Guest Post by Willis Eschenbach
I’ve been investigating the use of the “complete ensemble empirical mode decomposition” (CEEMD) analysis method, which I discussed in a previous post entitled Noise-Assisted Data Analysis.
One of the big insights leading to modern signal analysis was the brilliant idea of Joseph Fourier. He realized that any given waveform can be expressed as a combination of sine and cosine waves. However, there are other ways besides Fourier’s method to decompose a signal, including periodicity analysis, principal component analysis, and CEEMD.
Let me give you an example of a CEEMD analysis. Here are the intrinsic modes for the annual average number of sunspots from 1700 to 2014. The top row is the sunspot data itself. For intercomparison with other signals, it is standardized to a mean of zero and a standard deviation of one.
Figure 1. CEEMD analysis of the mean annual sunspot numbers. Top panel shows the sunspot data, standardized to a mean of zero and a standard deviation of one. Panels marked C1 – C7 show the intrinsic modes of the signal. The bottom line shows the residual, meaning what remains after the removal of modes C1 – C7 from the signal. Note that all intrinsic modes are displayed at their true size, with all scales being the same.
This is a “complete decomposition” of the raw data signal, meaning that if we add the intrinsic modes C1-C7 together plus the residual, it will faithfully and exactly reconstruct the original signal.
One thing I like a lot about the CEEMD analysis is that I can actually see how the underlying intrinsic modes vary over time. I’ve said before that ascribing an inherent cyclical mechanism to natural observations is fraught with problems. Figure 1 is a good example of these problems. Look at intrinsic mode C4. It has a small signal at about 22 years … but not all of the time. For most of the first century of the record there is little signal at all. Then there’s an intermittent small ~ 22-year signal from about 1780 to 1850 ,,, which fades out and after a few year hiatus is replaced by a single ~ 25-year cycle, and that in turn is replaced with a ~ 22-year cycle out to the end of the data.
Or we can consider intrinsic mode C6, which varies in a similar irregular fashion. Mode C6 has a couple of strong cycles with a period of around 90 years at about 1800, and it then kind of tails off to nothingness. This makes it obvious why it has been so hard to discuss the existence or non-existence of the so-called “Gleissberg Cycle”, which Gleissberg claimed was ~ 80 – 100 years. When cycles come and go like that, it is hard to draw any firm conclusions. I mean, the ~ 80 – 100 year cycle is definitely there … but it’s only there when it is there, and the rest of the time, well, it’s simply not there.
Unfortunately, these kinds of appearing and disappearing cycles are far too common in natural datasets. There is a great temptation to think that they can be used for forecasting purposes … and they could if we ignore Murphy’s Law, which says that as soon as you start prophesying, the cycle will die out. For example, if we looked at the sunspot data in the year 1900, we’d think that there was a strong, statistically significant hundred-year cycle in the data … but after 1900 the cycle simply fades out to nothing.
Having seen the actual waveforms of the intrinsic modes in Figure 2, we can look at the periodograms of the various intrinsic modes to see what kind of signals exist in each of the intrinsic modes C1 to C7.
Figure 2. Periodograms of each of the intrinsic modes C1 through C7 of the annual mean sunspot number, 1700-2014. These show the strength of waves of the various periods in each on the intrinsic modes.
Figure 2 shows that most of the energy is in the ~11 year cycle, which is in intrinsic mode C3. Because the sunspot cycle varies between ten and thirteen years, the energy is not a sharp spike, but has energy across that range.
As discussed above, intrinsic mode C4 can be seen to have a very small bit of energy in the 22 year range, but as we saw in Figure 1, there’s nothing regular enough in the data to give a strong signal.
Again as discussed above, intrinsic mode C6 seems to have some energy in the 90-100 year range … but as Figure 1 shows, the ~100 year signal in C6, while strong, is mostly visible in the first half of the data. This greatly increases the odds that it is a spurious signal that could disappear in a longer record.
So that is an example of a CEEMD analysis of a signal. It gives us a picture of the intrinsic modes (Figure 1) and the periodograms of those same intrinsic modes (Figure 2). It shows the strength and the ebb and flow of the underlying cycles in the data.
Now, how else can this kind of analysis be useful? Well, it can show whether and how two distinct observational datasets might be related. As an example, here is the CEEMD analysis of both the Nino3.4 Index and the Southern Ocean Index (SOI). The Nino3.4 Index is a detrended sea surface temperature dataset for an area in the tropical central Pacific covering 5° North to 5° South and 120° West to 170° West. The SOI, on the other hand, is an index of the atmospheric pressure difference between Tahiti and Darwin, Australia. The two indexes seem to be measures of the El Nino/La Nina pumping action. The SOI and the Nino3.4 move in opposite directions, so the SOI is usually displayed inverted so that peaks in the SOI correspond with peaks in temperature. The next two figures show the CEEMD analysis of the two datasets:
Figures 3 and 4. Upper figure shows the intrinsic modes resulting from the CEEMD analysis of the Southern Ocean Index (SOI, red) and the Nino3.4 Index (black). These two datasets cover the same period as the sunspot data shown in Figure 1, 1870 – 2011.
Again, we see that there are various cycles which are strong in part of the record, but disappear or are greatly diminished in other parts of the record.
Note the close correspondence of the decomposition of the two signals, both in terms of the strength and shape of the intrinsic modes, and in their periodograms. It is clear that regardless of the fact that the Nino3.4 Index and the Southern Ocean Index are measuring different variables, they are both a measure of the same phenomenon.
And it is equally clear that there is no significant sunspot signal in either the SOI or the Nino3.4 data—the CEEMD analysis shows little commonality. Unlike the sunspot data, in the SOI and Nino3.4 data there is little strength at 11 years, and little strength at around 90 years. And again unlike the sunspots data, the majority of the energy is in the short-cycle (2-6 year) part of the spectrum.
Anyhow, that’s why I’ve grown fond of the CEEMD analysis … it shows when datasets have related cycles, and when they are unrelated.
Pushing towards full moon tonight, with Jupiter and Arcturus vying for the moon’s attention … what a world …
My best to everyone,
w.
My Usual Request: Misunderstandings bring communication to a halt, so if you disagree with me or anyone, please quote the exact words you disagree with so we can all understand your objections. I can defend my own words. I cannot defend someone else’s interpretation of some unidentified words of mine.
My Other Request: If you believe that e.g. I’m using the wrong method or the wrong dataset, please educate me and others by demonstrating the proper use of the right method or the right dataset. Simply claiming I’m doing something wrong doesn’t advance the discussion unless you can tell us how to do it right.
Yearly Sunspot Data: SILSO
SOI Data: Here
Nino3.4 Data: NOAA
Code: I’m using the “CEEMD” function in the R package “hht” for the analysis.
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Signals could behave this way if they were harmonics of an elliptic fundamental.
Low frequency and high amplitude at aphelion, high frequency and low amplitude at perihelion.
The apparent ignorance of even undergraduate orthogonal function decomposition and the overall amateurish understanding of math and physics pervasive in the “climate science” global warming debates was a strong factor in getting me involved . Specifically it was the trivial case of getting people to understand the importance of the orthogonality of flat spectrum gray body temperature to which are added any non-flat spectral , ie : greenhouse effects . This continues to be the case when people mislabel a 255K value as some sort of fundamental “black body” temperature in our orbit when it is the result of a hypothetical step function spectrum . This causes them to fail to understand that the actual value of any computational significance is the flat spectrum value of , from Anthony’s recent post on TSI , about 278.3 . And it causes people to fail to understand the 180 year old observation this is the temperature of any gray , no matter how dark or light , body in our orbit , not just black .
If at some level in the upper atmosphere the absorption=emission spectra of the gases causes the temperature to decline from the gray body temperature to the 250s neighborhood , then that must be explained . But the “neutral” temperature which must be converged to as one leaves the atmosphere is the ~ 278 value .
NASA is complicit in promoting this ignorance by listing their step function calculated temperatures in their tables of planetary properties ( some absurdly irrelevant number in the 200s for Venus ) but not the computationally use gray body temperatures calculated simply by summing the total energy impinging on a point .
As I say , this is really trivial stuff . But until these fundamentals are settled understandings , you’re not talking physics .
I’d like to understand the history around Fourier’s insight . I have the impression that he was the first to realize that any pattern could be decomposed into , expressed in , other arbitrary orthogonal bases . I think it perhaps the next great leap after Descartes . But clearly the notion of decomposition into many other function sets rapidly evolved around the same time .
The earth is nothing like a grey body. It is a non-grey body that constantly changes reflectance in a semi-random fashion. Clouds make the whole situation even more complex.
You MUST understand the basics before you can understand the nuances . ALL real branches of applied physics follow this analytical approach . And it is the only way to start constructing a computational model providing an audit trail from the Sun’s output to our observed temperature .
And the gray body calculation explains 97% of our estimated surface temperature . So , in fact , we are very much like a gray body .
Furthermore , you give me a spectral map of the planet as seen from space , and I’ll give you back the additional couple of APL expressions to compute the equilibrium temperature for that map . But they will build on the computations for a gray sphere .
The basics are that if you observe earth ‘temperature’ over the equatorial pacific and do the same over the poles, you will find a difference. Earth does not have a uniform temperature. An average or mean temperature is a fictitious number. Neither the incoming energy from the sun is uniform on the surface and neither is the ‘blackbody’ radiation coming from the earth.
