Guest Post by Willis Eschenbach
I hear a lot of folks give the following explanation for the vagaries of the climate, viz:
It’s the sun, stupid.
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.
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I understand that you can not see the signal 11 years, but I understand that in this cycle we have a signal 100 years. Now you may find that the signal 11 years will be greater. I would be careful, because the current observations may be surprising, unless someone lived 100 years ago.
Since 2001, he began to as if the new epoch. I think so. I do not know how long it will take. Does you know?
Ulric Lyons says:
May 25, 2014 at 5:02 am
What comment am I supposedly ignoring? Folks, if I don’t answer your comment, just link to the dang comment and ask again, and remember the link. No need to cast asparagus on my morals or to claim I’m “ignoring” you. There is no way I can answer everyone. The day’s not long enough. Four hundred comments on this post alone.
As a result, I constantly practice comment triage—no answer, short answer, long answer. There are lots of reasons I don’t answer a given comment. Sometimes I don’t understand the claims. Sometimes there’s no “there” there. Sometimes I’m tired. Sometimes the person is just too insulting. Sometimes, I can’t be bothered to explain something to someone for the third time. So sue me …
In any case, what does your graphic supposedly show? Here’s the periodogram of the solar wind:
Largest peak at 10.5 years, clear sunspot connection. A cross-correlation analysis shows a lag of about two years between monthly sunspots and monthly solar wind, with a maximum correlation of about 0.35. Clear sunspot connection.
Regards,
w.
Matthew R Marler:
Thankyou for your question to me at May 25, 2014 at 10:21 pm.
I agree the answer provided by David A at May 25, 2014 at 7:07 pm and, therefore, I refer you to that.
Richard
PS I apologise for this response being terse but – as you can see from my above interactions with the Mods – my longer posts to this thread tend to be vanished by the WordPress system.
You are looking for an 11ish year correlation based on sunspots? How do you separate the sunspots that had no position to effect Earth. Of those that could, what was their size and scope. Of those how many expelled energy in a manner that might effect Earth. And of those that did effect Earth, what part of Earth did they effect and in which season (tilt and distance)? Further sunspots are not the only Earth effective sun phenomenon. You might get close looking at aurora records, its an energy transfer record, more earth directed activity, more aurora, less Earth directed activity, less aurora, no hot plate required:). Why not the solar polar field strength? Why not any number of things besides plaque on the sun?
The test is on though, lower solar activity (sunspots) according to popular theory backed by historical observation says our climate should get colder. We do not know however if this will effect both hemispheres. Do I think we will cool to an ice age? NO, I think ice age would be the result of a perfect storm scenario… happens but not often:)
thingadonta says:
May 25, 2014 at 5:20 am
It’s not like that at all. The hemispheric surface temperatures change up to 13° in a mere six months. And as someone pointed out above, in an eclipse the temperature plunges immediately. Where is your “lag effect”?
When the surface temperatures can change that rapidly, the “thermal inertia” explanation won’t hold water.
Regards,
w.
Here’s the deal. Earth is a water planet orbiting a variable star. Its climatic fluctuations are largely under the control of variations in the electromagnetic radiation & magnetic flux of that star & the modulations in delivery of those effects to the atmosphere & surface of that planet regulated by cosmic ray ups & downs & the orbital mechanics of the planet.
IMO the fact of these observations has been well established on various time scales from billions of years to decades. Changes in the outputs of the sun & in how its EM & magnetic effects, like the solar wind, are received on earth are IMO conclusively demonstrably felt here constitute the major forcing of climate here & on other bodies in the solar system.
Were it not for the CACA mafia, this fact would be intuitively obvious, IMO. The one observable fact upon which all with an open mind should be able to agree is that the atmospheric concentration of the trace gas CO2 at least for the past 600 million years does not correlate with major climatic changes.
Greg Goodman says:
May 25, 2014 at 5:31 am
Interesting, Greg, I hadn’t thought of that. Theoretically, you can use any resolution that you like. However, I think the gain is illusory. I don’t think you can tell anything at a resolution that is finer than the underlying time step of the data, whether that is monthly, daily, quarterly, or yearly.
