Guest Post by Willis Eschenbach
I thought I was done with sunspots … but as the well-known climate scientist Michael Corleone once remarked, “Just when I thought I was out … they pull me back in”. In this case Marcel Crok, the well-known Dutch climate writer, asked me if I’d seen the paper from Nir Shaviv called “Using the Oceans as a Calorimeter to Quantify the Solar Radiative Forcing”, available here. Dr. Shaviv’s paper claims that both the ocean heat content and the ocean sea surface temperature (SST) vary in step with the ~11 year solar cycle. Although it’s not clear what “we” means when he uses it, he says: 
“We find that the total radiative forcing associated with solar cycles variations is about 5 to 7 times larger than just those associated with the TSI variations, thus implying the necessary existence of an amplification mechanism, though without pointing to which one.” Since the ocean heat content data is both spotty and incomplete, I looked to see if the much more extensive SST data actually showed signs of the claimed solar-related variation.
To start with, here’s what Shaviv2008 says about the treatment of the data:
Before deriving the global heat flux from the observed ocean heat content, it is worth while to study in more detail the different data sets we used, and in particular, to better understand their limitations. Since we wish to compare them to each other, we begin by creating comparable data sets, with the same resolution and time range. Thus, we down sample higher resolution data into one year bins and truncate all data sets to the range of 1955 to 2003.
I assume the 1955 start of their data is because the ocean heat content data starts in 1955. Their study uses the HadISST dataset, the “Ice and Sea Surface Temperature” data, so I went to the marvelous KNMI site and got that data to compare to the sunspot data. Here are the untruncated versions of the SIDC sunspot and the HadISST sea surface temperature data.
Figure 1. Sunspot numbers (upper panel) and sea surface temperatures (lower panel).
So … is there a solar component to the SST data? Well, looking at Figure 1, for starters we can say that if there is a solar component to SST, it’s pretty small. How small? Well, for that we need the math. I often start with a cross-correlation. A cross-correlation looks not only at how well correlated two datasets might be. It also shows how well correlated the two datasets are with a lag between the two. Figure 2 shows the cross-correlation between the sunspots and the SST:
Figure 2. Cross-correlation, sunspots and sea surface temperatures. Note that they are not significant at any lag, and that’s without accounting for autocorrelation.
So … I’m not seeing anything significant in the cross-correlation over full overlap of the two datasets, which is the period 1870-2013. However, they haven’t used the full dataset, only the part from 1955 to 2003. That’s only 49 years … and right then I start getting nervous. Remember, we’re looking for an 11-year cycle. So results from that particular half-century of data only represent three complete solar cycles, and that’s skinny … but in any case, here’s cross-correlation on the truncated datasets 1955-2003:
Figure 3. Cross-correlation, truncated sunspots and sea surface temperatures 1955-2003. Note that while they are larger than for the full dataset, they are still not significant at any lag, and that’s without accounting for autocorrelation.
Well, I can see how if all you looked at was the shortened datasets you might believe that there is a correlation between SST and sunspots. Figure 3 at least shows a positive correlation with no lag, one which is almost statistically significant if you ignore autocorrelation.
But remember, in the cross-correlation of the complete dataset shown back in Figure 2, the no-lag correlation is … well … zero. The apparent correlation shown in the half-century dataset disappears entirely when we look at the full 140-year dataset.
This highlights a huge recurring problem with analyzing natural datasets and looking for regular cycles. Regular cycles which are apparently real appear, last for a half century or even a century, and then disappear for a century …
Now, in Shaviv2008, the author suggests a way around this conundrum, viz:
Another way of visualizing the results, is to fold the data over the 11-year solar cycle and average. This reduces the relative contribution of sources uncorrelated with the solar activity as they will tend to average out (whether they are real or noise).
In support of this claim, he shows the following figure:
Figure 4. This shows Figure 5 from the Shaviv2008 paper. Of interest to this post is the top panel, showing the ostensible variation in the averaged cycles.
