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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224 thoughts on “The Elusive ~ 60-year Sea Level Cycle

  1. JJ paper used SSA and some kind of 30 year filter, so very heavily damped.

    Perhaps the most signficant bit is that for the last century it’s clearly hovering about 2mm/y , not the 3.4mm/y claimed by satellite altimetry crews.

  2. Look, it is real simple and obvious to all but the most blind of observers – there is simply no way we have enough accurate data for sea level analysis, land or ocean surface temperature analysis, or basically any other fecking type of analysis one might wish to ‘believe’ one can undertake from current ‘datasets’ – It ain’t happening, and the sooner we realise how little we actually know, the better.. The earth and its biosphere is simply a massive perpetually moving and changing interaction of a bazillion (or more!) different positive and negative effects, all on different periodicities and interactive levels, NONE, I repeat – NONE – of which are known in any great detail ! (I won’t even comment on these bazillion interqctions being condensed into ‘models’! LOL)
    When folk finally realises this, the issue (whatever it may be) simply disappears. Willis has easily shown that even the best sea level data cannot be tortured and forced to give up any underlying trend – frankly, I don’t see that ANY of the current climate knowledge and data is any better.

  3. Where’s that paper that said 65% of tide gauges either show sea levels falling or no sea level rise at all…

  4. AJ says:
    April 25, 2014 at 3:09 pm

    Willis… if you need some code to import the psmsl data into a single R dataframe, the following might be of interest:

    https://sites.google.com/site/climateadj/nh-sea-level-reconstruction

    Thanks, AJ, your work in R is always of interest, and this is no exception. I’ll need to study it.

    On my end, I downloaded the 1413 files, and then read them into a matrix. I find a matrix a bit easier to work with than a data frame, likely my unfamiliarity with the latter. I’ll look to see how you do it.

    w.

  5. Willis, is it possible that the tide gauge data need to be corrected for any isostatic movement of the land-base?

  6. A guy wearing partially tied sneakers, no belt with his pants dragging down his butt gets a job at a dairy farm.
    Day 1
    Worker is not trusted with the tender areas of cows gets to clean up around the cows, lays down fresh hay, fills up the feed trough when he notices that his sneakers are a different color and filled with stuff. Has to borrow some twine to try and tie his pants up.
    Day 2
    Worker shows up with cheap calf high rubber boots. Discovers the manure gutter chain is jammed and spends a couple of hours working in a gutter full of soggy manure. Vomits a couple of times. His new rubber boots were too short and easily filled with the juicy stuff. Decides he really needs better fitting pants and a belt.
    Day 3
    Worker arrives with the cheapest pair of hip boots sold at the hardware store. Learns from the farmer that the gutter chain is under maintenance and it’s availability is still unknown. Worker spends the day filling a wheelbarrow and moving manure to the crap wagon. Worker falls into the wagon a couple of times trying to successfully dump the manure in the wagon. As often happens the worker learns that rubber boots keep things from leaking out very well. Worker realizes that he hasn’t lost his breakfast all day.
    Day 4
    Worker arrives at the farm wearing Carhartt overalls with good quality for the price clodhopper work boots. Sets to work without dilly or dallying.

    My apologies for the dairy farm worker introduction Willis. Just that I am getting rather amazed that you can open some of these data bases without losing your lunch. Even with repeated exposure it must be hard to keep your cookies where they belong sometimes.

    Thank you Willis for opening these cans of worm goo! Sixty year cycle… After living and fishing the Gulf of Mexico for a number of years with it’s odd tides I certainly believe that it is difficult to find common frequencies amongst tidal records.

  7. Latitude says:
    April 25, 2014 at 3:52 pm

    Where’s that paper that said 65% of tide gauges either show sea levels falling or no sea level rise at all…

    Hang on … OK, here’s the data:

    The very high and low values for the rate of sea level rise are almost all from short records. If we restrict it to records longer than 20 years, here’s the result:

    Out of that 820 records, about 20% of them show falling levels.

    w.

  8. Any studies on how much sea level rise can be attributed to displacement by post-glacial rebound?

  9. Hi Willis
    Scafetta is another author showing an ~ 60 year cycle in long sea level data.
    Discussion on common errors in analyzing sea level accelerations, solar trends and global warming Pattern Recognition in Physics 1, 37-58, doi:
    10.5194/prp-1-37-2013, 2013

    “(1) multidecadal natural oscillations (e.g. the quasi 60 yr Multidecadal Atlantic Oscillation (AMO), Norther Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO) need to be taken into account for properly quantifying anomalous background accelerations in tide gauge records such as in New York City;”

    See Fig. 2, Fig. 3 analyzing New York’s 1893 to 2011 record.
    Look forward to your evaluation or testing of his methods.

  10. power spectrum of Church and White GMSL

    http://climategrog.wordpress.com/?attachment_id=935

    This is just a quick rough but seems reminiscent of what I found looking at the Jevrejava data last time this came up.

    circa 10.x and 20.x possibly suggestive of solar , and 8.82 pretty surely the lunar apsides yet again.

    So the same conclusion I’ve been pointing to for a couple of years now: attempts to attibutre the solar signal has phase crisises because there is interference patterns with the close by lunar periodicity.

    Until climatology gets a grip on the importance of lunar apsides which seems to crop up anywhere there is the subject of water, they will not find a stable solar signal.

    But since that suits them fine and they can pretend it’s all “stochastic noise” + AGW , I don’t suppose they are going to be looking too hard.

  11. Yeah interesting that median rate of sea level rise is 1.8 mm/y. The same as what i get eyeballing Jevrejava’s rate of rise graph above.

    Now if [satellite] alitmetry says 3.4mm/y this it must all be piling up in mid ocean where there are not tide gauges !!

  12. Thank you sir. It is so much more difficult to expose the null than it is to imply a trend/cycle. You have done the near impossible.

  13. Out of that 820 records, about 20% of them show falling levels
    ===
    Bingo!

    Here’s what I was looking for:

    Abstract
    The location of tide gauges is not random. If their locations are positively (negatively) correlated with SLR, estimates of global SLR will be biased upwards (downwards). We show that the location of tide gauges in 2000 is independent of SLR as measured by satellite altimetry. Therefore PSMSL tide gauges constitute a quasi-random sample and inferences of SLR based on them are unbiased, and there is no need for data reconstructions. By contrast, tide gauges dating back to the 19th century were located where sea levels happened to be rising. Data reconstructions based on these tide gauges are therefore likely to over-estimate sea level rise.
    We therefore study individual tide gauge data on sea levels from the Permanent Service for Mean Sea Level (PSMSL) during 1807 – 2010 without recourse to data reconstruction. Although mean sea levels are rising by 1mm/year, sea level rise is local rather than global, and is concentrated in the Baltic and Adriatic seas, South East Asia and the Atlantic coast of the United States. In these locations, covering 35 percent of tide gauges, sea levels rose on average by 3.8mm/year. Sea levels were stable in locations covered by 61 percent of tide gauges, and sea levels fell in locations covered by 4 percent of tide gauges. In these locations sea levels fell on average by almost 6mm/year.

    https://suyts.wordpress.com/2013/09/20/vindication-for-suyts-new-tidal-gauge-sea-level-paper-out-reports-1mmyr-sea-level-rise/

  14. Looking at the power spectrum , I think I can see where Jevrejava’s 60 years cycle is coming from (assuming here data is similar to Church and White.

    http://climategrog.wordpress.com/?attachment_id=935

    cos(2.pi.x/7.5) + cos(2.pi.x/10.2) = cos (2.pi.x/56.67) * cos(2.pi.x/8.64)
    It’s the old radio amplitude modulation thing again.

    Now I’m not a expert on SSA but it’s a bit like principal component analysis and picks out pairs frequencies. So it seems to be pulling out the 57 year periodicity.

    Now a limitation of Periodicity Analysis is that it will only pull out a direct and constant amplitude repetition. Much of climate is about resonances and interference patterns and I fear P.A. may not be the most flexible tool to dig this sort of thing out.

    I did also find a strong and highly symmetric AM triplet in the sub-annual frequencies that shows a very similar modulation frquency:
    p2=0.96225
    pc=0.94693
    p1=0.931378
    # as A.M. 0.947 * 58.060 ; triplet asymmetry: -0.039 %

    Now that’s close enough with the accuracy of extracting the modulator from a triplet that this is very likely the same thing.

    Now looking at the SI of the Chambers paper that is the subject here they are fitting 55 years.

    [ Willis, look at the SI and you will which records they used. ]

  15. Solid.

    I hope people who have issues with what willis
    Has done download the data and have a wack
    At it. I suspect some will give willis homework. Or at least
    Try to give him more work to do.

    I have no issues.
    Solid

  16. On this matter of sea levels rising there is huge variation amongst scientists, from Al Gore’s figure of 65m per century to NIWA’s 1998 Lyttelton study of 1mm per year (10cm per century); a disparity of 65,000%. That degree of error disqualifies claims of plausibility, even throwing doubt on NIWA’s work.
    With that degree of uncertainty, it is difficult to see how anybody can be sure the sea is rising at all. How, it may be asked, can 1mm change in sea level be calculated, averaged over one entire year or a hundred years, when even a flat sea at rest undulates more than that with waves every few seconds, and tide height just in one day varies by some two metres?
    To say a tide height is higher we require knowing higher than what? To fairly compare tide heights one needs a past reference height to compare with one in the present. Finding the former is not possible because (at least 10) factors that influence tide height do not together repeat. We are talking of phase of the moon, lunar declination, perigee cycle, high and low pressure zones that can suck heights up or depress them, and winds onshore that can blow water into a harbour or offshore depleting a bay.
    Equinox tides are higher than solstice tides. The sea is warmer in summer, therefore higher. Underwater earthquakes, eruptions, and fissures raise local sea-levels, most non-recordable and/or undetectable. Rainfall at sea, river flows and land run-offs contribute to sea-levels. Temperature changes control density and water volumes, ever-shifting in the ocean, and the direction of currents both deep, mid and surface, alter sea height. Cycles of glaciers’ advance and retreat change heights of the ocean.
    In short, we haven’t a clue how high sea levels are ever supposed to be in any fixed place, to a tolerance of 1mm per year, when everything connected to the sea is in constant flux. The sea is not a lake or a pond. No computer model can pretend that it is, just for the sake of a neat result. Examinating old photographs, sketches and tide markers reveals high watermarks unchanged on NZ and Australian beaches, apart from erosion due to changing currents.
    Disappearing sand is a cycle, a function of lower than normal sea levels because lower water undermines foreshore and top sand collapses. Without higher water to re-deposit sand higher up the shoreline, over a long time period a beach can ebb slowly away. Higher tides deposit more sand because sand is heavier than water – surf brings sand in by momentum of wave action, and leaves it there when water recedes. Erosion cycles are just that, cycles. If this was not cyclic, all sand on all beaches would have gone long before now.
    Without monitoring over all oceans we cannot know if sea-levels are rising. We only have measuring devices on 0.4% on the earth’s surface where humans live. Special buoys now report via satellite using Argos transmitters, but we need to wait several centuries to achieve a reliable average to comment on any future century’s departure from average.
    Antarctic and Arctic ice are thickening, which means sea levels are dropping. On Tuvalu and other island atolls it is the land that is moving, not the sea. Small atolls like Tuvalu cannot be sliding under the sea whilst beaches in Australia and NZ stay unaffected. How would the sea decide which countries to send beneath the waves?
    As we emerge from this interglacial the poles are the smallest they have been in a while and some sea-levels the highest they aspire to. Some countries are still rebounding after the last ice age- Scotland is rising and the south of England is lowering. The west of Australia is rising whilst the east is dropping. There is a similar differential between NZ’s north and south island.
    The high watermark on any beach varies up and down the sand by about a metre every 10 minutes. To that add just 1 millimetre per year, the thickness of a grain of sand. If this varies over 60 years it may be 60mm, or about 2.5 inches. If you stood there for 60 years you could miss it if you blinked.

  17. [sarcasm]
    Tide gauges only measure the height of the tide, not the quality of the tide. Due to global warming we’re getting more and more rotten tides, the kind that dump heroin needles and dead whales (killed by ocean acidification, heat stroke, and depression) on our beaches. It won’t be safe for children to play in the surf no matter what the sea-level is unless we drastically curtail CO2 emissions so that the tides return to a healthy normal.
    [/sarcasm]

  18. Latitude I advise you to read that paper carefully before jumping all over the result because you like it. I’m not impressed for a number of reasons.

  19. http://nweb.ngs.noaa.gov/heightmod/NOAANOSNGSTR50.pdf

    http://tidesandcurrents.noaa.gov/sltrends/sltrends_station.shtml?stnid=8764311

    Mean Sea Level Trends for Stations in Lousiana

    Water-level records combine data on ocean fluctuations and vertical motion of the land at the station. The sea-level variations determined by these records include the linear trend, the average seasonal cycle, and the interannual variability at each station. Monthly data through the end of 2006 were used in the calculation, and all stations had data spanning a period of 30 years or more.

  20. Note that if the gauge is in a subsiding location, the sink rate of the gauge must be subtracted from the apparent rise in the ocean. My understanding is that something like 74% of the seeming rise in the Gulf around Louisiana is really the land subsiding. The causes of the land change/land loss are as hotly debated as anything else related to climate but there is general agreement on the subsidence problem even though the causes and thus the corrective action (if any) remain unresolved.

    See:

    http://nweb.ngs.noaa.gov/heightmod/NOAANOSNGSTR50.pdf

    http://tidesandcurrents.noaa.gov/sltrends/sltrends_station.shtml?stnid=8764311

    Mean Sea Level Trends for Stations in Lousiana [sic]
    Water-level records combine data on ocean fluctuations and vertical motion of the land at the station. The sea-level variations determined by these records include the linear trend, the average seasonal cycle, and the interannual variability at each station. Monthly data through the end of 2006 were used in the calculation, and all stations had data spanning a period of 30 years or more.

  21. David

    “Hi Willis
    Scafetta is another author showing an ~ 60 year cycle in long sea level data.”

    in one station. New York City.

    Here is a clue. You have a bunch of stations. if you look at enough of them you will find a cycle.

    The bottom line is why would anyone think you could find a cycle.

  22. Pat Frank says:
    April 25, 2014 at 4:03 pm

    Willis, is it possible that the tide gauge data need to be corrected for any isostatic movement of the land-base?
    ———————————————–

    Whether isostatic rebound or subsidence, wouldn’t that just be automatically discarded by the linear detrending?

  23. Greg says:
    April 25, 2014 at 4:49 pm

    power spectrum of Church and White GMSL

    http://climategrog.wordpress.com/?attachment_id=935

    This is just a quick rough but seems reminiscent of what I found looking at the Jevrejava data last time this came up.

    Thanks, Greg. Here’s the problem I have with claims of 60-year periodicity in Church & White and Jevrejeva data. This figure shows the detrended C&W, old C&W, and Jevrejeva data:

    A couple comments. First, the three datasets have roughly the same shape. The simplified (presumably smoothed in some fashion) version of Jevrejeva shows the basic shape. The rate of sea level rise was steady until 1900, slowed up until 1930, sped up until 1960, mostly steady after that.

    Now, it’s true that the period 1900 to 1960 looks a lot like half of a 120-year cycle … but the rest of the record dispels that idea. While there is nothing resembling a 60-year cycle anywhere in that data. And since the purported 60-year cycle is the subject of this post, I’m taking that as supporting evidence that such a cycle isn’t there …

    Finally, while you are correct that there are some small cycles at 8 years or so, as you can see they don’t do much.

    Thanks for the link,

    w.

  24. Whether or not you’ll notice the approx. 60-year sea-level oscillation depends on the mix of tide-gauges that you choose to examine.

    For instance, the North American Atlantic coast displays a significant oscillation, but there’s an “inflection point” somewhere around Cape Hatteras, NC. North of Cape Hatteras (about 35.3°N latitude) the sea is currently “sloshing up” (but nearing its peak rate of sea-level rise), and south of Cape Hatteras it’s “sloshing down” (but nearing its trough). (Note that this slow “sloshing” is in addition to the linear trends, which also vary by location, due to PGR, land subsidence, etc.)

