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 27, 2014 5:29 am

Sorry.
it seems I copied and pasted the wrong link.
This is the graph I was referring to.
http://ice-period.com/wp-content/uploads/2013/03/sun2013.png

April 27, 2014 6:50 am

Willis says
I used to suffer from advanced cyclomania as well,
Henry says
In fact, a lot of people do, and a lot of people did,
before they started with the carbon dioxide nonsense.
Here is a good paper on that
http://www.cyclesresearchinstitute.org/cycles-astronomy/arnold_theory_order.pdf
Without the cycles we would not be here,
In fact the only way to understand climate is to identify the cycles we are in….
Here is my final report on that
http://blogs.24.com/henryp/2013/04/29/the-climate-is-changing/

Greg
April 27, 2014 8:35 am

Well, I’m just running power spectra for some of the long MSL records here and the one common factor so far is a strong circa 21 year peak.
Very little around 10.4-11 , nothing consistent.
Looks like polarity is fundamentally important. Just goes to show you never know what to expect before you get on your bike and start cycling 😉

Matthew R Marler
April 27, 2014 9:40 am

RACookPE1978:Each data field will vary over a 24 hour period.
However, each daily value – for example, Taverage (for the day), Tmaximum – Tminimum (for the day), relative humidity each hour, pressure, etc – will also vary periodicaly over the length of an entire year. Given 7 years of data, I would expect to be able to generate a function adequately calculating each parameter as a function of hour-of-day and day-of-year, right?

If you are fitting hourly data over several years, then you would add in the known periodicity of 365 days. You probably need an interaction term, as the day/night variation probably depends on the phase with respect to the 365 day period. I don’t know if “adequately” is correct here, but both the 24hr and 365day periods should be at least statistically significant for any station (e.g. series) with enough data. Note that NH and SH series should be a half period out of phase on the 365 day rhythm; the NH and SH series at the same longitude should be in phase on the 24hr rhythm.
As you wrote it, Tave is only once per day, so it should not have a 24 hour rhythm, but anything measured hourly probably does. If you expect a 24h or 365 day period, but not a sinusoidal shape, you can nonlinearly transform the cosine curve in a variety of ways to get a variety of shapes:
M. R. Marler, P. Gehrman, J. E. Martin, S. Ancoli-Israel, The Sigmoidally-transformed Cosine Curve: A Simple Mathematical Model for Circadian Rhythms with Symmetric Non-sinusoidal Shapes. Statistics in Medicine, 25:3893-3904, 2006, presented at the poster session at the Joint Statistical Meetings, Aug 2005.
Transforms like that have been used to model the on/off behavior of melatonin and cortisol secretion, among others.

Matthew R Marler
April 27, 2014 9:58 am

Willis Eschenbach: In other words, what you propose is exactly what I’ve done.
Except that you only used a small number of long sequences. You can’t find a weak signal that is present in all series that way. And from the point of view of a statistical test, you used up all the degrees of freedom on effects that are not there. Generally, if you have an actual hypothesis of a particular period, as here, you get better statistical power with a method that focuses on that period, and less chance of seeming to identify a spurious “period” in a selected series. (Lower type 1 and type 2 error rates.)
I wrote that, because of lags, the series will not all have the same phase, but they should have phases within a few years of each other. If you find that the phases are all concentrated in a narrow region of years, instead of being uniformly distributed, that is additional evidence that the period is actually there, assuming that it has some external driver. In the example of the prolactin rhythm in healthy adult men, for example, all the peak time (phase hour of the cosine) estimates were within a few hours of each other, though no peak or peak time was itself well estimated — something that has an extremely low probability of occurrence if in fact the rhythm is absent.)

April 27, 2014 9:59 am

Greg says
the one common factor so far is a strong circa 21 year peak.
Henry says
Well, you should concentrate on that one….!!!
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.
You will all figure it out if you study my final report on it.

Matthew R Marler
April 27, 2014 10:17 am

Willis Eschenbach: For example, for about three sunspot cycles in the second half of the 20th century, the sea level varied pretty much in parallel with the sunspots … and since the period of the sea level cycle is was hypothesized to be ~11 years from the sunspot period, then according to you only one 11-year cycle would be required to confirm the hypothesis, and three cycles would establish it beyond doubt..
That is definitely a good cautionary example. One would never want to draw too strong a conclusion from an analysis such as I presented. Is there a period in the data set that we have? That is only the first question, but I think it should be addressed with the statistically most powerful method. Does it persist in future data? Is it reliably related to hypothetical causal influences?
As always, thank you for your thoughtful work.