Now, I’ve used this technique myself. However, if I were to do it, I wouldn’t do it the way he has. He has aligned the solar minimum at time t=0, and then averaged the data for the 11 years after that. If I were doing it, I think I’d align them at the peak, and then take the averages for say six years on either side of the peak.
But in any case, rather than do it my way, I figured I’d see if I could emulate his results. Unfortunately, I ran into some issues right away when I started to do the actual calculations. Here’s the first issue:
Figure 5. The data used in Shaviv2008 to show the putative sunspot-SST relationship.
I’m sure you can see the problem. Because the dataset is so short (n = 49 years), there are only four solar minima—1964, 1976, 1986, and 1996. And since the truncated data ends in 2003, that means that we only have three complete solar cycles during the period.
This leads directly to a second problem, which is the size of the uncertainty of the results of the “folded” data. With only three full cycles to analyze, the uncertainty gets quite large. Here are the three folded datasets, along with the mean and the 95% confidence interval on the mean.
Figure 6. Sea surface temperatures from three full solar cycles, “folded” over the 11-year solar cycle as described in Shaviv2008
Now, when I’m looking for a repetitive cycle, I look at the 95% confidence interval of the mean. If the 95%CI includes the zero line, it means the variation is not significant. The problem in Figure 6, of course, is the fact that there are only three cycles in the dataset. As a result, the 95%CI goes “from the floor to the ceiling”, as the saying goes, and the results are not significant in the slightest.
So why does the Shaviv2008 result shown in Figure 4 look so convincing? Well … it’s because he’s only showing one standard error as the uncertainty in his results, when what is relevant is the 95%CI. If he showed the 95%CI, it would be obvious that the results are not significant.
However, none of that matters. Why not? Well, because the claimed effect disappears when we use the full SST and sunspot datasets. Their common period goes from 1870 through 2013, so there are many more cycles to average. Figure 7 shows the same type of “folded” analysis, except this time for the full period 1870-2013:
Figure 7. Sea surface temperatures from all solar cycles from 1870-2013, “folded” over the 11-year solar cycle as described in Shaviv2008
Here, we see the same thing that was revealed by the cross-correlation. The apparent cycle that seemed to be present in the most recent half-century of the data, the apparent cycle that is shown in Shaviv2008, that cycle disappears entirely when we look at the full dataset. And despite having a much narrower 95%CI because we have more data, once again there is no statistically significant departure from zero. At no time do we see anything unexplainable or unusual at all
And so once again, I find that the claims of a connection between the sun and climate evaporate when they are examined closely.
Let me be clear about what I am saying and not saying here. I am NOT saying that the sun doesn’t affect the climate.
What I am saying is that I still haven’t found any convincing sign of the ~11-year sunspot cycle in any climate dataset, nor has anyone pointed out such a dataset. And without that, it’s very hard to believe that even smaller secular variations in solar strength can have a significant effect on the climate.
So, for what I hope will be the final time, let me put out the challenge once again. Where is the climate dataset that shows the ~11-year sunspot/magnetism/cosmic rays/solar wind cycle? Shaviv echoes many others when he claims that there is some unknown amplification mechanism that makes the effects “about 5 to 7 times larger than just those associated with the TSI variations” … however, I’m not seeing it. So where can we find this mystery ~11-year cycle?
Please use whatever kind of analysis you prefer to demonstrate the putative 11-year cycle—”folded” analysis as above, cross-correlation, wavelet analysis, whatever.
Regards,
w.
My Usual Request: If you disagree with someone, myself included, please QUOTE THE EXACT WORDS YOU DISAGREE WITH. This prevents many flavors of misunderstanding, and lets us all see just what it is that you think is incorrect.
Subject: This post is about the quest for the 11-year solar cycle. It is not about your pet theory about 19.8 year Jupiter/Saturn synoptic cycles. If you wish to write about them, this is not the place. Take it to Tallbloke’s Talkshop, they enjoy discussing those kinds of cycles. Here, I’m looking for the 11-year sunspot cycles in weather data, so let me ask you kindly to restrict your comments to subjects involving those cycles.
