23! New! Papers!

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 [5] 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.

sst3.4 with two methods seasonal variationsFigure 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:

periodogram sst3.4 climatology boxcarFigure 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:

douglass figure 3aFigure 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:

2.1. Data

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 [1] 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,

w.

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…

Usoskin Et Al. Discover A New Class of Sunspots 2014-02-22

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…

The Effect of Gleissberg’s “Secular Smoothing” 2014-05-19

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…

Advertisements

295 thoughts on “23! New! Papers!

  1. From Gosselin’s site:

    The sun drives the climate: Proof of the 90 and 200-year cycles in the earth’s climate history

    By Dr. Sebastian Lüning and Prof. Fritz Vahrenholt
    (Translated/edited by P Gosselin)

    Solar activity fluctuates very much in cycles, among them the Gleissberg Cycle over 90 years, plus or minus 30 years. In March 2015 a study by Orgutsov et al. appeared in the journal Advances in Space Research which discovered the solar Gleissberg Cycle during the growth period of the northern hemisphere over the past 450 years. The authors suspected a solar impact on temperatures on plant growth. The abstract:

    Evidence for the Gleissberg solar cycle at the high-latitudes of the Northern Hemisphere
    Time evolution of growing season temperatures in the Northern Hemisphere was analyzed using both wavelet and Fourier approaches. A century-scale (60–140 year) cyclicity was found in the summer temperature reconstruction from the Taymir peninsula (∼72° N, ∼105° E) and other high-latitude (60–70° N) regions during the time interval AD 1576–1970. This periodicity is significant and consists of two oscillation modes, 60–70 year and 120–140 year variations. In the summer temperatures from the Yamal peninsula (∼70° N, ∼67° E) only a shorter-term (60–70 year) variation is present. A comparison of the secular variation in the Northern Hemisphere temperature proxies with the corresponding variations in sunspot numbers and the fluxes of cosmogenic 10Be in Greenland ice shows that a probable cause of this variability is the modulation of temperature by the century-scale solar cycle of Gleissberg. This is consistent with the results obtained previously for Northern Fennoscandia (67°–70° N, 19°–33° E). Thus, evidence for a connection between century-long variations in solar activity and climate was obtained for the entire boreal zone of the Northern Hemisphere.”

    A year earlier Ogurtsov and a Finnish colleague had already published another paper on the Gleissberg cycles in the Journal of Atmospheric and Solar-Terrestrial Physics. Back then they reported a solar Gleissberg Cycle in the nitrate concentrations in polar ice cores:

    Evidence of the solar Gleissberg cycle in the nitrate concentration in polar ice
    Two sets of nitrate (NO3−) concentration data, obtained from Central Greenland and East Antarctic (Dronning Maud Land) ice cores, were analyzed statistically. Distinct century-scale (50–150 yr) variability was revealed in both data sets during AD 1576–1990. It was found that century-type variation in Greenland and Antarctic nitrate correlates fairly significantly with the corresponding Gleissberg cycle: (a) in sunspot number over 1700–1970 AD; (b) in 10Be concentration in Central and South Greenland over 1576–1970 AD. Thus, presence of century-scale relationship between polar nitrate and solar activity was confirmed over the last 4 centuries. That proves that NO3− concentration in polar ice caps could serve as indicator of long-term solar variability.”

    Another crucial solar cycle is the Suess-de Vries Cycle. In February 2015 Hans-Joachim Lüdecke together with his colleagues Weiss and Hempelmann published an overview of the climatic link of this solar cycle in the journal Climate of the Past Discussions:

    Paleoclimate forcing by the solar De Vries/Suess cycle
    A large number of investigations of paleoclimate have noted the influence of a ~ 200 year oscillation which has been related to the De Vries/Suess cycle of solar activity. As such studies were concerned mostly with local climate, we have used extensive northern hemispheric proxy data sets of Büntgen and of Christiansen/Ljungqvist together with a southern hemispheric tree-ring set, all with 1 year time resolution, to analyze the climate influence of the solar cycle. As there is increasing interest in temperature rise rates, as opposed to present absolute temperatures, we have analyzed temperature differences over 100 years to shed light on climate dynamics of at least the last 2500 years. Fourier- and Wavelet transforms as well as nonlinear optimization to sine functions show the dominance of the ∼ 200 year cycle. The sine wave character of the climate oscillations permits an approximate prediction of the near future climate.”

    Also a paper by Tiwari und Rajesh published in May, 2014 in the journal Geophysical Research Letters is in full agreement with the above paper. In it the authors found the Suess-de Vries cycle in the precipitation distribution in Northwest China over the past 700 years:

    Imprint of long-term solar signal in groundwater recharge fluctuation rates from Northwest China
    Multiple spectral and statistical analyses of a 700 yearlong temporal record of groundwater recharge from the dry lands, Badain Jaran Desert (Inner Mongolia) of Northwest China reveal a stationary harmonic cycle at ~200 ± 20 years. Interestingly, the underlying periodicity in groundwater recharge fluctuations is similar to those of solar-induced climate cycle “Suess wiggles” and appears to be coherent with phases of the climate fluctuations and solar cycles. Matching periodicity of groundwater recharge rates and solar and climate cycles renders a strong impression that solar-induced climate signals may act as a critical amplifier for driving the underlying hydrographic cycle through the common coupling of long-term Sun-climate groundwater linkages.”

    ==========================

    Readers will also find a longer list of peer-reviewed papers showing the sun’s major impact on climate here. – PG
    – See more at: http://notrickszone.com/#sthash.kBO6bnMM.dpuf

    Review By German Experts Show That Even The 11-Year Solar Cycle Has Undeniable Impact On Global Climate

    By P Gosselin on 15. September 2015

    German geologist Sebastian Lüning and Prof. Fritz Vahrenholt focus on a number of papers that clearly show the sun’s unquestionable impact on the earth’s climate.
    ===================================

    The sun drives climate: 11-year cycles shown in natural climate archives
    By Dr. Sebastian Lüning and Prof. Fritz Vahrenholt
    (Translated/edited by P Gosselin)

    Solar activity fluctuates in sync with a series of characteristic cycles. The most well-known of these is the 11-year Schwabe cycle. Naturally, 11 years are a relatively short time with respect to climate. Due to the inertia of the climate system, large climatic impacts cannot be expected from these short cycles. Yet it is still worthwhile to take a closer look. A series of appearing papers over the past years has looked into the Schwabe cycle and searched for a possible climate coupling in historical datasets. The search was fruitful: the solar Schwabe cycle has a measureable impact, and one that should not be underestimated.

    We’d first like to start in Germany. Here a team of scientists led by Dominik Güttler of ETH Zürich studied 100-year old oak trees from the Medieval Warm Period in South Germany. Using C14 dating and counting tree rings, the scientists were able to find a clearly pulsating 11-year cycle. The paper appeared in January 2013 in the Proceedings of the Twelfth International Conference on Accelerator Mass Spectrometry. The abstract:

    Evidence of 11-year solar cycles in tree rings from 1010 to 1110 AD – Progress on high precision AMS measurements
    Oak tree rings from Southern Germany covering the AD 1010–1110 years have been analyzed for radiocarbon with accelerator mass spectrometry (AMS) at the laboratory at ETH Zurich. High-precision measurements with a precision down to 12 years radiocarbon age and a time resolution of 2 years aimed to identify modulations of the 14C concentration in tree ring samples caused by the 11 years solar cycles, a feature that so far is not visible in the IntCal calibration curve. Our results are in good agreement with the current calibration curve IntCal09. However, we observed an offset in radiocarbon age of 25–40 years towards older values. An evaluation of our sample preparation, that included variations of e.g.: chemicals, test glasses and processing steps did not explain this offset. The numerous measurements using the AMS-MICADAS system validated its suitability for high precision measurements with high repeatability.”

    The next stop is Italy in the Ionian Sea. Researchers there as well found the 11-year solar cycle in the climate archives of the last 2700 years. The study was published in March 2015 in the journal Climate of the Past. The abstract:

    A high-resolution δ18O record and Mediterranean climate variability
    A high-resolution, well-dated foraminiferal δ18O record from a shallow-water core drilled from the Gallipoli Terrace in the Gulf of Taranto (Ionian Sea), previously measured over the last two millennia, has been extended to cover 707 BC–AD 1979. Spectral analysis of this series, performed using singular-spectrum analysis (SSA) and other classical and advanced methods, strengthens the results obtained analysing the shorter δ18O profile, detecting the same highly significant oscillations of about 600, 380, 170, 130 and 11 years, respectively explaining about 12, 7, 5, 2 and 2% of the time series total variance, plus a millennial trend (18% of the variance). The comparison with the results of multi-channel singular-spectrum analysis (MSSA) applied to a data set of 26 Northern Hemisphere (NH) temperature-proxy records shows that NH temperature anomalies share with our local record a~long-term trend and a bicentennial (170-year period) cycle. These two variability modes, previously identified as temperature-driven, are the most powerful modes in the NH temperature data set. Both the long-term trends and the bicentennial oscillations, when reconstructed locally and hemispherically, show coherent phases. Furthermore, the corresponding local and hemispheric amplitudes are comparable if changes in the precipitation–evaporation balance of the Ionian sea, presumably associated with temperature changes, are taken into account.”

    In April 2014 Liang Zhao and Jing-Song Wang of the Peking National Center for Space Weather reported in the Journal of Climate on another Schwabe finding. The authors studied fluctuations in the east Asian monsoons and here too were able to see a clear influence by the 11-year solar cycle. The abstract of the paper:

    Robust Response of the East Asian Monsoon Rainband to Solar Variability
    This study provides evidence of the robust response of the East Asian monsoon rainband to the 11-yr solar cycle and first identify the exact time period within the summer half-year (1958–2012) with the strongest correlation between the mean latitude of the rainband (MLRB) over China and the sunspot number (SSN). This period just corresponds to the climatological-mean East Asian mei-yu season, characterized by a large-scale quasi-zonal monsoon rainband (i.e., 22 May–13 July). Both the statistically significant correlation and the temporal coincidence indicate a robust response of the mei-yu rainband to solar variability during the last five solar cycles. During the high SSN years, the mei-yu MLRB lies 1.2° farther north, and the amplitude of its interannual variations increases when compared with low SSN years. The robust response of monsoon rainband to solar forcing is related to an anomalous general atmospheric pattern with an up–down seesaw and a north–south seesaw over East Asia.”

    Two months ago a team of researchers led by Zhongfang Liu published a study on the North American winter climate in the journal Environmental Research Letters. Surprisingly the scientists found a strong impact by the 11-year solar cycle, which in part has an influence on the climate of the North American winter via the Pacific circulation system. Abstract:

    Solar cycle modulation of the Pacific–North American teleconnection influence on North American winter climate
    We investigate the role of the 11-year solar cycle in modulating the Pacific–North American (PNA) influence on North American winter climate. The PNA appears to play an important conduit between solar forcing and surface climate. The low solar (LS) activity may induce an atmospheric circulation pattern that resembles the positive phase of the PNA, resulting in a significant warming over northwestern North America and significant dry conditions in the Pacific Northwest, Canadian Prairies and the Ohio-Tennessee-lower Mississippi River Valley. The solar-induced changes in surface climate share more than 67% and 14% of spatial variances in the PNA-induced temperature and precipitation changes for 1950–2010 and 1901–2010 periods, respectively. These distinct solar signatures in North American climate may contribute to deconvolving modern and past continental-scale climate changes and improve our ability to interpret paleoclimate records in the region.”

    In the conclusion they write:

    Our results have shown the influence of the 11year solar cycle on the PNA associated atmospheric circulation pattern and winter surface climate in North America.”

    Also in the Bering Sea a team of scientists showed the impacts of the solar Schwabe cycle. Kota Katsuki and his colleagues found the cycle in the climate archives of 13,000 years ago. That study appeared in April 2014 in the Geophysical Research Letters:

    Response of the Bering Sea to 11-year solar irradiance cycles during the Bølling-Allerød
    Previous studies find decadal climate variability possibly related to solar activity, although the details regarding the feedback with the ocean environment and ecosystem remain unknown. Here, we explore the feedback system of solar irradiance change during the Bølling-Allerød period, based on laminated sediments in the northern Bering Sea. During this period, well-ventilated water was restricted to the upper intermediate layer, and oxygen-poor lower intermediate water preserved the laminated sediment. An 11-year cycle of diatom and radiolarian flux peaks was identified from the laminated interval. Increased fresh meltwater input and early sea-ice retreat in spring under the solar irradiance maximum follow the positive phase of Arctic Oscillation which impacted the primary production and volume of upper intermediate water production in the following winter. Strength of this 11 year solar irradiance effect might be further regulated by the pressure patterns of Pacific decadal oscillation and/or El Niño-Southern Oscillation variability.”

    Last but not least we have a classic paper on the Schwabe cycle by a group led by Hiroko Miyahara in 2009 appearing in the Proceedings of the International Astronomical Union:

    Influence of the Schwabe/Hale solar cycles on climate change during the Maunder Minimum
    We have examined the variation of carbon-14 content in annual tree rings, and investigated the transitions of the characteristics of the Schwabe/Hale (11-year/22-year) solar and cosmic-ray cycles during the last 1200 years, focusing mainly on the Maunder and Spoerer minima and the early Medieval Maximum Period. It has been revealed that the mean length of the Schwabe/Hale cycles changes associated with the centennial-scale variation of solar activity level. The mean length of Schwabe cycle had been ~14 years during the Maunder Minimum, while it was ~9 years during the early Medieval Maximum Period. We have also found that climate proxy record shows cyclic variations similar to stretching/shortening Schwabe/Hale solar cycles in time, suggesting that both Schwabe and Hale solar cycles are playing important role in climate change. In this paper, we review the nature of Schwabe and Hale cycles of solar activity and cosmic-ray flux during the Maunder Minimum and their possible influence on climate change. We suggest that the Hale cycle of cosmic rays are amplified during the grand solar minima and thus the influence of cosmic rays on climate change is prominently recognizable during such periods.”

    ===========================================
    If the 11-year cycle already has an impact, then just imagine the profound impact that the other loner term cycles have, such as the 78-year cycle and the 1000-year solar cycle. These surely cement the climate into longer term phases. -PG
    – See more at: http://notrickszone.com/#sthash.kBO6bnMM.dpuf

    • GS, I do believe their are roughly cyclic oscillations in the nonlinear dynamic (chaotic) climate system. That must be true from first principles of mathematics. lorentz attractors and all that. But I do not think, as a trained econometrician, that most ofmthe curve fitting stuff you cite shows much of anything. For one, most climate data is autocorrelated, so the BLUE theorem fails, so simple uncorrected OLS (including sinusoidal fit ‘nonlinear’ equivalents) are statistical garbage. To oversimplify more complex reasoning arriving at a simple math based conclusion. Von Neumann’s elephant trunk dictum, further simplified.

  2. Hi Willis

    In all your hunting for the 11-year signal, have you done a frequency analysis against the _rate_ of temperature change?

    Would that even be a statistically valid thing to do?

  3. Well I would have thought that the seasonal variations were part of the data. So I would never have thought of removing them if I wanted to know what the climate was doing.

    With enough filtering, you can remove everything that is changing, and just get a number for the climate.

    But I can understand your curiosity about this Willis, since we all thought the science was settled.

    g

  4. Willis, I certainly agree with your analysis of this paper. Having not read the other 22 either (with no intention of doing so, Jupiter really?, no comment on solar influences in general, especially as oceans ( and their vagarities) MUST BE a BIG deal given their heat capqcity and size.
    But I am more cautious about solar on longer time frames. Milankovich, and all that. After all, we are only warmed by that big gravity contained nuclear fusion reactor in the sky. Well, for another 4-5 billion years or so. Good enough for government work.

    • “we are only warmed by that big gravity contained nuclear fusion reactor in the sky”. Not according to Trenberth’s Earth energy budget, in which two-thirds of the warming comes from that greenhouse gas up in the atmosphere.

  5. The 23 New Papers on the impact of the sun on the climate may all be quite wrong, but what a relief to read something that is not rabbiting on endlessly about the effects of CO2 only! If you switched the Sun off, we would very soon be in trouble, if you switched CO2 off, people would not notice for quite a while.

      • Whilst your point about plant food is a good one, do not forget that without sunlight plants would not photosynthesise – so they would not be consuming CO2 and thus the availability of CO2 would essentially be a non issue.

        Even ignoring that point, there would be a delayed reaction whilst plants used the existing CO2 in the atmosphere, bringing it down from about 400ppm to about 170ppm, and then as you say, plants would well and truly notice.

        The sun is the giver of life. Whilst I have yet to see any convincing data/evidence that the sun is the no.1 driver (and Willis has exposed the weakness of much of it), I would not be at all surprised if, together with changes in the patterns of cloudiness (which changes may or may not be solar induced), once the science is fully known and understood, it is revealed that the sun is the no.1 driver.

        If temperatures do not increase over the next 10 years and if the sun continues to be in a quiet phase (whatever that might mean), it will be interesting to revisit this debate, and consider the arguments being advanced by those that support CO2 as the main driver, and those that support the sun. I can foresee a time when many will support the view that it is likely that the sun is the no.1 kid in town, even though we may still not understand why and how that is the case.

        Get the popcorn out; the next 10 to 15 years could be very interesting – for which ever flank one is on.

  6. Thanks for taking the time to do this Willis. I talked with David when he was working on this and expressed my skepticism. But then, I’m a skeptic. :-)

      • “””””….. 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. …..”””””

        Willis, I guess this is THEIR words, and not yours.

        When I see ” harmonics ” I tend to think in frequency domain, so in this instance they would be meaning things like : 1 cycle per year, 2 cycles per year, 3 cycles per year , etc

        Is this your understanding of what they MEANT to say.

        I would have thought (maybe silly me) that periods of 1 year, 2 years, 3 years , 11 years etc would be more likely of interest; but then as I say, I can’t be sure what they mean.

        Do they have it upside down in your opinion ??

        G

      • Willis, I also just noticed a weird behavior in the fig 1 plots; perhaps you know what is going on.

        So I ignored the grey background base data graph, and took the blue graph as the ‘ corrected ‘ signal.

        The red graph then; the boxcar filter would seem to be some low pass filter version of the blue graph.

        Now I usually consider low pass filters to at least be linear.

        So when I look at the BIG wiggles in the blue graph, the red filter seems to attenuate them some amount (maybe 50% ish)

        But then what about the smaller blue wiggles around 1999 to 2003 or thereabouts; post the big el nino.

        Suddenly the smaller wiggles in the blue are completely vanished in the red graph.

        How does that happen if the low pass box car process is a linear filter ??

        Seems weird to me.

        G

      • Well I see I goofed Willis. Their boxcar is applied to the original grey data.

        But the same mystery applies. The small wiggles are completely squelched and the big ones only mildly attenuated.

        G

      • And I hear you on the ChileYamal Christmas tree Willis. It’s as if way back when, someone core bored in the ground in South Africa, and came to the conclusion that the whole of Africa must be sitting on a giant layer of Type IIa diamond at a depth of about 18 meters.

        Well the Cullinan diamond was actually sticking out of a tunnel wall at a depth of 18 meters, but it might just as easily been found in a drill core.

        Tree ring core boring gives a one dimensional image of a three dimensional object, so why anyone pays attention to it, specially since it is accepted as a proxy for every physical variable known to science, is beyond me.

        Dendrochronology has proved its value several times in correcting some aspects of history that were wrong at first. But how you tell anything else from tree rings, and separate the variables, when you completely scoff at things like Nyquist, just boggles my one.

        In this instance Willis, ‘ you ‘ is synonymous with ‘ one ‘, and not intended to be Willis.

        g

        My all time favorite ‘ Far Side ‘ cartoon panel, is a perfect demonstration of the failure to pay attention to sampling theory.

        I paid $75 for a legal copy of the panel (very nice) but can’t post it. NOBODY is ever authorized to post ANY ‘ Far Side ‘ panel. Permission, if sought ,is NEVER granted, to ANYONE.

        g

      • George E. Smith September 23, 2015 at 12:44 pm

        “””””….. 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. …..”””””

        Willis, I guess this is THEIR words, and not yours.

        Sure ‘nuf.

        When I see ” harmonics ” I tend to think in frequency domain, so in this instance they would be meaning things like : 1 cycle per year, 2 cycles per year, 3 cycles per year , etc

        Is this your understanding of what they MEANT to say.

        I would have thought (maybe silly me) that periods of 1 year, 2 years, 3 years , 11 years etc would be more likely of interest; but then as I say, I can’t be sure what they mean.

        Sometimes they described them as “harmonics” and sometimes as “sub-harmonics”. In neither case was their meaning clear. It appears that they mean “exact-year intervals”, meaning 2, 3, or 4 years or any integer number of years.

        It was one of the reasons for my confusion.

        George E. Smith September 23, 2015 at 12:59 pm

        Willis, I also just noticed a weird behavior in the fig 1 plots; perhaps you know what is going on.

        So I ignored the grey background base data graph, and took the blue graph as the ‘ corrected ‘ signal.

        The red graph then; the boxcar filter would seem to be some low pass filter version of the blue graph.

        Now I usually consider low pass filters to at least be linear.

        So when I look at the BIG wiggles in the blue graph, the red filter seems to attenuate them some amount (maybe 50% ish)

        But then what about the smaller blue wiggles around 1999 to 2003 or thereabouts; post the big el nino.

        Suddenly the smaller wiggles in the blue are completely vanished in the red graph.

        That’s an example of why I said that the boxcar filter is a poor choice. In addition to bringing the strength of the cycles less than or equal to 1 year down to zero, it also attenuates the cycles with periods up to about six years. And if you look at Figure 2, you’ll see that the shorter the period, the greater the attenuation … exactly what you report above. For example, the attenuation at 18 months is about 50% … no bueno.

        w.

      • George,

        Except that the alerce series is many, many trees, recognized by none other than Phil Jones as a deadly threat to CAGW.

        Willis’ fantasy of a single tree is a figment of his fevered imagination.

        Tree ring data can be used to reconstruct temperature as well as precipitation, if properly checked against other proxy data, as has been done repeatedly with the Patagonian data, as for instance against ice core oxygen isotope results.

  7. So, then it appears that no amount of evidence will persuade you, as you ignore studies with which you can’t or won’t find fault..

    • “Old” papers summarized from 2010 (Stanford’s Dr. Svalgaard not among the authors):

      solar-center.stanford.edu/sun-on-earth/2009RG000282.pdf

      SOLAR INFLUENCES ON CLIMATE
      L. J. Gray,1,2 J. Beer,3 M. Geller,4 J. D. Haigh,5 M. Lockwood,6,7 K. Matthes,8,9 U. Cubasch,8
      D. Fleitmann,10,11 G. Harrison,12 L. Hood,13 J. Luterbacher,14 G. A. Meehl,15 D. Shindell,16
      B. van Geel,17 and W. White18
      Received 5 January 2009; revised 23 April 2010; accepted 24 May 2010; published 30 October 2010.
      [1] Understanding the influence of solar variability on the
      Earth’s climate requires knowledge of solar variability,
      solar‐terrestrial interactions, and the mechanisms determining
      the response of the Earth’s climate system. We provide
      a summary of our current understanding in each of these
      three areas. Observations and mechanisms for the Sun’s variability
      are described, including solar irradiance variations
      on both decadal and centennial time scales and their relation
      to galactic cosmic rays. Corresponding observations of variations
      of the Earth’s climate on associated time scales are
      described, including variations in ozone, temperatures,
      winds, clouds, precipitation, and regional modes of variability
      such as the monsoons and the North Atlantic Oscillation.
      A discussion of the available solar and climate proxies is
      provided. Mechanisms proposed to explain these climate
      observations are described, including the effects of variations
      in solar irradiance and of charged particles. Finally,
      the contributions of solar variations to recent observations
      of global climate change are discussed.
      Citation: Gray, L. J., et al. (2010), Solar influences on climate, Rev. Geophys., 48, RG4001, doi:10.1029/2009RG000282.

    • A gentle chide. LG. ‘No amount of evidence’ does NOT include multiple bad papers that fail to do the most basic statistical stuff properly correcting for serial autocorrelation. Like the recent dreck out of Stanford, that made the correction using totally obsolete approximation methods in order to posit there was no statistically significant pause. Bad ‘curve fitting’ by sceptics is no better than bad climate models by warmunists. Bad is bad, period. We all need to learn such distinctions.

      FWIW, I wait to dissect the new Evans series of posts forthcoming at JoNova, just like I did Monckton’s irriducible equation at CE (hint, it is mathematically much further reducible, even though Judith had trouble posting the math and had to resort to a .pdf supplement for the derivation). Truth wins in the end, enabled by knowledge of stuff like how to correct autocorrelated time series. Regards.

      • The sun is a variable star. Its various variations affect climatic phenomena on earth and other planets. IMO these effects are observable, statistically significant and dwarf whatever effect an increase in CO2 from 280 to 560 ppmv might have.

    • Lady Gaiagaia September 22, 2015 at 3:42 pm

      So, then it appears that no amount of evidence will persuade you, as you ignore studies with which you can’t or won’t find fault..

      I am not “ignoring” any studies at all. Quite the contrary. I have invited people including yourself to send me TWO LINKS, one to the paper and one to the data, of some paper you think is the bee’s knees, and I’ll take a look at it … apparently you either have trouble with reading comprehension, or simply think you’re above it all, as you have ignored my request completely. Not impressed.

      Come back when you actually have a paper you think is valuable. Until then, sorry, I’ll pass. The total amount of garbage out there is unfathomable, and if you are unwilling to do any work, I’m equally unwilling.

      w.

      • Except you provided one of two links he specifically requested before you ever posted. How is that male chauvinism?

      • Arsten,

        There was a link to a paper in the very comment to which he replied, asking for links.

        In my prior comment I was referring to all the many studies cited previously from the same site out of which Willis picked a single one.

        Why would he then ignore my additional link, while claiming not to ignore individual links? Instead of asking for another one, why not just respond to the one I already linked?

        Only reason I can come up with for my valid citation being ignored is chauvinism.

      • Yes, but that is not what he asked for. At the end of his very long post, he specifically requested two links (to the SAME research information: One for Data, one for the paper itself). You then provided a single link: to the paper itself. I even pulled the DOI and checked for supplemental data containing what they used as their paper’s data set, and I didn’t see it at the DOI location (which is not unusual, which is probably why he asked for the data link in the first place.

        I’m not trying to be combative, though I probably come across that way, I am just trying to understand your charge that he doesn’t want to listen to you because you are female.

        If I ask a person to check the oil level in a vehicle as well as the fuel level and you come back and tell me that the fuel tank is full but you haven’t bothered to check the oil, is it chauvinist to ask that person check the oil also, as I originally asked, before I grab a set of keys and go jumping in to drive?

        To me, the chauvinist thing to do is to assume you simply couldn’t perform that request (ostensibly because you are a woman) and then go and do it for you.

      • Lady Gaiagaia September 22, 2015 at 6:55 pm

        You ignored the one I cited.

        Typical male chauvinist attitude.

        Lady Gaiagaia, as I have told you twice now, I asked for TWO LINKS for any paper you wanted me to discuss—one to the paper, and one to the data. Far too many papers these days depend on unavailable data, and are thus unfalsifiable and useless. I can analyze data. I cannot analyze claims about data. So I said unless you provide both of those links, don’t waste my time.

        You have totally ignored my clearly worded request both times, while at the same time falsely accusing me of ignoring your single citation. I told you not to bother me unless you had a link to the data as well as to the paoer … was that too complex for you?

        And now, because you are either too stupid or too arrogant to follow simple instructions, you claim that I’m a “male chauvinist”? Good heavens, madam, don’t you realize you’re auditioning for the role of “knowledgeable scientist”, and not for the “bitchy feminist” role?

        w.

      • The data are available in the paper. Why do you need a separate link?

        Why not just read the paper? This attitude is why so many find your obstinate, willful ignorance so anti-scientific. You simply ignore papers that you know will show your claims about solar influences false.

        It also appears that you are misrepresenting the Chilean tree ring data, since you have so far not deigned to reply to my comment below on the alerce series. despite responses to other comments all around it. Both the alerce series and oxygen isotope data show a correlation with solar activity at a high confidence level.

      • Lady Gaiagaia September 22, 2015 at 9:10 pm Edit

        The data are available in the paper. Why do you need a separate link?

        I need a separate link in part because I don’t believe a single word that comes out of your mouth … and in part because I’ve been fooled too many times by that exact claim, and wasted too much of my time looking for unavailable datasets.

        Why not just read the paper? This attitude is why so many find your obstinate, willful ignorance so anti-scientific. You simply ignore papers that you know will show your claims about solar influences false.

        I have ignored nothing. I told you several times that studies without data are useless and that I do not have time in my life to read them all looking for supposed data. I have rooted around in too many lousy papers only to find, after following dead links or not finding any links at all, that the data is not available. So I’m no longer willing to go on snipe hunts trying follow a thousand false leads of the type you’re pushing.

        If you are willing to depend on studies that are not falsifiable because they have no data, that’s your choice, and given your actions to date, I’m not surprised.

        Me, I like falsifiable papers, especially when folks like you are challenging me to falsify them. And I can’t falsify them without the data. If you can’t be bothered to provide the data you claim establishes the case, I can’t be bothered to analyze the paper … why is that so hard for you to understand?

        It also appears that you are misrepresenting the Chilean tree ring data, since you have so far not deigned to reply to my comment below on the alerce series. despite responses to other comments all around it. Both the alerce series and oxygen isotope data show a correlation with solar activity at a high confidence level.

        Lady, you were willing to try to shut me up by falsely accusing me of being a “male chauvinist”, simply because you were either too stupid or too arrogant to follow a simple request. As a result, I have nothing but contempt for you, as that kind of bogus accusation is the exclusive province of a particularly nasty type of female coward.

        Unfortunately for you, I don’t care in the slightest about political correctness. So while your male friends may quail when they’re accused of being chauvinist, I’ve lived with both myself and my gorgeous ex-fiancee long enough to know who I am and am not, and as a result I find your accusation both vituperative and ridiculous. You’re one of the nastiest harridans I’ve encountered in a while, and you seem to have some special hatred for me … so why should I answer a single question you put forwards? I’m simply trying to defend myself from your untrue accusations here, I have zero interest in answering your questions, no way I’m volunteering to enter Medusa’s snake-pit. You burned that bridge a while back.

        Finally, if whatever you are calling the “alerce series and oxygen isotope data” actually shows what you claim, then why haven’t you sent me the link to the paper and the data? As I’ve said several times, send me the TWO LINKS and I’m happy to look at it … and I hold to my word on that even with unpleasant folk like yourself.

        w.

      • I did give you the link to the entire paper, including its data, in the comment to which I refer at September 22, 2015 at 7:22 pm, below. Dunno how you missed it.

        The well known alerce series includes lots of trees and solar influences have been found in the data for decades, in paper after paper. But the main reason that “consensus” advocates don’t like the data is because there is no hockey stick to be found in them.

        Please show the Chilean study which you claim was based upon a single tree, a putative Southern Hemispheric Yamal. I don’t believe what you assert without evidence. Your memory is not to be trusted. Even if there were one reliant on a single tree, it would be just one among the numerous Patagonian tree studies.

      • Lady Gaiagaia September 22, 2015 at 11:27 pm

        I did give you the link to the entire paper, including its data, in the comment to which I refer at September 22, 2015 at 7:22 pm, below. Dunno how you missed it.

        “Missed it”??? I saw it and ignored it, just as I said I would do to anyone who didn’t provide TWO LINKS, one to the actual data and one to the paper.

        If you can’t be bothered to provide the link to the actual data as used in the study, I can’t be bothered either.

        Is that hard to understand or something? As Mosh said, which letter in TWO is giving you problems?

        You seem to think you can force me to play by your rules if you just whine and bitch enough about what a baaaad man I am. Sorry, not gonna happen. I’ve been there and done that, and all it got me was a headache looking at crappy studies without data.

        No data link?

        Not interested in the slightest, as I told you a bunch of times.

        w.

        PS—For humor’s sake, I just took a look at the paper at your link. In fact, the entire paper does NOT include the data as you falsely claimed. Some of the data is totally unreferenced. Some is paywalled. And the first data I tried to find, Lonnie Thompson’s 1985 ∂18O data, is claimed to be at pangaea.de, but a search of that location finds no such thing.

        This is very typical. People like you frequently misrepresent the data as being available, when it is not. And in this case, it’s obvious that you didn’t even look to see if the data was where the authors claimed, you just flat-out made up your bogus claim.

        In other words, as usual, you’re full of … well, let me call it “misconceptions” and let it go at that.

      • Willis,

        No surprise that you ignored my request for the paper which you claim was based upon a single Chilean tree, rather than the whole alerce series or part of it.