To do it, however, you just sample more frequently. I use an “sapply” function to feed a list of numbers to a single function called “sinefit”. Sinefit returns a list of two things, the original data and a variable called “full”, which is the best-fit sine wave of the specified cycle length. The sapply function looks like this:
This says, apply a sequence of numbers from runstart to runend to a function of x, with said function of x being
The “range” function returns (min, max). The “diff” function returns the difference of the two, the peak-to-peak range of the best-fit sine.
To sample it more frequently, you’d need to change the statement
to something like
Of course, there may be knock-on effects …
Like I said, though, I think the gains are illusory. However, I could be wrong, play with it some and see what you think.
w.
at RACookPE1978 says:
May 25, 2014 at 11:28 pm
===================================
Jim Hansen is expecting our oceans to test your boiling pot analogy any year now.
Discussion reminds me of the pendulum swing (see video link below). There are times when things line up how the sun’s energy output has a positive correlation on various climatic events on Earth. Other times it takes 2, 3, or 4 occurrences to trigger a climatic event on Earth where they need to work in tandem (see the swing) to create this global warming or cooling. Sometimes it requires the sun’s help, others not so (think asteroid hit or volcanic explosion). It would seem paradoxical that the northern latitude ought to be hotter during winter when Earth is at its closet to the sun but with the Earth’s tilt half-way on the other side of the orbital path around the sun makes all the difference from what could easily be a scorching winter or a cold winter.
Willis, one expanded question for you. As energy cannot be destroyed, how long do you think the solar energy entering the tropical oceans to a depth of 50′ stays in the ocean before reaching the surface? How about 100′, 200′ 300′ up to its maximum of 800′?
Does additional solar insolation continue to enter the ocean and atmosphere while that older energy stays within the oceans?
What is the average residence time of solar insolation in the oceans during a strong solar cycle?
Does the average residence time of solar insolation change during a weak solar cycle as the solar spectrum changes, and therefore the average ocean depth penetration also changes?
You see it is really one question expanded on the same concept, so I am not really cheating.
Re MAC says:
May 26, 2014 at 12:56 am
==============================================================
Mac, the last paragraph in my post here says much the same thing…
David A says:
May 24, 2014 at 10:33 pm
In affect, major climate shifts happen when disparate forcing’s and feedbacks form a positive or negative harmonic.
Camp and Tung, as well as Lean and Rind find signatures of the solar cycle in the climate data, although that found by Lean is about half the amplitude as that by Camp and Tung.
http://www.amath.washington.edu/research/articles/Tung/journals/solar-jgr.pdf
http://onlinelibrary.wiley.com/doi/10.1029/2008GL034864/abstract
Stephen Wilde says:
May 25, 2014 at 4:00 am
Emergent phenomena are the negative system response to any forcing and they do indeed keep the system stable.
I would agree 100%, this comes under the heading of “dissipative structures” within Ilya Prigogine’s nonlinear thermodynamics:
http://en.wikipedia.org/wiki/Dissipative_system
In fact all climate / weather structures, whether ocean currents, gyres, ENSO, Hadley cells, depressions, cyclones, all these are Prigogine’s dissipative structures. Thus indeed, nonlinear dynamics are the only game in town regarding climate and looking elsewhere for an explanatory system for climate is to 100% waste one’s time.
Thus observing a change in the pattern of emergent phenomena is all one would expect to see in response to a forcing element.
Non sequitur from your opening correct statement. Ed Lorenz foundational paper “Deterministic nonperiodic flow”
http://www.astro.puc.cl/~rparra/tools/PAPERS/lorenz1962.pdf
– again without reference to which climate science is equally meaningless as cosmology while stubbornly ignoring Einstein’s relativity papers, –
shows that a nonlinear-chaotic system can change level by itself without external forcing. Nonlinear oscillators can be forced or non-forced. A change in system structure or regime can either be forced or not, so is not automatically diagnostic of a change – or even existence – of a forcing regime.
