Guest Post by Willis Eschenbach
Dr. Nir Shaviv and others strongly believe that there is an ~ 11-year solar signal visible in the sea level height data. I don’t think such a signal is visible. So I decided to look for it another way, one I’d not seen used before.
One of the more sensitive signal analysis tools in our arsenal is the Fourier transform. If we have a complex signal, like say the sunspot signal, Fourier analysis allows us to see just how strong the different frequencies are that make up the signal. To start with, Figure 1 shows the sunspot record.
Figure 1. New SILSO monthly sunspot record.
As you can see, there is a clear cyclical signal. However, the cycles vary in length. A Fourier periodogram reveals the strength of the various underlying signals:
Figure 2. Fourier periodogram of the data shown in Figure 1. Shortest period shown is four years, as there are no strong cycles with shorter periods.
As you can see, most of the power is in the 11-year and nearby cycles. There is cycle strength out to twelve years or so. There is also a second smaller group of cycles with a period of ten years, of about half the strength of the 11-year cycle.
Now, if there is actually a solar cycle in the sea level height as Dr. Shaviv believes, then it should peak somewhere around 11 years. To look for such a cycle, I decided to look at the sea level records from the tidal stations of the world. These are available from the Permanent Service for the Mean Sea Level. For your convenience in investigating the question, I’ve collated them as an Excel worksheet here.
I like to have an absolute minimum of three cycles of data to use for my longest term analysis. So I started by selecting all of the tide station datasets that have sixty years or more of data, to allow me to look at cycles up to about twenty years. There were 199 such records. Here are some sample periodograms of four of these longest tide records.
Figure 3. Four periodograms of long-term tidal records. Shortest period shown is four months. The scale on the left is the range (maximum minus minimum) of the fitted cycle as a percentage of the range of the underlying tide data.
The largest period in the tidal records, as we might expect, is a one-year cycle. There is also a smaller cycle visible at half a year (six months). However, as you can see, there is no readily apparent strong 11-year cycle, although Swinoujscie (top right) has a small hint of an 11-year cycle … or it may be a random fluctuation.
Now, the averaging of tidal data has some large problems. The different locations have widely varying tidal amplitudes, so the large swings tend to swamp the averages. As a result, I decided to average the periodograms rather than averaging the data. Since all of the periodograms are expressed in scaled units as percentages of the range of their individual underlying datasets, they are directly comparable. And since the random variations would average out, I figured that averaging them should reveal even small signals. Figure 4 shows the 199-periodogram average:
Figure 4. Average of the periodograms of the 199 long-term tidal station records. Note that the error bars are not the error of the mean, which is much narrower. Instead, they reflect the spread of the underlying individual results.
As with the four individual periodograms, the average clearly shows the one-year and the six-month cycles. And as expected, the averaging of so much data allows us to see even very small cycles. I note, for example, a cycle of a bit more than three and a half years. I’ve noticed this same signal before in other natural datasets, and I’ve never discovered its origin.
There is also a similar-sized small peak visible at about six and a quarter years, also of unknown origin.
But the purported ~ 11-year solar-related cycle? Nowhere to be seen. Not a hint, not a twitch.
Conclusion? If there is any ~ 11-year signal in the sea level height, it is so small as to be lost in the noise.
That was a main problem that I had with Dr. Shaviv’s study. He stated that there appeared to be a cycle in the short satellite sea level height data, and he claimed it was a solar cycle … but for me that’s backwards. For me, the starting point for investigation has to be noticing some verified unexplained anomaly in the actual observational records. First we have to find something unusual, then we can speculate as to its causes and consequences. For example, just what is the odd 3+ year cycle in Figure 4? Now that we know that cycle is real, we can speculate and investigate its origins.
So for me, until there is evidence of an actual ~11 year cycle in the sea level height, any speculation as to the possible solar nature of said unobserved cycle is wildly premature.
And that’s the story of the missing ~ 11-year cycle.
w.
