The Elusive ~ 60-year Sea Level Cycle

Guest Post by Willis Eschenbach

I was referred to a paywalled paper called “Is there a 60-year oscillation in global mean sea level?”  The authors’ answer to the eponymous question is “yes”, in fact, their answer boils down to “dangbetcha fer sure yes there is a 60-year oscillation”, viz:

We examine long tide gauge records in every ocean basin to examine whether a quasi 60-year oscillation observed in global mean sea level (GMSL) reconstructions reflects a true global oscillation, or an artifact associated with a small number of gauges. We find that there is a significant oscillation with a period around 60-years in the majority of the tide gauges examined during the 20th Century, and that it appears in every ocean basin.

So, as is my wont, to investigate this claim I got data. I went to the PSMSL, the Permanent Service for the Mean Sea Level, and downloaded all their monthly tidal records, a total of 1413 individual records. Now, the authors of the 60-year oscillation paper said they looked at the “long-term tide records”. If we’re looking for a 60-year signal, my rule of thumb says that you need three times that, 180 years of data, to place any confidence in the results. Bad news … it turns out only two of the 1,413 tidal gauge records, Brest and Swinoujscie, have 180 years of data. So, we’ll need to look at shorter records, maybe a minimum of two times the 60-year cycle we’re looking for. It’s sketchy to use that short of a record, but “needs must when the devil drives”, as the saying goes. There are twenty-two tidal datasets with 120 years or more of data. Figure 1a shows the first eight of them:

all tide records over 120 years 1-8Figure 1a. Tide gauge records with 1440 months (120 years) or more of records. These are all relative sea levels, meaning they are each set to an arbitrary baseline. Units are millimetres. Note that the scales are different, so the trends are not as uniform as they appear.

Now, there’s certainly no obvious 60-year cycles in those tidal records. But perhaps the subtleties are not visible at this scale. So the following figure shows the Gaussian averages of the same 8 tidal datasets. In order to reveal the underlying small changes in the average values, I have first detrended each of the datasets by removing any linear trend. So Figure 1b emphasizes any cycles regardless of size, and as a result you need to note the very different scales between the two figures 1a and 1b.

gauss all tide records over 120 years 1-8Figure 1b. Gaussian averages (14-year full-width half-maximum) of the linearly detrended eight tide gauge datasets shown in Figure 1a. Note the individual scales are different from Figure 1a.

Huh. Well, once the data is linearly detrended, we end up with all kinds of swings. The decadal swings are mostly on the order of 20-30 mm (one inch) peak to peak, although some are up to about twice that. The big problem is that the decadal swings don’t line up, they aren’t regular, and they don’t have any common shape. More to the current point, there certainly is no obvious 60-year cycle in any of those datasets.

Now, we can take a closer look at what underlying cycles are in each of those datasets by doing a periodicity analysis. (See the notes at the end for an explanation of periodicity analysis). It shows how much power there is in the various cycle lengths, in this case from two months to seventy years Figure 1c shows the periodicity analysis of the same eight long datasets. In each case, I’ve removed the seasonal (annual) variations in sea level before the periodicity analysis.

periodicity all tide records over 120 years 1-8Figure 1c. Periodicity analysis, first eight long-term tidal datasets.

Boooring … not much of anything anywhere. Top left one, Brest, has hints of about a 38-year cycle. New York shows a slight peak at about 48 years. Other than that there is no energy in the longer-term cycles, from say 30 to 70 years.

So let’s look at the rest of the 22 datasets. Here are the next eight tide gauge records, in the same order—first the raw record, then the Gaussian average, and finally the periodicity analysis.

all tide records over 120 years 9-16 gauss all tide records over 120 years 9-16 periodicity all tide records over 120 years 9-16Figures 2a, 2b, and 2c. Raw data, Gaussian averages, and periodicity analysis, next 8 stations longer than 120 years.

No joy. Same problem. All kinds of cycles, but none are regular. The largest problem is the same as in the first eight datasets—the cycles are irregular, and in addition they don’t line up with each other. Other than a small peak in Vlissingen at about 45 years, there is very little power in any of the longer cycles. Onwards. Here are the last six of the twenty-two 120-year or longer datasets:

all tide records over 120 years 17-22 gauss all tide records over 120 years 17-22 periodicity all tide records over 120 years 17-22

Figures 3a, 3b, and 3c. Data, Gaussian averages, and periodicity analysis as above, for the final six 120-year + tide gauge datasets. 