    So along the northeastern U.S. Atlantic coast, between northern NC and Eastport, ME, if you compare the rate of sea-level rise now to 10 to 30 years ago, you’ll see a measurable acceleration (increase in rate). That’s why the NOAA-calculated long-term average rate of sea-level rise at those stations is slightly higher when calculated using measurements through 2011 than when calculated using measurements through 2006.

    But along the southeastern U.S. Atlantic coast, between Wilmington, NC and the Florida border, if you compare the rate of sea-level rise now to 10 to 30 years ago you’ll see a measurable deceleration. That’s why the NOAA-calculated long-term average rate of sea-level rise at those stations is slightly lower when calculated using measurements through 2011 than when calculated using measurements through 2006.

    Here’s a spreadsheet. You can view it as a web page, or load it into Microsoft Excel or Kingsoft Office:

    http://sealevel.info/US_Atlantic_coast_sea-level_trends.html

    The column labeled “2006 trend” shows the trend calculated by linear regression of all sea-level measurement data through 2006, and the column labeled “2011 trend” shows the trend calculated using data through 2011. For the northern coast, Virginia-to-Maine, the 2011 trends are slightly higher than the 2006 trends, but for the southern coast, North Carolina-to-Georgia, the 2011 trends are slightly lower than the 2006 trends.

    BTW, you might notice that the northeastern U.S. “hot-spot” of apparently accelerated sea-level rise encompasses a stretch of Atlantic coastline that is about twice as long as the southeastern U.S. “cold-spot” of apparently decelerated sea-level rise. But before you make the mistake of concluding that that means the overall trend is acceleration, I should warn you that the entire U.S. Pacific coast is showing deceleration, all the way from Seattle to San Diego:

    http://sealevel.info/MSL_global_trendtable4.html

    The bottom line is that, due to small but measurable multi-decadal regional oscillations in sea-level, with the most noticeable oscillation period equal to about 60 years, when sea-level trends are calculated over short time spans (meaning less than 50-60 years), some stretches of coastline show apparent acceleration, and other stretches show apparent deceleration, neither of which are real long term trends.

    Overall, there’s been little or no change in the rate of sea-level rise in the last 85-90 years, despite the fact that atmospheric CO2 levels increased by about 90-95 ppm (30%). That’s about 4/5-ths of the total anthropogenic CO2 increase since the start of the industrial age. Yet it has caused no detectable acceleration in the rate of sea-level rise. In fact, the best studies show a very slight deceleration.

    BTW, who can guess which of those two opposing “trends” (apparent acceleration in some places, apparent deceleration in others) got written up in a high-profile paper in Nature? Now let’s not always see the same hands.

  25. Why does anyone expect to find a 60 year cycle in sea level? I’m personally convinced of 60 year atmospheric cycles, but water is so quickly isotactic that when you make a pile of it (like the enso warm pool), the rest of the ocean drops. Meltwater and steric change are surely insignificant at this timescale.

  26. Very interesting work, Willis, but your analysis is limited to datasets in the PSMSL database. As PSMSL writes; there exist several important long datasets not included in the PSMLS database:

    http://www.psmsl.org/data/longrecords/

    For example:
    Amsterdam from 1700
    Stockholm from 1774
    Kronstadt from 1773
    Liverpool from 1768
    Brest from 1711

    I would guess that the pay-walled paper is based on some these. They are long enough for at least 3 cycles of a 60 year period.

    /Jan

  27. daveburton says:
    April 25, 2014 at 9:34 pm

    Whether or not you’ll notice the approx. 60-year sea-level oscillation depends on the mix of tide-gauges that you choose to examine.

    For instance, the North American Atlantic coast displays a significant oscillation, but there’s an “inflection point” somewhere around Cape Hatteras, NC.

    When you show up with some evidence for that, we’ll talk. But assuming what is to be proven is not going to work.

    w.

  28. Jan

    The longer records are very interesting but as far as I am aware the tide gauges in all of them have been physically moved at least once so they are not necessarily like for like.

    Also isostatic change needs to be taken into account which is often greater than sea level rise (or fall)

    The rate of sea level change around the UK (up and down) can be seen in this paper-this would cover Liverpool but assumes isostatic change has always been constant.

    http://www.geosociety.org/gsatoday/archive/19/9/pdf/i1052-5173-19-9-52.pdf

    tonyb

  29. @Willis; I commend your effort to personally examine the data and check the science.
    I personally can not see how anything can effect sea levels on so short a time scale. As you know so well the oceans are MASSIVE and leveled by gravity, The land surface/ocean interface is much more likely to move a bit then the amount of volume in the oceans.

  30. David L. Hagen says:
    April 25, 2014 at 4:46 pm

    Hi Willis
    Scafetta is another author showing an ~ 60 year cycle in long sea level data.
    Discussion on common errors in analyzing sea level accelerations, solar trends and global warming Pattern Recognition in Physics 1, 37-58, doi:
    10.5194/prp-1-37-2013, 2013
    “(1) multidecadal natural oscillations (e.g. the quasi 60 yr Multidecadal Atlantic Oscillation (AMO), Norther Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO) need to be taken into account for properly quantifying anomalous background accelerations in tide gauge records such as in New York City;”
    See Fig. 2, Fig. 3 analyzing New York’s 1893 to 2011 record.
    Look forward to your evaluation or testing of his methods.

    Scafetta’s work has been uniformly irreproducible. Unfortunately, he doesn’t give enough information to redo his work, and he has consistently refused to show his data and code.

    As a result, what Scafetta does is merely anecdote, not science in any form, and I ignore it completely.

    w.

  31. Jan Kjetil Andersen says:
    April 25, 2014 at 11:24 pm

    Very interesting work, Willis, but your analysis is limited to datasets in the PSMSL database. As PSMSL writes; there exist several important long datasets not included in the PSMLS database:

    http://www.psmsl.org/data/longrecords/

    For example:
    Amsterdam from 1700
    Stockholm from 1774
    Kronstadt from 1773
    Liverpool from 1768
    Brest from 1711

    I would guess that the pay-walled paper is based on some these. They are long enough for at least 3 cycles of a 60 year period.

    /Jan

    As usual, Jan, your comments are quite valuable. I just took a look at the Amsterdam data … here’s the periodicity analysis:

    There’s a strong cycle around 37 years, and a smaller peak at 53 years, but nothing around 60 years.

    I’ll report on the rest as I do them, but I may not show the rest.

    w.

  32. “Why does anyone expect to find a 60 year cycle in sea level?”

    good question.

    why do people want to find the cycle is a better question

  33. Nice to see a clear analysis of some (relatively) concrete and reliable data. Interesting and informative.

  34. Interesting that the records for places close together on the Dutch coast are so dissimilar, for example Amsterdam, Ijmuiden, Hook of Holland, Den Helder, but then you would need to look at the river freshwater height as well. The amount of rain upriver days or weeks before can have a massive influence on the height of tide at any time.
    But very worthwhile digging.
    It would be interesting to look at somewhere with no land effect – Rockall, St. Helena, somewhere in the Azores?

  35. “There’s a strong cycle around 37 years, and a smaller peak at 53 years, but nothing around 60 years.”

    I found 57/58 years and the paper actuall is fitting 55 years. Don’t know why they report this as “60y”.

    http://onlinelibrary.wiley.com/store/10.1029/2012GL052885/asset/supinfo/grl29523-sup-0005-t02.txt?v=1&s=0603509b8451e52b1b7109d1181c2521b37ccae4

    ” Phase, and Implied Trend for 1993-2011 of a 55-Year Oscillation for Long-Tide Gauge Recordsa”

    It seems they decided 55 was about right and fitted it to each one by OLS. I see no indication of a method that in some way regressed all the data collectively. But I have not paid to get the full text.

    Steve Mosher says “The bottom line is why would anyone think you could find a cycle.”

    Will since there is 60y component is AMO, at least, that should be refelcted in the thermal component of MSL which we are told is a world threatening large problem.

  36. Greg says:
    April 26, 2014 at 1:23 am

    “There’s a strong cycle around 37 years, and a smaller peak at 53 years, but nothing around 60 years.”

    I found 57/58 years and the paper actuall is fitting 55 years. Don’t know why they report this as “60y”.

    http://onlinelibrary.wiley.com/store/10.1029/2012GL052885/asset/supinfo/grl29523-sup-0005-t02.txt?v=1&s=0603509b8451e52b1b7109d1181c2521b37ccae4

    ” Phase, and Implied Trend for 1993-2011 of a 55-Year Oscillation for Long-Tide Gauge Recordsa”

    Not sure why you quoted that. I was looking at the Amsterdam record. They don’t use Amsterdam in what you cited. What am I missing?

    Steve Mosher says

    “The bottom line is why would anyone think you could find a cycle.”

    Will since there is 60y component is AMO, at least, that should be refelcted in the thermal component of MSL which we are told is a world threatening large problem.

    Haaiiiieee, please, stop assuming what you are trying to prove. You have definitely NOT established the existence of a 60-year cycle in the AMO, nor (despite lots of handwaving claims) has anyone that I know of.

    w.

  37. Willis Eschenbach says:
    April 25, 2014 at 8:31 pm
    “While there is nothing resembling a 60-year cycle anywhere in that data. And since the purported 60-year cycle is the subject of this post, I’m taking that as supporting evidence that such a cycle isn’t there …”

    As you say C7W and JJ look similar in form. Jevrejava plot has a peak around 1890 and and 1960. That’s a separation of ~70. Now since the early one is on a strong downward slope the peak will be displaced earlier, contrariwise for later one. That does not seem incompatible with a circa 60 y cycle, for a rough eye-balling of the data.

    This cooling trend to warming is significant feature of both MSL and surface temperature that does not fit the AGW theme tune. This is why a lot of studies prefer to start in 1900 so they have nice simple storey about monotonic rise.

    If you fit an exponential or quadratic rise to support the foregone conclusion about GHG warming it goes badly wrong if your data goes back into 19th c. ( You can get away with 1880 as ‘internal variation’ but beyond that you’re in trouble).

  38. Looking at your P.A. for Amdterdam you will note as well as 53 there is peak at 64 and a smaller one at 60.

    Now if you combine 53 and 64 as cos+cos = cos * cos, as I detailed above for the short cycles that will give the _frequency_ averaged result of 57.9y modulated by 616 years. I think this is what the JJ SSA plot is showing. This is typical of what SSA does it produces pairs of modulated harmonics that result in a long term variation in amplitude. Unfortunately I don’t think JJ reports any figures, so we’re left guessing from the graphics.

    recall the triplet I found in the sub-annual peaks
    # as A.M. 0.947 * 58.060 ; triplet asymmetry: -0.039 %

    Almost identical modulation period.

    Now I don’t see why Chambers et al are fitting 55 and report “60” years. But there is definitely some significant energy in that part of the spectrum.

    My chirp-z analysis found two separate indications : 56.7 and 58 and your PA show 57.8. From such fundamentally different techniques, I think that can be regarded as corroborative.

  39. W. “Haaiiiieee, please, stop assuming what you are trying to prove. You have definitely NOT established the existence of a 60-year cycle in the AMO, nor (despite lots of handwaving claims) has anyone that I know of.”

    OK care with words . There is a circa 60y periodicity in last 120 y of SST , I am not concluding that this is permanent , fixed amplitude harmonic “cycle”. But if that variability is there is SST it could be expected to be reflected in MSL , which was the sense of my reply to Mosh’.

  40. W “Not sure why you quoted that. I was looking at the Amsterdam record. They don’t use Amsterdam in what you cited. What am I missing?”

    I was pointing out that they were fitting 55 not the reported “60”. Since your PA of Amsterdam has the two main peaks in that region with a mean freq corresponding to 57.9 , you are finding similar periods in one of the records they did not use.

    Now if you are going fit a single sine (which is what their model does) it fit to the mean frequency. This is period of combined AM modulated result of the two frequencies your PA found.

    The method is crude and their reporting inaccurate of their results but there is basic agreement with the more general frequency techniques that both you and I are applying.

  41. W: “Finally, while you are correct that there are some small cycles at 8 years or so, as you can see they don’t do much.”

    All this global averaging is stirring a lot different colours in the same pot. Hence you get muddy brown water. It takes spectral analysis to work out pigments were there before you stirred it up.

    Both C&W and Jevrejava are pots of muddy water. Despite that some peaks are fairly well defined.

    Indeed since most oceans communicate it all gets mixed anyway but with the added complication of varying lags. It’s actually surprising that anything survives.

    http://climategrog.wordpress.com/?attachment_id=935

    The amplitudes very small in the chirp-z plot because it is done at a very high spectral resolution and the frequency intervals a very narrow. However, we see just four peaks on the decadal scale.

    Each peak is spread by noise and experimental errors. To get the total power of a peak it needs to be plotted in freq, not period and the area under the peak calculated. In fact the 20 y peak is no broader than the 7.5y one.

    We can get a fair idea from the relative heights.

  42. I remember watching a television show that had a mystery that needed to be solved. Divers in lake Erie were finding trees that died growing out of the lake bed 40-50 feet underwater.
    It turns out, that as the land in the North continues to rebound from the last Glaciation, the Earth’s crust in the South is receding where it had been bulged out by the trillions of pounds of ice piled up at higher latitudes.
    If one accepts that 20% of the tide gauges are actually falling in large measure due to isostatic rebound, then they could be forgiven for believing that at least an equal number are rising at an accelerated rate due to the crust receding. (I claim at least an equal number, due to the higher populations and numbers of tide gauges further south.)
    Now I am no Climate Scientist, but from what I have seen, I have no doubt that they are pretty thorough in making adjustments where the land is rising and completely ignoring adjustments where it is falling.

  43. “Now I am no Climate Scientist, but from what I have seen, I have no doubt that they are pretty thorough in making adjustments where the land is rising and completely ignoring adjustments where it is falling.”

    Yep, that’s pretty much what happens at Boulder. Where we’re supposed to worry about rising “sea levels” that not of the wet variety.

  44. Perhaps they took into account a 60yr cycle in post-glacial rebound but forgot to mention it in their workings.

  45. A good read from Willis as usual. The “measures”, like anything in this global “debate” about minute changes over extremely insignificant geological timeframes, ie, the human timeframe, are unreliable. No mention of land level changes. And there are many examples of ZERO significant change in sea levels around the coast of England. Exeter, Plymouth, Gosport, Portsmouth, Emsworth to name a few sites that have shown ZERO signifgicant change in hundreds of years of Royal Naval history.

  46. I wrote, “Whether or not you’ll notice the approx. 60-year sea-level oscillation depends on the mix of tide-gauges that you choose to examine.
    For instance, the North American Atlantic coast displays a significant oscillation, but there’s an “inflection point” somewhere around Cape Hatteras, NC.”

    Willis replied, “When you show up with some evidence for that, we’ll talk. But assuming what is to be proven is not going to work.”

    I already did, Willis.

    First of all, I gave you that link, to a number of papers which claimed to have identified such an oscillation. Here are some quotes from some of those papers, and a couple of articles about them:

    http://pages.citebite.com/h3c1d6x3w8efh

    http://pages.citebite.com/j2d0k3n9tqpc

    http://pages.citebite.com/g3g1i6j3b9rrr

    http://pages.citebite.com/i3c5o5e8tyff

    http://pages.citebite.com/b3y1x6d4w0xjc

    http://hockeyschtick.blogspot.com/2012/09/new-paper-finds-natural-60-year.html

    http://www.worldclimatereport.com/index.php/2012/09/10/sea-level-acceleration-not-so-fast/

    These two are less directly related, but still interesting:

    http://archive.today/PheLu#selection-1217.138-1217.152 (this is about temperature, not SLR)
    https://en.wikipedia.org/wiki/File:Amo_timeseries_1856-present.svg (this is a graph of the ~60 year AMO)

    Note that the postulated ~60 oscillation is a regional effect. When the ocean is “sloshing up” in one place, it should be expected to be “sloshing down” in another. So if we had a good, even distribution of tide gauges, we wouldn’t expect to see the oscillation in a global average, because the peaks and troughs would cancel in the average.

    To detect evidence of an oscillation you should be looking at regionally averaged, rather than globally averaged, data.