Data and Code: I’ve put the sunspot and HadISST annual data online, along with the R computer code, in a single zipped folder called “Shaviv Folder.zip“
Willis: “I’m interested in evidence, not some hypothetical possibility of the oceans acting as a multi-bandpass future. ”
You are interested in evidence that supports your conclusions, anything else, not so much.
http://wattsupwiththat.com/2014/06/06/sunspots-and-sea-surface-temperature/#comment-1656878
Even when it’s basically what you chose to do yourself.
Is a correlation of 0.25 at 10y and 22y less important now than when wrote the article?
From Willis Eschenbach on June 8, 2014 at 12:09 am:
For a straight-walled cup that’s 3″ deep and filled to the brim, the average kinetic energy per molecule would need be enough for a ballistic trajectory at least 1.5″ high, the average height to clear the edge.
And it would be quite a sight to see the coffee flash transform into a supercold solid powder, with much latent energy irretrievably radiated away, before crashing back down into the cup as a cooler liquid with perhaps some ice as the kinetic energy is reclaimed.
In another thought model, there would have to be enough thermal energy in the coffee to expand a (perfectly insulated) hot air balloon from room temperature to generate enough lift to suspend that mass of coffee.
I don’t see it happening. Unless you drink your coffee far hotter than I do. And far stronger too, possibly.
Dear Willis,
Here are a few answers.
“Mmmm … were I in your shoes, I don’t think I’d advertise that attitude. Sounds a whole lot like “My mind is made up, don’t bother me with facts”
Willis, when studying a dataset and looking for an effect (such as a solar/climate link), you can either detect at some significance or place an upper limit at some significance. If however the upper limit is above the signal I expect, it means that the dataset is irrelevant for proving or disproving the effect, which implies that “I couldn’t care less”, caring more about it would be a waist of time. This wouldn’t be the case if the upper limit was below the signal I expect (it would make me worry), or the detection at a high statistical significance (and it would make me happy).
Tide gauge records:
It should be apparent from reading Shaviv 2008 that the tide gauge data is the dataset having the solar signal detected with the highest statistical significance, so ignoring it is kind of missing the whole point of Shaviv 2008. Namely, the tide gauges prove that the there is a solar signal and you can consistently see it in the noisier SST and even noisier heat content.
As for the tide gauge data record itself, I didn’t collect it so no point in republishing it (it is not my habit of publishing other people’s stuff, especially after having being threatened once with a lawsuit). I downloaded it from a repository available to anyone with an internet connection. The stations I used are those chosen by Holgate (so that I cannot be accused in cherry picking). The only difference (and it is clearly explained in the the paper) is that I averaged the derivatives of the stations and not differentiated the average to get the sea level change rate. This way I avoid the spurious jumps that you get in years with stations added and removed, which otherwise contributes a lot of noise (and which people didn’t realize and therefore remove before). Anyone can redo what I did.
Folding the data:
If you properly folded the data, than my apologies. I assumed you didn’t because the folded graphs have “years” and not a phase for their x-axis (which is still problematic towards the end of the cycles, besides being misleading).
Cheers
Nir
Alex said on June 8, 2014 at 1:48 am:
Meh. It’s Willis talking math, and also cycles and somehow the solar system. Thus a practical guarantee the comments will be infested by Goodman holding court, trying to impress with his madz math skillz, seeing how many he can direct to his site to be flabbergasted by the blabbergasting, as he ruthlessly attacks all perceived inferiors with neither mercy nor understandable explanations.
When Goodman sets up a meeting of his Superior Geniuses Only club and hangs out the “No Ignorant Morons” sign, I know the chances of learning anything in the Comments is vanishingly small. A quick glance through is all they’re worth, and likely that will be wasted effort.
Gary Pearse says:
June 7, 2014 at 5:32 pm
/////////////////////////////
Garry
What Konrad and I were discussing is whether DWLWIR effectively heats the oceans. I don’t want to discuss that at depth since it is not directly germane to Willis’ present article.