        See below for a comment containing the paper to which you refer. As I suspected, it was indeed about many trees, not just one, as you recalled incorrectly.

    • Lady GG, I’ll take your word for it that tree ring data can be used for other things than telling the age; BUT while that might be true for tree RINGS, I have to quibble about the relevance of a sliver of some tree rings that come from a bored core from a tree.

      A tree is a three dimensional object, a tree ring is a two dimensional object; presumably rings vary with the height above the ground. A tree core is a one dimensional object, and contains even less certain information, than a complete section of the tree.

      I wonder if you recall the sorry tale from many years agor, in National Geographic, about a newly graduated MS in Botany enthusiast, who wondered just how old a bristle cone pine might be. So (s)he hightailed it up into the White Mountains to look for old BC pine trees.

      After finding what looked like a pretty old tree, our educated hero proceeded to cut the tree down, and cut out a slice of the tree, to take back to hiser lab to count rings.

      The tree proved to be much older than anyone before ever thought they could be, and so (s)he proudly showed the results to colleagues.

      They were horrified that this MS botany graduate had killed the tree to find out its age, and asked if core drills had ever come up in classes.

      The embarrassed less than educated graduate, went back to the White mountains armed with a nice shiny new core drill, and started boring like a mound of termites.

      S(he) never ever found another bristle cone pine tree that was within 500 years as old as the dearly departed one he had a piece of back in the lab.. As I recall, the tree was either 4,500 or 5,000 years old.

      NG had a photograph of the sorry stump of what remained of the oldest known bristle cone pine tree.

      As you know, the bristle cone pine trees carbon dating series was used to correct the carbon dating scale, which previously was based on the assumption that 14C production in the atmosphere was absolutely constant.

      That ended up proving that some new pottery technology that was presumed to have travelled from Mesopotamia, to Spain, based on dating materials from ancient kilns, had in fact started in Spain, and gone the other way.

      Personally, I much prefer to study phenomena that are only affected by one physical variable, at a time, rather than trying to decide if a tree ring width is a consequence of age of the tree, or the Temperature, or the prevailing wind direction, or the local moisture history, or the sudden change in minerals as a tresult of some geological event like a land slide, etc etc etc.

      I place a lot of confidence in the presumed age of a tree from a drilled core, provided of course that the core goes precisely through the center of the tree.

      I assume you are familiar with a fundamental theorem of pattern recognition, that says essentially that pattern recognition is impossible.

      Well more pedantically (or nitpickerly as you choose), it says in effect that if you have a finite number of (n-1) dimensioned sections of an (n) dimensioned object, it is always possible to design a counterfeit object that is different from the subject object, but produces the exact same set of (n-1) dimensioned sections.

      Well that of course is the mathematical result. It might not prevent a human from properly identifying the face on Mars, from just x, y, t sections of an x, y, z, t object. t of course being the time of the photographs which varies the lighting shadows.

      A two year old child can identify a tree; ANY tree, and distinguish it from the AT&T tree; AKA a telephone pole. But a computer program can’t.

      g

    • I’d say there’s a 50% chance it will go down, and an equal chance it will go up, and that in neither case will the temperature change much.

      w.

      • Or then again, it may largely stay the same (ie., within the bounds of statistical meaning).

        If I was a betting man, I would cover all 3 bases since the future is a notoriously fickle thing.

  8. Instead of insinuating that the authors are hiding data to suit the conclusion as part of a deception it would be refreshing if Willis actually asked them why the additional data was not included. Actual research is a good substitute for inuendo IMO.

    • james –

      I understand your question, but can’t help but wonder:

      They have data from January of 1982 through December 2013, but only use data from the period January 1990 through December 2013.

      Why should one ask them why they didn’t use those 8 years? It is supposedly a “Peer reviewed paper” – shouldn’t the paper include the rationale for leaving out the 8 years? Shouldn’t one of the reviewers had questioned this? Or, is the rationale in the paper somewhere and Willis simply missed it?

      • I will make it simple for you Willis.

        Busting them for leaving out data = good.

        Speculating they have something to hide without asking the authors the basic question ‘why” = could improve quality of article.

        At the end of the day you may be right but the value of speculation is proportional to the amount of information used to derive it.

      • Thanks, James. We now have their answer, which is:

        “They are not using all of the data. … It generally means the data are being stubborn and uncooperative.”

        Reply:
        The data are not being stubborn and uncooperative – just the opposite. The missing data from 1982 to 2000 is the same as seen in figure 2 of reference 1.

        I’m sorry, but that is an explanation which does not explain. They have NOT ANALYZED a quarter of the data. Their explanation is that the missing data is displayed elsewhere … BZZZT! The question wasn’t “where can I find the missing data”. It was “why was only 3/4 of the data analyzed”?

        To include this data would bring in phase-locked segment #8 which would require additional redundant discussion. That is why we began at 1990.

        Their only explanation which is near to being on point is to say that to include the data would require “redundant discussion” … say what? They’re not analyzing a quarter of their data because they’d have to explain some things about it? That’s the goofiest reason I can imagine. So what if you have to discuss your results? That’s what science is, that’s what the “Methods” section and the “Discussion” section is all about. You analyze all of your data, and if you have to discuss some part of it, you do so.

        But you definitely don’t say that it you just skipped a quarter of the data because it would require more discussion. That’s not an explanation or an excuse for anything.

        Now do you understand why I didn’t write to ask them, James? Because in a scientific paper there is no excuse for not using all the data without a valid explanation … and doing so with no explanation, as we’ve seen here, very probably means you don’t have a valid explanation.

        w.

    • James of the West September 22, 2015 at 3:57 pm

      Instead of insinuating that the authors are hiding data to suit the conclusion as part of a deception it would be refreshing if Willis actually asked them why the additional data was not included. Actual research is a good substitute for inuendo IMO.

      So if it is such a good idea, why haven’t you done it? You can ask as easily as I can. I await the answer … although to date it appears you’d rather complain about my actions than take action yourself.

      In my case, I’m not interested in their excuses, as there is no excuse for not explaining why you’ve left out half the data. It’s not something you can do accidentally or without noticing, for heavens sake. A single sentence after they said:

      2.1. Data

      This study considers only data from the period January 1990 through December 2013.

      would have sufficed.

      But heck, if you want to hear them ‘sciencesplain it all away, go ahead and ask … report back with their explanation, I don’t mind watching tapdancing …

      w.

      • Who is writing and publishing an article on the paper? Whoever that is should do some research. I am commenting on your article and the research you used in your WUWT publication Willis. I am asking you how you jump to a conclusion of conspiracy without talking to the authors about their side of the data story. Note – I am asking you not just insinuating and casting aspersions on why you would fail to ask them about that.

      • James of the West September 23, 2015 at 12:27 pm

        Who is writing and publishing an article on the paper? Whoever that is should do some research. I am commenting on your article and the research you used in your WUWT publication Willis. I am asking you how you jump to a conclusion of conspiracy without talking to the authors about their side of the data story. Note – I am asking you not just insinuating and casting aspersions on why you would fail to ask them about that.

        Since I never said a single word about a conspiracy, I fear you must be thinking of someone else. This is why I ask people to QUOTE WHAT YOU ARE OBJECTING TO. You clearly believe I said something I never said.

        Regarding writing to the authors to find out their excuse for leaving out a quarter of the data, as far as I’m concerned there is no excuse for doing that without explanation in a scientific paper. I don’t care what their reasons are. It’s very bad juju, it’s a huge red flag, it’s just not done.

        And yes, the fact that they have done so does entitle people to speculate why. That’s what people do with things are missing or are hidden from view. They speculate about why they can’t be seen. If the authors didn’t want people speculating, it would have taken a couple of sentences to explain themselves.

        But they didn’t … so I speculated on why they didn’t.

        Finally, you want to know why I don’t intend to write to them at this point and ask them?

        Because they’ve already confirmed my speculation.

        You see, I’m sure both authors have read this post. David is a regular contributor here, and even if he weren’t, the reach of WUWT is awesome. Everyone who is anyone in the climate world reads it, and their friends will assuredly tell them I’ve written about them if they happen to miss it. Even the most alarmist of climate scientists read WUWT and my work regularly, they’d be fools not to, if only to see what the other side is up to now.

        As a result, if the authors actually had a good reason for leaving out a quarter of the data, I’m sure they would have posted it here in the comments by now. I know if someone said that you or I might have something to hide and we both knew we didn’t, I’d be front and center yelling and screaming that it wasn’t true, and my strong speculation is that you’d be right there as well, both loudly protesting our innocence … but then we’ve got nothing to hide, and that’s what innocent people do.

        Regards,

        w.

      • Using that flawed logic someone like yourself reading your analysis might regard with suspicion the reasons you did not want to conduct further research of the authors reasons for the older data rejection is because it may not support the thrust and inuendo of your article.

        I’m not making any such claim or assumptions – I am only suggesting that it would have been a better article with more diligent research to understand the reason for rejecting the older data and then making a more informed finding rather than in your own words “speculating” the reasons were because they “have something to hide”.

      • James of the West September 23, 2015 at 4:10 pm

        Using that flawed logic someone like yourself reading your analysis might regard with suspicion the reasons you did not want to conduct further research of the authors reasons for the older data rejection is because it may not support the thrust and inuendo of your article.

        Say what? What kind of bizarre logic is that? Where did I leave out a quarter of the data?

        I didn’t bust them for not taking the analysis far enough or not asking enough questions, as you seem to imply with your bogus “example”. How far an analysis goes is up to the scientist. They asked certain questions and didn’t ask other questions.I asked certain questions and didn’t ask other questions. So what? In my world, that’s just the choice of the investigator.

        I busted them for leaving out a quarter of the data without explanation. And I assure you, by that same logic, if I leave out a quarter of the data without explanation, I should be regarded with suspicion.

        An explanatory example needs to be what’s called “parallel” to the original situation to shed light on it. Yours has nothing to do with the original situation

        w.

      • Finally, James of the West, I say again: you think it’s important to ask the authors. I think it is meaningless, because if they had a reasonable explanation we’d have heard it long ago.

        Here’s the bottom line. You’re never gonna convince me that if some scientist leaves out a quarter of his data without explanation, it is my responsibility to go ask him why. I don’t have to ask him why, I don’t owe him a request, I don’t owe him a damn thing. The shoe is on the other foot.

        HE OWES US AN EXPLANATION FOR LEAVING OUT A QUARTER OF THE DATA.

        He hasn’t given one despite having plenty of opportunity to do so, both in the paper and here. You want to ask him about it, that’s your business. It’s none of my business. And if you think he should be asked, then I encourage you to do so and report back which your findings. It’sone of my business.

        So please, you’re wasting your time trying to convince me that his explanation is my responsibility. It’s not my responsibility in any form. Give it up.

        w.

      • See Dr. Douglass’ comment below, and apologize for your scurrilous accusations. If you’re a man, which all here have reason to doubt.

        BTW, having lived with a woman doesn’t mean that you’re not a chauvinist.

  9. Willis , I do not even have to read what you wrote because your mind has been already made up.

    IF the global temperatures start to trend down in response to this prolonged solar minimum before this decade is out , you will still say no solar/climate connection.

    Again Willis it is not the 11 year solar cycle one needs to look at but prolonged periods of minimum solar activity when certain criteria are met for a sufficient duration of time which I have outlined in detail.

    If my criteria is met that is when the question as to if a solar/climate connection exist wiLl be answered. Until then Willis you are guessing and your guess is no better or worsae then any one else that has an opinion that differs from yours, although you go out of your way consistently to prove your guess work is correct and us who support the solar/climate connection are wrong and yet you have no data to back up what you say,and the data you do present is a convoluted mess.

    This in contrast to my data which is clear and concise and that is what IN CONTRAST TO MINE WHICH IS CLEAR AND CONCISE

    • No it is not. It is wriggle matching. That the temperature may go down, or does go down, under your scenario, does not answer the question of right or wrong. You lack a defensible and plausible, mathematically sound and physically sound mechanism.

    • I agree, Salvatore, it’s not worth anyone’s precious time reading his stuff. He’s as blinkered as the warmists and just as unscientific……

      One wonders why?

      • And yet here you are, Jay, reading my stuff … go figure. Not only that, but you’ve been unable to find one single fault in any part of my science. So, being a dittohead, you go “ME TOO! ME TOO!” to Salvatore, and being a mudslinger, you claim that I’m “blinkered” despite not having a scrap of evidence to back up your claim.

        Useless. I truly hope you either start talking about the science, or just shut your mouth …

        w.

    • I’m confused. How many “global temperatures” are there? There’s only one globe we’re concerned with, and there is no physically meaningful temperature for it. So what are these other globes you’re referring to?

  10. I am just sending part two and as one can see this is to the point and has specifics which equate to a general climatic outcome.

    PART TWO

    HOW THE CLIMATE MAY CHANGE

    Below I list my low average solar parameters criteria which I think will result in secondary effects being exerted upon the climatic system.

    My biggest hurdle I think is not if these low average solar parameters would exert an influence upon the climate but rather will they be reached and if reached for how long a period of time?

    I think each of the items I list , both primary and secondary effects due to solar variability if reached are more then enough to bring the global temperatures down by at least .5c in the coming years.

    Even a .15 % decrease from just solar irradiance alone is going to bring the average global temperature down by .2c or so all other things being equal. That is 40% of the .5c drop I think can be attained. Never mind the contribution from everything else that is mentioned.

    What I am going to do is look into research on sun like stars to try to get some sort of a gage as to how much possible variation might be inherent with the total solar irradiance of the sun. That said we know EUV light varies by much greater amounts, and within the spectrum of total solar irradiance some of it is in anti phase which mask total variability within the spectrum. It makes the total irradiance variation seem less then it is.

    I also think the .1% variation that is so acceptable for TSI is on flimsy ground in that measurements for this item are not consistent and the history of measuring this item with instrumentation is just to short to draw these conclusions not to mention I know some sun like stars (which I am going to look into more) have much greater variability of .1%.

    I think Milankovich Cycles, the Initial State of the Climate or Mean State of the Climate , State of Earth’s Magnetic Field set the background for long run climate change and how effective given solar variability will be when it changes when combined with those items. Nevertheless I think solar variability within itself will always be able to exert some kind of an influence on the climate regardless if , and that is my hurdle IF the solar variability is great enough in magnitude and duration of time. Sometimes solar variability acting in concert with factors setting the long term climatic trend while at other times acting in opposition.

    THE CRITERIA

    Solar Flux avg. sub 90

    Solar Wind avg. sub 350 km/sec

    AP index avg. sub 5.0

    Cosmic ray counts north of 6500 counts per minute

    Total Solar Irradiance off .15% or more

    EUV light average 0-105 nm sub 100 units (or off 100% or more) and longer UV light emissions around 300 nm off by several percent.

    IMF around 4.0 nt or lower.

    The above solar parameter averages following several years of sub solar activity in general which commenced in year 2005. The key is duration of time because although sunspot activity can diminish it takes a much longer time for coronal holes to dissipate which can keep the solar wind elevated which was the case during the recent solar lull of 2008-2010 ,which in turn keep solar climatic effects more at bay. Duration of time therefore being key.

    If , these average solar parameters are the rule going forward for the remainder of this decade expect global average temperatures to fall by -.5C, with the largest global temperature declines occurring over the high latitudes of N.H. land areas.

    The decline in temperatures should begin to start to take place within six months after the ending of the maximum of solar cycle 24,if sub- solar conditions have been in place for 10 years + which we have now had. Again the solar wind will be needed to get to an average of below 350km/sec. which takes time because not only do the sunspots have to dissipate but also the coronal holes. In other words a long period of very low sunspots will be need to accomplish this. It will be a gradual wind down.

    Secondary Effects With Prolonged Minimum Solar Activity. A Brief Overview. Even if one or two should turn out to be true it would be enough to accomplish the solar /climatic connection.

    A Greater Meridional Atmospheric Circulation- due to less UV Light Lower Ozone in Lower Stratosphere.

    Increase In Low Clouds- due to an increase in Galactic Cosmic Rays.

    Greater Snow-Ice Cover- associated with a Meridional Atmospheric Circulation/an Increase In Clouds.

    Greater Snow-Ice Cover probably resulting over time to a more Zonal Atmospheric Circulation. This Circulation increasing the Aridity over the Ice Sheets eventually. Dust probably increasing into the atmosphere over time.

    Increase in Volcanic Activity – Since 1600 AD, data shows 85 % approximately of all major Volcanic eruptions have been associated with Prolonged Solar Minimum Conditions. Data from the Space and Science Center headed by Dr. Casey.

    Volcanic Activity -acting as a cooling agent for the climate,(SO2) and enhancing Aerosols possibly aiding in greater Cloud formation.

    Decrease In Ocean Heat Content/Sea Surface Temperature -due to a decline in Visible Light and Near UV light.

    This in turn should diminish the Greenhouse Gas Effect over time, while promoting a slow drying out of the atmosphere over time. This may be part of the reason why Aridity is very common with glacial periods.

    In addition sea surface temperature distribution changes should come about ,which probably results in different oceanic current patterns.

    https://wattsupwiththat.com/2015/09/01/the-arctic-iris-effect-dansgaard-oeschger-events-and-climate-model-shortcomings-lesson-from-climate-past-part-1/

    The above accounts for abrupt climatic changes within a glacial or inter- glacial period. Dr. Curry this is similar to your stadium theory.

    [Long, interesting summary. Thank you. .mod]

    • I like how you put it out there. You’ve stuck your neck out for quite some time. I am anxious to see the thought experiment progress… Of course I would love for the climate to cool, to take the wind out of the social science attack on carbon based energy!

    • “Even a .15 % decrease from just solar irradiance alone is going to bring the average global temperature down by .2c or so all other things being equal.”

      .15% decrease over how long a period of time?

      During a Solar Eclipse, people in the area directly shaded by the Moon notice a cooling but it becomes overwhelmed quickly once they are in the direct sunlight.

      It would seem that a minor change in the sun would need to last long enough to have a discernible effect on Earth.

      Just thinking out loud.

      • A 0.15% decrease from solar irradiance alone actually results in 0.38 k decrease, but would have to cover a length of period at least for the duration of one whole cycle. (9-14 years) Hence, no sun spots for at least 9-14 years would be required for a temperature change like this to be felt. During a normal sun cycle the period of no/very low sun spots is roughly 4 times shorter, so there is only about 0.1 k difference measured.

      • JohnWho September 22, 2015 at 4:25 pm Edit

        “Even a .15 % decrease from just solar irradiance alone is going to bring the average global temperature down by .2c or so all other things being equal.”

        .15% decrease over how long a period of time?

        Please tell me we’re not back on the fabulous planet with “all other things being equal” … this is the mistake the alarmists make over and over. Things in climate are NEVER equal. The climate REACTS to changes in forcing by increasing or decreasing the reflected energy.

        Next, the total swing in TSI from the ~2008 trough to the ~2013 peak over the most recent solar cycle is only ~ 0.75 W/m2 out of 1360 W/m2. This is a TSI change of only about 0.05%, so I’m not sure where you got your “.15% decrease”.

        Next, you need to remember that just as not all of the TSI makes it to the surface, not all of the variation in the TSI makes it to the surface. Some 30% of the 0.05% is lost to albedo, leaving only 0.035% of actual decrease. Another ~20% is absorbed by the atmosphere where it has only half the effect, since half of it is radiated upwards away from the surface. That’s another 0.005%, so in all we’re down to a surface decrease of 0.03% … three hundredths of one percent.

        The earth currently radiates about 390 W/m2. The Stefan-Boltzmann equation says that a permanent blackbody change from that condition to where it would be radiating 389.883 W/m2 (a 0.03% decrease) would involve a cooling of about two hundredths of one degree … and that is using the inordinately foolish assumption that all things are equal.

        Color me unimpressed …

        w.

      • Is it the new texting craze that gets people to leave out the zero in front of a decimal point, and also leave out spaces so that one can’t determine if (.) in front of 15% is the end of sentence period from the previous sentence, so can’t determine if it is a 15% change or a 0.15% change.

        Also can’t tell if the (.) is doing double duty as the period at the end of the sentence, and the decimal point from .15%

        g

      • Matt says.. “A 0.15% decrease from solar irradiance alone actually results in 0.38 k decrease, but would have to cover a length of period at least for the duration of one whole cycle.”
        =============================================================================
        How would one calculate that? Even ignoring all the other affects Salvatore mentions as possible, TSI at the surface alone we would have to know far more then we currently do. As mentioned by Salvatore , the duration of the affect is key.

        “Only two things can affect the energy content of a system in a radiative balance, either a change in the input, or a change in the residence time of some aspect of the energy within the system.”

        The greater the increase in residence time of the energy, the greater the potential energy accumulation!!

        What is the cumulative daily energy difference at the ocean surface between, oh say pick any recent active solar cycle, and the current solar cycle. Divide that total difference into disparate W/L. Determine the earth (land-ocean-atmosphere) residence time of each disparate W/L. Multiply each disparate W/L daily energy by the particular residence time. Some you will multiply by one, for the residence time may be one day only or less, and primarily in the atmosphere. Some daily differences you may multiply by 3000 or more, as the residence time may be that long, or much longer. Now tell us the total energy gain over that one solar cycle. Now do the same over three active cycles vs. say three minimum cycles. What is the total difference in the earths energy?

        It is similar to determining the oceans geo thermal heat total. The output is infinitesimal to solar energy, but to know how much geothermal heat is currently in the oceans, we need to know the mean residence time of the total geothermal output, which could be many hundreds of days.

      • In this sense (residence time of energy input) I maintain not all watts are equal. The residence time depends on both the materials encountered, and the WL of the watt under consideration. In a recent post Willis asserted that the LWIR re-striking the surface, via back radiation, was equal to the SW striking the surface,sans the clouds presence. Thus in this post just above, ignoring residence time, he limits the affect to a very small number. I have, on the basis of residence time, questioned the veracity of Willis’s proposition that, if the watt per square meter down welling LWIR due to clouds, is equal to the same watt per square meter down welling SW , sans clouds, then they make the same contribution to earth’s energy budget.

        I postulate that the SW radiation will enter the earths oceans to depth, having far longer residence time. I postulate that the same increase in LWIR will expend much if its energy in accelerating the water cycle, be lost in evaporation, and released at altitude, to be liberated by GHG molecules, the more numerous, the more likely to be quickly liberated from our “system” I assert that (as an example) 10 straight days of SW pumping into the tropical ocean, will accumulate for the entire 10 days, losing little to space; whereas 10 days of LWIR from clouds, will lose far more total energy to space. I postulate that the residence time of the WL of radiation, as well as the materials encountered, are the reason the residence time and total accumulated energy within the system varies, despite an equal wattage flow per square meter.

        As the residence time of some of this energy is multiple decades, then we would need to compare say three very weak cycles, verses three very strong cycles, to understand the total energy gained in the three strong solar cycles. But right now we simply do not know the residence time of disparate solar spectrum entering the SW selective surface of our GHL (Green house Liquid) oceans.

      • One more way in which not all watts are equal…

        “Heat is a curious thing. In general it is described as an average of the kinetic energy of a given mass, such as one square meter. But this average, does not define the energy intensity of individual molecules or photons which composed said mass. A thought experiment if you will. Take a very large pot filled with water, say 100 square feet in area base, and ten feet deep, so 1000 square feet. and super insolated with a concave bottom, thinner in the center.

        Now apply two different heat sources to this pot, both of which are say 100 watts per 1 square feet. The first source, example A, is a 100 square foot heating element, 10, 000 watts total, with the conducted heat perfectly distributed throughout. From this source, no matter how perfect the insolation of the pot of water, it can only get to the T of the heating element, at which point the net flow between the element and the pot will be equal.

        Now consider a very different 100 watts per square foot AVERAGE source; example B. Apply a very small, say 1/4 inch square super heated but still 10,000 watts total, and so still 100 watts per square foot of the pot base. Given time, this greater energy intensity source of equal watts per square foot input to example A, can yet heat the pot of water to far higher Temperature. Under theoretical perfect insolation, the entire pot can reach the T of the source.

        Comparing a flux in GHG LWIR to the energy intense SW flux striking a SW selective surface like the oceans, is like the example A verses B above. The watts per square meter flux is almost meaningless compared to the greater energy intensity of the SW flux and the thousands of times greater residence time of said SW flux striking the SW selective surface of the oceans, verses the very short residence time change in atmospheric energy due to increased GHG which also are far less energy intensive then the SW radiation penetrating the oceans. (Some of Konrad’s experiments may be useful here)

        Due to the very long residence time of SW ocean insolation, and to the relatively higher energy intensity of SW insolation verses LWIR, then a 100 year long flux in SW insolation, can accumulate for every one of those 100 years, whereas the direct affects of a change in GHG LWIR, is balanced tomorrow. Indeed, not all watts are equal.

      • “Please tell me we’re not back on the fabulous planet with “all other things being equal” … this is the mistake the alarmists make over and over. Things in climate are NEVER equal. The climate REACTS to changes in forcing by increasing or decreasing the reflected energy.

        Next, you need to remember that just as not all of the TSI makes it to the surface, not all of the variation in the TSI makes it to the surface. Some 30% of the 0.05% is lost to albedo, leaving only 0.035% of actual decrease. Another ~20% is absorbed by the atmosphere where it has only half the effect, since half of it is radiated upwards away from the surface. That’s another 0.005%, so in all we’re down to a surface decrease of 0.03% … three hundredths of one percent.”

        Willis,

        Solar irradiance (TSI), is a measure of the solar radiative power per unit area normal to the rays, incident on the Earth’s upper atmosphere. Therefore it is irrelevant what happens below the upper atmosphere in this context.

        Based on this, the general science consensus supports that the sun warms the planet by 255 k. Therefore 0.15% result in a proportion of 0.38 k, but this just based on the average temperature of the planet now. This does vary with each solar cycle being different. It does of course matter how this TSI penetrates the surface of the planet, but that is different all together and doesn’t change the solar irradiance observed in the upper atmosphere above the clouds. We don’t know yet how little changes in this TSI affects the atmosphere and the Earth’s climate. (all of it is guess work) We don’t know yet if varying different wavelengths from solar energy are more important during historical periods. This certainly changes how that extra 1 or 2 W/m2 warm the oceans or could warm more with no or any little change.

        Global cloud levels having been changing over recent decades and there is reasonable evidence suggesting changes in UV and/or ozone affect there formation. How do we know how other wavelengths changing with solar energy behave with clouds also? This is an area with so many questions, but with very few answers.

        “Next, the total swing in TSI from the ~2008 trough to the ~2013 peak over the most recent solar cycle is only ~ 0.75 W/m2 out of 1360 W/m2. This is a TSI change of only about 0.05%, so I’m not sure where you got your “.15% decrease”

        Solar peaks in the past have shown up to 2 W/m2 difference between maximum and minimum. The solar peak from maximum to minimum during the late 1600’s was probably around 0-0.1 W/m2. This period was cold not because the difference between maximum and minimum, but because of the difference over a long period compared to solar cycles with much larger maximum’s compared. The difference in a solar cycle mean almost nothing because what really matters is how long the period is, when it is up to 2 W/m2 or around 0.1 W/m2.

        ENSO is totally solar driven and it is hard denying this, when illustrated below only 3 El Nino’s out of 21 have occurred during maximum periods of the sun cycle. Strong El Nino’s have only occurred towards around the minimum once the maximum period is out of the way. A few El Nino’s start just in the top 50% period of sun spots, but complete when the sun spots are reduced below 50%. The El Nino is a way of removing excess energy from the upper ocean generated during the maximum phase of the sun spot cycle. Just randomizing El Nino’s you would expect around 10 complete during maximum phases of the sun.

      • David A September 23, 2015 at 3:36 am

        That’s just calculated from general science consensus that the sun warms the planet by 255k based on the planets temperature now. This value changes with each different solar cycle, but solar irradiance % refers to the upper atmosphere above the clouds and therefore the internal workings of the planet are excluded from this.

        To work out how changes in TSI contributes on the surface is different matter altogether and complicated to say the least if even possible at the moment. Added to your suggestions you need to know how these vary with albedo (clouds, snow and sea ice), oceans and humidity in the atmosphere. Nobody knows with any confidence how these are even affected by TSI, so without that it is impossible to say. The models are useless because they can’t model albedo, oceans and any TSI changes influence on internal mechanisms. I currently don’t know what changes in TSI contribute to the surface per cycle and not seen a paper that does (only guesses). Even the paper below don’t take these into account and include assumptions with a biased point of view towards global warming.

        “The “solar cycle signal” obtained by regressing the global mean temperature onto the TSI time series yields the regression coefficient of κ = 0.18 ± 0.10°K per Wm−2 of solar constant variation, suggesting a mean global warming of ∼0.16°K from solar min to solar max. Next we will use a spatial filter to obtain a cleaner solar-cycle signal.”

        http://onlinelibrary.wiley.com/doi/10.1029/2007GL030207/full

      • “To work out how changes in TSI contributes on the surface is different matter altogether and complicated to say the least if even possible at the moment.”
        =================================================
        Indeed. My thoughts just encompass the direct surface affect, sans cloud cover changes, jet stream changes, etc, and we do not know the answer to those questions I posed above, let alone the current OHC balance relative to long term solar input. From what you say we have some idea of mid term solar responses affecting OHC affecting ENSO.

        We do not even know the OHC of all below surface geothermal flux, primarily because we do not know the residence time of the input.

  11. What you won’t find is a strong El Nino occurring during the maximum activity of the cycle. A strong El Nino always occurs towards the end of the sun cycle or slight overlap with the next one starting.

    Why is this?

    Well I am probably the first person to ever notice this? It’s down to the solar energy building during the maximum part of the cycle and when it winds down the tap is lowered, causing a favored change in persistent very weak trade winds across the tropics. The result is strong release of energy from the upper ocean to the atmosphere. How strong they are depends on the amount of low level clouds especially around the tropical oceans during the solar cycle and how warm the upper oceans are.

    Therefore a quieter sun like during the LIA would favor more El Nino’s because the conditions are ideal in changing the trade winds across the tropical oceans. This matches proxies from Little Ice Age and other cooler periods where numerous El NIno’s where detected and I have described roughly how the mechanism fits in place. Lowered temporary solar energy, UV and/or ozone probably favor the conditions in the tropical atmosphere supporting persistent weaker trade winds that result in stronger El Nino’s.

    My prediction is that the next strong El Nino will not occur if there is another one this soon, until towards the end of the next solar cycle. A quieter sun cycle in future could actually increase the rate of weak/medium El Nino’s in between each end of solar cycle strong event.

  12. One makes their mind up on the facts as they are first presented. Only when good solid contradictory facts are later presented should you consider the alternative. SDP has had his mind made up a long time ago and chooses not to read contrary evidence. Therefore, never has (or is able) to change his mind.

    As usual I enjoyed Willis and his take on this.

  13. I have a question for those that know how the energy from the Sun is accounted for that I would like answered. Would greatly appreciate an answer to these three questions.
    1. When you look at the way the energy from the sun hits the earth only one spot is perpendicular to the sun and would receive the full/maximum amount. The remainder of the earth is at an angle. Is this decrease in energy because of this angle properly accounted for in all of the calculations and models.
    2. When the energy travels through the atmosphere, it will be traveling through the (about) 60 miles of atmosphere to reach the earth, is this and the fact that it will be traveling through an increasing amount of atmosphere for every beam that is not exactly perpendicular. Thus every where else, before it his the earth it will be traveling through from 60+ miles to well over 2,000 (??) miles of atmosphere before it hits the earth.
    3. Additionally, there is a tangential donut around the earth defined by donut shaped area 60 miles (plus?) thick and with a 7920 mile donut hole in the center (the earths diameter less the atmosphere) encompassing that area of sunlight that would never hit the earth BUT would hit the atmosphere surrounding the earth and absorbing energy from the Sun. Is this area accounted for?

    In my mind all of these are affected by the Sun or have an effect on how much the various gasses in the atmosphere adsorb, reflect, and radiate energy. However, the math is way beyond my capabilities. Thank you for your effort.

    • The math is detailed, but not really “complex” .. You can understand, and we are working those equations for you (for all the readers here) relevant to the polar latitudes through the year.

      The real key is in deciding what approximations and assumptions are valid, needed and appropriate to each source paper; and what approximations and assumptions lead you astray into gibberish and “flat plate earth” averages.

