. al., Nature, 2005)
http://www.nature.com/nature/journal/v438/n7064/full/nature04237.html
“For the Mt Pinatubo eruption, we estimate a reduction in ocean heat content of about 3 1022 J and a global sea-level fall of about 5 mm…
To better understand the processes involved, we focus on the Mt Pinatubo eruption (June 1991) because of its stronger radiative forcing and the better observations available. The primary driver of the fall in sea-surface temperature, GMSL and GOHC in the PCM is the rapid reduction in net solar flux at the ocean surface (Fig. 3a) of up to 6 W m-2 in late 1991. By early 1994, the net solar flux had virtually recovered to pre-eruption values”
Tom

William Astley
January 22, 2014 1:30 pm

To understand what is observed it is necessary to understand the mechanisms. Mass balance and temperature changes do not explain the recent ocean level changes. The paleo record shows unexplained large changes in ocean level that cannot be explained. Something not melting ice sheet or change in temperature is causing the ocean level to change.
The explanation as to why the ocean level changes following the periodicity of the solar cycle with a change in lead and lag is the same reason the boost satellites get from the close earth orbit maneuver has changed with time. We know gravity cannot change. There is a missing variable. Working with the laws of physics what variable could affect satellite orbits and ocean level.
(Ocean level is now falling based on raw satellite data. The raw data is adjusted to create the rise in ocean level.)
Mass and volume contributions to twentieth-century
global sea level rise
The rate of twentieth-century global sea level rise and its causes are the subjects of intense controversy1–7. Most direct estimates from tide gauges give 1.5–2.0 mm/yr, whereas indirect estimates based on the two processes responsible for global sea level rise, namely mass and volume change, fall far below this range. Estimates of the volume increase due to ocean warming give a rate of about 0.5mmyr21 (ref. 8) and the rate due to mass increase, primarily from the melting of continental ice, is thought to be even smaller. Therefore, either the tide gauge estimates are too high, as has been suggested recently6, or one (or both) of the mass and volume estimates is too low.
ftp://falcon.grdl.noaa.gov/pub/bob/2004nature.pdf

Greg Goodman
January 22, 2014 1:31 pm

No, that article is not about “smoothing techniques”. It uses frequency filters to look at inter-annual and decadal scale variation in individual ocean basins and points out exactly the mistake Willis is trying make here in misreading the ‘phase crisis’ problem.
” See last graph in this post:” refers you to the part which is relevant to my comment about doing better if we stop “correcting” the data. It shows how Hadley processing is corrupting the lunar signal that is a strong component in many ocean basins and is the same one Scafetta and BEST (land) data show. It is also found in HadCRUT, presumably due to land portion since the “Had” part has removed , or more likely corrupted it into something else.
With many regarding HadSST as the “gold standard” of climate records, it hardly surprising natural cycle signals are not being detected.

rgbatduke
January 22, 2014 1:32 pm

All good clean fun. Of course, the ultimate proof of a hypothesis like this “There is a predictive correlation between sunspot numbers and the rate of SLR” is easily tested, since the graph above inexplicably ends at the year 2000. We have the additional advantage that in the subsequent 14 years, the sun has been at its lowest ebb in 100 years, and we have multiple sources for measuring SLR including the old tidal gauge network and the newer satellite-based measurements.
If the hypothesis is correct, then, we should have very low levels of SLR for the indefinite future, as predictions of sunspot levels are pretty low indeed.
But that raises many questions. One is “which sunspot numbers were used”, since Our Friend Lief suggests that sunspot numbers reported in the early 20th century do not match those reported in the late 20th century. This is an ongoing problem, I understand, but it is apparently an important issue if early numbers are all smaller by an (in)consistent scale factor as has been suggested. Something to clarify.
The bigger issue I have, however, comes from the SLR data. Say what? That’s not the SLR data. This is:
http://en.wikipedia.org/wiki/File:Trends_in_global_average_absolute_sea_level,_1870-2008_%28US_EPA%29.png
Note that both tide gauge data and satellite data (from two sources) are presented. Now, the data presented in the figure above is SUPPOSED to be the first derivative of this curve — its slope. And it’s not.
I count approximately thirty years in the actual record from 1900 to 2000 where SLR was negative. There are at most fifteen such years visible in the plot above. The clustering of negative years is also completely different — they are nearly uniformly distributed in the actual SLR data, and are somewhat antibunched, with a negative year more likely than not to be followed by a (possibly strongly) positive year. The bunching evident in the graph above is difficult for me, at least, to discern.
So I have to question: Not only which sunspot data, corrected or uncorrected, but where the hell did the SLR data come from? Because it certainly didn’t come from the graph I link above. In the graph above, only 4 decades in the entire century had years with negative SLR. In the actual data, there isn’t a single decade without multiple years of negative SLR.
If you plotted sunspot numbers against the actual coarse grained first derivative of SLR, I don’t think there would be any correlation at all.
rgb

Greg Goodman
January 22, 2014 1:41 pm

tommoriarty says: “Consider…
Significant decadal-scale impact of volcanic eruptions on sea level and ocean heat ”
To support this claimed “decadal-scale impact” you quote a paper saying flux was back to normal by 1994. That’s less than three years.
The big problem is false attribution. There are temporally coincident cooling trends that are already under way _before_ the eruptions of both Mt P and El Chichon. Alarmists confound the two and make exaggerated claims of “decadal-scale impact” of volcanoes that allow them to jack up the CO2 sensitivity. This worked until we had a long period with virtually not volcanism. Then we saw the warming vs CO2 connection broke down.

Manfred
January 22, 2014 1:43 pm

Willis Eschenbach says:
January 22, 2014 at 3:01 am
Paul Westhaver says:
January 22, 2014 at 2:05 am
Willis,
Notwithstanding your gripes about the overall paper and the journal review process, If someone placed the curves in front of me, I;d be very interested i knowing why they are related. They are related without a doubt. So why?
No, they are not “related without a doubt”. What part of an R^2 of 0.13 and no statistically significant correlation seems unclear?
——————————————————–
Sorry Willis,
an r value is not necessarily an appropriate measure here. Considering its limitations is easy:
Two straight lines have an r value of 1, but the information content of that result would be close to zero.
However, if you have 2 identical curves with multipole ups and downs and thus a high information content, and shift the phase of one curve into different directions for its first and last half, you may end up with an r value of 0, though everyone would “see” that they are “related” or in this example even coherent.
That is why Shaviv can say that some correlations between solar variables and temperature are the best between any two variables in climate science.
And because of curves like this
I think, the existence of a solar amplifier is likely (though the r-value is “only” r=0.45)

Mike Rossander
January 22, 2014 1:50 pm

Good evening, Willis. Would you be willing to publish your data from the digitized graph? I’m afraid my skills in that area do not match up to yours.
I ask because I am curious what the correlation would have looked like if one excluded the single out-of-phase period (which could perhaps be edge-effect in the data?) or what the correlation might be if one hypothesizes that the apparent lagging in cycles 17 and 19 were artifacts of measurement error. Such statistical tinkering after the fact can’t prove anything of course but it might lead to more interesting questions.

January 22, 2014 1:52 pm

Manfred,
Exactly and good example. Correlation tells us if a particular factor is important. R^2 tells us if we how well we have put all the factors together into a predictive model. We can’t look at R^2 of 0.13 and conclude that sunspots are uncorrelated. That’s wrong. We can only conclude that a predictive model using only sunspots is incomplete. We have to do a correlation to determine if it is ‘correlated’ and clearly it is.
Who you gonna believe Willis, or your lying eyes? 😉

Greg Goodman
January 22, 2014 1:56 pm

RGB, firstly you link to global average SL , that is not what the paper is using,
Secondly I recall a paper from Jevrajeva 2002 (?) that did a careful reconstruction just form tide gauges that had very clear 60 cycles. It may have been plotting 2nd diff IIRC. The official GMSL now includes GAIA [sic] adjustments and inv. barometer and you don’t to look at non-barmon data any more.
Also there were HUGE “corrections” to Jason data. In 2011 the GMSL record roughly followed UAH TLT, then something bad happened to it.
http://climategrog.wordpress.com/?attachment_id=524
Yet another climate parameter gets taken to the cleaners. I regard what Colorado our now presenting as having no analytic value. It has gone through political correction training.

Greg Goodman
January 22, 2014 2:03 pm

Jevrejeva, S., J. C. Moore, A. Grinsted, and P. L. Woodworth (2008),
Geophys. Res. Lett., 35, L08715, doi:10.1029/2008GL033611.
Can’t find the graph but paper is worth a look.

richardscourtney
January 22, 2014 2:03 pm

Ian Schumacher:
At January 22, 2014 at 1:52 pm you ask

Who you gonna believe Willis, or your lying eyes? 😉

I don’t “believe” either. I accept the indication of the r^2 statistic, and that indication is that the paper is junk.
Richard

tommoriarty
January 22, 2014 2:04 pm

Greg Goodman said “No, that article is not about “smoothing techniques”. It uses frequency filters to look at inter-annual and decadal scale variation in individual ocean basins”
Yes, you are right. I mistakenly went to the home page of “Climategrog” instead of the sub-page that you specified. Sorry about that.
Greg Goodman also said “See last graph in this post:” refers you to the part which is relevant to my comment about doing better if we stop “correcting” the data. It shows how Hadley processing is corrupting the lunar signal that is a strong component in many ocean basins and is the same one Scafetta and BEST (land) data show. It is also found in HadCRUT, presumably due to land portion since the “Had” part has removed , or more likely corrupted it into something else. With many regarding HadSST as the “gold standard” of climate records, it hardly surprising natural cycle signals are not being detected”
This is only marginally relavent to the Holgate data.
It seems that you are implying a maxim that there are no appropriate corrections when you say “I suspect we’d do a lot better if we _stopped_ trying to “correct” data.” Did you mean the no data should ever be “corrected” (whatever that correction means) or are you referring to a specific reason that the Holgate data should not be corrected for the effect of Pinatubo in the early ’90s?

J Martin
January 22, 2014 2:19 pm

Willis, if you’d been asked to be one of the reviewers for the Copernicus papers, would you have said yes ?

January 22, 2014 2:25 pm

lsvalgaard says:
January 22, 2014 at 10:34 am

vukcevic:

The graph is based on the interaction of two magnetic fields.

No, just poorly done curve-fitting.

Dr. S.
Curve fitting is my speciality, it’s great fun.
Here is something you might like to amuse yourself when you got some spare time:
Any reason why (polarised) Aurora spectrum would have stronger correlation with the Earth’s core field than with the solar magnetic cycle that drives it?
Polarised?
Yes, following the NASA’s guidance on the mater: Solar coronal mass ejections CMEs in the even-numbered solar cycles tend to hit Earth with a leading edge that is magnetized north. Such CMEs open a breach and load the magnetosphere with plasma starting a geomagnetic storm.
Polarisation:
even numbered cycles Auroras = + (positive)
odd numbered cycles Auroras = – (negative)
Notice tiny 5 year ENSO signal in the Aurora’s spectrum, if Mr. Tisdale might like to know about, the explanation is waiting.
.

Greg Goodman
January 22, 2014 2:34 pm

Mike: “I ask because I am curious what the correlation would have looked like if one excluded the single out-of-phase period ”
I don’t think it’s necessary to question the data or think it’s just one odd-ball cycle. There are other factors at play in climate (as is detailed in the paper !!) .
It maybe possible to see something from a cross-correlation of the data shown but just annual points is not going to be very informative.
The point is that this kind of slipping in and out of phase is _exactly_ what you get when you have two close frequencies ( like 9.1 and 11 , for ex.) As Ian says , all the low correlation value tells is that saying SSN is the primary and sole cause of MSL or temp change is not correct.

January 22, 2014 2:40 pm

richardscourtney,
R^2 tests the predictive value of a model. Sunspots are not a model. They are ‘maybe’ a single factor in a model containing many factors. Correlation tells us if something is a likely factor and how important a factor it probably is. Sunspots are highly correlated and are therefore probably an important factor, but not the complete story for predictive purposes.
Do you disagree? Here are some examples.
Smoking is correlated with lung cancer. Smoking is not predictive though. Using smoking as the only factor for lung cancer would give us a poor R^2 value. Smoking is highly correlated with lung cancer though. This tells us that while not the only factor, it’s an important one.
The temperature in Edmonton is correlated with rotation of the Earth (whether the Sun is shining or not), however it rotation is not the only factor. Tilt is also important. If we build a model of temperatures that uses ‘only’ rotation, we will have a poor R^2 value. It doesn’t do a very good job of predicting absolute temperatures in Edmonton as daytime/nighttime temperatures only vary a small amount compared to seasonal temperature changes. However it has really high correlation. That tells us that Earth’s rotation is probably an important factor, just not the only factor and that we need to take other things into account.
I’m not a ‘sunspot guy’ and have no position on it, but when I see sunspot numbers correlation highly with something (correlation, not R^2) then I acknowledge that there is almost definitely something there.