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http://www.bing.com/search?q=solar+radiation+penetration+of+the+coen+surface&form=CMDTDF&pc=CMDTDF&src=IE-SearchBox
Any change will have eventual oceanic effects.
But if there is no long-term trend for hundreds of year, there will be no trend either in the ‘oceanic effects’, regardless of the penetration.
Willis,
Just a thought, if you took the derivative of the sea level data before you computed the FFT power spectrum it may in fact yield spectral information about the sea level change annual growth rate. Similar to what I did with the CO2 data using the WFT app below. Of course I had to do a 12 month mean to remove the annual cycle before taking the derivative.
http://www.woodfortrees.org/plot/esrl-co2/normalise/plot/esrl-co2/mean:12/derivative/normalise
Not sure what you mean by “yield spectral information about the sea level change annual growth rate”. In any case the FT of the derivative of any of the tide stations is dead flat at periods longer than one year.
Thanks for the question,
w.
Willis,
I mean that by taking the derivative, the amplitudes represent how much sea level changed from month to month instead of the amplitudes representing actual sea level value. That way, lets say that if at the peak of each solar cycle sea level increases a tiny bit more than it does at the trough of a solar cycle the FFT has a better chance of seeing the signal. Its probably a waste of time, but there is a chance it could make a difference. As you can see in the link below I took the FFT of the raw CO2 data with and without the derivative and there is an interesting cycle in the CO2 data associated with the growth that is not present in the raw data.
http://www.woodfortrees.org/plot/esrl-co2/normalise/fourier/magnitude/to:100/plot/esrl-co2/mean:12/derivative/normalise/fourier/magnitude/to:100
Kev-in-Uk August 19, 2015 at 10:41 am
The luminosity of the sun has increased by about 5% since the “Cambrian explosion” of life on the planet. If your theory were correct, then the earth should be significantly warmer now than it was when life began. But all of the evidence points the other way. Generally, we’re at the cool end of the last half billion years, and if your theory were true, we’d be at the warm end.
The temperature of the earth is NOT a function of the forcing as the current climate paradigm asserts without proof. Instead, the planetary temperature is kept within fairly narrow bounds by thermoregulatory mechanisms. These mechanisms are temperature-threshold based, not forcing based, and so they are relatively immune to small changes in forcing, even when the change is maintained. When we get a bit more sun, we get a bit more clouds and thunderstorms, and the overall temperature doesn’t change.
w.
Willis – not sure if that argument seems a little forced, excuse the pun, because obviously, as one part of the system shows a greater input (your luminosity) the output part (IR, albedo, etc) may also show a greater value – thereby ‘reducing’ the overall effect. e.g. taking the time lag considerations in account – in the same way as we assume we may be recovering from the LIA, the effect of that increased luminosity will take time to build and take time to disperse (back into space) after the supposed zenith of luminosity. Variations in the climate would be very difficult to ‘isolate’ or pin down within that likely large ‘laggy’ system. Even as a geologist, I take the palaeoclimate proxies with a sack of salt.
Even in your last paragraph, you have dismissed the lag in the system as saying the overall temperature doesn’t change – but in reality – it did, even if only for a few hours (as per your thermal cloud regulatory theory) – you could not suspect that if you were historically looking back at max/min temp data – so how would you know or posit such a mechanism existed? (in the absence of a current observation of thunderstorms I mean)
The cumulative effect of very minor changes will depend on the lag in the system and of course the climate regulatory systems you mention. That does not mean that they are not there — just we cant see or detect them within the noise of the system. In my opinion, even 300 years of SS data is not really a great indicator of solar activity if we consider that 1) old data may not be brilliant and/or directly comparable to modern data and 2) the sun may have changed a little since then anyway, and hence our modern observations/correlations need not necessarily be ‘correct’ when compared to old data. In the same way as I don’t fully accept the ice core temp proxies, because it is based on assumptions that modern temps and firn deposition can be directly ‘compared’ to past temps and firn deposition/compaction. It is a proxy, but has to be used with caution. I dunno, perhaps like C14 dating – it has to be given a wide range and heavily understated (likely or estimated) limits of accuracy.