I feel you would have a problem looking at the spectral images centred on the two locations I mentioned.
Wein’s Law of Black Body Radiation.
Studied this 30 years ago as a CCNY Meteorology major undergraduate.
The “neutral” temperature was defined as 277K.
Not sure why NASA would use anything else. Odd.
Switching the wires on a speaker doesn’t seem to effect the sound volume.
Well Wien’s law is well known to give the wrong answer. Only the Planck Law gives the correct answer.
But in fairness Wien’s Displacement Law IS perfectly valid. lambdamax T is constant.
Black body radiation is a function of the single variable T x lambda (wavelength)
In my opinion, this analysis on sunspots is not relevant to any discussion on sun-climate relations. TSI is equally useless, as if there is any sun-climate relationship, it will be due to an interaction. Thus, you could hold TSI constant, and still see fluctuations related to some cycle etc.
It would seem to me the more relevant metric would be short-wave rad received at the surface. Only then can you begin to compare any cycles in the sun’s energy output, magnetic output, etc with what is happening at the surface of the earth.
So there ya go Willis …. just gave you another fun project. Run your CEEMD on surface short wave data, or some proxy of short wave received at the surface, and get back to us with what you find! 🙂
just a foot note, Solarham is used by ham radio operators on a regular bases for many obvious reasons. And of course the short waves movement should have a variable heat effect. if my brain functions correctly here.
Short wave radiation as in under 1 micron wavelength or short wave radio?
The term “shortwave” as used in climate references, almost invariably means SOLAR SPECTRUM radiation, which contains 98% of its energy between 250 nm and 4.0 microns wavelength which is between 0.5 and 8 times the peak spectrum wavelength of 500 nm (for a roughly 6,000 K black body spectrum.
I wish people would say solar spectrum instead of short wave. LWIR of course usually refers to the BB like radiant emission from an earthlike body at about 288 K (for earth surface mean)
Earth’s surface Temperature can be found anywhere between about -94 deg. C in the midnight Antarctic highlands, to +60 deg. C in the hottest tropical northern deserts (all at the very same time, so maybe 150 deg. C range
And TSI cycling might be able to change that by about 0.07 deg. C. based on a simple BB based calculation, and neglecting cloud modulation which will wipe most of that out completely.
G
Well sunspot data is known to be periodic with an 11 year period. That’s why Willis’s analysis works. If you want to add all of the as yet not accurately modeled earth climate variations to that, you should take on that task yourself.
Given that the biggest terrafloputers are incapable of modeling that, you should hardly expect Willis’s X-box to do it for you.
G
It seems that the majority of the discussion on this post has devolved into various arguments about the what and how of the sun’s contributions to our planet’s _______ (fill in the blank with weather, climate, earthquakes, clouds, cycles and their attributions, etc.)
I, on the other hand, am a lot more interested in the mathematical analytical technique (that I had never heard of before) rather than the solar and ocean temp. applications that Willis has chosen to apply it too. That has got me wondering whether this analytical technique could be applied to some more practical things in life like:
Rapidly Identifying someones speaking, singing, or phone voice based on the timbre created by how their vocal chords propagate sound through various oral and nasal physical cavities.
Rapidly identifying spoken phonemes based on the changes in sound frequency spectra.
Rapidly identifying someone by using this analytical technique on the pattern of changes in intensity and color of one’s iris when scanned circumferentially around the pupil.
And more . . . .
Wish I had the time to learn the mathematical technique and then try some of these ideas out.
10 years ago I bought a DVD with on it a “multimedia” database containing text, graphics, photos and acoustic records about over 500 birds. You can select a bird name and get all of it. That’s top-down search.
What I nevertheless miss since beginning is the inverse path! A cheap but good directional microphone together with a software working like you seem to describe would so prettily help in finding bottom-up the corresponding bird’s description 🙂
Are you living under a haystack ?? such analytical methods have been in use for eons, for all kinds of practical and impractical uses.
g
And you can actually buy text books that show you how to do it yourself.
george e. smith –
Although EMD and EEMD were available earlier, CEEMD was only published as a novel enhancement to the technique in 2011.
Torres, M. E., Colominas, M. A., Schlotthauer, G., Flandrin, P. (2011). A complete ensemble empirical
mode decomposition with adaptive noise. 2011 IEEE International Conference on Acoustics,
Speech, and Signal Processing, pp.4144-4147, doi: 10.1109/ICASSP.2011.5947265.
An eon is a billion years. 5 years ago is not an eon.
No, I do not live under a haystack although I have mucked cow and sheep barns in my youth. Was that meant as some kind of an insult? I did not wish for a textbook. I wished for the free time to explore this.
Well, that settles it. The Sun has absolutely nothing to do with the earth’s climate except in the fevered imagination of true believers.
‘Absolutely nothing’ is not correct either. There is a 0.1 degree solar cycle variation of global temperature, but this effect is so small that it drowns in the noise and is very difficult to dig out.
I think about 70 mDeg. C, from a dumb BB calculation assuming 0.1% TSI cycle amplitude.
G
Leif,
I have read in the past that temperature is not the main effect of the ~11 year cycle, but that the effect is mostly on regional cloud/rain patterns.
The main effect in the cycle is in the UV range – ozone production in the lower stratosphere – increased temperature (~1°C) in the tropical stratosphere at high solar activity – jet stream position pushed towards the poles, including the accompanying rain patterns.
The link to the scientific report about the effect of the sun cycle on the lower stratosphere is gone now, but another alludes to that research, without mentioning the jet streams:
https://www.sciencedaily.com/releases/1999/04/990412075538.htm
http://onlinelibrary.wiley.com/doi/10.1029/2005GL024393/abstract stratosphere – troposphere interaction
And a few reports on clouds/rain/wind patterns still do exist:
http://onlinelibrary.wiley.com/doi/10.1029/2005GL023787/abstract rainfall Portugal
http://ks.water.usgs.gov/pubs/reports/paclim99.html stream flow Mississippi delta
http://venus.unive.it/rubino/paper/JGR08.pdf river Po discharge (Italy)
https://ams.confex.com/ams/96Annual/webprogram/Manuscript/Paper282153/Gachari_Research%20Article.pdf
rainfall in Kenya
And some more here:
https://wattsupwiththat.com/2010/07/22/solar-to-river-flow-and-lake-level-correlations/
Still the global effect of the ~11 year cycle may be small (even less on longer term), but the regional effect may be quite important.
may is a weasel word.
As Tweedledee said:
“if it was so, it might be; and if it were so, it would be; but as it isn’t, it ain’t”
Leif,
OK let’s reformulate:
Still the global effect of the ~11 year cycle probably is small (even less on longer term), but the regional effect is confirmed as quite important. including implied “AFAIK” as always in science…
That goes for you too…
So whatever you claim is only as far as YOU know.
Hi Ferdinand,
It is appalling that a serious response as yours based on a lot of published research is brushed aside with the excuse of your words choice.
I respect your efforts to educate others on atmospheric CO2 changes and the carbon budget. I want you to know that the effort you put on writing a serious response to Leif Svaalgard has not been a complete waste of time.
The link to that publication that you were looking for could be this one:
Crooks, S. A., & Gray, L. J. (2005). Characterization of the 11-year solar signal using a multiple regression analysis of the ERA-40 dataset. Journal of Climate, 18(7), 996-1015.
http://www.homogenisation.org/files/private/WG1/Bibliography/Applications/Applications%20%28C-E%29/coroks_and_gray_2005.pdf
That was later updated here:
Frame, T. H., & Gray, L. J. (2010). The 11-yr solar cycle in ERA-40 data: An update to 2008. Journal of Climate, 23(8), 2213-2222.
http://journals.ametsoc.org/doi/pdf/10.1175/2009JCLI3150.1
You are corrected that the solar variability influence on climate appears linked to atmospheric changes that drive changes in cloud cover and precipitations, and only secondarily on temperatures. The link of cosmogenic isotope variation to speleothem records in the monsoon area has been highlighted multiple times, and can be seen clearly in the figure in my previous post:
https://wattsupwiththat.com/2016/04/20/ceemd-and-sunspots/comment-page-1/#comment-2195306 (green curve in A)
We find the fingerprints of the solar effect on climate all over the Holocene. For example the 2.8 Kyr BP event, that coincides with Bond event 2, shows very clear correspondence between solar activity, precipitations and temperatures. This was probably one of the worst periods for mankind of which we have evidence, that plunged the Mediterranean world into the Greek Dark Ages.
http://i1039.photobucket.com/albums/a475/Knownuthing/Figure%2049b_zpsh0zjyeut.png
Bibliography for this figure:
Kaniewski, D. et al. 2013. Environmental Roots of the Late Bronze Age Crisis. PLoS ONE 8(8): e71004.
Bond, G. et al. 2001. Persistent Solar Influence on North Atlantic Climate During the Holocene. Science Vol. 294, pp. 2130-2136
Geirsdóttir, Á. et al. 2013. Abrupt Holocene climate transitions in the northern North Atlantic region recorded by synchronized lacustrine records in Iceland. Quat. Sci. Rev. 70, 48-62.
Shapiro, A.I. et al. 2011. A new approach to the long-term reconstruction of the solar irradiance leads to large historical solar forcing. Astronomy & Astrophysics 529, A67.
