Guest Post by Willis Eschenbach
I hear a lot of folks give the following explanation for the vagaries of the climate, viz:
And in fact, when I first started looking at the climate I thought the very same thing. How could it not be the sun, I reasoned, since obviously that’s what heats the planet.
Unfortunately, the dang facts got in the way again …
Chief among the dang facts is that despite looking in a whole lot of places, I never could find any trace of the 11-year sunspot cycle in any climate records. And believe me, I’ve looked.
You see, I reasoned that no matter whether the mechanism making the sun-climate connection were direct variations in the brightness of the sun, or variations in magnetic fields, or variations in UV, or variations in cosmic rays, or variations in the solar wind, they all run in synchronicity with the sunspots. So no matter the mechanism, it would have a visible ~11-year heartbeat.
I’ve looked for that 11-year rhythm every place I could think of—surface temperature records, sea level records, lake level records, wheat price records, tropospheric temperature records, river flow records. Eventually, I wrote up some of these findings, and I invited readers to point out some record, any record, in which the ~ 11-year sunspot cycle could be seen.
Nothing.
However, I’m a patient man, and to this day, I continue to look for the 11-year cycle. You can’t prove a negative … but you can amass evidence. My latest foray is into the world of atmospheric pressure. I figured that the atmospheric pressure might be more sensitive to variations in something like say the solar wind than the temperature would be.
Let me start, however, by taking a look at the elusive creature at the heart of this quest, the ~11-year sunspot cycle. Here is the periodogram of that cycle, so that we know what kind of signature we’re looking for:
Figure 1. Periodogram, showing the strengths of the various-length cycles in the SIDC sunspot data. In order to be able to compare disparate datasets, the values of the cycles are expressed as a percentage of the total range of the underlying data.
As you’d expect, the main peak is at around 11 years. However, the sunspot cycles are not regular, so we also have smaller peaks at nearby cycle lengths. Figure 2 shows an expanded view of the central part of Figure 1, showing only the range from seven to twenty-five years:
Figure 2. The same periodogram as in Figure 1, but showing only the 7 – 25 year range.
Now, there is a temptation to see the central figure as some kind of regular amplitude-modulated signal, with side-lobes. However, that’s not what’s happening here. There is no regular signal. Instead of there being a regular cycle, the length of the sunspot cycle varies widely, from about nine to about 15 years, with most of them in the 10-12 year range. The periodogram is merely showing that variation in cycle length.
In any case, that’s what we’re looking for—some kind of strong signal, with its peak value in the range of about 10-12 years.
As I mentioned above, when I started looking at the climate, like many people I thought “It’s the sun, stupid”, but I had found no data to back that up. So what did I find in my latest search? Well, sweet Fannie Adams, as our cousins across the pond say … here are my results:
Figure 3. Periodograms of four long-term atmospheric pressure records from around the globe.
There are some interesting features of these records.
First, there is a very strong annual cycle. I expected annual cycles, but not ones that large. These cycles are 30% to 60% of the total range of the data. I assume they result in large part from the prevalence of low-pressure areas associated with storms in the local wintertime, combined with some effect from the variations in temperature. I also note that as expected, Tahiti, being nearest to the equator and with little in the way of either temperature variations or low-pressure storms, has the smallest one-year cycle.
Other than semi-annual and annual cycles, however, there is very little power in the other cycle lengths. Figure 4 shows the expanded version of the same data, from seven to twenty-five years. Note the change in scale.
Figure 4. Periodograms of four long-term atmospheric pressure records from around the globe.
First, note that unlike the size of the annual cycle, which is half the total swing in pressures, none of these cycles have more than about 4% of the total swing of the atmospheric pressure. These are tiny cycles.
Next, generally there is more power in the ~ 9-year and the ~ 13-14 year ranges than there is in the ~ 11-year cycles.