Ultimately, what do people make of the varying solar cycles? – i.e. the differences between ramp up and slow down of the various cycles (based on SS numbers) – is it right to use this as a sole definer of actual solar activity and ergo, the actual solar insolation onto/into the earth? I think we are too reliant on this premise. Like Mosh says about the temp data, its the best we have. But it’s limitations are well known. Why is SS data not treated with the same contempt or scepticism?
regards
Kev
lsvalgaard
August 19, 2015 at 11:22 am
Of course I do not. There is no evidence that they are solar-related. And all the other solar parameters we know off follow the sunspot number. You are, of course, allowed to pick and chose what you want to believe, but that does not count as evidence
Leif, with respect, – that is somewhat ironic. You seemingly ‘believe’ in no significant solar variation based ultimately on what amounts to historical SS data? Fine, but that does not count as (direct) evidence either, especially as we know full well that insolation does indeed vary! What about solar parameters we don’t know or understand (because we only have limited observations)? Obviously, you can pick and choose too!
I’m not arguing one way or the other, per se – I am just pointing out that either extreme stance is not currently demonstrable – that solar is or isn’t a main driver. I simply find myself more within the LOGICAL deduction camp that, as the primary source of (almost) all energy on the planet, and more specifically ‘into’ the climate system (even via lagged systems such as oceans) – any variation in solar activity/energy/whatever – will most likely have a climatic effect. I don’t know how much, how long, or how significant, but it IS a logical derivation. The tendency to dismiss solar activity as minor, simply because we don’t yet understand it fully, or we have not yet been able to measure significant (or more accurately, what we ‘believe’ to be significant) changes is not really a viable stance in my humble opinion. Microscopic changes in things can and do indeed lead to much bigger effects – hence, looking at what is ultimately macroscopic indications (such as sunspots) is perhaps a little blinkered. I fully accept we have that data, and we should use it – but it is limited and this should be remembered at all times. Ditto for the surface temp record, etc – it is all rather limited in the big (and geologically time based) scheme of things.
regards
Kev
Any thing that drives the climate(solar) changes the climate if it changes.
we know full well that insolation does indeed vary! What about solar parameters we don’t know or understand (because we only have limited observations)
You are being a bit clever here going after a straw man. ‘Insolation’ usually has a meaning other than solar activity related [Milankovich, etc]. And about the things we don’t know we should keep silent as nothing meaningful can be said about them and they certainly cannot be taken as ‘evidence’. As a solar physicist I will state that we have a pretty good understanding [not compete – there is always more to learn – but pretty good] of the solar parameters we do know about.
Leif – you are twisting my meaning I think. I was of course referring to the published variation in solar influx of allegedly around +/- 1.3 w/m2. Even as 0.1% of total solar, it is a significant amount. How much do we actually know about the interactions of solar wind, cosmic rays, geomagnetic effects (sun and earth), etc, etc? (I don’t know, but I bet its not everything !). I mean, as a physicist, you should know full well that there is stuff still to be confirmed/proved, we only just recently confirmed Higgs bloody Boson!
Just as a for example – the Northern Lights are believed to be when solar wind particles are guided by the earths magnetic field and release their energy as light – this is why we see them. However, what is happening when we can’t see them? Do we deduce that there are no solar particles at that time? No, of course not – just the conditions are not the same to allow them to be ‘as’ visible, if at all. Should we therefore use the Northern Lights as a proxy for solar activity or the strength of the solar wind? or for the strength of the earths magnetic field, etc? Understanding the principle does not necessarily demonstrate the understanding of the whole picture or why we see/don’t see the interactions of the various components!
I don’t really see there is much else to say, but thanks for the discussion.
