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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April 28, 2014 6:48 am

Steven Mosher says
Not just the sun and not just co2. Instead its (the weather?) controlled by many factors. Some increasing slowly some decreasing some with cycles some random shocks.
Henry says
I cannot believe that after all these years you indeed still would think that a 100 ppm CO2 could possibly be a factor that is warming earth. I am sure I have explained this before but I will do it again. All that is happening as a result of a bit more CO2 is that the earth’s biosphere is booming.
http://wattsupwiththat.com/2011/03/24/the-earths-biosphere-is-booming-data-suggests-that-co2-is-the-cause-part-2/
Now as a result, the increasing greenery traps some heat. You can see that from my results. For example, in Las Vegas minimum temperatures rose sharply, as a desert was transformed into a green paradise. OTOH where mankind decided to cut the trees down, for example in Tandil, Argentina, minima dropped sharply, see here:
http://blogs.24.com/henryp/2011/11/10/de-forestation-causes-cooling/
So, all that is trapping some heat, is the increase in greenery. The problem is that people want more lawns, more trees and more crops. So in a nutshell, that is the whole AGW problem. It is as simple as that.
So, Mr. Mosher, I want to ask you a question. What do you want? We have grown used to you running away when you are asked something. But be honest now, don’t you think that more greenery and some* more warming is better than global cooling?
*Be careful. According to certain estimates from the 1970’s that (green) AGW cannot really be all that much, in total.
We will not be able to stop the natural global cooling, and subsequent loss in greenery, that is coming.
http://blogs.24.com/henryp/2013/04/29/the-climate-is-changing/

Greg
April 28, 2014 2:39 pm

W: “But then he thinks that the Jevrejeva smoothed data is susceptible to spectrum analysis”
No Greg thinks SSA _is_ a form of spectral analysis and Jevrejava did not report the numerical results, so I’m wondering/guessing what the components of that SSA were.
It looks similar to the 54 + 64 year periodicities you found in the Amsterdam long series but seem intent on insisting are not there.
As I pointed out many times, superposing two close cosines will produce a modulated cosine that has the average of the two components found by spectral analysis. Two non harmonic periodic repetitions will produce a similar effect plus some non harmonic “noise”.
It appears, as I said above, that there is some Mannain window-filling going at the start and end of JJ’s graph (simple repetition padding IMO).
http://climategrog.wordpress.com/?attachment_id=933
Just a quick lash up to reproduce that kind of form using values similar to (but different for brevity) the Amsterdam PA given by Willis:
http://climategrog.wordpress.com/?attachment_id=938
mean freq (54,64) = 58.6 years. This roughly reproduces the circa 60y bumps in J’s rate of change graph. This demonstrates how you can have a 58.6y variability with ZERO 58.6y component in the spectral analysis, be it FFT, chirp-z or P.A.
This is not numerology , it is pure, precise mathematics.
It would be preferable by far if scientists would actually publish their results rather than just pics, when they publish.
Now if that estimation of her results is correct the impression that acceleration is easing is false. There is a constant linear increase in rate of change in that plot.
So after all the virtual tide gauges, SSAs, EOFs and windowed wavelets we still don’t know the bottom line of Jevrejava’s study because she did not do us the courtesy of actually publishing her results.

Greg Goodman
April 28, 2014 4:22 pm

“In fact, multiple lines of evidence show that the system is highly resistant to changes in forcing”
That is one thing we agree on. At least in the tropics, which is a key region for the control of energy input.
The following article is still at a draft stage and lacks the punch line. The text needs work but I think the physics is pretty close.
Fitting the fully developed climate response not just the instananeous dT=lambda.dF gets closer to explaining what is going on.
http://climategrog.wordpress.com/?attachment_id=884
Having argued for some time that volcanic forcing was exaggerated, I found that it is under-estimated. It is just the final impact that is exaggerated.
Roy Spencer pointed out in on his site that TOA flux returned to pre-eruption levels after just a year, when AOD was still at 50% of its peak:
http://climategrog.files.wordpress.com/2014/03/tropical-feedback-adjusted.png?w=814
Because modellers refuse to use “parameters” that give the strong negative feedbacks like those Willis suggests they had to rig the carefully calculated optical forcing of Lacis et al in 1992.
I found the best fitted value was slightly more than Lacis ( 33 instead of 30) well with his range of values. But for that to work you need a strong negative tropical f/b that neuters the volcanic impact within a year.
The delay in response that comes from the thermal inertia explains the post-volcanic tropical recovery that I showed in my volcano stack analysis.
In view of the physics based work of Lacis , current values appear to be an attempt to fix the models by fudging the inputs instead of correcting the model.