Greg Goodman
January 22, 2014 2:43 pm

Tom: “Did you mean the no data should ever be “corrected” (whatever that correction means) or are you referring to a specific reason that the Holgate data should not be corrected for the effect of Pinatubo in the early ’90s?”
I mean there is far too much speculative “bais correction” going on. If you make an uncertain “correction” to the data you ADD to the uncertainty, you don’t reduce it. In the case of volanism we do not know accurately how much radiative change is produced and more importantly we don’t the climate response to that change.
Much of the time it would be more appropriate to recognise that there are uncertainties and live with them. The current state of play is that we spend most of the effort analysing the result of corrections as much as the data.
Most these data sets seem to be controlled by groups with a “message” who are less than objective about how they alter the data.
What currently gets called “global mean sea level” is some phantom hovering slightly above the waves and getting more so each year.
It’s getting damn hard to find any true climate data to analyse.

TonyG
January 22, 2014 2:45 pm

All these people saying the correlation is visibly obvious – the first thing that stood out to me when looking at the graphs was the LACK of correlation. In some spots, the peaks coincide, in others they’re opposite. To me, the lack of correlation is quite clear. Are we looking at different graphs?

DirkH
January 22, 2014 2:52 pm

Willis is a little too smug for my liking. If it helps his ego.
What does WUWT fear? Turned into a mob.

January 22, 2014 2:55 pm

Mike Rossander says:
January 22, 2014 at 1:50 pm
………….
I don’t know much about the SL measurements and have a view that are most likely within the error margin and hence taken on annual bases irrelevant.
However cycle 17 is a particularly important one, it shows what happens to the Earth’s magnetic field when the solar intra-cycle oscillations are close to the Earth’s orbital period.
http://www.vukcevic.talktalk.net/SC17.htm

January 22, 2014 3:02 pm

TonyG says:
January 22, 2014 at 2:45 pm
All these people saying the correlation is visibly obvious – the first thing that stood out to me when looking at the graphs was the LACK of correlation. In some spots, the peaks coincide, in others they’re opposite. To me, the lack of correlation is quite clear. Are we looking at different graphs?
######################
ah ya,
worse than that they used 9 tidal guages? which 9? what happens of you pick a different 9 than the original author Holgate.
worse than that the author might have stole the idea, inlcuding the bits about GCRs
http://climateaudit.org/2007/02/11/holgate-on-sea-level

richardscourtney
January 22, 2014 3:26 pm

Ian Schumacher:
At January 22, 2014 at 2:40 pm you ask me

R^2 tests the predictive value of a model. Sunspots are not a model. They are ‘maybe’ a single factor in a model containing many factors. Correlation tells us if something is a likely factor and how important a factor it probably is. Sunspots are highly correlated and are therefore probably an important factor, but not the complete story for predictive purposes.
Do you disagree?

I strongly disagree.
The coefficient of determination (r^2) does NOT test “the predictive value of a model” except within the limits of a model which is correlation between two variables.
When two data sets are correlated then the r^2 indicates the proportion of the variance of one variable that is predictable from the other variable. Hence, it is a measure of the certainty of a prediction of one variable from the other as indicated by their correlation.
For example, if r^2 is 0.850, then 85% of the total variation in y can be explained by the linear relationship between x and y (as described by the regression equation). The other 15% of the total variation in y remains unexplained.
Putting that in plain language, if the r^2 is low then confidence that there is a useful correlation is low.
In his article above, Willis says

And what did I find? Well, the R2 between sunspots and sea level is a mere 0.13, very little relationship.

In other words, almost all the variation between the two parameters is NOT explained by a correlation between them.
Of course, that does not disprove a possibility that they are not each related to a third parameter, but the paper does not suggest any such third parameter. All one can say is that the correlation between the two considered parameters is so poor that the correlation is NOT a model which enables one of the two parameters to be usefully predicted from the other.
Richard

Manfred
January 22, 2014 3:29 pm

@Steven Mosher
January 22, 2014 at 3:02 pm
This was Steve McIntyres comment:
“…Doesn’t it look like there’s something like an 11-year cycle in this? Remind you of anything? I know that it’s a bit of a mug’s game trying to identify solar cycles, but here’s a plot of sun spot numbers in the same period. The maxima and minima of the solar cycles seem to match the fluctuations in sea level rise rather uncannily. While the resemblance is impressionistic (I don’t have a digital version of Holgate’s series), offhand, I can’t think of any two climate series with better decadal matching…”
http://climateaudit.org/2007/02/11/holgate-on-sea-level/

January 22, 2014 3:43 pm

It’s late, past my bed time, I’ve forgotten but I did a graph on sea level ‘correlation’ while ago, so here it is:
http://www.vukcevic.talktalk.net/SeaLevel.htm

January 22, 2014 3:54 pm

yes Manfred, trust nobodies eye’s. That’s why the experts on the thread call for the data to do the calculation. Note too that nobody dug down to check the data.
Now, If I selected 9 stations out of 177 and proved to you that UHI wasnt real.. wouldnt the FIRST thing youd do is ask for the data?
If I told mcintyre that 9 tree rings out of 177 gave me a flat MWP.. what do you think he would do.
Its simple. Before you let your eye’s decieve you. Get the data. Check the data. Then get the code. Check the code. Otherwise even the best eyes are lead astray

J Calvert NUK
January 22, 2014 4:09 pm

To DirkH “What does WUWT fear? Turned into a mob.” And the earlier commenter who made the analogy to circling wagons and shooting arrows outwards not inwards. For me this should not be a matter of taking one ‘side’ or the other – or of finding evidence to support the paradigms of one’s own ‘side’. Sceptism is for individuals – not ‘sides’ or teams (or any grouping that could refer to themselves as ‘we’ or ‘us’). So don’t expect me to join-up with some great ’cause’.
Above all, I want to see good science – of the Feynmann “bending-over backwards to disprove one’s own theory” type. Not the grasping at any old straw to support one’s own theory type of junk science that has become all too prevalent (in lots of different subjects). The sort of junk science that is trotted-out in support of the CAGW agenda is a pet-hate of mine – which is why I lurk at blogs like this one.
Unfortunately, the papers Willis and others are criticising fall far short of Feynmann’s ideals – to put in mildly – Willis has been too kind in my opinion.

Greg Goodman
January 22, 2014 4:33 pm

For those interested: a really quick power spectrum from cross-correl, Jevrejeva d/dt(MSL) and daily sunspot area. No bells whistles or other decoration. 😉
http://i40.tinypic.com/206j7th.png
By far the largest peak at 5.405 years. Ubiquitous 9.03 present but small
20.27 will be rather uncertain
It’s only annual resolution CC but peak correlation of .335 at one year lag. Guessing by form, actual peak near 0.5 years.
5.4= 10.8 / 2 looks like the main solar linkage.

Greg Goodman
January 22, 2014 4:41 pm

Interestingly the is damn near the period I extracted from Arctic sea ice about a year ago that got Tamino in such a panic he spent a week trying to diss my efforts before getting is a sulk and slamming the door shut.
http://climategrog.wordpress.com/2013/03/11/open-mind-or-cowardly-bigot/
It is also quite close to the 5.8 semi cosine that I found as a repetitive pattern this year when evaluating decadal variation in Arctic Sea Ice.

Manfred
January 22, 2014 4:44 pm

Mosher,
eyes may deceive you, but also an r-value or similarly Persons’ correlation coefficient if you trust them without considering their limitations.
These are just widely used measures of correlation, but not the only ones.
A single phase shift in one data set can turn an r value from one to zero. And there may have been phase shifts in above data set. Or if you have 2 identical curves of shorts pulses, a small jitter may bring the r value from one to zero. And there may have been dating issues working that way.
For such curves with multiple ups and downs (like above or Bond et al 2001 etc), I think a better measure of correlation would be how often ups and downs in one curve occur in the other as well, plus compare similarity of the shapes of each 11 year cycle, what essantially is trying to quantifiy what your eyes tell you.

January 22, 2014 5:17 pm

I did a very interesting correlation in regards to PRP,
Why was Roger chosen as an “editor” and “reviewer” [with no relevant qualifications] and what do all the authors of the “Special Edition” have in common? It’s quite special…
They are all favorite discussion topics at Tallbloke’s Talkshop (ordered by word frequency);
1. Wilson (2,380)
2. Scafetta (1,180)
3.
4. Jelbring (812)
5. Morner (222)
6. Charvatova (213)
7. Solheim (59)

Greg Goodman
January 22, 2014 5:33 pm

Oops. Too late at night to be doing this sort of crap. Slip up in collating data missed out several years and screwed up the CC calcs.
Here’s a corrected SPD. Peak at 10.4 as expected max max CC at 0.68 years is relatively low at about 0.18
Estimate signif at 0.138 with 127 pts but way past bedtime, so take with a pinch.
http://i44.tinypic.com/sffddu.png

Greg Goodman
January 22, 2014 5:40 pm

Hey Pops, you should have done a control for significance against red noise. 😉
Even I score 135 and I’m banned !!
You do have a point though, the review process does look rather incestuous.

Greg Goodman
January 22, 2014 6:03 pm

Here’s the cross_correlation fn before spectral analysis. 79 year period corresponds to the phase crisis in 1920s.
http://tinypic.com/view.php?pic=2hojthe&s=5#.UuB3_aJw1uA

Greg Goodman
January 22, 2014 6:12 pm

Now quick calculation of what would mix with 10.45 to cause phase lag of one year in 79 …..
9.23 years.
So that’s why there’s a low corr coeff. Like I’ve been saying for some time it seems you can’t refute the presence of a periodicity just by trivial regression analysis.
This was a quick hack so that 9.2 could be N Scafetta’s 9.1 , my 9.05 or maybe 186/2 = 9.3
In view of other evidence I suspect it’s 9.05

January 22, 2014 7:35 pm

Willis,
I’m a fan. Your scientific and personal essays have been educational and lightened my days.
Unfortunately, your latest work was the proverbial straw on a load from many, many essays from many sources. But . . .
. . . I must, at long last, say something about an endemic statistical solecism. (I apologize for wasting band width if someone beat me to it — I didn’t read all the comments.)
Hypothesis testing is a concept most undergraduates and a surprising fraction of Ph.D.s in fields that rely on non-experimental data find . . . perplexing. I have no idea how many times, in thirty some years, I said something like the following to one, a few, or a room full of (usually) undergraduate students (pedantry alert) :
The null hypothesis that a human’s body weight and donut consumption (say) are unrelated (null: r = 0, alternate: r 0) might be tested by comparing the:
a) calculated and critical values of a correlation coefficient, r, given the sample size and desired confidence interval (0.95 by convention in fields that depend on non-experimental data) or
b) maximum acceptable risk to a decision maker of rejecting a true null hypothesis (alpha, chosen carefully (?) in advance, 0.05 by convention in fields . . . . The sum of the confidence interval and alpha is 1.0. Always.) and the risk given the sample (the glorious p-value).
The null hypothesis can be rejected if the:
a) calculated correlation coefficient (r-hat, a caret over lower case r for estimated) is not smaller than the correctly chosen critical value (sometimes r*) or
b) p (a calculated value) is not larger than alpha (the maximum acceptable risk of rejecting a true null hypothesis, a carefully (?) chosen value).
Both methods yield the same result. Always.
in regression, of which Fourier analysis is an example, (capital) R-square reports the ratio of explained to total variation in an estimated equation, ignoring the effect (for example) of serial correlation in time series data and the inclusion of explanatory (independent) variables with obscure theoretical connections to the response (dependent) variable, both of which inflate r-square.
Iff (if and only if) there is exactly one explanatory variable, r = sqrt(R-square).
(pedantry off)
You report p = 0.08, so, if the author tested an hypothesis whose alpha was the conventional 0.05 with a test statistic (not R-square), the author, you, and your readers are unable to reject the hypothesis that there is no relationship between the dependent and set of independent variable.
I know of no hypothesis test based on R-square. One might test the null hypothesis that none of the estimated coefficients are “significant” with the F-test and related p-value, but reading a sentence that combines an R-square and a p-value makes my head . . . never mind.
Thank you for the time, energy, diligence, . . . your work shows. I look forward to reading many more reports of your always interesting, always cogent, analyses and anything else you may post. All the best!