Well, words are important. If you mean irradiance, say that. If you mean insolation, say that.
As for the various effects you mention: we do know in some detail haw all that works. There are still further details to discover, but the basics is well-understood. You underestimate how MUCH we actually know.
You ask:
However, what is happening when we can’t see them?
We wait until it is dark, then we can see them [all the time if you are in the right place – they never go away]
Should we therefore use the Northern Lights as a proxy for solar activity or the strength of the solar wind?
they would in fact be good proxies for those things, and have been used for that.
The climate regulator you suggest Willis does not regulate climatic cycles all it does is keep the tropics in a more or less steady state.
The real climatic regulators I will post next.
Here is what regulates the climate , in a brief concise nutshell. This is what keeps it within a range bound to a degree.
Land/Ocean Arrangements and Land Elevation.
Milankovitch Cycles- where earth is in regard to these cycles.
Solar Variability- primary and secondary effects..
Geo Magnetic Intensity- which moderates solar activity.
Initial State Of The Climate- how far the climate is from the glacial /inter-glacial threshold.
Ice ,Snow, Cloud Cover Dynamic – which are tied to the above to one degree or another.
Intrinsic Earth Bound Climatic Items- such as ENSO which refine the climate trends.
Rogue Terrestrial Event- such as a Super Volcanic Eruption.
Rogue Extra Terrestrial Event – such as an impact.
What Willis fails to address is how his tropical regulator give the climate a semi cyclic beat. My climate regulators do.
In addition the regulator he proposes from a practical stand point does not make the climate stable as far as humans are concerned unless one thinks a glacial versus an inter -glacial period is of no significance.
This is what matters.
The primary frequency in the solar sun spot time series is 11 years +/-. The FFT will easily pull out this signal. The primary frequency in the tidal records is 1 year. The FFT does a great job in showing this as well. It would make more sense to filter the tidal record to eliminate the 1 year and shorter cycles. Also show the tidal record time series (which was not done). Figure 1, sun spot time series, Figure 2, sun spot frequency spectrum, Figure 3 tidal record time series filtered to eliminate 1 year and shorter frequencies and Figure 4, tidal record spectrum. Then you can argue that the sun spot time series is dominated by the 11 year cycle and the tidal record is dominated by noise.
Hi Willis,
3.5 year period frequency is very simple. It is Lowest Common Multiplier of orbital length of Earth, Venus and Mars. If you look it is more like 3.7 year period. This is 1350 days. Venus year is 225 days, Mars year is 687 days. 1350 is exactly 6 Venus years, 3,7 Earth years and very close 2 Mars years.
You simply found that each 3,7 year Venus and Mars are aligned for maximum impact on Earth tide.
I thought Shaviv said that there was an 11 year cycle in the rate of change of sea level not in the sea level itself…. Willis graph shows why an 11 year harmonic is a good approx for solar though.
http://www-das.uwyo.edu/~geerts/cwx/notes/chap15/lgm.html
This shows the range of global temperatures from glacial maximum periods to inter- glacial is quite significant especially in the higher latitudes.
Except that range is not a solar activity signal.
You do believe in Milankovitch Cycles?
They have nothing to do with solar activity.
But they do have to do with how much energy the earth receives from the sun at given locations which is at the heart of my solar/climate connection theory.
They have to do with the insolation at 65 degrees North. I don’t see any reference to that in your so-called ‘theory’.
2. the stage of where earth is in respect to Milankovitch Cycles is either going to work in concert or against the current trend the solar variability is exerting upon the climate. Right now I would say Milankovitch Cycles are on balance acting in concert with minimum prolonged solar activity.
3. the geo magnetic field can enhance given solar activity effects or diminish given solar activity effects upon the climate. A weaker field compounding
As you can now see point 2 brings out the role of Milankovitch Cycles and of course it has to do with N.H solar insolation. That is part of my theory.