So … once again, I end up back where I started. I still haven’t found any climate datasets that show any traces of the 11-year sunspot cycles. They may be there in the pressure data, to be sure, it is impossible to prove a negative, I can’t say they’re not there … but if so, they are hiding way, way down in the weeds.
Which of course leads to the obvious question … why no sign of the 11-year solar cycles?
I hold that this shows that the temperature of the system is relatively insensitive to changes in forcing. This, of course, is rank heresy to the current scientific climate paradigm, which holds that ceteris paribus, changes in temperature are a linear function of changes in forcing. I disagree. I say that the temperature of the planet is set by a dynamic thermoregulatory system composed of emergent phenomena that only appear when the surface gets hotter than a certain temperature threshold. These emergent phenomena maintain the temperature of the globe within narrow bounds (e.g. ± 0.3°C over the 20th Century), despite changes in volcanoes, despite changes in aerosols, despite changes in GHGs, despite changes in forcing of all kinds. The regulatory system responds to temperature, not to forcing.
And I say that because of the existence of these thermoregulatory systems, the 11-year variations in the sun’s UV and magnetism and brightness, as well as the volcanic variations and other forcing variations … well, they make little difference.
As a result, once again, I open the Quest for the Holy 11-Year Grail to others. I invite those that believe that “It’s the sun, stupid” to show us the terrestrial climate record that has any sign of being correlated with the 11-year sunspot cycles. I’ve looked. Lots of folks have looked … where is that record? I encourage you to employ whatever methods you want to use to expose the connection—cross-correlation, wavelet analysis, spectrum analysis, fourier analysis, the world is your lobster. Report back your findings, I’d like to put this question to bed.
It’s a lovely Saturday in spring, what could be finer? Gotta get outside and study me some sunshine. I wish you all many such days.
w.
For Clarity: If you disagree with someone, please quote their exact words that you disagree with. It avoids all kinds of pernicious misunderstandings, because it lets us all know exactly where you think they went off the rails.
Why The 11-year Cycle?: Because it is the biggest cycle, and we know all of the other cycles (magnetism, TSI, solar wind) move in synchronicity with the sunspots. As a result, if you want to claim that the climate is responding to say a slow, smaller 100-year cycle in the sunspot data, then by the same token it must be responding more strongly to the larger 11-cycle in the sunspot data, and so the effect should be visible there.
The Subject Of This Post: Please do not mistake this quest for the elusive 11-year cycle in climate datasets as an opportunity for you to propound your favorite theory about approximately 43-year pseudo-cycles due to the opposition of Uranus. If you can’t show me a climate dataset containing an 11-year cycle, your hypothesis is totally off-topic for this post. I encourage you to write it up and send it to Anthony, he may publish it, or to Tallbloke, he might also. I encourage everyone to get their ideas out there. Here on this thread, though, I’m looking for the 11-year cycle sunspot cycle in any terrestrial climate records.
The Common Cycles in Figures 3 and 4: Obviously, the four records in Figs. 3 & 4 have a common one-year cycle. As an indication of the sensitivity of the method that I’m using, consider the two other peaks which are common to all four of the records. These are the six-month cycle, and the 9-year cycle. It is well known that the moon raises tides in the atmosphere just as it does in the ocean. The 9-year periodicity is not uncommon in tidal datasets, and the same is true about the 6-month periodicity. I would say that we’re looking at the signature of the atmospheric tides in those cycle lengths.
Variable-Length Cycles, AKA “Pseudocycles” or “Approximate Cycles”: Some commenters in the past have asserted that my method, which I’ve nicknamed “Slow Fourier Analysis” but which actually seems to be a variant of what might be called direct spectrum analysis, is incapable of detecting variable-length cycles. They talk about a cycle say around sixty years that changes period over time.
However, the sunspot cycle is also quite variable in length … and despite that my method not only picks up the most common cycle length, it shows the strength of the sunspot cycles at the other cycle lengths as well.