Dang, falling relative sea levels in Figure 3a. Obviously, we’re looking at some tidal records from areas with “post-glacial rebound” (PGR), meaning the land is still uplifting after the removal of trillions of tons of ice at the end of the last ice age. As a result, the land is rising faster than the ocean …

How bizarre. I just realized that people worry about sea-level rise as a result of global warming, and here, we have land-level rise as a result of global warming  … but I digress. The net result of the PGR in certain areas are the falling relative sea levels in four of the six datasets.

Like the other datasets, there are plenty of cycles of various kinds in these last six datasets in Figure 3, but as before, they don’t line up and they’re not regular. Only two of them have something in the way of power in the longer cycles. Marseille has a bit of power in the 40-year area. And dang, look at that … Poti, the top left dataset, actually has hints of a 60-year cycle … not much, but of the twenty-two datasets, that’s the only one with even a hint of power in the sixty-year range.

And that’s it. That’s all the datasets we have that are at least twice as long as the 60-year cycle we’re looking for. And we’ve seen basically no sign of any significant 60-year cycle.

Now, I suppose I could keep digging. However, all that are left are shorter datasets … and I’m sorry, but looking for a sixty-year cycle in a 90-year dataset just isn’t science on my planet. You can’t claim a cycle exists from only enough data to show one and a half swings of the cycle. That’s just wishful thinking. I don’t even like using just two cycles of data, I prefer three cycles, but two cycles is the best we’ve got.

Finally, you might ask, is it possible that if we average all of these 22 datasets together we might uncover the mystery 66-year cycle? Oh, man, I suppose so, I’d hoped you wouldn’t ask that. But looking at the mish-mash of those records shown above, would you believe it even if I found such a cycle? I don’t even like to think of it.

Ah, well, for my sins I’m a scientist, I am tormented by unanswered questions. I’d hoped to avoid it, so I’ve ignored it up until now, but hang on, let me do it. I plan to take the twenty-two long-term records, linearly detrend them, average them, and show the three graphs (raw data, Gaussian average, and periodicity analysis) as before. It’ll be a moment.

OK. Here we go. First the average of all of the detrended records, with the Gaussian average overlaid.

mean detrended 22 tide recordsFigure 4a. Mean of the detrended long-term tidal records. Red line shows a 14-year full-width half-maximum (FWHM) Gaussian average of the data, as was used in the earlier Figures 1b, 2b, 3b.

Well, I’m not seeing anything in the way of a 60-year cycle in there. Here’s the periodicity analysis of the same 22-station mean data:

periodicity mean detrended 22 tide recordsFigure 4b. Periodicity analysis of the data shown in Figure 4a immediately above.

Not much there at all. A very weak peak at about forty-five years that we saw in some of the individual records is the only long-term cycle I see in there at all.

Conclusions? Well, I don’t find the sixty-year cycle that they talk about, either in the individual or the mean data. In fact, I find very little in the way of any longer-term cycles at all in the tidal data. (Nor do I find cycles at around eleven years in step with the sunspots as some folks claim, although that’s a different question.) Remember that the authors said:

We find that there is a significant oscillation with a period around 60-years in the majority of the tide gauges examined during the 20th Century …

Not able to locate it, sorry. There are decadal swings of about 25 – 50 mm (an inch or two) in the individual tide gauge datasets.  I suppose you could call that “significant oscillations in the majority of the tide gauges”, although it’s a bit of a stretch.

But the “significant oscillations” aren’t regular. Look at the Gaussian averages in the first three sets of figures. The “significant oscillations” are all over the map. To start with, even within each individual record the swings vary greatly in amplitude and cycle length. So the cycles in each individual record don’t even agree with themselves.

Nor do they agree with each other. The swings in the various tidal records don’t line up in time, nor do they agree in amplitude.

And more to the point, none of them contain any strong approximately sixty-year signal. Only one of the twenty-two (Poti, top left in Figure 3a,b,c) shows any power at all in the ~ 60 year region in the periodicity analysis.

So I’m saying I can’t find any sign in those twenty-two long tidal datasets of any such sixty-year cycle. Note that this is different from saying that no such cycle exists in the datasets. I’m saying that I’ve pulled each one of them apart and examined them individually as best I know how, and I’m unable to find the claimed “significant oscillation with a period around 60-years” in any of them.