      • “flat plate earth” averages. That is the term I was looking for and could not remember. As a PE (I assume that is what the PE1978 means) even you are not an EE, you learned that the average of an AC sine wave is ZERO. The actual measure of work, energy is represented by the RMS value of the sine wave. That is what creates many questions in my mind. The Earth radiates energy on the dark side, and absorbs energy on the side the Sun is shining on. On any square inch, the effect would be represented, roughly by a sine wave shape which, may have different heights. Additionally the three questions I asked above would factor into the amount of energy absorbed, but not, in my mind, the energy radiated outward, as empty space is always going to be perpendicular to every square inch on the dark side. That has to be factored in and I don’t see how you make a Flat Plate and average it.
        Then there is the effect of the third item I listed above. A significant (at least measurable and non-trivial) portion of the dark side is going to be shielded by a layer of the atmosphere that is significantly different than the area not in the “Gray Line” zone, that portion that because of the fact that the Sun is shining through it is warmer, and the ionosphere, (the thermosphere and parts of the mesosphere and exosphere), mesosphere, troposphere, and stratosphere and even the magnetosphere [the upper regions which are NOT spheroid in shape and are affected by the Suns AND Earth’s magnetic field and the Solar Wind) up there and will behave differently.
        Secondly, this area bends the rays and wraps around the dark side, this is confirmed by the differences in observed and actual (calculated) sunrise. Thus effect is extended beyond the calculated affected area (unless this is taken into effect.
        The point I think I am trying to make is that it is not a blue ball with light shining on one side and the other half (50.000000000000%) radiating energy into space. Seems to me it is closer to 50.5% vs 49.5% and that seems reasonable with only about 150 miles on the dark side being affected. I base the estimate of 150 miles on the fact that the “Gray Line” propagation area is about 10 degrees wide at the equator. Assuming that radiation is only affected by about 1/5 that ”area” would be 2 degrees or about 140 miles and that is about ½ a percent – rather conservative. Surely the electromagnetic energy rays of IR are affected in the same way as radio waves are? I have had good reliable, predictable communications with my ham radio using gray line propagation forecasts.
        I have a second degree in Applied Math (Got my degree in the early 70’s and predicted then that Computers would take over electronics- Had to get a math degree to get into the Computer Science courses) Even with all of my Engineering math courses and the additional ones required for the BS in math I have no idea where to start on a problem like this. How do the Climate Scientists, most of which have just a BA, even make an intelligent guess at this? Methinks they are over simplifying the problem and ignoring known effects.

    • usurbrain September 22, 2015 at 5:29 pm Edit

      I have a question for those that know how the energy from the Sun is accounted for that I would like answered. Would greatly appreciate an answer to these three questions.
      1. When you look at the way the energy from the sun hits the earth only one spot is perpendicular to the sun and would receive the full/maximum amount. The remainder of the earth is at an angle. Is this decrease in energy because of this angle properly accounted for in all of the calculations and models.

      Yes, the amount hitting a certain area is adjusted for the solar angle.

      2. When the energy travels through the atmosphere, it will be traveling through the (about) 60 miles of atmosphere to reach the earth, is this and the fact that it will be traveling through an increasing amount of atmosphere for every beam that is not exactly perpendicular. Thus every where else, before it his the earth it will be traveling through from 60+ miles to well over 2,000 (??) miles of atmosphere before it hits the earth.

      This is also accounted for, in that the sloped path through the atmosphere absorbs more sun than the straight-down path. Because of this, atmospheric absorption increases at high solar vertex angles. There’s an article on it here.

      3. Additionally, there is a tangential donut around the earth defined by donut shaped area 60 miles (plus?) thick and with a 7920 mile donut hole in the center (the earths diameter less the atmosphere) encompassing that area of sunlight that would never hit the earth BUT would hit the atmosphere surrounding the earth and absorbing energy from the Sun. Is this area accounted for?

      This one I don’t know the answer to, but I’d be surprised if it weren’t included. The guys building the models are not fools—instead, they are very smart folks who unfortunately have uncritically swallowed the untrue claim that ∆T = λ ∆F (change in temperature ∆T equals sensitivity λ times change in forcing ∆F) and they’ve built their models to match their misconceptions.

      However, there is only substantial absorption of solar energy below about 25 km. This gives the annulus an area of seven-tenths of a percent of the cross-sectional area of the earth … so even if it is not totally accounted for, it’s a very small difference in terms of most calculations.

      w.

      • …I’d be surprised if it weren’t included.

        Less than a year ago, I was surprised to learn at Judith Curry’s blog that climate models didn’t bother adjusting for the fact that the latent heat of vaporization of water varies by many percent between the temperature at the poles and the temperature at the equator.

        Surprised, but no longer shocked.

    • the bigger problem you have not mentioned is the state of the surface of the oceans when the light from the sun is hitting them. the reflective properties are continually changing,mainly due to wind, but also tide state and size.

  14. We still do not understand why the planet insists on repeated Ice Ages that last a long, long, long time punctuated by very brief warm Interglacials which all suddenly descend into super cold Ice Ages again. This stubborn cycle dominates this planet for the last 3 million years and it is getting worse, not better.

    Several things cause this but the only thing that can suddenly, regularly heat up the planet for brief Interglacials is the sun. You see, it is ‘hot’ and sometimes is a lot hotter, and it can switch gears very suddenly or turn off suddenly. No other system can do this with the abruptness of the Local Star that keeps our planet from being a frozen ball of waste wandering about the Milky Way.

    • So as I understand it, your claim is that inherent variations in the sun itself cause the ice ages? Do you have a citation for that claim?

      w.

    • “the only thing that can suddenly, regularly heat up the planet for brief Interglacials is the sun”

      I think you are mixing solar changes with insolation changes. It is the insolation changes that have the greatest effect on climate. That’s why while watching the Boston – Tampa Bay baseball game this evening, the fans in Boston had jackets on and I was on my lanai in shorts and a t-shirt. Same Sun, different insolation.

  15. Willis, you gotta lighten up a bit:
    “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.”
    Think of the Global Symmetry that poor tree represents, one southern hemisphere tree in Chile and one northern hemisphere tree in Yamal.
    \sarc maybe.

    • I don’t know which paper Willis has in mind, but the alerce series of Chilean and Argentine tree rings is well known. It’s based not upon a single tree but many standing “alerces” or their stumps. (Alerce, cognate with “larch”, is the local Spanish word for genus Fitzroya, named for the captain of HMS Beagle.) The series is notorious in “climate science” for showing that the Medieval Warm Period and previous past intervals of the Holocene were hotter than the Modern WP in the Southern Hemisphere, not just part of the NH. It’s mentioned in the Climategate emails:

      http://www.assassinationscience.com/climategate/1/FOIA/mail/1076359809.txt

      Here’s a 2008 paper which found a solar signal in both the alerce tree-ring series and oxygen isotopes in Antarctic and Peruvian ice cores:

      http://www.researchgate.net/publication/222664299_The_Medieval_and_Modern_Maximum_solar_activity_imprints_in_tree_ring_data_from_Chile_and_stable_isotope_records_from_Antarctica_and_Peru

      The Medieval and Modern Maximum solar activity imprints in tree ring data from Chile and stable isotope records from Antarctica and Peru

      ABSTRACT

      This work presents a study of the relations between solar and climate variations during the last millennia by spectral and multi-resolution analysis for oxygen-18 and tree ring width time series. The spectral and wavelet analysis of tree ring data shows that main solar cycle periodicities are present in our time series at the 0.95 confidence level. This result suggests the possibility of a solar modulation of climate variations detected in accumulated ice oxygen-18. Results of spectral and wavelet analysis have shown that both solar and climate factors are also recorded in the oxygen-18 data

      • You are correct. Willis only imagines that the study was of a single Chilean tree. In fact it was of many.

        Willis, you shouldn’t trust your memory. Somehow you seem to have conflated the South American studies with Yamal, half a world away.

        https://wattsupwiththat.com/2014/06/23/maunder-and-dalton-sunspot-minima/

        milodonharlani
        June 25, 2014 at 11:05 am

        Willis, here are authors & abstract from another of the studies (or one like it) which you refused to consider in comments to one of your 11-year posts:

        http://mtc-m16.sid.inpe.br/col/sid.inpe.br/marciana/2005/01.03.10.15/doc/2.1AS_Rigozo01.pdf

        Nivaor Rodolfo Rigozo (1,2), Alan Prestes(2),
        Daniel Jean Roger Nordemann(2), Ezequiel Echer(2),
        Luís Eduardo Antunes Vieira(2) and
        Heloisa Helena de Faria(2)
        1Faculdade de Tecnologia Thereza Porto Marques – FAETEC,
        CEP 12308-320, Jacareí, Brazil
        Fone: 55 12 39524231
        2Instituto Nacional de Pesquisas Espaciais – INPE,
        CP 515, 12201-970 São José dos Campos, Brazil.
        Fone: 55 12 39456840 – Fax 55 12 39456810
        E-MAIL: rodolfo@dge.inpe.br, prestes@dge.inpe.br, nordeman@dge.inpe.br, eecher@dge.inpe.br,
        eduardo@dge.inpe.br, hfarai@dge.inpe.br
        Abstract
        Tree ring index chronologies, representing standardized annual growth rates for Fitzroya cupressoides at
        Cordillera de la Costa de Osorno in Chile, have been employed for the search of solar periodicities during the last 400
        years. Spectral analysis of tree ring series by multitaper method has determined significant periodicities at about 21 and
        10.7 years. These values are close to two known present basic solar activity periods at 22 and 11 years (Hale and
        Schwabe cycles). Other periodic component appears at 5 years, which may also be related to solar variations. The short
        periods found probably may be due the environmental and climatic influences. The application of band pass filter
        techniques shows that the 11 year cycle present in tree ring series correlates with the sunspot numbers with a time lag
        of about two years, since AD 1700, the extent of accurate sunspot record interval.

      • I was sure that Willis’ claim about a single tree had to be wrong, and now we know that it was. The paper to which he refers in his post covered tree ring series, not a lone alerce.

        The most charitable explanation for Willis’ error is failing memory and confusing the Chilean paper with Yamal. But why give him the benefit of the doubt, when he ascribed nefarious motives to Douglas’ allegedly “hiding” data, without bothering to contact him?

        We’ll see if he’s man enough to correct his mistake and retract the screed about a single Chilean tree.

      • Perhaps it was like Briffa’s Yamal, where multuple cores were studied, but only one showed a hockey stick, and was therefore made dominant in the conclusion.

  16. Its the sun stupid.

    I remain open minded that maybe just maybe the sun’s nuanced behavior might have some measurable impact on earth.

    Monarch Butterflies have multi generational routes to Mexico &back.

    Why? Some observed things are, as of yet, not understood.

    Gravity…

    • Indeed:

      https://www.terrapub.co.jp/onlineproceedings/ste/…/CAWSES_231.pdf

      Mechanisms for solar influence on the Earth’s climate
      Joanna D. Haigh
      Imperial College London
      E-mail: j.haigh@imperial.ac.uk

      Solar radiation is the fundamental energy source for the atmosphere and the global
      average equilibrium temperature of the Earth is determined by a balance between
      the energy acquired by the solar radiation absorbed and the energy lost to space by
      the emission of heat radiation. The interaction of this radiation with the climate
      system is complex but it is clear that any change in incoming solar irradiance has
      the potential to influence climate. There is increasing evidence that changing solar
      activity, on a wide range of time scales, influences the Earth’s climate although details
      of the mechanisms involved remain uncertain. This article provides a brief review of
      the observational evidence and an outline of the mechanisms whereby rather small
      changes in solar radiation may induce detectable signals in the lower atmosphere.1

      http://www.imperial.ac.uk/…/Solar-Influences-on-Cli..

      Grantham Institute for Climate Change
      Briefing paper No 5
      February 2011

      Solar influences on Climate
      PROFESSOR JOANNA HAIGH

      • Thank-you.

        I see the terrestrial thermal system as complex transfer function operated on by a gain supplied by the sun and the lack of sun at night.

        The output is therefore related to gain, as well as the function. Roy Spencer has taken the position that the transfer function is natural and the output is unaffected by small variations in gain. I speculate that the transfer function is not well understood and therefore the effect of variations of gain cannot be, as of yet, predicted.

        I tend to keep an eye to the seemingly obvious.

    • He also could have sent Douglas an email asking about the allegedly “hidden” data. Or called him up.

      • Lady Gaiagaia September 22, 2015 at 7:58 pm

        He also could have sent Douglas an email asking about the allegedly “hidden” data. Or called him up.

        As could you … but nooo, you want a man to do your work for you …

        w.

      • I didn’t write an article accusing him of “hiding” data. Your not having bothered to contact makes you not only lazy but scurrilous.

      • Willis you quack me up, “…noooo…”. Your brand of misogyny is like that of the rascal offspring could Monty Python and Saturday Night Live have a baby. Perfect. And while I don’t have all the necessary equipment, I will still proudly stand with you and your ex-fiancee as a typical male chauvinist.

    • Andrew, this is why I ask people to quote me. I didn’t pay a dime for Douglass’s paper, I linked to the paper in the head post, and I have no idea why you think otherwise.

      w.

  17. Paul Westhaver September 22, 2015 at 6:49 pm

    Its the sun stupid.

    I remain open minded that maybe just maybe the sun’s nuanced behavior might have some measurable impact on earth.

    Thanks for the comments, Paul. If you are claiming that “It’s the sun, stupid”, it certainly appears that you’ve made up your mind …

    Monarch Butterflies have multi generational routes to Mexico &back.

    Why? Some observed things are, as of yet, not understood.

    Gravity…

    And? … What do monarch butterflies have to do with the sunspot cycle and whether it affects the climate?

    Part of the problem is that people constantly misunderstand my motive in all of this. I started out like you. I too was convinced that “it’s the sun, stupid”. I believed in Hershel’s wheat price claims and the like, and I thought that finding evidence that the sunspot cycle affects the climate would be a piece of cake … foolish me.

    Instead, every piece of “evidence” that the “ITSS” folks folks have put up has vanished like the morning dew when examined dispassionately. So far, it’s been nothing but a parade of Chilean trees.

    I’d love to find some strong evidence that some phenomena related to the sunspot cycle affects the climate. It would be great to come up with such evidence. That’s why I’ve looked at cosmic rays and solar wind and the like, because at one time I truly believed they influenced the climate.

    But I’m a scientist and an honest man, and despite looking everywhere I could think of, and lots of places other folks pointed me to, I can say categorically that I’ve never seen even medium evidence that something related to sunspots influences the climate, much less strong evidence.

    However, like you, I remain open minded … hence this post. Once again I was hoping to find something real … once again I was disappointed.

    w.

    • All that glisters is not gold WE.

      I would look to your fusiform gyrus on this one. It has let you down.

      “What do monarch butterflies have to do with the sunspot cycle and whether it affects the climate?”

      I don’t know, and that is the point, sort of. The point was obvious I thought. We observe a behavior and as scientists wonder why such behavior is as it is observed. Like the monarch’s multi-generational migration. Just because we don’t know why the monarch does this, does not change the observed fact that it does migrate. The explanation is elusive.

      The most influential object in the universe to earth is not the earth, it is the sun, by orders of magnitude. To me that is obvious. It is an observation. Why or by what mechanism the sun alters the earths behavior, I don’t know. I doubt it is the Iditarod, ocean drilling of oil, Jupiter…people. I look to the big fat dynamic nuclear bomb 93,000,000 miles away.

      The sun has an exceedingly high likelihood to be the cause of earthly climate variations. Simply based on size.

      As for the Meme “It is the Sun Stupid”… it says quite a bit.

      To recalcitrantly ignore the sun as a variation source or trigger, is anti-curious, anti science and a bad bet.

      • It may well be that monarchs navigate by the sun (!) and take their life cycle cues from seasonal changes in sunlight. Another, not mutually exclusive hypothesis is that generations leave as yet unidentified chemical clues or cues on trees.

    • Paul Westhaver September 22, 2015 at 8:55 pm

      All that glisters is not gold WE.

      I would look to your fusiform gyrus on this one. It has let you down.

      “What do monarch butterflies have to do with the sunspot cycle and whether it affects the climate?”

      I don’t know, and that is the point, sort of. The point was obvious I thought. We observe a behavior and as scientists wonder why such behavior is as it is observed. Like the monarch’s multi-generational migration. Just because we don’t know why the monarch does this, does not change the observed fact that it does migrate. The explanation is elusive.

      So your point is that the explanation for some things are unknown … yes, I knew that. Again, what does that have to do with whether the sunspot cycles affect the climate? Yes, both are unknown. Is that supposed to be profound or insightful?

      The most influential object in the universe to earth is not the earth, it is the sun, by orders of magnitude. To me that is obvious. It is an observation. Why or by what mechanism the sun alters the earths behavior, I don’t know. I doubt it is the Iditarod, ocean drilling of oil, Jupiter…people. I look to the big fat dynamic nuclear bomb 93,000,000 miles away.

      I fear that’s far too vague to be of use. What do you mean by “alters the earth’s behavior”? AS I CLEARLY SAID,

      First, it’s obvious that the sun affects the climate. Without the sun, we’d be pretty cold.

      But whether the sun affects the climate is not the question. The question is whether we can find any sign of the 11-year cycle in surface temperature datasets. The answer to date seems to be “no” … and saying “It’s the sun, stupid” just shows that you haven’t grasped the question.

      The sun has an exceedingly high likelihood to be the cause of earthly climate variations. Simply based on size.

      True … but again, that says NOTHING about whether the ~ 11-year solar changes affect the surface climate in any meaningful way.

      As for the Meme “It is the Sun Stupid”… it says quite a bit.

      To recalcitrantly ignore the sun as a variation source or trigger, is anti-curious, anti science and a bad bet.

      Dear heavens, I’M THE ONE PUTTING IN THE WORK INVESTIGATING THE SUNSPOT QUESTION, not you!! You are seriously accusing me of “ignoring the sun” when this whole post and all the rest of my seventeen other posts listed above are about nothing but the sun??? Eighteen posts about the sun and I’m supposed to be ignoring it? How does that work?

      I’m the one doing close analyses of the solar data, and you’re the one waving your hands and talking about butterflies and making inane statements like “It’s the sun, stupid”. Which one of us is “ignoring the sun”? SPOILER ALERT! … it’s not me.

      w.

    • Stephen Wilde September 22, 2015 at 8:35 pm

      Willis and Leif should get married :)

      Dude, you are one sick puppy. You probably find that humorous. Ho ho ho. My opinion of you just went down.

      That said, there is one ‘paper’ which I regard as the ‘best’ thus far but it doesn’t meet Willis’s criteria:

      http://joannenova.com.au/2015/01/is-the-sun-driving-ozone-and-changing-the-climate/

      Since it consists solely of your own hypothesis and is unsupported by data about the surface, I can see why it doesn’t fit my criteria. However, I’m interested in the claimed 2% variation in ozone levels. Where is the link to the data on that?

      w.

      • Studies of the effect of UV flux variation on climate via ozone are numerous. Many are in .pdf format, but should be easy for you to find, if you were willing to look and not afraid of what you’d find.

        They go back decades:

        Effects of Solar UV Variability on the Stratosphere

        Lon L. Hood
        Lunar and Planetary Laboratory, University of Arizona, Tucson, Arizona

        Previously thought to produce only relatively minor changes in ozone
        concentration, radiative heating, and zonal circulation in the upper stratosphere,
        solar ultraviolet (UV) variations at wavelengths near 200 nm are increasingly
        recognized as a significant source of decadal variability throughout the stratosphere.
        On the time scale of the 27-day solar rotation period, UV variations produce a
        stratospheric ozone response at low latitudes that agrees approximately with current
        photochemical model predictions. In addition, statistical studies suggest an
        unmodeled dynamical component of the 27-day response that extends to the low
        and middle stratosphere. On the time scale of the 11-year solar cycle, the ozone
        response derived from available data is characterized by a strong maximum in the
        upper stratosphere, a negligible response in the middle stratosphere, and a second
        strong maximum in the tropical lower stratosphere. The 11-year temperature
        response derived from NCEP/CPC data is characterized by a similar altitude
        dependence. However, in the middle and upper stratosphere, disagreements exist
        between analyses of alternate temperature data sets and further work is needed to
        establish more accurately the 11-year temperature response. In the lower
        stratosphere, in contrast to most model predictions, relatively large-amplitude,
        apparent solar cycle variations of geopotential height, ozone, and temperature are
        observed primarily at tropical and subtropical latitudes. As shown by the original
        work of Labitzke and van Loon [1988], additional large responses can be detected
        in the polar winter lower stratosphere if the data are separated according to the
        phase of the equatorial quasi-biennial wind oscillation. A possible explanation for
        the unexpectedly large lower stratospheric responses indicated by observational
        studies is that solar UV forcing in the upper stratosphere may influence the selection
        of preferred internal circulation modes in the winter stratosphere.

      • I said …

        I’m interested in the claimed 2% variation in ozone levels. Where is the link to the data on that?

        Lady Gaiagaia thought she was responding when she said …

        Studies of the effect of UV flux variation on climate via ozone are numerous. Many are in .pdf format, but should be easy for you to find, if you were willing to look and not afraid of what you’d find.

        Lady G, I asked for a link to the data, not a link to “studies of the effect”. Studies are a dime a dozen, and most of them are data-free. Without the data, the studies are unfalsifiable and thus USELESS. Try reading what is written before babbling useless inanities.

        w.

      • I left it for Steven to provide that. The linked study cites the authors. Why does everyone else have to do your research for you?

        It took me seconds to find this one from 2014 just by searching on “2% variation ozone levels climate”:

        http://www.nature.com/ngeo/journal/v7/n5/full/ngeo2138.html

        Nature Geoscience | Letter

        Tropospheric ozone variations governed by changes in stratospheric circulation

        Jessica L. Neu, Thomas Flury, Gloria L. Manney, Michelle L. Santee, Nathaniel J. Livesey & John Worden

        The downward transport of stratospheric ozone is an important natural source of tropospheric ozone, particularly in the upper troposphere, where changes in ozone have their largest radiative effect1. Stratospheric circulation is projected to intensify over the coming century, which could lead to an increase in the flux of ozone from the stratosphere to the troposphere2, 3, 4. However, large uncertainties in the stratospheric contribution to trends and variability in tropospheric ozone levels5, 6, 7 make it difficult to reliably project future changes in tropospheric ozone8. Here, we use satellite measurements of stratospheric water vapour and tropospheric ozone levels collected between 2005 and 2010 to assess the effect of changes in stratospheric circulation, driven by El Niño/Southern Oscillation and the stratospheric Quasi-Biennial Oscillation, on tropospheric ozone levels. We find that interannual variations in the strength of the stratospheric circulation of around 40%—comparable to the mean change in stratospheric circulation projected this century2—lead to changes in tropospheric ozone levels in the northern mid-latitudes of around 2%, approximately half of the interannual variability. Assuming that the observed response of tropospheric ozone levels to interannual variations in circulation is a good predictor of its equilibrium response, we suggest that the projected intensification of the stratospheric circulation over the coming century could lead to small but important increases in tropospheric ozone levels.

      • Gosh, Lady G, still no link to the data. Reading comprehension is not your strong suit, I take it.

        Pass …

        w.

      • Even if TSI remains fairly stable, subtle changes in the composition of the wavelength of EMR from the sun may have an impact since the oceans absorb energy at different depths depending upon wavelength.

        In a 3D world, a watt is not necessarily a watt. I would suggest that not all watts here on planet earth are born equal. The place where a watt resides (or buried) may yet prove to be rather material since the time for that energy to be picked up (resurface whatever) could well depend where it is in the system.

        A 3D world is very different to the 2D world so much beloved by climate scientists.

      • Lady Gaiagaia September 22, 2015 at 10:54 pm

        Here are observations of O3 changes in Nepal:

        http://www.atmos-chem-phys.net/10/6537/2010/acp-10-6537-2010.pdf

        Tropospheric ozone variations at the Nepal Climate
        Observatory-Pyramid (Himalayas, 5079ma.s.l.) and influence of
        deep stratospheric intrusion events

        The good thing about you, Lady G, is that I don’t have to write new comments. I can just recycle my old comments, which makes the whole operation “sustainable” …

        Willis Eschenbach September 22, 2015 at 10:32 pm
        Gosh, Lady G, still no link to the data. Reading comprehension is not your strong suit, I take it.

        Pass …

        w.

      • What a silly comment. Go back to your playpen. You are like a young undisciplined child constantly interrupting adult talk.

      • Careful, Pamela. You’ll be accused of being a Female Chauvanist Pig in a second…;-)

        Seriously, Lady GG. Do you think that just maybe you could:

        a) Back off on the ad hominem (literally!) and engage in civil discourse with all and sundry, especially on issues where you disagree with somebody? That’s when we need to be our most polite, not our most irrelevantly aggressive, sarcastic, and sexist. Where accusing somebody you’ve never met of being a MCP as a substitute for actual argumentation is both sexist and reveals far too much of your travails in life so far for comfort. Amazingly enough, humans can disagree without being CP’s.

        Sometimes disagreements are honest, and about objective stuff, not gender, however much you may or may not have been abused on the basis of gender in the past. Heck, on the Internet you could even be a male disguised by a female handle, since I’m guessing that your name is not, in fact Gaiagaia and that you have not, in fact, been ennobled by royalty.

        b) In this specific case, WE always puts the standard request in for data to support assertions of this or that that disagree with his observations or conclusions. Note well, the entire blog is fairly tolerant to exceptions to this rule, and there is no limit on the number of people who propose truly absurd theories and hypotheses and explanations or who state fond hopes and personal opinions as if they were known facts without any effort to even say “I think” or “In my opinion” to qualify them. But that is why Willis wants you to pony up properly linked data, not theory, model, or observational evidence that may or may not be relevant to the discussion at hand.

        c) For what it is worth, I’m in precisely the same boat as Willis. One of the first papers on this that I read was Friis-Christensen (if I recall the spelling correctly) that apparently showed a slam-dunk causal connection between solar activity and climate, including the dip in the 1945-1970 range that CO2 did not explain. They asserted a really high correlation. It was very reasonable to conclude that it was correct. However, over years of looking at a lot more work, it became clear that a lot of their assertions simply did not hold up, even before learning of Lief’s work on sunspots (and I think you are being foolish if you don’t take that works seriously — I’ve read through it in detail and it is damned solid science in a way that FC was not, with three independent and highly accurate measures all in agreement as far as solar activity is concerned). I remain open minded — the coincidence of the Maunder minimum with the LIA is suggestive although not conclusive — but rest easy since we are supposedly going to enter another Maunder-like minimum by the next solar cycle and we can all find out the very best of ways instead of fruitlessly speculating without sound data.

        Outside of that, I have read but am not yet convinced by the GCR connection. I have meditated on self-oscillation in chaotic systems and the possibilities (unproven) of some sort of phase locking to a weak signal. I have examined various data transforms for evidence of an 11 year signal or some sort of truly systematic correlation between temperature and solar state. However, we are doubly crippled in this because we do NOT have very accurate measurements of solar state back into the remote past, and our measurements or inferences of “global temperature” are a bit of a joke, IMO, before 1950, and a full-blown knee-slapper back in 1850. So it is very difficult to make any assertions at all that are more than speculative and so weighted with Bayesian Prior baggage as to be worthless as reliable statements of probable fact or predictive knowledge. In the end, I am like Willis — open minded but not convinced, and not about to be convinced by still more words. Show me the data with a clear, unambiguous signal.

        After all, I can make up unprovable hypotheses too. My favorite one is that dark matter is inhomogeneously distributed in the galaxy, and because of its gravitational oddness and lack of strong interaction with ordinary matter it orbits, if at all, nearly independently of the galactic bands of stars. As the solar system moves around the galactic center, it therefore passes through these invisible bands of gravity-modulating “stuff”, which strongly interact with only one thing in the solar system — the core of Mr. Sun. Or Ms. Sun (to prove that I’m not a MCP:-) if you prefer. There they transiently affect core density, the efficiency of the fusion cycle, and — after a lag of hundreds of centuries — the energy output at the surface of the sun and its structure. It also causes transient changes in the orbital radius of the planets, pulling them smoothly into slightly expanded or contracted orbits outside of the usual progression of orbital resonance etc.

        According to my unprovable hypothesis, the Ordovician-Silurian ice age was caused by precisely this — at a time when CO2 was over ten times higher than today. It was probably responsible for all of the ice ages over the Phanerozoic era, and explains why we cannot find any good proximate cause for those ice ages. We hit a gap in dark matter, the sun cools in 100,000 years or so, the Earth’s orbit gets a bit larger over the same interval, and glaciation starts and feeds back. So simple.

        Now, if only we could see dark matter! But sadly, it is dark. So my theory cannot be disproven (yet, anyway) and it explains all of the observations (or none of them). Should we believe it?

        Of course not. It isn’t that it could not be true. It could be true — that’s the scary thing. We act as if we know all of physics, but obviously we don’t. We don’t even have sound enough theories of dark matter to imagine a signal we could detect indirectly outside of the ice ages themselves, and since changes of the sort I propose would occur on secular timescales of hundreds of thousands of years, lagged, we could hardly expect to detect them in astronomical observations, especially if we weren’t looking for them, mixed in with ordinary stellar variability. It’s an invisible fairy theory, and hence not refutable or provable. It is like saying that the Ice Ages were God’s Will. Even if true, it is hardly useful.

        d) So PLEASE. Show me the money. Not papers on ozone variation that MIGHT be correlated with god-knows-what, where the correlation MIGHT or might not be causality. I have yet to see a really convincing plot correlating solar activity with temperature, especially with the increasing temperature that might or might not be accurately portrayed by the contemporary anomaly models. There simply isn’t enough data, and the data that there is doesn’t have good enough error bars, and taking the data at face value and ignoring the error bars, there is not a compelling correlation or an explanation for the exceptions.

        BTW, I suppose that I need to show my non-MCP credentials in order to converse with you on a civil basis as I do not hide my actual name or identity on this list and you can clearly see that I am Male and hence apparently to be despised by default (at least, if I disagree with you or criticize your style of argumentation so far). I’ve been married for 36 years to one woman who is a physician, smart as hell, who makes more money than I do. I’m not exactly stupid myself, and at the very least have a very good education and a knowledge and experience of physics, math, statistics and computational modelling that easily exceeds that of 99.99% of the human species. I leave it to you to estimate the probability of my being an MCP and still married and successfully employed in a physics department where I work with, over, and under, competent, smart women all the time, and teach classrooms full of the same. If you wish to make the allegation that I am one in lieu of an actual argument or response to the above, feel free, but personally I think it would be more edifying to address the issues at hand, not irrelevant gender inequality issues that are nothing but distracting baggage in this context.

        rgb

  18. I couldn’t get past the second time phase-locked was put in quotation marks, i.e. “phase-locked”. Why put it in quotes? It is a euphemism for something else or should all new concepts get quotation marks as if they are aliens from another dimension?

    http://www.lifebuzz.com/quotations/
    http://www.unnecessaryquotes.com/

    Oh, and why are we doing periodograms? Are we incapable of understanding Fourier Transforms or are we just trying to be cool?

    • To phase lock an oscillator, first it helps to have an oscillator. Sadly, we can’t find the oscillator. We have both too many and too few, as one might expect in a chaotic system. Oscillators exist in absolute abundance. Look at the gyres in the actual observed surface current distribution in the Gulf Stream, for example — turbulent rolls spinning off at all length scales, each of them with a not-too-sharp rotational period. Look at the related twists in atmospheric circulation. Look at the Monsoon. Look at the multidecadal oscillations (which sadly are not even close to sharply periodic, but which are structurally oscillations, sort of, anyway).

      Now find one with the right period, one that matches e.g. the 11-ish year solar cycle. Not too easy to do. First, one could hardly be surprised at finding one, given so many to choose from, but at the larger/longer spatiotemporal scales, the periods we observe just don’t match up very well. ENSO is not an 11 year cycle — or even properly periodic. Neither is the SOI, the PDO, the AO, the NAO, the Monsoon cycle. Look at the thermohaline circulation (another place where a wide range of periods exist). None of the large periods AFAIK match up. Look at the temperature series. They just don’t have much signal at 11 years, however one looks for it (periodograms, FTs, wavelet transforms). 3-something years yes. 5 something years yes. I see those a lot. But 11 years — it isn’t just me, a LOT of people would like to find them, but they are elusive and not robust or convincing as a “cause” when one finds one.

      The second problem is that we don’t really have a good theory of phase locking in chaotic systems. We do know that open fluid dynamcal systems (like a heated pan of water on a stove) will self-organize into e.g. convective rolls with a turnover period, and we do know that the structure and periods of rolls that emerge depend sensitively on boundary conditions, initial conditions, forcing, and more. Stir the pot and when they reform they can easily have a different structure with different periods, and it isn’t clear that the self-oscillations in the gyres are subject to phase locking in the usual sense of the term. Hence the quotes — the Earth’s climate is a lot more chaotic and complex than a heated pan of water, it has many quasiperiodic structures (many of them named!) that strongly modulate the dissipation of energy, it could be that some of those oscillators are resonant with and phase lock to weak oscillations in the primary driver (the sun) but we have damn all theory to predict or explain or even describe it if they do, and we cannot find much evidence of it happening when we look.

