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.
I want to say a few more words about “obvious” patterns. Someone commented above:
Someone else said yes, there is a pattern, it’s so clear that an eight-year old can see it.
Humans have succeeded as well as we have in part because we are superb at recognizing patterns, and learning from them. It’s one of our more amazing skills. The downside of this is that we often ascribe patterns where none actually exist. By that I mean the pattern may exist, but it’s just there by chance.
As examples of this, I offer the constellations in the stars, and the faces in the clouds.
We see both patterns … and they are definitely there, even an eight-year old can see them.
But they have no meaning. They are not a clue to an underlying reality, as many patterns are.
To deal with this conundrum, to assist us in determining which patterns are indications of something deeper and which patterns are just faces in the clouds, we invented a kind of math called statistics.
For example. Suppose I pull out a coin, flip it four times, and each time it comes up heads.
Does that mean the coin is weighted to produce heads? In other words, does that pattern have meaning, or is it random?
With math, we go OK, one chance in two of getting heads if the coin is random.
One chance in four of flipping two heads in a row.
One chance in eight of three heads.
One chance in sixteen of four heads.
So we know that IF the coin is random, there is one chance in sixteen that we’ll get four heads in any given run of four flips.
So … is the result, four heads in a row on the very first try, significant or not? That’s only half a question. The other half is the level of significance we want to see before saying yep, it’s real. For climate science the standard is very easy to achieve, one chance in twenty.
As a result, four heads in a row would NOT be considered statistically significant, because with a fair random coin, we would expect that outcome once in sixteen tries.
However, if we flip the coin again, and get heads, the odds of that are one in thirty-two. And that WOULD be considered a statistically significant result in climate science. Like I said, it’s a low threshold.
However, the sea level – sunspot correlation above, DESPITE THE APPARENT PATTERN, couldn’t even get over that low bar.
There is another curiosity that bears emphasis. If the chances are one in 32 of five heads in a row … if we do say a dozen runs of five flips each, what are the chances of five heads in a row coming up in one of the runs?
To do this, we multiply the odds of it NOT happening (31/32) times itself 12 times … and subtract that from 1. That gives us 1-(31/32)^12 = 32% chance of finding a five-head run in twelve tries. In fact, there’s a simple rule for finding a relationship that is individually statistically significant at the one in twenty level—just keep looking.
As Robert Brown said above, this can be a huge unrecognized problem. If a researcher hypothesizes a relationship, and looks at one dataset to see if it is real, the appropriate level of significance in climate science is one in twenty.
But if the researcher doesn’t find it, and looks at say five other datasets where the effect might be noticed … at that point, the odds of finding a “1 in 20” occurrence are down to one in four … and so now, in the six datasets, the researcher needs to find a signal with odds such that (( x-1 ) / x )^6 = 0.95, which solves to odds of about one in a hundred …
This comes into play, for example, if the researchers have free choice among the datasets to be selected for analysis. For example, the two sea level datasets used by Shaviv and by the author of the study in question are made from twelve and nine tidal station records respectively … out of a few thousand possibilities.
Finally, many natural datasets are like the surface of the ocean. There are a variety of waves that appear, and have strength for a while … but then they fade out and some other wave of a different height and period takes over … then that wave slowly both lengthens and decays, and there is a period of calm, which we notice and name the “Maunder Minimum”, and then another new and different wave starts building up …
My general point is, natural datasets are like trailer-trash ex-husbands. My best advice is, DON’T TRUST THOSE SUCKERS, THEY ARE LYING TO US JUST LIKE LAST TIME! It is treacherously easy to chase such will-o-the-wisps forever. It is ludicrously simple to drop a trend and a sixty-year cycle onto the HadCRUT temperature data and declare there is a relationship, even an eight year old can see the pattern … but just like a celebrity marriage, that lovely relationship likely won’t last.
So in response to those folks who insist that the pattern is there before their eyes, I cannot dispute that any more than I can dispute a face in the clouds.
However, only the math can let us know whether it is significant … and even there, we find lots of hidden pitfalls.
My best to all, this continues to be a most interesting discussion.
w.
PS—having now looked at the Shaviv dataset, and the Church and White dataset, it’s clear that the sea level data won’t bear the weight of the conclusion that sunspots correlate with sea level. I also took a quick look at the CERES data, no relationship there either. So the math seems to have been right, it was either a spurious or a very weak correlation.
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” ?
Leif, do you have a link to whatever might be the longest observationally based and count-corrected sunspot dataset?
Thanks,
w.
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).
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.173.2162&rep=rep1&type=pdf
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.
Manfred says:
January 23, 2014 at 7:11 pm
~ 0.00.
Sure. However, it’s a very different problem because the increase in CO2 is so highly autocorrelated, and is also essentially a straight line. As a result, establishing significance between CO2 and anything is highly difficult … hence the unending search for a CO2 “fingerprint” that to date hasn’t appeared.
Definitely. If your math is done correctly, it shouldn’t matter. You gain R^2 by smoothing, but you lose significance because of high autocorrelation. If it’s not significant unsmoothed, odds are good that it’s not significant smoothed. Following the guidance of the estimable William Briggs, I make a practice of never smoothing data used to calculate significance.
The other problem is that smoothing can actually introduce bogus, spurious correlations. I wrote a post on that some years ago, hang on … OK, six years ago, here you are.
It’s not the first time I’ve seen variations on that theme, and I wasn’t any more impressed the first time than I am now.
More to the point, I’ve looked at too many natural datasets to be impressed by that kind of thing. Heck, one reason I wrote about this was that I looked at it and estimated by eye that there was no statistically significant relationship … which turned out to be the case.
w.
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
Manfred says:
January 23, 2014 at 7:32 pm
Don’t believe everything you read. I just digitized Shaviv’s data, and compared it to the Lean TSI data from here … R2 = 0.09, p-value 0.09 … not sure why the difference. Perhaps it’s the use of a different TSI set … which just reveals the weakness of the method.
Spreadsheet here …
w.
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.
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.
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:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.173.2162&rep=rep1&type=pdf
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
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
http://www.vukcevic.talktalk.net/OldLadysHalo.htm
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.
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.
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
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
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
‘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.
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.
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.
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
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
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.
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.
How about that Richard? Follow me?
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.
lsvalgaard says:
Since there are not, this is a non-issue.
………
Andreas Baumgaertner from UCAR thinks there are : Page 11
http://sisko.colorado.edu/FESD/MeetPresentFiles/Jul1_2013/Baumgaertner.pdf
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
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.
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