Guest Post by Willis Eschenbach
Over at Pierre Gosselin’s site, NoTricksZone, he’s trumpeting the fact that there are a bunch of new papers showing a solar effect on the climate. The headline is Already 23 Papers Supporting Sun As Major Climate Factor In 2015 …Burgeoning Evidence No Longer Dismissible!, complete with exclamation mark … sigh. Another person who thinks that because a paper is published in a scientific journal it’s not “dismissible” … skeptics of all people should know better than that. In any case, I figured I should at least take a look, and so as not to pick favorites, I grabbed the first paper in Pierre’s list.
Turns out that the very first paper was one of two discussed back in January here on WUWT. I didn’t see the WUWT post at the time, so it’s now my sad duty to pick up my shovel, put on my hip-boots, and wade into the mire.
Both papers are by David Douglas and Robert Knox. David has published occasionally here on WUWT. So I grabbed that first paper, yclept “The Sun is the climate pacemaker I. Equatorial Pacific Ocean temperatures”, available here.
I must confess that their Abstract left me scratching my head … it says:
Equatorial Pacific Ocean temperature time series data contain segments showing both a phase-locked annual signal and a phase-locked signal of period two years or three years, both locked to the annual solar cycle. Three such segments are observed between 1990 and 2014. It is asserted that these are caused by a solar forcing at a frequency of 1.0 cycle/yr. These periodic features are also found in global climate data (following paper). The analysis makes use of a twelve-month filter that cleanly separates seasonal effects from data. This is found to be significant for understanding the El Niño/La Niña phenomenon.
They claim that there are climate phenomena that are “locked to the annual solar cycle” … say what? Almost every climate phenomenon I know of is locked to the annual solar cycle with some variable amount of delay. How is that possibly news? I didn’t get that when I read it, and re-reading the paper hasn’t helped much.
Upon first reading, I thought that the secret might lie in the fact that they say they have a filter that “cleanly separates seasonal effects from data”. So maybe they’re not talking about just being phase-locked to the plain old solar cycle … because they’re looking at the data after the seasonal effects have been removed in some special way.
Now I have some interest in filters, so I looked to see what they were talking about. The normal way to remove the “seasonality”, the month-by-month changes in temperature, is to take monthly averages and subtract them from the data. These monthly averages for January to December are usually called the “climatology” for the region. However, the authors don’t like that process for some reason. They describe their own procedure as follows:
2.2. Methods: precise separation of high- and low-frequency effects
Studies of many geophysical phenomena start with a parent signal G0, such as a temperature or wind speed record, containing a component of interest mixed in with a seasonal component at frequencies of 1.0 cycle/yr and its harmonics. The component of interest might show ENSO effects with multi-year periodicity. An important task is to separate the seasonal component from G0 to obtain the one of interest. A moving average is one of the methods used to make this separation. Such a filter of length one year, which we denote by an operator F, is the most precise for seasonal components, as Douglass  has shown in a study of SST3.4 (the parent signal).
OK, they’re using a 12-month moving average filter, often called a “boxcar” filter, to remove the climatology. Not a choice I’d make, because it messes with the data at other cycle lengths. Let me demonstrate this problem in two ways. First, here is the underlying data (gray line in background), along with same data with the climatology (monthly averages) removed (blue line with dots), and finally in pale red, the results using their 12-month moving average “boxcar” filter.
Figure 1. The monthly sea surface temperatures for the “Nino 3.4″ region, which extends 5° north and south of the equator from 120°W to 170°W in the tropical Pacific Ocean. The gray line in the background is the original data. The blue line with dots shows the normal method of removing the seasonal variations, by subtracting the monthly averages (the “climatology”) from the original data. The pale red line shows the result of applying their 12-month moving average “boxcar” filter to the original data.