Michael D Smith
January 22, 2014 7:39 pm

Willis, will you please post the digitized data you took? Thanks

Matthew R Marler
January 22, 2014 8:43 pm

The other reason it’s a tragedy is that they were offered an unparalleled opportunity, the control of special issue of a reputable journal. I would give much to have the chance that they had. And they simply threw that away with nepotistic reviewing, inept editorship, wildly overblown claims, and a wholesale lack of science.
I think you have overreacted.
What is the alternative hypothesis to H0: R^2 = 0? How about H1: R^2 >= 0.10? For something that is influenced by many different agents, R^2 = 0.10 is too large to ignore, even though it is sometimes (in some behavioral research) customarily called “small”. ( Most of the risk factors for high blood pressure, such as obstructive sleep apnea, have R^2 <= 0.10, but they are important in a large population.) Then the probability of obtaining R^2 = 0.15 is at least 0.32, making the outcome that was reported 4 times as likely given H1 than H0. Say for the sake of argument that you were willing to place prior odds on H1 at 1:4, 0.2 probability that H1 is true, 0.8 probability that H0 is true? Then by Bayes' rule the posterior odds would be 1:1, or H0 and H1 equally likely. Even though p = 0.08 is not strong evidence, it does support H1 more than H0.
(That's a sketch. A full development would need some more detail, including explicit examination of conditional independence.)
Null hypothesis testing with conventional levels of statistical significance is not the only important method of statistical inference.
The full paper is worth reading. It's too bad that the authors and editors have not made the exact data and code used available publicly, but that does not make the paper a waste of reading time. If you an I ran the scientific publishing industry then data and code would have to be presented as a condition of review, not merely of publication, but we don’t, and in the mean time we have to work with people who don’t agree with us; and that includes readers on this forum, most likely.

January 22, 2014 8:43 pm

vukcevic says:
January 22, 2014 at 2:25 pm
Curve fitting is my speciality, it’s great fun.
But it ain’t science, and should not be peddled as such.

Matthew R Marler
January 22, 2014 8:45 pm

Ah, nuts. The italics are supposed to end before “I think you have overreacted.”
and the word “review” is to be italicized in “as a condition of review“.
[Fixed. -w.]

Paul Westhaver
January 22, 2014 8:56 pm

Willis,
I don’t believe that your categorical dismissal of a pattern is appropriate.
I see a pattern. It is there. My mathematics skills limit me in that language but my pattern recognition machinery is very convincing. I suppose I could work on it but it has been a while since I did such comparisons.
I suggest that the data sets are better related than scatter plots.
So I say why is my pattern recognition machinery telling me that there is a pattern? I see groupings within the set. so dealing with the whole set without accommodation localized features seems like a half-hearted attempt at being a critic.
I suggest “seemingly unrelated regression analysis” as a subject matter.
Keep an open mind.

Paul Westhaver
January 22, 2014 9:37 pm

JCasey
I see similar frequencies as well with locally varying phase and amplitude.
A periodic function and many of its harmonics will also fail the R and P tests.
I would normalize the data sets against a common aggregated periodic function. Then do a test. I bet that will yield a close correlation. I wish I had time to do it. Maybe I will later.

Paul Westhaver
January 22, 2014 9:45 pm

One last comment.
The underlying relationship will be revealed by looking at the outlier sets. That is where all the information resides. I as an act of discipline, assume that the two sets would otherwise be superimposed. (This is where I differ from Willis) Examining the regions of greatest error, to me, are the areas of interest.

tallbloke
January 22, 2014 11:33 pm

Hey Willis,
getting anywhere with the reproduction of the 350 yr SSN record with the randomly chosen orbital periods I gave you yet? Yo said you could do it easily with all the extra ‘free parameters’. Y’know, wiggling trunks and all that? Don’t forget R.J. Salvador’s model also reproduces the Maunder minimum well, Svalgaards previously stated acid test.
Talk is cheap. A little less condescension and a little more action baby.
– Anthony

tallbloke
January 22, 2014 11:38 pm

REPLY: So because you hate what Willis has to say, along with the maths to back it up (you’ve presented none), it was OK to violate my trust? Interesting. – Anthony
I violated your trust by alerting the PhD’s to the ad hom and bad math attack about to be launched on them by Willis, and you sacked me from the voluntary 30 hour a week moderation job I was doing for you. It was a merciful release from a situation supporting behaviour I could no longer stomach.
Willis’ maths left a lot to be desired on that occasion Anthony, and you refused the PhD’s the right of reply here where the attack on them was posted. So they asked me to post it at the talkshop where they could get a fair crack of the whip.
Can you or Willis find anything wrong with their maths? I doubt it.
You were also quite thankful when I went to bat for you in your times of need.
It is unfortunate, the situations you have created for yourself through your own actions. which further isolate you. Best of luck in the future. – Anthony

tallbloke
January 23, 2014 1:26 am

Anthony says:
I said then after they way they emotionally and unprofessionally exploded over something not yet published that you could have them and their reply.

Willis had already infuriated them with the avalanche of ad hom attacks and misrepresentations of their work in his previous posts. They demonstrated to you that his maths was bunk and that posting this next attack was ill advised. But you posted it anyway and denied them a right of reply.
You were a lot more contrite then when I explained why I could no longer trust you, and you understood why and agreed with my decision to terminate your access. I find it telling that you would use my trust that way. It is a sad commentary on your ethics.
I knew we’d no longer be able to work together after you condoned WIllis’ behaviour, and that was a matter for regret. I was certainly sorry we’d arrived at the parting of the ways. I have no regrets over my ethical choice to put correct maths and decency in debate before loyalty to a blog which permitted the personal and scientific abuses Willis indulged in, and your condoning them.
You were also quite thankful when I went to bat for you in your times of need.
I was. But that innings was before the events we parted company over. I maintained decorum about those events so far as was possible since then. But the unwarranted attack you and Willis are now making on the integrity of the members of our research group is the last straw for me. We’ll keep going with our productive research though.
Best of luck in the future. – Anthony
I believe in making my own luck, but thanks for the sentiment, and the same to you.
Rog

January 23, 2014 1:58 am

There are two fundamental problems here.
1) Just how flexible the concept of ethics has become, and
2) Whether or not integrity has anything at all to do with the concept of ethics anymore.
The solution(s) might very well lie closer to the end of the next ice age. Perhaps more propitiously, the one after that. Assuming we actually manage to get to either one, or both………
“An examination of the fossil record indicates that the key junctures in hominin evolution reported nowadays at 2.6, 1.8 and 1 Ma coincide with 400 kyr eccentricity maxima, which suggests that periods with enhanced speciation and extinction events coincided with periods of maximum climate variability on high moisture levels.”
state Trauth et al in “Trends, rhythms and events in Plio-Pleistocene African climate”, Quaternary Science Reviews 28 (2009) 399–411 http://www.manfredmudelsee.com/publ/pdf/Trends-rhythms-and-events-in-Plio-Pleistocene-African-climate.pdf
We are at an eccentricity minima right now. It will most likely be 200kyrs (two more post-MPT100kyr glacial/interglacial cycles) before we are at the next eccentricity maxima, e.g our next potential hardware braincase-capacity upgrade.
Yet, to this very day, try as we might, chimps STILL do not post their data or their code……go figure
Enjoyed this Willis, Anthony, et al etc. Stay thirsty my friends, and remember, it’s only as bad as it hits your wallet, ye old universal gaia language.
Strange doth it seem, methinks, how much scrutiny be given the trees at the expense of the forest. Especially given the half-precessional-cycle+ age of the Holocene.

January 23, 2014 2:24 am

Apologies to all. The last sentence of my last post was actually a partial, still being edited, response to a comment on a sports blog (another tab that was up). I really do not know how it ended up at the end of the response intended here. I did not paste anything before clicking “Post Comment” here. Yet here is the last sentence from a longer comment on some other blog on something else altogether. How a sentence not actually complete from another tab got appended here I really do not understand.
Again, my apologies.
Obviously a reboot, at a minimum, is in order……followed by some serious system scanning for bots and malware.
[Fixed. -w.]

Chris Wright
January 23, 2014 2:39 am

Willis Eschenbach says:
January 22, 2014 at 9:36 am
“Dear God, would all of you folks making this or a similar claim please do the freakin’ math! ….”
Willis, did you read the rest of my post? The next sentence is:
” Of course, the eye can be easily fooled.”
I simply observed that there does appear to be a striking correlation, but nowhere did I state that the correlation is real or proven. That’s why I suggested some kind of Monte Carlo analysis to give an indication as to how frequently a similar correlation could be produced by chance.
I made some reasonable criticism and suggested more analysis is needed. Isn’t this what science is supposed to be about?
Best regards,
Chris

January 23, 2014 2:43 am

tallbloke says:
January 22, 2014 at 11:38 pm

ROFLMAO! What did you actually prevent? It took N&Z two weeks to write a rebuttal anyway. Was a critique by Willis (which he does all the time) worth abusing your moderating privileges? Most authors respond in the comments and a debate ensues. I have never seen such a hysterical reaction before …wait, maybe there is a trend here.
I had no idea anyone took the Nikolov & Zeller stuff so seriously. I initially looked to see if it was published (still not) and moved on, or was that being saved for the second “special” edition of PRP? Fits my PRP special edition correlation to the Talkshop theory ,
Nikolov (2,200)
Who seriously reads these comments by the author and does not step back for a minute,

kzeller says:
January 25, 2012 at 9:51 am
[Various Math] = A SIMPLER MIRACLE […] You folks just don’t get it do you, you’re not seeing the forest for the trees: Willis’ rendition of our MIRACLE is also a MIRACLE!!!!!!! What is the Miracle you don’t see? […] Why is this a miracle? […] We are handing WUWT ‘THE NAIL’ to the AGW coffin and you guys have forgotten about the coffin and are fixated on the details of the nail!”

It reads like WUWT is being punked.

richardscourtney
January 23, 2014 2:49 am

Paul Westhaver:
To ensure that I am not addressing it out of context, I am copying all your post addressed to Willis which is at January 22, 2014 at 8:56 pm.

I don’t believe that your categorical dismissal of a pattern is appropriate.
I see a pattern. It is there. My mathematics skills limit me in that language but my pattern recognition machinery is very convincing. I suppose I could work on it but it has been a while since I did such comparisons.
I suggest that the data sets are better related than scatter plots.
So I say why is my pattern recognition machinery telling me that there is a pattern? I see groupings within the set. so dealing with the whole set without accommodation localized features seems like a half-hearted attempt at being a critic.
I suggest “seemingly unrelated regression analysis” as a subject matter.
Keep an open mind.

One needs to keep an open mind, but not so open that your brains fall out.
The assessed paper suggests

A stronger effect related to solar cycles is seen in Fig. 2, where the yearly averaged sunspot numbers are plotted together with the yearly change in coastal sea level (Holgate, 2007).

So, it is claimed there is an effect of “yearly change in coastal sea level” which is RELATED to “solar cycles“ as indicated by “sunspot numbers”.
Willis is assessing the claimed effect.
There are two possibilities which are
(a) The “sunspot numbers” and the “yearly change in coastal sea level” each varies with similar periodicity BUT their variations ARE NOT RELATED.
Or
(b) The “sunspot numbers” and the “yearly change in coastal sea level” each varies with similar periodicity BECAUSE their variations ARE RELATED.
Willis has provided cogent evidence of (a); i.e.
(a) The “sunspot numbers” and the “yearly change in coastal sea level” each varies with similar periodicity BUT their variations ARE NOT RELATED.
His evidence includes
Lack of coherence indicated by varying synchronicity over the assessed period.
Lack of correlation indicated by low R^2 and p values.
Lack of causal mechanism to induce a relationship.
Possible cherry picking of sea level data.
and Leif Svaalgard adds possible cherry picking of sunspot data.
Simply,
1.
the paper provides no evidence for existence of the relationship claimed by the paper,
2.
analysis of the data used in the paper indicates that the relationship claimed by the paper does not exist
3.
and the paper uses dubious data for each of the parameters which the paper claims are related.
However, you say you “see a pattern”. Yes, you do. The “pattern” is that the “sunspot numbers” and the “yearly change in coastal sea level” each varies with similar periodicity. But so what? Nobody disputes that those two parameters varies.
Willis has demonstrated that the variations of those two parameters are NOT related although the paper claims they are related.

I hope this has clarified the matter.
Richard

January 23, 2014 3:40 am

My comment got censored at the Talkshop,
“Good thing you alerted them in time, otherwise we would of had to wait longer than two weeks for the rebuttal – an eternity when dealing with Blog science. Thanks to your swift action, crisis averted!”
I don’t know why?