    In fact, the actual worldwide distribution of long-term tide gauges is rather uneven, so it is not unimaginable that you might find some oscillation signal in a global average, but the lack of such a signal doesn’t tell you much. If you want to test whether there are ~60 year regional oscillation patterns, you need to examine regional data.

    Now, with that in mind, recall that I wrote, about two such regions:

    North of Cape Hatteras (about 35.3°N latitude) the sea is currently “sloshing up” (but nearing its peak rate of sea-level rise), and south of Cape Hatteras it’s “sloshing down” (but nearing its trough). (Note that this slow “sloshing” is in addition to the linear trends, which also vary by location, due to PGR, land subsidence, etc.)

    So along the northeastern U.S. Atlantic coast, between northern NC and Eastport, ME, if you compare the rate of sea-level rise now to 10 to 30 years ago, you’ll see a measurable acceleration (increase in rate). That’s why the NOAA-calculated long-term average rate of sea-level rise at those stations is slightly higher when calculated using measurements through 2011 than when calculated using measurements through 2006.

    But along the southeastern U.S. Atlantic coast, between Wilmington, NC and the Florida border, if you compare the rate of sea-level rise now to 10 to 30 years ago you’ll see a measurable deceleration. That’s why the NOAA-calculated long-term average rate of sea-level rise at those stations is slightly lower when calculated using measurements through 2011 than when calculated using measurements through 2006.

    Here’s a spreadsheet. You can view it as a web page, or load it into Microsoft Excel or Kingsoft Office:

    http://sealevel.info/US_Atlantic_coast_sea-level_trends.html

    The column labeled “2006 trend” shows the trend calculated by linear regression of all sea-level measurement data through 2006, and the column labeled “2011 trend” shows the trend calculated using data through 2011. For the northern coast, Virginia-to-Maine, the 2011 trends are slightly higher than the 2006 trends, but for the southern coast, North Carolina-to-Georgia, the 2011 trends are slightly lower than the 2006 trends.

    NOAA did the linear regressions for those tide gauges. In 2007 they did them using sea-level measurement data through 2006 for each gauge. In 2012 they redid the calculations for the U.S. gauges (only), using data through 2011. (I see that they’ve since updated them again, using data through 2012, but I haven’t updated my spreadsheet — sorry.)

    Now, if the ocean is “sloshing up” along one section of coast, and “sloshing down” along another, think about what you should expect to see when comparing the 2011 linear regressions to the 2006 linear regressions.

    In regions where the ocean is “sloshing up,” it means that the current year-to-year rate of sea-level rise is temporarily higher than its long-term average. So in those regions, as the analyzed time interval is extended (from 2006 to 2011), the current temporarily higher rate of SLR should cause the long-term average to increase. That is what has happened along the U.S. Atlantic coast, from northern North Carolina through Maine. For example, here’s Boston:

    http://tidesandcurrents.noaa.gov/sltrends/sltrends_update.shtml?stnid=8443970

    In regions where the ocean is “sloshing down,” the current year-to-year rate of sea-level rise is lower than its long-term average. So in those regions as the analyzed time interval is extended (from 2006 to 2011), averaging in the current (lower) rate of SLR should cause the long-term average to decrease. That is what has happened along the U.S. Atlantic coast from mid- North Carolina through Georgia, as well as along the U.S. Pacific coast. For example, here’s Wilmington, NC:

    http://tidesandcurrents.noaa.gov/sltrends/sltrends_update.shtml?stnid=8658120

    So that gives some evidence of an oscillation, but it doesn’t tell us its period. The question is, how long does the “slosh” persist, before it persistently reverses?

    That’s no mystery. Here’s a quote from Zervas: “there was statistically significant multidecadal variability on the U.S. east coast with higher rates in the 1930s, 1940s and 1950s and lower rates in the 1960s and 1970s.”

    Do you see it? Three decades down, followed by at least two decades up. Noting that ~2/3 of the U.S. east coast is once again seeing higher SLR rates, and has been for at least 20 years, it looks like about a 50-60 year oscillation: 1930-1960 up, 1960-198x down, 198×-2014 up, which is roughly consistent with the other papers.

  47. I found sea level trends in the PMSL database to have a trend of 0.29 mms/year from 1930 to 1980 (there isn’t enough global coverage before 1930). The rate rose to 1.4 mms/year from 1980 to 2009. Tiny hint of a 60 year cycle in there.

    I recently checked the database to see which gauges had been updated for 2010 or 2011 data. 300 gauges had been updated for 2010 or 2010 and 2011 and the average rise was 2.26 mm/year over an average of 1.7 years.

    I guess I am more interested in is there acceleration in sea level from the tide gauges (I don’t think we can trust the adjustment algorithms from the satellites). Sounds like you now have solid database Willis in order to do this very important work.

  48. I agree. The amount of modelling, calibration and “bias” adjustments needed to get sub millimetre accuracy of sea level with a typical swell of about a metre when you can only see the bottom of the swell from 1,336 kilometres away is a joke.

    There is just too much opportunity for personal bias and preconceptions to creep in, even if those involved are doing their best to be objective: which I seriously doubt in many cases anyway.

    Tidal gauges are the best we have, though like all this geo stuff the goalposts are forever moving as well as the ground they are dug into.

    ” (there isn’t enough global coverage before 1930).”

    Well that’s a problem if you’re going to just cut off in 1930 because you won’t see the pre-1990 drop that is apparent in a large proportion of these tidal records. That automatically invites the erroneous conclusion of a small rise becoming larger rise. And it’s a small step from there to erroneous “accelerated global warming”.

    This whole obsession with drawing straight line “trends” through everything is one of the biggest problems in climate science.

    A linear model is wholly inappropriate and has no place on any such graph. Draw another one since 1995 and you will equally conclude ‘decelerating’ sea rise. Draw another up to 1940 and and you start to see a long term cycle.

    As always it is selection bias that determines the result.

  49. Steven Mosher says: I guess greg missed my point while willis got it.

    You do make an art of not saying what mean , so don’t complain if your cryptic insinuations are not always read correctly.

    Greg says:
    April 26, 2014 at 2:04 am

    W. “Haaiiiieee, please, stop assuming what you are trying to prove. You have definitely NOT established the existence of a 60-year cycle in the AMO, nor (despite lots of handwaving claims) has anyone that I know of.”

    OK care with words . There is a circa 60y periodicity in last 120 y of SST , I am not concluding that this is permanent , fixed amplitude harmonic “cycle”. But if that variability is there is SST it could be expected to be reflected in MSL , which was the sense of my reply to Mosh’.

    ====

    I think I get your point but if I don’t perhaps you ought state clearly what it is rather than let people have to guess what you intended to infer.

  50. so why would there be a 60 year global cycle in tidal levels? Where would the water go? There might be cycles at individual stations, but if the water is low at one it must be high at another, so unless the volume changes any sort of global average would hide the effects.

    There might be a lunar cycle, as the moon’s orbit carries it above and below the equator, moving water north and south. This might be related (18 years) to drought and locust cycles (17 years), but it may simply be co-incidence.

  51. Greg says:
    April 26, 2014 at 6:53 am
    I think I get your point but if I don’t perhaps you ought state clearly what it is rather than let people have to guess what you intended to infer.
    ============
    academics (like the Law) avoid plain language like the plague, trying to pretend the reason we lesser mortals can’t understand them is because they are smarter then everyone else. when in reality they are poor communicators.

    the art of good communication is to take the complex and reduce it to plain language so that the meaning is clear to all. failure to achieve this reflects ego more than intelligence.

  52. “This might be related (18 years) to drought and locust cycles (17 years), but it may simply be co-incidence.”

    I think perigee (8.85y) is more important in tidal effects, though this is complicated by how this ties in with declination angle.

    http://climategrog.wordpress.com/?attachment_id=935

    The major challenge is to decrypt and identify any periodic signal in the various time series before trying to explain them. This is usually done backwards. Someone starts with a pet theory and goes out to look for a direct correlation. When it’s not there as a direct unchanging correlation someone comes along and claims “therefore X is not a factor in climate”.

    Climate is complex and has many causal influences. Some add other modulate each other, some have two way feedbacks eg radiation – temperature – radiation. which leads to exponential convolution responses.

    One thing it isn’t is simple direct correlations and straight line trends. Sorry fellas ;)

  53. Steven Mosher says:
    April 25, 2014 at 7:49 pm
    The bottom line is why would anyone think you could find a cycle.
    =======================================================
    Easy
    Everyday up, everyday down
    Sometimes several times a day

  54. “Where would the water go? ” It does not go anywhere, it’s thermal expansion.
    =================
    the thermal inertia of the oceans makes a mockery of trying to measure this. the signal would be smeared over hundreds of years. there is no practical means to separate this from the noise given the relatively short time we’ve been keeping records.

  55. ” failure to achieve this reflects ego more than intelligence.”

    Well it could be the result of being trained in language as an art form rather than a as dry, a down to earth means communicating factual information. Females also seem to be good at communication by innuendo then claim men are stupid because they need everything explaining.

    Personally being a stupid , science trained male , I prefer people to say want they mean first time rather than waste half a day with inappropriate replies and arguments about who said what and what they really meant.

  56. “the thermal inertia of the oceans makes a mockery of trying to measure this”

    I think if you read up on this you will find vertical mixing is very limited and the thermal effect is just as important as melting land bound ice caps.

  57. Much of the height difference in W. Pac warm pool is due to a “lens” of lighter warmer water rather this the idea of it “piling up” because of trade winds. As you say water is not too good at piling up unless pulled by barometric effects or tidal forces.

  58. I guess I can update some of my charts. 1930 to 2011 for tide gauges (although the last two years only have updated records for 306 of the 500 gauges which are supposed to be included in the database now).

    And then compared to other sea level reconstructions back to 1960 and the satellite composite (updated to mid-February and with its methodology revised again for the 30th time with a rise of 3.26 mms/year).

  59. Greg says:
    April 26, 2014 at 7:32 am
    Much of the height difference in W. Pac warm pool is due to a “lens” of lighter warmer water rather this the idea of it “piling up” because of trade winds. As you say water is not too good at piling up unless pulled by barometric effects or tidal forces.
    ====
    Also gravity from sea floor mountains and volcanoes….

  60. Greg says:
    April 25, 2014 at 7:26 pm
    Latitude I advise you to read that paper carefully before jumping all over the result because you like it. I’m not impressed for a number of reasons.
    ===========
    Greg says:
    April 26, 2014 at 7:26 am
    Personally being a stupid , science trained male , I prefer people to say want they mean first time rather than waste half a day with inappropriate replies and arguments about who said what and what they really meant.
    ====
    I didn’t say one way or the other….care to take your own advice?

  61. Several months ago, while looking up past sea level records, I was intrigued by the recent fall in the rate of sea level rise which occurred during the cooling trend over the same period. So I thought it might be interesting to look at sea levels over the past several hundred years, and took a look at the sea level curve back to 1700 AD (Jevrejeva et al.,2008). I first picked out some peak warm and cool periods and looked for anything resembling the 60-year PDO/AMO cycles. There was a nice sea level spike in 1850 during that warm period and a big downward spike in ~1804 during the Dalton cool period, but those didn’t stay up or down very long. By and large, sea level seemed to be oblivious to global temperature trends and no 60-year cycle seemed apparent, just as Willis found in much greater detail.
    But what was even more interesting was an obvious change in sea level pattern that began about 1850. From 1700 to 1850, although sea level bumped up and down, the 150 year trend was essentially flat. Sea level in 1850 was the same as it was in 1700. There was a warm period about 1850 and sea level showed a spike that lasted a few years, then dropped back down to the 150 year level. But then something remarkable happened–sea level broke out of its 150 year stable pattern and began to rise steadily at a rate of about 2 mm/year for the next 150 years and it’s still going on. The post 1850 rise doesn’t seem to show any obvious cyclic pattern and doesn’t seem to react to warming and cooling trends. So the really interesting question is, what happened about 1850 to change from a 150-year stable sea level pattern to a constantly rising sea level pattern that has lasted for 150 years with no apparent regard for global warming and cooling? You might expect that during warm periods, in addition to sea water expansion you would also get some contribution from glacier melting, but that isn’t obvious in the sea level record, at least at this scale. So the really interesting question here is, what happened after 1850 to change from a 150 year pattern of sea level stability to the present 150 year pattern of pretty constant sea level rise that seems to be oblivious to global temperature changes?

  62. lat: “I didn’t say one way or the other….care to take your own advice?”

    I said exactly what I meant. I’m not going to go into an detail appraisal of another paper and spam this discussion. I did leave a comment on the thread you linked to. I just suggested here that you read that paper carefully and make up your mind.

    attractive as the idea of 1mm/y may be, their paper did not impress me enough to give any credence to their results. It has some good points but I would not trust it without reproducing it and I don’t have time for that.

  63. Don, bearing in mind that Willis plotted this detended, I’d say there is about a 15 year lag between d/dt(GMSL) and SST(t) just as a rough visual appraisal. I would not conclude “oblivious”.

  64. BTW since we would expect GMSL to vary with SST not it’s diff, I also note that 15y is pi/2 lag for a 60y cycle.

  65. Willis, there is a superfluous space character clinging to the right end of the url in the link you put up to the large .xlsx sheet with the 1412 stations data. It clobbers the intended purpose and you only get small (700 bytes of html text ) error notice from dropbox if you try to download directly from it.

    [Didn't see it. What paragraph? Mod]
    [Fixed, thanks, Mod and Bjorn. -w.]

  66. @ Don Easterbrook/ Bill Illis/Willis
    I think the point is that warmer water expands and therefore takes up more volume.
    On the other side, more rain will also cause increase in sea level.
    As we consider this plot

    http://www.woodfortrees.org/plot/hadcrut4gl/mean:60/plot/hadcrut3vgl/mean:60/plot/hadcrut4gl/trend/plot/hadcrut3vgl/trend/plot/hadcrut4gl/last:360/trend/plot/hadcrut3vgl/last:360/trend

    and provided it is true,
    it seems that the whole of earth warmed up a bit, since 1850
    it follows that as the water was heated more, over the years, by the sun,
    the subsequent increased evaporation provided that extra heat that gave us more warmth.
    The warmth is good for the greening of earth. The subsequent greening (also of the oceans), also with the help of mankind, traps more heat. The biosphere is booming. Hence the explanation of increasing water levels.

    I hope this explains it, Don?

    Willis found the 44 or 45 year cycle in the record, also in Vlissingen and if you take the two Amsterdam cycles it also gives an average of 45.
    Strangely enough, these results do make sense to me.
    My own results on maximum temperatures led me to this plot:

    http://blogs.24.com/henryp/2012/10/02/best-sine-wave-fit-for-the-drop-in-global-maximum-temperatures/

    This shows that events related to whatever drives the weather (i.e I mean rain mostly) is on some 87 or 88 year cycle, but after every half cycle of 44 years you are back to zero.
    Various investigations which I can quote show that the Gleissberg and DeVries/Suess cycles are real and happening. In both cycles we are now coming to the cooling side of the wave.
    Notwithstanding the “current” records

    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

    my results seem to point to a greater rate of global cooling,

    happening now.

  67. Because the 60 year period was hypothesized based on considerations external to this data set, and prior to analysis of this data set, you can simply test (using a model that is a straight line plus a cosine curve with amplitude and phase estimated) whether there is a significant 60-year period in each series. Then count how many of the corresponding F-tests of the cosine curve, out of ca 1400, are statistically significant, or do the histogram/pdf estimate of the p-values. You don’t have too many parameters for each series: mean, slope, amplitude of cosine, phase of cosine, residual error variance, autocorrelation coefficient (most likely, first-order autoregressive noise is adequate for each series, something that you can test.)

    Your rule of requiring a series at least 3 times as long as the longest period is a great rule for when exploring whether there is any periodicity in the data; but when the period is hypothesized a priori, it is not necessary. Also look at the histogram/pdf estimate of the phases to see whether the diverse series are all in phase. Given the dynamics and known lags (as in your “ENSO pump” graphical analysis), there is no necessity that the series all be in phase.

    If you don’t want to, I may do this. I have done it lots of times with circadian rhythm modeling where the rhythm, if present, is known to have a period of 24 hours. (I said “if present”; it came as a surprise that there is a circadian rhythm in blood concentrations of prolactin in healthy men; no one to my knowledge has tested whether there is a circadian rhythm in blood testosterone in healthy women, but if there is one it has a period of 24 hours.) You only need data over one full period to estimate and test the rhythm. If you suspect some other periodicity, then you need at least 3 days data for exploratory analyses.