So I’m tossing the question over to you. For your ease in analyzing the data, which I obtained from the PSMSL as 1413 individual text files, I’ve collated the 1413 record tide station data into a 13 Mb Excel worksheet, and the 22 long-term tidal records into a much smaller CSV file. I link to those files below, and I invite you to try your hand at demonstrating the existence of the putative 60-year cycle in the 22-station long-term tidal data.

Some folks don’t seem to like my use of periodicity analysis, so please, use Fourier analysis, wavelet analysis, spectrum analysis, or whatever type of analysis you prefer to see if you can establish the existence of the putative “significant” 60-year cycles in any of those long-term tidal datasets.

Regards to all, and best of luck with the search,

w.

The Standard Request: If you disagree with something someone says, please have the courtesy to quote the exact words you disagree with. It avoids all kinds of trouble when everyone is clear what you are objecting to.

Periodicity Analysis: See the post “Solar Periodicity” and the included citations at the end of that post for a discussion of periodicity analysis, including an IEEE Transactions paper containing a full mathematical derivation of the process.

Data: I’ve taken all of the PSMSL data from the 1413 tidal stations and collated it into a single 13.3 Mb Excel worksheet here. However, for those who would like a more manageable spreadsheet, the 22 long-term datasets are here as a 325 kb comma-separated value (CSV) file.

[UPDATE] An alert commenter spots the following:

Jan Kjetil Andersen says:

April 26, 2014 at 2:38 pm

By Googling the title I found the article free on the internet here:

http://www.nc-20.com/pdf/2012GL052885.pdf

I don’t find it any convincing at all. They use the shorter series in the PSMSL sets, and claim to see 64 years oscillations even though the series are only 110 years long.

The article has no Fourier or periodicity analysis of the series.

/Jan

Thanks much for that, Jan. I just took a look at the paper. They are using annually averaged data … a very curious choice. Why would you use annual data when the underlying PSMSL dataset is monthly?

In any case, the problem with their analysis is that you can fit a sinusoidal curve to any period length in the tidal dataset and get a non-zero answer. As a result, their method (fit a 55 year sine wave to the data) is meaninglesswithout something with which to compare the results.

A bit of investigation, for example, gives the following result. I’ve used their method, of fitting a sinusoidal cycle to the data. Here are the results for Cascais, record #43. In their paper they give the amplitude (peak to peak as it turns out) of the fitted sine curve as being 22.3. I get an answer close to this, which likely comes from a slight difference in the optimization program.

First, let me show you the data they are using:

If anyone thinks they can extract an “~ 60 year” cycle from that, I fear for their sanity …

Not only that, but after all of their waffling on about an “approximately sixty year cycle”, they actually analyze for a 55-year cycle. Isn’t that false advertising?

Next, here are the results from their sine wave type of analysis analysis for the periods from 20 to 80 years. The following graph shows the P-P amplitude of the fitted sine wave at each period.

So yes, there is indeed a sinusoidal cycle of about the size they refer to at 55 years … but it is no different from the periods on either side of it. As such, it is meaningless.

The real problem is that when the cycle length gets that long compared to the data, the answers get very, very vague … they have less than a hundred years of data and they are looking for a 55-year cycle. Pitiful, in my opinion, not to mention impossible.

In any case, this analysis shows that their method (fit a 55-year sine wave to the data and report the amplitude) is absolutely useless because it doesn’t tell us anything about the relative strength of the cycles.

Which, of course, explains why they think they’ve found such a cycle … their method is nonsense.

Eternal thanks to Jan for finding the original document, turns out it is worse than I thought.

w.

 

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Bart
April 29, 2014 1:24 pm

HenryP says:
April 29, 2014 at 12:48 pm
Might we suppose the WWII glitch could be related to disruption of regular measurements during that time?
I will bookmark your page and try to take a look when I have time. I’m skeptical, in general, of planetary influences, because gravitational coupling is so weak, but… well, I’ll take a look.

Reply to  Bart
April 29, 2014 1:40 pm

@bart
Thx!
It is the planets that affect the sun. I am sure we will see another polar switch on the sun in or around 2016.
Do understand that there is quite a lag between energy in (maxima) and energy out (mean earth temp.)