January 23, 2014 4:50 am

By Team PRP defending the indefensible they are creating a skeptic turkey shoot. They are full of bullet holes and bleeding out, while we have a band of zealots going around telling everyone they have not been shot. So instead of admitting they should not have stepped in front of a loaded gun, they keep telling us it is just a flesh wound!

January 23, 2014 5:22 am

lsvalgaard says:
January 22, 2014 at 8:43 pm
But it ain’t science, and should not be peddled as such.
Oh yeah, or is it just because you happen to find implications somewhat distressing to the ‘sun is impotent’ idea.
This specific curve fitting is based on two well known sets of data:
– Sunspot count from the world’s standard (SIDC)
– Geomagnetic data from the four world’s top scientists in the field (Jackson, Bloxham, Gire and LeMouel).
Both sets of data is used by NASA, NOAA and Institut de Physique du Globe de Paris among others.
I am sure you actualy delighted by this finding that climate and solar magnetic cycle have strong and direct relationship, but as a true scientist choose to remain sceptical until my results are verified by the academic establishments.
Italian professor Giovanni Gregori announced: “I am presently smoothing an 8 volume set (8,000 pages, completed) on the electromagnetic coupling between solar wind and Earth. It has been a hard 10-year job.”

Paul Westhaver
January 23, 2014 6:16 am

Richard Courtney,
You’ve made my point exactly. Thank-you,
Willis’ analysis ignores normalization of the OBVIOUS periodic behavior.
What I asserted, as did JCasey etc, is that both sea level rise and sunspot number share a periodic influence. Not that one drives the other. Rather that they are both influenced by something shared. Gosh knows what that is.
The data shows that and despite Willis’ narrow application of statistics on the broad set the pattern remains.
It is contingent on us to discover what the connection is.
I find it remarkable that something so obviously a pattern has to be so viciously dismissed with such lame application of stat analysis. Seems to me there is a politic at work here that is outside my experience.
So be it.
There is a pattern, I call it like I see it. The weakness then is not in my eyes but in Willis’ inability to extract the periodic behavior and THEN apply the regression analysis. Maybe he can’t. I can’t at the moment. But it is there.
I suggest a good frequency analysis be done. My eye says there is a fundamental, and maybe a low F modulation and a linear superposition. That is what I would filter out, if I had the time.

Paul Westhaver
January 23, 2014 6:29 am

George in Pennsylvania,
Do you think you are up to a Fourier type analysis? Seems to me the data set to a bit small for that but I bet you’ll get something. As for the donuts and my mass, something tells me there is also a connection.

January 23, 2014 6:39 am

To Willis:
http://wattsupwiththat.com/2014/01/21/sunspots-and-sea-level/#comment-1545217
There Willis says: “However, I just did a quick search of both the first WUWT post and the Shaviv paper that you reference … neither one of them contains the graph under discussion. But its not just that … neither one of them even mentions sunspots once.”
Willis further proved the case that he does not know how to read a scientific work, he simply jumps around and he is quite confused on basic scientific concepts.
This is the figure of Solheim that Willis reports in his post:
http://wattsupwiththat.files.wordpress.com/2014/01/sea-level-change-and-sunspots.jpg
Here sea level changes and sunspot number oscillations are compared.
Here is the figure of Shaviv reported in WUTW post that I linked above:
http://www.sciencebits.com/files/pictures/research/calorimeter/calorimeter2.gif
Here sea level changes and a solar irradiance reconstruction, which Willis does not know is made mostly with the sunspot number record and presents the same cycles, are compared.
Here is the figure of Archibald:
http://wattsupwiththat.files.wordpress.com/2009/04/sea-level-rate-of-change-and-solar-cycles-510.jpg?w=640
where sea level changes and solar cycles are compared.
Those who understand a minimum of these things realize that the three figures are exactly the same thing because the issue was to compare sea level changes and solar cycles. If the solar cycles are expressed in sunspot number or in total solar irradiance is the same, evidently. But Willis did not get it, and Anthony doesn’t it either!

January 23, 2014 8:11 am

vukcevic says:
January 23, 2014 at 5:22 am
This specific curve fitting is based on two well known sets of data
And that is where the pseudo-science comes in. You have not described in detail how the two sets of data are combined and by what process they should be combined.

January 23, 2014 9:04 am

lsvalgaard says:
January 23, 2014 at 8:11 am
vukcevic says:
January 23, 2014 at 5:22 am
This specific curve fitting is based on two well known sets of data
And that is where the pseudo-science comes in. You have not described in detail how the two sets of data are combined and by what process they should be combined.
……………….
Aaahh, memory is sometimes like a hazy sunset…
See email Dec 10 2012
from vukcevic to leif@….
Alternatively, you can derive same waveform directly from your aurora data, since aurora contains both solar and Earth’s magnetic oscillations.

January 23, 2014 9:13 am

vukcevic says:
January 23, 2014 at 9:04 am
Aaahh, memory is sometimes like a hazy sunset…
See email Dec 10 2012 from vukcevic to leif@….

That is how I can state it is pseudo-science
Alternatively, you can derive same waveform directly from your aurora data, since aurora contains both solar and Earth’s magnetic oscillations.
No, they don’t. Only solar.

January 23, 2014 9:20 am

Dr.S.- No, they don’t.
Vuk – Oh yes they do, see my comment with the ‘OldLadysHelo’ link

January 23, 2014 9:31 am

vukcevic says:
January 23, 2014 at 9:20 am
Vuk – Oh yes they do, see my comment with the ‘OldLadysHelo’ link
already here you disqualify yourself from serious consideration.
You can take that from this world-famous expert on solar-terrestrial physics and aurorae.

richardscourtney
January 23, 2014 10:06 am

Paul Westhaver:
Your post at January 23, 2014 at 6:16 am
http://wattsupwiththat.com/2014/01/21/sunspots-and-sea-level/#comment-1546284
either misunderstands or willfully misrepresents my post at January 23, 2014 at 2:49 am
http://wattsupwiththat.com/2014/01/21/sunspots-and-sea-level/#comment-1546134

I did NOT confirm your post. I refuted it.

There is NO RELATIONSHIP to investigate. I explained this.
You are being deceived by appearances. I explained this, too.
The two variables each have similar periodicity but there is NOT a causal link between them.
Richard

Paul Westhaver
January 23, 2014 10:15 am

Richard,
With respect, you may well have intended to flame my post or refute it but you, in fact, confirmed what I said.
I have no control over what you say. You said what you said.
You are in no position to say that ther is no relationship when there is clearly one recognizeable by an 8 year old child. ( I asked one who was nearby what they saw)
It seems that even the least inform among us see that there is a common behavior in the thwo plots and that they look alike.
It is contingent on science (scientists)to investigate the reason we think that is so. To dismiss an obvious relationship as a trick of the eye or brain is shoddy incurious science.
I see what I see. I see a relationship to something.
Your state agreement OR refutations are no substitute investigation and analysis.
I see a pattern.
Plain and simple.

January 23, 2014 10:59 am

Willis Eschenbach says:
January 23, 2014 at 10:54 am
Here is the true relationship between sunspots (I’ve used the NASA values) and sea level changes (Church and White values)
It looks like the solheim values are just a smoothed version of C&W, perhaps with a 5-year window. What happens if you smooth C&W and compare with Solheim?

rgbatduke
January 23, 2014 11:12 am

One last comment (I shouldn’t even take the time as I’ve got deadlines rapidly approaching). The paper at the top only uses 9 specific coastal sites that appear to be correlated with sunspots (although there are many more sites to choose from, IIRC, tide data is available from most the major harbors around the world on hundred year timescales is it not?) This exposes the paper to the criticism of cherrypicking/data dredging. In the figure caption they indicate that they only included nine of 177 that were available. Really? Why THESE nine? Could it be because when these nine were averaged, it looked like there was a correlation, where when all 177 were averaged, there was nothing?
So sorry, Willis, your R^2 and in particular your p-values are incorrectly computed. You have to use:
http://en.wikipedia.org/wiki/Bonferroni_inequalities
(or a Bonferroni correction) to assess the p-value, lest you fall into the trap of believing that green jelly beans cause cancer. Choosing 9 out of 177, I can confidently state that one must adjust even the p-values you obtain to nothingness. If one looks at all possible groupings of the tide gauge data and chooses a finite set that happens to exhibit a (comparatively poor) correlation, you have to take into account all of the rejected permutations that had even less. Otherwise we’ll be able to prove that ESP is real, that prayer works, pretty much anything we like. In fact, this is sort of like what the warmists do when they point to a bad storm and go “look, must be due to global warming”.
So its worse that we thought. So to speak.
rgb

January 23, 2014 11:32 am

Willis Eschenbach says:
January 23, 2014 at 11:23 am
You ask, if we selectively weed out and ignore the data that doesn’t agree with our hypothesis, will the correlation get better and make our hypothesis look stronger?
some people actually do this. They call it Bayesian estimation. Here is an example:
“The regression slope was found to be somewhat different if Least Median Squares (LMS) or Bayesian Least Squares (BLS) were used [Lockwood et al., 2006a, and references therein], but the procedures converged on very similar regression lines if outliers were progressively removed. Hence we here use OLS but the largest outliers were removed until the regression converged on a stable line” from ‘Reconstruction of Geomagnetic Activity and Near-Earth Interplanetary Conditions over the Past 167 Years: 1. A New Geomagnetic Data Composite, M. Lockwood et al. [Ann. Geophys. 2013]’.

richardscourtney
January 23, 2014 11:39 am

Paul Westhaver:
I am replying to your post addressed to me at January 23, 2014 at 10:15 am. For convenience of others, this is a link to it
http://wattsupwiththat.com/2014/01/21/sunspots-and-sea-level/#comment-1546575
It begins by saying

With respect, you may well have intended to flame my post or refute it but you, in fact, confirmed what I said.

Well, everybody knows what is to follow when a message begins saying, With respect”, and your post does not disappoint.
There is some confirmation of your view in my post at at January 23, 2014 at 2:49 am
http://wattsupwiththat.com/2014/01/21/sunspots-and-sea-level/#comment-1546134
I said there

However, you say you “see a pattern”. Yes, you do. The “pattern” is that the “sunspot numbers” and the “yearly change in coastal sea level” each varies with similar periodicity. But so what? Nobody disputes that those two parameters vary.
Willis has demonstrated that the variations of those two parameters are NOT related although the paper claims they are related.

So, I said, “you say you “see a pattern”. Yes, you do.”
That is the ONLY confirmation of what you said that I have provided.
But the apparent “pattern” which you see is spurious for the reasons I explained.

You have persistently ignored my explanation and now say

You are in no position to say that ther is no relationship when there is clearly one recognizeable by an 8 year old child. ( I asked one who was nearby what they saw).

Rubbish!
I wrote

Willis is assessing the claimed effect.
There are two possibilities which are
(a) The “sunspot numbers” and the “yearly change in coastal sea level” each varies with similar periodicity BUT their variations ARE NOT RELATED.
Or
(b) The “sunspot numbers” and the “yearly change in coastal sea level” each varies with similar periodicity BECAUSE their variations ARE RELATED.
Willis has provided cogent evidence of (a); i.e.
(a) The “sunspot numbers” and the “yearly change in coastal sea level” each varies with similar periodicity BUT their variations ARE NOT RELATED.
His evidence includes
Lack of coherence indicated by varying synchronicity over the assessed period.
Lack of correlation indicated by low R^2 and p values.
Lack of causal mechanism to induce a relationship.
Possible cherry picking of sea level data.
and Leif Svaalgard adds possible cherry picking of sunspot data.
Simply,
1.
the paper provides no evidence for existence of the relationship claimed by the paper,
2.
analysis of the data used in the paper indicates that the relationship claimed by the paper does not exist
3.
and the paper uses dubious data for each of the parameters which the paper claims are related.

Your “position” is that you ignore all that.
And you have the gall to say to me

Your state agreement OR refutations are no substitute investigation and analysis.

I summarised the “investigation and analysis” which you refuse to consider because it refutes what you want to be true.
And you assert that I should ignore the results of that “investigation and analysis” because it does not agree with the view of “an 8 year old child”!!!

Richard

January 23, 2014 1:39 pm

Dr. S.
Here it is Aurora and aurora data alone

January 23, 2014 2:26 pm

vukcevic says:
January 23, 2014 at 1:39 pm
Here it is Aurora and aurora data alone
You claimed that aurorae and ‘magnetic’ oscillations of the Earth itself were related. Show that. Aurorae and solar wind magnetic field are causally related but that is not what you claim. Pseudo-science piled on higher just makes it worse.