  68. some references, or blatant self-promotion:

    9. M.R. Marler, R. Jacob, J.P. Lehoczky, and A. Shapiro. Statistical analysis of treatment and activity effects in 24hour ambulatory blood pressure monitoring. Statistics in Medicine 7:697716, 1988.

    21. M.L. Rao, G. Gross, A. Halaris, G. Huber, M.R. Marler, B. Strebel, and P. Braunig. Hyperdopaminergia in Schizophreniform Psychosis: A Chronobiological Study. Psychiatry Research, 47: 187203, 1993.

  69. The question was asked – why do we expect to see a 60 year cycle? For me, that brought up the question, what would I expect to see in the data given what I know and believe? Thinking about it, my answer was that I think that there has not been a great deal of change in the rate of melting of glaciers in the last 150 years – nothing out of the ordinary in the context of the Holocene. The same is true for the ice caps. Therefore, I think the main reason for sea level changes above and beyond that caused by sinking or rising land masses is changes in heat content of the ocean. The heat content of the ocean given it’s size and depth are long term slow changes. And given how the ocean sloshes around, I would expect the data to be somewhat noisy. Looking at Willis’s data, it seems consistent with this view. But what about oscillations in part of the ocean? On that point, there are “oscillations” that are well known such as the PDO or AMO among others. I would expect that those could have a regional impact on sea level gauges. I’m not sure about the degree of impact or the exact period of any oscillation but given the PDO and AMO are real and do have an impact on the ocean and planet, they should have some impact on sea level. Therefore, I would not be surprised if Daveburton is correct about regional oscillations.

  70. [Didn't see it. What paragraph? Mod]

    You won’t “see” it , its come non displaying character. (maybe non-braking_space char)

    Same problem with the CSV file. The .csv extension has an invisible cruft-char too.

  71. Oh no, its a common space char #32 aka “%20″ . Means if you do Save_as you get an error page in HTML and no data, that is mysteriously unavailable usless you quote the filename and add the invisible space after .csv

    I suspect the others are the same.

  72. Two general comments. First, The 18.6-year Metonic Cycle for tides is recognized as being large enough to affect surveyed coastal boundaries. I made a comment about that in “Sea level rate of rise shown to be partially a product of adjustments” Posted on January 24, 2013:

    http://wattsupwiththat.com/2013/01/24/sea-level-rate-of-rise-shown-to-be-partially-a-product-of-adjustments/

    In part, I quoted the American Council of Surveying and Mapping reference to an approximate 18.6-year cycle known for some time as the Metonic Cycle. The cycle is the basis for
    the 19-year tidal epoch used to define the sea level datum. See ACSM Bulletin at the
    NOAA website:

    http://tidesandcurrents.noaa.gov/publications/Understanding_Sea_Level_Change.pdf

    My full comment is at

    http://wattsupwiththat.com/2013/01/24/sea-level-rate-of-rise-shown-to-be-partially-a-product-of-adjustments/#comment-1208839

    Second, NOAA published a technical report on vertical land motion relative to sea level

    http://tidesandcurrents.noaa.gov/publications/Technical_Report_NOS_CO-OPS_065.pdf

    available at FEMA’s coastal website here

    http://www.fema.gov/coastal-flood-risk-resources#Guidance

    Technical Report NOS CO-OPS 065 “Estimating Vertical Land Motion from Long-Term Tide Gauge Records” is written for coastal engineering and design community, rather than the climate science community. Long term sea level rise is taken as 1.7 mm/year from IPCC 2007, not the often-quoted 3 plus values. Of particular note is this paragraph in the introduction:
    [begin quote]
    The purpose of the methodology is to provide a more accurate estimation of local VLM at tide stations with 30-60 years of data rather than just simply subtracting the estimated global sea level trend of 1.7mm/yr from the observed relative mean sea level trend. Relative sea level trends calculated from shorter data periods are more likely to be affected by anomalously high or low oceanographic levels at the beginning or end of their series. By removing the regional oceanographic variability as calculated based on longer-period stations, both more accurate and more precise estimates of land motion are possible at shorter-period stations.
    [end quote]
    Note “. . .anomalously high or low oceanographic levels at the beginning or end of their series.” These anomalies might be another consequence of what you described in your earlier post:

    http://wattsupwiththat.com/2014/04/24/extreme-times/

  73. Ah, well, something I hadn’t noticed before, the PSMSL archive happens to attach ID numbers by station age, so even though there’s no column for that, the ID column itself acts lets you list the stations by age, more or less.

    http://www.psmsl.org/data/obtaining/

    Some of the oldest *do* show some acceleration, mostly but not all via a pivot point in the 1920s between linear trends. But thankfully for skeptics at least, the official Church & White 2011 update finally included a simple average of tide gauges that I extracted and added a poor man’s trend line to, and it shows utterly no acceleration, one of the most devastating plots for climate alarmists since they are now claiming the impossible that the deep oceans are heating up but not then also expanding more than usual:

  74. Matthew R Marler says:
    April 26, 2014 at 10:25 am

    … you can simply test (using a model that is a straight line plus a cosine curve with amplitude and phase estimated) whether there is a significant 60-year period in each series. … If you don’t want to, I may do this. I have done it lots of times with circadian rhythm modeling where the rhythm, if present, is known to have a period of 24 hours.

    Let me take you up on this then please.

    The DMI has provided me with a text file from 2007 – 2013 for each hour’s dry bulb temperature, pressure,wet bulb temperature, wind speed and wind direction for an Arctic site at 80 north latitude.

    My assumptions:
    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?

  75. Also seems what ever Willis is using stores csv files in old mac format with no linefeed chars.

    dos2unix -c mac “Willis Files.cvs”

  76. Sea level changed ~2 mm/yr first half of 20th and ~2,5 mm/yr last half of 20th but dropped to 0-0.5 mm/yr after high volcanic activity, so there should be a ~9 and a ~80 yr cycle.

  77. “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?”

    A sort of 365.25×24 element climatology you mean?

    Check out Wilis’ links to the PA software and use 365.25×24 as the window repetition.

    “each daily value ….will also vary periodicaly over the length of an entire year.”

    otherwise I have a simple awk script that can do this for an arbitrary window length. I can post that up if you’re interested.

  78. lgl says:
    Sea level changed ~2 mm/yr first half of 20th and ~2,5 mm/yr last half of 20th but dropped to 0-0.5 mm/yr after high volcanic activity, so there should be a ~9 and a ~80 yr cycle.

    ===
    Any correlation you are seeing there is in your own head.

  79. Greg says:
    April 26, 2014 at 11:59 am

    (replying to RACookPE)
    “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?”

    A sort of 365.25×24 element climatology you mean?

    Check out Wilis’ links to the PA software and use 365.25×24 as the window repetition.

    “each daily value …. will also vary periodically over the length of an entire year.”

    otherwise I have a simple awk script that can do this for an arbitrary window length. I can post that up if you’re interested.

    If I understand you correctly, yes – sort of.

    But if we were to create a look-up table with a simple average of each hour’s data, then the “weather” variations over every day would need further smoothing and manipulation, right? Since I’m trying to approximate the as-found average “weather” on an hour-by-hour basis up north to get hourly heat transfer approximations, I’d prefer to “smooth” the data over the entire year with a single function. However, since no part of any weather pattern is a simple sinusoid at any time interval, you can’t really reduce things to nice simple sine or cosine curves. (Which could be a part of the problem of trying to find “perfect” periodicities in past yearly data…)

    For example, I can develop an equation adequately reproducing the DMI average temperature plot for 80 north latitude (from the WUWT Sea Ice page) over a 365 day year.

    So, Tave(day-of-year) = A + B*(DOY) + C*(DOY)^2 + D*(DOY)^3 + E*(DOY)^4 + F*(DOY)^5 + … which works as long as DOY never gets below 0 nor higher than 366. Not elegant, but adequate.
    Then, if Tmax-Tmin varies over the year over the 7 years of data, that too, can be calcualted on a day-of-year basis.
    And, if T(hour) varies consistently and predictably over the year between Tmax (about 14:00 hours) and Tmin(05:00 hours), then getting T(hour-of-year) for every day of the year is straightforward.

    From T(hour-of-year), P(hour-of-year), wind speed (hour-of-year), and relative humidity(hour-of-year), you can get Heat lost by evaporation(hour-of-year), heat lost by convection(hour-of-year), and heat lost by long-wave radiation (hour-of-year).

  80. Ah ha! As my spectral analysis of the Church & White reconstruction showed the 7.5 and 10.2 I found would produce a 57y modulation

    I just dug NY Battery out of W’s csv file took the first diff and applied a 2y filter to make it more legible:

    http://climategrog.wordpress.com/?attachment_id=936

    The circa 60y modulation is clearly visible.

    I also found a sub-annual periodicity had a 58 y modulation too.

  81. “Tave(day-of-year) = A + B*(DOY) + C*(DOY)^2 + D*(DOY)^3 + E*(DOY)^4 + F*(DOY)^5 + … which works as long as DOY never gets below 0 nor higher than 366. Not elegant, but adequate.”

    How many poly terms will you need to represent 365 days worth of fluctuations?! I would have thought an FFT approach would have been more suitable. Or perhaps more flexible the PA breakdown that Willis used.

    FFT is limiting because of the way the frequency intervals work. That’s why I use chirp-z. which frees it up. You can then pick off the precise frequencies of the main peaks and build a truncated harmonic reconstruction.

    While I think of it, if you are working with wind speed consider whether v^2 is what you need to use. Most things like sea evaporation and energy depend upon the square.

  82. Greg
    Who said correlation?
    I said sea level change drops after high volcanic activity. Show me one large eruption without a sea level change drop a few years after.

  83. “And, if T(hour) varies consistently and predictably over the year between Tmax (about 14:00 hours) and Tmin(05:00 hours), then getting T(hour-of-year) for every day of the year is straightforward. ”

    I very much doubt that you will find a constant-ish daily cycle that holds through the Arctic winter and the daylight periods.

  84. “Show me one large eruption without a sea level change drop a few years after.”

    Well it’s hard to see on that messy upside down pastiche you presented but it looks like Santa Maria misses and for Mt Pinatubo the drops starts before the eruption so suggesting causation gets a bit tricky unless you close both eyes and peep through your lashes.

    El Chichon happens once the AOD has nearly returned to normal , then you have massive heg swings that have nothing to do with any volcanic events. That again makes attributing coincident events rather speculative if they are not different from the variability in the rest of the data when there are no volcanoes.

    That is why I mentioned correlation. If there’s a connection , as it appears you are trying to suggest, there must be a correlation. Otherwise the effect is a just like seeing a face in the clouds.

  85. Thank you for interesting and as far as I see correct analysis of data and final conclusion. I really wonder what method could have the paper authors used.

    I tried to download your data files to take a look at it myself but I got an “Error (400)” for both links.

  86. See notes above for the link. There’s an extra spaced got added to the end like “.cvs ” and “.xlsx ”

    Copy the link and paste in a new window, then edit off the space ;)

  87. Just had a look at Honolulu. 7.5 and 10.2 still notable peaks but very strong 9.08 and 21.4 y too.

    This is the ubiquitous 9.1 +/-0.1 that can be found just about everywhere in SST it seems and Scafetta claims in aurora data. This is lunar. The 21.4 looks solidly Hale cycle . Surprised to see that come out so clearly.

    It seems that, like SST, this needs to be analysed regionally otherwise a lot of valuable information gets muddied out.

    Chambers et al got that much right and looked at the details of phase too. Its a shame that they did not do a more general frequency analysis.

  88. Let me add some confusion. I have not seen anywhere in the discussion or the literature consideration of the local water column density. The comings and goings of estuarine circulation near the tidal gauges, or the meanderings of oceanic currents with different densities are not at all considered. The continuous and dynamic isostatic adjustment of the water column is not considered. For example, if you have a change to a low column density by the gauge station, the water level will go higher, etc.

  89. Greg says:
    April 26, 2014 at 1:39 am

    Willis Eschenbach says:
    April 25, 2014 at 8:31 pm

    “While there is nothing resembling a 60-year cycle anywhere in that data. And since the purported 60-year cycle is the subject of this post, I’m taking that as supporting evidence that such a cycle isn’t there …”

    As you say C&W and JJ look similar in form. Jevrejava plot has a peak around 1890 and and 1960. That’s a separation of ~70. Now since the early one is on a strong downward slope the peak will be displaced earlier, contrariwise for later one. That does not seem incompatible with a circa 60 y cycle, for a rough eye-balling of the data.

    Sorry, I’m not seeing that. A sixty year cycle spends thirty years below the line and thirty years above the line. Those three datasets all are going level and then spend 60 years below the line … and at the end, they go linear again, and then up, not down. As I said above, that looks vaguely like half a 120 year cycle, but there’s no 60 year cycle in there anywhere. Periodicity analysis of C&W finds a peak at 42 years, but nothing around 60 years.

    w.

  90. Greg says:
    April 26, 2014 at 1:57 am

    Looking at your P.A. for Amdterdam you will note as well as 53 there is peak at 64 and a smaller one at 60.

    Now if you combine 53 and 64 as cos+cos = cos * cos, as I detailed above for the short cycles that will give the _frequency_ averaged result of 57.9y modulated by 616 years. I think this is what the JJ SSA plot is showing. This is typical of what SSA does it produces pairs of modulated harmonics that result in a long term variation in amplitude. Unfortunately I don’t think JJ reports any figures, so we’re left guessing from the graphics.

    recall the triplet I found in the sub-annual peaks
    # as A.M. 0.947 * 58.060 ; triplet asymmetry: -0.039 %

    Almost identical modulation period.

    I hate to say it, Greg, but for me that’s nothing but numerology. Fifty-seven point nine years? Six hundred sixteen years? Triplet asymmetry? Fifty-eight point zero six zero years, 58.060 … that’s six hundredths of a year with a claimed precision to the nearest thousandth of a year? A thousandth of a year is what, about eight hours?

    You’re just pulling numbers out of your … immediate environment, with no visible limits on what you might grab, and not the slightest attempt at providing a physical mechanism.

    So I fear you’ll have to address your numerological insights to someone else. I’m not interested in sparring with someone who does that. On my planet that’s an advanced case of cyclomania, and to date no one has found a cure. I used to suffer from advanced cyclomania as well, that’s why I got into periodicity analysis, for all the wrong reasons. So I know what the mania is like, but eventually the disease ran its course … I can only hope it works out that way for you, but in the meantime, you might find yourself more at home over at Tallblokes Talkshop.

    w.

  91. daveburton says:
    April 26, 2014 at 3:53 am

    I wrote, “

    Whether or not you’ll notice the approx. 60-year sea-level oscillation depends on the mix of tide-gauges that you choose to examine.
    For instance, the North American Atlantic coast displays a significant oscillation, but there’s an “inflection point” somewhere around Cape Hatteras, NC.”

    Willis replied,

    “When you show up with some evidence for that, we’ll talk. But assuming what is to be proven is not going to work.”

    I already did, Willis.

    First of all, I gave you that link, to a number of papers which claimed to have identified such an oscillation. Here are some quotes from some of those papers, and a couple of articles about them:

    http://sealevel.info/Schlesinger_and_Ramankutty_1994_p1.png

    I have a funny habit, Dave, that’s related to the limited amount of time I have. When someone sends me a list of links, I’ll stop at the first link that is not on topic. In addition, I’ll stop at the first paywalled link. I don’t have time to screw around with someone’s idea of good stuff. When it goes off the rails, I stop.

    In this case, your first link to support your claim that there is an “inflection point” in North Carolina turns out to be paywalled. It claims that there is a “65-70 year” cycle in the global temperature. Further, it claims that this cycle is “the statistical result of 50–88-year oscillations for the North Atlantic Ocean”.

    Fifty to eighty-eight year oscillations in the North Atlantic? How is a claim like that considered to be science? That’s so vague as to be without meaning.