      Personally, I think that the annual variation is uninteresting, so that FT like decompositions of climate are irrelevant for periods less than a year. I would like most of all to see convincing explanations for the multiannual oscillations first, and best — the first few peaks after a year in Willis’ periodogram above. I’ve seen very similar structure in decompositions of other climate measures in the past (mostly posted on this blog). They appear to be connected in some way with ENSO, perhaps acting as a sort of “explanation” for the pattern of long and short, weak and strong El Nino’s, but as Willis points out above, this could be coincidence as easily as causality. There is bound to be some structure in a FT of noisy data, but there is no assurance that the structure is meaningful.

      The 5.5 year peak is actually suggestive of a solar cycle connection, as it corresponds roughly to \pi/2 in a 22 year cycle. That is indeed the time from peak to trough, trough back to peak again. But this is odd indeed from a causal point of view, as it suggests SSTs follow the extrema of the solar cycle, not their magnitude per se. However, it is not completely insane in a chaotic theory, where period doubling is a common signal of chaos. Then the question is: in a chaotic oscillation, can a primary oscillation phase lock to a weak signal and then double the period to move in and out of phase with the primary driver instead of slaving to it? Not exactly phase locking, obviously, but a sort of harmonic double resonance phenomenon that absolutely requires nonlinearity to happen.

      rgb

      • “To phase lock an oscillator, first it helps to have an oscillator.”
        I didn’t read the paper but they should have a mathematical definition of phase locked that does not depend on the signal being broad-banded or not. If it satisfies the mathematical definition of phase locked then it is mathematically phase locked and defies the skepticism of a mouth breather like WIllis. Now to establish if it is physically phase locked requires some sort of arguments about mechanisms.

        “There is bound to be some structure in a FT of noisy data, but there is no assurance that the structure is meaningful.”
        You would enjoy CS Daw’s papers.

        PS – “open fluid dynamcal systems” They don’t have to be open for the rest of your statements to be true. I say this because there are many systems which are closed or piecewise or quasi closed and still are dynamical. IIRC (and I may not) Lorenz system was closed.

      • “The second use of FFT (the one almost always used today) is for the RESULT of computing an FFT of some time sequence.”

        Not so. It was an algorithm. It is now an algorithm. It will always be just an algorithm. C-T is one of the algorithms, the fastest when one has 2*n number of data of a constant sampling rate.

        An FFT is not the result of an FFT even if children use the term as such. This term is not fungible.

    • Dinostratus September 22, 2015 at 10:55 pm

      I couldn’t get past the second time phase-locked was put in quotation marks, i.e. “phase-locked”. Why put it in quotes? It is a euphemism for something else or should all new concepts get quotation marks as if they are aliens from another dimension?

      Dino, in addition to the excellent reasons put forwards by rgbatduke, I put “phase-locked” in quotes for two very good reasons: first, they have a wildly non-standard definition, and second, they haven’t established the putative “lock”.

      Here’s their definition:

      By phase locking we mean that a signal of frequency f1 has a fixed phase with respect to that of a second signal of frequency f 2 = (n/m) f 1 , where n and m are integers.

      I don’t see how that is even possible. If the two frequencies (or periods) are different, then the difference in phase is constantly changing. And what would a “phase-locked” signal whose period is 511/1303 years look like, and how would it stay “locked” to the one-year signal?

      It seems to me that all their definition really means in practice for them is that the length of the period is some integer times one year. It has nothing to do with the phase. In addition, it seems they’ve restricted “n” in their “n/m” definition to the value “1”, because they only consider integral full-year periods in their “locking” calculations.

      Next, in any reasonably complex climate signal, you find a wide mix of frequencies (periods). Now, their claim is that any cycle with a period of m/n years is “phase-locked” to the sun … but that’s true of any cycle whose length is a rational number … how on earth is that possible? Where is the “lock”?

      Anyhow, that’s my take on it. According to the authors, any signal can be claimed to be “phase-locked” to a one-year signal … let’s take a signal that is 31 / 57ths of a year long. Clearly phase locked to one year, by their definition, since both 31 and 57 are integers.

      w.

      PS—Next time, for the sake of your own reputation … how about you just ask, and leave out the sarcasm? I put the term in quotes for a very good reason—the way they use it, it has a totally and completely non-standard meaning. As they actually use the term, it simply means that the period is an integral multiple of one year, which is not phase-locked on my planet. Your sniggering just makes you look foolish.

    • Dinostratus September 22, 2015 at 10:55 pm

      Oh, and why are we doing periodograms? Are we incapable of understanding Fourier Transforms or are we just trying to be cool?

      A periodogram is just a different way to demonstrate the results of a Fourier analysis. Since they are in period rather than in frequency, I find that for most people (myself included), they are much more understandable than the usual frequency-based representation.

      And in fact, to help people understand the frequency-based Fourier plots, it’s quite common to label the peaks with their corresponding period … and since we obviously need to label the points with their corresponding periods, why not just display the result in periods to start with?

      Finally, I use periodograms because they are linear in period rather than linear in frequency. With linear in frequency graphs, all the interesting short-period stuff gets all jammed up at the left of the graph and it’s hard to separate.

      With a periodogram, on the other hand, I can use a log scale on the x-axis and clearly display both the short- and long-period cycles.

      Now, perhaps you prefer the frequency-based representation of the results … or perhaps you are “just trying to be cool”, as an acquaintance of mine remarked. After all, it’s cool to be like the real sciencey guys who can do the frequency-to-period conversion in their heads …

      But me, I write for the educated layman, and I see no point in putting forward a frequency-based graph if I need to label it with the corresponding periods in order to understand it.

      Finally, your ongoing assumption that I don’t consider these kinds of questions, or I don’t understand what I’m doing, or I’m “just trying to be cool” is quite boring. I’m a very successful author for a reason—I think very carefully about all of the aspects of my presentation of ideas and data. I work and rework, write and rewrite, read and re-read. I don’t put in quotes for no reason, I think about each thing I quote and why I quote it. I don’t just toss out some standard representation of a result. I strive for a presentation that is clear and understandable. Yes, I know that some folks might prefer something else, but I have to write for the average reader, not someone who can convert frequency to period in their head.

      And given that fact about my writing, your pre-judgements and resulting snark about my choices merely makes you look petty and pitiful when I explain the reasons for my choices.

      So if you value your reputation, I suggest that you first ask your questions in a neutral manner, and IF my answer is unrealistic or illogical or something, THEN pull out the stops.

      w.

      • A periodogram is just a different way to demonstrate the results of a Fourier analysis.

        So then plot 1/time and reverse the axis. There is a certain right of passage involved in understanding how data is plotted, good reasons for why the traditions persist and you’re not helping anyone, including yourself, by using training wheels.

      • Dinostratus September 25, 2015 at 6:20 pm

        A periodogram is just a different way to demonstrate the results of a Fourier analysis.

        So then plot 1/time and reverse the axis.

        Learn to read. I already explained the many reasons I don’t display my results in frequency. Don’t like it? So what?

        There is a certain right [sic] of passage involved in understanding how data is plotted, good reasons for why the traditions persist and you’re not helping anyone, including yourself, by using training wheels.

        Dino, I see you highly disapprove of most everything I do, claiming to be so much wiser and so much more knowledgeable than I.

        Just to be clear, one of us is a random anonymous internet popup who is unwilling to sign his own name to his own words, and the other of us is one of the better-known climate science bloggers on the planet, with publications in the scientific journals.

        Now, Nature magazine thought my science was just fine, as did the other journals I’ve published in. I think I’ll take their word over yours. The journals have to stand behind their choices, as do I … whereas you can walk away and disown everything you’ve ever posted with no consequences.

        So in short, I think I’ll keep doing it my way … it’s working pretty well, regardless of what you laughably call a “right” of passage …

        w.

        PS—Despite your obvious contempt, I’m the one with the balls to do the new, original scientific investigations and analyses and put it out there on the web for folks to attempt to take apart … not you. And some folks even succeed at finding scientific errors in my work, it does happen … but not you. You don’t even try.

        Instead, you’re with Lady Googoo and the folks who hide behind an alias and ignore the science and whine about my methods of presentation, and get all snooty about whether to look by frequency or by period, and the axes of my graphs and other such meaningless trivia.

        Like I said, amigo, you’re not doing your reputation any good with this line of patter … I’d advise you to give up while you’re behind.

      • Willis – quite true.

        I have been doing DFT/FFT for 40 years – almost exclusively for sound/music where we work almost exclusively with frequency. About 3 weeks ago, inspired by one of your posts, I wrote an app note for my readers regarding plotting period, using a length-11 “toy” cycle as an example. It might help someone.

        http://electronotes.netfirms.com/AN424.pdf

        Bernie

      • Bernie Hutchins September 25, 2015 at 7:03 pm

        Willis – quite true.

        I have been doing DFT/FFT for 40 years – almost exclusively for sound/music where we work almost exclusively with frequency. About 3 weeks ago, inspired by one of your posts, I wrote an app note for my readers regarding plotting period, using a length-11 “toy” cycle as an example. It might help someone.

        http://electronotes.netfirms.com/AN424.pdf

        Bernie

        Thanks, Bernie, that’s an interesting app note. Working in frequency is the best choice for something like sound or music, where an A is 440Hz and nobody discusses or cares what the period is.

        On the other hand, in climate science it’s rare to deal in frequency. People talk about the “11-year” sunspot cycle or the putative 80-year Gleissberg cycle, not the one-eleventh cycles per year sunspot cycle or the one-eightieth cycles per year Gleissberg cycle. As a result, in climate science I find that most folks have an intuitive understanding of the period-based display of the periodogram.

        On the other hand, a graph showing f, 2*f, 3*f, 4*f, where f is the reciprocal of the number of data points, which are typically in months … not at all intuitive. In fact, as I tried to point out to Dino, in climate science most of the time you end up having to label the peaks of such frequency-based graphs as to their period length … so why not just show it period-based to start with?

        Regards,

        w.

      • DFT…. music to my ears. A phrase someone with 40 years of experience would use. I’ve come to hate the phrase FFT because it makes it sound like the results are some how better than a FT. PhD’s will actually write “Here is the FFT” not even knowing that the FFT is actually the algorithm and not the data. I’m tempted to ask, “So which FFT algorithm did you use, Cooley Tukey?” but I don’t because it would just slow them down.

      • “so why not just show it period-based to start with?”

        Why has the traditional way to plot the data persisted over the years? What are the advantages?

      • Dinostratus September 26, 2015 at 2:52 am Edit

        “so why not just show it period-based to start with?”

        Why has the traditional way to plot the data persisted over the years? What are the advantages?

        Thanks, Dino. The traditional way has persisted because it usually makes perfect sense. In many, perhaps most fields where the Fourier Transform is used, you generally deal in frequency and not period. Consider music, the example I discussed above but which you apparently didn’t read. Every musician knows that A is 440 Hz, but nobody ever thinks about it in terms of its period. As a result, presenting a Fourier analysis of sound in frequency terms makes perfect sense, and presenting such an analysis in terms of the periods of the sound would just make people shake their heads. The same is true of radio and electromagnetic waves in general, people discuss the “Ham Radio Bands” or the microwave range as extending from one frequency to another, not from one period to another. Radio stations are discussed in terms of their frequency, not their period. So the traditional way persists because in all of those kinds of fields, frequency is the subject of interest.

        However, in climate the opposite is true. Nobody discusses the “1/11th cycles/year frequency sunspot cycle”. Instead, we almost invariably are looking at the periods – 11 years for the sunspot cycles, three or four decades for the PDO, 100,000 years for the ice ages, 18 months for the QBO, 28 days for the lunar cycle, 18 years for the orbit of Jupiter … we almost never deal in frequencies in climate, it’s all periods.

        And as a result, I put forwards my results as a periodogram, because (obviously unlike whatever field you are familiar with) … the period and not the frequency is the subject of interest. We don’t discuss whether the Gleissberg frequency is 1/80th cycles/year or 1/90th cycles/year, we discuss whether the period of the Gleissberg cycle is 80 or 90 years long.

        w.

        PS—The traditional way to plot also has persisted because folks like you are stuck in tradition to the point of insisting on it even when it makes no sense at all, sometimes in a most aggressive and unpleasant fashion, as though presenting it in the traditional way were a law of nature or something … but then that is true of many, many traditions, so no surprise there.

      • Dinostratus wrote September 26, 2015 at 2:37 am:

        ———————————————————–
        “DFT…. music to my ears. A phrase someone with 40 years of experience would use. I’ve come to hate the phrase FFT because it makes it sound like the results are some how better than a FT. PhD’s will actually write “Here is the FFT” not even knowing that the FFT is actually the algorithm and not the data. I’m tempted to ask, “So which FFT algorithm did you use, Cooley Tukey?” but I don’t because it would just slow them down.”
        ——————————————————————

        The term “FFT” has TWO meanings. Technically it originally referred to a fast algorithm for computing the DFT, the DFT being a frequency-sampled DTFT (Discrete Time Fourier Transform). None of the three terms (or operations) FFT, DFT, or DTFT should ever be confused with the (integral-integral pair) FT. It is silly to suggest that a FFT would be “better than” a FT – unless you know nothing about either.

        The second use of FFT (the one almost always used today) is for the RESULT of computing an FFT of some time sequence. If you have a time sequence x(n), the FFT of x(n) and the DFT of x(n) both refer to the EXACT SAME (output) sequence of frequency samples [ typically X(k) ].

        The term “FFT” is almost never pronounced out as “Fast Fourier Transform”, and is interchangeable with “DFT” to be the output X(k). There is no reference to an algorithm. [In passing, remember that Cooley-Tukey get credit for one FFT algorithm but the FFT can be traced back to – ready – Gauss.]

        I have elaborated on these issues here:

        http://electronotes.netfirms.com/AN410.pdf

        and suggested that FFT is the better choice of term (avoid DFT) because the terms DFT and DTFT are often confused.

        Bernie Hutchins

      • “In many, perhaps most fields where the Fourier Transform is used, you generally deal in frequency and not period.”

        Simply not true but I’ll be charitable (because I know you’ll look foolish), provide one study that this is the case and your assertion is simply not good enough.

        Maybe the fact that you simply do not know why people show FT’s is the reason you use Periodograms. Have you ever admitted that to yourself? That maybe you just don’t understand?

      • “FFT can be traced back to – ready – Gauss”

        Oh yes. I once had an applied mathematics professor who showed us how to do discreet Laplace transforms, very close to DFT’s, similar to how Gauss did his. He published his work in the 1930s. I bet he was one of those people who cried when they first saw Visicalc.

      • So I read your pdf. You define a LT over a boundary as opposed as from an initial time. I’ve never seen it as such. I don’t think that’s correct.

        Bonus question. I see you’re in Ithaca. I was taunting Willis a few weeks ago and he mentioned how great some heat transfer guy at Duke was (funny). I mentioned the heat transfer work at Cornell and “that guy who died a few years ago” which was disrespectful. Who was that guy? He did pool boiling problems for the Navy, iirc.

      • Dinostratus wrote comments on Sept 26 at 12:37 pm and 12:46 pm, apparently to me. Three paragraphs total – and not one coherent thought. So I can’t help much. Here are my best efforts.

        (1) “how to do discreet [sic] Laplace transforms, very close to DFT’s”
        —That would be numerical integration.

        (2) “some heat transfer guy at Duke was (funny).”
        — Almost certainly Robert G. Brown

        (3) “I mentioned the heat transfer work at Cornell and “that guy who died a few years ago” which was disrespectful. Who was that guy? He did pool boiling problems for the Navy, iirc.”
        —Sadly – many have died. Not sure how many boiled pools!

        Bernie

      • Bernie, pretty important comment here, “So I read your pdf. You define a LT over a boundary as opposed as from an initial time. I’ve never seen it as such. I don’t think that’s correct.”

        Care to comment? If you can’t even properly define a LT then it kinda makes your work…. suspect.

      • Dinostratus said : September 26, 2015 at 6:33 pm

        “Care to comment? If you can’t even properly define a LT then it kinda makes your work…. suspect.”

        OH STOP IT! Grow up!

        Have you never seen a two-sided LT.

        And the LT is totally irrelevant to the FFT issue here.

        I’m through with your games.

      • No. I’ve never, ever, never seen a two sided LT. There is a fundamental reason a LT is found by integrating over the first moment of jw while a LT is found by integrating over s that has everything to do with the bounds of the integral.

        At best I thought I was mistaken. Then again, maybe it was a typo and I wanted to give you an opportunity to correct it. I feared it was a conceptual error. Unfortunately, what I find is that it is a conceptual error. It’s kind of disappointing for me frankly. I get tired of WIllis’s self aggrandizing hand waving and was hoping for something different.

  19. Please give Willis, and everyone reading this blog, a break and just get the link to the data that he asks for!!! He’s not lazy – you are if you want to promote your favorite solar-climate study and not do the work to find or get the data – after all Willis will be doing the truly heavy lifting in trying to verify the paper.

    If you are too lazy to get the data, then please save your precious energy and don’t post the link to the paper.

    I can’t believe all the juvenile bitching about just complying with a simple and logical request…

  20. Solar wind bursts

    Willis,
    You write article after article about the sun and appear to have never investigated how the sun changes and how the sun affects planetary climate (i.e. it appears you are not interested in the sun or how the sun modulates planetary climate and have not read papers about the subject and thought about the subject. It appears you like to write biased articles on the solar modulation of planetary cloud cover for what ever reason.). We need someone how has done the research to explain what is currently happening to the sun and how that change will affect planetary climate. I volunteer as soon as there is significant cooling.

    If you look at the graph at this site Ap (blue line at the bottom of the graph, Ap is a measurement of the disturbance of the earth’s geomagnetic field by solar wind bursts) you will see that the start of the current El Niño correlates with the sudden increase in solar wind bursts.

    You will also see that Ap is currently the highest measured in the solar cycle while the number of sunspots is the lowest.

    http://www.solen.info/solar/

    Solar wind bursts cause the planet to warm by creating a space charge differential in the ionosphere which in turn causes current flow a high latitude regions of the planet to the equator. The return path for the current is in the ocean. This process is called electroscavenging.

    The solar wind burst effect lasts from 2 to 5 days, so a large number of small solar wind bursts has more climatic effect than a single solar wind burst.

    The solar wind bursts are primary caused by coronal holes, not by sunspots.

    What causes coronal holes to appear on the sun where, when, how many times, the shape of the coronal hole, the area of the coronal hole, and strength of the coronal hole is not known and does not correlate with the number of sun spots or the time in the period of the sunspot.

    Comment:
    Coronal holes rotational speed matches that of the core of the sun, rather than the ‘surface’ of the sun. The surface rotational speed of the sun decreases by 40% comparing the equator of the sun to high latitude regions of the sun. Sunspots which float on the surface of the sun, rotate at the same speed as the surface of the sun. Coronal holes do not. This observational fact supports the assertion that what is causing coronal holes to appear is something deep within the sun rather than convection zone of the sun.

    Sunspots and coronal holes both affect the strength the strength and extent of the solar heliosphere which is the name for the tenuous gas and pieces of the magnetic field that are thrown off the sun. The heliosphere extends well past the orbit of Pluto.

    The solar heliosphere block GCR (galactic cosmic rays, mostly high speed protons). So when the solar heliosphere is strong the pieces of magnetic field in the solar heliosphere block GCR so there are less GCR striking the earth.

    The increased GCR will cause the planet to cool at high latitude regions, if there are not solar wind bursts to remove the cloud forming ions. GCR will only cause the planet to cool at high latitude regions as the earth’s magnetic field in lower latitudes blocks the GCR.

    Note the difference in the regions of the planet that are affected by solar wind bursts and solar heliosphere’s modulation of the amount of GCR that strikes the earth. Electroscavenging both high latitude regions and the equator while solar heliosphere only high latitude regions. This comment is true as long as the geomagnetic field is not strongly tilted or is in an excursion.

    Geomagnetic specialists in the last 10 years have found the earth’s geomagnetic field tilt abruptly changes and the earth’s geomagnetic field strength abruptly changes (factor of 5 to 10).
    The change in the tilt of the geomagnetic field cause the regions where GCR affects the earth’s climate to move to lower latitudes which causes cooling.

    During the very, very, large geomagnetic excursion there are suddenly multiple magnetic poles on the surface of the earth. This fact and the fact that the geomagnetic field drops in strength by factor of 5 to 10 causes the planet to cool. We are currently experiencing what appears to be abrupt start to a geomagnetic field excursion. The earth’s geomagnetic field strength started dropping in strength at 5% per decade in the mid 1990s where for the last 150 years it has been dropping at in strength at 5% per century. Why this so is not known. This is a paradox as the maximum drop in field strength that a change in the liquid flow in the core can cause is 5% per century as there is a back emf generated in the liquid core that resists very, very fast field changes and there is no known internal mechanism that can causes massive abrupt flow of magma in the liquid core of the planet to suddenly start in the 1990s.

    By physical constraints on the problem the sudden abrupt change to the geomagnetic field must have been causes by a sudden change in charge on the surface of the earth. By the a process of elimination there is only one object in our solar system that could possibly cyclically cause sudden charge differences which is the sun. The observational fact that we are currently experience a drop in the geomagnetic field strength that is ten times faster than possible for a core based change in the earth, provides support for the assertion that the sun and hence other stars are significantly different that the standard model. Paradoxes change or invalidate theories.

    http://sait.oat.ts.astro.it/MmSAI/76/PDF/969.pdf

    Once again about global warming and solar activity
    By K. Georgieva, C. Bianchi and B. Kirov
    Solar activity, together with human activity, is considered a possible factor for the global warming observed in the last century. However, in the last decades solar activity has remained more or less constant while surface air temperature has continued to increase, which is interpreted as an evidence that in this period human activity is the main factor for global warming. We show that the index commonly used for quantifying long-term changes in solar activity, the sunspot number, accounts for only one part of solar activity and using this index leads to the underestimation of the role of solar activity in the global warming in the recent decades. A more suitable index is the geomagnetic activity which reflects all solar activity, and it is highly correlated to global temperature variations in the whole period for which we have data

    In Figure 6 the long-term variations in global temperature are compared to the long-term variations in geomagnetic activity as expressed by the ak-index (Nevanlinna and Kataja 2003). The correlation between the two quantities is 0.85 with p<0.01 for the whole period studied. It could therefore be concluded that both the decreasing correlation between sunspot number and geomagnetic activity, and the deviation of the global temperature long-term trend from solar activity as expressed by sunspot index are due to the increased number of high-speed streams of solar wind on the declining phase and in the minimum of sunspot cycle in the last decades

    http://gacc.nifc.gov/sacc/predictive/SOLAR_WEATHER-CLIMATE_STUDIES/GEC-Solar%20Effects%20on%20Global%20Electric%20Circuit%20on%20clouds%20and%20climate%20Tinsley%202007.pdf

    The role of the global electric circuit in solar and internal forcing of clouds and climate

    The solar wind affects the galactic cosmic ray flux, the precipitation of relativistic electrons, and the ionospheric potential distribution in the polar cap, and each of these modulates the ionosphere-earth current density. On the basis of the current density-cloud hypothesis the variations in the current density change the charge status of aerosols that affect the ice production rate and hence the cloud microphysics and climate [e.g., Tinsley and Dean, 1991; Tinsley, 2000]. The underlying mechanism is that charged aerosols are more effective than neutral aerosols as ice nuclei (i.e., electrofreezing) and that the enhanced collections of charged evaporation nuclei by supercooled droplets enhance the production of ice by contact ice nucleation (i.e., electroscavenging). Both electrofreezing and electroscavenging involve an increase in ice production with increasing current density [e.g, Tinsley and Dean, 1991; Tinsley, 2000]. The current density-cloud hypothesis appears to explain solar cycle effects on winter storm dynamics as well as the day to-day changes of Wilcox and Roberts Effects [e.g., Tinsley, 2000]. Kniveton and Todd [2001] found evidence of a statistically strong relationship between cosmic ray flux, precipitation and precipitation efficiency over ocean surfaces at midlatitudes to high latitudes, and they pointed out that their results are broadly consistent with the current density-cloud hypothesis.

    C) Satellite measurement of planetary cloud cover that confirms planetary cloud cover is modulated by GCR and solar wind bursts

    Mechanism where Changes in Solar Activity Affects Planetary Cloud Cover
    1) Galactic Cosmic Rays (GCR)
    Increases in the suns large scale magnetic field and increased solar wind reduces the magnitude of GCR that strike the earth’s atmosphere. Satellite data shows that there is 99.5% correlation of GCR level and low level cloud cover 1974 to 1993.

    2) Increase in the Global Electric Circuit
    Starting around 1993, GCR and low level cloud cover no longer correlate. (There is a linear reduction in cloud cover.) The linear reduction in cloud cover does correlate with an increase in high latitude solar coronal holes, particularly at the end of to the solar cycle, which cause high speed solar winds. The high speed solar winds cause a potential difference between earth and the ionosphere. The increase in potential difference removes cloud forming ions from the atmosphere through the process “electro scavenging”. Satellite data (See attached link to Palle’s paper) that confirms that there has been a reduction in cloud cover over the oceans (There is a lack of cloud forming ions over the oceans. There are more ions over the continents due to natural radioactivity of the continental crust that is not shielded from the atmosphere by water.)

    As evidence for a cloud—cosmic ray connection has emerged, interest has risen in the various physical
    mechanisms whereby ionization by cosmic rays could influence cloud formation. In parallel with the analysis
    of observational data by Svensmark and Friis-Christensen (1997), Marsh and Svensmark (2000) and Palle´
    and Butler (2000), others, including Tinsley (1996), Yu (2002) and Bazilevskaya et al. (2000), have developed the physical understanding of how ionization by cosmic rays may influence the formation of clouds. Two processes that have recently received attention by Tinsley and Yu (2003) are the IMN process and the electroscavenging process.

    http://www.albany.edu/~yfq/papers/TinsleyYuAGU_Monograph.pdf

    Atmospheric Ionization and Clouds as Links Between Solar Activity and Climate

    • William Astley September 23, 2015 at 1:06 am

      Solar wind bursts

      Allergic to verbs?

      Willis,
      You write article after article about the sun and appear to have never investigated how the sun changes and how the sun affects planetary climate (i.e. it appears you are not interested in the sun or how the sun modulates planetary climate and have not read papers about the subject and thought about the subject. It appears you like to write biased articles on the solar modulation of planetary cloud cover for what ever reason.).

      Hogwash. I have 18 posts on exactly how the sun changes. I have thought and read and written extensively about the subject … have you?

      I note that you have not quoted a single thing I’ve said, nor have you objected to any one of my findings, nor pointed out anything that you think is wrong in my claims. Instead you wave your hands, make vague unpleasant allegations, and throw mud … which on my planet is a sure sign you have no actual ammunition.

      We need someone how has done the research to explain what is currently happening to the sun and how that change will affect planetary climate. I volunteer as soon as there is significant cooling.

      You can’t explain what’s happening until it cools down? How does that work? Too hot for you?

      If you look at the graph at this site Ap (blue line at the bottom of the graph, Ap is a measurement of the disturbance of the earth’s geomagnetic field by solar wind bursts) you will see that the start of the current El Niño correlates with the sudden increase in solar wind bursts. …

      Pass. You provide lots of claims like

      “The increased GCR will cause the planet to cool at high latitude regions, if there are not solar wind bursts to remove the cloud forming ions. GCR will only cause the planet to cool at high latitude regions as the earth’s magnetic field in lower latitudes blocks the GCR.”

      The problem is that you provide no evidence to back that claim up. No temperature records, no cross-correlation analysis, no backup for the idea that “solar wind bursts” are able to “remove the cloud forming ions”. Instead you just put it out there and expect me to believe it … and the website you cite doesn’t even mention “solar wind bursts”, so I have to assume you made that up and there is no scientific definition of a “solar wind burst”. The website also contains lots of pretty pictures, but I can’t find data on “solar wind bursts” there anywhere.

      Sorry, I’m a skeptic. Call me crazy, but I need data before I’ll believe things.

      w.

      • The problem is that you provide no evidence to back that claim up. No temperature records, no cross-correlation analysis, no backup for the idea that “solar wind bursts” are able to “remove the cloud forming ions”. Instead you just put it out there and expect me to believe it … and the website you cite doesn’t even mention “solar wind bursts”, so I have to assume you made that up and there is no scientific definition of a “solar wind burst”. The website also contains lots of pretty pictures, but I can’t find data on “solar wind bursts” there anywhere.

        Willis says which of course is not quite true. I have along with others have provided Willis with much evidence to support a solar/climate relationship. What is more correct is Willis does not view the evidence as convincing which is his opinion nothing more nothing less.

        On the other hand those of us who believe in a solar/climate connection think the evidence Willis has presented to counteract our evidence in our opinion is also not convincing.

        My answer is let us see what the global temperature response will be to this prolonged solar minimum event that is currently taking place . Maybe this will clear up matters.

      • Willis,

        I provided a link to two peer reviewed papers (Tinsley’s paper is a review paper which includes multiple references to other peer reviewed papers) in my quote which supports exactly what I said,

        I have more papers I can quote but you appear to not be interested in doing science. What you are doing is trying to show off or attempting to entertain as opposed to try to solve a holistic problem. You appear to have done no research into the problem. You declined to look at the graph of Ap. Solar wind bursts are causing the warming.

        Did you miss this paper? Do you need new glasses?

        http://sait.oat.ts.astro.it/MmSAI/76/PDF/969.pdf

        Once again about global warming and solar activity
        By K. Georgieva, C. Bianchi and B. Kirov
        Solar activity, together with human activity, is considered a possible factor for the global warming observed in the last century. However, in the last decades solar activity has remained more or less constant while surface air temperature has continued to increase, which is interpreted as an evidence that in this period human activity is the main factor for global warming. We show that the index commonly used for quantifying long-term changes in solar activity, the sunspot number, accounts for only one part of solar activity and using this index leads to the underestimation of the role of solar activity in the global warming in the recent decades. A more suitable index is the geomagnetic activity which reflects all solar activity, and it is highly correlated to global temperature variations in the whole period for which we have data

        In Figure 6 the long-term variations in global temperature are compared to the long-term variations in geomagnetic activity as expressed by the ak-index (Nevanlinna and Kataja 2003). The correlation between the two quantities is 0.85 with p<0.01 for the whole period studied. It could therefore be concluded that both the decreasing correlation between sunspot number and geomagnetic activity, and the deviation of the global temperature long-term trend from solar activity as expressed by sunspot index are due to the increased number of high-speed streams of solar wind on the declining phase and in the minimum of sunspot cycle in the last decades

        Where you aware that planetary cover decreased during the warming period of the last 20 years? The amount of warming due to the decrease in planetary cover is sufficient to explain all in the last 20 years.

        Science requires the ability to compose hypotheses. Note hypotheses is plural, not singular. Have you heard the comment the scientific problems are solved by ‘scientific’ imagination?

        The hypothesis have certain logical characteristics and features.

        The problem which we are trying to solve is what causes the planet to cyclically warm and cool in the past?

        Note the past cyclical warming and cooling is in the same latitudes as recently warmed. Is it possible that the warming in the last 150 years is primarily due to natural causes, rather than the increase in atmospheric CO2.

        Greenland ice temperature, last 11,000 years determined from ice core analysis, Richard Alley’s paper. William: As this graph indicates the Greenland Ice data shows that have been 9 warming and cooling periods in the last 11,000 years.

        https://wattsupwiththat.files.wordpress.com/2012/09/davis-and-taylor-wuwt-submission.pdf

        Davis and Taylor: “Does the current global warming signal reflect a natural cycle”

        …We found 342 natural warming events (NWEs) corresponding to this definition, distributed over the past 250,000 years …. …. The 342 NWEs contained in the Vostok ice core record are divided into low-rate warming events (LRWEs; < 0.74oC/century) and high rate warming events (HRWEs; ≥ 0.74oC /century) (Figure). … …. "Recent Antarctic Peninsula warming relative to Holocene climate and ice – shelf history" and authored by Robert Mulvaney and colleagues of the British Antarctic Survey ( Nature , 2012, doi:10.1038/nature11391),reports two recent natural warming cycles, one around 1500 AD and another around 400 AD, measured from isotope (deuterium) concentrations in ice cores bored adjacent to recent breaks in the ice shelf in northeast Antarctica. ….

    • William Astley September 23, 2015 at 10:13 am

      Willis,

      I provided a link to two peer reviewed papers (Tinsley’s paper is a review paper which includes multiple references to other peer reviewed papers) in my quote which supports exactly what I said,

      Are you as stupid as Lady Gaiagaia? I said specifically I was not interested in anyones blather about wonderful solar studies unless you provide TWO LINKS, one to the paper and one to the data.