What is clear from Figure 1 is that the 12-month boxcar filter (red line) is removing much more of the variation in the data than is the normal method of subtracting the climatology (blue line). The red line has smoothed away much of the short-term fluctuation in the original data. And while this result of theirs is of interest, it is not accurate to claim as they do that:
Such a filter of length one year, which we denote by an operator F, is the most precise for seasonal components …
I said I would show the boxcar filter problem in two ways. Here’s the other way to illustrate the difference between climatology and boxcar filter, using a periodogram. A periodogram shows the strengths of the various cycles that make up the signal. Here are the periodograms of the three different datasets used in Figure 1:
Figure 2. Periodogram showing the cycle strengths of the original SST3.4 data (gray, only visible at cycles of one year or less), the original data with climatology removed (blue), and the 12-month moving average “boxcar” filter of the original data favored by the authors (red).
As one might expect, the original data (gray line) shows a strong cycle at both one year and six months (0.5 years). The precision of the climatology method (blue line) is shown by the fact that it only affects the data at frequencies of one year or less. Above that, you can’t even see the original data (gray line) because other than a slight difference at 13 months, the original gray line is hidden by the blue line, meaning that subtracting the climatology has not affected the cycles of other lengths at all.
Now contrast that with the effect of their filter (red line). In addition to doing what the authors desired, that is to say removing the one year and six month peaks, it has an unwanted side-effect. It has greatly reduced the strength of the cycles between one year and six years or so as well. This is the same thing we saw in Figure 1, where their boxcar filter (red line) was smooth and did NOT show the short-term cycles.
Setting that difficulty aside, let me move on to what they do with their boxcar filtered data. Their main scientific claim is that even though they’ve removed the annual variations with their boxcar filter, there are still some time periods that show evidence of either a two-year or a three-year cycle. They call these intervals “phase-locked” with the sun. Here’s their graph detailing those periods:
Figure 3. This shows Figure 3a from their paper. The thick black lines at the bottom indicate what they call “previously reported climate shifts”, although they give no citation for where they were “previously reported”. My guess is that these “climate shifts” were “previously reported” by the authors themselves, but then I’m a skeptical fellow. ORIGINAL CAPTION: Fig. 3. a. Low frequency index aSST3.4 (red) and NOAA anomaly index Nino3.4 generated by the climatology method (blue).
Before discussing this Figure, a momentary digression. Here is a giant red flag from their paper:
This study considers only data from the period January 1990 through December 2013.
They are not using all of the data. The dataset is already short, only from January of 1982 through December 2013 at the time of their writing the study, or a total of 32 years of observations. Despite that, they’ve thrown away no less than eight years of the data, a full quarter of the available information … why? Unfortunately, the only discussion of that question I could find in their paper is the short sentence I quoted above.
Now, when someone does that, my urban legend alarm goes off. It generally means that the data are being stubborn and uncooperative … but I digress, back to Figure 3.
Looking at that Figure, I gotta say … whaaaa? This is their finding that justified publication in a scientific journal? This is the sum total of the first paper of the widely-hyped TWENTY-THREE NEW PAPERS ON SOLAR blah blah blah? This is it?
If you don’t see why I am so incredulous, there are several reasons. The first reason is the length of the time periods. Let’s take a look at interval #11 at the right of their Figure 3. It covers 5 3/4 years … and they are claiming a 3 year “phase-locked” cycle exists there? That’s not even two full cycle lengths! In climate science you need to have four cycle lengths to say whether a cycle is real and persistent or not … and often even that is sometimes not enough. And here, they are declaring a cycle is alive and well on the basis of not even two full cycles of data. This is meaningless.
And the same is true for time intervals #9 and #10 above. Neither of them are even three cycles in length. Declaring the existence of a “phase-locked” interval on that basis is foundation-free.
Second, what did they expect? If you have a complex signal like the SST3.4, it has cycles of a variety of lengths going on in it. Take a look at the periodogram in Figure 2 above. The original data has all kinds of cycle lengths which have some strength even after removing the annual signal by either method. So OF COURSE there are sections which have a stronger two-year or a three-year cycle in them, just as there are sections with stronger or weaker 2.5 year cycles in them.
Third, they make much of the fact that the cycles they’ve found are exact-year periods. Yes, those exact-year cycles are there, but per the periodogram, they’d do better by looking for cycles at 2.5 years, 3.75 years, and 5.5 years …
Here’s the problem. No matter what length cycle you look for … you’ll find it. So their claim that the periods are locked or related to the sun are nonsense—they’ve only looked at sun-related (exact-year) timespans, and surprise, that is what they have found.