A Couple of my Previous Searches for the 11-Year Sunspot Cycle:
Looking at four long-term temperature records here.
A previous look at four more long-term temperature records.
Atmospheric Pressure and Sunspot Data:
Tahiti to 1950 and Tahiti 1951 on (note different units)
Darwin to 1950 and Darwin 1951 on (note different units)
Sunspots These are from SIDC. Note that per advice from Leif Svalgaard, in the work I did above the pre-1947 values have been increased by 20% to adjust for the change in counting methods. It does not affect this analysis, you can use either one.
For ease of downloading, I’ve also made up a CSV file containing all of the above data, called Long Term Atmospheric Pressure.csv
And for R users, I’ve saved all 5 data files in R format as “Long Pressure Datasets.tab”
Code: Man, I hate this part … hang on … let me clean it up a bit … OK, I just whacked out piles of useless stuff and ran it in an empty workspace and it seemed to fly. You need two things, a file called madras pressure.R and my Slow Fourier Transform Functions.R. Let me know what doesn’t work.
Discover more from Watts Up With That?
Subscribe to get the latest posts sent to your email.
And last the PDO shows a very good anomaly at the 11 year cycle, I wish they had the non AU adjusted data for the PMOD so I could see were the one year cycle is. I guess I will have to write my own program to do the FFT, it really should be over sampled by a factor of 4 before you do the forward FFT, because it allows you to resolve the lower frequencies better. I suspect the PDO is interfering with the 11 year cycle. The PDO should show up as the strongest peak on the power spectrum for any dataset and the side lobes can interfere with the 11 year cycle if the data is not sampled fine enough.
http://www.woodfortrees.org/plot/sidc-ssn/from:1979/fourier/magnitude/from:1/to:50/normalise/plot/jisao-pdo/from:1979/fourier/magnitude/from:1/to:50/normalise/plot/pmod/from:1979/fourier/magnitude/from:1/to:50/normalise
Willis Eschenbach says:
May 27, 2014 at 12:08 pm
SH is ~81% ocean; NH ~61%, for global average of ~71%.
Once again, there are those here who seem to think the PDO drives something. Folks, that is a step too far. It is an index derived from global SST anomaly data removed except from the North Pacific area, and then more statistical stuff is done to that data. So all the drivers have driven. What happens next and which part of the oceanic/atmospheric teleconnection drives the next happening is up for debate.
Ok so the Arctic Sea Ice shows that the one year cycle is centered at sample 35, the 11 year cycle is centered at approximately at sample 3 and the 30 year PDO cycle is below 1 and probably aliased in this analysis. The plot below shows that RSS global and Hadcrut4 Southern Hemisphere match the SSN 11 year cycle. The small peaks that follow the 11 year cycle on RSS and HADCRUT 4 are the longer than normal lag between cycle 23 and cycle 24, And the PDO index shows the strongest peak. So RSS, HADCRUT4 Southern and the PDO show the 11 year cycle.
What did I win? lol
http://www.woodfortrees.org/plot/sidc-ssn/from:1979/fourier/magnitude/from:1/to:50/normalise/plot/jisao-pdo/from:1979/fourier/magnitude/from:1/to:50/normalise/plot/nsidc-seaice-n/from:1979/fourier/magnitude/from:1/to:50/normalise/plot/rss/from:1979/fourier/magnitude/from:1/to:50/normalise/plot/hadcrut4sh/from:1979/fourier/magnitude/from:1/to:50/normalise
Willis: it would be the poles I suspect.
LT says:
May 27, 2014 at 6:23 am
First, let me congratulate you. You’ve done what so many folks don’t do. Instead of providing us all with the benefit of your opinions, hypotheses, and ideas, you actually went and got the data and looked at it yourself. Well done.


Having said that, there are a few problems with your analysis.