      Is there some part of “TWO” that escapes your grasp?

      Sheesh … and as I pointed out above, your cited website has pretty pictures but no data.

      w.

      • William Astley September 23, 2015 at 10:13 am

        You’ve been told multiple times on here that your plot of Alley’s data is incorrect but still you keep trotting it out! Clearly it is you who is not willing to do real science.

  21. Can I just chip in and say NoTricksZone makes a valuable contribution to the debate and provides useful colour on the state of play in Germany, the heart of climate darkness. That Willis and others may take issue with a positing from time to time should not take away from that.

    • I can only agree, Steve. Pierre is one of the good guys in general, and like me, at times he swings and misses …

      w.

  22. Hi, my first post here (I think) so please be gentle. From what I can gather from the debate here is it a fair statement Willis that you fail to find any robust data that implies any signature of the solar cycles’ 11 year period in the climate record BUT// you accept that there are longer term solar variations that are a (possibly direct) cause of a change in climate say over 100’s of years, the Maunder minimum being an example….Are these fair statements?

    • Apologies Willis, Scrub the second part about Maunder, just found your 2014 guest post (for some reason it didn’t appear from the search engine when I typed maunder minimum but did when I typed “Maunder”) on Maunder and Dalton Sunspot Minima.

  23. This paper reminds me of the interesting phenomena that occurs when you have two clocks ticking away within earshot (esp. at night when you can’t get to sleep).

    For a while, the clocks are happily clicking in sync – then they seem to quickly drift apart and seem almost random for a while, then they “phase lock” again and seem to click in sync for a while, before they again drift apart.

  24. Willis,

    You seem to be restricting your investigation to direct effects at the surface due to the 11 year solar cycle?

    “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:”

    Whereas in actual fact, many studies link longer solar cycles with changes in the stratosphere which can and do affect climate at lower levels. But even assuming I’m wrong about this restriction, if somebody were to send you ANY paper and data which purported to find a link or a mechanism whereby solar activity affects climate over decadal/multidecadal/centennial time scales, you would still not guarantee to publish a formal rebuttal:

    ” 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.”

    Thus, you will publicly affirm any paper which your own analysis confirms is valid . . . . . but all those which you do not confirm as valid, but which you do not formally, publicly debunk, we are still supposed to assume therefore that they are invalid?

    • I’m sorry, Jaime, but you’ve misunderstood me. What I said was that if the paper and data turn out to be garbage, I may not write a response. I’ve written about tons of garbage already.

      But if it stands up, if their analysis is correct and they DID find a real solar effect here on the ground, I’ll write about it.

      w.

      • Hmmm, not really sure I did misunderstand you, but anyway, best of luck going through all those papers and accompanying data and throwing out the ‘garbage’.
        I suspect you will not find the evidence which you suspect you will not find but for reasons contrary to those which I suspect you will attribute your lack of success to in that regard!

      • Jaime Jessop September 23, 2015 at 12:48 pm

        I suspect you will not find the evidence which you suspect you will not find but for reasons contrary to those which I suspect you will attribute your lack of success to in that regard!

        Oh, well isn’t that just the cutest, most educated way I can imagine of nastily accusing me of scientific malfeasance … piss off, jerkwagon. I do good, honest, transparent, out-in-the-open science, and I report exactly what I’ve found, along with the data and the code that supports my findings.

        Since you can’t find anything wrong with my data and code or my conclusions, you resort to puerile fancied-up personal insults. Go crawl back under your rock, I’m not your punching boy. When you find something scientifically wrong with my work, crawl back out and tell us. Until then, talk to the hand.

        w.

      • Wow, extremely touchy, aggressive and insulting to boot. I could respond in kind but that would achieve very little. You just continue with your ideologically-driven “good, honest, transparent, out-in-the-open science” crusade against an entire area of scientific research linking solar activity with climate change. I do wonder why you aggressively and insultingly blog about your triumphs on WUWT rather than taking your findings to the appropriate people – the authors and the publishers of the papers you so merrily and so thoroughly ‘debunk’, in order that their ‘garbage’ can be taken out of circulation.

      • Jaime Jessop September 24, 2015 at 1:12 am

        Wow, extremely touchy, aggressive and insulting to boot.

        Oh, that’s rich. You open the bidding by saying that I’ll fail to find what I want because I’m not really looking, just pretending to look, as I quoted above … and you call me insulting?

        As I said above, you can’t find any scientific arguments with my work, and as a result you have shifted to unpleasant innuendo with no foundation.

        And unlike some people, I won’t put up with that. You don’t get to freely insult me by saying I’m not really looking, that’s a crock of excrement, despite wrapping it up in pretty, flowery words. When you do that, I will indeed respond in kind. Consider me to be karma incarnate. When you slam me, it hits you in the face.

        Don’t like it? Then don’t open the bidding with baseless innuendo and poorly-veiled insults.

        Physician, heal thyself.

        w.

      • Before you launched into your foul-mouthed indignant and abusive tirade, you might have stopped to consider more carefully what i was questioning – and that which I was implying with my “pretty, flowery words”, points which you have not adequately responded to IMO. I was NOT questioning the scientific integrity of any work which you have done thus far; I was merely questioning the scope of that endeavour and whether it permitted you to claim that there is no scientific evidence for a solar link to climate change.

        So let’s dispense with the pretty and flowery and I’ll ask you again: do you claim that there is no valid scientific evidence which demonstrates a solar influence upon climate over multi-decadal/centennial, even multi-centennial time scales? That being the case, I should be interested to know what external forcing of climate change you postulate in its place, or do you consider that randomised internal variability only has propelled climate variability throughout the Holocene? I’ll ask you again: what do you hope to prove/achieve by this current ‘gauntlet throwing exercise’ when you do not guarantee to formally, publicly debunk any paper which is thrown at you, when the conditions which you impose are likely to result in only a very small cross-section of papers from the scientific literature on solar/climate change correlations/mechanisms being presented to you?

      • Jaime Jessop September 24, 2015 at 2:20 am

        Before you launched into your foul-mouthed indignant and abusive tirade, you might have stopped to consider more carefully what i was questioning – and that which I was implying with my “pretty, flowery words”, points which you have not adequately responded to IMO. I was NOT questioning the scientific integrity of any work which you have done thus far; I was merely questioning the scope of that endeavour and whether it permitted you to claim that there is no scientific evidence for a solar link to climate change.

        Say what? I’ve made no such claim. Here’s what I said in the head post:

        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.

        You see, I’m well aware that it isn’t possible to demonstrate a negative. There’s no way to show, for example, that the ~11-year solar fluctuations have no effect on man-in-the-moon marigolds. All I can say is that I’ve looked at piles of claims and haven’t found one yet that hangs together.

        So let’s dispense with the pretty and flowery and I’ll ask you again: do you claim that there is no valid scientific evidence which demonstrates a solar influence upon climate over multi-decadal/centennial, even multi-centennial time scales?

        “Ask me again“? You never asked me that before, how is this “again”?

        To answer your question, however, I have never to my knowledge made such a claim, nor anything like it. Consider my quote from the head post:

        First, it’s obvious that the sun affects the climate. Without the sun, we’d be pretty cold.

        Pretty clear, no?

        And consider my previous comment:

        I fear that’s far too vague to be of use. What do you mean by “alters the earth’s behavior”? AS I CLEARLY SAID,

        First, it’s obvious that the sun affects the climate. Without the sun, we’d be pretty cold.

        But whether the sun affects the climate is not the question. The question is whether we can find any sign of the 11-year cycle in surface temperature datasets.

        So if that is truly what you were questioning:

        1. Learn to read. The answer was in both the head post and the comment thread.

        1. Learn to quote. If you think I’ve made a claim about something, QUOTE MY EXACT WORDS!

        Regards,

        w.

      • OK Willis, so you are only saying that there is no “sign of the 11-year cycle in surface temperature datasets”. So your comment “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” was a bit off target really when not all (not even a majority) of those papers purport to show any effect on ‘climate’ during the 11 year cycle. The Chinese paper for instance says,
        “A recent study demonstrates the existence of significant resonance cycles and high correlations between solar activity and the Earth’s averaged surface temperature during centuries.”
        Clearly a longer period is involved. Who really cares that much whether the Sun does affect surface temperature over 11 years when climate is usually referenced by periods of at leat 30 years? That is the issue. Is there a solar signal in the surface datsets going back over 150 years? In the paleo records stretching back over centuries, milennia?

  25. Some just make up their mind (Like most warmists).There was a guy over a Lucia’s “Phil” me thinks who insisted that NH ice was definitely melting and posted lengthy postings with citations ect about 7 years ago if I recall. I wonder where he is now? Just a note: each day without sun is -10C so if today 30C tomorrow 20C next day 10C next day -10C ect. The sun has no effect on climate… These non-solar people will simply “fade away” from blogging as did poor ol Phil and his ice. LOL

  26. The interesting result isn’t in the paper, it is in your reanalysis of the data from the paper. The two peaks in the periodogram around 2.33, 3.33 and 5.5 years appear to be quite robust and show up in global temperature decompositions of many types (e.g. Fourier transforms, wavelets, periodograms). As you like to put it, this is visible to the Mark I eyeball as well as being fairly clearly responsible for a lot of the systematic “wiggle” in the various temperature series.

    The interesting thing here is the that all of these frequencies seem related to ENSO’s not-quite-periodic behavior. ENSO’s period runs from 2 to 8 years, and lo, if we multiply out these numbers we find that 2.3*3.3 = 7.6 years. 2.3 * 5.5 = 12.7 years. 3.3*5.5 = 18 years. 2.3*3.3*5.5 = 42 years. Allowing for the fact that these numbers are not exact and the quasi-periodic self-resonances of the climate are broad and possibly variable, it suggests that the climate cycle (including ENSO) has heterodyning interference where certain periods can produce an unusually strong or weak ENSO because of addition or cancellations across these three (possibly primary) cycles. These intervals all tend to show up in the recent ENSO record.

    Are they phased locked to solar phenomena in some way? Dunno. But one thing I do know is that they are bloody odd intervals (1/3 of a year progressive offsets?) and that there should be something we can identify as the “oscillators” in question — cycle times for currents, bobbles in the jet streams, whatever.

    rgb

    • Thanks, Robert, always a pleasure to hear from you. You say:

      The interesting result isn’t in the paper, it is in your reanalysis of the data from the paper. The two peaks in the periodogram around 2.33, 3.33 and 5.5 years appear to be quite robust and show up in global temperature decompositions of many types (e.g. Fourier transforms, wavelets, periodograms). As you like to put it, this is visible to the Mark I eyeball as well as being fairly clearly responsible for a lot of the systematic “wiggle” in the various temperature series.

      The interesting thing here is the that all of these frequencies seem related to ENSO’s not-quite-periodic behavior. ENSO’s period runs from 2 to 8 years, and lo, if we multiply out these numbers we find that 2.3*3.3 = 7.6 years. 2.3 * 5.5 = 12.7 years. 3.3*5.5 = 18 years. 2.3*3.3*5.5 = 42 years. Allowing for the fact that these numbers are not exact and the quasi-periodic self-resonances of the climate are broad and possibly variable, it suggests that the climate cycle (including ENSO) has heterodyning interference where certain periods can produce an unusually strong or weak ENSO because of addition or cancellations across these three (possibly primary) cycles. These intervals all tend to show up in the recent ENSO record.

      I’ve wondered about these cycles for several years now, and have commented on them before. I have no idea what their cause might be, but I’ve seen combinations of them in some climate datasets … but not others.

      I always get a bit uneasy when I start adding and subtracting cycles, however. It’s far too easy for me to fool myself in that regard. Nature is naturally cyclical, and these cycles can persist for a while … or they can last a few years and disappear. And the number of possible combinations of even just three numbers is quite large, particularly when we start to include things like the synodic periods.

      The ~5.5 year cycle shows up in the periodogram of the sunspot data (actually around 5.75), but I have no clue where the shorter cycles come from.

      Always more to learn, and the planet only throws curveballs …

      w.

  27. Yeah, Willis, this has become really tiresome. It’s a fact that the sun cycles vary a mere 1 W/m2 around the mean. So proponents are automatically supporting some “magic” feedback that blows that up to some arbitrarily large effect. Don’t they realize that’s the same thing the warmunators do w/CO2?

    • Except that there are demonstrable mechanisms which show how the so-called “amplification” works, but about which true believers like Willis refuse even to read, just like “climate change” advocates refusing to acknowledge skeptics.

      Same, same.

      • Lady Gaiagaia September 23, 2015 at 5:58 pm

        Except that there are demonstrable mechanisms which show how the so-called “amplification” works, but about which true believers like Willis refuse even to read, just like “climate change” advocates refusing to acknowledge skeptics.

        Same, same.

        You pathetic lying jerkwagon, we’ve been over this before. I have clearly offered to analyze whatever you want me to read. All you have to do is provide me a link to the paper and the data used in the paper. Without the data, I can’t possibly determine whether or not your whizbang “demonstrable mechanisms” has actually been demonstrated. That’s how science works. You put up the claims and the data, and people try to poke holes in either or both. And I’m more than happy to do that … but science goes nowhere without data, and you’ve refused and refused, time after time, to provide it.

        So your claim that I am “refusing to read” something or other is just another of your endless lies. You’re the one doing the refusing. If you want to blindly believe in fairy tales spun without data, that’s up to you. Me, I need data to do an analysis of the tales.

        But because you are either too stupid or too lazy to provide links to the data, instead you accuse me of “male chauvinism”, of not being a proper investigator … classy. Real classy.

        You are one sick puppy, Lady, if you are a lady at all. From your actions, my best guess is that you’re actually a pimply-faced sixteen year old boy hunched over a computer in mom’s basement, sniggering behind your hand at the brilliant social statements you’re making.

        So anyhow, Pimply … I hope you don’t mind if I call you “Pimply”, because to call you a Lady would be a lie … anyhow, Pimply, you do what you want. You’ve proven to us all that you are either stupid, lazy, a sock-puppet, or a troll, and I don’t particularly care which one.

        w.

      • Wow.

        Willis plain and simple for all to see.

        Sad, really, because you are at the end of your meaningless existence, during which time you have contributed nothing, but only tried to hijack the observations of your betters.

        ‘Nuff said.

    • Yeah. And don’t forget — that is 1 watt/m^2 out of an average of roughly 1370 (at the TOA) in a cycle that varies annually by 91 W/m^2 as the Earth traipses around its elliptical orbit, with a marginal effect that is further reduced by albedo and etc.

      With that said, sure, chaotic self-organized systems can sometimes phase lock to weak driving signals if there is some sort of structural resonance in the dynamics to phase lock to. If there was some oceanic gyre that just happened to have an 11 year cycle, one could imagine some sort of resonant amplification of an effect. In a noisy dissipative system with no strong natural frequencies that match, though, it is certainly a lot more challenging to see how this would work.

      So sure, it isn’t impossible. On the other hand, it is far, far from certain, and the onus of proof is very much on anyone who would assert otherwise.

      Myself, I maintain a cheerfully open (but skeptical) mind. You want to make me believe? Show me proof that goes beyond possible accident or numerology. Propose a mechanism that actually sounds plausible. Time will always tell, as more, better, data rolls in. In the meantime, I think we all remain blissfully ignorant about the way the climate really works, what is important and what is not. After all, it has a fair number of moving parts…

  28. Willis you can not attack my theory with your approach to the data you use to try to spin it in favor of your thinking because my theory is specific with a specific outcome. In other words my theory has no BS or excuses it is either going to be correct or wrong. That keeps you from be able to prove it is wrong.

    What is unfortunate however, is if the global temperatures decline while the sun is in this prolonged solar minimum state and the solar criteria meets what I have called for, you will probably still not admit that I was correct or even that I might be correct.

    At that time your take on the climate along with AGW theory should both be on their way of being obsolete or if not obsolete will be diminished to a large degree.

    That time should be here before this decade ends in my opinion.

    Maybe you will prove me wrong and come on board, but I doubt it.

    If on the other hand the global temperature response should be steady or rise in response to this prolonged solar minimum event ,I would unlike yourself admit to being wrong.

    That being the important difference which is I am open minded enough to know if I am wrong I am wrong, and at that point if it should come I will face the reality.

    In contrast AGW enthusiast for sure and you Willis (?),with your take on the climate(which is wrong in my opinion in some aspects) I do not believe will come around.

    • Sal, give it up! I am so tired of pointing out that just because two things happen at the same time, does not in any way point to a cause and effect!!!! So no, you will not be proven right or wrong. But I am beginning to contemplate stubborn (and I am being overly nice).

    • I personally will cheerfully re-examine the issue if global temperatures significantly cool in the coming solar minimum. So will many people. I hope you will be equally cheerful in discarding your hypothesis in the event that the Earth refuses to cooperate and either warms or remains neutral (or really even if it only weakly cools) in the coming years.

      At the same time, bear in mind that (as Pamela pointed out) even if it cools or strongly cools, it doesn’t mean that your theory is correct, only that the GCMs are even wronger than wrong, where they are already pretty wrong. In that case, it won’t mean that CO2 is not a greenhouse gas and doesn’t produce warming, either. It will only mean that the range of temperature modulation available to CO2 at its current concentration is lower than most people think (where at least some people, e.g. Lindzen and Choi, think it is pretty small and I’ve read serious papers that suggest that certain evidence from soundings suggest that it is even smaller than L&C think it is), lower than the range of natural variation.

      Note well, that as a chaotic nonlinear system with strong internal feedbacks and an enormous cyclic reservoir, the Earth doesn’t actually require any reason at all to suddenly warm or cool. As in one could lock everything — solar input, CO2 concentration, pollution, aerosols, etc — to single fixed values and run the climate forward at full resolution for a hundred years starting from precisely given initial conditions separated by no more than the beating of butterfly wings here and there and you’d still end up with a rather enormous range of possible outcomes. I’m pretty sure some of those outcomes would show strong cooling. Runs like that happen even in the GCMs that on average show strong warming! At that point, everything is a matter of probabilities — looking for “a cause” as if there is such a thing becomes a bit of a joke.

      I rather expect that this is the joke Mother Nature is playing on us. Humans are reductionists. We can’t imagine four whole dimensions without getting a headache. Trying to cope with a system with ten to a high power number of dimensions and the vast permutation of possible dynamical trajectories therein is something we cannot to do. We invented God and Satan for precisely the same reason. Who can praise or blame the butterfly? Who can accept that we have no control, no ability to predict, no ability to prevent, no real ability to even imagine?

      Microscopic physics is nicely predictable. Causes are clear and easily enough understood. But more is different. Long before one reaches planetary complexity we get things like me. And not even I can predict which keys I’m going to strike next, what my next thoughts or words or actions will be, because I’m partially governed by a vast, vast pool of effective entropy that can reroute my “will” in the blink of an involuntary eye.

      It’s like this. When I used to go fishing, sometimes he’d catch fish at his end of the boat and I would catch nothing at my end, fishing for the same fish with the same bait and casting into the same place. His explanation? I “wasn’t holding my mouth right”.

      I have friends who have accused me of being the reason Duke lost the NCAA championship back in 1979 in the final game. I watched the game with family members (including UNC fans) instead of the same friends I watched every other game with. This was enough to influence the game.

      In a couple of minutes, I’m going to get up, scratch a bit, and go into work. If I scratch three times, the world will continue to warm. If I scratch twice and cough, it will remain “paused” or even cool a tiny bit for the next decade. However, if I blow my nose and then discover I’m too late to even scratch at all, the world will plunge into rapid cooling, which will persist as long as I hold my mouth right and avoid watching championship games with UNC fans.

      It’s like that. Out of every moment, there are future trajectories that solve the equations of motion for the Universe that pack into and fork out of a tight little bundle grounded at every little spatiotemporal volume, probably all the way down to the Planck scale. Those trajectories are probably fractally distributed and intertwined. Perhaps there is some significant probability distribution that favors one generic bundle of possible future climate states over some other, perhaps not — it might even be remarkably uniform with only the illusion of some simple linear causal relationship, sometimes. But even if this is statistically true, this isn’t like statistical mechanics where the most probable thing is somehow homogeneous and capable of producing a thermodynamic average state that is indistinguishable macroscopically from all other most probable states. This is chaotic statistics — anything could happen and nothing is so unlikely as to be forbidden outside of egregious failures of the first or second laws.

      I’ll try to remember not to scratch, but YOU remember that if it cools, I predicted it — heck, I “caused” it! — and gave you a perfectly plausible reason. Just like I made Duke lose, way back in 1979.

      rgb

      • I know close enough to nothing about the climate that it might as well be nothing. But I do know one thing: no one has ever quantified the fraction of global warming that is supposed to be AGW.

        If they still can’t measure what they insist must be there, then it’s just too tiny to worry about.

  29. Not understanding your beef at all. The Sun is very obviously the primary driver of climate. The dirt-simple observation that day / night variation in insolation can cause changes of 40 – 50 deg F ought to be your first clue. There is good correlation between solar behavior and climate variation, which you seem to demonstrate with your figures above, and then proceed to dispute it. The most recent study by German climate scientists Horst-Joachim Lüdecke, Dr. Alexander Hempelmann and Carl Otto Weiss clearly show that global cooling is the bigger concern. They are very critical of the IPCC for continuing to push CO2 as more of a climate driver than the sun. You are on the wrong side of this issue!

    • Like many people, you miss the point. Yes, the sun is the main driver of climate, duh. I said that in the head post. That’s not the question.

      The question is, does the ~11-year sunspot cycle have any effect on the climate down here at the surface? I’ve looked, and I can’t find any such effect.

      And while Horst-Joachim Lüdecke, Dr. Alexander Hempelmann and Carl Otto Weiss may say that we are headed for global cooling until 2080, they don’t “clearly show” that. They claim it will happen, but the truth is … we don’t know. We simply don’t know if the world will be cooler or warmer in fifty years, and anyone who says they do know is hallucinating.

      Those three scientists base their claim on the superposition of cycles that they claim to have identified in the climate data. However, a quick look at their paper shows that they claim to have identified a 235-year cycle by using Fourier analysis on 250 years of data … that kind of nonsense may impress you, but me, I just laugh when I see that.

      w.

  30. richard verney September 23, 2015 at 6:11 am

    In a 3D world, a watt is not necessarily a watt. I would suggest that not all watts here on planet earth are born equal. The place where a watt resides (or buried) may yet prove to be rather material since the time for that energy to be picked up (resurface whatever) could well depend where it is in the system.

    A 3D world is very different to the 2D world so much beloved by climate scientists.

    Thanks, Richard. While it makes a difference where a watt resides, a watt is still a watt.

    And I haven’t a clue what you mean by “the 2D world so much beloved by climate scientists”. It makes me think that you’ve never looked at the code for the climate models, which is most assuredly 3D. There are many valid and cogent objections to the mainstream climate science … but claiming they’re not looking at a 3D world is not one of them.

    w.

  31. Wow, all this arguing and ZERO people have actually dealt with Willis’s request for two links: Paper and Data.

    I mean an expert is offering to do work for FREE for your gain in knowledge and all you can do is complain about very simple, realistic requirements. If you’d ever gone data spelunking yourself you’d really find that getting the data is at least half the work, plus or minus 100%…. which is a terrible indictment of the current scientific publishing process.

    The rudeness shocks me. It really is terrible human behavior to look this gift horse in the mouth.

    Willis, this is really great analysis, you hit it out of the ballpark again. I’ll peruse the 18 papers and find the one that looks the most compelling and has data, based on my experience and training in signal processing. It might take a day or two to complete. The answer might well be “none of the above” though…

    Please do share the source code though. I think I need to learn “R”, the packages are mostly better than Octave. I’ll be happy to share ports of some of my Octave/Matlab analysis libraries to “R” with you in return. The endpoint extension defaults (zero padding) in Octave, Matlab, and “R” are just wrong for what we are doing here (low frequency signal analysis), and that’s mostly what I try to fix.

    best regards,

    Peter

      • Peter you are not being objective and there are two sides to this issue despite what Willis tries to convey.

        It’s such a trivial effort to put Willis on the spot and make him do the work he promised. Why not just do it?

        You’ve put hours and hours of work into quoting endless pages of unfalsifiable stuff here. Basic communications principle: Keep It Simple. You aren’t doing that, you are attempting to win an argument in your own fashion. It’s a waste of time.

        I’m not going to continue this thread, I’m busy putting together a spreadsheet with these columns:

        “Paper Number” “Press Release” “Paywall Copy” “Free Copy” “Data”

        And the cell contents will be empty or hyperlinks. The table will speak for itself. I suspect there will be lots of empty cells.

        Peter

    • Too funny

      “The endpoint extension defaults (zero padding) in Octave, Matlab, and “R” are just wrong for what we are doing here (low frequency signal analysis), and that’s mostly what I try to fix.”

      Depends which package you are talking about in R

  32. The rudeness shocks me. It really is terrible human behavior to look this gift horse in the mouth

    Peter you are ridiculous. I did not notice your above comment. Ridiculous and all the other contributions are garbage?

  33. David A September 23, 2015 at 4:52 am

    One more way in which not all watts are equal…

    “Heat is a curious thing. In general it is described as an average of the kinetic energy of a given mass, such as one square meter. But this average, does not define the energy intensity of individual molecules or photons which composed said mass. A thought experiment if you will. Take a very large pot filled with water, say 100 square feet in area base, and ten feet deep, so 1000 square feet. and super insolated with a concave bottom, thinner in the center.

    Now apply two different heat sources to this pot, both of which are say 100 watts per 1 square feet. The first source, example A, is a 100 square foot heating element, 10, 000 watts total, with the conducted heat perfectly distributed throughout. From this source, no matter how perfect the insolation of the pot of water, it can only get to the T of the heating element, at which point the net flow between the element and the pot will be equal.

    Now consider a very different 100 watts per square foot AVERAGE source; example B. Apply a very small, say 1/4 inch square super heated but still 10,000 watts total, and so still 100 watts per square foot of the pot base. Given time, this greater energy intensity source of equal watts per square foot input to example A, can yet heat the pot of water to far higher Temperature. Under theoretical perfect insolation, the entire pot can reach the T of the source.

    Mmmm … it seems you are conflating the temperature of the source with the energy flux. A flux of energy is is a flow. If it stops flowing … well, it’s no longer a flux.

    You say that you have a heater which delivers a flux of 100 W/m^2. But it’s not doing that. Instead, it is delivering less and less energy as the pool warms up. This is because as you point out the energy flux is proportional to ∆T, the difference in temperature between the pool and the energy source. At equilibrium, the temperature difference is zero, so it is delivering exactly zero energy flux to the pot of water.

    As a result, your attempt to compare it to some other energy source fails. Neither one is delivering 100 watts per square metre, so your claim that they are equal or equivalent is simply not true. Because the ∆T is so much larger in one case than the other, the energy fluxes will also be different, they will not be 100 W/m2.

    A more realistic scenario to compare fluxes would be to have the energy delivered to the pot of water by a light source, one focused and one diffuse, but with the same energy (100 W/m2) entering the pots. What would happen then?

    Well, if the two pots were perfectly insulated, each light source would continually add 100 W/m2 to the pot of water, and it would all boil away … equally so, because in that case they actually are both delivering 100 W/m2 to the pots.

    Best regards,

    w.

  34. David A September 23, 2015 at 3:57 am

    In this sense (residence time of energy input) I maintain not all watts are equal. The residence time depends on both the materials encountered, and the WL of the watt under consideration. In a recent post Willis asserted that the LWIR re-striking the surface, via back radiation, was equal to the SW striking the surface,sans the clouds presence. Thus in this post just above, ignoring residence time, he limits the affect to a very small number. I have, on the basis of residence time, questioned the veracity of Willis’s proposition that, if the watt per square meter down welling LWIR due to clouds, is equal to the same watt per square meter down welling SW , sans clouds, then they make the same contribution to earth’s energy budget.

    Thanks for your comments, David. First, although it is possible that I said that, I doubt it greatly. It doesn’t sound like something I’d say. I’d need a citation for that.

    In any case, lets consider your example of the ocean:

    It is similar to determining the oceans geo thermal heat total. The output is infinitesimal to solar energy, but to know how much geothermal heat is currently in the oceans, we need to know the mean residence time of the total geothermal output, which could be many hundreds of days.

    Suppose we have an ocean at equilibrium, and we turn on say 0.1W/m2 of geothermal heat at the bottom. How hot will the ocean get at equilibrium?

    Well … it will heat up until it is losing (through a combination of radiation, conduction, and evaporation) an additional 0.1 W/m2. At that point it is back at equilibrium.

    Note that the “residence time” of the heat does not appear anywhere in that calculation. At equilibrium it must lose an extra 0.1W/m2, regardless of how long that heat is “residing”. To do that, it increases in temperature by a given amount, again regardless of how long that heat is “residing”.

    In any case, I don’t understand how you calculate what you are calling the “residence time” of heat in a situation at equilibrium. Heat is entering the ocean at a rate of 0.1W/m2, and at the same time the ocean is losing heat at the same rate. Since one watt is not distinguishable from any other watt, and since the heat is flowing in and out of the ocean at the same constant rate at equilibrium … how long is the “residence time” in this situation? One day? One week? 1,000 years?

    w.

  35. My money is on the sun causing these very small global temperature fluctuations over the past 10,000 years that everyone seems so concerned about. I have a feeling the end of the Modern Maximum will really start to show up in the temperature data beginning late 2016.

    • IMO, variation in solar activities, modulation of irradiance by orbital mechanics, affecting insolation, oceanic circulation driven by insolation and feedback effects such as albedo explain well observed decadal, centennial, millennial, myriadal, hundreds of thousands of years and longer term climatic fluctuations. At the longer time scales, continental arrangements driven by plate tectonics is also important.

      CO2 is mainly an effect, not a cause of climate change, far from being the major control knob.

      • Gloria, quite a bit of your list has undergone fairly extensive research. Of the published research, a lot of it will be immensely poorly done without regard to measures of robustness (a familiar result on both sides of the debate). Error bars are often missing or are themselves filtered to remove outliers. Statistical maneuvers often outstrip necessary steps to deal with degrees of freedom, resulting in making an elephant wriggle its tail.

        That said, you have touched on an important paradigm. Our Earth’s oceanic/atmospheric/topographic teleconnected interplay results in a wickedly complex and wide-ranging intrinsic chaotic system all by itself. Add to that finding the effects of comparative sprinkles added or taken back sourced from external variations, would be like finding a single needle not in a haystack but in the highly variable globby ocean, or floating through our immense swirling unmixed atmosphere.

      • The sun obviously controls climate. I’m reminded of mid-20th century geologists who refused to accept the obvious fact that continents move (despite glaringly apparent fit of South America with Africa and abundant fossil evidence) because no one had proposed a plausible mechanism, or late 19th century physicists who couldn’t accept the age of the earth as determined by geologists for lack of an explanation, and of early 19th century biologists who couldn’t embrace “transmutation” (evolution) for lack of same.

        But it’s even worse with the solar d#niers, since science does know some if not all of the mechanisms by which apparently small changes in solar parameters work large changes in planetary climates.

      • Let me try to add reality to your statement. The Sun obviously is the source of our general climate. At issue is whether or not the very small regular variations in solar output can overcome Earth’s own intrinsic and quite large chaotic variations (something that thermodynamic experts would be quite familiar with) to push those chaotic variations against their natural chaotic patterns and to indeed drive them here and there in a more regular pattern. Your statement would lead anyone to conclude that if there were an amplification device that responded to solar sourced regular variations, the response here on the ground to that amplified driver would also be less chaotic.

        So what is the best way to see if regular extrinsic low energetic variations can significantly affect highly energetic and chaotic intrinsic variations?

        To feet on the ground flora and fauna, observation after observation after observation demonstrates they are stuck in and must respond or die in a chaotic system, not a regulated one. To feet on the ground measures in the oceans and atmosphere, here too it seems the chaotic random walk remains in charge.

        In other words, the signal you look for in earthly systems cannot be found in direct large or fine tuned observations and measures. It seems silly then that the hunt continues for an invisible signal. In terms of gold standard research methods, the solar variation effects search fails the first and most important step: observations made here on Earth through several solar cycles.

  36. In reply to:

    Salvatore Del Prete September 23, 2015 at 8:42 am

    Did you miss this paper which provides data to support exactly what I said in my above comments?

    Geomagnetic field ‘activity’ is caused by solar wind bursts. The geomagnetic does not abruptly change for no reason. Geomagnetic field changes can be used to determine the frequency of the solar wind bursts.