Having digested all of that, I had to ask … so what?
Seriously, so what? What does one get by rooting through a pile of cycles and choosing some to focus on?
Well, here’s their answer to the question about why all this matters:
This study confirms the results of  that some of the largest maxima/minima in the oscillations of the phase-locked state correspond to well-known El Niños/La Niñas. For example, the sequence 1996 La Niña – 1997/98 El Niño – 1999 La Niña corresponds to a minimum–maximum–minimum portion of phase-locked segment #9.
Before I get into El Ninos, this quote brings up an issue that has bugged me throughout, similar to the issue of their omitting eight years of data … where are the “phase-locked segments” numbers #1 through #8? How come they didn’t show them or say one word about them? And since there is eight years of missing data, it doesn’t seem possible that the “phase-locked segments” #1 through #8 could be there. In any case, one rule that has rarely failed me, in climate science as in life, is that when a man hides something … it means he’s got something to hide. But again I digress … back to the El Ninos.
Their claim is that some of the largest maxima and minima in the El Nino 3.4 index correspond with El Ninos and La Ninas … again, I was dumbfounded. Large maxima in the El Nino 3.4 region correspond with large El Ninos? Who would have guessed? Why do the authors imagine that it’s called the “Nino3.4” region?
In any case, given their claims above about El Ninos, it appears that the scientific value of the 2-year and 3-year so-called “phase-locked sections” is to understand and thus better predict El Ninos. And to be sure, new theories can indeed have value if they can make testable predictions, regardless of how outré their claims or explanations might seem. Sooo … here is their daring prediction based on their work:
The climate system is presently (June 2014) in a phase-locked state of periodicity 3 years. This state, which began in 2008, contains a maximum (El Niño) at about 2010 followed by a minimum (La Niña) followed by a maximum (weak El Niño at about 2013). If the climate system remains in this phase-locked state, the next maximum will not occur until about 2016 – i.e., no El Niño before that date. On the other hand, if a maximum occurs before then, it will signal the end of the phase-locked segment (and therefore a climate shift).
I gotta admit, I lost the plot entirely when I read that. If the climate system stays “phase-locked” it means an El Nino at the next maximum, unless no El Nino occurs at the next maximum, in which case it means a climate shift.
Given that the data they are using is SSTs of the the Nino 3.4 region, and given that El Ninos are defined inter alia by maxima in the sea surface temperature anomalies in the El Nino regions … I don’t even know what that prediction means. The only thing I can compare it to is Will Rogers’ unbeatable formula for making money in the stock market:
Buy a stock, and when it goes up, sell it. And if it doesn’t go up, don’t buy it.
Onwards to their conclusions, I can’t resist one more quote:
6. Conclusions and summary
Phase-locked sequences are found in Pacific Ocean SST3.4 temperature data during the periods 1991–1999, 2002–2008 and in 2009–2013. These three sequences apparently being separated by climate shifts. It is asserted that the associated climate system is driven by a forcing of solar origin that has two manifestations: (1) A direct phase-locked response to what is identified as a solar forcing at a frequency of 1.0 cycle/yr for the whole time series;
I couldn’t make it to the second “manifestation”, I was laughing so hard. It is boldly “asserted” that the temperature of the Pacific Ocean is “phase-locked” to “what is identified” as “a forcing of solar origin”??? You mean that the ocean temperature follows the sun? Who would have guessed? Who was the genius that first identified that it was “a forcing of solar origin”? That definitely proves that the sun has an effect on the climate, all right, no gainsaying that …
All kidding aside, let me put something on the table. First, it’s obvious that the sun affects the climate. Without the sun, we’d be pretty cold. And yes, according to this paper the temperature has what is usually called an “annual cycle” but which they refer to as a “phase-locked response to what is identified as a solar forcing at a frequency of 1.0 cycle/yr” … However, related to cycles longer than one year, things get murky pretty fast. In particular, consider the long-time hunt for some sign of the ~11-year solar sunspot-related cycle on the climate.