    Next, here’s one of their rock-solid, crystal-clear references for one of their claims:

    Houghton, J. T., Jenkins, G. J. & Ephraums, J. J. (eds) Climate Change: The IPCC Scientific Assessment (Cambridge Univ. Press, 1990).

    For me, anyone who cites the entire IPCC three volume circus, without any attempt to provide a reference to a volume in the report, much less to a chapter in a volume in the report, much less to a page in a chapter in a volume in the report … anyone who does that is a fraud. They are not practicing science of any kind. That is religion, just airily waving at the Holy Book and saying it’s in there somewhere, you go find it.

    So … since the very first reference you directed me to was an off-topic, paywalled, non-scientific study, I stopped right there. I don’t care what else you think is a citation to support your ideas. That paper was garbage. And when a man promotes garbage, the fact that there may be a diamond in there somewhere is not enough to keep me looking.

    SO … if you’d like to point to the actual page of the actual paper that says something about an inflection point in North Carolina, bring it on. For the rest of your citations … sorry, not interested. Your first reference put me off my feed, and I’m a former well driller who is not fool enough to continue digging a dry hole …

    w.

  92. I’ve updated the links to the data, my apologies. For some reason WordPress added a space at the end of each one.

    w.

  93. Matthew R Marler says:
    April 26, 2014 at 10:25 am

    Because the 60 year period was hypothesized based on considerations external to this data set, and prior to analysis of this data set, you can simply test (using a model that is a straight line plus a cosine curve with amplitude and phase estimated) whether there is a significant 60-year period in each series.

    Matthew, that is exactly what the periodicity analysis does, except it is not limited to a cosine curve. It shows whatever curve is actually there. The standard deviation of that actual curve is a measure of the amount of power at that cycle length. Note that this is the actual power at that period, not the sine-wave power at that period.

    In other words, what you propose is exactly what I’ve done.

    Then count how many of the corresponding F-tests of the cosine curve, out of ca 1400, are statistically significant, or do the histogram/pdf estimate of the p-values. You don’t have too many parameters for each series: mean, slope, amplitude of cosine, phase of cosine, residual error variance, autocorrelation coefficient (most likely, first-order autoregressive noise is adequate for each series, something that you can test.)

    Figuring statistical significance of the periodicity analysis has been on my to-do list, but the nature of the analysis means that it’s going to be a monte carlo analysis and thus subject-specific. Thanks for reminding me. I think as a result of this discussion I know how to do it, but we’ll see.

    Your rule of requiring a series at least 3 times as long as the longest period is a great rule for when exploring whether there is any periodicity in the data; but when the period is hypothesized a priori, it is not necessary.

    Matthew, thanks for that and for the link to the circadian rhythm analysis. I have no doubt that for the kind of analysis that you are used to doing, one cycle may be adequate. However, climate is another kettle of fish entirely.

    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..

    However, when we look at the full record, within a few cycles in either direction the sea levels and the sunspots are way, way out of phase. It turns out the relationship is entirely spurious. The relationship appears, lasts for three good cycles, then the relationship vanishes. Go figure.

    In Summary: Not only is one cycle far, far from enough to do what we want to do, to establish that a cycle exists in some climate variable.

    In cases like the one I mention, even my recommended three cycles of data are still not enough to establish that a cycle exists.

    It’s a problem that signal engineers and statisticians from other fields encounter when they start looking at climate. Regular signals in their fields are the rule, and their assumptions are based around regular, repeating cycles that vary little in either frequency or amplitude. Circadian rhythms occur every day “like clockwork” as they say.

    But in climate, regular repetitive cycles are very rare, and very little runs “like clockwork”. Instead, we see pseudo-cycles at all frequencies. For a while, it sure looks like there’s an 11-year cycle in the sea level … but then the 11-year cycle fades away, and is replaced for a while by what sure looks like a 17- year cycle … followed by a long flat period … out of which the 11-year cycle reappears again, but this time reversed in phase … I’m sure you see the problem.

    In that morass of appearing and disappearing pseudo-cycles that we routinely find in climate observations, your idea that one cycle is enough to establish the veracity of a hypothesized cyclical linkage is not even wrong. Three cycles is a practical rule of thumb for a minimum, and as the example above shows, sometimes even three cycles of what looks like significant data is not enough.

    Best regards,

    w.

  94. Mr Eschenbach,

    Movement of the Tanana ice at Nenana tripped the clock at 4:48 pm AkDT (UTC -10) on April 25. That’s the 8th (of 98) earliest ice-out.

    http://www.newsminer.com/news/alaska_news/breakup-tripod-falls-on-the-tanana-river/article_80a4a176-ccde-11e3-bfd7-0017a43b2370.html

    Though you’d want to know as you went to the trouble to write a feature that appeared the middle of last May about the abnormally, ultimately a new record (by ~3 1/2 hrs), late ice-out. I count 5 posts twixt yourself and Mr. Watts over the six days leading up to that event last year.

  95. “Whether or not you’ll notice the approx. 60-year sea-level oscillation depends on the mix of tide-gauges that you choose to examine.”

    Yes, Mann learn the same trick with proxies

  96. Neil Jordan says:
    April 26, 2014 at 11:22 am

    Two general comments. First, The 18.6-year Metonic Cycle for tides is recognized as being large enough to affect surveyed coastal boundaries. I made a comment about that in “Sea level rate of rise shown to be partially a product of adjustments” Posted on January 24, 2013:

    http://wattsupwiththat.com/2013/01/24/sea-level-rate-of-rise-shown-to-be-partially-a-product-of-adjustments/

    Mmmm … well, I fear that your citation doesn’t say a word in support of your claim. Instead, it says:

    This lunar cycle, first determined by Meton of Athens in 432 BC, captures a long-period change in the amplitude of the tide due to the orbital paths of the Earth and Moon relative to the Sun. The Metonic Cycle was selected because it includes daily, monthly, annual, and decadal changes in the amplitude of tides over 19 years.

    Figure 2 shows the variation introduced into the amplitude of the annual mean ranges of the tide during the Metonic Cycle. The heavy black curve is the annual mean range, or the difference in height between Mean High Water and Mean Low Water. The time elapsed from peak-to-peak is the Metonic Cycle.

    In other words, they say that the Metonic cycle affects the RANGE of the tides.

    However, does it affect the HEIGHT of the tides? Well … not much. Rather than dispute with you on this point, I encourage you to take a running 19-year average of any one of the long-term tidal sets, and then show us all the claimed cycle. If it is there, such an averaging should reveal it … however, don’t be surprised when you don’t find anything.

    Yes, the ACSM citation points out that the LMSL, the lower mean sea level, is recalculated once every Metonic cycle of 19 years, which does indeed “affect the surveyed coastal boundaries” … but that is to adjust for the slow secular trend in the sea level. It has nothing to do with the variation in RANGE caused by that cycle.

    w.

    PS—If you don’t want to do the math yourself, then you could occupy yourself with trying to find the 18.6 year cycle in the 22 periodicity analyses above … good luck.

  97. Neal Jordan, here’s the thing about the Metonic cycle. People think it’s a slow underlying sinusoidal cycle. It’s not. For example, here’s the actual repeating 18.6 year cycle in the Cuxhaven II tidal data. I’ve shown four full cycles for clarity.

    As you can see, the 18.6 year Metonic cycle is by no means a slow sinusoidal up and down. Instead it is a complex waveform that just happens to repeat every 18.6 years. So if you’re waiting for it to “affect surveyed coastal boundaries” as you state above, you’re going to wait a long time. They don’t adjust coastal boundaries based on such small short-lived variations.

    w.

  98. Re Willis Eschenbach says: April 26, 2014 at 10:59 pm
    and Willis Eschenbach says: April 26, 2014 at 11:57 pm
    Thank you for your comment re the LMSL, the challenge, and the Metonic cycle information. I wasn’t trying to ferret out another cycle and won’t step into that quagmire, but was trying to bring in an important aspect of sea level that defines a property boundary. The 19-year tidal epoch is an averaging period that is considered when establishing the boundary between public and private property. The current tidal epoch is 1983 through 2001 (the previous 19 years) per NOAA http://tidesandcurrents.noaa.gov/datum_options.html and is updated every 19 years except for a 5-year cycle in Alaska and Gulf of Mexico. Unfortunately, this technical reality tends to be overlooked by those who would arbitrarily define the boundary based on some future hypothesized sea level from a computer model rather than a sea level actually measured according to established procedures (e.g. “Evidence and Procedures for Boundary Location”). The complexity of this is described for example in “Tidal Boundary Delimitation: Some North American Perspectives”:

    http://www.csc.noaa.gov/mbwg/_pdf/biblio/Nichols.pdf

    The pages are out of order, but all are there. See the Borax decision on Page 563. The tidal boundary is indeed a quagmire. See “Rules of the Game: Establishing Property Lines”:

    http://www.profsurv.com/magazine/article.aspx?i=70829

  99. 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 meaningless without 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.

  100. recall the triplet I found in the sub-annual peaks
    # as A.M. 0.947 * 58.060 ; triplet asymmetry: -0.039 %

    “I hate to say it, Greg, but for me that’s nothing but numerology. Fifty-seven point nine years? Six hundred sixteen years? Triplet asymmetry? Fifty-eight point zero six zero years, 58.060 … that’s six hundredths of a year with a claimed precision to the nearest thousandth of a year? A thousandth of a year is what, about eight hours?”

    Look, that’s just the output of the calculation, I’m not suggesting it’s five figure accurate. Like I already comments earlier when I said 56.7 and 58 probably represented the same thing. So don’t get pedantic about the extra digits. That’s a distraction and nothing else.

    If you don’t understand the significance of triplet asymmetry, this is a check against taking any three peaks in row a and saying “wow a triplet”. There are other checks but this is my first line sanity check, since amplitude modulation will produce a perfectly symmetric pattern in the frequency domain. Another check is the side-band having the same height.This also checks out in this case.

    ” As I said above, that looks vaguely like half a 120 year cycle, but there’s no 60 year cycle in there anywhere. Periodicity analysis of C&W finds a peak at 42 years, but nothing around 60 years.”

    Well actually up-down-up would be the full “cycle” but I agree variability of that length is there, though we would both agree there is no grounds to call it a “cycle”. What I was trying point out is that unless you “detrend” for that too it will separate the other peaks which lie on the down slope and the up slope, leaving them separated by about 70y (visual est.).

    To say “nothing around 60 years.” is to refuse to see the results of your own analysis.

    The ‘power index’ at 60 is about 3.7 . That is not equal to “nothing”. There is also notable signals at 53 and 64 as I already pointed out. So there is significant energy in that part of the spectrum not “nothing”.

    “You’re just pulling numbers out of your … immediate environment, with no visible limits on what you might grab, and not the slightest attempt at providing a physical mechanism.”

    One visible limit is triplet asymmetry that you dismissed without understanding what it meant. Hopefully that is now clearer.

    As for physical mechanism, hey what do you want? A PhD in each blog post? What is being discussed here is a first grasp of what patterns may or may not be present in the data. It is necessary to analyse the data and decide what is there FIRST. I’m not trying to colour my analysis by guessing about causation and specific mechanisms beyond the fact that some physical effects will be simply additive and will be superimposed, others will modulate each other physically, so this kind of A.M. should be expected to be present and should be actively sought out if we are get the most information from these noisy data sets.

    “So I fear you’ll have to address your numerological insights to someone else. I’m not interested in sparring with someone who does that.”

    Well unfortunately you always seem to regard this as a combative process. Almost all your replies to everyone’s comments are in the spirit of fending off an attack defending what you posted unchanged.

    I prefer to contribute to a common effort to understanding the data and what it can tell us about climate. You have an incisive and enquiring mind and a broad life experience that gives you insights that desk bound academics lack. I also have a broad range of expertise and experience that I try to bring to bear.

    If you are inclined to reject the idea of cyclic content that’s a useful check on what I’m seeing and argued objectively rather than dismissively it would be helpful. I too am not interested in intellectual sparring contests, pet theories and pet rebuttals. I’m interested in seeing what information can be extracted from this messy data.

    Now as I pointed out above it looks like the circa 60y effect is more clearly seen in the modulation of the amplitude and that is what my spectral analysis suggested. Here we can clearly see this modulation in rate of change at NY Battery. No spectral mumbo jumbo, no numerology:

    http://climategrog.wordpress.com/?attachment_id=936

    Jevrejava’s SSA ; the freq. mean of your 53 and 64 PA periodicities; my sub-annual triplet and combination of 7.5 and 10.2 spectral peaks all point in the direction of 58 years that is seen directly at Battery.

    It seems much clearer as a modulation than as a simple change in height, where the signal is admittedly weak ( the Chambers paper that was the subject of this post only finds small amplitude effects).

    Now that is only the very first step of recognising the patterns. The next step is to try to understand how they relate to each other ( is the smaller height effect a non-linearity left-over from the modulation effect), how does this vary geographically. All that is necessary before we even begin to speculate about physical causation.

  101. “Eternal thanks to Jan for finding the original document, turns out it is worse than I thought.”
    I agree it was pretty poor. The only thing I like is they (attempt to ) investigate the geographic dependency rather than muddying everything into a global average.

  102. Note there is a peak about 21y in Cascais there. This was a notable peak in the various spectra I’ve done. Particularly strong at Honolulu, which is one of the rare mid-ocean sites.

  103. Greg says:
    April 27, 2014 at 2:32 am

    Jevrejava’s SSA ; the freq. mean of your 53 and 64 PA periodicities; my sub-annual triplet and combination of 7.5 and 10.2 spectral peaks all point in the direction of 58 years that is seen directly at Battery.

    Um … er … well … uh … perhaps on your planet, a “sub-annual triplet and combination of 7.5 and 10.2 spectral peaks points in the direction of 58 years that is seen directly at Battery”.

    On my planet, that is a string of words that sounds important but contains no meaning at all.

    So Greg, truly, as I said before I’m not interested in trying to parse your explanations. To me that’s numerology, and is useless. Like I said, there’s no use addressing that to me. I find nothing of value in your work, of which the quoted paragraph is quite typical.

    However, my inability to find anything of value in your work doesn’t mean it has no value. I think it’s meaningless numerology myself, but obviously on your planet the statement you made has deep and profound meaning.

    So once again I encourage you to take your ideas where people appreciate such statements, such as Tallbloke’s Talkshop. My threads are not the place for them. Over there, people talk your language, they may be interested in discussing your ideas, and unlike me, they are unlikely to tell you what I will, which is that in my opinion, they are absurd numerological excursions of no scientific value.

    Best regards,

    w.

  104. “My threads are not the place for them.”
    This is still an open forum for discussion last time I looked, or do you somehow imagine you “own” the discussion every time you post an article? It’s not the first time you’ve made such comments to others.

    “… on your planet the statement you made has deep and profound meaning. ”

    Hmm, that sounds a little like Lew-talk to me.

    “However, my inability to find anything of value in your work doesn’t mean it has no value. ”

    Indeed. It is difficult to explain technical stuff like interference patterns and modulation sidebands in blog comments. I’m sorry I have not been able to get this across. I think the fact you have clearly made up your mind and are fairly dismissive and refractory to the idea does not help. Hopefully it may click for others.

    best regards, Greg.

  105. Correction to earlier comments I made about NY Battery. It is the beat period that is close to 60y (this is half the modulation frequency). In fact I’d put this nearer to 63 years.

    Willis’ P.A. graph of Amsterdam found a peak at 64 in tide height. These are similar latitude sites. Again, as with SST, geography is probably important in understanding all this Lumping everything into a global is must like mixing all the colour in the paint box. You end up with muddy brown water.

  106. Greg says
    Lumping everything into a global is must like mixing all the colour in the paint box. You end up with muddy brown water. (sic, you are writing too fast?)
    Henry says
    Truth is that most data sets that study climate are not globally representative.
    This is (apparently) because nobody has spent some good time thinking on how to get a globally representative sample.
    I found / recommend that there is a basic set of rules to follow:

    1) the number of sample stations SH and NH must be equal
    2) the sum of latitudes of all sampling stations must balance to zero or close to it
    3) longitude is not important if you (want to) observe the average change per annum,
    One year includes the effect of seasonal shifts and irradiation + earth rotates once every 24 hours. So balancing on longitude is not required as the differences due to longitude cancel each other out over a year.
    4) if you study temp. or weather, chose your stations 70% at sea/ 30% inland
    5) if possible, all continents included
    6) subject to the sampling procedure 1-5 above, samples must be selected randomly, any place on earth. They maybe even close to each other, if the conditions 1-5 are met.