1sky1
April 28, 2014 5:27 pm

“Periodicity analysis” in climatology pre-dates Sethares & Staley’s IEEE paper by at least half a century. That’s indeed how the annual cycle is established for subtraction from monthly data in obtaining “anomalies.” The authors clearly state, however, that “the p-periodic basis elemnts are fundamentally coupled together” and nowhere even hint that this a suitable technique for general signal analysis purposes. In fact, they call attention to the “integer periodicity limitation.”
Ironically, with dominant tidal constituents crudely removed by monthly averaging of tide-data and the annual cycle removed by Willis here, nothing truly periodic is left in the sea-level data to analyze with this simplistic technique.

Bart
April 28, 2014 6:48 pm

The ~60 year quasi-cyclical component contained within the temperature data is not some sorta’, kinda’ looks similar phantom. It’s an amazingly precise formation (given the quality of the data) which has repeated twice with extremely close repetition of amplitude and period. The likelihood of it being just a random luck of the draw is very small. The coolly dismissive reference to Slutsky is an exercise in burying one’s head in the sand.

April 28, 2014 11:41 pm

As predicted, it seems Steven Mosher has left the arena. He does not want to be challenged.
Look at the rubbish he writes here:
http://static.berkeleyearth.org/pdf/skeptics-guide-to-climate-change.pdf
All that wasted effort by climate scientists on the harmless 100 ppm CO2 having been added to the atmosphere instead of doing a new evaluation of exactly how much energy is really consumed by the biosphere (land + oceans)

Greg
April 29, 2014 1:31 am

Bart says The ~60 year quasi-cyclical component contained within the temperature data is not some sorta’, kinda’ looks similar phantom
Which part of the title are you having trouble following Bart?
“The Elusive ~ 60-year Sea Level Cycle”

Bart
April 29, 2014 8:33 am

Greg says:
April 29, 2014 at 1:31 am
Just trying to keep the obvious quasi-cyclicality in the temperature data from being tarred under the same rubric, Greg. As has been discussed elsewhere, I believe the ~60 year quasi-cycle in the temperature data results from a combination of lunar tidal and solar intensity forcings. So, I would not expect to see an actual kinetic ~60 year oscillation in the tides, themselves.
What I would expect to see in the tides is the intrinsic slosh modes of the ocean basins being excited by the rigid body motion of the Earth. I have very preliminary findings suggesting that this, in fact, can be observed. Eventually, I will post these results for others to review. But, this is a very complicated problem, involving transmission delay across the oceanic expanses and reverberations against the shorelines. I have not had time to thoroughly examine my results to make a strong case as of yet.

April 29, 2014 9:42 am
April 29, 2014 9:45 am

Willis says
http://wattsupwiththat.com/2014/04/25/the-elusive-60-year-sea-level-cycle/#comment-1624529
Henry says
why being so aggressive toward Bart?
What I have seen from him is that he is brilliant in mathematics, and he told us he is working on it, did he not?