We humanoids have been looking for a definite clear effect on the climate that could be attributed to the solar variations associated with the sunspot cycle ever since William Herschel made his failed prediction (see below) about sunspots and wheat prices a couple of centuries ago. If such a clear definite effect had ever been demonstrated, we wouldn’t be still having this discussion. After hundreds and hundreds of people starting with Herschel and up to and including myself and others have looked over a total period of two centuries for evidence of such an effect, one thing is clear:
If something associated with the ~11-year sunspot cycle is having an effect on the climate, it is a very small effect, otherwise it would have been both identified and verified beyond question years ago.
At this point, the hunt for such evidence has become so obsessive that I was seriously presented with a paper that the commenter assured me clearly demonstrated that something associated with the ~11 year sunspot cycles was indeed having a measurable effect on the climate. It turned out the that evidence was in the form of tree ring records … tree ring records from one single core from one single tree in Chile.
One Chilean tree! That’s how desperate some folks are to have their ideas validated … and how desperate the scientific journals are for things to publish.
Now, given the number of One Chilean Tree papers published each year, including this paper discussed above, there’s no way that I could possibly deconstruct them all. First off I have to read and understand their paper. Then I have to go get the data they used and replicate their study, as I did above for this study. I have to do my own analyses until I’m clear where they’ve gone off the rails. Then I have to produce the graphics, which better be error-free, and write the paper, which hopefully is error-free or I will be properly and quickly (and fortunately) informed of my mistake(s).
Finally, I have to upload the paper to the web, upload all the graphics, connect up all the links, tag it and categorize it, spell-check it, and proof-read the preview to make sure it’s all correct. Oh, and pick the featured image, can’t forget that. From your side it just magically appears on your screen … on my side, each one is a pile of work.
So I’m declaring right now, I’m not touching the other 22 papers listed by Pierre. At this point, the onus is on you. I’m just one guy, no graduate students or associates, I can’t stem the flood of Chilean trees. So … if you think that something associated with the sunspot cycle (TSI, EUV, solar wind, GCRs, heliomagnetic field, pick your poison) is having an effect down here at the surface of the earth where we live, and you think you have the scientific paper that conclusively demonstrates it, then you are welcome to send me TWO LINKS:
• A link to a non-paywalled version of the paper. I’m not paying $37 to read about another Chilean tree.
• A link to the exact dataset(s) used by the authors in their study.
Don’t bother me with data dumps of five or twenty-three papers, not interested. I want the one paper that YOU think is the best, second place doesn’t interest me. I won’t guarantee to write about whatever paper it is, but I will write about it if the data and the analysis stands up. Remember that one link is not sufficient. I need a link to both the non-paywalled paper and to the data they used. Please, no papers about solar effects on the thermosphere or the Van Allen belts, read my request again.
Best to all,
PS—In these parlous times, if you disagree with someone (unlikely, I know, but it happens), please quote the EXACT WORDS YOU DISAGREE WITH. This allows all of us to know both who you are addressing, and what specifically what you are objecting to.