    Pity there is not one global data set that keeps to these (very) simple rules. That is why I trust my own data set more than any other.

    .

  107. “Truth is that most data sets that study climate are not globally representative.”

    Neither are effects global. Nor are the feedbacks the same in the tropics and at temperature latitudes. So the mixing does nothing but muddy the waters. Even if done carefully.

    This is to a large extent a convenient non-accident. Having mangled any evidence of how climate really operates they just turn round and say “it’s stochastic” ! They then focalise on carefully cropped period with upwards trend and conclude AGW+noise.

  108. Greg says
    Neither are effects global.

    Henry says
    Ultimately what we get from the sun – or what is allowed through the atmosphere –
    affects the whole of earth. Nothing else is really relevant. Greenery and man made greenery may trap the heat a little bit, but it is not much compared to the sum total.(0.5 degrees C?)
    Based on my own results for the change in (global) maxima, I can predict that we will enjoy global cooling for the next 35 or 40 years.
    Had I known about the diminishing strengths of the solar polar fields:

    https://www.google.co.za/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&docid=Zf89nf4rQ3_kNM&tbnid=x0HB82mkwt-syM:&ved=0CAUQjRw&url=http%3A%2F%2Fice-period.com%2F&ei=zPNcU8GRI6HW0QXui4DYAQ&bvm=bv.65397613,d.ZGU&psig=AFQjCNHOdANkKQwNlTCXv6eSmUS2CWij-Q&ust=1398687049323680

    beforehand, I could have seen what is coming. I refer to that graph as the scissors. The key lies in knowing what is happening TOA as a result. Based on my results, I predict that around 2016 the solar poles will switch again. Then we start the scissor in the opposite direction.

  109. 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/

  110. 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 ;)

  111. 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.

  112. 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.)

  113. 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.

  114. Greg says:
    April 27, 2014 at 3:10 am

    “My threads are not the place for them.”

    This is still an open forum for discussion last time I looked, or do you somehow imagine you “own” the discussion every time you post an article? It’s not the first time you’ve made such comments to others.

    Greg, I can’t prevent you from posting your garbage here. I made a polite request. If you don’t wish to be polite, don’t expect me to be.

    w.

  115. 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.

  116. Greg says:
    April 27, 2014 at 3:40 am

    Correction to earlier comments I made about NY Battery. It is the beat period that is close to 60y (this is half the modulation frequency). In fact I’d put this nearer to 63 years.

    Oh, this is hilarious.

    Before, your bogus calculations were as follows:

    “Jevrejava’s SSA ; the freq. mean of your 53 and 64 PA periodicities; my sub-annual triplet and combination of 7.5 and 10.2 spectral peaks all point in the direction of 58 years that is seen directly at Battery.

    So before, you were insisting, in the face of my skepticism, that all of that crap somehow magically pointed directly at 58 years.

    And now, one day later, you are ludicrously trying to convince us that they all now point at 63 years.

    I guess you don’t realize how pathetic all that flailing about reads from this side of the screen. You’ve just confidently assured us that you understand all these deep questions, and the answer is 58 years.

    Now, without explanation, without telling us why the “7.5 and 10.2 spectral peaks” and Jevrejeva’s SSA suddenly have had their sights adjusted, now you very confidently assure us that they point at 63 years.

    Your claims are pathetic, Greg. You may be an expert in your field, but your grasp of climate is a joke.

    w.

  117. @Greg
    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?

  118. Matthew R Marler says:
    April 27, 2014 at 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.

    Thanks, Matthew. Two points. First, we only HAVE a small number of long tide data series. I’ve used every series 120-years or over in the PSMSL dataset.

    Next, you seem to have forgotten that I also calculated the average of those 22 series, and found no signal there either.

    Look, Matt. I put the damn data out there and asked people to find the putative signal for a reason. It was to avoid this kind of nitpicking. I told you I can’t find the signal. It is useless and meaningless to speculate that there is some reason why I can’t find it but that the signal is really there. I can’t find it. Either you can find it or you can’t.

    So here’s the present situation:

    If you think the elusive ~60-year signal is there, it is now your job to find it.

    Greg seems to think the challenge is to find several different astronomical cycles that combine to “point to” the signal I can’t find.

    You seem to think the challenge is to explain why I can’t find it.

    Neither of those are the challenge. The challenge is to find the elusive hypothesized signal. To date the score looks like this:

    I can’t find the signal in the data.

    You can’t find the signal in the data.

    Greg can’t find the signal in the data. But then he thinks that the Jevrejeva smoothed data is susceptible to spectrum analysis, so that’s no surprise.

    I’ve done further analyses of a couple of other long-term records and couldn’t find the signal.

    And finally, I’ve shown above why the authors’ analysis method (find a non-zero signal at 55 years and declare victory) is nonsense. This goes much further than my initial goal, because I just wanted to show that the signal didn’t exist. However, I’ve also been able to show that their results are totally bogus, due to their ludicrous analysis method.

    So unless one of you guys can find the elusive signal, I’m about to declare victory and go home.

    w.

  119. HenryP says:
    April 27, 2014 at 10:37 am

    @Greg
    Best is to ignore Willis. He is just like Leif. Must be horrible to live with people like that. No doubt they are single.

    I’m married for 36 years to my gorgeous ex-fiancee, a powerful woman who laughed greatly at your foolish, groundless, and untrue accusation.

    Yes, you’re free to ignore me, just as you are free to continue ignoring both Leif and the laws of physics … your choice.

    w.

  120. Matthew R Marler says:
    April 27, 2014 at 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.

    Thanks, Matthew, always good to read your comments. However, I keep waiting to hear from you what “the statistically most powerful method” reveals about the putative signal, whatever that method might be.

    Because until you or someone actually finds such a signal, asking “does it persist in further data” is kinda premature, don’t you think?

    w.

  121. 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.

  122. 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.

  123. 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.

  124. 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.

  125. “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.

  126. 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/

  127. 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.

  128. 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.

  129. 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.

  130. Steven Mosher says:
    April 25, 2014 at 5:54 pm

    “I have no issues.”
    _________________
    I ain’t touchin’ that…

  131. 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/

  132. 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.

  133. Steven Mosher says:
    April 27, 2014 at 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.

    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.

    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. If you run the numbers you’ll find that tropical clouds around noon make a NET difference in downwelling radiation of around 400 W/m2 or so. This is a 24/7 average of 400/24 ≈ 16 w/m2 reduction in incoming sunlight per hour of time difference in cloud onset.

    So a small change in the onset time of tropical clouds (which is typically around 11AM) is more than enough to rebalance the system.

    Next, there is only one number of interest in the equation. It’s not the temperature of the mixed layer of the ocean. It’s not the temperature of the atmosphere at two kilometres of altitude.

    It’s the surface air temperature. As a result, any variation in the rate at which the surface loses heat to the middle/upper troposphere has the possibility to “restore balance” without much increasing the surface temperature.

    By that last one, what I mean is that in addition to a temperature the surface has a throughput, the amount of energy passing through the surface. And the two are not related. As long as losses rise to meet increased input there will be very little surface temperature change.

    Steven, your idea that the only way to restore the balance is a corollary of the basic climate science misunderstanding. This is the curious claim believed by almost all climate scientists that

    ∆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.

    Unfortunately, the equation I give above is the central climate paradigm of our times, that global temperature changes not only slavishly but linearly follow the changes in total forcing … and it is totally incorrect.

    In fact, multiple lines of evidence show that the system is highly resistant to changes in forcing, due to a host of both parasitic losses, as well as emergent climate phenomena which act to cool the surface in a variety of ways. These act in concert to restrict temperature changes to a very narrow range, e.g. ± 0.3°C over the entire 20th century …

    w.

  134. “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:

    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.

  135. “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.

  136. 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.

  137. 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”

  138. 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.

  139. 1sky1 says:
    April 28, 2014 at 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.

    I told you I couldn’t find the cycles. I invited y’all to find them.

    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?

    w.

  140. Bart says:
    April 28, 2014 at 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.

    You been taking lessons from 1sky1? The only thing that will impress people is when you find the cycles that you claim exist. I can’t find them, and to date, all you’ve shown us is your big mouth, and all you’ve given us are overblown claims. Color me unimpressed. Come back when you can demonstrate the existence of the cycles.

    w.

  141. HenryP says:
    April 28, 2014 at 11:41 pm

    As predicted, it seems Steven Mosher has left the arena. He does not want to be challenged.

    He gave you valuable information. In return, you rubbished his name and ignored his information. Why should he not leave? You attack him for things he hasn’t done, viz:

    Look at the rubbish he writes here:

    http://static.berkeleyearth.org/pdf/skeptics-guide-to-climate-change.pdf

    While Mosher does do some consulting for Berkeley Earth, I doubt very much if he agrees with everything they might publish. I don’t see his name on that pamphlet anywhere. How do you know he wrote it?

    Now, if you ever get tired of ignoring the science and abusing the abusing the scientists with ad hominem arguments, if you want to challenge the Slutsky paper that Mosh pointed to, I invite you to do so. I can hardly wait to see that, it should be hilarious to see you try to overthrow Slutsky’s claims.

    And if you can’t overthrow Slutsky’s claims … then why are you trying to bite Mosher’s ankles for pointing at it?

    w.

  142. 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

  143. 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.

  144. Steven Mosher says:
    April 29, 2014 at 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.

    Thanks, Mosh. Your words in this very comment prove my point about you excluding possible solutions (the “excluded middle).

    In my comment to you above I identified two other ways to restore the balance. Instead of discussing either one of those two ways to restore radiative balance, you merely repeat the tired meme that there are no other ways to restore the balance than increasing temperature. Well, you used slightly different words, instead of saying “there are no other ways”, you said “None have been identified”. And you did this just after I identified (and not for the first time) two other ways.

    Nor is this the first you’ve heard of this. I’ve been falsifying this particular bit of alarmist lunacy since at least 2011, when I discussed Dr. Andrew Lacis making the exact same bogus claim you make above. I encourage people to read that post, I encourage you to re-read it. From that post:

    Or as Dr. Andrew Lacis of NASA GISS recently put it (emphasis mine):

    As I have stated earlier, global warming is a cause and effect problem in physics that is firmly based on accurate measurement and well established physical processes. In particular, the climate of Earth is the result of energy balance between incoming solar radiation and outgoing thermal radiation, which, measured at the top of the atmosphere, is strictly a radiative energy balance problem. Since radiative transfer is a well established and well understood physics process, we have accurate knowledge of what is happening to the global energy balance of Earth. And as I noted earlier, conservation of energy leaves no other choice for the global equilibrium temperature of the Earth but to increase in response to the increase in atmospheric CO2.

    This is why I called your restating of that idea above “the tired alarmist argument”, because it is exactly that, and it is no more true now than it was then. My comment at the time is still very apropos. I said:

    This means that there are more ways to restore the radiation balance in heaven and earth than are dreamt of in your philosophy, Dr. Lacis …

    So once again, in your most recent comment you have very neatly excluded the middle ways of restoring radiation balance by claiming that “none have been identified”, despite my just identifying two of them for you, changes in cloud albedo and increased energy throughput …

    Q.E.D.

    Finally, despite my providing a variety of evidence in support of my hypothesis that the temperature of the earth is thermally regulated by emergent phenomena such as thunderstorms, you continue to try to denigrate my work by referring to it as “magic” and “unicorns”. This is unworthy of you as a scientist. I have asked you in the past to stop such puerile judgement-laden accusations, that’s just mud-slining.

    I ask you again. If you have scientific objections to my work, you have ample opportunity to do anything from providing a refutation in a comment to writing a long and detailed post on WUWT showing where my hypothesis is wrong, and I sincerely invite you to do any or all of that.

    At the same time, I also invite you to stop your underhanded attempt to appeal to people’s emotions to discredit my work by describing it as magic or voodoo or unicorns or the like. I’ve asked before. Enough, already.

    In friendship,

    w.

  145. 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?

  146. ∆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.

  147. @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/

  148. 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 !

  149. 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.

    • @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.)

  150. Bart says:
    April 29, 2014 at 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.

    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 …

    And why did you throw away the first fifty years of HadCRUT? What is it with cyclomaniacs and cherry picking? The full data is here … and it doesn’t show what you claim.

    w.

  151. 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.

  152. “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 ? ;)

  153. 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.

  154. 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.

  155. 1sky1 says:
    April 29, 2014 at 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.

    First, you seem to have not finished the head post before jumping up to tell me where I’m wrong. At the end of that post is 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:

    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

    Next, I studied and utilized their method, again as I described in the head post. I found it to be not valid in the way they used it. They fitted a 55-year sine wave (fitted phase and amplitude) to the various tide records. I showed an example in the head post of one of the datasets they used, Cascais:

    Rather than use their bogus method of just fitting a 55-year cycle and declaring success if it is “significant” by some measure, I swept the analysis across the frequencies. Here is the result.

    As you can see, their claim that the Cascais dataset contains significant power in the 55-year period is a total misinterpretation of the data produced by their own method.

    Next time, do your homework before breaking your silence …

    Finally, there seems to be some part of the following that escaped you in the head post. Perhaps it was in the part you didn’t bother to read:

    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.

    As a result, I don’t give a rat’s if you think I’m using the wrong method. I’ve admitted I haven’t found the elusive ~60 cycle. I’ve also used the Chambers method and not found the claimed 55-year cycle. Like I said, I gave it a good shot, and NOW IT’S YOUR TURN!!!

    I designed this post to give people a chance to demonstrate that the elusive 60-year cycle exists, because I couldn’t find it. I’ve provided the data that Chambers et al. used in a convenient format. I collated the information in 1,413 individual files into one Excel spreadsheet and a smaller 22-long-term station file so nobody could say they couldn’t get the data. This is your change to put up or shut up. Unfortunately, to date you have done neither.

    You have the opportunity. Either come up with the results and the data and code that reveals power in the 55-year period in not one but a majority of the long-term tidal records as Chambers claimed, or stop bothering us with your claims that you’re so much smarter than I am. You may be … but so far, you’re all mouth. You’re happy to tell me where you think I’m wrong … but strangely reluctant to show the results you get when you do it right.

    w.

  156. 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).

    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.

  157. 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.

  158. 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).

  159. 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 !

  160. 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.

  161. 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.

  162. 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.

  163. 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:

    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?

  164. 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.

  165. @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.

  166. 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

  167. 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

  168. “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.

  169. 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.

  170. Bart : “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.”

    Could you elaborate ? Link ?

    thx

  171. Willis:

    There you go ad hominem again, with your put or shut up meme! Meanwhile, you
    remain clueless as to what’s involved in analyzing tidal data for major
    constituents. You need HOURLY data for at least a month for LS analysis
    (see http://drs.nio.org/drs/bitstream/2264/59/4/Mahasagar_24_1.pdf). And
    for a fuller set of tidal constituents, in accordance with Doodson’s
    time-honored method, you need at least a year’s worth of hourly data. (see ftp://canuck.seos.uvic.ca/docs/MFTides/heights.pdf)

    Nor is your comprehension any better about analyzing MONTHLY sea-level data,
    which is a can of worms with all sorts of factors (eustatic and steric
    variations, wind set-up, rainfall runoff, etc.) affecting the record even
    in geologically stable locations. As your LS fitting of pure sinusoids
    to the Cascais record shows, you fail to realize that it’s the presence of a
    strong trend that produces your rising values as period increases. And, of
    course, the well-established Rayleigh criterion for resolving sinusoids in
    a record of finite length remains terra icognita for you. (It specifies
    that two sinusoids can be resolved when the spectral peak of one
    sinusoidal component falls on the first null of the second frequency in the
    record-length dependent spectral window)

    As disappointing as the Chambers et al. paper turned out to be (with it’s
    simplistic bias + trend + sinusoid conception), at least they had sense
    enough to remove the trend in their analysis and use the mean of
    the best-fit period to the GMSL. And little is gained by removing the
    annual cycle by monthly anomalization, as you do, rather than by their yearly averaging when looking for multidecadal oscillations, which no experienced geophycist would expect to be periodic.