Matthew R Marler
January 23, 2014 2:40 pm

Willis, thanks for correcting my error.

January 23, 2014 3:20 pm

What I am claiming it is that geomagnetic storms are modulated by small changes in the Earth’s magnetic field (generated by differential rotation within the liquid core).
You have to be patient, another university professor and a prominent scientist will have the first sight of entire endeavour.
You ascertain it is just a ‘pile of pseudoscience’, I guess you are being polite. For most of my practical engineering carrier,I dealt with data and signals, so science or pseudoscience aspect doesn’t bother me a single iota, but for the benefit of your serenity let it stay as pseudoscience. It is my bedtime; see you at another thread at another time. Keep well.

RC Saumarez
January 23, 2014 3:43 pm

I hope I’m not being provocative, but your statements about correlation make no sense to me.
What does an autocorrelation function of ~0.8 actually mean?
How does this effect the “R^2” value?
I realise that I am being incredibly stupid but I can quite easily envisage signals that has an “aurtocorrelation” of 0.999 and an R^2 of 0.000. Alternatively I can envisage a signal that has an ” autocorrelation of 0.000 (ideally) yet can have an autocorellation of 1.0.
I realise that I cannot match your brilliance, but I would be very interested in your explanation.
P.S.: I see you have discovered “Mathematica”.. God help us all! I would point out that this is a symbolic language for mathematical manipulation – it is not a substitute for understanding (or hard work)

January 23, 2014 3:49 pm

vukcevic says:
January 23, 2014 at 3:20 pm
What I am claiming it is that geomagnetic storms are modulated by small changes in the Earth’s magnetic field (generated by differential rotation within the liquid core).
You used to claim just the opposite: that the field in the core is modulated by geomagnetic storms, i.e. causality going the other way.
Now, on very long time scales [centuries] the intensity of geomagnetic storms is modulated by the strength of the geomagnetic dipole, as the latter determines the stand-off distance of the solar wind above the surface of the Earth and hence determines the size of the magnetosphere. The tilt of the Earth’s dipole against the solar wind direction introduces daily and seasonal variations [up to 30%] of the size of magnetic activity [and thus also of geomagnetic storms] as the solar wind sees a varying strength of the Earth’s magnetic field at the ‘sub-solar’ point as the Earth wobbles. This effect accounts for the well-known semiannual and UT-variation of geomagnetic activity http://www.leif.org/research/Semiannual-Comment.pdf but I don’t think this is what you have in mind as those variations average out over a year.

January 23, 2014 3:56 pm

RC Saumarez says:
January 23, 2014 at 3:43 pm
What does an autocorrelation function of ~0.8 actually mean?
How does this affect the “R^2″ value?

A high autocorrelation means two adjacent data points are almost the same [and so are not independent] so that when you think you have 1000 data points you may only have [say] 50 points. this affects the p-value making you believe that the correlation is more significant than it really is. A good example is the sunspot number: in a solar cycle there are 4000 days and thus 4000 daily values of the sunspot number, but since a high sunspot number on a given day is always followed by a high number the next day, the number of independent data points for a whole cycle is only about 25, not 4000.

RC Saumarez
January 23, 2014 4:26 pm

@Lsvalgaard
Why not consider the correlation between a sine wave and cosine wave?
Alternatively, why not consider the corellation between a random sequence and a time shifted random sequence?

January 23, 2014 4:30 pm

RC Saumarez says:
January 23, 2014 at 4:26 pm
Why not consider the correlation between a sine wave and cosine wave?
Alternatively, why not consider the corellation between a random sequence and a time shifted random sequence?

Yes, why not? Demonstrate your eloquence by elaborating on this as you feel the need for…

Manfred
January 23, 2014 7:11 pm

Come on Willis, I think you ignore other’s input. If you would do your maths with CO2 concentration (instead of sun spots) versus sea level, what r value would you get ? Would you also conclude the correlation is very weak or spurious ? Would you compute r with the noisy unsmoothed data set ? If you would have looked at above plot a month ago, before that mess, would you really have computed an r value without further effort of extracting the “pattern” ?

Manfred
January 23, 2014 7:32 pm

Don’t know if anybody is still reading here, perhaps everything has been said, except perhaps, that Shaviv computed an r value of 0.54 giving a p=10E-4 (even without further pattern matching / signal extracting effort).
Otherwise I would totally agree with Paul Westhaver / Ian Schumacher.
I comment again, because I would love to see an audit happen where the meat of the papers is (or should be), which is the planetary connection. And not correlations which have been commented numerous times at WUWT. (And in my view support the existence of a solar amplifier or quoting Steve McIntyre first impression of above plot again “…offhand, I can’t think of any two climate series with better decadal matching…”)
And the planetary connection here may be really interesting to discuss, as it is claimed as follows in the paper:
“The coastal sea level variation cannot be explained as due
to expansion/contraction of the oceans due to heating/cooling
during a solar cycle as proposed by Shaviv (2008) simply because,
near the shore, the thermal expansion becomes zero
since the expansion is proportional to the depth (Mörner,
2013b). The good correlation and nearly in-phase response
between solar activity and sea level indicates that this is a direct
mechanical response – and not a thermal response that
needs time to heat up and cool, and therefore shows delayed
response.”
So they disagree with Shaviv (!) and say the expansion is not a thermal response but mechanical.
I would be interested in an expert comment on this part “….simply because, near the shore, the thermal expansion becomes zero since the expansion is proportional to the depth (Mörner, 2013b).”
The Moerner paper is paywalled, so I could not check the reference, but in my amateur’s view, I would have thought that expansion occurs instantly and that such expansion levels out quickly. The volume would expand in x,y,z and x,y would push water into the coastal regions immediately, while the z expansion may take longer as it is leveled by gravitational force.

January 23, 2014 7:55 pm

Willis Eschenbach says:
January 23, 2014 at 7:12 pm
Leif, do you have a link to whatever might be the longest observationally based and count-corrected sunspot dataset?
We are still working on hammering out the ‘final’ series, but it will be close to the series you get if you use the Official SIDC numbers and then increase all values before 1947 by 20%.
Our deliberations are detailed here http://www.leif.org/research/CEAB-Cliver-et-al-2013.pdf

Paul Westhaver
January 23, 2014 10:13 pm

Hi Richard.
A bit snarky?
Listen, an 8 year old child looked at the plot at my request and I asked the child what he saw. He said about the plots: “they are doing the same thing”.
When an 8 year old sees something and he tells you that the emperor has no clothes, you might want to listen. Maybe the naked emperor is a fool and his sycophants don’t have the courage to tell him.
I call them like I see them.
I see a pattern of some sort and no amount of bullying and browbeating from you will change that.
You are quite a tyrant want-to-be.

January 24, 2014 1:08 am

lsvalgaard says:
January 23, 2014 at 3:49 pm
………….
Yep, it appears to be both ways; a feedback positive or negative depending on the phase at the time of re-encounter at the pole. Perhaps you should revive the Svalgaard –Mansurov effect for the energetic solar events, apparently the ordinary SW has not sufficient energy.
Next step would be to apply the same process to the Ap index to see if it reproduces the signal, idealy data from a single polar region station, e.g. Thule if you got a long enough set.
Will keep you informed.

Chris Wright
January 24, 2014 2:47 am

As many have mentioned, the apparent correlation between the two graphs is quite dramatic. This is what Steve McIntyre said in 2007, when this graph was discussed at CA:
“The maxima and minima of the solar cycles seem to match the fluctuations in sea level rise rather uncannily. While the resemblance is impressionistic (I don’t have a digital version of Holgate’s series), offhand, I can’t think of any two climate series with better decadal matching. I think that this resemblance is pretty obvious.”
http://climateaudit.org/2007/02/11/holgate-on-sea-level/
If Holgate’s data is good, then the SLR shows a very distinct periodicy with a period close to 11 years. I’m not a statistician, but I would be fairly confident that the probability of this being purely random is vanishingly small. If it’s not random then there has to be a cause, pretty well by definition.
In my original post I specifically noted that the eye can be deceived. But in many cases the human eye can be far more true than vast constructs based on dodgy computer models – there have been many examples of this at WUWT, and some of them probably from Willis! In one example – a paper used incredibly complex computer model constructs to demonstrate a claimed link between the UK 2000 floodings and climate change – a simple eyeballing of the actual data clearly showed there was no overall trend.
Shaviv’s paper has been mentioned:
Shaviv uses different data but the result is very similar. He gets an R2 correlation of greater than 0.5, which is much more believable. Hopefully he had the original data and did not need to scan the graphs. His explanation of the effect, if I understand correctly, is that the solar cycle effects ocean surface temperatures (the top 10 meters) and this in turn effects SLR through expansion.
Is there a real and provable relationship between solar activity (in this case, sunspots) and SLR?
I don’t know. But Holgate’s data clearly shows that there’s more work to be done. That’s what makes science so fascinating.
And, yes, I agree wholeheartedly with Willis that the only thing that matters in the final analysis is mathematics, evidence and proof.
I did mention that doing a Monte Carlo analysis would be very useful, it would strongly indicate the probability that the apparent correlation was caused by chance. Any takers?
Chris

January 24, 2014 3:05 am

lsvalgaard says:
January 23, 2014 at 3:49 pm
………..
Hi again
I did a quick run on the Ap data; resulting graph is now added to the one from the aurora data
It confirms previous findings, significance could be important if a physical process can be identified correctly (i.e. if indeed there is one !).
I am surprised that no one has done it before since Ap was accurately measured since (was it ?) 1930s.

January 24, 2014 4:54 am

Some further observations from the Old Lady’s Halo (Old Lady = Terra, Halo = Aurora) graph
1880 -1925 there is reasonable agreement pulse for pulse for both down and up short term trends.
1925 – 1945 it is a mess, I suspect due to strongest geomagnetic jerk in recorded data:
http://www.geomag.bgs.ac.uk/images/image018.jpg
peaking in 1925
1945 – 2010 again there is reasonable agreement pulse for pulse for both down and up short term trends, albeit amplitudes of pulses in the temperature domain are considerably less prominent.
Since I assume that Ap measurements have maintained same standard, although decline in the strength of of the Hudson Bay magnetic pole has not been fully compensated by the slow rise in the strength of the Central Siberia one. The temperature side of the equation there are number of factors (from CO2 to data processing methods) which could explain rising discrepancies.
R2 for any of the above three time periods is negligible, but these type
Just to add: 3 year moving average is used in the temperature domain.

richardscourtney
January 24, 2014 7:00 am

Paul Westhaver:
Hi Paul:
re your post at January 23, 2014 at 10:13 pm.
All “snark” was yours.
I took the trouble of explaining the issues to you twice and your response was – and is – that my explanation must be wrong because an 8 year old child sees the pattern you can see.
I twice agreed that you did see a pattern but I explained that the pattern is meaningless. Willis also explained that to you.
But you persisted – and still persist – in claiming my understanding and explanation should be ignored because an 8 year old child can see a relationship. That is an insult. My offence at the insult does not indicate I am “quite a tyrant want-to-be”.
It only indicates that I am offended by gratuitous insults and respond appropriately.
Richard

richardscourtney
January 24, 2014 7:34 am

Chris Wright:
Many things oscillate naturally and some could have a periodicity close to ~11 years by chance. Hence, observation of something having similar periodicity to the solar cycle periodicity does merit investigation of whether that similarity is or is not a chance coincidence.
At January 24, 2014 at 2:47 am you say

If Holgate’s data is good, then the SLR shows a very distinct periodicy with a period close to 11 years. I’m not a statistician, but I would be fairly confident that the probability of this being purely random is vanishingly small. If it’s not random then there has to be a cause, pretty well by definition.

Why would it be “vanishingly small” when many things vary with different frequencies? These two have similar frequencies.
Of importance is WHY the SLR varies as it does.
J E Solheim published a paper which suggested that the SLR variation is related to the solar cycle. In other words, the solar cycle causes the SLR variation. And, as you say, their similar periodicities does imply that the SLR is solar driven.
But the analysis by Willis strongly suggests that the similarity of the SLR oscillation is purely by chance. I summarise his findings above at
http://wattsupwiththat.com/2014/01/21/sunspots-and-sea-level/#comment-1546134
Willis’ analysis certainly indicates no direct causal effect although there could be an indirect effect. Such indirect effect would result from the solar cycle determining the periodicity of ‘something else’ which – in turn – affects the periodicity of SLR. But nobody has suggested what such a ‘something else’ could be.
So, at present, Willis’ analysis indicates that there is no relationship between the solar cycle and SLR variation. Hence, the hypothesis of a direct causal relationship between the solar cycle and SLR variation is falsified.
This is a useful finding because it frees people to investigate whatever is the true cause of the SLR variation.
Richard

Jan Stunnenberg
January 24, 2014 9:20 am

‘richardscourtney’ says:
‘J E Solheim published a paper which suggested that the SLR variation is related to the solar cycle. In other words, the solar cycle causes the SLR variation. And, as you say, their similar periodicities does imply that the SLR is solar driven.’
NO! Must read: [..] In other words, the solar cycle AND the SLR variation MIGHT have A COMMON CAUSE. full stop
Whatever that cause might be – tidal forces of the planetary system and/or even electromagnetism … etc. – the effect is verifiable in the record. At earth: as SLR. At the sun as SSN.
The COMMON CAUSE has yet to be discovered.