But your other implication of negative system response and thus negative feedback is correct – I would say. I noted this important contribution from George E Smith below:
george e. smith says:
May 25, 2014 at 5:42 pm
“”””””……joeldshore says:
May 24, 2014 at 7:06 pm
george e. smith says:
BUT ! back to a recent (not so long ago) Willis essay, on some observations of a Volcanic dimming incident(s) I believe made in Hawaii (shoot me if I’m wrong Willis) where for some months, there was a measured, very significant reduction (20-25% sticks in my mind) in the SURFACE solar irradiance; that decayed exponentially over a few months to a year or so (in that region) BUT, the local temperature anomalies showed no observable temperature change at all (in that region.
It was a striking demonstration that the weather / climate feedback loop, can squelch a host of solar irradiance variation, that happens over time intervals, that are clearly much longer than the thermal time constants.
Well the day night temperature cycle proves that the thermal response can be pretty darn fast, compared to a three month SI deprivation.
So that essay of yours Willis proved to me quite solidly, that there is a very active negative feedback, temperature regulating loop in play.
In the final analysis there HAS to be strong negative feedback and self-stability of the climate system. In addition to all the other evidence for this is the dim sun paradox. Climate remaining in a similar temperature regime over the phanerozioc despite 5-8% change in the sun’s heating output. The most likely (certainly the most parsimonious) explanation for this is Gaia type self regulation of the climate with the participation of the biosphere. This powerful self-regulation has to be considered in any discussion of climate “forcing”. Thus I consider the term climate “forcing” to be very problematic and unhelpful, suggesting the egregious fiction of a passive climate system.
Thus generally speaking I take Willis’ side against the “peleton” of cyclists in regard to solar or any other “forcings”.
Gary Palmgren says:
May 25, 2014 at 5:36 am
Mmm … sounds interesting, but exactly how do you propose to “use the data”? Cross-correlation? I’m happy to show that. I enjoy the periodograms because they reveal things like the tides in the atmospheric pressure data, but like I said in the head post, use what you want.
An interesting proposal. You ask, “Can your analysis find the original magnitude and lag?”. I’ve never tested that. Before testing it, I would say that no, it will not find the lag, it’s not designed to do that. Nor will it find the “original magnitude”.
However, what it will do regardless of magnitude and lag is to identify the power in the appropriate cycle lengths. So, let me go do it … well it took an hour, but here you go. First, the proxy data. I created the proxy data by first lagging the sunspots with a standard exponential lag, with a scale factor lambda of 0.2 and a time constant tau of four years. Note that this is not just a four-year delay of the signal, it is a true lagging of the input data.
To that I added uniform (non-gaussian) noise at the ratio of eight parts noise to one part signal. Here is the resulting proxy data:
And here is the periodogram of the proxy data …
Despite what is a huge transformation of the data, with a four-year exponential lag smearing the results, and despite having eight times as much non-gaussian noise as there is lagged signal, the periodogram clearly reveals the relationship with the sunspots …
Interesting thought, Gary, thanks for the suggestion.
w.
TimTheToolMan says:
May 25, 2014 at 6:47 am
Jeez, Tim, could you please be a little more unpleasant? Particularly when it appears you haven’t grasped what I’m doing. So I’ll explain it again.
Along with the sunspots, UV runs on an ~11 year cycle, in sync with all the rest of the solar changes. If it’s a long sunspot cycle it’s a long UV cycle. Yes, the details of the rise and fall are different, but it is still an ~11-year cycle.
As a result, regardless of the cycle-to-cycle fluctuations you mention, if the UV were having a significant effect on climate, it would be seen as an ~11-year cycle in some climate dataset.
Which is why I’m looking for an 11-year cycle. Because that way I don’t have to consider either the exact shape or the nature of the cycle, whether it’s solar wind or magnetism or UV.
Hope that helps,
w.
Sparks says:
May 25, 2014 at 7:55 am
Without having the data, my comments are:
1) LINKS! LINKS TO THE EXACT DATA! Your graph is useless without that.
2) What is the correlation between the raw datasets?