I think researchers are finally starting to look at the right place to find the solar-climate effect, and reanalysis looks like a powerful tool for the task.
Shapiro, A.I. et al. 2011
The general feeling is that Shapiro et al. are not correct and that their reconstruction is seriously off. So relying on them is hazardous. We can enter into a discussion of that if we must.
Ferdinand,
Also you were referring to this article in your first link:
Shindell, D., et al. “Solar cycle variability, ozone, and climate.” Science 284.5412 (1999): 305-308.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.175.4146&rep=rep1&type=pdf
Leif,
“The general feeling is that Shapiro et al. are not correct and that their reconstruction is seriously off. So relying on them is hazardous. We can enter into a discussion of that if we must.”
It is not necessary, Leif. The main point for the figure is the existence of the two grand solar minima at 2950 and 2750 years BP. Those lows in solar acrivity at 1000 and 800 BC are also present in Steinhilber reconstruction so it is not easy to dismiss them:
http://www.landscheidt.info/images/steinhilber.png
pochas94
Some people are heliocentric and some are geocentric. ‘ It’s the CO2,stupid’. ‘It’s the sun,stupid’. Others just like to argue.
Sure, that the Sun has little to do with Earth’s climate is settled. As settled as the overbearing influence of CO2 on climate.
Meanwhile research on solar influence on climate continues:
Mitchell, D. M., et al. “Signatures of naturally induced variability in the atmosphere using multiple reanalysis datasets.” Quarterly Journal of the Royal Meteorological Society 141.691 (2015): 2011-2031.
http://onlinelibrary.wiley.com/doi/10.1002/qj.2492/pdf
“A multiple linear regression analysis of nine different reanalysis datasets has been performed to test the robustness of variability associated with volcanic eruptions, the El Niño Southern Oscillation, the Quasi-Biennial Oscillation and with a specific focus on the 11-year solar cycle. The analysis covers both the stratosphere and troposphere and extends over the period 1979 – 2009. The characteristic signals of all four sources of variability are remarkably consistent between the datasets and confirm the responses seen in previous analyses. In general, the solar signatures reported are primarily due to the assimilation of observations, rather than the underlying forecast model used in the reanalysis system. Analysis of the 11-year solar response in the lower stratosphere confirms the existence of the equatorial temperature maximum, although there is less consistency in the upper stratosphere, probably reflecting the reduced level of assimilated data there. The solar modulation of the polar jet oscillation is also evident, but only significant during February. In the troposphere, vertically banded anomalies in zonal mean zonal winds are seen in all the reanalyses, with easterly anomalies at 30◦N and 30◦S suggesting a weaker and possibly broader Hadley circulation under solar maximum conditions. This structure is present in the annual signal and is particularly evident in NH wintertime. As well as the ‘top-down’ solar contribution to Northern Annular Mode variability, we show the potential contribution from the surface conditions allowing for a ‘bottom-up’ pathway. Finally, the reanalyses are compared with both observed global-mean temperatures from the Stratospheric Sounding Unit (SSU) and from the latest general circulation models from CMIP-5. The SSU samples the stratosphere over three different altitudes, and the 11-year solar cycle fingerprint is identified in these observations using detection and attribution techniques.”
As someone said, observation trumps theory.
Since the ‘finding’ is the result of “A multiple linear regression analysis of nine different reanalysis datasets” it is statistics and not observation.
It’s the World Wrestling Federation of solar science. I love it! Thank you, Leif, for all of your insights. You live up to your aka ” the Donald Trump of solar science”, but with more factual information.
So oceans and heat storage don’t count. Oh.
On the sun?
Not only storage but circulation and redistribution into the atmosphere.
Yes, a multistage problem with cycles
Hi Willis,
I think a CEEMD of the Hadley temperature history might be very interesting.
be careful of any signal analysis of a variable that is, strictly speaking, non physical and an index only.
we call it the land ocean INDEX .
That said, if you go hunting for signals you will find them.
Pushing data through meat grinders is a poor excuse for science.
Start with a physical theory.
Explain why you think that theory should result in a signal in the temperature record.
Then you might be on solid ground looking for it
Mosh,
My point is that there are known and suspected causes for short term temperature variation (like ENSO), of varying time influence, where this kind of decomposition might help to isolate and define short and longer term variation. The ‘secular change’ in temperature versus shorter term changes is important if you want to understand the influence of GHG forcing.
Thanks to Willis Eschenbach for this very interesting guest post and of course also to Leif Svalgaard for his accurate (and very, very patient) reactions.
I read about the amazing fight of LS and his colleagues worldwide to reestablish a correct SSN evaluation after Waldmeiers lone hand play.
A little question alongside this post: does the correction have an effect even on WE’s CEEMD plot, or is its influence restricted to evaluations of the sun’s activity during the XXeth cy?
There are corrections throughout the series, but the largest one from 1947 on.
lsvalgaard,
Is this accurate too?
There may be a 0.1 degree influence on the variation of global temperature from adding 100 PPM in CO2, but this effect is so small that it drowns in the noise and is very difficult to dig out.
Most climate scientists would put the increase to something like 0.5 degrees for a 100 ppm increase in CO2. Whether that is accurate, I don’t know, but that is what the data show.
lsvalgaard,
Has there really been a verified cause/effect experiment done to confirm that 100 ppm change in CO2 concentration will cause a half a degree change in world-wide air temperature? or when you say “that is what the data show” do you really mean “that is what various theoretical computer models show”?
If you know this to be true, I would like to read the paper that has actually VALIDATED the cause/effect relationship between CO2 and world-wide air temp with “data” not modeling. IMHO the “data” shows nothing of the kind. The “data” shows no substantial change in temp for the last 18 years even though the concentration of CO2 has gone up 44 ppm. I will continure to believe the actual “data” until the models are validated.
I would also like to see your ‘data’ that you believe.
I don’t think there is any way to VALIDATE the result. How would you do that?
Don V,
On laboratory scale, the absorption effect was measured at different air pressures and CO2-water-CH4 levels. That was combined in an overall model based on the average 1976 atmosphere with the cloud/rain patterns of that year. The line by line calculations were done by the US military in the HITRAN model, later simplified in the MODTRAN model, which can be found in interactive form here:
http://climatemodels.uchicago.edu/modtran/
You can change the CO2 levels for the “1976 US standard atmosphere” with fixed relative humidity and look at the “Upward IR Heat Flux” for 280 and 380 ppmv, keeping the other variables constant. Then for 380 ppmv, give the ground offset a stepwise increase until the outgoing IR equals that at 280 ppmv. That is what the warming of the surface would need to cancel the 100 ppmv CO2 extra, based on its absorption characteristics.
Of course that is the physical base only, without any positive or negative feedbacks to the increased temperature.
Climate models add a lot of positive feedbacks like water vapor and clouds, while reality shows no water vapor feedback and clouds are a negative feedback…
In looking at input signal (solar) vs potential output signal (ENSO, PDO, temps etc), everyone seems to assume that the earth-atmosphere-ocean system doesn’t act as a filter or that the filter is essentially white.
I would say that is a very large & untested assumption. In geophysics, we never see natural earth filters with a white spectrum. Understanding how the earth-atmosphere-ocean system acts as a filter on input is essential before you can do any interpretation on output signal.
This may (or may not ) be a fruitful line of investigation.
lsvalgaard April 21, 2016 at 9:08 am says
“Your ‘Hale Cycle’ is nowhere in the data. You made it up by making every other solar cycle have negative solar activity”
If this is not Hale cycle, it must be Mosher’s unicorns.
http://www.vukcevic.talktalk.net/LODvsHALE.gif
Show a graph of the Hale Cycle ‘data’ that you computed the spectrum from.
you can do it, here is the data,
http://sbc.oma.be/data1.html
instructions : Extracting higher frequencies (shorter periods) can be achieved by using any of the known high pass filters. However, for the convenience and benefit of the easy reproducibility, the 21 year (cantered) average can be subtracted from the ‘raw ’ geomagnetic data. The obtained difference represents higher frequencies. Since the geomagnetic data is only available for the 1840-1990 period, the obtained result is truncated by 10 years at each end.
From your colleague Ken Schatten
Solar Polar Fields and the 22-Year Activity Cycle: Observations and Models
http://link.springer.com/chapter/10.1007%2F978-1-4939-2584-1_11#page-2
published on line 1 July 2014
tell Ken he got it wrong ! there is no such a thing as “the 22-Year Activity Cycle”
They mean [and say in the paper] ‘the 22-year magnetic cycle’. This not the same as a 22-year cycle in solar activity. The clearest example is perhaps the butterfly diagram:
http://solarscience.msfc.nasa.gov/images/bfly.gif
But you are evading the issue. Show us the Hale Cycle you used to calculate your spectrum.
No, that is not the solar Hale Cycle. Show the solar Hale Cycle, if you have one
“Except there is no 22-year cycle in solar activity.”
Solar Polar Fields and the 22-Year Activity Cycle: Observations and Models
“They mean [and say in the paper] ‘the 22-year magnetic cycle’.”
PRICELESS !
I think Mosher got it, sunspots are not magnetic activity, they could be unicorns.
I’m done, bye for now.