  172. Steven Mosher says

    Instead of cheery picking 10 stations, use them all

    http://berkeleyearth.lbl.gov/regions/alaska

    Henry says

    FCirst, I don’t do “cheery” picking
    I selected 10 stations, randomly, inland.
    I was not interested in looking @sea, now, and I am not interested in results before 1998
    (your Berkeley seems to report only from 1990).
    If you had followed the whole thread you would know why.
    but here is a good method to get a global representative sample:

    http://wattsupwiththat.com/2014/04/25/the-elusive-60-year-sea-level-cycle/#comment-1622942

    Clearly the Berkeley global data set lacks that balancing,as it reports a (global) warming rate of almost double that of other global data sets, including my own, from 1990.
    Otherwise, why don’t you do an anlysis of all the data available from the Elmendorff Army base in Anchorage. It has data from 1942, If you analyse the maximum temperature record there and look at the average change in K/annum from 1972-2014 and from 1942-1972 you should get a part of an a-c wave that looks like the second graph shown here:

    http://blogs.24.com/henryp/2012/10/02/best-sine-wave-fit-for-the-drop-in-global-maximum-temperatures/

    Clearly there is still some serious cooling coming up in Alaska, Note the difference in the sine wave from Anchorage and the global a-c wave.

  173. @steven mosher
    I am interested in seeing that calibration record? That must be of a new thermometer? I was talking about re-calibration.
    As to error, I have deliberately chosen to look only at the average change from the average over periods of time, i.e. linear regression, in K/annum, which excludes a lot of error, especially calibration and differences between stations. Also, in older records, I trust maximum and minimum temps. more than means as the latter required presence of people and physical work, taking readings during the day.

  174. Henry.
    Ur method is not BLUE. Its not proven. Its not tested.
    Its wrong. Its so bad its not even wrong.
    The simplest out of sample test would show you that.
    Its an amateur untested flawed unreviewed piece of garbage.

    Here is a simple test. Use your 10 stations to predict the 100s you ignore. Then measure your error of prediction.
    It will suck. Then use the 100s you ignore to predict the ,10 you “randomly” selected. Note the improvement over
    Your boneheaded approach.

  175. 1sky1: “As your LS fitting of pure sinusoids
    to the Cascais record shows, you fail to realize that it’s the presence of a
    strong trend that produces your rising values as period increases.”

    The periodic analysis W used fits non harmonic patterns. It’s basically like finding the mean annual “climatology” but with varying window lengths , not just 12mo.

    However, your criticism is still valid, It is the upward trend that produces what W showed and reflects his lack of experience and understanding of periodic analysis.

    One of the first things to do is to remove the auto-regression and the non stationary mean, which is why the first thing I usually do with this kind of data is work with the first difference not the time-series itself.

  176. Bart says:
    April 30, 2014 at 7:27 pm
    See here.

    http://s1136.photobucket.com/user/Bartemis/media/ssn2.jpg.html?sort=3&o=22

    Yes, Isn’t that something I linked you to about six months ago :)
    I was wondering whether you had found a separate source or had reproduced it.

    Oddly that links into the Stockholm temp record that our host has just published about ( comment published in Nature CS). That got me to look at SPD of Stockholm and look what I found:

    http://tinypic.com/view.php?pic=lfq5g&s=8#.U2ILc6KBwrQ

    I should stress that is hot off the press and unchecked, so caveat emptor.

  177. Sorry is that your reworking of what the other Bart did that was produced on Talkshop?

    I think you may have just explained the self convolution, that was one thing I did not get in the original. Nice one.

  178. @steven mosher
    why don’t you give me all your Alaska data from Berkeley from 1998, or from 2000, is also OK, if that is more readily available, and we compare that result with the -0.55K/decade I get for Alaska
    (I chose 1998 because that was when earth was at its warmest)

    Clearly, it is people like you standing in the way of progress,
    Progress would be for you to tell the world that we are globally cooling.

    .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

  179. @Mr. Mosher
    we are all waiting for you to tell us your (berkeley) result of the amount of cooling taking place in Alaska since 1998 or 2000 in K per decade
    If you do not reply we will all know or notice that you are a fake and a fraud, always trying to defend AGW

  180. HenryP says:
    May 1, 2014 at 2:50 am

    @steven mosher
    why don’t you give me all your Alaska data from Berkeley from 1998, or from 2000, is also OK, if that is more readily available, and we compare that result with the -0.55K/decade I get for Alaska

    The Berkeley Earth Alaska data is here. Similar to the rest of the planet, the trend doesn’t go positive until you get back to 1992-present, and it doesn’t become significantly positive until you get all the way back to 1973-present … in other words, according to the Berkeley Earth data, there has been no significant warming in Alaska in four decades …

    Again according to Berkeley Earth, the trend 1998-present is on average -.47°C/decade, and the trend 2000-present is -1.27°C/decade.

    All the best,

    w.

  181. Willis says
    Again according to Berkeley Earth, the trend 1998-present is on average -.47°C/decade, and the trend 2000-present is -1.27°C/decade.
    henry says
    thanks Willis. I appreciate. I am not that much interested in before. Clearly they (berkeley) are a bunch of uninformed government employees, following the masterplan, an orgaisation that has employed or engaged people like Mosher. This helps!! It proves that my estimate of cooling in Alaska of -.55K per decade since 1998 is spot on.
    The acceleration to -1.27K/decade from 2000 is somewhat worrying to me. Can it be that much difference? I will check that.

  182. Greg says:
    May 1, 2014 at 12:42 am

    1sky1:

    “As your LS fitting of pure sinusoids
    to the Cascais record shows, you fail to realize that it’s the presence of a
    strong trend that produces your rising values as period increases.”

    The periodic analysis W used fits non harmonic patterns. It’s basically like finding the mean annual “climatology” but with varying window lengths , not just 12mo.

    However, your criticism is still valid, It is the upward trend that produces what W showed and reflects his lack of experience and understanding of periodic analysis.

    Since I detrended the Cascais record before doing the analysis, all that your bogus long-distance “diagnosis” proves is that once again, you guys are all hat and no cattle. The increasing trend is obviously NOT because of the trend as both of you stupidly claim, because I removed the trend first.

    You could have asked me if I’d detrended the data, but noooo, both of you are too smart to do that.

    Nice try at discrediting me … unfortunately, the only ones discredited are yourselves. Next time do your homework first. If you had actually run the analysis before opening your mouths, you wouldn’t both look like idiots. Instead, you were so eager to prove me wrong that you both ended up wrong yourselves …

    w.

  183. I checked,
    according to my ten sample stations , Alaska cooled by -1.05K/decade since 2000.
    That worries me, as it proves my theory that we are cooling from the top, and it is accelerating as times moves on. I can see it from my global tables as well, just adding the results of 2012 and 2013. The cooling is accelerating.Exactly as predicted.

    http://blogs.24.com/henryp/2012/10/02/best-sine-wave-fit-for-the-drop-in-global-maximum-temperatures/

    Pity that Mr Mosher does not care.

  184. Greg:

    What W. does with the gap-filled Casais data is LS curve fitting of sinusoids of different periods, not the simple data averaging over different bin-lengths that you presume. What mislead me into thinking that the trend, which always consists of low-frequency components, was not removed was his reference to the previous chart in his write-up. Notwithstanding this mistake, which he conveniently seizes upon to call us both idiots, what he fails to grasp is the irrelevance of such curve fitting to detecting irregular multidecadal oscillations in geophysical data. And he fails to show what happens for periods longer than 80yrs.

    BTW, first differencing of data is an effective mean-and-trend-suppressing stratagem, but it also severely attenuates the lowest frequencies. I’m under the gun to finish a technical report this week; I’ll have more to say when I’m finished.

  185. To Willis (and Anthony)
    “Scafetta’s work has been uniformly irreproducible. Unfortunately, he doesn’t give enough information to redo his work, and he has consistently refused to show his data and code.
    As a result, what Scafetta does is merely anecdote, not science in any form, and I ignore it completely.”

    Anthony, when will you start to correct your guests (Willis and Mosher in primis) about this incorrect statement that my work cannot be reproducible? Holm already let you know that it can be easily reproduced with due scientific knowledge which Willis evidently does not have. Do you understand that the statement has been demonstrated to be false and constitute “defamation” under the civil code?

    Said this, about the topic addressed by Willis, as usually he does not seem to understand the issue. In the case of sea level it is caused locally by different phenomena which couple climatic effects such as a changes of the volume of the glaciers, changes in the Earth rotations and changes in the ocean currents.

    The phenomenon is quite complex. Let us discuss changes in water current. Water can move from one region to another. This means that in one location the sea level can rise and in another it can decrease and the phenomenon get quite complex depending where you are. Thus the 60-year oscillation is not in phase everywhere nor it is expected to be see everywhere.

    In some record it is quite evident in other it is less evident.

    As Mosher noted above in my paper

    Scafetta is another author showing an ~ 60 year cycle in long sea level data.
    Discussion on common errors in analyzing sea level accelerations, solar trends and global warming Pattern Recognition in Physics 1, 37-58.

    http://www.pattern-recogn-phys.net/1/37/2013/prp-1-37-2013.pdf

    I analyze the record from New York and this long record (since 1860) show it quite clearly.
    See my figure 3.

    However, Mosher did not noticed that In my paper I have not analyzed only the record from NY but in Figure 2 I have analyzed the global sea level reconstruction by

    Jevrejeva, S., Moore, J. C., Grinsted, A., andWoodworth, P. L.: Recent global sea level acceleration started over 200 years ago?, Geophys. Res. Lett., 35, L08715, doi:10.1029/2008GL033611, 2008.
    ftp://soest.hawaii.edu/coastal/Climate%20Articles/Jevrejeva_2008%20Sea%20level%20acceleration%20200yrs%20ago.pdf

    This global reconstruction of sea level does show a quasi 60 year oscillation. since 1700.

    Note that in their paper Jevrejeva et al wrote:

    “Superimposed on the long-term acceleration are quasiperiodic fluctuations with a period of about 60 years. ”

    The issue, now, is to see whether the patter is an artifacts of the data. To show this one needs to compare with other records since 1700.

    This was done in

    Scafetta N., 2013. Multi-scale dynamical analysis (MSDA) of sea level records versus PDO, AMO, and NAO indexes. Climate Dynamics. in press.

    http://people.duke.edu/~ns2002/pdf/10.1007_s00382-013-1771-3.pdf

    See figure 10 where the there is comparison with the NAO index since 1700, and this index shows the same quasi 60-year oscillation.

    This is enough to respond to Willis both about the merit of the 60-year oscillation in the SL and the reproducibility of my work.

  186. 1sky1 says:
    May 1, 2014 at 5:25 pm

    Greg:

    What W. does with the gap-filled Casais data is LS curve fitting of sinusoids of different periods, not the simple data averaging over different bin-lengths that you presume. What mislead me into thinking that the trend, which always consists of low-frequency components, was not removed was his reference to the previous chart in his write-up.

    You and Greg both claim that your failure to read what I wrote is my fault. Gotta love it, both you guys claiming that I’m in charge of your eyeballs.

    Notwithstanding this mistake, which he conveniently seizes upon to call us both idiots, …

    Hold up there, cowboy. You seized upon what you thought was my mistake and used it to try to hammer me over the head.

    Nor is your comprehension any better about analyzing MONTHLY sea-level data, which is a can of worms with all sorts of factors (eustatic and steric variations, wind set-up, rainfall runoff, etc.) affecting the record even in geologically stable locations. As your LS fitting of pure sinusoids to the Cascais record shows, you fail to realize that it’s the presence of a strong trend that produces your rising values as period increases. And, of course, the well-established Rayleigh criterion for resolving sinusoids in a record of finite length remains terra icognita for you.

    You attacked my comprehension and claimed I “fail to realize” something based on nothing more than the fact that you were so eager to prove me wrong that you didn’t bother to read what I said.

    So yeah, when you try that kind of nonsense, I will point out your foolishness in big letters.

    Doesn’t feel so good when the shoe is on the other foot, does it? Next time, read before you write. Neither of you are acting like savants. If you were, you would read my words very, very carefully before you set out to attack my ideas. That’s generally a mistake even a bear of little brain would avoid, I’m not known to suffer fools gladly.

    … what he fails to grasp is the irrelevance of such curve fitting to detecting irregular multidecadal oscillations in geophysical data.

    You claim (without a scrap of evidence) that such curve fitting is “irrelevant” to detecting “irregular multidecadal oscillations” … do you really think that an unsupported anonymous opinion swings any weight on WUWT? You may be a pezzonovante in your world. Here, you’re just another popup with a big mouth who claims I’m responsible for what you read. You may be right … but your unsupported word that you are right is a joke. You have no credibility, nobody who posts anonymously does, because you don’t have to stand behind your words. I do, so I’m careful about what I say. You, on the other hand, can make any claim you want and never have to back it up. You can disappear and reappear as “2sunthree” and never take responsibility for every and anything you’ve said. As a result, your unsupported claims have no weight at all.

    Note that I’m note saying it’s wrong that you are anonymous. Many people are, some for valid and good reasons, some just to cause mischief and avoid responsibility. But the motives don’t matter, I’m talking about results. I’m just pointing out that anonymous unsupported opinion is valueless, so if you choose to be anonymous, you have to live with that.

    But in any case, since detecting irregular oscillations is not what I was using the method for, so what? I wasn’t looking for irregular oscillations. I was looking at whether the claimed regular 55-year signal was or wasn’t present in the data.

    And he fails to show what happens for periods longer than 80yrs.

    Two reasons. First, as I said above, I was looking for a 55-year cycle, the one that the authors claimed to find. As such, the 80+ year data has no probative value either way, so it was ignored.

    Second, unlike some people, I won’t speculate on any cycles longer than half the length of the dataset … that’s just numerology in my book. I don’t even like cycles that long, my rule of thumb is to not believe anything with less than three cycles … and even that is often not enough, as I discussed above. Let me repeat what I said, it’s important:

    I have no doubt that for the kind of analysis that you are used to doing, one cycle may be adequate. However, climate is another kettle of fish entirely.

    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..

    However, when we look at the full record, within a few cycles in either direction the sea levels and the sunspots are way, way out of phase. It turns out the relationship is entirely spurious. The relationship appears, lasts for three good cycles, then the relationship vanishes. Go figure.

    In Summary: Not only is one cycle far, far from enough to do what we want to do, to establish that a cycle exists in some climate variable.

    In cases like the one I mention, even my recommended three cycles of data are still not enough to establish that a cycle exists.

    So I’ll leave you to speculate on an 80-year cycle in a 120-year dataset, 1sky1. Me, I’ll pass.

    Finally, I note again that all you do is complain about my methods … if you’re so smart, if you know the right method for what I’m doing, then how come you can’t find the purported 55-year signal?

    w.

  187. Nicola Scafetta says:
    May 2, 2014 at 1:42 pm

    To Willis (and Anthony)

    “Scafetta’s work has been uniformly irreproducible. Unfortunately, he doesn’t give enough information to redo his work, and he has consistently refused to show his data and code.
    As a result, what Scafetta does is merely anecdote, not science in any form, and I ignore it completely.”

    Anthony, when will you start to correct your guests (Willis and Mosher in primis) about this incorrect statement that my work cannot be reproducible? Holm already let you know that it can be easily reproduced with due scientific knowledge which Willis evidently does not have. Do you understand that the statement has been demonstrated to be false and constitute “defamation” under the civil code?

    Nicola, welcome back. I haven’t a clue who Holm is. And sadly, my statement is in fact true. Without data and code, your work is anecdote, not science.

    When you are willing to supply your code and your data, at that point your work can be checked for errors. Until then, it can’t be done, so your work can’t be falsified. You have been asked for code and data many times, by Steve McIntyre, by Steven Mosher, and by myself.

    Your response, shockingly to me at least, has been the same response that Michael Mann gave, and the same response that Phil Jones gave, which is that your meagre explanations of what you have done are quite sufficient, and that you are under no obligation to reveal either your methods or your data. In other words, you spit on the idea of scientific transparency, you hide your code and your data from public view … and you want to be treated as a scientist?