3) What does the cross-correlation look like for all months lag?
4) Why just November and March?
5) The centered moving average is a horrible thing. It munges the data mightily. And when you are looking at sunspot data, the 11-year centered moving average is the absolute worst choice possible. See “Sunny Spots Along The Parana River” for a full discussion of these issues.
Thanks, getting closer …
w.
James Hall says:
May 25, 2014 at 8:51 am
James, all the data sources are listed and linked to at the end of the head post.
w.
Willis: “something like
seq(runstart, runend, by=.1)
Of course, there may be knock-on effects …
Like I said, though, I think the gains are illusory. However, I could be wrong, ”
Thanks Willis , I’d got that far, as I think I indicated. It’s the knock on effects (getting it to scale and plot properly) that were the problem. And since I don’t have the familiarity with I can’t spend the time learning just fix trivial stuff like that.
I wrote out the sunP data to text and used gnuplot, a tool I master fairly well. It’s a few more pasted commands but no real prob. for me however, it would be good to have this build into SFT.
You can see on Tahiti/SSN triplet plot, I had smooth curves not clunky join-the-dots resolution.
Yes, it is beneficial. Look around 5y mark as it stands it’s pretty unusable resolution. It would be nice to have something a bit better than 1/12 intervals around the 11 year mark too, for a bit more precision in this case.
There is not a direct linear link between TS resolution and the step intervals. For example with 200y of data you should be able to determine a 5.5 y period pretty damn accurately. More so as you get shorter, not less so. On the contrary the long periods are very inaccurate anyway but get more and more resolution which slows the whole thing down for no benefit.
One month TS resolution does not imply 1mo resolution of period. It’s reciprocal really.
To Pamela Gray:
A belated thank you for providing a link to a paper relating trends, up to 2000, in kHz ionosphere transmission to models of interaction from an atmospheric greenhouse effect . ( The authors subtracted solar periodicity).
The earlier quoted literature in the paper will hopefully fill in some much needed tutorial reading on the background to this influence.
Greg Goodman says:
May 25, 2014 at 9:33 am
I warned you there would likely be knock-on effects, but that’s just a fact of life, not a fault of R.
In any case, I’m still not convinced that the procedure will give any real increase in information. Here’s the difference, for example, between monthly and annual data:
I think that if I increase the resolution on the sunspot periodogram while still using the annual data, it won’t look anything like the real monthly data … I’ll report back on that one.
w.
@Willis Eschenbach:
“Mike, your tone is unpleasant…”
Writing a formal criticism is “unpleasant”? Just a question: Are you really grown up?
“…your logic is absent…”
These are basics of climate science which you are about to deny. 🙂
“…you provide no facts…”
“My friend, I don’t trust anyone, including myself.”
Here is your answer for providing no facts. Why should I prove anything when you don’t trust anybody and don’t even get along with the basics?
“…your citations are non-existent.”
Which citations?
P.S.: OK, I admit trusting nobody in climate science too (I think you know why). And, to make it clear, your mistake in your research might be comparing temperatures DIRECTLY with solar activity, ignoring the lag between change in solar activity and climate (which can be 10 to 15 years long) and ignoring all other possible climate influences, even anthropogenic ones (and – very important – their feedbacks). Even if sun controls climate, you won’t get a high correlation just by comparing it with the global temperature data (alone with the lag, it’s like comparing sine with cosine). You have to correct all noise and all other influences first.
Sorry for being that rude sometimes. But I also want to thank you. Because of people like you, I isolate myself from possible conspiracy theories and keep getting the facts on my own, which is necessary for working science.
P.P.S.: You deleted my comment while quoting it 1:1 in your comment you little genius. 😉
But it’s still good that I backed up most of my comments here.
I’m no climate or solar expert and I’ve not had time to fully digest Willis’s analysis or all the comments. But I can’t help feeling in some ways we are oversimplifying it in other ways making it too complex, and looking at it the wrong way. Imagine we lived in a time before we had the basic laws of thermodynamics necessary to forecast weather. So the attempts to look for a solar link are a bit like trying to understand weather from observations like ‘red sky at night, shepherds delight’. Similarly I wonder if the solar influence is just as complex as weather forecasting if you don’t know what’s actually going on – a complex system with lots of inputs and interactions, but relatively simple once you’ve figured out the thermodynamics, it’s just a matter of improving forecast accuracy by increasing your measurements and computing power.