April 29, 2014 11:05 am

Willis
‘Ah, the famous fallacy of the excluded middle. Once again you recycle the tired alarmist argument that when forcing gets “out of balance” the only way to restore balance is by increasing temperature.”
Wrong.
Quote my words. I never would argue this is the ONLY way to restore balance
There might be other ways to restore the balance. None have been identified.
Maybe its unicorns.
The argument that the system restores balance by warming is a hypothesis. There might be other ways, you are welcomed to describe in detail ( with equations) some other way.
Quote my words. I like that

April 29, 2014 11:26 am

Henry
Perhaps now you understand why I am cryptic. My hope is that you were curious. That the cryptic comment would get you to read, RATHER THAN BLATHER.
But even when I give you a synopsis you choose to blather.
“Steven Mosher says
Not just the sun and not just co2. Instead its (the weather?) controlled by many factors. Some increasing slowly some decreasing some with cycles some random shocks.
Henry says
I cannot believe that after all these years you indeed still would think that a 100 ppm CO2 could possibly be a factor that is warming earth. ”
#############################
of course its a factor. All the working physics I know tells me that if your increase the forcing
to a chaotic system That there will be an effect. Think butterflies on steriods. It will have an effect.
The question is : What effects? how big? when will manefest? will they make any noticeable difference. You farting will have an effect. Hopefully small.
##########
I am sure I have explained this before but I will do it again. All that is happening as a result of a bit more CO2 is that the earth’s biosphere is booming.
http://wattsupwiththat.com/2011/03/24/the-earths-biosphere-is-booming-data-suggests-that-co2-is-the-cause-part-2/
1. what makes you think that increase c02 has any effect here?
2. You havent proved that it isnt something else
3. The greening is well within natural variability
4. We have had a greener planet in the past with lower Co2
5. C02 cant cause the planet to green, its only a trace gas
/sarc off
####################
“So, Mr. Mosher, I want to ask you a question. What do you want?
1. world peace.
2. A conservative in the white house
3. A new pair of shoes.
“We have grown used to you running away when you are asked something. But be honest now, don’t you think that more greenery and some* more warming is better than global cooling?”
1. You dont owe me answers. I dont owe you answers.
2. I am under no rational obligation to ‘stick around’
3. I gave you something to read. I cannot demand you read it, you cannot demand
that I stick around to see if an old dog learned a new trick
4. Information is power. I choose who gets to learn. Your test is to take the clues
and learn. I will not spoon feed lazy people.
5. I provide a reading list. be a good student or not. your choice.
Now for your stupid question.
more greenery? how much
some warming? how much?
global cooling? how much?
better? better for who? better when? how much better?
I choose not to bother with value questions. The value judgement is conditioned by the facts.
And we dont know how much warmer or how much greener. We know the direction..
more c02 ( that trace gas) will create a warmer world ( all things being equal ) and will
create a greener world ( all things being equal)
How do we know?
Well we do lab experiements with plants and we do lab experiements with C02.
We know from these experiments that more c02 means a greener world and a warmer world.
And we know from biology (for plant growth) and from physics (for warming) why this is.
What next? well the world is not a lab. we cant do controlled experiments.
We can observe that in the real world when we add c02 we get a warmer world (as predicted in 1896) and we also get a greener world.
The evidence for both theories — more c02 = warmer greener– is about the same.
I accept both.

April 29, 2014 11:54 am

steven mosher says
blah blah blah
henry says
the question was if you want a greener world (= a warmer world)
or not

April 29, 2014 12:04 pm

Steven Mosher says
1. what makes you think that increase c02 has any effect here?
henry says
is it possible that S.Mosher really has never ever heard of photosynthesis?