That said, the issue is whether the heating of the oceans is entirely by solar, or does DWLWIR play a significant role? Konrad has performed an experiment that suggests that DWLWIR does not effectively heat the oceans. I was considering the position from a different perspective, namely real world conditions that are encountered every day over the oceans.
The issue is what is going on in say the 10metre atmosphere layer above the ocean, the pico layer of the ocean, the first few micron layer of the ocean, the first few millimetre layer,and say the first 10 metres of the ocean. this raises the issue is a photon just a photon, or does the place where the photon is absorbed play a material role.
In general according to the K&T energy budget cartoon, DWLWIR ad approximately twice the energy of solar. Given the absorption charactericis of SWIR in water (and bear in mind that sea water is not pure water), Solar IR is being absorbed in a volume represented by about the first 20 metres of water (some is absorbed at depths well below that). Given the absorption characteistics of LWIR in water, DWLWIR is absorbed within 10 microns, with 60% of all DWLWIR absorbed fully within just 4 microns.
Thus in broad terms as much energy that solar imparts into a volume of 20 metres of water is through DWLWIR being absorbed in just 4 microns (ie, 60% of all DWLWIR is fully absorbed in 4 microns and DWLWIR has twice as much energy as Solar SWIR – 60% of double is aprroximately the same in broad terms),
Solar SWIR does not boil away the oceans because all the energy is absorbed within a large volume, ie. a 20 metre layer. If the absorption characterics of SWIR was different such taht 80% of it was fully absorbed within just 4 microns (I have upscaled it to bring it in line with DWLWIR – K&T energy budget cartoon suggests DWLWIR is approximately twice that of Solar IR), the oceans would have boiled away long ago.
The issue is why if DWLWIR is heating the oceans, it does not lead to rapid and copious evaporation of the ocean? Potentially, there is so much energy being absored within the first 4 microns that it would give rise to about 14 to 18 metres of rainfall annually (which of course we do not have). So this raises the question whether the DWLWIR absorbed within the first 4 micron layer can be dissipated to depth before it would cause rapid and copious evaporation. It cannot be dissipated to depth by conduction since the first 4 microns is coller than the first few millimetres of the oceans. The temperature flux profile is upwards and hence energy absorbed in the first 4 microns cannot ‘swim ‘ against that ‘tide.’ The only other process suggested is ocean overturning. Willis in one of his comments explains this diurnal event. It is a slow mechanical process which could not dissipate energy faster than the rate of absorption of DWLWIR fuelling evaporation.
There are fundamental difficulties with the interaction iof DWLWIR and the oceans. The fact that we do not see copious amounts of evaporation provides some suppport for the view that DWLWIR does not effectively heat the oceans. Konrad’s expiriment suggest that it does not. I do not challenege his expiriment, but additionally postulate that there are other processes involved in the real world conditions encountered by planet Earth that may mean that not all the DWLWIR actually finds its way into the ocean, but instead much of it remains in the atmosphere above the oceans, never entering them.
Why is this relevant? well realy as to whether one accpets the gross energy flow budget, or the net energy flow budeget at the one that best describes real world conditions encountered on planet Earth.
.
KDK “Thus a practical guarantee the comments will be infested by Goodman holding court, trying to impress with his madz math skillz, seeing how many he can direct to his site to be flabbergasted by the blabbergasting”
I’m not trying to redirect anyone anywhere, I use my climategrog site as pastebin because this site does not let commenters insert graphics. I used to use tinypic until they put so much crap in the way, so I starteda WordPress site where it is at least clean and legible. This also means I can add a description of how plots are derived and what the data is.
Since I seem to be almost the only one apart from Willis actually prepared to do anything other than talk and handwave, that may appear “superior” to some. Remind me of what you have ever contributed to scientific analysis here.
John Reistroffer says:
June 7, 2014 at 11:58 pm
Thanks, John. Yes, there are stacks of people who have made all kinds of claims about finding signals from both the duration and the strength of the sunspots in the climate datasets. Unfortunately, I haven’t found one yet that withstands serious examination.