Like many of the difficult engineering failure investigations I’ve worked on that at the time seem inexplicable, the answer may be surprisingly simple: The data doesn’t make sense, the clever guy comes up with a theory that get’s more and more complex, people fall for it (just as I fear people are falling for the CO2 AGW theory) because the clever guy seems to be able to explain away all the contradictory evidence by making the theory more and more complex, no one can argue with him so most people believe him. But suddenly one day we find the real answer and laugh because it’s surprising how simple it is “it’s just the laws of physics”. I think the most likely way to understand the solar link (if it exists at all) may be by finding the right way of looking at the data rather than finding the mechanism, with the right way of looking at the data suddenly the mechanism(s) may suddenly appear very obvious and simple ‘just the laws of physics’ that we’re already very familiar with. BUT, by finding the right way of looking at the data I do not mean what I fear might often be happening which is making the mistake of playing around with the data and theoretical model until you find a way of making it fit your theory, it should just naturally fall into place with no assistance at all just by laying out the data. That’s often how you know you’ve got the right answer, if it’s hard work and complicated to make it fit (like the CO2 AGW theory seems to be to me) that’s probably a sign you’ve got it wrong.
Perhaps the first mistake is to think we should be looking at sunspots. I presume they are only an approximation of solar outputs of which there may be several and each doing different things. I’m assuming we have outputs such as radiation of which the spectrum is constantly changing; uv may have a different affect to ir, etc. Magnetic field, which presumably is actually changing on a 22 year cycle, i.e. 11 years of one polarity might be having a different affect on some aspects of weather/climate/the thermodynamics to a similar but opposite 11 year cycle with the opposite polarity. Presumably we have solar wind, charged particles, flares/solar storms as well. Maybe some times a much higher proportion of the flares miss us, other times a higher proportion hit us because of our orbit and/or how they come out of the Sun. Maybe there are other factors from outside our solar system which can be variable of their own accord or be modulated by solar activity. Each of these may affect different aspects of the thermodynamic system (weather/climate/energy transfer/release/storage) and perhaps there are interactions between many of these factors. Then all of this may depend on what’s happening with our planet, if the north hemisphere or southern hemisphere are facing the Sun at the time, or if it happens during night or day. Then there’s what our weather systems etc are doing at the time too, just as the affect of a racket hitting a ball will depend what that ball is already doing when you hit it. If we’re looking at cycles, say 11 years, then it only needs a few years, where some other factor disrupted the results one way in one cycle and the opposite way during another cycle to confuse our analysis, so I think we may need to think more in terms of ‘weather’ rather than ‘climate’.
Perhaps one affect is on the jetstreams, but again it may not fall into the usual measures of performance we are using, e.g. a fluid flow may take route a or b or anywhere inbetween depending on some forcing factor, but if the flow is weak perhaps the forcing factor has less control on the direction and it is mostly stuck somewhere near the middle between a and b constantly changing and never getting to a or b, but if the flow is strong it tends to flip very quickly between a or b, never inbetween except briefly as it flips and then stays in one of those extremes for longer.
I’m wondering if another big mistake is to be thinking in terms of just one output such as temperature, precipitation, humidity, pressure, etc. A mistake to be thinking global average, to be thinking smoothed signals, to be thinking of fixed locations. A mistake to be thinking cycles. Instead we should be considering the whole thermodynamic system, all the changes of energy, including potential energy and kinematic energy, how chaotic or disorganised the system is becoming, how is the weather changing. Some things might influence the energy balance, some might influence temperature, some kinematic energy, some potential energy, some heat content, etc, etc. Perhaps one thing to look at would be how the ‘dead reckoning’ longrange weather forecasts go wrong and can we correlate that with anything outside our planet. I am just guessing, but maybe a largish proportion of that’s usually blamed on jetstream behaviour? If so maybe we then also need to look at what’s driving jetstream(s) between, say state ‘a and b’ and what’s affecting how well that’s controlled, i.e. is something (else) making the flow stronger and that’s what makes the controlling factor more effective. A different factor controlling strength to the one driving it between state a and b.