Greg
April 29, 2014 4:11 pm

“the SST 2 (green) seems a bit strange around the WWII area”
This is hardly news to anyone. McIntyre was onto this about a decade ago. I call it Folland’ Folly. A pretty ridiculous 0.5 step adjustment to SST was made at 1945. It now get blended to it does not stick out like a sore thumb but I doubt where that makes it more justified.
http://judithcurry.com/2012/03/15/on-the-adjustments-to-the-hadsst3-data-set-2/

Greg Goodman
April 29, 2014 4:56 pm

Bart says: “But, I don’t expect to see it in GMSL, as I explained above.”
OK, I don’t know what you have on this. My hypothesis is that it is basin-wide, long period tides in the thermocline. They have the same origin as surface tides but the interface at the thermocline has a density difference about a thousand times less that the air/water surface. This means that the typical period of the oscillations will be about 1000 times longer.
Compared to a 12h tide that is the stimulus that the surface tides respond most strongly too, that is about 1.4 years. ie we are looking for a response measured in a few years. I suspect this is the origin of El Nino. Look at 3D animations of the thermocline depth and it is quite simply a slow acting wave.
These “pseudo periodic” phenomena are just several harmonic responses added to each other, similar to the complex variations in surface tidal amplitudes. From time to time there’s “spring tide” and we call it El Nino.
If you look at what is currently happening in the equatorial depth profile of temp, you see an El Nino like formation in the Pacific that is not coming from the surface waters as usual “slosh” myth would have it. It must be coming from non equatorial latitudes.
Consider that the recent lunar eclipse was just about 10d after the equinox and you have maximum tidal forces focused on the equator thus drawing water in bulk form both N and S Is suspect we will see a stronger El Nino build up next year as the eclipse will be within days of the equinox. Such a resonant tidal pattern does not have to be in phase with the driving force, bit it’s the first thing to look for.
The whole Pacific seems to have regions both in-phase and anti-phase with PDO. That again suggests basin-wide tidal oscillations.
These are just qualitative observations, I don’t have numbers and wave equations !
There is a 15% difference in max apogee and min perigee. That implies a 45% change in lunar tidal at the most extreme. That is one hell of change in 14 days. How that cycle ties in with lunar declination angle means the whole thing will look “chaotic” unless some serious analysis is performed.
This is why many are tempted to jump to the easy conclusion and attempt to sound learned in their ignorance by using fancy words like “stochastic” to mask their failure to analyse the system.
Hopefully some of that will key in with what you are doing.
regards, Greg.

Greg Goodman
April 29, 2014 5:01 pm

“Dear heavens, you are seriously claiming that you can diagnose is an ~70 year cycle in 115 years of data? That’s Nostradamus level madness …”
Hell, mainstream manage to predict 100 into the future with a 95% certainty, based on a sloppy match to 1960-1990 hindcasts, . Why can’t we play too ? 😉

Greg Goodman
April 29, 2014 5:27 pm

http://www.woodfortrees.org/plot/hadsst3nh/detrend:0.8
I’m more inclined to see a folded (rectified) cosine than a pure harmonic.
Same abs(cos(2.pi.x/135)) idea seems to match Arctic ice
http://climategrog.wordpress.com/2013/09/16/on-identifying-inter-decadal-variation-in-nh-sea-ice/

Greg Goodman
April 29, 2014 5:28 pm
1sky1
April 29, 2014 7:03 pm

Chambers et al. use”long tide gauge records in every ocean basin to examine whether a quasi 60-year oscillation observed in global mean sea level (GMSL) reconstructions reflects a true global oscillation, or an artifact associated with a small number of gauges.” The means by which they reach their conclusion that there is such an oscillation remains behind the paywall. I merely provide the heads-up that “periodicity analysis” is not suitable for finding such a quasi-periodic oscillation, whose amplitude, phase, and frequency can vary widely. On the basis of PSMSL’s averaging of tide-gauge data over calendar (not lunar) months and your removal of a linear trend and annual cycle, I simply conclude that “nothing truly periodic is left in the sea-level data to analyze with this simplistic technique.” Signal components can be obscured or destroyed by inept data handling, in the direct converse of the Slutsky effect.
You respond with: “Instead of doing even a scrap of actual work, you want to stand around and tell me how smart you are. Here’s a protip, 1sky1. You’ll look smart when you find the cycles. Until then, you just look unpleasant. Those that can, do. Those that can’t, stand around and bitch. So far, you’re in Group B. If you’re so damn smart, then where are your results?”
I make no claim, one way or another, on the open question of a ~60yr component in GLOBAL sea-level variations. You’re the one who does that! Your personal attack is a hilarious attempt to turn the tables. It’s solely YOUR responsibility to provide a scientifically CREDIBLE analysis.