First, the datasets are too short. There’s only 32 years of data there in the shortest dataset. On my planet, I need a bare minimum of data which is three times the length of the cycle I’m looking for. In this case we’re looking for a cycle in the range of 10-12 years, so we need about 36 years of data.
Next, the datasets are different lengths. The PMOD data ends in 2011, and the other two go to 2013.
This makes a difference in the Fourier analysis. The “value of the frequency axis” is in units of frequency, where “1” is the base frequency, “2” is twice that frequency, “3” is three times the frequency, and so on.
The base frequency, in turn, is one cycle per dataset length. So if you have different dataset lengths, the base frequencies are different, and thus the numbers on the frequency axis mean different cycle lengths … no bueno.
To convert to cycle lengths, such as the 11-year sunspot cycle, you need to divide the dataset length (~33 years in this case, in fact 32 years for one and 34 years for the other two) by the value on the frequency axis. So “1” is 33 year cycles, “2” is 16.5-year cycles, “3” is 11-year cycles, “4” is 8.25-year cycles, and so on. You can see why I prefer my method …
In any case, here’s my analysis of the WFT data that you used. First, the data itself
I’ve trimmed the longer datasets to the length of the shorter one. Next, the periodograms:
Contrary to my usual practice, I’m showing cycles up to half the length of the dataset … in any case, the RSS data shows nothing in the range of 11 years.
All the best, you took a good shot at it,
w.
Pamela, I think the 30 year periodicity of the ocean surface temperature, pressure etc.., That cycle is what people refer to when they say that it drives multi decadal variations in global temperatures and precipitation patterns. It is difficult to draw a line in the sand when no human understands the cause of the 30 year periodicity, but it would seem logical that it is some type of deep ocean current system modulated by something. But the cycle is there nonetheless and it drives temperatures.
The cycle for which part? The North Pacific?
Willis,
What is causing the 2nd strongest peak on your RSS at approximately 11 – 12 years? The 11 year cycle would be very weak on a power spectrum.
Hey, Pamela –
Just curious but do you have a response for the link I provided that shows the movement of the ITCZ over thousands of years? Can the ENSO/PDO explain the apparent influence of the sun on that movement?
Willis said
“As others have commented, the difference lies both in the thermal mass and the relative areas of the ocean and the land. Only the skin of the land is warmed, while the ocean warms and cools deeply. As a result, while overall the planet is 3.4°C warmer in July (aphehelion, furthest from the sun) than in January (perihelion), the ocean is only 0.08°C warmer … but the land on average is a full ten degrees warmer in July, because so much of it is getting heated in the northern hemisphere.”
According to NCDC the global ocean is 0.6°C warmer in July (20th century average):
http://www.ncdc.noaa.gov/sotc/global/
Maybe there is more cold upwelling in the southern ocean during its summer.
I interpret the last spectrum you gave as proof of the 11 year cycle, the 5 – 7 year lobes are more than likely ENSO effects that have an average periodicity of 5 to 7 years, the 11 year cycle would be much weaker than the ENSO effects, so I think that it is it.
James says:
May 27, 2014 at 6:52 am
It’s an interesting thought, James. I use the integral of other series, notably the PDO, to determine the timing of regime changes. The discrete integral is also known, of course, as the “cumulative sum” of the data.

You need to be a bit cautious, however, as the result is very sensitive to the choice of where you are taking the anomaly from. If you take your anomaly about the zero point of the index (say the PDO index, for example), you get a very differently shaped integral than if you take your anomaly about the mean of the PDO index. My practice, at least for the first cut, is to take the integral using the anomaly about the mean of the data. One result of this is that the integral begins and ends at the same value.
With that as prologue, let me go see what the normalized integral of the sunspots taken about their mean might give us … hang on … OK, here you go …
w.
Greg Goodman says:
May 27, 2014 at 6:53 am
Interesting, Greg. I’ll have to take a closer look at that one.
w.