    Note it is the frequency of the solar wind bursts that is important. The solar wind affect on planetary cloud cover only lasts for 3 to 5 days. So a single large solar wind burst only affects the planet for 3 to 5 days. The persistent coronal holes produce a series of solar wind bursts. Coronal holes is the cause of the warming in the last 30 years.

    The planet gets colder when there are less solar wind bursts in a period and gets warmer when there are more solar wind bursts.

    When the solar wind bursts stop (i.e. When the coronal holes move to high latitude regions on the sun or disappear, the earth will abruptly cool, as GCR is the highest it has ever been (in the age of space measurements), for this period in a solar cycle.

    http://cosmicrays.oulu.fi/webform/query.cgi?startday=09&startmonth=05&startyear=1977&starttime=00%3A00&endday=15&endmonth=09&endyear=2015&endtime=00%3A00&resolution=Automatic+choice&picture=on

    http://sait.oat.ts.astro.it/MmSAI/76/PDF/969.pdf

    Once again about global warming and solar activity
    We show that the index commonly used for quantifying long-term changes in solar activity, the sunspot number, accounts for only one part of solar activity and using this index leads to the underestimation of the role of solar activity in the global warming in the recent decades. A more suitable index is the geomagnetic activity which reflects all solar activity, and it is highly correlated to global temperature variations in the whole period for which we have data.

    The real terrestrial impact of the different solar drivers depends not only on the average geo-effectiveness of a single event but also on the number of events. Figure 5 presents the yearly number of CHs, CMEs and MCs in the period 1992-2002. On the descending phase of the sunspot cycle, the greatest part of high speed solar wind streams a affecting the Earth comes from coronal holes (Figure 5), in this period their speed is higher than the speed of the solar wind originating from other regions, and their geoeffectiveness is the highest. Therefore, when speaking about the influence of solar activity on the Earth, we cannot neglect the contribution of the solar wind originating from coronal holes. However, these open magnetic field regions are not connected in any way to sunspots, so their contribution is totally neglected when we use the sunspot number as a measure of solar activity.

  37. Willis:

    You didn’t post a hyperlink to the SST data. Where did you get it?

    When I googled for SST temperatures, I found the NOAA data set starts at 1990, which might answer the implied question:

    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.

    NOAA data: http://www.cpc.ncep.noaa.gov/data/indices/wksst8110.for

    best regards,

    Peter

  38. Peter Sable September 23, 2015 at 9:53 am

    Wow, all this arguing and ZERO people have actually dealt with Willis’s request for two links: Paper and Data.

    True ‘dat …

    I mean an expert is offering to do work for FREE for your gain in knowledge and all you can do is complain about very simple, realistic requirements. If you’d ever gone data spelunking yourself you’d really find that getting the data is at least half the work, plus or minus 100%…. which is a terrible indictment of the current scientific publishing process.

    The rudeness shocks me. It really is terrible human behavior to look this gift horse in the mouth.

    As Jack Nicholson said in the movie, people don’t want the truth. They’d rather wave their hands and post claims and accuse me of “ignoring” their oh-so-brilliant ideas. As to their rudeness, anonymous internet popups like Lady Gaiagaia and the rest specialize in that because they never have to stand behind their words. They can say anything, it will never come back to bite them … and so they do exactly that.

    Willis, this is really great analysis, you hit it out of the ballpark again. I’ll peruse the 18 papers and find the one that looks the most compelling and has data, based on my experience and training in signal processing. It might take a day or two to complete. The answer might well be “none of the above” though…

    Interesting, Peter.

    Please do share the source code though. I think I need to learn “R”, the packages are mostly better than Octave. I’ll be happy to share ports of some of my Octave/Matlab analysis libraries to “R” with you in return. The endpoint extension defaults (zero padding) in Octave, Matlab, and “R” are just wrong for what we are doing here (low frequency signal analysis), and that’s mostly what I try to fix.

    I fear my code is not only not turnkey, and is not only not user-friendly, it’s best described as “user-aggressive”. However, I’ve stuck the code and what I think are the necessary subsidiary functions into a folder here. Let me know what parts give you trouble.

    You definitely need to learn R. It’s a language on the way up, and is remarkably easy to learn. Steve McIntyre encouraged me to learn it, and I’ve never looked back and never regretted the time it took.

    Regarding Fourier analysis, I use a type of Fourier analysis that I developed myself, only to find out had already been invented. As Tamino most courteously pointed out:

    The method you describe is very clever. It’s also known (in the astronomical literature at least) as the Date-Compensated Discrete Fourier Transform, or DCDFT (Ferraz-Mello, S. 1981, Astron. J., 86, 619).

    I’ve included the code for that as well. I call it the “Slow Fourier Transform”, but it has some huge advantages, one being that it doesn’t require your data to be either complete (accepts NA’s) or evenly measured (it will handle geological data, for example, with irregular time intervals).

    My thanks to you,

    w.

    • “Slow Fourier Transform”

      Yes, I reviewed this a year or so ago. Hopefully you are windowing the data now :-). I’ll check for that.

      BTW How in the world do we exchange emails? I’d rather provide feedback on “user-aggressive” code in a private forum. I’m a software engineer, my feedback would be harsh but useful I think, but rather not do that in public. Maybe I could teach you to write “user-aggressive” code that’s also pretty and reusable. I have a couple of tricks that I did to teach myself to do that.

      Peter

      Reply: I’ll forward your email to Willis. After that it’s up to him. ~mod

      • Peter Sable September 23, 2015 at 11:54 am

        “Slow Fourier Transform”

        Yes, I reviewed this a year or so ago. Hopefully you are windowing the data now :-). I’ll check for that.

        Hanning, Hamming, or no windowing are the current options, with Hanning the default.

        BTW How in the world do we exchange emails? I’d rather provide feedback on “user-aggressive” code in a private forum. I’m a software engineer, my feedback would be harsh but useful I think, but rather not do that in public. Maybe I could teach you to write “user-aggressive” code that’s also pretty and reusable. I have a couple of tricks that I did to teach myself to do that.

        Peter, my public email is willis.eschenbach at yahoo [dot] com. However, I’m more than happy to endure the slings and arrows of outrageous criticism in public. Here’s the thing. Although my role always appears to be as a participant in a bunch of conversations between me and commenters, in fact I’m not really writing for the commenters. I’m writing to the commenters, but I write almost exclusively for the lurkers. Heck, many of the commenters will never change their minds … but some of the lurkers will. So I write for them.

        In the same way, I’m sure that whatever you might teach me about programming, there are many others out there who might benefit even more than I would. So I’m glad to do it on the web.

        And as to you abusing my coding style … that will be a welcome relief from the personal attacks I’ve gotten from trolls like “Lady Gaiagaia” and “Dinosaurus” or whatever aliases they’re hiding behind, I can’t keep them straight.

        Be aware, however, of a couple facts before you start. One is that I wrote my first computer program in 1963, which is now over a half-century ago. And I have continued to write programs ever since. Over the course of my life I’ve become fluent in, and written many, many programs in, the computer languages Basic, C/C++, Mathematica (but I was only good in two of the four built-in languages), Hypertalk, Databus, Fortran, VectorScript, Pascal, Alcom, Lisp, Visual Basic for Applications, and now, of course R. So I’m far from being a novice in these matters.

        Second, I’m not much concerned about the re-usability of my code. When I want reusability I write a function. In general, in addition to demanding total accuracy in the calculations, I write and program for speed, and I don’t mean computer speed. I mean writing and debugging speed.

        You see, I’m turning out fairly sophisticated analyses, not PhD theses or full scientific papers in any sense, but intricate and time-consuming statistical and other analyses of reasonable size datasets. Since November 2009 I’ve written some 580 posts for WUWT, a rate of about one every three-four days. Not only that but all but the last couple months of that time I was employed, and in addition I’m expected (and I expect myself) to answer all scientific objections to my work, and to learn from comments and corrections from good folk like yourself.

        So I don’t have time to pretty up my code or make it turnkey. It’s just got to get the job done flawlessly and fast.

        Part of the reason that I like R so much is that you can run any chunk of code you like. A line. Half a line. A single word. All you do is select the exact part of the code you want, hit Ctrl-Enter, and it runs. So in general I’m not much concerned about the order of the lines of my code. It will often have sections upstream that depend on sections downstream. So what? When I run it again later it lets me know if I haven’t set some variables so I find them and set them, and it got the job done, and in record time.

        As a result, I don’t think I have written a single program in R that is designed to be run from top to bottom like traditional computer programs. I suppose you could describe them as “interactive programming experiences demanding total user immersion” if you wanted to be nice .. or as “terribly designed random pieces of junk that run really well” if you wanted more realism.

        In addition, often I may just put in a note like “# from program foobar.R”, meaning I’ve gotten the variables or the datasets from the “foobar” program. Sure, I could copy all of the relevant code into the program I’m working on, but why? I can go there and get my data in thirty seconds and I’m on my way. Remember, my only considerations are speed and accuracy.

        Another result of this ability of R to run any selected code is that debugging is very easy. If you have a problem with a line of code, you just run part of it. And of course this affects how I write. Often I could use something like “sapply” to run a loop much faster … but instead I may write it as a loop (pretty much a no-no in R programming) simply because it’s easier for me to walk through line by line. I can’t remember the last time I set a debugging breakpoint …

        If you’re going to use R, there’s only one editor you should use, and that is RStudio. Like R, it’s free, and cross-platform (Mac, PC, Linux). It has autocomplete for all functions and variables, and if you Cmd-Click on a function name it will take you to the function’s code, with a back arrow to return to where you were … sweet. Plus you can collapse a function or a section of code down to a single line, then expand it when you want to see it again. Nonpareil.

        Anyhow, there you have it. Having said all of that, I’m still more than willing to learn whatever you might have to show me that will make me a better programmer. As I’ve often said … perfect is good enough, and in this case I’m a long ways from there even after fifty years of programming.

        My regards to you,

        w.

      • So I don’t have time to pretty up my code or make it turnkey. It’s just got to get the job done flawlessly and fast.

        Thus you describe climate models, most PhD student code, etc. Your code is what we called “stream of consciousness” programming. The only time I ever do that is when I’m very unsure about what to do, and I nearly always throw it away or refactor it before showing it to anyone else.

        The problem with this method is it’s subject to large numbers of errors and much harder to falsify and is not repeatable. That should hit your science philosophy buttons I hope…

        There’s a concept called “unit testing” that is designed to both make it easier to falsify, and also make less mistakes, and makes testing and usage repeatable. The side effect is your code is reusable. (it’s hard to unit test if you don’t have units…) For example, if you are writing something like SlowFFT, or in my case edgeSaferFiltering, you need to test against numerous waveforms such as ramps, pink noise, sine waves, square waves, sine waves with beat frequencies, real climate data etc. So using the unit test method, you write a function that is testable with any waveform and whatever input parameters are required. At the same time you write the tests themselves (which are pretty simple). Now you have tested, and as a side effect, reusable code. You check both the code and the tests in. You can now prove that you attempted to falsify your own code.

        The other piece of philosophy is don’t write functions that are more than 60 lines long. This is about what fits onto one page on most monitors. This is because page-at-glance has value, and it restricts the complexity of code to something that is testable. (you could actually run complexity metrics of course, but usually that’s overkill if you follow this simple rule).

        So again the reason to partition everything into functions , objects, or whatever is a natural unit for the language being used is that it’s not the re-usability of the code that’s important, though that’s a good side effect, it’s that the code is testable, (aka falsifiable), and that process is repeatable.

        Peter

      • Peter Sable September 23, 2015 at 2:08 pm Edit

        So I don’t have time to pretty up my code or make it turnkey. It’s just got to get the job done flawlessly and fast.

        Thus you describe climate models, most PhD student code, etc. Your code is what we called “stream of consciousness” programming. The only time I ever do that is when I’m very unsure about what to do, and I nearly always throw it away or refactor it before showing it to anyone else.

        I can’t see how a climate model could possible be “stream of consciousness” programming. My code never runs top to bottom. Theirs does so, over and over, iteratively. What am I missing?

        The problem with this method is it’s subject to large numbers of errors and much harder to falsify and is not repeatable. That should hit your science philosophy buttons I hope…

        I completely disagree. My programming is remarkably error-free, as evidenced by the fact that I’ve been found wrong so few times in my analyses.

        As to being “hard to falsify”, in general code is falsified by looking at the outputs, so I’m not clear how that applies to the code itself. If my code says that 2 + 2 = 5, you don’t need to examine to code to falsify it.

        As to repeatability, when I produce code that requires repeatability, say for a scientific study, I make it turnkey. Different code for different purposes.

        There’s a concept called “unit testing” that is designed to both make it easier to falsify, and also make less mistakes, and makes testing and usage repeatable. The side effect is your code is reusable. (it’s hard to unit test if you don’t have units…) For example, if you are writing something like SlowFFT, or in my case edgeSaferFiltering, you need to test against numerous waveforms such as ramps, pink noise, sine waves, square waves, sine waves with beat frequencies, real climate data etc. So using the unit test method, you write a function that is testable with any waveform and whatever input parameters are required. At the same time you write the tests themselves (which are pretty simple). Now you have tested, and as a side effect, reusable code. You check both the code and the tests in. You can now prove that you attempted to falsify your own code.

        I don’t understand how this differs from what I do. I wrote the Slow Fourier Transform code, for example. Then I tested it against a bunch of different waveforms. Is “unit testing” different than that? I have another function I use a lot, “plotdecomp”. It decomposes a waveform into its seasonal, loess trend, and residual portions and plots them onscreen. I wrote it. I tested it by using it on everything I could think of. Is that “unit testing”?

        The other piece of philosophy is don’t write functions that are more than 60 lines long. This is about what fits onto one page on most monitors. This is because page-at-glance has value, and it restricts the complexity of code to something that is testable. (you could actually run complexity metrics of course, but usually that’s overkill if you follow this simple rule).

        Mmm … I try to follow this, and I have literally dozens of short functions, much shorter than 60 lines. They do things like generate a mask on a 1°x1° global grid, or convert a 3-D data array (latitude, longitude, and time) into area-weighted monthly averages.

        However, if you’ve seen the global maps that I regularly use to display global analysis results, it’s about 200 lines long … and that is already utilizing a whole raft of the short subsidiary functions of the type I mentioned above. Heck, it’s got 18 variables alone, and each one is important … rotation of the globe, display units, number of digits to display, it’s hard to imagine making that sucker much shorter.

        However, your point about length is well taken.

        So again the reason to partition everything into functions , objects, or whatever is a natural unit for the language being used is that it’s not the re-usability of the code that’s important, though that’s a good side effect, it’s that the code is testable, (aka falsifiable), and that process is repeatable.

        I took a total of two computer classes. One was a six-week class in 1963, where I learned the rudiments of programming in Algol on punch-cards. The other was a 1-semester course in 1973, where I learned to program in Databus. Both languages are long-dead, and absolutely no attention was paid to code structure in either case, it just had to work. Other than that, I taught myself all the rest … so I have a whole raft of bad programming habits.

        Over that time, however, I’ve come to depend on functional programming. So I generally wrap up whatever I can into a function. It’s helped that the RStudio editor has a “Extract Function” menu item that will turn any chunk of code into a function. So what you are preaching is definitely the direction my programming is heading. And I see that I can take it further.

        One thing I have to thank you for. I realized that I need to make some “Setup” source files. I’ve done that for the CERES data, and the setup file opens the data file, and extracts all of the contents into named variables. But for some reason, until this discussion I’ve never thought of doing it for my host of other datasets. So each time I need say the sunspot data, at present I need to hunt it up. But what I’m going to do is make a “Sunspots Setup.R” file that will load the various variables of interest and print out the variable names in the console. Same thing for my tides data, a “Tides Setup.R” file will handle that nicely, and so on to viscosity, as the poem says …

        And that, of course, means that your programming ideas have already borne fruit, so well done.

        All the best,

        w.

      • I wrote it. I tested it by using it on everything I could think of. Is that “unit testing”?

        To properly follow the unit test methodology, you need to keep the source code for your unit tests and run them every time you modify the code. That way you can ensure you didn’t mess it up. It also documents to others you attempted to falsify your code. It’s helpful to get them all down to automated “pass/fail” status, though that’s an art in the world of signal processing and stats. I still have to all too often graph the result and use an intuitive pass/fail criterion from a graph.

        You should also have your source code under revision control. I use git. It’s intuitive and easy and has plugins for most environments. I’ve had to do the “oh crap what did I mess up” and go back to earlier versions of files many times. Besides it documents how you evolved the code.

        If my code says that 2 + 2 = 5,

        Yes, for 1-2 processing steps this process is overkill. For 3 or more, it’s too easy to have funny results and not realize you might have slightly offsetting errors. So I just do it as habit. (though testing Octave functions is actually a PITA, I violate it sometimes and go top-bottom).

        One thing I have to thank you for. I realized that I need to make some “Setup” source files.

        Funny, I only started that in the last month or two. So I guess you’ve been reading my posts. Or I got a lucky inspire somehow.

        Well, enough back-slapping. I’m now trying to figure out how the “18 papers” are really on 5 papers. Not one of which is doesn’t have obvious flaws such as no data, broken hyperlinks, or “well, not that useful of a result even if it is valid”, which is what the above paper is looking like now. BTW if you’re interested the paper he referenced (aka his own), actually has far better detail on methodology and examines far longer stretches of data than the one above. I still think the answer is “oh, so ENSO always starts exactly on a year subharmonic interval. Who cares”. It’s either the definition of ENSO index, or it’s simply it requires lack of sun (or full) sun to initiate whatever process there is. Well, since the sun initiates Spring and the lack of sun initiates Fall, meh, whatever.

        http://www.pas.rochester.edu/~douglass/papers/Douglass_Pacific_2011.pdf

        Peter

      • Hypertalk!!! I know this!!! Such a language arts, user friendly code. Loved it. Spent many 24 hour periods with nothing but coffee writing that code. “If [this], then [that], else [do this].” And if you didn’t get the syntax correct it would give suggestions or at least underline the section that was incorrect, instead of the damnable “error, go straight to jail” message.

      • A course in computer coding is now a part of Kahn Academy. I’ve got some wicked smart kids in 6th grade who regularly tinker with Kahn’s version of computer programming to make a ball bounce across the screen, or draw shapes. In one day, they quickly outperformed my slow attempts to make even a single dot appear on the screen.

  39. Where is 2014, 2013 ?

    Adolphi, Florian, et al. “Persistent link between solar activity and Greenland climate during the Last Glacial Maximum.” Nature Geoscience (2014)

    Barlyaeva, Tatiana V. “External forcing on air–surface temperature: Geographical distribution of sensitive climate zones.” Journal of Atmospheric and Solar-Terrestrial Physics 94 (2013): 81-92

    Biktash, L. Z. “Evolution of Dst index, cosmic rays and global temperature during solar cycles 20–23.” Advances in Space Research 54.12 (2014): 2525-2531

    Buizert, C., et al. “Precise Interhemispheric Phasing of the Bipolar Seesaw during Abrupt Dansgaard-Oeschger Events.” AGU Fall Meeting Abstracts. Vol. 1. (2014)

    Chambers, Don P., Mark A. Merrifield, and R. Steven Nerem. “Is there a 60‐year oscillation in global mean sea level?.” Geophysical Research Letters 39.18 (2012)

    Czymzik, Markus. “Mid-to Late Holocene flood reconstruction from two varved sediment profiles of pre-alpine Lake Ammersee (Southern Germany).” (2013)

    Knudsen, Mads Faurschou, et al. “Evidence for external forcing of the Atlantic Multidecadal Oscillation since termination of the Little Ice Age.” Nature communications 5 (2014)

    Lam, Mai Mai, Gareth Chisham, and Mervyn P. Freeman. “Solar wind‐driven geopotential height anomalies originate in the Antarctic lower troposphere.” Geophysical Research Letters 41.18 (2014): 6509-6514

    Lassen, Knud, and Peter Thejll. Multi-decadal variation of the East Greenland Sea-Ice Extent: AD 1500-2000. DMI, (2005)

    Leal-Silva, M. C., and VM Velasco Herrera. “Solar forcing on the ice winter severity index in the western Baltic region.” Journal of Atmospheric and Solar-Terrestrial Physics 89 (2012): 98-109

    Mantua, Nathan J., and Steven R. Hare. “The Pacific decadal oscillation.” Journal of oceanography 58.1 (2002): 35-44

    National Research Council. The Effects of Solar Variability on Earth’s Climate: A Workshop Report. Washington, DC: The National Academies Press, (2012)

    Nieuwenhuijzen, H. “Terrestrial ground temperature variations in relation to solar magnetic variability, including the present Schwabe cycle.” Natural Science 2013 (2013)

    Schlesinger, Michael E., and Navin Ramankutty. “An oscillation in the global climate system of period 65-70 years.” Nature 367.6465 (1994): 723-726

    Sfîcă, L., and M. Voiculescu. “Possible effects of atmospheric teleconnections and solar variability on tropospheric and stratospheric temperatures in the Northern Hemisphere.” Journal of Atmospheric and Solar-Terrestrial Physics 109 (2014): 7-14

    Sha, Longbin, et al. “A diatom-based sea-ice reconstruction for the Vaigat Strait (Disko Bugt, West Greenland) over the last 5000yr.” Palaeogeography, Palaeoclimatology, Palaeoecology 403 (2014): 66-79

    Shaviv, Nir J., Andreas Prokoph, and Ján Veizer. “Is the solar system’s galactic motion imprinted in the phanerozoic climate?.” Scientific reports 4 (2014)

    Solheim, Jan-Erik, Kjell Stordahl, and Ole Humlum. “The long sunspot cycle 23 predicts a significant temperature decrease in cycle 24.” Journal of Atmospheric and Solar-Terrestrial Physics 80 (2012): 267-284

    Tiwari, R. K., and Rekapalli Rajesh. “Imprint of long‐term solar signal in groundwater recharge fluctuation rates from Northwest China.” Geophysical Research Letters 41.9 (2014): 3103-3109

    Todorović, Nedeljko, and Dragana Vujović. “Effect of solar activity on the repetitiveness of some meteorological phenomena.” Advances in Space Research 54.11 (2014): 2430-2440

    Vanniere, B., et al. “Orbital changes, variation in solar activity and increased anthropogenic activities: controls on the Holocene flood frequency in the Lake Ledro area, Northern Italy.” Climate of the Past 9.3 (2013): 1193-1209

    Zhao, X. H., and X. S. Feng. “Periodicities of solar activity and the surface temperature variation of the Earth and their correlations.” Chin. Sci. Bull.(Chin. Ver.) 59 (2014): 1284-1292

    Zhao, X. H., and X. S. Feng. “Correlation between solar activity and the local temperature of Antarctica during the past 11,000 years.” Journal of Atmospheric and Solar-Terrestrial Physics (2014)

  40. http://www.ncgt.org/newsletter.php?action=download&id=130

    Kolvankar (2011) shows that 98% of all earthquakes satisfy a linear relationship:
    (1) GMT = EMD + SEM + const

    http://www.ncgt.org/newsletter.php?action=download&id=143

    EARTHQUAKES OCCUR VERY CLOSE TO EITHER 06:00 OR 18:00 LUNAR LOCAL TIME

    Abstract: If an earthquake (EQ) has to occur at some location and on some day, almost always it happens during either one of two time intervals close either to 06:00 or to 18:00 LLT (lunar local time). This law applies to ∼98% of case histories. The procedures are presented that are suited to assess the exact duration of the time lag with a 95% (or higher) confidence limit.

    • Thanks, LGL. I do appreciate a man who takes the time to do his own analysis. Belief is for suckers.

      I redid your analysis and got a slightly weaker trend, 0.004°C per sunspot, but looking much the same.

      However, a Monte Carlo analysis of the results has bad news. Using the sunspot data versus red-noise pseudodata, it turns out you get a trend that large about half the time. So while the result is interesting, it’s a long ways from being even near significant.

      The sunspot data is funny that way, because it is “peaky”—there’s much less data in the peaks than in the intervening periods of low activity. This means it will give larger trends against random red-noise data.

      Keep calculating …

      w.

      • Keep calculating … I did, and I have to retract my “SST increase 0.2 C over a strong solar cycle (SSN=200)”
        It turns out the 0.001C/sunspot is a result of the longterm increase in SSN and temp 1880-2015.
        For instance, shifting the temp record 2 or 5 years makes very little difference.

      • lgl, there are considerations when using scatter plots and linear trend lines to demonstrate relationships. So here are my thoughts, take them or leave them.

        When examining a relationship between an independent and dependent variable, you need to have scatter plots for both to start with. You have imposed an already manipulated temperature trend line with a scatter plot for SSN. What is the R-squared value of your temperature trend line? How noisy is it? What is its R-squared value to its idealized linear trend line? .98, .70, .45? I do not like using someone else’s scatter plotted trend line without knowing what its R-squared value is to its idealized trend line. The closer the R-squared value is to 0 (IE .98 versus .45), the less faith I have in the data being anything other than random performance. Scatter plots that are widely scattered around a linear trend line will have an R-squared value close to 0. Scatter plot performance that throughout the series tags along to the idealized trend line will have an R-squared value closer to plus or minus 1.

        The second consideration then would be the trend line validity and reliability of both the dependent and independent variable if you have raw data for both. As I said, a trend line though data points is most reliable when its R-squared value is at or near 1. This is a relationship I use every day. I take data on student improvement over time to determine whether or not performance demonstrates a reliable trend over time plotted against time in intervention. Without the R-squared value, I will be unaware that closely fitting data points to an idealized trend line can also be duplicated with widely scattered points that result in almost the same trend slope. If I don’t go on to calculate the R-squared value, I have not done due diligence in determining whether or not my trend line for each scatter plot (the independent and dependent variable) is valid and reliable. The bottom line, both trend lines should have similar R-squared values to make your hypothesis show reliable and valid relationships when one data set is plotted against the other’s linear trend.

        In my case, the independent variable, length of time in an intervention, is clearly going to be a linear trend with an R-squared value of 1. But I also need to make sure the intervention is performed in a reliable, repeatable fashion in order to use time in intervention as a reliable independent valid variable. In other words the quality of the independent variable has to be high or else I cannot suggest a relationship between time and what I did during that time. The dependent value, student performance, will have a much noisier R-squared value to its idealized trend line, being the dependent value. Here again, if performance is scattered, I have less faith in performance being a result of the intervention and is instead random performance.

        Finally, I think you may have your graph backwards. The scatter plot should be your temperature series, it being the noisier data set and the dependent variable, and your trend line should be SSN, it being the tighter data set and the independent variable.

    • No we are not. And that difinately includes your mechanism absent, throw everything on the wall, hope something sticks, hypothesiseseseseseseses.

      • How many times must the incontrovertibly demonstrated mechanisms by which changes in solar activity influence climate be linked here before you will bother to read them?

      • Lady Gaiagaia September 23, 2015 at 6:19 pm

        How many times must the incontrovertibly demonstrated mechanisms by which changes in solar activity influence climate be linked here before you will bother to read them?

        How many time? As I’ve said all along, exactly once, if they are accompanied by a link to the dataset that establishes their incontrovertibility.

        Because if they are not accompanied by data, then they cannot be falsified and are therefore not “incontrovertible”.

        w.

    • Willis,

      Do you never wonder why so many laugh at you for demanding TWO links whenever you have no scientific answer to the data presented.

      Where I come from, that’s called cowardice or worse.

      • “So many laugh at me”? Near as I can tell, the majority of posters understand that without data I cannot determine whether a study is true or not. It’s just you and Salvatore and some ditto-heads out there that can’t seem to understand that.

        So while there may indeed be laughter … I fear you are mistaken about its target. Your monomoniacal trolling is actually quite humorous, and your vacuous claim that a study without data has scientific value is indeed a joke.

        w.

      • Your Excellency Lady Gaia,

        I am beginning to understand how revolutions begin – namely, when aristocrats make themselves obnoxious beyond tolerance.

  41. Willis,
    Thank you for all your efforts at going through these much heralded papers. As just a lowly B.S. degreed engineer, you do a great job of making sense of all the technical obfuscation that appears to be the norm for climate science nowadays. The graphs you painstakingly produce really illustrate where the authors miss the boat.

  42. Response to Willis Eschenbach’s comments on the Douglass/Knox paper “The Sun … I”

    Almost all of the comments by Eschenbach in regard to the Douglass/Knox paper “The Sun is the Climate Pacemaker I …” are wrong. We (Douglass/Knox) will reply to a few of them.

    ————————————–

    “Almost every thing I know is locked to the annual Solar cycle…”

    Reply:
    Most know about the seasonal effect (winter vs summer) at high latitudes caused by changes in solar irradiance whose average is 342W/m2. However, there is another effect at 1 cycle/year caused by the eccentricity of the earth’s orbit. The amplitude of this solar irradiance is 11.3 W/m2 and peaks in early January. This solar forcing is the “Pacemaker” as described in the 2nd paragraph of section 2.2 of our paper.

    ——————————————

    “… they give no citation for where were ‘previously reported’ . My guess is that these ‘climate shifts’ were ‘previously reported’ by the authors themselves…”

    Reply:
    No, they are from many sources and are described in a paper by Douglass (reference 1). In reference 18 in this paper a list of climate shifts is given from various studies. [Note. As every scientist knows, references are given so that the reader may know where an assertion of the author may be checked. With the help of Google any reference can be downloaded in less than a half hour. For the Douglass/Knox papers there is link just below the references. One click and a list appears. A second click gives you the paper in question. All in less than a minute.]

    ————————————————-

    “They are not using all of the data. … It generally means the data are being stubborn and uncooperative.”

    Reply:
    The data are not being stubborn and uncooperative – just the opposite. The missing data from 1982 to 2000 is the same as seen in figure 2 of reference 1. To include this data would bring in phase-locked segment #8 which would require additional redundant discussion. That is why we began at 1990.

    ———————————

    ‘”precision of the climatology method”

    Reply:
    Section 4 is entirely about how the “climatology” method is flawed.

    —————————————-

    Where are “…phase-locked segments #1 through #8” ?

    Reply:
    Two clicks away in reference 1, as mentioned specifically in the Introduction. All of them are clearly identified in figure 2. Also in this figure the “climate shifts” are shown as blue arrows.
    ———————————————

    “ … cycles at 2.5 years, 3.75 years, 5.5 years…”

    Answer: The peaks for periods greater than 1 year in Eschenbach’s periodgram are meaningless. They are some kind of average of segments of 2-year, 3-year and segments containing neither.

    ———————————————

    David Douglass
    Robert Knox

    • “One click and a list appears. A second click gives you the paper in question. All in less than a minute.”

      Willis’ unwillingness to obtain papers and data himself reminds me Maynard G. Krebs’ aversion to work. He also doesn’t observe the usual form for writing scientific papers.

      Thanks for clearing up the supposed mystery of the allegedly “hidden” data. Much as so many here expected, no offense committed.

      • No. In that case, it’s laziness and contempt for the scientific method. It’s all about the megalomania and desperate attempt to achieve something at the end of a wasted life. IMO.

        IIRC, Willis has a healthy, happy daughter, in which case, not totally wasted.

      • Yes, Pamela, not only my grasp on the scientific method, but elementary math and statistics. Willis, the rank newbie (not amateur, since he would have to love statistical analysis for that term to apply), has been repeatedly shown up for the poseur he is here by real experts in statistical analysis.

        This AW’s blog, so of course he’s free to publish whatever he wants, but Willis’ “analyses” are so laughably wide of the mark that I sometimes wonder if he isn’t a Mannian plant. I refer you, just for a single ludicrously erroneous post, to the evisceration the psych major Willis suffered at the expert hands of 1sky1 and Dinostratus (if I remember their handles correctly) on his risible NAO “analysis”.

        To say nothing of his pretending to have discovered tropical oceanic cloud formation as a weather phenomenon, which Gore-like claim was deflated by a real expert, Dr. Spencer.

        IMO, AW should rid himself of this obnoxious poseur.

      • Lady GG,

        Sorry, and I may well and perhaps not without reason, be censored for this comment, but in my considered opinion, Willis is a statistical stupe, a mathematical moron and arithmetic a$s.

        As has been amply demonstrated not only by Dr. Douglass here, but in every single one of Willis’ lame attempts to dispute the glaringly obvious fact of solar influence on climate.

        If moderators want to silence me, I’ll understand.

      • Again, childish, interruptive comments instead of showing your work. Put up your research. And if you want to go head to head with it, I’ll put up mine. Granted, it’s not in climate. It’s on the Auditory Brainstem Response to high frequency tone pips. My previous career. While I am a one-hit wonder in published research, that one was a hit out of the park. Since then, instead of studying brains, I grow them. Even the ones that don’t work so well.

    • David, I have never and would never cite a paper that cites references that are germane to my thesis, in order to cite references germane to my thesis. I would cite those papers directly with comments.