Bart
April 29, 2014 7:38 pm

Willis Eschenbach says:
April 29, 2014 at 4:46 pm
Oh, come off it, Willis. Nothing before 1900 is reliable. The error bars are as large as the variation. There is no scientific precept which says you have to fit all the data you have, no matter how unreliable or unrepresentative it may be. Science would never advance under such a rule.
As for how long the mode has been excited in this way anyway, who can say? This is NOT a wholly deterministic system we are dealing with. The cycles can ebb and flow. This is normal when you are dealing with highly nonlinear and complex, natural systems.
Here, we have two whole, very regular, cycles. It is meaningful. It is not coincidence or, rather, if it is, it is one helluva big one.
I wish I could find the conversations I had with, I think it was Pat Frank, on these pages when we were trying to determine if there had been a turnaround in the mid-2000’s as you would expect of an at-least quasi-cyclical phenomenon with ~60 year period. I remember stating quite clearly that I did not think we could say so yet. The idea was gaining currency in many corners. SkS even had a page dedicated to debunking any notion of a ~60 year periodicity.
By 2009, I think it was, it had become evident that the turnaround had arrived in about 2005, right on time. You can wait around for the next one if you like. It should happen sometime in the 2030’s. The rest of us are moving on.
Greg Goodman says:
April 29, 2014 at 4:56 pm
We may be heading down similar paths. I have been pursuing a lead based on the recognition of this phenomenon. I believe there is possibly, if not likely, a beat phenomenon of the tides with the solar cycle alternately storing and releasing heat which leads to the ~60 year quasi-cycle.
“This is why many are tempted to jump to the easy conclusion and attempt to sound learned in their ignorance by using fancy words like “stochastic” to mask their failure to analyse the system. “
Depends on what they mean by “stochastic.” If they mean, “it’s totally random or chaotic, and we can’t do anything with it,” then it is a cop out.
Stochastic does not generally mean unpredictable. There is structure here, along with processes which are so complicated or chaotic that a probabilistic model is needed to make it all tractable. We have fairly deterministic forcing from lunar and solar tides, but very complex fluid dynamics and variable solar activity.

April 29, 2014 11:01 pm

bart says
By 2009, I think it was, it had become evident that the turnaround had arrived in about 2005, right on time. You can wait around for the next one if you like. It should happen sometime in the 2030′s. The rest of us are moving on.
henry says
you can see it clearly from 2002 onward
http://www.woodfortrees.org/plot/hadcrut4gl/from:1987/to:2015/plot/hadcrut4gl/from:2002/to:2015/trend/plot/hadcrut3gl/from:1987/to:2015/plot/hadcrut3gl/from:2002/to:2015/trend/plot/rss/from:1987/to:2015/plot/rss/from:2002/to:2015/trend/plot/hadsst2gl/from:1987/to:2015/plot/hadsst2gl/from:2002/to:2015/trend/plot/hadcrut4gl/from:1987/to:2002/trend/plot/hadcrut3gl/from:1987/to:2002/trend/plot/hadsst2gl/from:1987/to:2002/trend/plot/rss/from:1987/to:2002/trend
Strangely enough, from my own data, especially on maxima, I could already see it coming from 2000 and you can calculate from the change in the warming speed that the original change at the sun took place in 1995. So, it takes 7 to 10 years to notice the change…..
http://blogs.24.com/henryp/2013/02/21/henrys-pool-tables-on-global-warmingcooling/
Bart, in the end for that graph for the drop in the speed of maximum temperatures I decided it must be sinusoidal, as otherwise we would end up in an ice age?
http://blogs.24.com/henryp/2012/10/02/best-sine-wave-fit-for-the-drop-in-global-maximum-temperatures/
Do you agree?
So, whatever is coming out on earth (means) is also a bit like a wave, although not necessarily having to look exactly like that of the energy being allowed through the atmosphere. Hence, my prediction that arctic ice will only start increasing again from 2020-2040 (corresponding with 1930-1950 in the Gleissberg)
The fiddling with the data in the past does not make it any easier for us. Also, I have challenged anyone to show me a re-calibration certificate of a thermometer before 1940. Nobody has been able to show me one yet. That makes me very skeptical of all the data before 1950. I would trust my own data more than any other data set. Also, all global data sets are not properly globally balanced, see here,
http://wattsupwiththat.com/2014/04/25/the-elusive-60-year-sea-level-cycle/#comment-1622942
My own data set tells me that we already dropped by -0.2 K since 2000. I am busy updating my tables and adding 2012 and 2013 to my data set and it makes it even looking worse than that……
Note that we are globally cooling from the top latitudes downward. For example, it has been cooling significantly in Alaska, at a rate of -0.55K per decade since 1998 (Average of ten weather stations).
http://oi40.tinypic.com/2ql5zq8.jpg
That is almost one whole degree C since 1998.
And then you have people like Steven Mosher who tried to twist and turn these data (from Alaska).
Instead, we should warn the farmers in Alaska that it is not going to get any better there for the next 3 decades.