Your so-called theory is just hand waving. No numbers, no comparisons, no nothing. As Feynman said: “the easiest one to fool is yourself”. You do a good job at that.
Leif that is your opinion time will tell.
yes, when Hell freezes over…
Again differences of opinion.
If all you got is opinion, then we shouldn’t worry too much.
A periodogram of the TOPEX and Jason data show an 11 year period in the sea level data.
The University of Colorado Sea Level Research Group publishes monthly data for the Global Mean Sea Level. The data can be downloaded here. It is a combination of data from Jason-1, Jason-2 and TOPEX. It is the data that was used by Dr. Shaviv et al. in their paper.
Unfortunately, the data file contains the date in decimal years format (e.g., “1993.0409”). I wrote a program to convert the decimal years to year-month-day format. The data is reported approximately every 10 days (but sometimes there are 20 days between data values), which means there are two or three data values per month. The periodogram works best if the time between measurements are all the same. So I wrote another program to average the values for each month. That way the data is in monthly format (available here; sorry its a Word document because WordPress will not let me load text files into my media library). I then used the following R-code to read the data, calculate the periodogram and plot the result, which is shown in Figure 1.
sldat <- read.table( "c:/temp/sl_global_avg.txt", header = TRUE, row.names=1)
slpgram <- spec.pgram( sldat, plot = FALSE)
plot(slpgram, log="no", main="Sea Level\nRaw Periodogram",lty=1,col="black" )
Figure 1. The periodogram for the TOPEX and Jason Sea Level data.
The frequencies and periods of the five heighest peaks in Figure 1 are shown in Table 1. The period in months is obtained by taking the recipricol of the frequency. The period in years is obtained by dividing the period in months by 12 months/year. Figure 2 shows a close-up view of the first frequency peak at 0.00740 month-1.
Frequency
Period
Period
month-1
months
years
0.00740
135
11
0.0222
45
4
0.0852
11.7
1
0.167
6
0.5
0.481
2.1
0.2
Table 1. Frequencies and Periods
Figure 2. A closeup view of the periodogram for the TOPEX and Jason Sea Level data with the frequency at 0.00740 month-1 marked by a vertical line.
The data shows an unmistakable peak at a frequency of 0.00740 month-1. That is, the data shows an unmistakable peak at a period of 11 years, which is roughly the same as the solar cycle. Therefore, Dr. Shaviv and his collegues are justified in assuming a harmonic solar component in the seal level data.
In the case of stochastically varying signals, raw periodograms are not statistically CONSISTENT estimators of the power spectra, no matter how long the record. Furthermore, power spectrum peaks at nearly the same frequency are far from clear evidence of causal physical relationship between two signals They could readily be concomitant variables, physically dependent in some way upon an unknown third variable. What is absent in all claims of solar components in sea-level data is any demonstration of high cross-spectral coherence. Even with high coherence, we would meet only a necessary, but not sufficient, condition for acausal connection.
You are quite right. Just because an 11-year cycle appears in a data set does not mean that it must be related to the solar cycle. At absolute most, one can say only that it could be related to the solar cycle. Necessary, but not sufficient. But, it is still worth investigating.
The data goes from 1992 to 2015. That is not enough to establish an 11-yr ‘unmistakable’ peak.
Yes. After I was looking more at the periodogram, I would agree that the word “unmistakable” is much too strong. The 11-year period appears at the very edge of the chart, which means that the cycle just barely emerges from the data. So one should say only that there is a peak (without any adjectives), but it should be taken with a grain of salt. But is there in the data, and it is still worth investigating. And I would still say that Dr. Shaviv et al. were justified in their use of an harmonic solar component to try to tease out a fit to that 11 year cycle.
I would expect though, that as more data comes in, it is more likely that the peak will grow rather than vanish. But then again maybe it will vanish.
nhill, I say again, three cycles is my absolute minimum length for periodograms of natural datasets, and I much prefer four cycles … and I’ve been fooled by five cycles.