Greg
April 29, 2014 12:06 pm

∆T = λ ∆F
where lambda ( λ ) is the climate sensitivity, delta-T (∆T) is the change in global surface air temperature, and delta-F (∆F) is the change in forcing.
To believe that equation, you have to, must, need to believe that (as you strongly imply in your post) only a change in temperature can rebalance the system … sorry, not true in the slightest.
===
There are a couple of other ways of restoring the balance. The biggest one, of course is a slight change in the time of onset of tropical clouds.
====
You seem to be implying that ∆T = λ ∆F only refers to Plank feedback, it does not. It includes anything , including cloud formation and tropical storms that is a reaction surface temperature.
It implies that if there is a change in radiative forcing this will result in a change (leaving the linearity to one side) in the equilibrium temperature. It does not dictate that the radiative response to temperature is the only thing causing this result.
For example, if there is a strong negative feedback in the tropics, that we both consider to be the case, that feedback has to be a reaction to something and it is not directly reacting to the presence of more or less radiation. As your explanation would have it, it is a local response to higher SST changing the frequency and timing of tropical storms.
Now as with steam-engine centrifugal governess and the rest, even a strong negative feedback will not restore the non forced state of the system, it will settle at a much reduced but finite change in the controlled variable. SST in this case.
A strong negative feedback will thus give a small lambda, not zero. A new equilibrium is reached at a slightly higher temperature that maintains the required frequency / timing of TS and cloud cover.
The fact that TS have internal +ve f/b and are highly non-linear , does not prevent the averaged f/b effect of the change in timing from being approximately linear and being part of “lambda”.
My draft article shows that a volcanic forcing 50% stronger than currently used values causes a feedback that peaks about 12 mo after the eruption when AOD is still at 50% of its peak value.
http://climategrog.wordpress.com/?attachment_id=884
That is presumably the minimum in the induced drop in SST and the latest onset of TS, if that is the principal f/b. Since the feedback is strong, the temperature change is small. Roy Spencer got reasonable results trying to correlate the change in TAO and SST but in view of size of the change in relation to the variability in the rest of the tropical SST record, attribution seems seems speculative (unless I’m missing the means to do that). It may be corroborative but I don’t see it as conclusive.
That model can explain the initial few years. It does not explain the definitive offset in TLS, and the likely warming of the troposphere that this implies.
http://climategrog.wordpress.com/?attachment_id=902
Whether that can also be shown to be shown to be a persistent negative feedback mechanism or simply a one-off counter effect seems beyond currently available data.
In short, I don’t see why your tropical feedback hypothesis cannot fit the equation you cited. At least as a first approximation.

Bart
April 29, 2014 12:11 pm

Willis Eschenbach says:
April 29, 2014 at 9:29 am
It’s not that hard to see. Very regular. Same amplitude. Same rise time. You have to not want to see it to miss it.

April 29, 2014 12:48 pm

@bart
http://www.woodfortrees.org/plot/hadcrut4gl/from:1900/detrend:0.75/plot/hadsst2gl/from:1900/detrend:0.75/plot/hadsst3gl/from:1900/detrend:0.75
the SST 2 (green) seems a bit strange around the WWII area, perhaps it (the cooling) is a bit anomalous there ?\
so it (the bending point) could run a bit further naturally to around 1950
The on-going decline towards (current) global cooling seems inevitable
I am not against a mixture of solar/earth-lunar cycle, setting the earth’s temperature.
However, the energy coming in is what will set the tone, in the end, I am sure.
Back in 1985, William Arnold had it all figured out that the Hale-Nicholson cycle of 21-22 years is steered by the motion of the planets. My own results merely confirmed his findings plus I was able to get the turning dates figured out, give or take a year.
The Hale-Nicholson cycle is exactly a quarter of the Gleissberg, 87 years
You will all figure it out if you study my final report on it. Take out some time for that some day and let me know what you think?
http://blogs.24.com/henryp/2013/04/29/the-climate-is-changing/

Greg
April 29, 2014 1:11 pm

Bart says: “It’s not that hard to see. Very regular. Same amplitude. Same rise time. You have to not want to see it to miss it.”
Bart, if it’s so easy to see why do you link yet again to SST ? Tell me about GMSL , I want to see it.
I see indications of it spectral analysis but there’s a lot else in there, and Jevrejava’s graph, which is also a spectral technique seems to show it (whatever that is in numbers is anyone’s guess).
I see frequency components in Willis’ Amsterdam plot that will result in a 57y component but again there’s a lot else in there.
My conclusion is that there is a circa 60y signal buried in there but there’s so much else going on it is not readily seen in the time series.
If, as Chambers suggests, there is different phase geographically, global averaging will lose it.
“It’s not that hard to see. Very regular. Same amplitude. ”
Let’s see it !

Bart
April 29, 2014 1:19 pm

Greg says:
April 29, 2014 at 1:11 pm
But, I don’t expect to see it in GMSL, as I explained above.