Greg
April 30, 2014 5:29 am

Bart: “We may be heading down similar paths. I have been pursuing a lead based on the recognition of this phenomenon. I believe there is possibly, if not likely, a beat phenomenon of the tides with the solar cycle alternately storing and releasing heat which leads to the ~60 year quasi-cycle.”
I did an article about this time last year that showed that there was a signficant circa 9y signal in many SST ocean basins. I pointed out that would disrupt any solar signal leading to just the kind of “phase crisis” that is used to refute the presence of a solar ( Schwabe cycle ) infulence on climate.
http://climategrog.wordpress.com/2013/03/01/61/
Recent attempts to explain the beats phenomenon to Willis ended in tears.
There is strong 9.1 in cross-correlation of N.Atl and N.Pac SST. In fact I’m fairly sure that this period ( also reported by Scaffeta ) is itself a combination of 8.85 (perigee) and 9.3 ( 18.6/2 ) . The mean freq of those two being 9.04, usually reported as 9.1 +/- 0.1 . That gives a modulation of 365 _years_ . I won’t go into that now but you may like to reflect that its a one day per year drift.
Since the solar cycle if far from being this regular the result is not so nice and simple but does produce variations of around 60 years.
There’s also 8.83 in the power spect. of Church & White’s GMSL that I linked above.
http://climategrog.wordpress.com/?attachment_id=935
I think one of the major causes of the lunar signal is water mass being attracted in and out of the equatorial regions. The leads to variations between tropics and temperate latitudes that get neatly average away in the usual : one-size-fits-all global means. There’s a residual but it causes get lost due to phase difference.
Once we start to regional analysis, and apply normal systems engineer approach there is a chance we may start to learn a little about what drives climate.

Greg
April 30, 2014 5:43 am

I think you have a mistake in your link:
T1 = 11*9.3/(11+9.3) = 5 years
It is the mean freq 11*9.3/(11+9.3)*2 , ie 10.1 y with those figures, that gets a 120 y modulation ( ie 60y beats).

Greg
April 30, 2014 5:47 am

That’s the problem, people see a circa 10y seems to ‘roughly match’ SSN for a while , but then gradually drifts out of phase, ending up completely in anti-phase. Those will little understanding of interference patterns take this as definitive proof that there is NO solar signal and it’s all wishful thinking and cherry-picking. And there is a lot of cherry-picking going on by those who don’t understand the phase shift and just choose to look at the bits which fit !

Greg
April 30, 2014 6:03 am

The modulation profile of 120 y modulation is the 60y “beats” that have a folded cosine amplitude ( ie not sinusoidal ) this is what I think we see in NH SST and cyclone energy.
http://climategrog.wordpress.com/?attachment_id=215
This is one area where the P.A. technique Willis favours may be helpful. Fourier techniques lead a lot of people to assume harmonic variation when it is not the case.
For example you look a spectra of NH SST it will tell you about 60y , not 120, unless you are skilled enough to look for the other harmonics the build up the true profile. This is why we need engineers on the job not environmental activists.

Greg
April 30, 2014 6:13 am

For the modulation envelop of “beats” to be visible after it has all been smoothed by natural processes or filtered in data processing requires either some process to “rectify” the signal ( like is done in radio demodulation) or some non linearity in the system.
One possible ‘rectifier’ is wind speed , which affects many variable with it’s magnitude and thus looses the sign or direction. There is also plenty of scope for non linearities ( again wind for ex. ) that will also lead to a residual of the 60y envelop.
Some try to dismiss this kind of analysis as “cyclomania” without realising that this is exactly the kind of interaction that leads to chaotic nature of climate.
Yes it is complicated, as are many engineering problems. Saying it random is a cop out.