With a 22-year dataset, you have two cycles … useless for an 11-year signal. It’s one of the problems with Shaviv’s analysis.
Grab a long dataset and run a periodogram on it, then divide it in have and do the same on the two halves. See what happens to the cycles longer than about a quarter of the data. Not pretty.
w.
Well, that didn’t work. Neither the table nor the figures appear correctly in the comments. They can be viewed at my blog.
The problem with using a small number of tide gauges is that what we’re trying to measure-indirectly by measuring the surface height) are changes in the ocean’s volume due to thermal expansion. To reliably do that, we need to measure the height across the entire ocean, because sea level can rise and fall in individual places relative to the “mean” height.
It’s a shame the altimetry data only go back so far.
If sunspots are the accellerator pedal then Shaviv hypothesises that there will be a signal in the sea level acceleration but Willis focused on a relationship between accellerator pedal position and the speedo reading – I can be doing 100 mph with my foot off the pedal or 10 mph with my foot flat to the floor. Looking at sea level seems like a fundamental error.
Okay, I’m going to be a squeaky wheel on this one. Willis, I suspect there is a severe flaw in what you are doing here, you don’t seem to be aware of it and nobody else is paying any attention to it:
There is far too much noise in the tidal data to be able to see the 11-year signal even if it were present.
This is easy to prove and I’m working to get my data and graphs accesible (I will try to post links later today). Willis, you should be able to easily do this yourself too. All you have to do is add a fake 11-year signal to the tidal data and then see if the periodogram can find it. I’ve tried this experiment with data from several locations (e.g. Brest) and the results are similar with all of them.
I have taken Willis’ published tidal data and added a 5.0mm peak-to-peak sine wave at various periods from 11 to 12 years and with various phase offsets (details below). You can see a change in the periodogram around 11-12 years, but it is so small compared to the noise that I don’t think anyone could reasonably say “there’s a signal there”.
So, while this is a great discussion to have — especially the part about “where would or should one look for 11 year signals?”, I propose that Willis is working with data which cannot possibly show this signal — even if it is there. GIGO.
I am sorry to be so adamant on this point but making un-warranted conclusions (there’s no signal there) from dodgy data (there’s too much noise) is just plain wrong.
Details: Since the tidal data has a resolution of 1mm I have used this formula to create the fake signal:
fake(t) = round(2.5mm * cos(omega * t + phase offset))
omega is chosen to yield a period between 11 and 12 years. Then I added this fake signal to the raw tidal data before generating the periodogram.
Willis, I do not know the exact details of your construction of the periodograms so I cannot duplicate your results exactly. I de-trended the data, offset to zero mean value and then applied a Hanning window before computing the discrete Fourier transform.
Finally, I’m fallible too…if I made a mistake here then well, I made a mistake and you have my apologies in advance. However, I have checked my work several times through and cannot find any mistakes. If you find that I’ve made an error I can accept that too and won’t take it personally if you speak poorly of me!
Okay, here’s all of the tools and graph results that prove that a +-2.5mm 11-year signal in the tidal data is not easily detectable. The only thing I could not make available is the CSV version of Willis’ spreadsheet, but you can generate that easily by using “Save As…” in Excel to save a CSV format file.
Scripts were run in Matlab but were tested in Freemat too.
This example uses only a windowed periodogram and there are more advanced ways to do periodograms out there, so perhaps the signal might be detectable that way but certainly not as shown here.
http://wxobserver.wordpress.com/2015/08/20/tidal-data-tools-and-results-archive/
Please post any comments here as opposed to the blog linked above as it is not regularly checked for comments.
It seems to me that low presence of a ~11-year cycle in Fourier analysis indicates that variations of solar activity don’t have much effect on sea level. Sea level variations are driven by temperature, via thermal expansion (and its inverse), and runoff/formation of accumulations of water (including ice) on land. This seems to indicate that climate sensitivity to solar variation is low.