January 24, 2014 10:02 am

Jan Stunnenberg says:
January 24, 2014 at 9:20 am
…………..
There are number of articles positing that the geomagnetic storms affect the global atmospheric pressure, there is also something called Svalgaard-Mansurov effect ( which I think is real but its discoverer think it is not).
Atmospheric pressure changes will affect the rate of sea level change.
If you compare geomagnetic disturbances to the sunspot cycle than there is considerable discrepancy between the two.
http://www.esa-spaceweather.net/spweather/workshops/proceedings_w1/POSTER4/figure_01.gif
So Perhaps Willis could look at the geomagnetic data, but my concern is that changes of few mm are well within margin of error.

January 24, 2014 10:16 am

vukcevic says:
January 24, 2014 at 10:02 am
There are number of articles positing that the geomagnetic storms affect the global atmospheric pressure, there is also something called Svalgaard-Mansurov effect ( which I think is real but its discoverer think it is not).
You are confusing the Svalgaard-Mansurov effect which is very real with the Mansurov-effect which is not.
If you compare geomagnetic disturbances to the sunspot cycle then there is considerable discrepancy between the two.
It is wrong to call it a ‘discrepancy’ as the difference itself is related to the solar cycle and is due to the prevalence of coronal holes on the declining branch of the cycle. All this is well-understood.

January 24, 2014 11:08 am

OK, bos, I stand corrected on the difference.
Svalgaard-Mansurov effect is related to the polar cap magnetic deflections, associated with ionospheric currents flow resulting from the release of magnetic tension on newly open magnetic field lines.
The Mansurov effect refers to North–south asymmetry of geomagnetic and events with effect on the troposphere.
It would resolve many problems if Mansurov happen to be correct.
Burns et al 2007 do think Mansurov effect is real
The Mansurov effect, which for the Southern Hemisphere consists of a positive association between the By component (east-west) of the interplanetary magnetic field (IMF) and the ground-level pressure for stations poleward of ∼80° magnetic latitude, is confirmed for Vostok (78.5°S, 106.9°E; magnetic latitude 83.6°S) using modern data. The magnitude of the association is small (0.19 hP per nT; 1.2% common covariance) but statistically significant (at the 96.1% level). A more substantial association exists, with a slight delay (2–3 days) and a cumulative influence, between the
………….
We confirm a previously reported Sun-weather linkage (the Mansurov effect), provide evidence that the mechanism operates via the atmospheric electric circuit and present data supporting an inferred and more substantial surface pressure response to changes in the global atmospheric circuit.
http://onlinelibrary.wiley.com/doi/10.1029/2006JD007246/abstract

January 24, 2014 11:25 am

If there are geomagneticaly induced currents at altitude of 10-15km, how could one be certain that the atmospheric events are unaffected.
http://www.vukcevic.talktalk.net/GEC.jpg

January 24, 2014 11:51 am

vukcevic says:
January 24, 2014 at 11:25 am
If there are geomagneticaly induced currents at altitude of 10-15km, how could one be certain that the atmospheric events are unaffected.
Since there are not, this is a non-issue.

Paul Westhaver
January 24, 2014 12:29 pm

Richard,
You had not contributed anything to the rational explanation as to why a perceived pattern is not real. Only ad hominem to me.
Your lack of a an attempt at any possible reasonable hypothesis as to the basis of why a pattern is commonly perceived yet is absent in W.E’s analysis or yours, is the reason I am unconvinced by his and your assertions.
Why don’t you comment on whether my assertion that a simple Fourier type adjustment of each data set might reveal an error?
I argue that Willis really hasn’t done the data set justice. He simply ran a comparison and tossed it out, out of hand. Seems to me that based on the Sunspot number approximation, where he got ok mapping, then he ought to be able to do the same with these 2 data sets. Then compare and resolve the adjustments.

TonyG
January 24, 2014 12:46 pm

Paul,
A PERCEIVED pattern is a matter of what you see coupled with the human brain’s tendency to recognize patters, WHETHER OR NOT THEY REALLY EXIST.
For example – the constellations. The stars have no pattern. But we PERCEIVE a pattern, regardless.
The degree of matching of two patterns is NOT something that is visually determined. It is done mathematically. That is to prevent perception from biasing the results. Perception is SUBJECTIVE, and differs from person to person. For example, I DO NOT see the correlation that everyone else appears to see. But that only MY perception. What matters more is the mathematical correlation. The mathematical calculation of the correlation is OBJECTIVE, and it has been done. According to the math, there is NO correlation.
What you’re doing is no different than people who see Jesus’ face on their breakfast toast, or dragons in the clouds.

January 24, 2014 12:37 pm

lsvalgaard says:
Since there are not, this is a non-issue.
………
Andreas Baumgaertner from UCAR thinks there are : Page 11

Paul Westhaver
January 24, 2014 12:43 pm

Willis,
You sound pretty confident that there is no common root to the two data sets.
I don’t agree with you.
I don’t think you tested the data sets creatively enough to expose the pattern error.
It is the ERROR between the two data sets that reveals the key.
Professionally I do this all the time. I just am too busy to tackle this set just now. Signal analysis is my area of interest.
I would force that data onto a strong correlation by adjusting the amplitudes, phases and linear trends etc.then examining the error pattern of a series of best fits.
All of the information is hidden in the outliers and the errors.
I think you prejudiced your analysis by categorizing the apparent agreement as a perceptual aberration, corrupting your objectivity. Also, you assumed that there is cause-effect relationship, which would mask an intermediate.
Why don’t you come back to it later after some reflection. You might have a Eureka moment?
PW

January 24, 2014 12:44 pm

vukcevic says:
January 24, 2014 at 12:37 pm
Andreas Baumgaertner from UCAR thinks there are : Page 11
You have to learn to accept what I teach you. The induced currents [and the curved arrow] refer to currents induced 400 km below ground, not at 10-15 km altitude.

Paul Westhaver
January 24, 2014 1:37 pm

Tony G,
Perception of something like a face is hard-wired in the brain, apparently. I am not a neuro psychology expert. I will not dismiss that people are good pattern finders. We are, as a general rule. Galileo notice a pattern in a oscillating chandelier and from that he derived a rule for the period of the pendulum. Good thing his wasn’t distracted by the cat-calls of his detractors. History is replete with people who are mocked because they recognize a patterns before others.
My favorite example is Georges Henri LeMaitre.
Rather than suggesting that the there is a cause and effect relationship between sun-spot number and sea level change I suggest that they share a common root of some sort.
Maybe ocean evaporation rates vary or terrestrial drought vary. We know that cloud formation rates vary based on cosmic ray production which is related to the sunspot cycle. Maybe something else. Clearly the earth is affected by sunlight in many ways.
According W.E.s 1st attempt at a correlation he see no relationship. Fine.
WE is one guy. The Job may be for someone else who is less interested is dismissing the data sets and more curious at picking beneath the paint, so to speak. As always, I vote in favor of curiosity and against people who claim “settled science”.
Tony G. you should abide by Roger Bacon’s (Opus Maius 1267) 4 obstructions to discovering the truth which are:
1) the example of weak and unreliable authority;
2) continuance of custom,
3) regard to the opinion of the unlearned, and
4) concealing one’s own ignorance, together with the exhibition of apparent wisdom.
Quatuor vero sunt maxima comprehendendæ veritatis offendicula, quæ omnem quemcumque sapientem impediunt, et vix aliquem permittunt ad verum titulum sapientiæ pervenire: videlicet fragilis et indignæ auctoritatis exemplum, consuetudinis diuturnitas, vulgi sensus imperiti, et propriæ ignorantiæ occultatio cum ostentatione sapientiæ apparentis.” [Roger Bacon. (1267). Opus Maius.
Bravado is the first betrayer of #4.
Again I see something and I can’t say what it is. But I see something.
Say what you want. I see it.
PW

Jan Stunnenberg
January 24, 2014 1:45 pm

TonyG says to To Paul Westerhagen:
‘According to the math, there is NO correlation.’
Wrong, it’s only Willis’Math that did so.
Paul Westerhagen seems clever enough to me, to be able to ‘feel’ uncomfortable with that kind of ‘math’.

January 24, 2014 1:49 pm

Usoskin & Korte:
We conclude that changes of the regional tropospheric ionization at midlatitudes are defined by both geomagnetic changes and solar activity, and none of the two processes can be neglected.

Paul Westhaver
January 24, 2014 2:00 pm

The color purple doesn’t exist. Yet we see it still.
You cannot find the color purple anywhere on the visible light spectrum.
Our brains adequately decipher red, ted to yellow to blue very well and yield us a good approximation of the spectrum even though we only have blue red and green receptors.
Purple is a color invented by our brains to resolve the difference in intensity by non-adjacent spectrum receptors in the absence of an intermediate.
So, even though purple is not real, the ratio of the two colors that make purple is real and quantifiable. So purple is real…? but as a difference calculator.
Maybe the data sets are like that. Willis never checked for something like that.

Jan Stunnenberg
January 24, 2014 2:01 pm

Sorry Paul Westerhagen.
I’ve just submitted my comment as you submitted yours.
However your’s tells it all.

January 24, 2014 2:12 pm

vukcevic says:
January 24, 2014 at 1:49 pm
Usoskin & Korte:
We conclude that changes of the regional tropospheric ionization at midlatitudes are defined by both geomagnetic changes and solar activity, and none of the two processes can be neglected.

As you have a tendency to misunderstand things, you should provide a link so we can see what you misunderstood. Now, I can guess where your confusion comes from: The main sources of electric fields and currents in the Global Electric Circuit are thunderstorms in the troposphere and the dynamo situated in the ionosphere and magnetosphere produced by tides generated in there and tides propagating upward from the lower atmosphere. Again, learn from what I say, rather than digging your holes ever deeper.

Paul Westhaver
January 24, 2014 2:32 pm

vukcevic,
I speculate that what happens on the earth may be the result of solar activity, Radiation, solar wind, solar farts, etc… all that stuff, seen and unseen and yet-to-be-discovered.
Is there a good study of cloud cover (anywhere/everywhere) vs sunspot number (or a proxy)?
I suspect that there is a transfer function of some kind between solar activity and terrestrial variables, like sea level change etc. Maybe the TF is an aggregate of all of these. Why not?
PW