Bart
April 30, 2014 7:46 am

Greg says:
April 30, 2014 at 5:29 am
Interesting. Thanks. In looking at a few tide gauges via PSD analysis, I have not been able to tease out a 9.3 year signal. But, I have seen specific formations repeating at a 1/9.3 years^-1 interval, which suggests further modulation, which I have thought might be indicative of forcing at 1/9.3 years^-1 modulated by reflections off the shoreline due to the physical limits of the basins. But, I have not had time to delve into this very far, and this is just my first impression.
I will have to review your findings to see where they lead.
Greg says:
April 30, 2014 at 5:43 am
No, it’s the correct formula, because
cos((2*pi/T1)*t) * cos((2*pi/T2)*t) = 0.5* ( cos((2*pi*(1/T1+1/T2))*t) + cos((2*pi*(1/T1-1/T2))*t) )
You are going the other way:
cos((2*pi/T1)*t) + cos((2*pi/T2)*t) = 2*cos(pi*(1/T1+1/T2)*t) * cos(pi*(1/T1-1/T2)*t)
This idea is that the intensity of the Sun is “gated”, if you will, by the physical state of the oceans, whether they are extended beyond their basins to receive more radiant energy, or settled in them to consolidate what they have received. That produces a modulation, as in the first formula.
Greg says:
April 30, 2014 at 6:03 am
Yes, it’s not really a “beat”. Because the periods are so close together, you really cannot designate one the carrier and one the signal. It looks like this. Which looks not unlike this.

April 30, 2014 10:57 am

henry@bart
I see you did some work on SSN finding a 20 and 23.6
That seems a lot like the Hale-Nicholson cycle to me.
Last time I looked at SSN I was sure that the way they recorded SSN has changed over time.
Anyway, since then I keep a look at this graph:
http://ice-period.com/wp-content/uploads/2013/03/sun2013.png
Clearly, you can see that the polar field strengths of the sun are dropping?
In fact, even without having the original data, I can see a binomial best fit, hyperbolic, coming from the top down and reaching its lowest point at around 2015 or 2016. From the bottom, parabolic, reaching its highest point around 2015 or 2016.
You agree?
All of that makes my theory very simple:
I figure that there must be a small window at the top of the atmosphere (TOA) that gets opened and closed a bit, every so often. Chemists know that a lot of incoming radiation is deflected to space by the ozone and the peroxides and nitrous oxides lying at the TOA. These chemicals are manufactured from the UV coming from the sun. Luckily we do have measurements on ozone, from stations in both hemispheres. I looked at these results. Incredibly, I found that ozone started going down around 1951 and started going up again in 1995, both on the NH and the SH. Percentage wise the increase in ozone in the SH since 1995 is much more spectacular.
The mechanism? We know that there is not much variation in the total solar irradiation (TSI) measured at the TOA. However, there is some variation within TSI, mainly to do with the E-UV. It appears (to me) that as the solar polar fields are weakening, generally a larger amount of more energetic particles are able to escape from the sun to form more ozone, peroxides and nitrogenous oxides at the TOA of earth/.
You concur?

Bart
April 30, 2014 11:55 am

HenryP says:
April 30, 2014 at 10:57 am
Yes, it is very clear that the approximately 10, 10.8, 11.8, and 134 year components evident in the SSN data are coming from rectification of processes centered at approximately 20 and 23.6 years.
As for solar UV, I do not know of any source where that has been tracked. I supposed it couldn’t have been tracked for very long.
Your idea is not the one I am pursuing, so of course I cannot concur with it 😉 But, it would be very interesting to see long term data on UV variation if it exists anywhere.

April 30, 2014 12:42 pm

@bart
thx
if you read william arnold’s report,
you will find that he puts 4 cycles (Hale-Nicholson) in an a-c wave (4 quadrants) , which gave me the idea of putting the drop in the speed of maximum temperatures in an a-c wave.
he is not in favor of the 11 year Schwabe
but prefers the 22 year Hale-Nicholson
which seems a lot like an average of the 20 an 23.6 that you found…….
it might be a good idea to go back to his (ancient?) report as my own findings support exactly what he found, albeit that his dates might be a bit out.
http://www.cyclesresearchinstitute.org/cycles-astronomy/arnold_theory_order.pdf
It is not solar UV TOA that is important
we are looking beyond UV, at X-ray and further towards zero um coming from the sun
that is what is making Ox, HxOx and NxOx at the TOA.
In a way these chemical reactions TOA protect us from certain death due to radiation.
They absorb or deflect harmful UV and non-harmful UV to space.
If there is more of it they deflect more of it
but it is exactly the UV which is what mostly heats up our oceans……
That is what is causing cooling and warming periods.