I went to take a look at the first result on your list, Christensen and Lassen. I find that they have used an 11-year running mean filter on the sunspot data, a truly destructive choice of filters. Not only is it a boxcar filter, with all that implies. The choice of an 11-year filter in a sunspot dataset with a strong signal that varies both above and below 11 years munges the data horribly. See my post called “Sunny Spots Along The Parana River” for a full discussion of the problems caused by this kind of filtering. The short version is that it turns your sunspot data into garbage, and any conclusions based on the procedure are useless.
One more study looked at … how about you guys crank up your cranial engines and actually think critically about these studies before dumping them on my plate? It just makes you look foolish when you cite junk.
In any case, what I’m looking for is not a study. It is a dataset and a corresponding procedure for extracting the 11-year cycle. I’m not the guy propounding this damn theory that there is an 11-year cycle out there in the climate data. If you want people to believe it, we need evidence. Not just pointing at yet another random theory in yet another random study. Half of all scientific studies are crap, and I’m skeptical about the other half. The fact that some claim got printed means nothing to me. Actual evidence of a dataset and a procedure that you know works is what I’m waiting for people to show me.
Thanks anyhow,
w.
michaelwiseguy says:
June 7, 2014 at 9:41 pm
You didn’t get what I said, likely my lack of clarity. The south pole receives no more or less solar energy than the north pole. Yes, the instantaneous insolation is lowest in the antarctic winter. But that is exactly compensated for by the difference in speed. The earth spends less time in the warmer zone and more time in the cooler zone. Both poles receive exactly the same amount of heat over the year. It is guaranteed by the laws of physics.
As a result, there is no “un-even solar heating of the polar regions due to the distance from the Sun”.It doesn’t exist. Both poles get the same amount of energy. The same is true of the entire southern hemisphere. It gets the same total amount of energy from the sun as does the northern hemisphere, despite the ellipticity of the orbit. Strange, but true.
w.
Konrad:
Proof does not exist? I have been running these experiments since 2011 –
http://i47.tinypic.com/694203.jpg
I am not lying to you. I am not a climastrologist. I work in engineering.
Just conduct the experiment as shown. Remember –
“Tell me I’ll forget. Show me I’ll understand. Let me do it I will KNOW.”
Type is cheap. Do the experiments and you will know. I have little interest in just publishing my results. I am more interested in others replicating experiments.
=====
If you work in engineering, you know that your jpg diagram of two black boxes in no way represents “proof” , so why is that all you posted? It’s nonsense.
You equally know that comments like ” I can assure you” or “I am not a climastrologist” carry ZERO weight either.
Since no one else seems to have done this and you “have little interest” in publishing any results you may ( or may not ) have, that leaves us where were with NO proof. So your earlier claim that there is proof is unfounded. I did not call that lying, That is your choice of words.
If you have something lets see it. If you don’t, stop posting false claims that proof exists when it does not.
My impression (which could obviously be mistaken) is that you failed to get any credible results or never even got as far as constructing the experiment. You hope that someone else will do the donkey work and “reproduce” what you failed to produce. You will presumably then claim credit for the “original” work that “proved” you can’t heat ventilated water with IR.
Now I’d be very happy if I am mistaken and you get motivated to publish if you have some credible results. Thus far, it’s getting a bit like Murray Salsby syndrome.
From Greg Goodman on June 8, 2014 at 3:54 am:
And that makes it official. If it ain’t Willis waxing nostalgic about atolls or fishing or building pergolas that are proofed against climate change, his work just ain’t worth reading anymore since Goodman might be there. If you don’t want to see manure excreting, stop looking at bungholes.
Steven Mosher says: June 8, 2014 at 12:24 am
Willis ….“However, I think at this point I’ve heard every conceivable excuse for not being able to find the 11-year signal …”
Mosher …… I think it highlights what one gets when one starts to look at data with no real “idea” of how the system works.