  43. People can discuss, dispute, accept or reject the science that indicates the influence of solar activity dominates when it comes to climate change. But what is undeniable is that over the last 2000 years, there is good correlation between solar activity and climate change while there is no correlation between carbon dioxide and those major climate changes over the last 2000 years.

    Over the last 2000 years, a period in which atmospheric carbon dioxide was relatively stable, there has been the Roman Warm Period, the Dark Ages Cold Period, the Medieval Warm Period, the Little Ice Age, and the recent warming trend. Very cyclical… just as solar activity is very cyclical.

    So … I am inclined to believe that the key influence on earth’s planet is very likely the sun and that the IPCC’s mantra is utter rubbish as demonstrated by Dr Giaever’s presentation in the following video:

    • A brave man and a great scientist, but I wish he hadn’t said “degrees K” instead of just “K”.

      Also, chimps barter bush baby meat in exchange for sex. Humans aren’t the only bartering species, contrary to Matt Ridley’s view. Otherwise, I’m totally down with Matt.

      • PS: However, not much in the video about the effect of the sun on climate, which is the topic under discussion in these comments. It is however devastating to the repeatedly falsified hypothesis of CO2 as the control knob on climate.

  44. William Astley

    September 23, 2015 at 1:06 am
    —————————————–

    Interesting William. Changes in the Earth’s magnetic field in particular.

    Aren’t we due for a “Solar Cycle Up Date” here at the WUWT?

  45. As Mosh observed above, not only does Lady Gaiagaia struggle with the word “two”, I feel that the meaning of the word “data” is completely foreign to her. Hint: It’s plural.

    This is quite depressing for me. As I observed in an email to Willis:

    “It is unfortunate that so many of my ideological allies are such dimwits. It makes us all look bad in very serious ways.”

    ~ ctm

    • I’m quite sure that my understanding of Latin is at least as profound as yours.

      Why is it that on this blog the obviously math challenged megalomanic Willis gets to chose which data sets to which he will reply, while ignoring others?

      If you are typical of moderators here, then anyone seriously interested in science, to say nothing of math and statistics, should avoid this site like the plague.

      Thanks for making clear to me that avoiding science is the purpose of this blog.

      Bye bye and ta ta.

      But please be aware that real scientists and genuine statisticians laugh at you in general and the presumptuous ignoramus Willis in particular.

  46. David Douglass September 23, 2015 at 3:34 pm

    David, thank you for your genteel responses, perhaps better than I deserved. My comments below.

    Response to Willis Eschenbach’s comments on the Douglass/Knox paper “The Sun … I”

    Almost all of the comments by Eschenbach in regard to the Douglass/Knox paper “The Sun is the Climate Pacemaker I …” are wrong. We (Douglass/Knox) will reply to a few of them.

    ————————————–

    “Almost every thing I know is locked to the annual Solar cycle…”

    Reply:
    Most know about the seasonal effect (winter vs summer) at high latitudes caused by changes in solar irradiance whose average is 342W/m2. However, there is another effect at 1 cycle/year caused by the eccentricity of the earth’s orbit. The amplitude of this solar irradiance is 11.3 W/m2 and peaks in early January. This solar forcing is the “Pacemaker” as described in the 2nd paragraph of section 2.2 of our paper.

    ——————————————

    I fear I’m not following this. The “2nd paragraph of section 2.2” says in toto:

    In the present analysis only anomalies are treated. Thus we replace a parent series G0 by G = G0 − ⟨G0⟩, where ⟨G0⟩ is the average of the parent series over the period January 1990 to De- cember 2013.

    This says nothing about a “Pacemaker”. So I did a search for “pacemaker”, and the only mention of a “pacemaker” is in the title of your study. Nor is eccentricity mentioned anywhere in your paper, not once.

    I think you must mean the second paragraph of section 3.2, although that doesn’t really help much either. It simply claims that the variation in SST3.4 temperatures is due to TSI, which seems reasonable. It says:

    hSST3.4. This index has an amplitude of about 1 ◦ C. The auto- correlation function shows a sustained oscillation of 1.0 cycle/yr whose amplitude is not decreasing, implying an external forc- ing F S . Since the oscillation frequency is 1.0 cycle/yr, F S almost certainly originates in the solar irradiance, which has an annual component of amplitude of 11.3 W/m2. Its maximum occurs in early January when Earth is at perihelion. The average of the monthly values of hSST3.4 has a maximum during May; one would expect this maximum to occur several months after perihelion, since the temperature of Earth responds to the solar irradiance with a delay of several months, an effect seen clearly in outgoing longwave radiation

    Unfortunately, this isn’t true. The actual situation is much more complex than the one you describe. You are correct that for the entire globe the total solar insolation (TSI) peaks in January and June. Here are the observations, the seasonal variations, and the (very small) residuals from the CERES data. The January/June peaks are quite visible.

    Figure S1. Incoming top-of-atmosphere total solar irradiation (TSI) for the globe. Top panel shows observations. Middle panel shows the repeating identical seasonal component. The bottom panel shows the residuals, the deviation of the observations from the seasonal expectation.

    The top panel shows the ± 11 W/m2 swing that you discuss in Paragraph 2 of 2.2 above. You can see the variation in TSI linked to the sunspots in the bottom panel. So far, so good.

    However, around the Equator, things get strange. Half the year the sun is in the north, half the year it’s in the south. In addition to the annual cycle you discuss, this imposes a half-year cycle as well. This changes the situation considerably. Overall, the variation in TSI in the equatorial Nino3.4 region looks like this:

    Figure S2. Incoming top-of-atmosphere total solar irradiation (TSI), Nino 3.4 region. Top panel shows observations. Middle panel shows the repeating identical seasonal component. The bottom panel shows the residuals, the deviation of the observations from the seasonal expectation.

    As you can see, the TSI over the Nino3.4 region is absolutely nothing like you’ve described above. First, the six-month signal is quite large. How large? I’ll get to that.

    More to the point, the peak insolation in the Nino3.4 region is NOT in January, as you state in your paper. It is in March. But wait, it gets worse. The variations in TSI are wildly asymmetrical. The peak insolation is in March … but the minimum insolation is only three months later, in June.

    Next, as to the question of which cycle is larger—the six-month cycle due to the sun moving north/south, or the one year cycle as the earth moves closer and further from the sun? TSI falls off as the cosine of the zenith angle. The sun moves 23.45° North and South over the year. Measured at the Equator at the time when the sun is furthest from the Equator, TSI is 416 W/m2 * cos(23.45) = 381 W/m2. This is a variation of ± 17 W/m2, compared to your accurate quotation of ± 11 W/m2 for the variation due to eccentricity. This difference in strength is also visible in the periodogram of the Nino3.4 TSI:

    Figure S3. Periodogram, top-of-atmosphere total solar irradiation (TSI), Nino 3.4 region

    Here you can see the two cycles, six months and one year. And as you can see, the 6-month cycle is the stronger.

    To sum up: The “Pacemaker” you have described is nothing like the reality. Rather than having a peak in January and a minimum in July as you claim, it has a peak in March and a minimum in June. And rather than the strongest cycle being the annual eccentricity cycle as you claim, the strongest (and fastest changing) cycle is the six-months-north, six-months-south familiar annual cycle.

    Now, here’s where it gets very interesting. As we’re all aware, the ocean is not at the top of the atmosphere. So I decided to look at what is actually happening down at the surface. After all, in general that’s what the sea surface temperature swings are most related to—not TSI, but how much sunlight they actually receive at the surface. Fortunately, that information is available in the CERES data. As usual, I was surprised by my findings. First, here’s an overview of the full period of record:

    Figure S4. Absorbed surface solar radiation, Nino 3.4 region. Top panel shows observations. Middle panel shows the repeating identical seasonal component. The bottom panel shows the residuals, the deviation of the observations from the seasonal expectation.

    Now, when I looked at Figure S4, my jaw dropped. I looked at the bottom graph, and I thought, oh my goodness, that looks very much like the inverse of the SST3.4, the very data we’ve been discussing. SO … I ran to calculate the CCF, the cross-correlation function between the absorbed solar anomaly and the sea surface temperature anomaly (climatology removed in both cases) … here’s that graph:

    Figure S5. Cross-correlation of the absorbed surface solar radiation anomaly and the temperature anomaly, Nino 3.4 region. Climatology removed in both cases. The lag shows the amount that the reduction in solar lags the temperature change.

    And that, to me is a most lovely graph. I was correct in that the absorbed solar anomaly is indeed the inverse of the temperature anomaly. This is among the strongest observational evidence for my thermostat hypothesis that I have ever found. What a surprise to find it when I was least expecting it!

    The reason I was excited is that this shows is that as the sea surface temperature (SST) anomaly warms in the Nino3.4 region, the downwelling solar decreases. Decreases! The surface is getting warmer, and in response there is less solar radiation at the surface.

    I say that this is because of increased clouds from the warmer surface reducing the incoming radiation, just as is predicted by my thermostat hypothesis.

    So David, I find I have to thank you. This discussion with you has led me to this very strong evidence, which otherwise I might never have found … life is a strange thing.

    To close out this part, let me show a detailed view of Figure S4, the variations in surface absorbed radiation.

    Figure S7. Closeup of Figure S4Absorbed surface solar radiation, Nino 3.4 region. Top panel shows observations. Middle panel shows the repeating identical seasonal component. The bottom panel shows the residuals, the deviation of the observations from the seasonal expectation.

    As you can see, once again there has been a transformation of the previous cycle. Whereas the TSI peaked in March and was lowest in June, at the surface the relative size of the two peaks is swapped. Now the peak in surface insolation is in October, and the minimum is still in June … go figure.

    In any case, that’s the reply to the first of your comments, and it certainly didn’t end up going the direction I thought it would.

    In that regard, let me leave you with a final pair of graphs. These show the decomposition of the SST3.4 temperature itself, the subject of your paper.

    and here is a closeup of the recent period:

    It was the peaks in 2003, 2007, and the big peak in 2010 that made me think that the bottom panel of Figure S4, the absorbed solar, might be the inverse of the SST. It has valleys at the same points.

    As you can see, the SST peaks in May as you’ve said … but the actual situation is more complex than that. The peak is in May, but the minimum is not until eight months later, in January.

    Finally, where in all of that confusion of a combination of a larger 6-month cycle and a smaller annual cycle are we supposed to find your “Pacemaker”? Things are nowhere near that simple.

    Thank you again for your detailed reply, and for the unexpected bonus of stumbling across evidence for my hypothesis. It’s late, I’ll reply to your other comments at a subsequent time … but just the above graphs are enough to falsify your argument that the January peak in global TSI was making the temperature peak in May. Instead, at the surface (which is the only sun the ocean knows, it knows nothing of TSI), the solar peak is in October …

    But not only that. Rather than the temperature and the solar input varying in parallel as you and most other solar researchers assume, they are inversely related … and that fact alone is enough to falsify your whole argument.

    Best regards, more later,

    w.

    • Willis,

      “Rather than the temperature and the solar input varying in parallel as you and most other solar researchers assume, they are inversely related..”

      I don’t think that’s quite fair. Your residual in S4 equals the derivative of Nino3.4 SST:

      so, the temperature and the integral of the solar input is varying in parallel (like it is supposed to when water is involved), temp is not the inverse of solar input.

      • I don’t understand this at all. The residual in S4 has nothing to do with the sea surface temperature. The underlying data of the absorbed solar is calculated from the CERES dataset with no reference to the Nino3.4 SST.

        More to the point, it seems that you are claiming that the natural course of things is that when the surface insolation goes up, the ocean temperature goes down … how does that work?

        w.

      • Willis, no

        solar input = derivative of Nino3.4 SST
        then
        integral of solar input = Nino3.4 SST

        Run the integral and you will see. I have done it for the all tropics (-30/30N) here:
        virakkraft.com/CERES_EBAF_Surface_Net_Shortwave_Flux_Tropics.xlsx

      • lgl, it seems you missed my question, so I’ll ask it again.

        … you are claiming that the natural course of things is that when the surface insolation goes up, the ocean temperature goes down … how does that work?

        Note that this inverse relationship is NOT true in much of the world. Elsewhere, when net surface insolation goes up, temperature goes up.

        But in the Nino3.4 region, the opposite is true.

        I say that this is because the temperature is regulating the clouds, which in turn are regulating the surface insolation. Warmer ocean ==> more clouds ==> less insolation.

        w.

      • Willis
        That’s not what I’m claiming, so your question is meaningless.
        I’m claiming SST is increasing when the input is positive (anomaly > 0)

      • Willis Eschenbach September 24, 2015 at 1:22 pm

        lgl, it seems you missed my question, so I’ll ask it again.

        … you are claiming that the natural course of things is that when the surface insolation goes up, the ocean temperature goes down … how does that work?

        lgl September 24, 2015 at 3:48 pm

        Willis
        That’s not what I’m claiming, so your question is meaningless.
        I’m claiming SST is increasing when the input is positive (anomaly > 0)

        lgl, the data shows that when surface solar absorption increases the SST goes down. That’s a fact. You claim this is explainable because of the integral, or the differential … but regardless of your explanation, you obviously think that a surface cooling down when it receives MORE solar radiation is somehow the natural course of things.

        Hence my question. It’s particularly relevant because in most parts of the world, the surface is reacting in the way we’d expect—when the solar input goes up, the temperature goes up.

        But not in the Nino3.4 area. There, the situation is reversed. There, when the solar input goes up, the temperature goes down.

        I say that this is because in the tropics the temperature is controlling the solar input, through the relationship

        Warmer ocean ==> more clouds ==> less insolation, or alternately,

        Cooler ocean ==> less clouds ==> more insolation.

        You, on the other hand, seem to think that such a response (temperature goes down when forcing goes up) is actually just the normal course of things … but if so, why is it not true over the rest of the planet?

        w.

      • Willis

        lgl: “I’m claiming SST is increasing when the input is positive (anomaly > 0)”

        Willis: “you obviously think that a surface cooling down when it receives MORE solar radiation”

        Hm… wonder how your brain works …

        Anyway, “the data shows that when surface solar absorption increases the SST goes down. That’s a fact.” Yes, like the integral of a sine wave is in counterphase with the sine wave half the time but still a result of the sine wave.
        http://woodfortrees.org/plot/sine:10/last:300/normalise/plot/sine:10/last:300/integral/normalise

        And, it’s also a fact that when the solar input is high the SST increase is also high, and solar input leads SST change.

        But this whole thing is a loop and it’s pointless discussing where the starting point of a loop is, so to close the loop you initiated:

        Warmer ocean ==> more clouds ==> less insolation =>
        => Cooler ocean ==> less clouds ==> more insolation =>
        => Warmer ocean ==> ….

  47. Wow, this is a great thread!! (OK, I am a little drunk) :)

    Thank you Anthony for your wonderful site and Willis for his post ( I love your stuff) and David Douglass and Robert Knox for coming into the lions den and engaging. It’s all really interesting and educational stuff and although my brain struggles a bit I love it!
    Thanks again to all!

  48. Pretty rude response from Gosselin. He doesn’t actually pick one paper as you asked, instead throwing another four on the pile.
    http://notrickszone.com/2015/09/24/4-new-papers-show-suns-impact-on-global-climate-german-scientists-sun-is-a-major-climate-factoR

    I have no idea about the technicalities, but I’ve noticed the ‘solarians’ or whatever you call them tend to be dodgy after Willis’ articles. So they spend so much time to get the paper written and published, and then they can hardly be bothered to answer the criticisms.

  49. Willis has a way of convoluting the data and presenting it in a very misleading mess to put it mildly to make it somehow try to support his wrong assertions when it comes to solar/climate relationships.

    In addition Willis needs to look at prolonged periods of active or inactive solar activity.

    Willis has convinced me that I am more correct then ever through his feeble attempts to show otherwise.

  50. Salvatore Del Prete September 24, 2015 at 8:29 am Edit

    Willis has a way of convoluting the data and presenting it in a very misleading mess to put it mildly to make it somehow try to support his wrong assertions when it comes to solar/climate relationships.

    This is great! I’m in the data convolution business … Salvatore, your complaint about my use of actual data to demonstrate relationships reminds me of the Homer Simpson quote:

    Facts are meaningless. You could use facts to prove anything that’s even remotely true! –Homer Simpson

    w.

  51. Willis, I am just saying to you what you have said to those of us who support a solar/climate relationship which is we are in the business of using and presenting convoluted data.

    The feeling is mutual and only time will tell the truth.

    • That’s bovine waste products without a quote to back it up. Are you too stupid or too arrogant to follow a polite request, viz:

      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.

      Find a quote where I said that someone is “in the business of using and presenting convoluted data” and we can discuss it. Until then, you’re just slinging mud, which proves you’re out of scientific ammunition.

      I will not allow you put words into my mouth that I never said, that’s underhanded and deceptive.

      w.

  52. Having gotten some sleep, let me return now to the responses that Drs. Douglass and Knox have kindly provided to some of my objections to their work. My thanks again to them for responding, it is unusual and noteworthy in a time when many scientists refuse to defend their work in public.

    To the responses, they’ve given the quotation of my words first, my thanks to them for clearly identifying their objections:

    “… they give no citation for where were ‘previously reported’ . My guess is that these ‘climate shifts’ were ‘previously reported’ by the authors themselves…”

    Reply:
    No, they are from many sources and are described in a paper by Douglass (reference 1).

    So indeed, they were previously reported by the authors themselves, as I said …

    In reference 18 in this paper a list of climate shifts is given from various studies. [Note. As every scientist knows, references are given so that the reader may know where an assertion of the author may be checked. With the help of Google any reference can be downloaded in less than a half hour. For the Douglass/Knox papers there is link just below the references. One click and a list appears. A second click gives you the paper in question. All in less than a minute.]

    I fear you have not answered my objection, which was the lack of a a citation. I’m sure you know where they were “previously reported”, since you wrote it. And they may indeed be in your list of references. However, telling me that the information is only two clicks away doesn’t help—the entire web is a couple of clicks away.

    The problem is that without a citation or link of some kind, there’s no way to know who or what was “previously reported”.

    “They are not using all of the data. … It generally means the data are being stubborn and uncooperative.”

    Reply:
    The data are not being stubborn and uncooperative – just the opposite. The missing data from 1982 to 2000 is the same as seen in figure 2 of reference 1.

    The question is not “where is the data”. The question is why you didn’t analyze it.

    To include this data would bring in phase-locked segment #8 which would require additional redundant discussion. That is why we began at 1990.

    So indeed, the data was being stubborn and uncooperative, as I said …

    More to the point, I’m sorry, but saying in effect “If I included all the data I’d have to discuss it more“ is not a sufficient reason to not analyze a quarter of the data.

    And that is ten times as true when no reason is given in the paper for not analyzing such a large chunk of the data … you gotta admit, the optics of that are horrible.

    ‘”precision of the climatology method”

    Reply:
    Section 4 is entirely about how the “climatology” method is flawed.

    Yes, I had read and understood that. Your argument is that a boxcar smooth is better than removing the climatology because it is smoother. This allows you to use it to split the data into a high frequency-low frequency pair which you can analyze separately. Fair enough.

    However, this splitting of the dataset is for a very different purpose than the purpose behind removing the climatology. So I see the two methods as being complementary to each other, as they are used for different purposes.

    In terms of the precision, the climatology removal is more precise. I say this because it doesn’t mess with any longer periods. As the periodograms show, the boxcar filter not only removes the 1-year signal, it also reduces cycles up to about 6 years in length.

    Where are “…phase-locked segments #1 through #8” ?

    Reply:
    Two clicks away in reference 1, as mentioned specifically in the Introduction. All of them are clearly identified in figure 2. Also in this figure the “climate shifts” are shown as blue arrows.

    I did not catch that in the intro, my bad. But that wasn’t my main point. I’m sorry for my lack of clarity. What I meant was, where are the other segments in the actual analysis? What I was interested in was the fact that some of these segments would likely be in the missing quarter of the data … so why weren’t they analyzed?

    And indeed, you say that segment #8 is in the missing data … but as you say, you didn’t include it because it’s too hard to explain. Got it.

    “ … cycles at 2.5 years, 3.75 years, 5.5 years…”

    Answer: The peaks for periods greater than 1 year in Eschenbach’s periodgram are meaningless.

    Fourier analysis is now “meaningless”, but only for periods over one year? How on earth did I miss the memo? Heck, it’s even got a peak at 3 years … is that “meaningless” as well? Because that’s the very cycle that you claim is so important …

    They are some kind of average of segments of 2-year, 3-year and segments containing neither.

    I fear you have a fundamental misunderstanding of the nature of Fourier analysis. It doesn’t “average” frequencies, ever. It just shows the strengths of the individual cycles of different period lengths (or frequencies). In the actual data the various cycles can be totally intermingled, going into and out of phase … doesn’t matter. The Fourier analysis separates them all out, and shows the individual strengths at each period length. For example, if there is a cycle of period 3 years and one of 5 years, you’ll get a beat frequency in the actual data. But that doesn’t matter to the Fourier analysis. It shows it as 3 and 5 years, and more to the current point, it never shows up as an average period of 4 years.

    So those cycles are assuredly real. However, we do not know whether they are persistent because the data is only 32 years long, far too short to look at most frequencies.

    To sum up:

    • As I showed in the first half of my response to the authors above, when the absorbed solar radiation at the surface goes UP in the Nino3.4 Region, the surface temperature goes DOWN. This fact alone is sufficient to falsify their entire argument.

    • The authors claim that the TSI solar input to the Nino3.4 Region peaks in January and is at a minimum in July. This is not true at the TSI, and it is even less true at the surface, which is what the ocean is actually responding to. Again this is very strong evidence against their argument.

    Finally, even if those were not true, I’m still very uneasy about their method. They are looking for periods when there is either a 2-year or a 3-year cycle … but they’ve ignored all other possible cycle lengths. In addition, they are declaring “phase-locked sections” based on less than two cycles of data … sketchy. It seems to me that they are doing what is functionally a very simplified and clumsy version of wavelet analysis. If they’d actually done wavelet analysis it could have been interesting … if their theory hadn’t already been falsified by the two issues listed just above.

    w.

    • So robustness is lacking. If an effect is found in a manipulated data set (versus direct observation), authors are ethically bound to determine the robustness of that effect when in observation mode. And effect may be found, but it may not mean anything to boots on the ground conditions. Robustness is meaningful across observations and data manipulation.

  53. Willis
    I am quite familiar with the CERES web site but can not find the TSI data in the first panel of your figure S4.

    Please tell me how to download this data.

    Thanks

    David Douglass

    • David, there are two datasets at the CERES website. One is the TOA data, and one is a derived (although very accurate) set of surface data. Hang on …

      OK, go here, and select the “Solar Flux” field. There’s only one subfield, called “Incoming Solar Flux”. Click on the “Get Data” button at the bottom.

      You likely know the rest, but the data is in the form of an NetCDF file. It contains the observations in an array of 180 x 360 x 168 cells, which is 180° latitude by 360° longitude by 168 months of data.

      Best regards,

      w.

  54. Now I might have missed it, but as far as I know, to date not one person has taken me up on my offer to analyze their one best solar study if they sent me TWO LINKS, one to the paper and one to the data. I mean, this is a chance to show me up, a chance once and for all to show I’m wrong … but not one person came to the party.

    But there is some good news. Many folks have peanut buttered over this lacuna by concealing it beneath a thick layer of vigorous and vociferous abuse directed at my many noble, amazing, and even awe-inspiring qualities … so at least there’s been something to amuse us all while we’re waiting.

    w.

  55. Willis:

    I’m still trying to find you a paper with data. Wow, it’s really really hard.

    For example the Soon, Connolly & Connolly paper looks intriguing, they go through great lengths to explain all the problems with TSI and temperature reconstructions, which toggles my “they’ve read Feynman” bit.

    However, where the heck is the data? They did their own temperature reconstruction using rural-only stations, but don’t link to it. Googling for Soon I can’t even find his academic home page, the first two Google pages are filled with smears (as is Wikipedia). As far as the TSI reconstruction, there are so many and Google can’t find the one they use in the paper.

    I look in the references section, and it’s ALL papers. No data. It’s as if the data is an afterthought.

    coming from the software world, this is just stupid. You just upload your software to github. Handy URL right there. Done.

    Peter

    https://nextgrandminimum.files.wordpress.com/2015/09/soonconnollyconnolly15-sep4-esrunformattedpreprint.pdf

    • Peter, many thanks for your perseverance. The lack of data is no surprise to me. It’s one of the things that drove me to only accept papers for analysis if there is a clear link to the data. People would give me links to half a dozen papers with a supercilious sneer, claiming the papers clearly established the sunspot cycle/climate connection … and after a pile of searching I’d find out that not one of them had data to support their claims.

      The surprise to me is how many people buy into the data-free papers, and quote them as though the peer-review were enough for them. You’d think skeptics would be more … well … skeptical. I mean, most every skeptic knows that peer-review is meaningless, but as soon as some new solar claim surfaces, the true believers snap it up and start retailing it at a rate of knots as though it were established scientific fact …

      And you are correct that this is all easy stuff. You just upload your data and code to some convenient location. I use Dropbox, so anyone can easily find the data (and code as appropriate) for my work. And for many datasets, you don’t need to upload them, just provide a link to the one(s) that you used.

      Anyhow, your dedication is much appreciated. I just wish that the people claiming over and over that “it’s the sun, stupid” would do the work you’re doing, so that they can see just how thin on the ground actual data is … but then, I suppose that’s exactly why they don’t do it …

      My best regards to you,

      w.

    • Sorry, the paper is by Willie Soon, Ronan Connolly, and Michael Connolly. An EDIT button would be nice, wouldn’t it…?

    • Thanks, Smokey. There are two huge problems with the study.

      The first is that they are using the old sunspot data. This data incorrectly says that there was an increase in solar strength over the last couple centuries. Using that data automatically invalidates any conclusions they might draw from it.

      The second is that it seems that they can’t find any correlation with any of the standard temperature datasets, so instead they made up their own special dataset. This is an amalgam of the temperature records of “rural” stations in China, the US, Ireland, and the Arctic …

      In addition to the fact that their reconstruction doesn’t look much like any of the standard temperature records of the same period, I have a huge problem with the procedure. There are literally dozens of areas of the planet that they could have chosen for their reconstruction, giving untold possible reconstructions. So there are quite probably one or more reconstructions that one could create that would give a good correlation with sunspots, or CO2, or the price of US postage stamps, or the increase in world population over the time … so what?

      As a result of both of those problems, I wouldn’t even have a clue how to calculate the statistical significance of any results one might get from comparing a purpose-built temperature reconstruction with an incorrect sunspot dataset … but it’s not gonna be pretty, and there’s no way it could be significant. Bad data in, bad data out is the iron-clad rule.

      My thanks to you, and to the authors of the study for making their data available.

      w.

      • Hi Willis,

        Somebody mentioned you had just commented on our recent Earth-Science Reviews paper (http://www.sciencedirect.com/science/article/pii/S0012825215300349) here. Unfortunately ESR is a paywalled journal, but as Smokey notes above, I’ve put a copy of a pre-print here (http://globalwarmingsolved.com/data_files/SCC2015_preprint.pdf) and a copy of the SI here (http://globalwarmingsolved.com/data_files/SCC2015-SI.zip).

        You say there are two huge problems with our paper. I suspect this may actually be due to a misreading of what we said? I appreciate the paper is quite long, so it’s possible this has led to some confusion…

        Willis: “The first is that they are using the old sunspot data. This data incorrectly says that there was an increase in solar strength over the last couple centuries. Using that data automatically invalidates any conclusions they might draw from it.”

        Actually, this is not correct. First off, we didn’t actually use sunspot numbers for our analysis! To be honest, I’m not sure where you got that impression, as we were quite critical of studies which placed too much reliance on sunspot numbers (whichever version) as an exact proxy for solar variability. But, I do appreciate it is a long paper, and quite a lot of other studies have relied on sunspot numbers, so it is possible you just assumed this was another example.

        Instead, we used some of the different published TSI reconstructions which were available in the literature, i.e., some of those in Figure 8. It is correct that most of these reconstructions use sunspot numbers as one of their solar proxies, and since all of these reconstructions predate Clette et al., 2014’s recent update (which AFAIK was only published this summer?), the versions of the sunspot numbers that they would have used would have been one of the other datasets. But, while most of the reconstructions rely quite heavily on sunspot numbers, most of them use more than one solar proxy. Indeed, the updated Hoyt & Schatten, 1993 reconstruction (which we discuss in detail in Section 5) actually consists of seven different solar proxies – see Section 2.2.4.

        Moreover, in our discussion of the sunspot number records (Figure 4), we consider all three of the main versions, i.e., the original Wolf a.k.a. Zurich a.k.a. International numbers (see Figure 4a); Hoyt & Schatten’s “Group sunspot number” (see Figure 4b) and Clette et al., 2014’s recent update (see Figure 4c). Did you read Section 2.2.1 where we compare and contrast all three of these datasets as well as several other solar proxies aside from the sunspot numbers?

        At any rate, although the Wang et al., 2005 TSI reconstruction – which the latest IPCC Global Climate Model hindcasts (“CMIP5”) mostly relied on – is very closely related to the Hoyt & Schatten Group sunspot numbers (see Figure 9), and therefore our comparison of our Northern Hemisphere composite to the CMIP5 hindcasts in Figure 25 is indirectly based on a comparison with what you call “the old sunspot data”, this was the IPCC’s choice, not ours! We actually criticised this choice (in both Sections 2.2.4 and 4.5), and argued that climate modellers should consider a much wider range of TSI models for their hindcasts.

        We argue that, at a minimum, they should have considered all 8 of the various TSI reconstructions in Figure 8 that have been published recently in the literature, and not just the four “low solar variability” reconstructions on the right hand side of Figure 8.

        Do you not agree yourself?

        Moreover, a major point that we stressed in Section 2.2 is the fact that, while initially most of the solar proxies seem to imply fairly similar trends, when you look in detail at each of the solar proxies, there are quite a few differences between each of them – sometimes subtle, but sometimes quite pronounced.

        In your comment, you note one such difference, i.e., the differences between Clette et al.’s recent reanalysis of the sunspot number data (Figure 4c) and the two other more commonly used sunspot number datasets – Hoyt & Schatten’s group sunspot numbers (which the Wang et al., 2005 TSI reconstruction recommended by CMIP5 relies on) and the original Wolf/Zurich/International dataset (Figures 4b and 4a respectively). But, as we illustrate graphically in Figures 4 and 5, there are quite a lot of plausible solar proxies, each implying slightly different trends and peaks.

        You seem to be arguing that researchers should not rely on the datasets in Figure 4a or 4b, but rather rely on the new one in Figure 4c. Is that correct?

        If so, we’ve actually gone further and recommended that instead of simply relying on one of the ~20 solar proxies in Figures 4 and 5, we should be recognising the subtle and not-so-subtle differences between all of the different plausible solar proxies.

        Do you not agree?

        Willis: “The second is that it seems that they can’t find any correlation with any of the standard temperature datasets, so instead they made up their own special dataset”

        Actually, this is not correct either, and seems to miss the point of the second half of the paper. As we summarised in the abstract and explained in the Introduction (Section 1), there were two main goals of this paper:

        1. To try and accurately and fairly review the extensive debate over solar variability.
        2. To try and minimise the urbanization bias problem associated with the standard global/hemispheric temperature reconstructions, by developing a new estimate of Northern Hemisphere surface air temperature trends since 1881.

        So, we did NOT develop this dataset because we “…can’t find any correlation with any of the standard temperature datasets” – we specifically developed the dataset a priori to try and minimise the urbanization bias problem. Not only that, but in Section 4.1, we carried out a detailed discussion of how our new estimate compares and contrasts with the previous land-based estimates, i.e., the ones which included both rural and urban stations.

        Willis: “There are literally dozens of areas of the planet that they could have chosen for their reconstruction, giving untold possible reconstructions. “

        Initially, this seems like a fairly obvious and valid criticism. Before I had analysed the GHCN dataset (in considerable detail!!!), I would have thought the same as you… However, remarkably when you look carefully at the GHCN dataset, there is a serious shortage of regions with a large number of fully rural stations with reasonably long and complete station records.

        I’m not sure how familiar you are with the GHCN dataset, but have you looked in detail at the spatial and temporal distribution of the fully rural stations? Most of the fully rural stations in the GHCN actually only have data for the 1951-1990 period! There are quite a lot of regions which have reasonably long and complete station records… but, these stations are generally not fully rural ones!!!!

        We tried to identify as many regions as we could for which we there was enough data to construct a reasonably reliable long-term rural estimate. You would (rightly!) expect that there should be “dozens” of such areas, but the reality is that there currently is not!