Greg
January 24, 2014 2:42 pm

Willis : “Here are the Jevrejeva annual sea level changes plotted against sunspots …”
Where did you get that data from Willis ?? Did you digitise fig 3 from the paper by any chance. If you did you should read the caption: it is the SSA 30year window analysis, which they say is similar to 30y running average. That may explain the lack of any decadal detail !
Data should be available from PMSL but it’s not responding my end right now.
http://www.psmsl.org/products/reconstructions/gslGRL2008.txt‎
Suggest KNMI
http://climexp.knmi.nl/getindices.cgi?WMO=PSMSLData/gsl_ann&STATION=global_sea_level&TYPE=i&id=someone@somewhere&NPERYEAR=1

rgbatduke
January 24, 2014 2:47 pm

A high autocorrelation means two adjacent data points are almost the same [and so are not independent] so that when you think you have 1000 data points you may only have [say] 50 points. this affects the p-value making you believe that the correlation is more significant than it really is. A good example is the sunspot number: in a solar cycle there are 4000 days and thus 4000 daily values of the sunspot number, but since a high sunspot number on a given day is always followed by a high number the next day, the number of independent data points for a whole cycle is only about 25, not 4000.
Excellent summary. I wrote a whole Phys. Rev. paper on this once upon a time, elaborating (among other things) how one can actually determine the autocorrelation time (or number of samples per “independent” sample) by looking at the scaling of the variance of the data. It is easy to make egregious claims for the precision of an entirely spurious experimental result if one has thousands of samples but the scaling of the variance indicates that you really only have a hundred.
I was actually doing it the other way around — I was doing numerical simulations using (and comparing) several Markov Chain processes — Metropolis, Heat Bath, a cluster/metropolis method and a mixed cluster/heat bath method. Metropolis was cheap and generated enormous numbers of samples, but the samples had an enormous autocorrelation as one might expect. Heat bath did better — it isn’t accept/reject and so every spin in the model was changed in every step. Cluster methods alone did break up spatial correlations quickly but — surprise! They were accept-reject AND preserved large blocks of spin bonds in unchanged states per move, and actually slowed down the decay of energy autocorrelation (accept/reject methods more or less guarantee that SOME bonds in a lattice are not thermalized for many, many sweeps). Best of all was a mix of cluster steps to break up egregious spatial correlations at a variety of length scales and heat bath sweeps to facilitate the ergodic progression of the bond energies. All revealed by comparing how the standard deviation of the sample compared to what one would expect from a knowledge of the system variance and the number of samples in question.
This is one reason I’m very skeptical about climate science conclusions in general. There isn’t one autocorrelation time or important time scale in climate science, there are dozens (at least) — there is probably a continuum of them to where Laplace transforms are more appropriate to speak of than an single exponential decay process. And it isn’t clear that these times themselves are stationary; it may not even be that the usual concept of an “autocorrelation time” HOLDS for a climate system, even in an approximate sense.
That’s why I read papers on Hurst-Kolmogorov statistics with great interest. Many climate variables appear to exhibit an H-K pattern of autocorrelation — stretches of order decades of approximately uniform variation followed by a discrete jump to a new stretch of order decades. “The pause” is just such a stretch in the GASTA , where the 1997/1998 Super-ENSO is coincident with the preceding jump, and where quite a few of these intervals are visible e.g. here:
or even more clearly here:
http://www.woodfortrees.org/plot/uah/from:1977/to:2014
or here:
In the satellite era in the last two of these curves, note well that one does quite well imagining that the climate was locally stable from 1980 to 1998 give or take a year or two — call it twenty years. Then it jumped by around 0.3 C over a very short period — two or three years, with large oscillations attending the jump — and has been stable since for anywhere from 13 to 17 years depending on just where you want to put the jump and whether you want to discretize it or extend it over a 2-4 year period that includes the initial very sharp rise, the overcorrection, and the smaller scale bounces that could just as easily belong to the new equilibrium.
Similar jumps are clearly visible in SST graphs generated by Bob Tisdale. Similar jumps are clearly visible in rainfall patterns (one of the places where they were first observed and analyzed by hydrologists like Koutsoyiannis).
Guestimating (looking at longer timeseries and allowing for both poorer data and various thumbs on the scales) 15 to 25 years is one highly non-exponential autocorrelation time — the Earth seems to like to spend 1-2 decades going only slowly up or slowly down interspersed with intervals where it goes up or down comparatively rapidly. Much of even this cannot really be trusted — IMO global climate data itself has a clear trend of decreasing reliability as one goes back into the past (that no one ever gets to see as no one prints the curves with even guestimated error bars, but the estimated errors in things like HADCRUT4 now are pretty large, on the close order of the 30 year supposed warming “signal” of a few tenths of a degree).
If one reduced the last 36-odd years of satellite data — the most reliable data source out there for an unbiased global temperature anomaly — to the two independent “climate” samples it probably represents, and decorated each of those points with an error estimate of 0.1 to 0.2 C (based solely on the variance around the sample means of the observational data taken in two 18 year chunks) nobody would dare to say anything about a trend in the climate. One point at an anomaly of $0 \pm 0.1$ C centered on 1986, one point at an anomaly of $0.2 \pm 0.1$ C centered on 2005. The probable error (accounting for the scaled autocorrelation and hence sample independence on a smaller scale WITHIN these intervals pretty much completely overlaps and suggests a mean warming of perhaps 0.2/C over 18 years, or roughly 1.1 C/century if it made the slightest bit of sense to extrapolate two data points in any graph ever built!
The problem here is that we really do not know the various climate autocorrelation times (plural) and so we do not know how much “significance” to assign to any apparent linear trend in the highly correlated data with its own internal dynamics that is essentially noisy but stable for apparent decade-plus intervals.
To conclude, I hate to see Bayesian reasoning bad-mouthed, so I’ll just point out that the correct application of Bayesian reasoning in problems like this is along the lines of the stuff Willis was discussing, to automagically correct one’s initial prior estimates of probability until they are in asymptotic agreement with the observational data.
For example, one might examine as he did flipping a two-sided coin. You might begin with a strong bias, an experience-based belief that two-sided coins are likely to have a probability 0.5 for heads, 1 – 0.5 for tails, and 0 for landing perfectly on edge, making them perfect Bernoulli Trial objects with a nice binomial distribution of outcomes.
Bayesian analysis in principle gives one a way to systematically and nearly smoothly correct this belief on the basis of data as one starts to actually flip the coin. Initially one’s prior for heads might be 0.5, but after observing 78 flips in 100 flips, one would/should have a posterior probability that is much higher than 0.5. This is the flip side of ordinary p-values. The Binomial distribution might allow one to compute the probability of getting 78 heads out of 100 flips of an unbiased coin — the usual definition of the p-value — but it does not tell you what your best estimate for the probability of heads is given the data.
The problem with Bayesian reasoning is that it is often misapplied or used as a means of legitimizing an improbable estimate. By weighting your prior beliefs highly, you can essentially demand a lot of evidence — probably far too much evidence — before you start bending your posterior estimate much. There are right and wrong ways to do it, but in the hands of a clever person Bayesian analysis can conceal a kind of statistical lie that resolves to “the result of my analysis shows that the probability of the event is p, very near where I expected/wanted/needed it to be”. Taleb reviews a very similar case in The Black Swan where a Scientist refuses to alter his prior estimate for the probability of a tail in the face of the observational evidence of 100 straight heads because he knows that 2^100 events can happen (one in 10^30 tries, give or take a bit) where Joe the Cab Driver immediately recognizes this as “a mugs game” — the coin is very, very probably fixed so that p(heads) is nearly unity, at least in the hands of the clever person flipping the coin.
This is very similar to the problems with concluding either correlation or causality from the data. Suppose I flipped a truly unbiased coin several thousand times. Over several thousand flips, it is very likely to see sequences of heads and tails at least 8 or 9 long. If somebody wishing to demonstrate that the coin is biased were allowed to pick samples out of this data and concentrate their analysis on some subset, all they have to do is point to one of these long sequences (which have low probability of happening in any given sequence of coin flips even as they will occur quite reliably if one honestly samples the unbiased coin) and say — LOOK — the coin produced ten highly correlated flips in a raw! The coin must be biased! Our brains make us especially susceptible to this — all of the mundane flips sequences are boring; even though the probability of getting HTTHTHHTHH. is exactly equal to the probability of getting HHHHHHHHHH, the latter is exciting and we notice it while forgetting all of the equally unlikely but less strikingly patterned sequences. Picking particular sequences of data that favor some desired conclusion is called “cherrypicking” and is a cardinal sin of science. The tendency to discover favorable sequences and cherrypick them is called “confirmation bias” and is a even worse sin as the latter can happen by accident but confirmation bias involves deliberate action e.g. presenting the average of 9 tidal stations that we know appear to have some sort of correlation but deliberately concealing — often even from ourselves — the fact that 9 stations picked at random from the full set of stations would exhibit no such visible correlation or worse, that the unbiased average of all of the data exhibits no such correlation.
Climate science (on both sides) research in much of the humanities and soft sciences are rife with confirmation bias and cherrypicking and overreaching conclusions drawn from inadequate or incorrect statistical analysis. A lovely example of the latter is the infamous hockey stick. An example of the former is D’Arrigo’s infamous testimony: “if you want to make cherry pies, you have to be willing to pick cherries”. Whether or not the misuse of statistics is deliberate or accidental, much of published climate science is statistically incompetent. A properly cautious approach would simply refuse to draw conclusions about trends, causes or effects until far, far more reliable data is collected and analyzed by somebody other than a collection of world-saving zealots — on either side. Let me be clear — we do not have sufficient evidence to reject the hypothesis of catastrophic warming by the end of the century (human caused or otherwise) any more than we have sufficient evidence to accept it. At the moment, the evidence in favor of the hypothesis is weak, but because of the uncertainties in things like autocorrelation, the range of so-called “natural” variation, the effect of a variety of processes that we know have timescales on the order of several decades (e.g. the multidecadal oscillations, the observed multidecadal variability of the sun, multidecadal oceanic turnover processes) it is far from sufficient to positively assert that CO_2-linked global warming will not, in fact, eventually cause real catastrophic e.g. sea level rise. There just isn’t any evidence that it is doing so yet.
For fans of proper Bayesian analysis, there is a delightful article here:
http://marginalrevolution.com/marginalrevolution/2005/09/why_most_publis.html
that analyzes a surprising but plausible claim: Most published research findings are false! This is probably not true in all fields (I’d like to think it isn’t true in physics, for example, because we police the field at roughly the level where any further stringency would eliminate the fringe from which new revolutions rarely but significantly spring) but in fields like medicine it is probably (and tragically) true!
It is interesting to see where climate science falls in the schema given at the bottom of this article.
1) I’d have to say climate science is very high in what he calls background noise, the number of hypotheses tested. Examples of this are frequent on WUWT — when every biologist has to add the magic words “to look for evidence of the impact of global warming” to a grant proposal to study pinkfooted bungie-jumpers, when every population study, every medical study, every study of coral reefs or the migratory patterns of beetles invokes the phrase on the presumption that such evidence exists (that has to be presented sufficiently compellingly to help influence a grant officer into funding the work when otherwise there is nothing particularly special to look for sheer Bayesian analysis dictates that a significant fraction of such studies will find something to report at the level of marginal statistical significance.
2) Many results supporting a supposed coming catastrophe are reported for tiny sample populations, or highly constrained and limited environments. Last week we heard about the supposed extinction of a subpopulation of a butterfly in a part of a single meadow subject to uncontrolled and confounding environmental alterations in addition to any supposedly discriminable change in climate. Again, many of these studies of marginal subpopulations or specific locations can easily turn out to be at best statistical accidents — happening to test green jelly beans first and never bothering to report that jelly beans in general have a null result, and even green jelly beans have a null result unless they were tested on one particular Sunday.
3) Small effects are to be distrusted. Wow. Talk about defining an entire science! The entire predicted catastrophe is less than a one percent effect on the absolute temperature scale. All observed warming to date in the sixty-odd years where it could be attributed to human-produced CO_2 is on the order of two-tenths of one percent of the absolute temperature (some fraction of which is almost certainly natural) and there has been no warming at all for the last quarter of that era precisely where one would expect it to be the strongest.
4) Multiple types of evidence are desirable, but are not forthcoming. The GCMs that are the sole basis for predictions of warming fail to predict global warming, they fail to predict tropospheric warming, they fail to predict rainfall, they predict storm intensification that has not happened, they do not predict the correct patterns of what warming there has been, and they utterly failed to predict what has happened with e.g. Antarctic sea ice, the Greenland ice pack, sea level rise, sea surface temperatures, El Nino, the PDO, and well, pretty much anything. It isn’t clear that there is anything that GCM’s have gotten right!
The problem there is that when multiple biological studies find supposed evidence for negative sequellae attributable to GW that supposedly took place over the last 15 years, that is not evidence for CAGW when in fact no statistically discernible warming whatsoever took place over the last fifteen years. So a huge body of what people consider to be “evidence” is in fact evidence — against the hypothesis, and for the presence of an enormous degree of confirmation bias in the literature given that people are finding effects for a cause that in fact has not budged since many of the researchers were in middle school.
5) This also is a problem here. Much of the literature can be discounted when they find evidence of global warming in e.g. the biosphere when no discernible global warming occurred or is occurring since the 1997-1998 ENSO, and only 0.2 C of warming occurred then. Changing “global warming” to “climate change” is intended to hide this since “climate change” cannot be falsified given that the climate is always changing, but does not repair the science. Then there is the deification of Mann by the IPCC and the consequent corruption of the mainstream science. Post-Mann climate science has been all about a literature controlled by the authors of a few individual papers, some of which have been shown to be terrible examples of statistics. Briffa, Jones and others published many papers clearly showing the MWP before Mann “erased” it. Climategate clearly revealed the naked backstabbing, gatekeeping, and behind the scenes pressure to suppress the competing voices that ordinarily keep science honest. It has taken fifteen years plus where the sky stubbornly refused to fall to gradually free the editors and referees to consider papers that don’t conform to a narrowly politicized conclusion as evidenced by the presence of one or more catch phrases about global warming even where it ends up being irrelevant (the mirror image of the grantseeking process alluded to above). One shudders to think about how long it will take to de-politicize the granting agencies themselves.
6) There are damn few papers published that test other people’s theories in climate science, at least as far as I’ve seen. If there were, would not the GCMs largely have been rejected at this point? WUWT has indeed covered at least one paper that tested GCMs directly against one another for a toy problem, where they failed miserably, but only a handful of researchers dare to question the party line, often at the expense of being called names by their peers and being subjected to a brutal and withering peer review process in journals where the editors are routinely subjected to pressure from a small cadre of climate scientists. Indeed, a properly skeptical test of the fundamental theoretical basis for the prediction of CAGW would be most welcome, as those predictions are busy not coming true and everybody knows it but nobody will say so in the literature!
7) Don’t reject papers that fail to reject the null hypothesis. Boy, Climate Science in a nutshell, at least if the null hypothesis is that human CO_2 has had a negligible effect on the climate, one impossible to disentangle so far from natural climate variation and impossible to directly observe even with sophisticated instrumentation and hence discernible at best by dubious analyses that attempt to discriminate recent warming as being due to CO_2 from previous but identical warming that was supposedly natural, e.g. comparing the first and second half of the 20th century.
If a non-expert in the field cannot tell the difference between GASTA in the first half of the 20th century (when most climate scientists agree that CO_2 levels had not yet begun to change at a significant rate from pre-industrial levels) and the second half of the 20th century (where all or almost all of the warming is openly attributed to CO_2 in IPCC ARs) then one has little basis for rejecting the null hypothesis.
rgb