__________________________________
Willis, Mosher,
What boggles my mind, is why nobody has seriously looked into this before, as a part of all the Climate slush-funds that are sloshing their way around the world. Why has nobody fully analysed all the possibilities? And why is it left to an amateur to debunk the many absurd claims that have been made (no offense, Willis, but I don’t think you receive a stipend from an educational establishment for your endeavors).
Clearly there are cycles in the climate record, with the 60-odd year cycle being perhaps the most obvious. So why do we not know what the primary driver of that cycle is? And saying it is the PDO is not an answer, for what drives the PDO?
Personally I think this cycle has to be something other than a natural resonance that just ‘happens’. Something is driving it. I feel the Sun probably is a primary influence, but how? Surely, these are the questions that Mann et all should be investigating and answering, rather than just scaring everyone with absurd extrapolations of temperature and disasters. How can the IPCC say they understand the Earth’s climate out to the next 150 years, if they do not understand the rather obvious 60-year climate cycle?
And sorry, Willis, please don’t ask me to do the math first – I would not know a Fornier Transformation from a Ferret. But surely there is someone in the IPCC or in academia that does. Or, failing that, why not just pay Willis to look. Geeez, in terms of the amount of money wasted on climate ‘science’, giving Willis a stipend and an office would be a drop in the (warming or cooling) ocean.
Ralph
Willis, the buoy’s don’t measure the surface temperature, the skin where the evaporation and radiation actually takes place. They measure the water temperature just under the surface which clearly shows the night vs day pattern, just as the air temperature above the surface does too.
Do you know of a source that actually measures the surface temperature? I can also confidently predict (postdict?) that if you match the wind speeds to your buoys temperature data that the wind speed dropped allowing the temperature to rise.
Also if the wind speed isn’t high enough the surface temperature will show the night vs day pattern, if it is clear skies. Pointing the IR gun skyward, the bottom of the darker clouds is generally in the 76˚ F range while the clear sky areas is around 34˚ F at night and in the 40˚ F range during the day. (I use Fahrenheit because it is a finer scale, whether it is more accurate or not is another thing)
I am extremely familiar with the buoy data, as well as all of the weather forecast models, Chris Parker, satellite data, etc. etc. As the Admiral gets very upset if the sailing conditions are not to her liking and my number one goal in life is to keep the Admiral happy.
Willis: “In any case, what I’m looking for is not a study. It is a dataset and a corresponding procedure for extracting the 11-year cycle. ”
Since we are all agreed that the “solar cycle” is it not a single fixed period no one is going to be able to extract such thing even if there is a solar signal to be found. It has to be linked directly to some proxy of solar “activity”.
You chose cross-correlation to examine that, which seems a very sensible first step. Unfortunately you seem to have chosen annual averages. one thing for which Shaviv2008 could be criticised for. That appears to degrade the signal to a level you regard as non existent.
When I do the same thing with monthly data and find what your own criteria suggest should be significant, you steadfastly ignore the result and discuss anything else instead.
http://climategrog.wordpress.com/?attachment_id=959
Strange.
Greg says: ” Remind me of what you have ever contributed to scientific analysis here.”
KDK replies:
http://wattsupwiththat.com/2014/06/06/sunspots-and-sea-surface-temperature/#comment-1657029
Hmm, just as I thought: nothing. Just wanted to check. Thanks for the confirmation.
@ur momisugly Greg Goodman on June 8, 2014 at 4:40 am:
It’s okay, Greg. I forgive you.
P.S.
As a matter of interest, Willis, will your many analyses of the data show up a variable cycle, like the Sunspot Cycle – which appears to vary between 10 and 14 years? Does a FT of the raw SSN data, for example, show a prominent peak at 11 or so years?
So can we easily detect variable cycle-lengths in the temperature record? Is this why longer cycle lengths are more prominent in the temperature data, because the shorter cycle lengths are variable and jumbled? Is the 60-year PDO climate cycle a culmination of many variable smaller cycles of about 12 years in duration?
And please don’t ask me for the math. As mentioned previously, I would not know a Fornier Transformation from a Ferret. But I am interested in the results.