After looking at the analysis you did on OHC, it clearly shows the 11 year cycle too. I think you are expecting a large spike on the power spectrum but you will not get one, it will be very small, perhaps a small 3 DB variance is all you are going to get, as you know the 11 year cycle does not change the suns output by much from cycle to cycle. And neither does earths temperature.
Well, yes, but it is even worse than that.
Antarctica is 14.0 Mkm^2, surrounded by fixed ice shelves of 3.5 Mkm^2. + a minimum sea ice of 3.5 Mkm^2 = 21.0 Mkm^2 of “permanent reflective surfaces” (So the “permanent ice” around Antarctica = 4 % of the earth’s surface.)
The Antarctic sea varies between that 3.5 minimum up to 19 – 20 Mkm^2 maximum. (But that 20 mkm^2 of varying sea ice around Antarctica represents AN ADDITIONAL 4% of the earth’s surface – which ain’t “trivial” though!).
So overall, the Antarctic sea ice varies between 70 south latitude (at sea ice minimum) up to 59 south (at sea ice maximum.) The Antarctic land ice is essentially fixed between 70 south and the pole. Overall, there is no “land mass” at all as far as Antarctica goes, just varying amounts of “ocean or reflective ice” between 59 south and the south pole.
It is only the Australia, the larger part of South America, and the little bit of Africa that are south of the equator. Essentially none of those (obviously!) are ice-covered any part of the year. As i recall, the actual “exposed land area south of equator is right at 33.8 Mkm^2, or the size of the Antarctic total ice cap at sea ice maximum.
By eye (which may or may not be an effective tool!) it is not a 11-year simple cycle beat, but a pattern of 3 cycles high, 3 cycles lower, 3 cycles higher, 3 cycles lower of sunspot activity that repeats regularly. You could allow for a short lag delay, but over 33 years of rising influence, followed by 33 years of slightly declining influence, and any lag “spreads out” and would not necessarily be visible.
Thus, would not there be 33 higher period, and then a 33 year lower period that drives (yields!) the net 66 – 68 periodic cycle of temperatures that is seen superimposed on the far longer 450 year long term rise and fall of the past 2100 years?
Sorry Shawnhet, it’s your speculation. How do you think the Sun changes the ITCZ? I have already presented a paper that explains its movements. 1000’s of years ago our oceans and atmosphere were still working the way they do now. If you add in huge loads of ash and sulfur in the air there is more than enough power in intrinsic variables to cause ITCZ shifts. Your solar theory? Not so much. Not enough power to shift such a massive thing. But I would like to hear your speculative solar mechanism as you understand it.
My premise continues to be ENSO processes can explain ITCZ shifts without considering solar variables.
http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=10&cad=rja&uact=8&ved=0CIcBEBYwCQ&url=http%3A%2F%2Fshadow.eas.gatech.edu%2F~kcobb%2Fseminar%2Fchiang00.pdf&ei=q-yDU63gMtStyASVwoL4Bw&usg=AFQjCNFMGGLJQwHfoINNc5eOVF7jGh5xJw&sig2=Ylae3ppGMfzh2wasT2rSQw
RACookPE1978 says:
May 27, 2014 at 2:06 pm
For most if not all of the Paleozoic, most land was in the southern hemisphere. Those tectonic plates get around, even without throwing sea & land ice into the mix.
Well, Willis regardless is you realize it or not, you stand corrected the 11 year cycle shows up in your analysis as well as mine. I suppose it will take you some time to realize it.
Shawnhet says:
May 27, 2014 at 1:27 pm
OK, admittedly this is modeling, but at least the study mentions paleoclimatic data regarding shifts in the ITCZ. It finds that extratropical clouds (!) & ice contribute to movements of the ITCZ.
http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-11-00116.1
To me, that the air & waters north & south of the zone would affect it, as well as the ENSO, seems pretty likely.