Running away and shirking to show the Hale cycle graph. This is your typical and standard reaction when cornered. Be a man and do what you need to do.
Leif says:
“but that is what the data show”
A spurious correlation. For years the US stock market correlated well with the height of ladies’ hemlines. And yes, one can come up with a mechanism for that, too, but I won’t get into that here. Spurious relationship.
Check out http://www.leif.org/EOS/Nagoya-Lean-2012.pdf and take up your opinion with Judith Lean.
Tried to download it several times. It gets to 41.09mb and stops. In any event, too many exogenous variables involved for anyone to buy the certainty of any such .5 degree quantification. A better statement might be that ” there is some data that indicates such a relationship”.
Well, it is 42 Mb, but is is based on Figure 2 of the following paper:
http://www.leif.org/EOS/2010GL045777.pdf and the more technical:
http://www.leif.org/EOS/LeanRindCauses.pdf and
http://www.leif.org/EOS/123222295-Lean-Trends.pdf
“And it is equally clear that there is no significant sunspot signal in either the SOI or the Nino3.4 data—the CEEMD analysis shows little commonality.” – Willis
As a suggestion, if you use TSI from the PMOD composite ftp://ftp.pmodwrc.ch/pub/data/irradiance/composite/DataPlots/composite_42_65_1602.dat instead of SSNs you might get a different result.
Most people naturally use SSNs first instead of TSI as the sun’s energy proxy. They do track together annually over solar cycles, with an R2 of .87 (as does F10.7), but it’s the timing, duration, magnitude, and direction up or down of TSI that ultimately matters to the Earth’s response, not particularly exactly when SSNs are high, most of the time.
Using annual averages, PMOD TSI peaked in 2002, v2 SSN peaked in 1979, and F10.7cm peaked in 1989.
Secondly, SOI/MEI/ONI/OHCI ENSO parameters follow TSI in time, as it takes time for the build-up of TSI to do it’s work raising temps, OHC, and creating pressure differentials – meaning there’s a TSI response lag to consider.
2015 was the highest SORCE TSI year, and the highest since 2002, but the SSNs weren’t.
Year 1au TSI F10.7 v2 SSN
2015 1361.4321 117.5 69.8
2014 1361.3966 145.8 113.3
2013 1361.3587 122.8 94.0
2012 1361.2413 119.9 84.5
2016 1361.1986 99.1 56.2
2011 1361.0752 113.4 80.8
2003 1361.0292 127.4 99.3
2004 1360.9192 106.5 65.3
2010 1360.8027 80.1 24.9
2005 1360.7518 91.7 45.8
2006 1360.6735 80.0 24.7
2007 1360.5710 73.1 12.6
2009 1360.5565 70.6 4.8
2008 1360.5382 69.0 4.2
http://lasp.colorado.edu/data/sorce/tsi_data/daily/sorce_tsi_L3_c24h_latest.txt
I’m not so sure FFT and CEEMD are exclusively the best ways to understand what we’re looking at – and you didn’t say they were, although CEEMD is interesting. Thanks.
lsvalgaard
“Speculation is a poor substitute for observations. Even if you fervently believe that the Sun MUST influence the climate big time, the evidence simply is not there.”
Obviously, speculation is a poor substitute for observations. The problem is that observations are missing that would show what caused the large climate cycles over the last few thousand years such as the Medieval Warm period and Little Ice age.
Due to the absence of observations, and the fact that the Sun has the most influence on climate, I speculate that changes in the sun are most likely the cause. If it is not the sun, then I would speculate that the next most likely possibility seems to be volcanic followed by speculation that the type of “variations” in the climate can combine in ways that reinforce trends that lead to periods of cold or warm as have been seen in the past.
In the absence of observations, I think speculation is healthy so long as those speculating understand that is what they are doing. Unlike the speculation on “catastrophic global warming” and “catastrophic climate change” which somehow became “holly writ” even as observations began to accumulate that made the “catastrophic” part very unlikely.
In the absence of observations, I think speculation is healthy so long as those speculating understand that is what they are doing.
And that they label it as SPECULATION, instead of pretending it is not.
There are other possibilities like natural warming and cooling trends in the oceans regulated by their circulation and their ability to move warmer water to the north where it radiates to space more easily. Two different oceans with different warming and cooling cycles sometimes in sync sometimes not in sync, sometimes in sync for a little while some times totally in sync and so on, creating natural variability. Add to this warming tropical trends that alter cloud cover and its affect on albedo and varying ice cover and its affect on albedo in the south and the in the north lower ice cover equals higher thermal radiation to space and you have a pretty unpredictable chaotic system. Throw in volcanic activity, maybe large solar variability or not but at least small variability and the system gets more unpredictable. you can add in any other natural cycle you want and to complicate the system more and you don’t need any magic control knob to understand why with climatic change. over larger times scales the movement of the continents greatly alters the circulation of the oceans and the tilt of the earth on its axis affects how the system absorbs energy from the sun.
“””””….. like natural warming and cooling trends in the oceans regulated by their circulation and their ability to move warmer water to the north where it radiates to space more easily. …..”””””
Nothing could be further from the truth.
“Thermal” (BB like) electromagnetic radiation radiance varies as the 4th power of Temperature, so the very last thing that is going to happen to energy in warmer waters that move north, will be increased radiation to space.
The VERY HOTTEST tropical (high) deserts in the middle of the hottest summer days, is where you should look for efficient cooling of the earth; at rates almost twice that for the global nominal 288 K Temperature, but the very coldest places like the Antarctic highlands or the northern polar regions, can only radiate at rates as low as one sixth of the global mean rate.
The polar regions DO NOT COOL THE EARTH.
G
George;
Bad choice of words on my part, was trying to make the point that as the waters in the arctic warm and the ice melts this allows for a transfer of heat from the oceans to the atmosphere and then to space. When the arctic is frozen it acts as a valve limiting this path, creating a control valve the helps regulate the system. was not trying to imply that the arctic was the main source of heat transfer in the system.
off topic but this one surprised me.
“The average temperature on Venus makes it the hottest world in the Solar System, with its thick atmosphere trapping heat and giving rise to scorching temperatures of 460°C (860°F) on the surface.But measurements taken by Venus Express at an altitude of 130 to 140 kilometers (81 to 87 miles) above the surface have revealed the atmosphere near the poles has temperatures far below that on Earth. In fact, the polar atmosphere on Venus drops to -157°C (-251°F), which is 70 degrees colder than expected.”
Where is this quote from?
A quick search finds this …5th para. From there it’s a short hop to doi:10.1038/nphys3733, available here: http://phys.org/pdf380286576.pdf
Arrgh! Not the actual paper unfortunately but a lot more detail. Sorry about that.
Breaking:
http://dailybail.com/home/sune-sets-sunedison-files-for-bankruptcy.html
The sun definitely is in complete control of the weather & climate.
High TSI in 2015 directly caused high temps in 2015, and all the extreme weather events in 2015.
Observe in the TSI data I posted before that TSI in 2015 was the highest since 2002. The 2015 ENSO started in Feb/March just as TSI for SC24 peaked, driving 2015 temps up to record levels.
Since the final ENSO temp peak in Feb/Mar this year, SSTs and OHC have dropped. People are now calling for a strong La Nina this year – but the La Nina will just be the response to present and future lower TSI this year. 2016 will not be a record temperature year because falling TSI won’t support it.
OHC started dropping right after last year’s second SORCE TSI peak in November, accelerating its drop as TSI has dropped off more recently:
Month SORCE TSI
2015.042 1361.5359
2015.123 1361.8859
2015.204 1361.6749
2015.288 1361.6690
2015.371 1361.4732
2015.455 1361.3152
2015.538 1361.4622
2015.623 1361.1664
2015.707 1361.1063
2015.79 1361.3139
2015.874 1361.3646
2015.958 1361.2527
2016.042 1361.2892
2016.124 1361.3051
2016.206 1361.1865
2016.292 1360.8044
http://lasp.colorado.edu/data/sorce/total_solar_irradiance_plots/images/tim_level3_tsi_24hour_3month_640x480.png
http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_update/heat-last-year.gif
In spite of the fact of the lagged TSI response to SSN, we can use SSNs going back in time to get a fairly good general idea of the sun’s influence on temps post-1865.
The modern maximum in solar activity occurred during the 70 years from 1935 to 2004, when the annual average v2 sunspot number was 108.5, versus the previous 70 years since 1865, when the SSN annual average was 65.8 (lower than this year’s 69.8), a 65% sustained higher sunspot level for 70 years.
That means TSI was on the whole higher for those 70 years than the previous 70 years, making the sun’s TSI the only realistic viable candidate for the cause of ‘global warming’ since 1850. Remember the Dalton minimum cold years? It was low SSNs and low TSI.
http://www.leif.org/research/Kopp-et-al-New-TSI.png
I try not to call what the sun does a trend or a cycle, such as the Gleissberg, but clearly, there was an overall major energetic step change upward in solar output in the 20th century that has already peaked.
You are just playing your usual cherry-picking games. A better comparison would be the years 1712-1798 SN=83.2, 1823-1901 SN=81.6, 1933-2015 SN=98.5, a meager 20% higher than the two other periods.
You have to consider the temperature variation from 1700 to now. Part if the Dalton minimum low temps were due to volcanic activity [e.g. ‘the year without a summer’]
You’ve already played these cards on me, including your ‘cherry’ picking argument, many times.