Greg
April 30, 2014 2:17 pm

Bart: “Yes, it’s not really a “beat”. Because the periods are so close together, you really cannot designate one the carrier and one the signal. It looks like this. Which looks not unlike this.”
Well if it’s not a beat don’t call it a beat. No reason not to suggest one of two close frequencies modulating the other , just don’t confuse things by calling it “beats” which has a well established meaning.
Having two additive frequencies is more relevant to SPD since that is unfailingly what fourier linke techniques do. They have no way to express a modulated signal so always give additive components. If there is a physical modulation, FFT will always give the lefthand side of you second expression which is identical to the real modulaiton on the RHS.
Half the sum of the frequencies ( aka the average ) is what you see in the time series. It is amplitude modulated by half the difference. Since the eye (like the ear) usually missed the fact that the phase inverts we tend to see the repetition as being twice as fast as it is mathematically. This gets called beats. So thiat is simply the freq difference.
>>
No, it’s the correct formula, because
cos((2*pi/T1)*t) * cos((2*pi/T2)*t) = 0.5* ( cos((2*pi*(1/T1+1/T2))*t) + cos((2*pi*(1/T1-1/T2))*t) )
>>
That’s a bit of a special case because the both amplitudes are the same on the LHS. Physically things are not often modulated right down to zero amplitude like that. Usually the higher frequency (carrier in the radio analogy) is greater in amplutude and does not totally disappear at any stage. This leaves a symmetic “triplet” of frequencies. In radio, modulation depth is cnosen to be less than half ( which is confusingl called 100% modulation 😉 ) . This leaves the central peak at least twice the size of the side bands. There is no reason that limitation will be present in a natural modulation.
An example of the a triplet in arctic ice coverage:
http://climategrog.wordpress.com/?attachment_id=757

April 30, 2014 2:21 pm

Alaska.
Instead of cheery picking 10 stations, use them all
http://berkeleyearth.lbl.gov/regions/alaska
The problem with picking 10 stations is that your error of prediction is large.
Remember an “average” of temperatures is really not a physical thing. What it is is this: a prediction.
Its a prediction of what the temperature would be at locations where you didnt measure.
And its easy to test. You take your stations ( there are 300 ) you build the average with 150
That allows you predict what you are likely to see at other locations. You then test that by
using the out of sample data.
When you show your method minimizes error and is BLUE then your estimate using all the data
is BLUE and minimizes error.
Until you do this analytic test your method is just garbage. worse than HADCRUT

Greg
April 30, 2014 2:31 pm

“It looks like this: http://s1136.photobucket.com/user/Bartemis/media/60yearcycle_zps994247be.jpg.html?sort=3&o=0 which looks like this http://www.woodfortrees.org/plot/hadcrut4gl/from:1880/detrend:0.75/mean:15/mean:15
Well this is what I mean about muddying the waters. NH and SH behave differently. I suggest keeping them separate. Land changes twice as fast as SST and with slightly different phase. Mixing the two again muddles the signal and impedes analysis.

Greg
April 30, 2014 2:38 pm

http://www.woodfortrees.org/plot/hadsst3nh/from:1880/detrend:0.6
To me the bottoms look pointed and the tops round. That’s why I’m inclined to see a folded cosine.
Whether this dataset resembles real climate and whether the face I see in the clouds is really the face of God, is another question. 😉

April 30, 2014 2:39 pm

Henry P
‘The fiddling with the data in the past does not make it any easier for us. Also, I have challenged anyone to show me a re-calibration certificate of a thermometer before 1940. Nobody has been able to show me one yet. That makes me very skeptical of all the data before 1950.”
Well
1. you should be skeptical of all data.
2. A calibration document tells you nothing because.
A) you have to trust the document
B) you have to trust that the device did not go out of calibration
3. Mis calibration, sensor drift, sensor change, is all grouped under the nugget effect
You and others are used to doing bottoms up error estimates. Kriging works the other
way around. What you end up with after minimizing the error is a correlation at distance
zero which is translated into an error due to ALL SOURCES of error. For example,
if you have 10 stations and you fit the field to these stations by minimizing the fit error
your residual tells you something.
4. The earliest US records I have seen for which there is a calibration record is 1804.
Guess who created these records?
The really cool thing is you can hold out these calibrated records and use the uncalibrated
records to create an expectation. Then test your prediction.