April 27, 2014 10:37 am


Best is to ignore Willis. He is just like Leif. Must be horrible to live with people like that. No doubt they are single. They simply donot or cann ot accept anyone simply disagreeing with them.
Anyway, I forgot to tell you about the deVries/Suess cycle.:
http://www.nonlin-processes-geophys.net/17/585/2010/npg-17-585-2010.html
Abstract. Spectral analyses performed on records of cosmogenic nuclides reveal a group of dominant spectral components during the Holocene period. Only a few of them are related to known solar cycles, i.e., the De Vries/Suess, Gleissberg and Hallstatt cycles. The origin of the others remains uncertain. On the other hand, time series of North Atlantic atmospheric/sea surface temperatures during the last ice age display the existence of repeated large-scale warming events, called Dansgaard-Oeschger (DO) events, spaced around multiples of 1470 years. The De Vries/Suess and Gleissberg cycles with periods close to 1470/7 (~210) and 1470/17 (~86.5) years have been proposed to explain these observations. In this work we found that a conceptual bistable model forced with the De Vries/Suess and Gleissberg cycles plus noise displays a group of dominant frequencies similar to those obtained in the Fourier spectra from paleo-climate during the Holocene. Moreover, we show that simply changing the noise amplitude in the model we obtain similar power spectra to those corresponding to GISP2 δ18O (Greenland Ice Sheet Project 2) during the last ice age. These results give a general dynamical framework which allows us to interpret the main characteristic of paleoclimate records from the last 100 000 years
end quote
So the DeVries cycle is about 10 x the Hale-Nicholson cycle. I just wish I knew where we are in this cycle, I suspect we are on the cooling side? Have you any ideas on that?

April 27, 2014 11:50 am

Steven Mosher
I got one word
Henry says
Over the years you have proven yourself to be a man of few words.
They did not help much, either.

April 27, 2014 12:08 pm

Henry: RIF
but for you, here is a synopsis
“Business cycles, and their rhythms, have long fascinated and perplexed economists. Why do economic booms alternate with recessions, decade after decade? And why do graphs of long-term data on gross domestic product, employment and other economic indicators form undulating patterns similar to physical phenomena such as ocean waves or sound waves? Over the past 150 years, all sorts of explanations have been put forth for recurrent peaks and valleys in economic activity—economists have hypothesized forces as seemingly far-fetched as sunspot activity and rainfall patterns as the cause of these cyclical patterns in national and world economies.
By the early 20th century, some researchers believed that chance occurrences like wars, crop failures and technological innovations played a role in business cycles. But no one fully appreciated how crucial random (or “stochastic”) processes are to the workings of the economy until Eugen Slutsky, a Soviet statistician and econometrician, did the math. A middle-aged professor working at a Moscow think tank, Slutsky was virtually unknown to economists in Europe and the United States when he published his landmark paper on cyclical phenomena in 1927.1
In a bold statistical experiment, Slutsky demonstrated that random numbers subjected to statistical calculations similar to those used to reveal trends in economic time-series formed wavelike patterns indistinguishable from business cycles. The implication was that a similar stochastic process—“the summation of random causes,” as Slutsky described it—might be at work in the actual economy, causing prosperity to ebb and flow without the agency of sunspots, meteorological patterns or other cyclical forces.”
In short, cyclical behavior can arise from the summation of random forces.
Finding a cycle ( 60 year or otherwise) tell you Nothing about its cause. Even if it is correlated with another 60 cycle, that still tells you nothing about the cause.

April 27, 2014 1:43 pm

Now let me explain my question. People think they Will find a cycle because they hope the climate governed by one cause. Its not. Not just the sun and not just co2. Instead its controlled by many factors. Some increasing slowly some decreasing some with cycles some random shocks.

Greg
April 27, 2014 4:26 pm

Thank you.You were correct. I did not get what you meant. It would have been more effective to say that a few days ago when there was still some traffic on this article.
“… the climate governed by one cause. Its not. Not just the sun and not just co2. Instead its controlled by many factors. ”
I point i have re-iterated many times, including in this thread.
That is why global averages do not advance our understanding, they just muddy the waters.
It’s like listening to 20 different types of music all at once in the hope that it will all average out and you will discover some fundamental truth about musical structure.
If you choose the start and end points of the “ensemble” carefully you may conclude that, on the average, music tends to gets louder and louder the longer it goes on. At which point you’ll whip off the headphones before it reaches a tipping point and blows your ear-drums out.
Yes, the human mind does like simple explanations. Another favourite is the word stochastic.