HERSCHEL: Before you get all steamed up and start yelling at me about how you know for a fact that the astronomer William Herschel proved that wheat prices varied with the sunspots, read On the insignificance of Herschel’s sunspot correlation, published in Geophysical Research Letters. I’d written a post on the subject a couple years ago that came to the same conclusion, but I never published it because I came across that link, and the author did it so much better. If you have specific problems with that paper, feel free to list them. While you are at it, you might profitably contemplate the concept of “scientific urban legends” …
FURTHER READING: If you have not done so, you might enjoy reading my previous posts on the sunspot-cycle question …
Riding A Mathemagical Solarcycle 2014-01-22
Among the papers in the Copernicus Special Issue of Pattern Recognition in Physics we find a paper from R. J. Salvador in which he says he has developed A mathematical model of the sunspot cycle for the past 1000 yr. Setting aside the difficulties of verification of sunspot numbers for…
Congenital Cyclomania Redux 2013-07-23
Well, I wasn’t going to mention this paper, but it seems to be getting some play in the blogosphere. Our friend Nicola Scafetta is back again, this time with a paper called “Solar and planetary oscillation control on climate change: hind-cast, forecast and a comparison with the CMIP5 GCMs”. He’s…
Cycles Without The Mania 2013-07-29
Are there cycles in the sun and its associated electromagnetic phenomena? Assuredly. What are the lengths of the cycles? Well, there’s the question. In the process of writing my recent post about cyclomania, I came across a very interesting paper entitled “Correlation Between the Sunspot Number, the Total Solar Irradiance,…
Sunspots and Sea Level 2014-01-21
I came across a curious graph and claim today in a peer-reviewed scientific paper. Here’s the graph relating sunspots and the change in sea level: And here is the claim about the graph: Sea level change and solar activity A stronger effect related to solar cycles is seen in Fig.…
Sunny Spots Along the Parana River 2014-01-25
In a comment on a recent post, I was pointed to a study making the following surprising claim: Here, we analyze the stream flow of one of the largest rivers in the world, the Parana ́ in southeastern South America. For the last century, we find a strong correlation with…
There’s a new post up by Usoskin et al. entitled “Evidence for distinct modes of solar activity”. To their credit, they’ve archived their data, it’s available here. Figure 1 shows their reconstructed decadal averages of sunspot numbers for the last three thousand years, from their paper: Figure 1. The results…
Solar Periodicity 2014-04-10
I was pointed to a 2010 post by Dr. Roy Spencer over at his always interesting blog. In it, he says that he can show a relationship between total solar irradiance (TSI) and the HadCRUT3 global surface temperature anomalies. TSI is the strength of the sun’s energy at a specified distance…
The Tip of the Gleissberg 2014-05-17
A look at Gleissberg’s famous solar cycle reveals that it is constructed from some dubious signal analysis methods. This purported 80-year “Gleissberg cycle” in the sunspot numbers has excited much interest since Gleissberg’s original work. However, the claimed length of the cycle has varied widely.
Cosmic Rays, Sunspots, and Beryllium 2014-04-13
In investigations of the past history of cosmic rays, the deposition rates (flux rates) of the beryllium isotope 10Be are often used as a proxy for the amount of cosmic rays. This is because 10Be is produced, inter alia, by cosmic rays in the atmosphere. Being a congenitally inquisitive type…
ABSTRACT: Slow Fourier Transform (SFT) periodograms reveal the strength of the cycles in the full sunspot dataset (n=314), in the sunspot cycle maxima data alone (n=28), and the sunspot cycle maxima after they have been “secularly smoothed” using the method of Gleissberg (n = 24). In all three datasets, there…
It’s The Evidence, Stupid! 2014-05-24
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…
Sunspots and Sea Surface Temperature 2014-06-06
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…
Maunder and Dalton Sunspot Minima 2014-06-23
In a recent interchange over at Joanne Nova’s always interesting blog, I’d said that the slow changes in the sun have little effect on temperature. Someone asked me, well, what about the cold temperatures during the Maunder and Dalton sunspot minima? And I thought … hey, what about them? I…
Splicing Clouds 2014-11-01
So once again, I have donned my Don Quijote armor and continued my quest for a ~11-year sunspot-related solar signal in some surface weather dataset. My plan for the quest has been simple. It is based on the fact that all of the phenomena commonly credited with affecting the temperature,…
Volcanoes and Sunspots 2015-02-09
I keep reading how sunspots are supposed to affect volcanoes. In the comments to my last post, Tides, Earthquakes, and Volcanoes, someone approvingly quoted a volcano researcher who had looked at eleven eruptions of a particular type and stated: …. Nine of the 11 events occurred during the solar inactive phase…
Early Sunspots and Volcanoes 2015-02-10
Well, as often happens I started out in one direction and then I got sidetractored … I wanted to respond to Michele Casati’s claim in the comments of my last post. His claim was that if we include the Maunder Minimum in the 1600’s, it’s clear that volcanoes with a…
Sunspots and Norwegian Child Mortality 2015-03-07
In January there was a study published by The Royal Society entitled “Solar activity at birth predicted infant survival and women’s fertility in historical Norway”, available here. It claimed that in Norway in the 1700s and 1800s the solar activity at birth affected a child’s survival chances. As you might imagine, this…