Greg Goodman
January 24, 2014 4:03 pm

I posted this a couple of days back but it seems to have disappears 😕
Anyway I’ve cleaned the graphs up on posted as description of the derivation.
Cross-correlation:
http://climategrog.wordpress.com/?attachment_id=760
Power spectrum:
http://climategrog.wordpress.com/?attachment_id=759
There is a smallish but significant correlation between the Jevrejave annual MSL and sunspot area. This show clear evidence of a modulation due to interference pattern with a second periodicity. The other frequency causing the modulation can be estimated by:
1/79.1+1.10.49 = 9.23 years.
This suggestive of a lunar cycle. Periods of 9.1 +/- 0.1 years have been reported by Nicolas Scafetta and Berkeley Earth project.
I have been tyring to point for some time that you cannot detect or refute an particular driver (like solar) by trivial single variable regression of correlation.
Willis challenged me to show it and I think that does.

Bart
January 24, 2014 4:14 pm

Greg Goodman says:
January 24, 2014 at 4:03 pm
“I have been tyring to point for some time that you cannot detect or refute an particular driver (like solar) by trivial single variable regression of correlation.”
Throw out all the other comments on this thread. That is the only one needed.

richardscourtney
January 24, 2014 4:43 pm

Jan Stunnenberg:
In my post at January 24, 2014 at 7:34 am I wrote

Willis’ analysis certainly indicates no direct causal effect although there could be an indirect effect. Such indirect effect would result from the solar cycle determining the periodicity of ‘something else’ which – in turn – affects the periodicity of SLR. But nobody has suggested what such a ‘something else’ could be.
So, at present, Willis’ analysis indicates that there is no relationship between the solar cycle and SLR variation. Hence, the hypothesis of a direct causal relationship between the solar cycle and SLR variation is falsified.
This is a useful finding because it frees people to investigate whatever is the true cause of the SLR variation.

At January 24, 2014 at 9:20 am you have replied saying in total

‘richardscourtney’ says:

‘J E Solheim published a paper which suggested that the SLR variation is related to the solar cycle. In other words, the solar cycle causes the SLR variation. And, as you say, their similar periodicities does imply that the SLR is solar driven.’

NO! Must read: [..] In other words, the solar cycle AND the SLR variation MIGHT have A COMMON CAUSE. full stop
Whatever that cause might be – tidal forces of the planetary system and/or even electromagnetism … etc. – the effect is verifiable in the record. At earth: as SLR. At the sun as SSN.
The COMMON CAUSE has yet to be discovered.

The “must read” applies to you and not me.
Firstly, my words “at present” are true and have a meaning which your reply ignores.
Secondly, I said the possibility of a common cause exists but the study under discussion falsifies a direct cause.
Thirdly, you assert a common cause but provide no evidence for it.
So what? I see no reason to accept an unjustified assertion that something DOES exist when – as I pointed out – it may exist but there is no evidence for it.; e.g. the reason for fairies at the bottom of your garden has yet to be discovered.
Richard

richardscourtney
January 24, 2014 4:58 pm

Paul Westhaver:
Your post at January 24, 2014 at 12:29 pm begins saying

Richard,
You had not contributed anything to the rational explanation as to why a perceived pattern is not real. Only ad hominem to me.
Your lack of a an attempt at any possible reasonable hypothesis as to the basis of why a pattern is commonly perceived yet is absent in W.E’s analysis or yours, is the reason I am unconvinced by his and your assertions.

No! That is absolutely untrue except possibly that you may be “unconvinced”.
The only ad homs. were from you at me; e.g. untrue assertion that I used “snark”, my explanation is less cogent than the perception of an 8 year old child, I am “quite a tyrant want-to-be”, etc..
Both I and Willis explained to you that the human brain often generates the perception of patterns which do not exist so mathematics are needed to determine if perceived patterns are real. If you want to continue your pestering then try it on Willis because his tongue will be more lashing than mine and I cannot be bothered to reply to more of your insulting and offensive twaddle.
Richard

richardscourtney
January 24, 2014 5:21 pm

rgbatduke:
Your long but excellent post at January 24, 2014 at 2:47 pm contains much ‘good stuff’. One sentence jumped out at me because only yesterday I replied to a series of questions put to me on WUWT and I provided an answer which effectively said the same; i.e.

Let me be clear — we do not have sufficient evidence to reject the hypothesis of catastrophic warming by the end of the century (human caused or otherwise) any more than we have sufficient evidence to accept it.

Indeed, I said that to my questioner, I explained why it is true and I suggested further study that would assist the questioner’s understanding of it. But the questioner then responded with a post which tried to pretend I had said that there would no global warming! I suspect his intention was to induce me to say that and when he failed he tried to pretend I had.
Reality is important. What we want reality to be is not important. But as this thread clearly demonstrates, some people want to pretend reality is as they ‘see’ it and not as available evidence indicates.
Richard

Greg Goodman
January 24, 2014 5:35 pm

Richard: “Reality is important. What we want reality to be is not important. But as this thread clearly demonstrates, some people want to pretend reality is as they ‘see’ it and not as available evidence indicates.”
Indeed, and other some people want to pretend reality is as they ‘see’ it and do not understand what the available evidence indicates.”
http://climategrog.wordpress.com/?attachment_id=760

richardscourtney
January 24, 2014 6:37 pm

Greg Goodman:
At January 24, 2014 at 5:35 pm you say to me

Indeed, and other some people want to pretend reality is as they ‘see’ it and do not understand what the available evidence indicates.”
http://climategrog.wordpress.com/?attachment_id=760

I agree.
And I observe that your link provides some debatable data which its presenter says only “suggests” something and rightly does not claim it “indicates” anything.
Richard

Paul Westhaver
January 24, 2014 7:01 pm

I see what I see…

Manfred
January 24, 2014 8:06 pm

richardscourtney says:
January 24, 2014 at 7:34 am
But the analysis by Willis strongly suggests that the similarity of the SLR oscillation is purely by chance.
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Richard,
if curve fitting is not regarded as science, computaton of a single p value is hardly an “analysis”, which “strongly” suggests something without having a “look” at more specifics and details.
That language is not matching the evidence brought forward in support.
And readers notice that, and rightfully may get annoeyed, because they are not used to be manipulated like that, but enjoyed much more sophisticated and constructive discussions, many of them thanks to Willis.
And curiously, exactly that overdoing of an argument may be rightfully critizised in a few PRP papers as well. and it is therefore neither a serious-minded nor a clever option to citizise their work.
You say, maths is superior to visible inspection. Maybe, maybe often, but not in this case. The opposite is true.
Firstly, what you call maths is just a single value. And by visible inspection you may not only find out as well, that the p value may be low, but also why.
It is due to 2 phase shifts at the start of the data set and around 1990. Without these phase shifts, the datasets would be beating nearly synchronously all along the data.
The p value may still be low due to different amplitude patterns, but the beat is obvious.
The first thing of interest would be to investigate if the phase shift around 1990 is real. Comparing with Shaviv’s data set suggests, that it may be not, as in his data set the spread thereafter is smaller. Perhaps part of it due to Mt Pinatubo ?
The second thing of interest would be, if the beat continues with recent data.
The third thing of interest would be, if the beat is also visible in the satellite sea-level data.
Then you may be closer to conclude, if the relationship is thermal / mechanical.or due to a third common driver or, perhaps more likely “purely by chance”.

January 24, 2014 10:54 pm

Willis,
Regarding R^2 equals correlation.
R^2 isn’t very good at helping you find components of a signal. The correlation I (and others) are referring two is of the signal processing variety (Sum of the product, not sum of the square of the differences). This allows you to be wrong in phase, in scale, and still ‘detect’ a correlation. In addition there is another type of correlation known as synchronicity. R^2 is only valid for linear models. Consider Milankovitch cycles ‘correlation’ with ice-ages. If you were to do an R^2 you’d probably get a very a very low value. This is because the Earth is a complex non-linear feedback loop system and correlation assumes a linear model. Whatever Milankovitch cycles do and how they do it, it is probably more of a ‘trigger’ then a direct driver. Blindly using R^2 you’d probably get a value so low that you’d assume that Milankovitch cycles are irrelevant. However, synchronicity is amazing. No rational person can look at the synchronicity of Milankovitch cycles and ice ages and conclude “it’s just a coincidence”.
Regarding dismissing the graph:
However, you also linked to some other data that was far less flattering for sun spots (both synchronicity and statistical correlation). As I’ve said I’m not a ‘sun spot guy’. I objected to your use of R^2 meant for linear models and dismissal of the graph above when to the eye there appears an obvious synchronicity. Does the synchronicity hold for longer periods? Who knows? I thought you were too quick to dismiss it based on linear model metrics. However, If other data shows that this paper is essentially ‘cherry picking’, then that is a different story.

Paul Westhaver
January 24, 2014 11:07 pm

Has this paper been accepted for publication?
Solar Forcing of the Streamflow of a Continental Scale South American River
P. J. D Mauas, E. Flamenco, A. P. Buccino
http://ruby.fgcu.edu/courses/twimberley/EnviroPhilo/Mauas.pdf
http://arxiv.org/abs/0810.3882
From the Abstract:
Solar forcing on climate has been reported in several studies although the evidence so far remains inconclusive. Here, we analyze the stream flow of one of the largest rivers in the world, the Parana in southeastern South America. For the last century, we find a strong correlation with the sunspot number, in multidecadal time scales, and with larger solar activity corresponding to larger stream flow. The correlation coefficient is r=0.78, significant to a 99% level. In shorter time scales we find a strong correlation with El Nino. These results are a step toward flood prediction, which might have great social and economic impacts.
River stream flow may impact sea level change?

Manfred
January 24, 2014 11:40 pm

Hi Willis,
re failed Shaviv 2008 p=0.54 replication.
Shaviv used Lean 2000. Is your data identical ?
Shaviv removed secular trends.
Your zscore uses column D and not detrended values in E ?

Greg Goodman
January 25, 2014 1:42 am

” and when we look at the Jevrejeva sea level records its worse yet …”
What is your basis for that claim? What you posted earlier appears to be your own digitised version of Jevrejeva’s fig3 but you did not answer about where you got what you are calling “Jevrejeva sea level records “.
Their fig 3 is effectively 30 year smoothed, the J 2008 data does not look anything like what you posted. If you are now using the correct data, perhaps you need to clarify that you are not still referring to your previous error.
You asked for demonstration of a case in climate where the cause sometimes leads and sometimes lags the effect. That is the wrong question. That does not happen , neither did I suggest it did.
It a missing variable problem. What can give the _impression_ of effect periodically leading the cause is when you have two or more causes creating and interference pattern.
I demonstrated above, through cross-correlation of MSL and sunspot data, that there is a concurrent cause and that it has a frequency close to 9.2 years. The interference of those two causes produces the phase crisis that has already frequently been noted in trying to simplistically relate global temp change to SSN.
If there are two such causes, the result goes in and out of phase with both of them individually. If you calculate the corr-coeff over a full cycle of the interference pattern it will be ZERO.

If you do the CC over some non integral multiple of the interference patter as is the case here you will get a low correlation That does not mean the either of the two causes does not exist.

Is there any part of that , that you do not follow or disagree with?