The modern period was more active. Using annual v2 SSNs for three consecutive 9 solar cycle periods
1712-1809 ave = 78.7, 18.4% less than the most recent 9 cycles
1810-1913 ave = 71.7, 22.4% less than the most recent 9 cycles
1914-2009 ave = 95.1
‘Cherry’ picking:
You have always attempted to cover the actual variation in SSN and TSI with the broadest hand-wave ever seen, with your repeated assertion to only use variation from 1700 onward, as though the actual real solar variation within that time frame didn’t matter. Very unscientific of you.
Clearly, Dr. Leif Svalgaard, you are not being scientific at all if you’re going to make this statement in contradiction to the facts:
“Part if the Dalton minimum low temps were due to volcanic activity [e.g. ‘the year without a summer’]”
From http://berkeleyearth.lbl.gov/auto/Global/Complete_TAVG_summary.txt
The year without summer occurred in 1816; Tambora erupted in 1815.
Temperatures fell for years as solar activity dropped long before Tambora erupted. 1816 saw a minor temperature decrease given the amount the hype Tambora generates wrt it’s supposed cooling effect. The cooling effect from zero sunspots in 1810 was as deeper and longer than the Tambora cooling effect.
Year BEST v2 SSN
1800 0.027 24.2
1801 0.116 56.7
1802 0.127 75
1803 -0.027 71.8
1804 0.308 79.2
1805 0.008 70.3
1806 -0.152 46.8
1807 -0.268 16.8
1808 -0.992 13.5
1809 -1.509 4.2
1810 -1.619 0
1811 -1.687 2.3
1812 -1.519 8.3
1813 -0.845 20.3
1814 -1.006 23.2
1815 -1.356 59
1816 -1.624 76.3
1817 -1.593 68.3
1818 -0.729 52.9
Dr. Svalgaard, you really ought to check your facts first before giving wrong unsupported unscientific opinions. Any discussions with me from now on should reflect this new knowledge. Your education is costing me a lot of time.
ditto.
And your new cherry-picks [~20%] are a far cry from your previous 65%.
There were large volcanic eruptions in 1809 and 1812 [e.g. Maion].
I’ll show this again, so you can familiarize yourself with it:
http://www.leif.org/research/SN-1700-2015.png
Not much variation over the centuries.
Check out https://en.wikipedia.org/wiki/List_of_large_volcanic_eruptions_of_the_19th_century
“1808/1809 mystery eruption 1808 Greenland and Antarctic ice samples suggest an undocumented eruption roughly half the magnitude of Mount Tambora occurred, contributing to the 1810s being the coldest decade in at least 500 years.[1] Recent searches of documents suggest that it may have taken place in South Western Pacific Ocean around Dec 4, 1808 and observed in Colombia from December 11, 1808.[2]”
And Mayon was 1814, not 1812 [my bad]
You’re not learning anything Leif. All the comparison I made are valid, they just cover different time periods. What you want to do with your childish accusations of cherry-picking is prevent anyone from delving into the nuances of solar activity over time at all, so your viewpoint on the climate can prevail – an argument you have clearly lost.
You obviously have an incapacity to learn:
“There were large volcanic eruptions in 1809 and 1812 [e.g. Maion].”
So what? Temps started dropping in 1804. The deepest cold occurred after the year without sunspots, not after the volcanic eruption years 1809 or 1812. I just told you all that a few minutes ago and yet you come back with the same old same old. You didn’t learn a thing!
I think there is not much to learn from you. You how a graph with temperatures rising and solar activity [and thus TSI] falling. As I said: what more is there to say.
And you are certainly free to indulge in any flights of fancy you wish. As for me: you are not convincing.
You have to ignore the fact that PMOD was highest in the early 2000s in order to maintain your point of view. You are bordering on stupid here.
You said “temperatures rising and solar activity [and thus TSI] falling”
Get a clue – TSI rose until 2002, it did not fall, OK?! Those are the facts. Are you disputing them?
I showed you the PMOD rankings. Once again your hand-waving explanation comes up short.
You will continue to be shown by me how wrong you are about your opinion about the sun-earth climate connection, why you are wrong, and why your activist-like statement in your sunspot number paper about the trend in solar activity not causing global warming is not only wrong, but so easily proven wrong as to make me wonder how that ever made it through peer-review! It will be challenged.
You are clearly NOT AN EXPERT on the sun-earth climate connection, despite your claims.
Here is Claus Froehlich’s [maintainer of PMOD] latest composite of TSI:
http://www.leif.org/research/TSI-Composite-Froehlich.png
Study it carefully.
Bob Weber April 21, 2016 at 11:04 am Edit
Thanks, Bob, but consider the size of the variation in TSI. The TOA solar varies by 0.3 W/m2 (24/7 global average) from peak to trough of the sunspot cycle. The average difference over longer periods, of course, is much smaller than that, on the order of a maximum of 0.1 W/m2.
If you think that a 0.1 W/m2 change in TOA insolation is “the only realistic viable candidate” for global temperature variations, well … I’d say several things.
First, you are committing the “Error of the Excluded Middle”, meaning you have left out all kinds of possibilities. IF a change of 0.1 W/m2 can indeed change the global temperature, that is less than the change in forcing from the CO2 change over that time period, so that could be the cause of the warming. It is also equivalent to a change from a global average albedo of ~30% to ~ 30.03%, a trivially small change, so that could be the cause of the warming. Or an equally tiny change in the global average wind speed would reduce evaporation by that same small amount, 0.1 W/m2.
Next, given the size of the system, you are looking at a meaninglessly small change in forcing, far too small to make a difference. The average downwelling energy at the earth’s surface is on the order of a half a kilowatt per square metre. The TSI change that you think is the “only realistic viable candidate” is a tenth of a watt per square metre. That is a total change of 0.002%, a whacking great TWO TEN-THOUSANDTHS OF A SINGLE PERCENT variation in the forcing … sorry, but that is far too small to cause anything measurable.
Finally, IF the smaller long term TSI variations like your seventy year period above made a difference, then the larger 11-year variations would make a larger difference … but we do not see that in the record.
Regards,
w.
Willis: “Thanks, Bob, but consider the size of the variation in TSI. The TOA solar varies by 0.3 W/m2 (24/7 global average) from peak to trough of the sunspot cycle. The average difference over longer periods, of course, is much smaller than that, on the order of a maximum of 0.1 W/m2.”
That is the variation for the data available. The sun has been observed for too short of a time to know what variations are possible or what variations occur over several thousand years. The variations that happened for example between 1640 to 1700 are not measured variations. The graphs of TSI for that period of time usually are a relatively flat line. But that is a speculative estimate. I know, some may say it is based on theory and the theory they think is robust. I think it is not robust at all given it is not based on observations of TSI for the time in question.
“Next, given the size of the system, you are looking at a meaninglessly small change in forcing, far too small to make a difference. ”
I agree that the known forcing you are talking about is obviously too small to have a large impact on temperature. But is that the only forcing that happens over time periods of thousands of years? I don’t know if it is or isn’t. Nor am I sure how changes in ultra-violet impact temperature over longer periods or even how much ultra-violet emissions will vary over a longer period of thousands of years.
I know those number crunches sound convincing… To me, the idea that TSI doesn’t vary enough to make a difference is as much a myth as CO2 driving temperatures.
The eleven year variations are clear in this graphic that I first pointed out to you two years ago:
http://climate4you.com/images/SunspotsMonthlySIDC%20and%20HadSST3%20GlobalMonthlyTempSince1960%20WithSunspotPeriodNumber.gif
My opinion is that people wrote equations and thought they understood TSI with those equations, before studying the whole system first. Secondly, many of those determinations occurred before the higher solar activity from 1980-2003, before the system as a whole could be adequately studied, w/o enough data.
In other words people jumped to conclusions about TSI and CO2, and the inertia of those ideas is still with us.
On a practical level today, I’ll give you one tip: compute, plot, and overlay the rate of annual PMOD change over top of the SST/SIDC graphic, and learn what happens when TSI ramps up at the onset of a solar cycle. If there’s still some fight in you after you do that, I’ve give you some more to think about. 😉
If you want to wait and see, I’ll be showing you those graphics in my solar paper.
Your graph clearly shows that temperatures are rising while solar activity [and TSI] is falling. Enough said.
Leif says, “Your graph clearly shows that temperatures are rising while solar activity [and TSI] is falling. Enough said.”