April 27, 2014 7:59 pm

“That is why global averages do not advance our understanding, they just muddy the waters.”
Wrong.
As a system we know the climate is determined by certain governing equations: namely
energy out = energy in + energy stored. At equilibrium when its all said and done, energy out = energy in. When that system is out of balance we know it can restore balance by increasing
temperature. The global temperature (at 2m) is a system diagnostic. It does not tell us the whole picture, but it gives us a slice of the system state. The OHC is perhaps a better diagnostic but the record there is short. A global average doesnt muddy the water unless you look at it as the only metric. It provides a glimpse into the state of the system.

April 27, 2014 11:51 pm

Willis says
In this case, Mosh’s one word (accompanied by a very informative link) is all that was needed …
Henry says
well, it seems to me Mr. Mosher realizes that you cannot say that in one word, as he now says:
Steven Mosher says
energy out = energy in + energy stored. At equilibrium when its all said and done, energy out = energy in. When that system is out of balance we know it can restore balance by increasing
temperature. The global temperature (at 2m) is a system diagnostic. It does not tell us the whole picture, but it gives us a slice of the system state. The OHC is perhaps a better diagnostic but the record there is short. A global average doesnt muddy the water unless you look at it as the only metric. It provides a glimpse into the state of the system.
Henry says
I would re-phrase his equation:
energy-in = energy-out – energy stored.
A reasonable proxy for energy-in is maximum temperatures.
And who else but me is looking at that now?
http://blogs.24.com/henryp/2012/10/02/best-sine-wave-fit-for-the-drop-in-global-maximum-temperatures/
A reasonable proxy for energy-out is means.
On my own results, because it has been globally balanced, I can do a reasonable bi-nomial fit on means as well.
Now, my A-C wave for the drop in maximum temperatures obviously does not reflect exactly at the same time what happens to temperatures on earth. As stated, earth has an intricate way of storing energy in the oceans. There is also earth’s own volcanic action, lunar interaction, the turning of Earth’s inner iron core, electromagnetic force changes, etc.
So, I am not looking for a 60 year cycle. I know what is important is to look at the cycle of what energy is coming in. That is the 87 year Gleissberg, consisting of 4 Hale-Nicholson cycles. These are the ones I can see.
I hope that my planets arrive in time, as otherwise I do know where we will end up with the global cooling.
If you are stubborn enough not wanting to take a look at the energy coming in, ie. that what is being allowed through the atmosphere, (not TSI only), then you will not be able to predict as to what climate change is coming.
http://blogs.24.com/henryp/2013/04/29/the-climate-is-changing/

Matthew R Marler
April 27, 2014 11:58 pm

Willis: However, I keep waiting to hear from you what “the statistically most powerful method” reveals about the putative signal, whatever that method might be.
Fair enough: if I think that the statistically most powerful method is to focus on the 60 year period in all the data, I ought to do it myself.

Matthew R Marler
April 28, 2014 12:27 am

Steven Mosher: http://economics.sas.upenn.edu/~fdiebold/Teaching/Course2010/slutsky.pdf
Good link.
It’s worse than that. Everything that you think you discover in a finite time series might not continue as the process that is measured continues. Every finite time series can be fit within any predefined accuracy by a polynomial of high enough order, and you can be nearly certain that the polynomial will not adequately predict the future. Contrariwise, a chaotic process with an approximate period does not stay in phase, and if you have a long enough time series you can’t identify the approximate period, even if it persists.
What to do when the “signal” you are looking for probably isn’t there, and is likely weak if it is there (due to many other causal agents acting concurrently)? Big Pharma tests thousands of compounds annually for clinically relevant biological activity, and abandons research on 99% of them. Some companies decide to skip that endeavor and manufacture drugs that other companies discover and patent. It might be more efficient not to do any research at all, and it certainly has a lower type 1 error rate, if that is what concerns you most; but if there is anything to discover, the generic companies won’t discover it.
Except for a few obvious rhythms, discovering periodicities requires assiduous work and produces a high type 1 error rate. I think we are stuck with that.

Greg
April 28, 2014 4:13 am

Mosh’ “A global average doesnt muddy the water unless you look at it as the only metric.”
Sadly the whole concept of “global warming” is, by definition, just that.
Then the magic work “stochastic” is used to discount everything but the long term trend as “internal” , gleefully ignoring that there is no reason that the long term variation is not , by equal argument, also “stochastic”.
We’ve wasted the last 30 years having heated arguments about a useless metric.

Alan Robertson
April 28, 2014 5:38 am

Steven Mosher says:
April 25, 2014 at 5:54 pm
“I have no issues.”
_________________
I ain’t touchin’ that…