Otherwise, wxobserver is on-spot about noise exceeding signal.
As for the distinct peaks: The 1-year peak seems to be from one or more annual cycles, such as annual variation of wind direction at a coastal tide gauge. The .5-year peak is the 2nd harmonic – which can come from the annual variation deviating from a sinusoid by having its annual high-point or its annual low-point deviating from a sinusoid in the direction of either going spiky or being limited/squashed/clipped.
Willis,
It seems quite sure that there is little 11-year solar signal in sea level, neither in temperatures.
One question remains: does the water vapor/clouds/precipitation feedback remove these cycles in favor of an 11-year amplitude in the water cycle?
There are indications for an 11-year cycle in river discharges mainly in the mid-latitudes: Mississippi catch area, Portugal, Po (Italy), Nile and in South Africa… Partly caused by shifts of the jet stream to the poles or equator during the solar cycle (at maximum: more UV – ozone – higher temperatures in the lower stratosphere – more temperature difference between equator and poles).
I have the links, but a previous comment still is in moderation (I hope), because of too many links…
As you have both datasets for precipitation and the solar cycle, maybe an idea for a follow-up article…
Willis – I enjoy your posts. The theory that solar cycles influence cloud formation and our climate seems plausible to me. But, I agree with you about not expecting their periodicity to be directly reflected in sea-level measurements. I’m an electrical engineer, and cordially offer a few remarks.
Because global sea-level changes in recent centuries are dominated by thermal expansion, they are driven primarily by changes in mean ocean temperature (averaged globally and over all depths). Because of their immense thermal capacity, it takes centuries to completely warm the oceans from the surface. Assuming roughly constant geothermal warming, recent changes in mean ocean temperature are largely the net result of the variable warming applied to the surface over the last several centuries. This is like a low-pass filter, smoothing out short-term (less than a century) variations in mean temperature.
It’s like a pot of water on a gas stove. If the gas is rapidly turned on and off by a Morse code operator, a frog basking in the pot would only sense gradual delayed warming, with no way to know whether he was warmed by Shakespeare or a Michael Mann paper (although some might say Mann’s work should have a telltale stench).
But, it’s always good to look at real data. The solar data certainly seems oscillatory, but if the period varies significantly, Fourier techniques would give a dull peak. An alternative would be to compute the correlation between the solar data and a composite sea-level time-series and see if there’s a meaningful peak. (I wouldn’t expect one.) If using Fourier methods, of course there are endless ways to do it, such as Welch’s method with various window and segmenting options, as others mentioned, but I don’t think that was really the problem here.
The suggestion of looking at the derivative of sea level makes sense theoretically, but IMO wouldn’t help in practice because the 11 year signal and its derivative were both pretty thoroughly filtered away, and even if a trace remained, taking a derivative would amplify spurious noise.
Clearly the bottom line is that there are much better places to look for solar cycle signatures.
Leif says to me and my reply that is all you got also is an opinion with nothing to back it up with.
If all you got is opinion, then we shouldn’t worry too much.
lsvalgaard: http://www.leif.org/research/2006GL027817-Milankovich.pdf gives a 404 error. I didn’t see it in http://www.leif.org/research/ either.
Can you upload it?
Thanks
Yes, you found it. But that you were the only one complaining shows that nobody else even bothered to have a look. Very telling.
lsvalgaard/Leif: Nevermind, found it! It’s http://www.leif.org/EOS/2006GL027817-Milankovich.pdf instead of under leif.org/research.
Milankovitch Cycle as I have said are a part of the big climate picture but fail to explain the 1470 year climatic cycle within the big picture and the countless abrupt climatic changes that have taken place on the order of the YD. The YD period not being unique in the historical climatic record.
Other climatic items are in play to account for this which I have explained in may of my previous postings.
Tidal data is nowhere near good enough to extract anything other than the 1 year signal. You need to use satellite altimetry data — properly corrected for ENSO — but alas you will need to wait a few more decades.