This will be the second time I suggested to you today to check your facts first. The SIDC numbers in the image are not the same as TSI, as you know. The PMOD composite TSI during the 1978-now period ranks the highest TSI years annually and 2002 is the top year, with three 2000-2002 of the top five years 2000-2002, which completely obliterates your point:
Rank Year PMOD F10.7 v2 SSN
1 2002 1361.7132 179.5 163.6
2 2000 1361.7102 179.4 173.9
3 1980 1361.6522 198.6 218.9
4 1981 1361.6424 202.6 198.9
5 2001 1361.6349 181.3 170.4
6 1989 1361.6224 213.5 211.1
7 1979 1361.5670 191.9 220.1
8 1990 1361.5669 189.8 191.8
9 1991 1361.4917 208.1 203.3
10 1999 1361.4889 154.1 136.3
11 1992 1361.3106 150.5 133.0
12 1983 1361.3050 119.6 91.0
13 1982 1361.2916 175.1 162.4
14 2015 1361.2647 117.6 70.4
15 2003 1361.2381 128.7 99.3
16 1998 1361.2250 118.1 88.3
17 2013 1361.2142 122.8 94.0
18 2012 1361.1778 119.9 84.5
19 2014 1361.1654 146.1 113.3
20 1988 1361.1209 141.0 123.0
21 2016 1361.0771 106.4 62.5
22 2011 1361.0529 113.4 80.8
23 1978 1361.0445 164.3 158.9
24 2004 1361.0075 106.4 65.3
25 1993 1361.0014 109.7 76.1
26 1984 1360.8737 100.9 60.5
27 1997 1360.8582 81.0 28.9
28 1994 1360.8361 85.8 44.9
29 2005 1360.8343 91.7 45.8
30 1987 1360.8100 85.3 33.9
31 2010 1360.8086 80.0 24.9
32 1995 1360.7475 77.2 25.1
33 1986 1360.7273 74.0 14.8
34 2006 1360.7247 80.0 24.7
35 1985 1360.7176 74.7 20.6
36 1996 1360.6935 72.0 11.6
37 2007 1360.5934 73.1 12.6
38 2008 1360.5711 69.0 4.2
39 2009 1360.5569 70.6 4.8
the SIDC numbers in the image are not the same as TSI
It is well-known and generally accepted that sunspot numbers and TSI vary in sync, so a plot of sunspot numbers is also to good approximation a plot of TSI. E.g.
http://cdn.phys.org/newman/gfx/news/hires/2009/1-320851main_tsi2_full.jpg
In fact the sunspot number is the basis for reconstruction TSI in the past: TSI = TSIo + a * SN^0.7, where
TSIo is the base TSI when no spots are present, and ‘a’ a suitable coefficient to be applied to the sunspot number SN.
This shows how to calculate TSI from the SSN:
http://www.leif.org/research/TSI-and-SSN-Froehlich.png
Willis, you’ll be surprised to learn that TSI varies by more than 0.1% on shorter than annual scales.
From SORCE and PMOD TSI, you can compute the real range of TSI variability in terms of daily, monthly, or yearly data. I find that daily data are the most useful for real world analysis of regular weather and extreme events, etc.:
Annual data TSI variation range
SORCE, 0.07%
PMOD, 0.09%
Monthly data TSI variation range
SORCE, 0.12%
PMOD, 0.14%
Daily data TSI variation range
SORCE, 0.39%
PMOD, 0.46%
That is nearly a 4-5X variation on the daily scale compared to the oft-cited canonical 0.1% variation.
Everything in climate comes down to daily data. Models based on 0.1% on all time scales are missing the boat on that one count alone. There’s just more to it than that number alone, as we are always compelled to believe. The variation range is different for each solar cycle too, and each should be treated separately, not broad-brushed with the same 0.1%.
These comparisons don’t include the definitely higher TSI from SC19, or the possibly lower TSI from the Maunder Minimum, since we don’t have instrumental data for it, but if they were included, the actual possible range in TSI over time could be higher yet, maybe 0.6% to 0.75%, why not 1%? We don’t know.
And you try to advance your view using obsolete TSI reconstructions [blue, red, and green].
A more honest graph of solar activity would be
http://www.leif.org/research/SN-1700-2015.png
Note the equal height plateaus in every century.
Dr. Svalgaard, the graphic I posted came from your research page. If you don’t like it, take it off your page.
Why should I do that? It nicely shows how the TSI models based on the old sunspot number are wrong.
Here is much better one [from 2007]:
http://www.leif.org/research/TSI-LEIF.png
You are welcome to use that in your paper.
ferdberple April 21, 2016 at 6:08 am
Oh, please, DO THE EXPERIMENT FIRST! No, Ferd, an 11 year and a 22 year cycle will NOT “be reduced via CEEMD to a mathematical 11 year cycle of varying amplitude”, you just made that up without trying it first.
In fact, and completely contrary to your fantasies, CEEMD splits a combination of an 11-year and a 22-year signal very neatly back into the 11-year and the 22-year components, just like you would expect.
Please, please, folks, do your homework before making your claims. It saves me work falsifying your nonsense, and it saves you embarrassment …
w.
From your Figure 1 you make a subsequent statement:
“Unfortunately, these kinds of appearing and disappearing cycles are far too common in natural datasets. There is a great temptation to think that they can be used for forecasting purposes…”
The raw data for sunspots in Figure 1 suggested to me: Polymorphic Ventricular Tachycardia (torades de pointes) whereby the heart ventricle responds to a signal that recirculates through the electrical conducting system. There is a cycle, and it is dangerous, as it can lead to ventricular fibrillation, complete ventricular chaos.
However, my point is, there is a recirculation signal that can be mapped. The importance from the viewpoint of capturing this signal is in the automatic defibrillators found in many public places. With torades de pointes, there is no algorithm for the signal sensing device to capture a requisite spike in electrical activity so as to “shock” the heart, first into standstill and then allowing the normal conduction system to resume its rhythm.
The cycle exists, its just hard to find the necessary mathematics to develop a sensor for the signal; i.e. to forecast and use the next electrical spike. I would surmise that there are other natural signals that have cycles that are hard to capture and quantitate, let alone forecast.
‘Torsades’, but nice pointe.
========
kim
Of course you are correct. I had to re-type it as my “auto-correct” kept denying me my spelling. Eventually, I lost the “s”. Thanks.
A ‘Bubble’ for Hubble: Iconic Telescope Snaps Spectacular Birthday Photo
http://www.space.com/images/i/000/054/957/i02/bubble-nebula-hubble-26th-anniversary.jpg?1461250794?interpolation=lanczos-none&downsize=640:*
My new desktop background !
I haven’t read all comments, so I may have missed something. But –
Willis – I am very interested in your analysis, because of the conclusions of some of your earlier analyses. As you say “When cycles come and go like that, it is hard to draw any firm conclusions“. When a cycle comes and goes, is it an illusion, or is it a real phenomenon influenced by some other factor so that at times it is suppressed and at times it is visible? Absent further information, it could be either. I feel that in the past, when you have been examining possible climate/solar/etc factors, you have been too quick to dismiss their existence/influence on the basis that they aren’t always visible. I have often felt that a “don’t know” finding would have been better than an “doesn’t exis’t” finding. I would be really interested if you could revisit some of the past analyses in light of this one. [OK, OK, I should do the work myself. Sorry, but I don’t have your statistical skill.].
Leif – You have made a logical error. When others refer to a cycle of activity, you have incorrectly dismissed that by referring only to energy.
When others refer to a cycle of activity, you have incorrectly dismissed that by referring only to energy
‘Activity’ requires energy to have any effect. Hence energy is a measure of activity and activity is an index for energy. The dictionary defines activity as “Measurable amount of work performed to convert inputs into outputs”. Work performed over time is energy. Work refers to an activity involving a force and movement. Energy is the capacity for doing work.
Wrong. You have stated that activity requires input energy, but then you have tried to portray hemispherical activity as something that can be measured by the whole sphere’s output energy : “A useful definition of solar activity is one that involves the energy the sun puts out, and the effect of that on the Earth“. It may well be useful to look at those things, but it is not a definition of solar activity. You say, “The magnetic field in a given hemisphere has a 22-year cycle, but the field in the other hemisphere has the opposite 22-year cycle, so as we get stuff from both hemispheres, the two cycles cancel out, and the overall effect is just the unsigned solar activity 11-year cycle.“. ie, you are dismissing the magnetic field 22-year cycle because it doesn’t deliver a net energy change at Earth. But change of magnetic field direction is clearly an activity and, as you say, the magnetic field in a given hemisphere does have a 22-year cycle. [Thinks: There is a two-step cycle – left right – in walking, even if two people are walking together in opposite step.].
And as for effect of that on the Earth, no-one knows what that is.
vukcevic April 21, 2016 at 7:40 am
vuk, I just took a look at the astronomical LOD data you referred to, and I find no evidence of any significant 22-year cycle. I ran a CEEMD analysis on your data. As you said, it does show a small cycle at 22 years in the intrinsic mode C4. But as you can see, as with the solar data, the cycle fades in and out. As a result, the strength is trivially small.
Sorry, not convinced.
w.
Thanks for looking. ‘Not convinced’ it is fine with me.
For benefit of anyone else who would like to have a go, data is here:
http://sbc.oma.be/data1.html
Result of a simplest of analysis shows that LOD is made of two major components
100 year period (peaks about 1870 and 1970) with +3 to -3 ms p/p, and a smaller component of +0.6 to -0.6 ms p/p, with basic periodicity of 21.5 years + two very minor of 16 and 32 years
http://www.vukcevic.talktalk.net/GMF-LOD1.gif
Waveform shown above in red is now compared to the solar magnetic cycle
http://www.vukcevic.talktalk.net/LODvsHALE.gif
Reproducibility is a basic tenent of science.
p.s. the same Jackson et al data is used by NASA-JPL in their CAM calculations
No, that is not solar data and therefore cannot with reason be called the ‘Hale Cycle’. You are still evading the issue: show us a graph of the solar data you used for the spectrum.