Ralph
Willy
I have followed with interest your efforts to detect the 11-year solar cycle in meteorological data and the lack of evidence for any correlation. So I am wondering whether longer-term variations in solar activity have had any effect. The 14C and 10Be radioactive isotopes provide good proxies for solar activity. Their anticorrelation with the historic temperature swings of the Little Ice Age and the Medieval Warm Period may not be long enough for any solid conclusion. However, Bond et al 2001, Science 294, 2130 found good correlation over 12 000 years in cores of layered ocean sediments. In another example Neff et al 2001 Nature, 411, 290 found excellent correlation of 14C with the delta18O proxy for monsoon rainfall in a stalagmite from a cave in Oman from 6200 to 9600 years ago. I would welcome your views on such studies. Even though we do not know the mechanism, how good is the evidence for a solar contibution to climate change?
From ralfellis on June 8, 2014 at 5:10 am:
WoodForTrees has the SIDC SSN info:
http://woodfortrees.org/plot/sidc-ssn/from:1750/to:2014
It can do a FT for you. Check the Help link for commands and tips.
http://woodfortrees.org/plot/sidc-ssn/from:1750/to:2014/fourier/from:1/to:50/magnitude
Over the record, it’s doing a FT, limited from 1 year up to 50 years. “Magnitude” is something done because that particular FT method uses complex numbers (they have real and imaginary components), there are others that don’t. “Magnitude” joins the imaginary portion with the real part so the info isn’t lost because the imaginaries aren’t being displayed.
9 and 12 years are stronger than 11 years over the full record. The 24 years makes sense because the poles flip during the 11+ year cycle, so it takes 22++ years for the full Hale cycle.
You now have a tool you can play with, with the datasets it has access to. Go forth and explore.
I read that some indigenous peoples of Alaska are losing their traditional fishing grounds because of AGW driven permafrost melting causing the shoreline to subside into the ocean.
I pulled up a satellite image of an unrelated coastline, determined from the well-accepted Beysian/Confusium correlation applied to beach gravel distributions that 1,256,342 years ago the average tidal fluctuation was 3.25 mm per parsec less than today. And, no, I did not forget to regress the dilithium cross-compounded monolith by the well established factor of wtf^2+(hoo-nos). Sheesh, anybody who knows anything knows how to do that.
Applying statistical slicing, dicing and leveling it appears highly likely that a large scrap tire dump outside Podunk, OK is the only possible source of this frightening threat to humanity and the only way to mitigate this end to life as we know it is to redistribute these tires via drone to each & every household using gender neutral genetic marker addresses.
My data, software, and methods are in a sealed and booby trapped canister at location 75.225 degrees W, 0.000 degrees N. Prove me wrong, I dare you, otherwise my work is unimpeachable.
“to each & every household using gender neutral genetic marker addresses.”
What about those with trans-gender addresses?! Sorry not P.C. : recommend rejection.
Kakada.
(((So can we easily detect variable cycle-lengths in the temperature record?)))
You now have a tool you can play with, with the datasets it has access to. Go forth and explore.
___________________________________
Thanks, Kakada, that is most interesting. But I think I will need a Willy to explain what it means.
http://woodfortrees.org/plot/sidc-ssn/from:1750/to:2014/fourier/from:1/to:50/magnitude
From what I see, the 11 year sunspot cycle is missing in this Fourier Transformation of SSNs, while the double cycle is very prominent. And this is despite the approx 11 year cycle being the most prominent when visually looking at a SSN graph. And yet the triple or quadruple cycle is missing from the Fourier. Why does a double cycle display, but not the single, triple or quadruple?
So could the sunspot cycle be visible in the climate record as a quintuple (60-year) resonant or heterodyning multiple of the smaller (approx 12-year) sunspot cycle??
Ralph
Ralph “From what I see, the 11 year sunspot cycle is missing in this Fourier Transformation of SSNs, while the double cycle is very prominent.”