    That excuse was nonsense when Mann and Jones said it back in the day. You should be ashamed to be trying to sell us the same line of nonsense a decade later.

    To me, it’s simple, and Mosher said it best. If you don’t provide code and data, it’s not science—it’s just an advertisement. I say it a bit differently—no code, no data, no science.

    So, let us know when you want to become a scientist and reveal your code and data, Nicola. Until then, your advertisements are fascinating but unsatisfying …

    Said this, about the topic addressed by Willis, as usually he does not seem to understand the issue. In the case of sea level it is caused locally by different phenomena which couple climatic effects such as a changes of the volume of the glaciers, changes in the Earth rotations and changes in the ocean currents.

    Nicola, perhaps you didn’t understand what I’m doing here. I’m not trying to analyze the global sea level as you seem to assume. I’m not trying to understand the causation of the changes in sea level like glaciers and stuff. That’s all immaterial to what I’m doing here.

    I am doing one thing, and one thing only—attempting to establish the truth value of the authors’ statement that:

    … there is a significant oscillation with a period around 60-years in the majority of the tide gauges examined …

    So far, I have found very little evidence of such a cycle. Nor have you provided any such evidence.

    You go on to say:

    The phenomenon is quite complex. Let us discuss changes in water current. Water can move from one region to another. This means that in one location the sea level can rise and in another it can decrease and the phenomenon get quite complex depending where you are.

    Again, the complexity of the phenomenon and the water currents are not of interest in this instance. I’m just looking for the 55-year cycle, not trying to explain it, so the causes are immaterial.

    Thus the 60-year oscillation is not in phase everywhere nor it is expected to be see[n] everywhere.

    Yes, Nicola, according to the authors the 60-year cycle IS expected to be seen everywhere. They claim it is present in “the majority of the tide gauges”. That’s what I didn’t believe when I first read it, and what I’m trying to determine. So far, very few of the tide gauge datasets show such a cycle.

    In some record it is quite evident in other it is less evident.

    As Mosher noted above in my paper

    Scafetta is another author showing an ~ 60 year cycle in long sea level data.
    Discussion on common errors in analyzing sea level accelerations, solar trends and global warming Pattern Recognition in Physics 1, 37-58.

    http://www.pattern-recogn-phys.net/1/37/2013/prp-1-37-2013.pdf

    Yeah, I tried reading that. I got as far as this:

    However, Sa2012’s result does not appear robust because, as I will demonstrate below, the geometrical convexity observed in the NYC tide gauge record from 1950 to 2009 was very likely mostly induced by a quasi 60 yr oscillation that is already known to exist in the climate system.

    I’m sorry, but saying that something is “very likely mostly induced by a quasi 60 year oscillation” is not science in my book. “Very likely mostly induced by a quasi …” is just waffle words. It’s handwaving to try to cover up the fact that you have no physical mechanism and are just blowing wind.

    Indeed, you used the word “quasi” no less than thirty times in your paper. You have “quasi 60 year oscillations”, for example … what does that mean? Is an oscillation with a 40-year period a “quasi 60 year” oscillation? How about a 50-year period? How about a 70-year period? You never say what a “quasi 60 year” oscillation is, which makes your claims unfalsifiable.

    Then you have “quasi-secular oscillations”, and a “quasi-millennial solar/climate cycle”.

    “Quasi-secular”?

    Of course, we mustn’t forget this gem “Finally, the alternating quasi regular large green and red areas evident at scales from 30 to 110 yr indicate a change of acceleration (from negative to positive, and vice-versa) that reveals the existence of a quasi 60–70 yr oscillation since 1700.”

    Your claim is that quasi regular 20 to 110 year changes in sea level acceleration reveal a quasi 60-70 year oscillation? Really?

    Does that kind of claim really pass for science on your planet, Nicola? Because around here, that dog won’t hunt …

    In any case, here’s what you claim is caused by a 60-year oscillation, Fig. 1a from your paper:

    What you show is the change in tides as a slow quadratic curve (which you call a geometric convexity above). Say what? At no time can you use a quadratic curve to represent tides. Quadratic curves go to infinity at both ends, hardly a characteristic of the tides.

    That’s enough of a problem, but the real problem comes when you try to explain said quadratic curve as the result of a 60-year sine wave … so I can see why you say the quadratic curve is “very likely mostly induced by a quasi 60 yr oscillation”.

    Because it would be very hard to explain mathematically how a 60-year sine wave would create a 60-year section of a quadratic curve … try fitting the two together sometime.

    I analyze the record from New York and this long record (since 1860) show it quite clearly.
    See my figure 3.

    I saw your Figure 3. It says very clearly at the top “PS of Sea Level Rise in New York (1893-2011)”. In your comment above, on the other hand, you claim that the data in Figure 3 goes back to 1860 … you see why without the data and code your work is just anecdote? Without the data and code there’s no telling what dataset you used for Figure 3. I suspect that the note on the graph itself is correct, and that you have discarded the earlier data … but without your data and the code there’s no way to know.

    However, Mosher did not noticed that In my paper I have not analyzed only the record from NY but in Figure 2 I have analyzed the global sea level reconstruction by

    Jevrejeva, S., Moore, J. C., Grinsted, A., andWoodworth, P. L.: Recent global sea level acceleration started over 200 years ago?, Geophys. Res. Lett., 35, L08715, doi:10.1029/2008GL033611, 2008.
    ftp://soest.hawaii.edu/coastal/Climate%20Articles/Jevrejeva_2008%20Sea%20level%20acceleration%20200yrs%20ago.pdf

    This global reconstruction of sea level does show a quasi 60 year oscillation. since 1700.

    Well, I found the Jevrejeva dataset here.

    Before looking at it, let me quote a bit from the Jevrejeva paper and discuss how they constructed a tide record back to 1700:

    [4] The ‘‘virtual station’’ GSL calculated from 1023 tide gauge records [Jevrejeva et al., 2006], optimally solves the sampling problem of station locations. Detailed descriptions of these time series are available from http://www.pol.ac.uk/ psmsl. All data sets were corrected for local datum changes and glacial isostatic adjustment (GIA) of the solid Earth [Peltier, 2001]. The reconstruction preserves volcanic signatures [Grinsted et al., 2007] and also has published standard errors [Jevrejeva et al., 2006], available from http://www.pol.ac.uk/psmsl/author_archive/jevrejeva_ etal_gsl/.

    Gotta admit, I get nervous when someone claims to have “optimally solved” a problem involving the average of an intensive quantity like sea level.

    I’m also not impressed by a claim that the method “preserves volcanic signatures”, as I’ve never seen any evidence that volcanoes had the slightest effect on sea level. In any case, they continue:

    [5] We extend the record backwards from 1850 using three of the longest (though discontinuous) tide gauge records available: Amsterdam, since 1700 [Van Veen, 1945], Liverpool, since 1768 [Woodworth, 1999] and Stockholm, since 1774 [Ekman, 1988]. We remove the linear part of each record, which contains the land movement component, by comparing each time series with the existing GSL for the period of overlap. In order to estimate the global sea level from these three stations, we assume implicitly that the mean trend from the three records is the same as that globally for the 18th century. And one notes that geological evidence supports relatively stable global sea level over the last 2 millennia [Lambeck et al., 2004]. By far the major source of error when using the three stations as an extended record of GSL is how representative tide gauges from a single ocean basin can be of global sea level; we estimate this error to be ±6 cm from jack knife error estimates when global data are available [Jevrejeva et al., 2006].

    [6] We provide a solution for the problem associated with decadal and multi-decadal variability using a method based on the Monte Carlo Singular Spectrum Analysis (MC-SSA). We remove 2–30 year variability, determine the time variable trend and examine temporal variability in acceler- ation in global sea level during the past 300 years.

    OK, so they have 150 years of tidal station data that has been carefully analyzed. It has been GIA corrected, and local discontinuities have been removed.

    And on the front end of that, they’ve spliced an altered 3-tide-station average …

    Now, I might use that result for something simple, or for a rough estimate of historical rates of sea level rise, Nicola. I’d never use it for an analysis of the cycles. I can’t begin to list the number of simplifying assumptions made in there. They assume that land movement is linear, probably justified. They assume that the mean trend of Amsterdam, Liverpool, and Stockholm is equal to the global trend not only in the long run but in the short run, probably not justified. They assume that they can simply spice their three-station average on the early end of an actual global average and analyze it like it were a single dataset. They assume that their “optimum solution” is optimum. They assume that there is one “best” method for averaging intensive data. They assume that there is a “signature” of volcanoes in the sea level, and whatever that signal might be, they’ve designed their analysis to preserve it (with unknown side effects). They assume that their GIA adjustments are not causing any long-term fluctuations in the results. And there are likely more assumptions I’ve overlooked.

    Note that in their paper Jevrejeva et al wrote:

    “Superimposed on the long-term acceleration are quasiperiodic fluctuations with a period of about 60 years. ”

    I did note that. Since Jevrejeva provides no evidence for these “quasiperiodic fluctuations”, whatever that might mean, I fail to see how his bald claim qualifies as science. He provides no Fourier or other analysis of the results. As far as I can tell he looked at the data and said yep, definitely quasiperiodic fluctuations … and you got all impressed. I see nothing in his claim to back up that analysis.

    Me, I just went and checked his data in the easiest manner. I compared the cyclical components of the first half of the data (the average of 3 tide gauges) with the second half of the data. If his reconstruction is valid, we should find the same cycles in the two halves … guess what. We don’t. In fact, the cycles in the two halves of the Jevrejeva data are different at most cycle lengths. This is the simplest of tests, and the one test that folks like yourself tend to overlook …

    The issue, now, is to see whether the patter[n] is an artifacts of the data. To show this one needs to compare with other records since 1700.

    Nope. To do this, one only needs to show that the two halves of the data have very different cycles. If they do, then the cycles are artifacts which are not representative of the entire dataset. Here’s that analysis:

    If the first 150 years of the average of the three tide gauges were a valid proxy for the cyclical nature of the next 150 years of global sea level, the first 150 years of data would contain the same cycles as the last 150 years. However, the cycles are nothing like the same.

    Q.E.D.

    Can’t say that the Jervrejeva paper was impressive or convincing, Nicola. Doubling the length of the tide record by using three individual tide gauge records? That seems … well, like a big stretch. For example, suppose some climate alarmist tried to extend the HadCRUT global temperature data back to 1700 by using three temperature records … how much weight would you put on that?

    Me, not much … but that’s what Jevrejeva’s done. And even if it is somewhat valid as a rough guide, finding cycles in it is meaningless.

    This was done in

    Scafetta N., 2013. Multi-scale dynamical analysis (MSDA) of sea level records versus PDO, AMO, and NAO indexes. Climate Dynamics. in press.

    http://people.duke.edu/~ns2002/pdf/10.1007_s00382-013-1771-3.pdf

    See figure 10 where the there is comparison with the NAO index since 1700, and this index shows the same quasi 60-year oscillation.

    This is enough to respond to Willis both about the merit of the 60-year oscillation in the SL and the reproducibility of my work.

    Your last citation provides as a reference the very paper we are discussing here … which I’ve shown above to be using a flawed method to make their claim.

    In addition, they are not using “the NAO [North Atlantic Oscillation] index since 1700″. They are using a reconstruction of that index back to 1675 based on “pressure, temperature and precipitation measurements and proxy data”. They say of the reconstruction that “Predictive skill varied for different sub-periods depending on data availability.” This alone would invalidate any cyclical analysis of the data, but there’s more. They say that predictive skill “was highest for autumn and winter and was generally better for the EU than for the NAO index.” And there’s more: “Wavelet analysis suggested significant low-frequency variability, especially for the spring, summer and annual averaged indices.”

    As a result, the cyclical components of that reconstruction due to the incorporation of a host of other datasets, each of which contains its own characteristic oscillations, means that the reconstruction is totally unsuitable for the kind of Fourier analysis you show in your paper.

    You also are way into “quasi” in that paper as well, with “quasi 20-year and 60-year oscillations”, a “quasi 60–70 year natural oscillation”, “quasi secular-long records”, “quasi decadal, bi-decadal and 50–90 year oscillations”, “quasi-predictable variability of the ocean–atmosphere system”, “quasi periodic oscillations are observed, as indicated by the quasi-periodic shifts”, “synchronous quasi bi-decadal oscillations”, “quasi uniform accelerations”, “repeat quasi- periodically for three hundred years”, and the “quasi millennial oscillation typically found in numerous climatic and solar indexes”.

    With your planet containing “quasi oscillations” at decadal, bi-decadal, 50, 60, 70, 90, and 1000 years … I’m quite sure that you can find a quasi oscillation at any cycle length you might choose. As I said in my most recent post:

    Whenever one of these good cycle-folk says “a period around” I know they are investigating the upper end of the stress-strain curve of veracity

    This holds for undefined “quasi oscillations” as well. That’s just handwaving.

    For example, suppose in some tidal dataset there is no visible oscillation with an 11 year period for 30 years. Then for thirty years such an oscillation emerges and persists, only to disappear again for the last ten years of the record.

    Is that a “quasi 30-year oscillation” or not?

    And if so, what if it only appears after 100 years, and then disappears after 30 years and is not seen again in the record … is it still a “quasi 30-year oscillation” or not? What if it only appears for one clearly defined cycle … quasi 30-year oscillation or not?

    I’m sure you see the problem. We have no idea what you mean by “quasi”. If you don’t see the problem, here’s another example. In the Jevrejeva data, the first half has a strong cycle at 25 years. The second half has no such cycle. Is that a “quasi 25-year cycle” or not? If you answer no, then what if it was present in the first and last thirds of the record? Or present in the first half and last quarter of the record but not in the third quarter? Still not a “quasi 25-year cycle”.

    As a result, your statements are absolutely not falsifiable. With no definition of what a “quasi oscillation” might be, or what a “quasi 20-year cycle” might exclude or include, there is no way to show that your statements are wrong …

    And of course, that means that your statements are not science in any sense of the world. Science is concerned with falsifiable statements. If a statement is not falsifiable, it’s not part of the realm of science. The statement “The Buddha was enlightened” is not falsifiable, for example, so it’s not part of science. Doesn’t make it bad or wrong, it’s just not falsifiable, so science can say nothing about it.

    The same is true about all of your statements about “quasI’ periods and “quasi” oscillations and “quasi secular-long records”. None of those statements are falsifiable, so none of them have anything to do with science.

    Nicola, you need to understand something. Until you come up with data and code, I and others are under no obligation to treat your results as anything more than advertisements for your claimed brilliance.

    Now, you may not like that. And you may have gotten away with much less in the past, as many other scientists have.

    But this is 2014, and the rules have changed. Scientists have recognized that the English language is inadequate to describe what your computer program does. To truly check what you’ve done, we have to look at your code and data. Even if you describe in detail what you think you’ve done, your description of your procedures doesn’t mean that you have successfully implemented them in computer language in the way that you think you have. A detailed description also can’t demonstrate that there are no bugs in the code. To determine that, we have to see the code and data. It’s called “scientific transparency”. Unless you give us your code and data, we can’t determine if your computer code contains bugs … so we are under no obligation to believe a word you say.

    Now, you may not like that, but that’s how it is. Until you depart the company of Mssrs Mann and Jones and you reveal your data and code, most modern scientists will treat your results as just advertisements. Oh, you can hang out at Tallbloke’s and bask in the adulation of those that can’t tell anecdote from science … but around here, you won’t get any traction without data and code.

    That’s what I meant when I said:

    “Unfortunately, he doesn’t give enough information to redo his work, and he has consistently refused to show his data and code.
    As a result, what Scafetta does is merely anecdote, not science in any form, and I ignore it completely.”

    It’s not complex. Science absolutely requires transparency. You insist on keeping your code and data totally opaque. You can tell me the logical conclusion of those two true statements.

    My best to you,

    w.

  188. 1sky1: “BTW, first differencing of data is an effective mean-and-trend-suppressing stratagem, but it also severely attenuates the lowest frequencies. I’m under the gun to finish a technical report this week; I’ll have more to say when I’m finished.”

    Which is exactly what red-noise, random walks and auto-regression is all about.

    It is the auto-regression that creates a lot of the long term variability. That does not make the variability less real but working with first diff puts any random noise back into a “white noise” spectrum.

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