I wonder if it’s a big mistake to think in terms of, say something directly influencing global average temperature. Perhaps we should think of something changing weather patterns, which might mean changing hot and cold spots (and probably not in one location on the surface, i.e. it is a ‘hotspot’ in the ‘weather’, not a location on the surface), after many years we might see that, because of the way we measure average global temperature it appears as a trend, but it is such an indirect connection it would be very misleading to be analysing it that way. Of course the energy balance has to apply but I wonder if it is such a complex system with so many freedoms to do its own thing we should not just focus on energy balance, and must also consider that energy balance is not just temperature, heat content, but also potential and kinetic energy and disorder. Maybe we have long cycles in jetstream behaviour, the different behaviour may cause different weather patterns, but not show up on our smoothed and average measures of temperature, rainfall etc, but might be more conducive to glacier advance and after several decades we notice a slight drop in global temperature, because the thermodynamic system has allowed more snow and ice, not necessarily because of a change in energy in/out of our planet or ‘weather system’.
That may sound very complex and almost impossible for a partime enthusiast with limited resources, but I don’t think it necessarily needs to be nearly as complicated as I made it sound. I think we just need a different approach to the one Willis and most others are taking to find, prove or disprove a solar link.
It’s obvious there are some other parts of the code need changing if the data interval is different because for R it’s just a series of numbers. That much is expected but I can’t be bother to learn R just to fix it. I’m sure you will know what the tweak. The 1/8 is odd.
Note your annual points all seem to lie exactly on the monthly curve.
A lot of that detail is windowing artefacts anyway. The ‘triplets’ around 7.5,8.5, 14 and 18 are probably just reflections of the main three peaks.
I think there is some real energy around 9 and 13 but it should probably not be splitting like that. In fact, I think the whole 16-18 band is artefacts too. I have concluded that from other work, not from this spectrum.
That does not mean that extra precision in locating the main peaks would not be valuable. That is desirable.
Paul UK: “I think the most likely way to understand the solar link (if it exists at all) may be by finding the right way of looking at the data rather than finding the mechanism, with the right way of looking at the data suddenly the mechanism(s) may suddenly appear very obvious and simple ‘just the laws of physics’ that we’re already very familiar with.”
You are correct. The first step is to define what the solar signal is before trying to decide whether it is present or not. This was one of Willis’ initial points. I don’t think we have got that far yet. Which is why I’m putting so much effort into analysing the spectral information.
One thing seems pretty clear, it’s not just a simple 11y cycle and those expecting that is what a solar influence on climate should look like will certainly not find it. Many will then, erroneously conclude there is no correlation with solar.
If you don’t have an clear idea what you’re looking for your sure ain’t going to find it , even if it’s there in front of you.
I’m working on finding “the right way of looking at the data”. 😉
Mike Jonas says:
May 25, 2014 at 7:52 pm
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Well, maybe there are flaws, maybe there aren’t. Filtering out the vitriol, and a few (what appear to be) misdirected responses based on some kind of misunderstanding of the article, I actually see a very useful process unfolding here — something akin to how I would imagine the scientific world working.
I don’t expect people to self-censor errors in understanding, but it’s not helpful, and does not do this site any good, for readers to have to waste time (and endure a vaguely icky feeling) looking at vulgar, ad-hominem nonsense.
It would seem to me that people who are intelligent enough to respond to the thrust of Mr. Eschenbach’s post should have matured to the point where they can ask themselves if their post is worthy of the obvious effort that Mr. Eschenbach (or any other writer) has invested in bringing us an interesting article, whether one may or may not agree with the argument contained therein, and self-censor accordingly.
There is something to be admired about maturity and self-control. There is nothing to be admired about the lack of either.