Sorry Ralph but WTF.org is a crock for anything more than fitting inappropriate linear trends to non-linear data and distorting it with crappy running means. It is totally inappropriate to offer FT as a click-go-tool like that. Also it is so badly present that I, having done plenty, can’t relate to the crap way the result is plotted.
FT does require quite a bit of understanding before you can make any sense of what it does. It’s well worth looking into but splashing about on WTF.org will be totally fruitless and frustrating. Don’t even try.
Willis’ “slow FT” code is available to play with and is probably more intuitive that a true FT. Look at this recent threads for code, examples and ample discussion by lots of people with varying degrees of knowledge of the subject.
You will also find answers to some of the questions you asked above, SSN contains three close frequencies around 11 years plus circa 22y. This is why it is a shape-shifter. You will find lots of detail on that.
No point in repeating the highlights here, suggest you go and read the threads. As always, more chaff than wheat so take coffee and biscuits with you. 😉
“Is the 60-year PDO climate cycle a culmination of many variable smaller cycles of about 12 years in duration?”
IMO, it is likely that the various long period climate “oscillations” are the result of interactions of lunar and solar influences with periods around 9y and 10-11y respectively. But climate is complex and the last 30y have largely been wasted attempting to prove a foregone conclusion to the exclusion of all else.
Perhaps in another ten years a better understanding of climate as more than a single variable problem will have emerged.
Once again I must be failing terribly at what WAS, I thought, the scientific method. First observe. If it cannot be observed it is likely to be such a small affect it can safely be ignored. That is the case with TSI. It does indeed have the potential to cause a cyclic change in temperature and can be mathematically calculated. But it can be readily overcome by much more powerful intrinsic variability, thus can be safely ignored, buried as it is in noise. Many here on either side of the solar debate advocate that position: ignore TSI and yes Earth has powerful intrinsic-driven variability (and think so correctly IMO). Yet, readily ignoring a mechanized plausible mathematically defensible solar related addition to Earth’s temperature while admitting to powerful intrinsic variability does not seem to inform them about ignoring other far less powerful solar parameters. The logic escapes me that a small little mutt can be ignored but lets focus in on the hair on a flea’s ass. Even more, let’s focus in on that after we have split it in two and statistically curled it.
From ralfellis on June 8, 2014 at 7:16 am:
Because the “11 year cycle” is only a half cycle. At the bottom of an 11-yr portion, the magnetic poles flip, which is actually a drawn-out thing where they might not flip simultaneously. The Sun is a messy place.
The Sun has to pass through the bottom of another 11-yr portion for the magnetic poles to return to the previous orientation.
So what you see are Hale cycles, the flipping and the return together, which shows up as a 24-yr cycle.
The “triple” would be three half-cycles. The “quadruple” would only be the second harmonic (Frequency * 2) of the Hale cycle. So neither should show up much. Although given the variations in the “11 year” length, a “triple” could be anywhere from 28 to 36 years long.
Of course, things change when you throw away some data. I’m going to start at 1880, when the GISTEMP dataset starts, and throw away the first 130 years, half the data.
http://woodfortrees.org/plot/sidc-ssn/from:1880/to:2014/fourier/from:1/to:50/magnitude
Now we’re down to only five full Hale cycles, using the 24-yr amount. Before we might have had eleven, or ten. The 24-yr high peak is gone. But there are many iterations of the “11 year cycle” remaining, shown peaking at 13 but still strong at 12 and 11 years.
Let’s look. We’ll do a FT of GISTEMP too. I’m using the “normalise” function to rescale both the same for easy comparison, usable as we’re just matching peaks.
Also with only 134 years of data, looking for a 60 year cycle is a stretch, I’ll bump it out up to only 65 years.
http://woodfortrees.org/plot/sidc-ssn/from:1880/to:2014/fourier/from:1/to:65/magnitude/normalise/plot/gistemp/from:1880/to:2014/fourier/from:1/to:65/magnitude/normalise
Where is that “60 year” cycle? Can you really claim there is anything between the two that really matches up, except perhaps the Hale cycle at 24 years, which might be spurious in the temperature data?