If you didn’t use any solar data, all you have to say is “I did not use any solar data” and be done with it.
Dr. Svalgaard
If you like to calculate solar magnetic periodicity it is as simple as it can be.
Here we go:
This illustration shows solar magnetic field evolution from 1880 to 1990 making it 110 years of solar history.
http://www.vukcevic.talktalk.net/LODvsHALE.gif
Now we count the blobs created by sunspot magnetic field, in either solar hemisphere, lets take red ones in the N. H, there are 5 in whole of 110 years, that makes it 110/5 = 22. There is your sunspot 22 year periodicity, often referred to as the ‘Hale Cycle’.
We can do same with blue blobs again it is 110/5 =22, or we can do it with either with the red or the blue blobs in the south Hemisphere, the answer is always 22 years.
There is no getting away from it:
Sunspots activity creates the 22 year magnetic cycle in the N. Hemisphere, and equally the sunspot activity creates the 22 year magnetic cycle in the S. Hemisphere.
I see nowhere in there 11 year magnetic cycle.
Therefore logic obliges me to say : Sunspot activity has 22 year periodicity, but if you wish to refute what is physically happening up there, and stick to outdated early 1600s numerical construct of counting, without what that count may represent, you are free to do so.
There you have it.
No, as your Figure shows solar activity has a clear 11-year cycle. Magnetic energy does not depend on the sign. And at any time the Earth is facing magnetic fields from both hemisphere, one with one polarity and one with the other, so again only an 11-year cycle. But you are again evading the issue. Show a graph of the solar Hale Cycle that you used to calculate the power spectrum. If you don’t have any, say so.
Now doc, what you are trying to say (but for some reason unwilling to do so) is that none of the solar magnetic energy can penetrate deep enough to alter the core’s angular momentum (CAM), and I agree with that entirely.
Now, since the changes in the LOD are synchronous in periodicity and phase with the positive orientated magnetic field (B>0, red) switching from the N to the S hemisphere and vice versa (see graph above), that leaves you with two rather uncomfortable alternatives:
a) Solar activity has an effect on ocean & atmospheric temperatures and circulation which in turn via angular momentum exchange affects the Earth’s rate of rotation (LOD). The last 2 stages are well known facts, but you wouldn’t have any of it because of the ‘solar activity has an effect on ocean & atmospheric temperatures and circulation’.
b) There is a common (not recommended for myself to name it, but you have freedom to do so) driver for both, the 22 year solar magnetic cycles and the synchronised 22 year LOD quasi-periodic oscillation, but that could be even more embarrassing for the settled science.
p.s. yesterday at the supermarket this kid was screaming and kicking would not move from the sweet counter until his mother bought him packet of sweets.
Now doc, what you are trying to say (but for some reason unwilling to do so) is that none of the solar magnetic energy can penetrate deep enough to alter the core’s angular momentum (CAM),
This I have told you many times. The core is effectively a superconductor and magnetic fields cannot penetrate into the core, and in addition, the core field is many orders of magnitude larger than the solar wind’s.
The rest of your speculation is not valid either, so no need to comment further on that. But you are still evading the issue: show us the solar Hale Cycle you used to compute your power spectrum. If you didn’t use any solar data for that, say it here now. Be a man.
You need to calculate Hale Cycle spectrum?
Simple, use data from Mt. Wilson Obs & Wilcox Solar Obs
http://www.vukcevic.talktalk.net/HCS.gif
data is given in 10 days blocks, need to convert into years.
Of course I use solar data, Steven Mosher failed to provide the unicorns’ data.
I do not calculate ‘power’, I calculate relative amplitudes spectrum, so if you want a power spectrum you do your own calculations, sir.
Let’s see what you get. Good luck with it.
vukcevic
“that leaves you with two rather uncomfortable alternatives:
a) Solar activity has an effect on ocean & atmospheric temperatures and circulation which in turn via angular momentum exchange affects the Earth’s rate of rotation (LOD). The last 2 stages are well known facts, but you wouldn’t have any of it because of the ‘solar activity has an effect on ocean & atmospheric temperatures and circulation’.
b) There is a common (not recommended for myself to name it, but you have freedom to do so) driver for both, the 22 year solar magnetic cycles and the synchronised 22 year LOD quasi-periodic oscillation, but that could be even more embarrassing for the settled science.”
Dr. Svalgaard
“ your speculation is not valid, so no need to comment further on that.”
Ahaa.
Not valid ?
Why not?
To quote your own wards: “say it here now. Be a man.”
On its face. We have gone over this too many times to waste bandwidth on.
But you are evading the issue: show the solar Hale data that you used to compute your spectrum.
If you don’t or can’t that is, of course, an admission that you didn’t have any. So we shall see.
Can’t you read?
Data from 1976 is from Stanford University
http://wso.stanford.edu/Polar.html#latest
data before 1976 is from the data file you emailed to me some years ago.
The graph representation is couple of comments above.
“We have gone over this too many times to waste bandwidth on.”
No you never gave an answer, you are just prevaricating.
Now your turn to come ‘clean’ and answer the above two points
a) Solar activity has an effect on ocean & atmospheric temperatures and circulation …..or
b) There is a common driver for both ….
or both.
Over to you.
Before you said the data was from Jackson et al. [field at core boundary]. Now you say something else. In neither case, the ‘data’ is not solar activity.
a) Solar activity has an effect on ocean & atmospheric temperatures and circulation …..or
b) There is a common driver for both ….
No to both. Any resemblance [if there is one] is just coincidence or worse [e.g. man-made].
So, you did not use any solar activity data as your “Hale Cycle’ data. This is now established.
and again
see https://wattsupwiththat.com/2016/04/20/ceemd-and-sunspots/comment-page-1/#comment-2196659
else I give up.
Instead of running away as usual, take some time to try to understand what I just said in my replies.
If not, giving up [permanently] does seems to be a good strategy. Hope you mean it and follow through.
vukcevic April 21, 2016 at 3:52 pm
Thanks for the data, Vuk. First, there are three datasets at your cited location, labeled “Rough”, “Intermediate”, and “Smooth”. The datasheet says:
Despite that, you have used the “Rough” data … what is the reason for that choice?
…
Ah … never mind, I just did the analysis and found out the reason. The “Rough” data contains a strong ~ 22-year cycle … but the preferred “Smooth” data doesn’t contain any significant 22-year cycle at all.
And that alone, dear vuk, should tell you something about the data itself … when you have to cherry-pick the data by choosing the non-preferred version, simply because the preferred version doesn’t show what you claim is true, then I have to conclude that your claim is very weak.
There is a further problem, however. I showed elsewhere in this thread the LOD analysis using your astronomically determined LOD link, which also showed no 22-year cycles … but that astronomically derived data is very different than your more recent link to the core angular momentum LOD data.
As you can see, we get very different LOD values from the two methods … and that doesn’t even count that you are not using the preferred angular momentum data …
Short answer? Our data about the LOD is far too internally inconsistent to say much of anything about LOD and sunspots.
w.
vukcevic April 21, 2016 at 3:58 pm
No, it is not the same data at all. You have used the “Rough” data, where NASA is using an entirely different dataset, the “Smooth” data. See e.g. here.
w.
Honesty has never been a strong suit with Vuk.
vukcevic April 22, 2016 at 4:45 am
It would be simple to use that putative data, but only if you GIVE US THE VERDAMMT LINK!! I’m not going on a snipe hunt only to have you be using some different dataset, like you did with the “Rough” and “Smooth” LOD dataset. Link to your data or stop handwaving about it, thanks.
w.
You need to calculate Hale Cycle spectrum?
Simple, use data from Mt. Wilson Obs & Wilcox Solar Obs
In addition, those data show the solar polar fields which have nothing to do with any ‘Hale Cycle’.
They show the difference between the north and south pole magnetic flux density. A technical detail: the polar fields go from maximum [when they are zero] to the next maximum. The Hale sunspot polarities go from minimum to minimum.
Mr Eschenbach
re your remarks and questions
– I like using wherever possible the least processed or corrected (rough) data, not intermediate or smooth whatever that was meant to be.
– LOD is affected by many factors
http://op.gfz-potsdam.de/champ/media_CHAMP/luehr_2_geodyn.gif
and since Jackson and Bloxham are world recognised experts on the subject, presumably they calculated the LOD portion as inferred from the changes in the magnetic field, I am happy to go by their results, but it is up to you if you trust their work or not.
– polar field data, from 1976 see here http://wso.stanford.edu/Polar.html#latest data 1965-1976 are from a file emailed to me by Dr. Svalgaard number of years ago, I’m happy to email it, or you may prefer to get a copy from dr. S himself (info was given here https://wattsupwiththat.com/2016/04/20/ceemd-and-sunspots/comment-page-1/#comment-2196585)
Finally I have no objection to your finding using your own method concluding ‘not convinced’, or as Dr. S. might have said ‘junk’.
vukcevic April 22, 2016 at 1:49 pm
Not responsive. You said you were using the same data NASA used, viz:
That was NOT TRUE. You are not using the same data NASA used, and your excuse for not using the same data doesn’t address your false claim.