Pamela,
I am talking about the Pacific Decadal Oscillation, the one that causes the planet warm and cool in 30 year cycles. The one in which the Pacific emits more heat than it absorbs for 25 – 30 years and then switches phase, and then abosrbs more heat that it emits for 25 30 years. The cycle that no one understands what drives the mechanism.
Willis Eschenbach says:
May 27, 2014 at 1:46 pm
Thank you for the reply!
Yep. I just think it is more meaningful to talk about it as an integral. Even though we are using discrete approximations for the model, the real process is continuous and will ‘integrate’ in that sense.
Correct, and I am aware of the high variation in the integral, depending on the baseline sunspot number used. If the average sunspot number is used, then you can see a strong 11 year signal in the integral. That makes sense, though, because if the mean is right in the middle of a sin wave, the integral will be a cosine of the same frequency.
I did try it with that, and the fits were terrible. Picking the average sunspot value seems to me to push the baseline value too high, because means will be pulled up by highly active sun seasons. A highly active season might make us want to attribute more energy to them, an effect that the increasing mean would reduce.
The model I linked to and repeated, while rudimentary, gives a simple way of finding a baseline energy value. Under the assumption that sunspots are a proxy for energy input to the system, the optimizer tries to find out what sunspot number represents no net flux of energy in or out of the system (given the simplified form of the model, of course). I found the model to fit through the middle of temperatures well, and the residual was a ~70 year oscillation, which seems reasonable given the long term ocean oscillations. In other words, the model (in a super rough way) approximates energy flux for the earth as a whole, as well as an energy transfer process in the earth (oceans vs. atmosphere) that might explain further oscillations in atmospheric temperatures.
That’s all conjecture involving statistical models, so I’m not going to hang my hat on it or anything (I don’t even want to claim that I’ve shown something to be causal). You can do the exact same analysis with CO2 (and I have) and get fits that are just as good. The sunspot way assumes energy input to the system is what varies more, while the CO2 way assumes that energy retention is what varies more. Both have led to me to find models that shoot right up the middle of the temperature data and left me with a 50-70 year oscillation (with smaller ones, of course).
That’s a lot of rambling, but I wanted to justify the selection of the baseline value for the integral that wasn’t the mean, and also compare that to another option to provide balance. I think both physical mechanisms (energy intake and energy retention) are valid and working in combination.
My point was just to show that there is an interpretation of sunspots that allows for, in a data modeling sense, a relationship between the sun and temperature to exist that explains the lack of a strong 11 year cycle in temperature data. The short short version: I think you (Willis) are absolutely correct about not finding 11 year cycles in the data you haven’t found the cycles for. I just think that those findings don’t necessarily support a case for downplaying the sun. That’s certainly a matter of degree, though, and I’ll leave it up to climate and solar scientists to argue about the mechanisms.
Pamela Gray says:
May 27, 2014 at 2:43 pm
The oceans may have been working the way they are now but the ITCZ has been in quite *different* positions in the past than it is today. This is a pretty big problem for your hypothesis that you will have to deal with. Since the changes in those positions track pretty well with changes in the solar proxies and you have *no idea* how or why the ENSO might have changed in that time frame – I’d say that’s game, set and match to me 😉 You cannot wave away the good confirmation between solar and movement of the ITCZ by claiming that XYZ might have happened. Sure, gazillions of things might have happened but if they did this does not explain why *solar* correlates so well with it.
Again, the best hypothesis is the one that explains all the data not just the stuff that is consistent with your hypothesis – your hypothesis is missing some pretty big pieces.
I am frankly baffled at how you think the sun doesn’t have the power to affect the ITCZ – you are aware that the ITCZ moves during the year, aren’t you? I would claim that this is predominantly caused by the sun’s position during the year to get more or less sunlight – since you think that the sun is unable to cause this apparently – perhaps you can explain how you think it happens.
milodonharlani says:
May 27, 2014 at 3:14 pm
Thanks for the link, Milo. I’ll definitely take a look at it in more detail – it looks interesting 🙂