Hey, it was you who said you were using both the astronomical LOD data and the Jackson/Bloxham LOD data … I’m just pointing out that they disagree. It’s none of my business who you go by … but since you said you go by both and they disagree with each other, it is certainly valid to ask why you claim to go by both.
w.
reply is here
https://wattsupwiththat.com/2016/04/20/ceemd-and-sunspots/comment-page-1/#comment-2197602
In C5, there’s a 30-year cycle, in C6 a 60-year cycle, even in the residual there’s a possible 150-year cycle. All approx, of course. The 22-year cycle can be seen to be fading in and out, the longer cycles may well be fading in and out but the period is too short to tell. What causes the cycles? It would be easy to suppose that the 60-year cycle relates to the PDO, but the PDO itself is thought to have faded in and out. http://tinyurl.com/hj6elhv (“[..] a ∼50 to 70 year periodicity in the PDO is typical for the past 200 years but, was only intermittently a strong mode of variability prior to that. Between AD 1600 and 1800 there is a general absence of significant variability within the 50 to 100 year frequency range. Significant variability within in the frequency range of 50 to 100 years reemerges between AD 1500 and 1300 and AD 1200 to 1000. [..]”. So, is the PDO real or an illusion? If real, then cycles fading in and out does not mean that the cycles are not real. In which case, perhaps we have a real 22-year cycle, it’s just that it fades in and out?
[PS. I used “relates to” above, rather than “caused by”, because I wouldn’t want to make any assumption about cause and effect.].
Sir, you are confused or avoiding to answer the question, I will assume it is the first, but suspect strongly the second..
I shall enlighten you, by going over it in the very simple terms.
There are two variables
1. solar magnetic field ‘oscillation’
2. LOD as inferred from geomagnetic data by Jackson et al.
Clear so far? Good.
I compare two graphically as in here
http://www.vukcevic.talktalk.net/LODvsHALE.gif
so far so good
then I calculate spectral composition of both data sets
– data and graph for Hale Cycle is my comments above at April 22, 2016 at 4:45 am and 2016 at 8:01 am
– data for LOD is from Jackson http://sbc.oma.be/data1.html
OK?
Let’s compare two spectral compositions
http://www.vukcevic.talktalk.net/H-Lspec.gif
Well that is as close as it can get, and anyone should be able to understand.
First of all, there is no 22-year solar activity oscillation. The polar fields only cover 4 cycles which is normally too short to show 22-yr periods, but more importantly, your physics is all wrong. What is shown on the polar field graph is the difference between the north pole and the south pole. At all times when there is a high field in the north, there is also a high field in the south, so no 22-year period in field magnitude. The energy of the field does no depend on the sign. Second, what we see at Earth is not the polar fields but comes from the near equatorial regions where the field [even with sign] does not show any 22-year cycle, so assuming that something that does not have a 22-year cycle causes something in the core [where it could even not penetrate to] with a 22-year cycle is not reasonable. Actually, it is worse: junk.
I think we have over this so many times that it is hardly worth flogging that dead horse over.
And third: what you show in blue and red colors here:
are not the magnetic field of the butterfly wings. The wings consist of bipolar active regions [each with spots of opposite polarities], so their net magnetic flux is close to zero. And, in any event, such small-scale features do not make it out into the solar wind; they are generally closed field lines. So, there is no 22-year cycle there either. The coloring is a made-up feature that has no existence on the sun.
Many will take your word for it, they are welcome to it, but you don’t fool me that easily, next time try a bit harder.
Let’s remember: “You can fool some of the people all of the time, and all of the people some of the time, but you can not fool all of the people all of the time.”
Funny, you play the ‘fool me’ game. Some time ago somebody was trying to convince me that the layers in Greenland’s inland ice was caused by the water of Noah’s flood sloshing back and forth several hundred thousand times during the 40 days and freezing instantly. When I tried to make him see the errors of his ways, he used that same, tired ‘fool me’ argument. The situation here is quite similar.
Despite all the obfuscations the most likely scenario is:
Solar activity has an effect on ocean & atmospheric temperatures and circulation which in turn via the angular momentum exchange affects the Earth’s rate of rotation (LOD).
Of course I can’t decisively prove it, but it is close to it, and all your efforts to sidetrack and rebuff and the ridiculous new ‘science’ (there is no 22 year cycle in the sun’s polar field) have failed. See you soon, with more data more uncomfortable ‘coincidences’.
This is a new obfuscation of yours. You used to claim that the changes were due to the solar magnetic field penetrating to the Earth’s core and changing the circulation of the molten material and hence causing a 22-year cycle in LOD. Now you sing a different tune. And it is true that changes in the atmosphere does influence the LOD, but since those changes don’t show any 22-year variation [perhaps because the sun doesn’t either], you have a problem. To be frank: your ideas are junk or worse.
You just invented that.
Find a quote, or admit you told a ‘porky’
I suspect you won’t do either, it made me laugh, in desperation people do odd things
You know well what you used to claim but seem to have forgotten about it. You find it and spare me the trouble.
already done it, see here
https://wattsupwiththat.com/2016/04/20/ceemd-and-sunspots/comment-page-1/#comment-2196833
vukcevic April 22, 2016 at 9:59 am
Actually, what we should remember is that when a man like vuk simply claims his opponent is wrong without either quoting what his opponent says, or indicating where his opponent is wrong, or supporting his own claims with citations, he’s just blowing wind. If he had real objections, he’d raise them—when a man starts throwing mud, it’s a sure sign he’s out of real ammunition.
In any case, vuk, you can’t fool us any of the time … in that comment, you’re just handwaving.
w.
see https://wattsupwiththat.com/2016/04/20/ceemd-and-sunspots/comment-page-1/#comment-2196659
“Ah … never mind, I just did the analysis and found out the reason. The “Rough” data contains a strong ~ 22-year cycle … but the preferred “Smooth” data doesn’t contain any significant 22-year cycle at all.
And that alone, dear vuk, should tell you something about the data itself … when you have to cherry-pick the data by choosing the non-preferred version, simply because the preferred version doesn’t show what you claim is true, then I have to conclude that your claim is very weak.”
Perhaps they realised that 22 year cycle is in there, and that might cause lot of problems (as you can see in the exchanges with Dr. S), deciding that after smoothing, the problem has disappeared and they recommend the use.
I emailed one of the authors about finding in there as you put it ‘contains a strong ~ 22-year cycle’ but never got a reply.
If authors publish two set of data, one with ‘controversial’ information embedded, and another mediocre one, sure I will cherry pick the one with ‘controversial’ information and do not apologise for that.
I am looking for the unknown even if I can’t always or ever give an acceptable interpretation.
Not much more I can add to the matter.
this is reply to Mr. Eschenbach @ur momisugly April 22, 2016 at 12:11 pm
Mr. Eschenbach,
– thank you for pointing what appears to have been an omission in my original comment regarding JPL, I had no idea about version they used, but I made it clear that I use ‘rough’ version. Which version is used in the paper you quoted is their choice, an early ‘monochrome’ version paper referred to the same data file link I quoted.
– as far as I understand it, they are ‘rough’ and ‘smooth’ versions of the same data.
I choose to use ‘rough’ version which has as yet unexplainable solar 22 cycle.
If you prefer to use ‘smooth’ version which has eliminated the ‘as yet unexplainable solar 22 cycle’ that of course is your personal choice.
– in my view, if the authors of the data have any reservation about accuracy of the ‘rough’ version it would have been withdrawn. Recommendation to use ‘smooth’ version is most likely in order to avoid controversy, but they keep the ‘rough’ version as the most prominent and the first in the data list, possibly to protect their scientific integrity in view of the future science evolution.
– why the astronomical observations of the LOD differ to the one inferred from the geomagnetic data is clear to me, but I have no intention of going into it. If you suspect that the geomagnetic version of the LOD is worthless than all this concern about the ‘rough’ or ‘smooth’ versions, and what and why I use, or what and why JPL uses, is absolute waste of time.
You personally may see all this in a different light, I have no complaint about any of that. We may be at the opposite side of scientific argument, but I did like your stories very much.
Mr. Eschenbach,
I have no intention to turn in any prolong discussion on something which I think has no particular significance. I wish you all the luck and success in your research, and look forward to occasional excursion to the land of creative writing.
p.s. I hope this ends up in right place.
vukcevic April 23, 2016 at 1:19 am
No, it is not my “personal choice”, it is the recommendation of the authors.
And no, they are not “three different versions of the same data”. They are the results of three different model runs with different settings.
Vuk, the original authors say that the “smooth” version is the preferred version. If you want to use a non-preferred version, you’ll have to tell us why, and “because it has the 22-year cycle I’m looking for” is not one of the choices.
w.
From the outside looking in, I am about as convinced that the sun drives the climate as I am CO2 drives the climate – i.e. – they both probably have some influence but neither seem to be the definitive driver.
WUWT tends to focus on these 2 drivers but for all the discussion , the one thing I come away with is that there must be other, more important factors out there, but that there isn’t much discussion / posting on it.
We have a significant amount of discussion on short period effects of sea temps (ENSO, PDO, NAO cycles etc) but how about the possibility of longer period effects ? The heat uptake and release processes seem to be an area that could be a significant driver of longer term climate variation, but there seems to be little analysis of it for longer cycles. I would love to see those engaged in analysis , such as Willis & Bob Tisdale dig into that area & see if there is anything to it … or not.
This blog has done a pretty good job of saying what isn’t significant in driving climate…. I would like to see more on what is significant in driving climate.