        The U.S. is of course a region with a high density of fairly long and complete rural stations… thanks to the USHCN, and fortunately thanks to the Surfacestations project (which I know you were involved in!), we also have some idea on their microclimates. But, for the rest of the world, there is a surprising shortage of fully rural stations with long & complete records…

        Willis: “In addition to the fact that their reconstruction doesn’t look much like any of the standard temperature records of the same period”

        Actually, as we discuss in Section 4, while our new Northern Hemisphere temperature reconstruction implies the previous land-based estimates have slightly overestimated the 1980s-2000s warming and underestimated the 1950s-1970s cooling, it is actually quite consistent with SST (Section 4.2), glacier length-derived estimates (Section 4.3) and tree ring-derived estimates (Section 4.4). Our new reconstruction doesn’t show a very good fit to the CMIP5 hindcasts (whether using their “Natural” and/or “Anthropogenic” forcings) – see Section 4.5. However, it does show some evidence which is consistent with an influence from stratospheric volcanic cooling (Section 4.6).

        – Ronan

        P.S. Sorry for the long reply. In the words of Blaise Pascal, “I have made this [comment] longer than usual because I have not had time to make it shorter”!

  56. Ronan Connolly October 3, 2015 at 4:49 pm Edit

    Hi Willis,

    Somebody mentioned you had just commented on our recent Earth-Science Reviews paper (http://www.sciencedirect.com/science/article/pii/S0012825215300349) here. Unfortunately ESR is a paywalled journal, but as Smokey notes above, I’ve put a copy of a pre-print here (http://globalwarmingsolved.com/data_files/SCC2015_preprint.pdf) and a copy of the SI here (http://globalwarmingsolved.com/data_files/SCC2015-SI.zip).

    You say there are two huge problems with our paper. I suspect this may actually be due to a misreading of what we said? I appreciate the paper is quite long, so it’s possible this has led to some confusion…

    Willis: “The first is that they are using the old sunspot data. This data incorrectly says that there was an increase in solar strength over the last couple centuries. Using that data automatically invalidates any conclusions they might draw from it.”

    Actually, this is not correct. First off, we didn’t actually use sunspot numbers for our analysis! To be honest, I’m not sure where you got that impression, as we were quite critical of studies which placed too much reliance on sunspot numbers (whichever version) as an exact proxy for solar variability. But, I do appreciate it is a long paper, and quite a lot of other studies have relied on sunspot numbers, so it is possible you just assumed this was another example.

    I don’t understand this comment at all. You say below that all of the reconstructions used the sunspot numbers, and you agree that in fact they used the old sunspot numbers:

    Instead, we used some of the different published TSI reconstructions which were available in the literature, i.e., some of those in Figure 8. It is correct that most of these reconstructions use sunspot numbers as one of their solar proxies, and since all of these reconstructions predate Clette et al., 2014’s recent update (which AFAIK was only published this summer?), the versions of the sunspot numbers that they would have used would have been one of the other datasets. But, while most of the reconstructions rely quite heavily on sunspot numbers, most of them use more than one solar proxy. Indeed, the updated Hoyt & Schatten, 1993 reconstruction (which we discuss in detail in Section 5) actually consists of seven different solar proxies – see Section 2.2.4.

    The TSI reconstructions all used the old sunspot numbers. You used the TSI reconstructions. Ergo, your analysis depends on the old sunspot numbers. What am I missing here?

    Moreover, in our discussion of the sunspot number records (Figure 4), we consider all three of the main versions, i.e., the original Wolf a.k.a. Zurich a.k.a. International numbers (see Figure 4a); Hoyt & Schatten’s “Group sunspot number” (see Figure 4b) and Clette et al., 2014’s recent update (see Figure 4c). Did you read Section 2.2.1 where we compare and contrast all three of these datasets as well as several other solar proxies aside from the sunspot numbers?

    You did discuss and consider the three main versions (Wolf, Hoyt/Schatten, Clette). However, in the event you used the old ones, so I’m not sure what good discussing and considering them did.

    At any rate, although the Wang et al., 2005 TSI reconstruction – which the latest IPCC Global Climate Model hindcasts (“CMIP5”) mostly relied on – is very closely related to the Hoyt & Schatten Group sunspot numbers (see Figure 9), and therefore our comparison of our Northern Hemisphere composite to the CMIP5 hindcasts in Figure 25 is indirectly based on a comparison with what you call “the old sunspot data”, this was the IPCC’s choice, not ours! We actually criticised this choice (in both Sections 2.2.4 and 4.5), and argued that climate modellers should consider a much wider range of TSI models for their hindcasts.

    As I read this, you are saying that indeed you DID use the old sunspot numbers, but only because it was the IPCC’s choice. Fair enough.

    We argue that, at a minimum, they should have considered all 8 of the various TSI reconstructions in Figure 8 that have been published recently in the literature, and not just the four “low solar variability” reconstructions on the right hand side of Figure 8.

    Do you not agree yourself?

    Mmmm … my preference would be to study the eight individual TSI reconstructions first, and see if I agreed with ANY of them. Next, I’d be clear and say that none of them might be right … and that at a maximum, one of them might be right. Given that, if you consider them all, the same would be true … bad odds.

    Next, when you consider all 8 of them, you need to use the Bonferroni correction on your results … which means that if you think that a p-value less than 0.05 is necessary for significance, when you consider eight of them you need to find something with a p-value of less than 0.05 / 8 = 0.006 … which is a tough ask.

    Moreover, a major point that we stressed in Section 2.2 is the fact that, while initially most of the solar proxies seem to imply fairly similar trends, when you look in detail at each of the solar proxies, there are quite a few differences between each of them – sometimes subtle, but sometimes quite pronounced.

    My point exactly. All of them can’t be right.

    In your comment, you note one such difference, i.e., the differences between Clette et al.’s recent reanalysis of the sunspot number data (Figure 4c) and the two other more commonly used sunspot number datasets – Hoyt & Schatten’s group sunspot numbers (which the Wang et al., 2005 TSI reconstruction recommended by CMIP5 relies on) and the original Wolf/Zurich/International dataset (Figures 4b and 4a respectively). But, as we illustrate graphically in Figures 4 and 5, there are quite a lot of plausible solar proxies, each implying slightly different trends and peaks.

    You seem to be arguing that researchers should not rely on the datasets in Figure 4a or 4b, but rather rely on the new one in Figure 4c. Is that correct?

    Yes, I agree.

    If so, we’ve actually gone further and recommended that instead of simply relying on one of the ~20 solar proxies in Figures 4 and 5, we should be recognising the subtle and not-so-subtle differences between all of the different plausible solar proxies.

    Do you not agree?

    Not in the slightest. As I pointed out, and as you agree, there are a number of differences in all of the ~ 20 solar proxies. This means that at most only one is right, and 19 are wrong. The idea that if we just consider all of them we’ll be closer to the truth seems … well … optimistic.

    Willis:

    “The second is that it seems that they can’t find any correlation with any of the standard temperature datasets, so instead they made up their own special dataset”

    Actually, this is not correct either, and seems to miss the point of the second half of the paper. As we summarised in the abstract and explained in the Introduction (Section 1), there were two main goals of this paper:

    1. To try and accurately and fairly review the extensive debate over solar variability.
    2. To try and minimise the urbanization bias problem associated with the standard global/hemispheric temperature reconstructions, by developing a new estimate of Northern Hemisphere surface air temperature trends since 1881.

    So, we did NOT develop this dataset because we “…can’t find any correlation with any of the standard temperature datasets” – we specifically developed the dataset a priori to try and minimise the urbanization bias problem. Not only that, but in Section 4.1, we carried out a detailed discussion of how our new estimate compares and contrasts with the previous land-based estimates, i.e., the ones which included both rural and urban stations.

    It sounds to me like you are saying that because the regular datasets contain some unknown measure of distortion due to the UHI (the “urban bias problem”), that you can’t find the solar signal in them.

    How is that different from what I said, viz:

    … they can’t find any correlation with any of the standard temperature datasets, so instead they made up their own special dataset.

    I take a lot of grief because I make obvious assumptions. In this case, I assume that if you’d been able to find the desired correlations with the datasets containing UHI you would have used them and not bothered with making your own … but you didn’t use them.

    Willis:

    “There are literally dozens of areas of the planet that they could have chosen for their reconstruction, giving untold possible reconstructions. “

    Initially, this seems like a fairly obvious and valid criticism. Before I had analysed the GHCN dataset (in considerable detail!!!), I would have thought the same as you… However, remarkably when you look carefully at the GHCN dataset, there is a serious shortage of regions with a large number of fully rural stations with reasonably long and complete station records.

    I’m not sure how familiar you are with the GHCN dataset, but have you looked in detail at the spatial and temporal distribution of the fully rural stations? Most of the fully rural stations in the GHCN actually only have data for the 1951-1990 period! There are quite a lot of regions which have reasonably long and complete station records… but, these stations are generally not fully rural ones!!!!

    We tried to identify as many regions as we could for which we there was enough data to construct a reasonably reliable long-term rural estimate. You would (rightly!) expect that there should be “dozens” of such areas, but the reality is that there currently is not!

    The U.S. is of course a region with a high density of fairly long and complete rural stations… thanks to the USHCN, and fortunately thanks to the Surfacestations project (which I know you were involved in!), we also have some idea on their microclimates. But, for the rest of the world, there is a surprising shortage of fully rural stations with long & complete records…

    I’m sorry, but that makes no sense. You’ve used four of what you call “regions”, viz, US, China, the Arctic, and Ireland. Why on earth would you limit yourself to “regions”? Heck, three of the four “regions” are countries … are there no rural stations in Canada? The sensible way would be to use all available rural stations. Instead, you use restrictive regions like “Ireland”. As near as I can tell, you have one Irish station with data before 1950 … WUWT?

    But regardless of the choice of regions, you’ve made it impossible to compute any statistics on your results, because you could have included other stations in your reconstruction and you did not do so.

    Finally, it doesn’t appear that you have any ex-ante criteria for station selection. For example, you say:

    In Connolly & Connolly (2014c), two of us (RC & MC) showed that there were only eight non-US fully rural stations in the Global Historical Climatology Network dataset with data for at least 95 of the last 100 years. In other words, there is a serious shortage of fully rural stations with long, up-to-date and relatively complete records.

    Despite that you have a whole bunch of non-US stations. You also say:

    For calculating the annual mean gridded temperature trends for each of the regions, we adopt the same approach used in Connolly & Connolly (2014c), i.e.,
    1. All stations meeting the required characteristics for a particular subset are identified.

    I fear I couldn’t find the “required characteristics for a particular subset”, but the idea that there are different requirements for different subsets is worrisome. Also worrisome is the idea that FIRST you selected the regions and then you only considered stations within the regions.

    In addition, despite using your own new corrections to other stations, you don’t correct stations in the Arctic for UHI …

    Aside from the Barrow and Fairbanks studies, there has been very little research into quantifying the extent of urban heat islands in the Arctic, although Konstantinov et al. (2014) recently presented some preliminary research finding urban heat islands for four Arctic cities (including Murmansk). So, at present, it is difficult to accurately quantify the magnitude of the net urbanization bias effect on the Arctic trends in Figure 17a.

    For this reason, we will not attempt to correct our Arctic estimate for urbanization bias. However, it is likely that urbanization bias has led to a slight overestimation of the recent warming period.

    So you’ve ended up with some adjusted stations done by your new and “improved” method (which it may be) and some stations adjusted by other methods, and some stations not adjusted at all.

    Willis:

    “In addition to the fact that their reconstruction doesn’t look much like any of the standard temperature records of the same period”

    Actually, as we discuss in Section 4, while our new Northern Hemisphere temperature reconstruction implies the previous land-based estimates have slightly overestimated the 1980s-2000s warming and underestimated the 1950s-1970s cooling, it is actually quite consistent with SST (Section 4.2), glacier length-derived estimates (Section 4.3) and tree ring-derived estimates (Section 4.4).

    Wait a minute. Your claim is that your instrument-based reconstruction is supported by the fact that it is “quite consistent” with the tree-ring and glacier-length reconstruction? Doesn’t it usually work the other way around, with instruments used to verify proxies? Color me unimpressed.

    Look, there are a couple of independently developed instrumental datasets for historical temperature (GHCN-based, and Berkeley Earth). They look a lot like each other. And, they both look a lot like the satellite records. Your reconstruction doesn’t look like either the satellite records OR the station based records. Given that, the fact that it looks like the tree-rings doesn’t mean much …

    Our new reconstruction doesn’t show a very good fit to the CMIP5 hindcasts (whether using their “Natural” and/or “Anthropogenic” forcings) – see Section 4.5. However, it does show some evidence which is consistent with an influence from stratospheric volcanic cooling (Section 4.6).

    Since the CMIP hindcasts are tuned to the accepted historical temperature record, they would not fit your record, so that is no surprise.

    And if it shows “some evidence which is consistent with” a volcanic influence, that means that it shows some evidence which is NOT consistent with a volcanic influence … sorry, but if you’ve read my work regarding volcanoes, I don’t find “some evidence” to be anything like persuasive. Most people doing that analysis forget that there is a 50/50 chance of temperatures going down after a volcano … so “some evidence” is meaningless.

    – Ronan

    P.S. Sorry for the long reply. In the words of Blaise Pascal, “I have made this [comment] longer than usual because I have not had time to make it shorter”!

    Not a problem. Thank you for taking the time and trouble to answer in detail, it’s the only way to make your meaning clear. Comments should be as short as necessary and no shorter.

    Regarding this whole thing, I would say that yours is a portmanteau study. You have simultaneously attempted an overview of TSI reconstructions, proposed an entirely new temperature reconstruction, and looked for a connection between the two. As a result, I fear you’ve ended up with none of them being firmly established.

    And in the end, the best we can say is that using TSI reconstructions based on outdated sunspot numbers, you’ve found a correlation with your just-born and completely unverified historical temperature estimation … I couldn’t even begin to guess at the statistical significance of such a finding, but I find it completely unconvincing as regards the solar influence on the climate.

    Best regards, and thanks again for your clear comment.

    w.

  57. Willis & Ronan,

    Thanks for the back & forth on this. Having looked through both sets of your comments, the paper & the data set, I must tentatively admit that I come down in favor of the new paper… while also admitting that it is by NO means a slam-dunk “proof.”

    Even though this paper does not seem to be the “DNA evidence” which proves solar culpability in re: the observed historical climate (at least, not beyond all reasonable doubt) I still feel that the datasets involved are derived in good faith and according to valid, logical premises; the conclusions based upon their comparison are thus both interesting and compelling, if not nearly the last word on the subject. If more definitive data, correlations and conclusions along the same lines can be uncovered, I doubt anyone will really be all that surprised in retrospect.

    THAT SAID: I think definitive confirmation/refutation of the solar cycle upon decadal/centennial climate will have to wait until we can, e.g., look back 100+ years from now at a much longer satellite record of both temperatures AND solar activity — a record which will hopefully be nearly unassailable in terms of continuity, accuracy and coverage, the lack of such records today having contributed mightily to our current predicament in terms of “which set shall we use?” or “should we create a new set?” and so forth — which will hopefully show clearly that these fellows were on a/an [right/wrong/irrelevant] (circle one option) track.

    Until then, we’ll just have to wait & see. Thanks again, both of you, for your time!

  58. Hi Willis,
    Ah, thanks for your response – I think we may have been talking at cross purposes, as I actually agree with many of the points you make, and in most cases we made similar points in the paper!

    Perhaps it would help to understand a bit of the background behind why Michael (my father) and I did this collaborative study with Willie Soon. For the last few years, Michael and I have been studying the various attempts which have been made to quantify global temperature trends. In early 2014, we submitted several papers discussing our analysis for open peer review at the Open Peer Review Journal (a website we set up as an ongoing attempt to try and open up the peer review process): http://oprj.net/category/articles/climate-science

    I know you are familiar with our “Global temperature changes of the last millennium” review of the various millennial temperature proxy reconstructions, since you made some very useful and helpful comments on it. But, have you had a chance to read any of our papers analysing the various weather station-based reconstructions yet?

    If not, then one of our main findings was that the insidious nature of the urbanization bias problem has been seriously underestimated by the various groups who developed what you call “the standard temperature datasets” – see in particular, “Urbanization bias I. Is it a negligible problem for global temperature estimates?”: http://oprj.net/articles/climate-science/28

    However, our approach to science has always been heavily driven by a sense of curiousity, and so, to us saying “the data used by the previous studies is problematic” is very unsatisfying – we want to know, “well what is the data saying then?”. So, in our “Urbanization bias III. Estimating the extent of bias in the Historical Climatology Network datasets” study (http://oprj.net/articles/climate-science/34), we decided to look in detail at the various monthly temperature datasets (of which the GHCN was – at the time – the most commonly used) and see if (aware of the insidious nature of the urbanization bias problem) we could do a better job…

    As we discuss in that paper (and as I mentioned in my comment yesterday), for the U.S., we were able to identify enough fully rural stations with reasonably long and complete station records to construct a regional estimate for U.S. temperature trends since the late 19th century. However, remarkably, for the rest of the world there were very few stations that met those requirements – which initially we had assumed (like you!) would have been fairly basic requirements. We did find a handful of fully rural stations which met those requirements in Europe, but each of these stations implied slightly different trends, and there was evidence that several of them may had been affected by non-climatic biases associated with station moves, changes in microclimate, instrumentation, etc.

    Unfortunately, aside from the USHCN component, the GHCN dataset doesn’t include any station histories. None of the other datasets (i.e., Berkeley Earth, ISTI, CRU, etc) do either! So, we concluded that without access to the station histories for these stations, it was very difficult to determine which (if any!) of the station records was most representative of the long-term rural temperature trends for the region.

    One of the main recommendations we made in that paper (and some of our others) was that effort should be made into compiling station histories for the station records, so that the non-climatic biases which are associated with long station records could be reduced. [As an aside, we found that there are serious problems with the automated homogenization algorithms that are currently being used – in the absence of actual station histories – to account for non-climatic biases ]

    We also suggested that, if one of the new datasets (ISTI or Berkeley Earth) were used, then it was likely that more stations meeting those requirements could be identified. However, while the compilers of the GHCN dataset had put quite a bit of effort into identifying how urbanized the stations were, this has not (yet!) been done for the ISTI or Berkeley Earth datasets – although Wickham et al. did carry out a preliminary categorisation of the Berkeley Earth stations (I gather with assistance from Steve Mosher).

    By the end of 2014, in discussion with some of the ISTI group, we had started analysing the ISTI dataset with a view to sorting the stations according to their degree of urbanization. This is ongoing work, but (like you) none of our climate research is funded, and so we can only carry it out in our spare time. :(

    This comment is already quite long, so I’ll continue in a separate comment! …

    • Ronan Connolly October 4, 2015 at 12:56 pm

      Hi Willis,
      Ah, thanks for your response – I think we may have been talking at cross purposes, as I actually agree with many of the points you make, and in most cases we made similar points in the paper!

      Thanks for continuing the discussion, Ronan, much appreciated.

      Perhaps it would help to understand a bit of the background behind why Michael (my father) and I did this collaborative study with Willie Soon. For the last few years, Michael and I have been studying the various attempts which have been made to quantify global temperature trends. In early 2014, we submitted several papers discussing our analysis for open peer review at the Open Peer Review Journal (a website we set up as an ongoing attempt to try and open up the peer review process): http://oprj.net/category/articles/climate-science

      I know you are familiar with our “Global temperature changes of the last millennium” review of the various millennial temperature proxy reconstructions, since you made some very useful and helpful comments on it. But, have you had a chance to read any of our papers analysing the various weather station-based reconstructions yet?

      If not, then one of our main findings was that the insidious nature of the urbanization bias problem has been seriously underestimated by the various groups who developed what you call “the standard temperature datasets” – see in particular, “Urbanization bias I. Is it a negligible problem for global temperature estimates?”: http://oprj.net/articles/climate-science/28

      Yes, it’s a problem. Is it “seriously underestimated”? I think it’s underestimated, but I don’t think anyone has a good handle on the size of it.

      However, our approach to science has always been heavily driven by a sense of curiousity, and so, to us saying “the data used by the previous studies is problematic” is very unsatisfying – we want to know, “well what is the data saying then?”. So, in our “Urbanization bias III. Estimating the extent of bias in the Historical Climatology Network datasets” study (http://oprj.net/articles/climate-science/34), we decided to look in detail at the various monthly temperature datasets (of which the GHCN was – at the time – the most commonly used) and see if (aware of the insidious nature of the urbanization bias problem) we could do a better job…

      As we discuss in that paper (and as I mentioned in my comment yesterday), for the U.S., we were able to identify enough fully rural stations with reasonably long and complete station records to construct a regional estimate for U.S. temperature trends since the late 19th century. However, remarkably, for the rest of the world there were very few stations that met those requirements – which initially we had assumed (like you!) would have been fairly basic requirements. We did find a handful of fully rural stations which met those requirements in Europe, but each of these stations implied slightly different trends, and there was evidence that several of them may had been affected by non-climatic biases associated with station moves, changes in microclimate, instrumentation, etc.

      Oooh, you are omitting data from your study because it doesn’t do what you expect … this is egregious data snooping.

      Unfortunately, aside from the USHCN component, the GHCN dataset doesn’t include any station histories. None of the other datasets (i.e., Berkeley Earth, ISTI, CRU, etc) do either! So, we concluded that without access to the station histories for these stations, it was very difficult to determine which (if any!) of the station records was most representative of the long-term rural temperature trends for the region.

      Agreed

      One of the main recommendations we made in that paper (and some of our others) was that effort should be made into compiling station histories for the station records, so that the non-climatic biases which are associated with long station records could be reduced. [As an aside, we found that there are serious problems with the automated homogenization algorithms that are currently being used – in the absence of actual station histories – to account for non-climatic biases ]

      Agreed.

      We also suggested that, if one of the new datasets (ISTI or Berkeley Earth) were used, then it was likely that more stations meeting those requirements could be identified. However, while the compilers of the GHCN dataset had put quite a bit of effort into identifying how urbanized the stations were, this has not (yet!) been done for the ISTI or Berkeley Earth datasets – although Wickham et al. did carry out a preliminary categorisation of the Berkeley Earth stations (I gather with assistance from Steve Mosher).

      By the end of 2014, in discussion with some of the ISTI group, we had started analysing the ISTI dataset with a view to sorting the stations according to their degree of urbanization. This is ongoing work, but (like you) none of our climate research is funded, and so we can only carry it out in our spare time. :(

      Here’s my question, which all of this hasn’t answered … why didn’t you a) put out ex-ante criteria for which stations you would use, and then b) use EACH AND EVERY station that fit your ex-ante criteria, whether or not they did what you expected it to do.

      Onwards to your next comment,

      w.

  59. …At any rate, at this point Willie contacted us. As you probably know, he has been carrying out a lot of research into evaluating the role of solar variability in climate change, and in particular into the possible influence of solar variability on recent global (and regional) temperature trends. However, until he read our papers, he had been assuming (like you!) that the standard temperature datasets were reasonably reliable. If the standard temperature datasets are significantly affected by urbanization bias (and other non-climatic biases), as our work suggests, then this could could have implications for his analysis. So, he wanted to know what dataset we recommended he should be using instead.

    We explained that, aside from the U.S. component, the GHCN dataset was surprisingly limited and problematic, and that our analysis of the ISTI dataset was still ongoing. However, after some discussion, we decided that if we looked in detail at the other regions on a case-by-case basis, there were actually a few more regions where we could carry out a cautious and caveated “best current guess” estimate, i.e., China, Ireland and the Arctic.

    This was what led to one half of our Earth-Science Reviews paper, i.e., the construction of a new mostly rural Northern Hemisphere temperature reconstruction from 1881-2014.

    Because Michael & I live in Ireland and are only ~4 hours drive from the main rural station in Ireland (i.e., Valentia Observatory), we were able to contact the station owners directly and they provided us with a detailed station history and useful parallel measurements associated with 2 of the main station history events. In other words, for this station, we were able to implement the recommendation we made in our 2014 papers. It would be nice to extend this analysis to include some of the other long rural records in Europe, and in Section 3.3, we recommend this should be done by the collection of similar station history information. We think something like what you guys did with Surfacestations could be a useful way to do this, but at any rate, we think this should be a top priority for improving the reliability of the temperature datasets…

    For the remaining two components (China & Arctic), we had to take a number of assumptions and provide cautionary caveats (for instance our concern about some UHI in the Arctic). However, in the interests of providing greater hemispheric coverage, we decided it would still be of interest.

    We spent a long time seeing if there were any other regions which we could also include with some measure of confidence in their reliability. For instance, you mentioned Canada – we did actually include all of the available Canadian Arctic stations in our Arctic composite, but we also considered expanding our Arctic composite to include sub-Arctic Canada (and similarly sub-Arctic Russia). However, as it is, of the four components, the Arctic component is the one we probably have the least confidence in… even though our estimate is very similar to the regional Arctic estimates derived from “the standard temperature datasets”. That is, the groups compiling the standard temperature datasets apparently would have more confidence in our Arctic component than we do!

    We are looking to see if we can improve on this Arctic component for future work, but we will probably not be using the GHCN for this.

    Although there are (as you imply) plenty of other regions in the Northern Hemisphere which have stations with fairly long and complete station records, these are urbanized stations!

    For us, the biggest gap in our Northern Hemisphere sampling is probably for the tropics – a point we stress in Section 4. But, again this is because there is a severe shortage of fully rural tropical stations with long records in the GHCN.

    To summarise, the main emphasis we took in our approach was quality over quantity. While the groups who developed the standard temperature datasets seem to feel it’s ok to ‘throw everything into the pot’ and see what comes out, we feel that this is not appropriate. What we have found is that, despite the claims of several groups, the underlying data is problematic. We think it is important to recognise this and treat the data carefully and be explicit about our caveats and assumptions.

    For our earlier work, we applied what we thought should have been fairly basic minimum requirements… but essentially the only part of the GHCN dataset which met those requirements was the U.S.! For this reason, in this study, we tried to lessen these restrictions a bit to try and increase our station coverage. This gave us two new regions, i.e., China and the Arctic, but we stressed in the paper that these new estimates still need to be treated cautiously (see the discussions in Section 3).

    We also followed up on our own recommendation that station histories could be used to help overcome some of the challenges, by tracking down the information for our local long-term fully rural station. This opened up a third region, Ireland. Yes, it’s a small country, and it would be nice if this analysis could be extended to include some of the other long-term fully rural stations in Europe. But, remember, all of this work is self-funded! We haven’t yet managed to track down the station histories for these other stations.

    It’s true if we completely dropped our basic requirements for the data (as the other groups have), then we would get basically the same result as them. Indeed, we did this in Section 4.1 (see Figures 20 and 21). It’s kind of like how if I construct a temperature proxy reconstruction using bristlecone pines, Yamal and upside-down Tiljander lake sediments, I can create a “hockey stick” too… ;)

    But, should we really abandon our basic requirements just so that we can be “part of the gang”? We don’t think so. We’re more interested in trying to figure out what the data is really telling us, even if it means going through it piecemeal… What do you think, yourself?

    Does that background give you a better insight into our approach?

    Also, does it explain why this assumption you made is wrong:
    Willis: “I take a lot of grief because I make obvious assumptions. In this case, I assume that if you’d been able to find the desired correlations with the datasets containing UHI you would have used them and not bothered with making your own … but you didn’t use them.”
    Michael & I weren’t even considering solar variability when we started this – our main goal was (and still is) simply to do the best we can with the currently available data to minimise UHI and other non-climatic biases, etc.
    Willie, on the other hand, had been using the standard temperature datasets all along, and probably would have continued to do so. But, once he realised that there were potentially serious problems with them, he decided this wasn’t good enough!

    I guess this brings us to the other half of our paper, i.e., the solar variability debate, but this comment is also very long, so I’ll continue in yet another comment…

    • Ronan Connolly October 4, 2015 at 12:57 pm

      …At any rate, at this point Willie contacted us. As you probably know, he has been carrying out a lot of research into evaluating the role of solar variability in climate change, and in particular into the possible influence of solar variability on recent global (and regional) temperature trends. However, until he read our papers, he had been assuming (like you!) that the standard temperature datasets were reasonably reliable. If the standard temperature datasets are significantly affected by urbanization bias (and other non-climatic biases), as our work suggests, then this could could have implications for his analysis. So, he wanted to know what dataset we recommended he should be using instead.

      As I said above, if the solar analysis had proven fruitful with the standard temperature datasets, Willie wouldn’t have needed a new dataset to compare with. You objected strongly to this characterization, but it appears to be true.

      We explained that, aside from the U.S. component, the GHCN dataset was surprisingly limited and problematic, and that our analysis of the ISTI dataset was still ongoing. However, after some discussion, we decided that if we looked in detail at the other regions on a case-by-case basis, there were actually a few more regions where we could carry out a cautious and caveated “best current guess” estimate, i.e., China, Ireland and the Arctic.

      This was what led to one half of our Earth-Science Reviews paper, i.e., the construction of a new mostly rural Northern Hemisphere temperature reconstruction from 1881-2014.

      Because Michael & I live in Ireland and are only ~4 hours drive from the main rural station in Ireland (i.e., Valentia Observatory), we were able to contact the station owners directly and they provided us with a detailed station history and useful parallel measurements associated with 2 of the main station history events. In other words, for this station, we were able to implement the recommendation we made in our 2014 papers. It would be nice to extend this analysis to include some of the other long rural records in Europe, and in Section 3.3, we recommend this should be done by the collection of similar station history information. We think something like what you guys did with Surfacestations could be a useful way to do this, but at any rate, we think this should be a top priority for improving the reliability of the temperature datasets…

      For the remaining two components (China & Arctic), we had to take a number of assumptions and provide cautionary caveats (for instance our concern about some UHI in the Arctic). However, in the interests of providing greater hemispheric coverage, we decided it would still be of interest.

      I still don’t understand why you are messing with “regions”. Why not just include all valid records, whether they fit into a “region” or not?

      We spent a long time seeing if there were any other regions which we could also include with some measure of confidence in their reliability.

      Same objection. You are stuck on the idea of “regions”, and as a result you are not using all of the data.

      For instance, you mentioned Canada – we did actually include all of the available Canadian Arctic stations in our Arctic composite, but we also considered expanding our Arctic composite to include sub-Arctic Canada (and similarly sub-Arctic Russia). However, as it is, of the four components, the Arctic component is the one we probably have the least confidence in… even though our estimate is very similar to the regional Arctic estimates derived from “the standard temperature datasets”. That is, the groups compiling the standard temperature datasets apparently would have more confidence in our Arctic component than we do!

      We are looking to see if we can improve on this Arctic component for future work, but we will probably not be using the GHCN for this.

      Although there are (as you imply) plenty of other regions in the Northern Hemisphere which have stations with fairly long and complete station records, these are urbanized stations!

      I’m sorry, but I simply do not believe that the only valid stations for your research are in the US, China, Ireland, and the Arctic. That makes no sense at all

      For us, the biggest gap in our Northern Hemisphere sampling is probably for the tropics – a point we stress in Section 4. But, again this is because there is a severe shortage of fully rural tropical stations with long records in the GHCN.

      Again, same objection. If there is a “severe shortage”, that’s all the more reason to use the few that remain.

      To summarise, the main emphasis we took in our approach was quality over quantity. While the groups who developed the standard temperature datasets seem to feel it’s ok to ‘throw everything into the pot’ and see what comes out, we feel that this is not appropriate. What we have found is that, despite the claims of several groups, the underlying data is problematic. We think it is important to recognise this and treat the data carefully and be explicit about our caveats and assumptions.

      But you haven’t done that. You haven’t established ex-ante criteria and only used what fit. Instead, you appear to have special criteria for each of your different “regions”.

      For our earlier work, we applied what we thought should have been fairly basic minimum requirements… but essentially the only part of the GHCN dataset which met those requirements was the U.S.! For this reason, in this study, we tried to lessen these restrictions a bit to try and increase our station coverage. This gave us two new regions, i.e., China and the Arctic, but we stressed in the paper that these new estimates still need to be treated cautiously (see the discussions in Section 3).

      Never mind about the “regions”. You’ve blinded yourself with your insistence on “regions”.

      We also followed up on our own recommendation that station histories could be used to help overcome some of the challenges, by tracking down the information for our local long-term fully rural station. This opened up a third region, Ireland. Yes, it’s a small country, and it would be nice if this analysis could be extended to include some of the other long-term fully rural stations in Europe. But, remember, all of this work is self-funded! We haven’t yet managed to track down the station histories for these other stations.

      It’s true if we completely dropped our basic requirements for the data (as the other groups have), then we would get basically the same result as them. Indeed, we did this in Section 4.1 (see Figures 20 and 21). It’s kind of like how if I construct a temperature proxy reconstruction using bristlecone pines, Yamal and upside-down Tiljander lake sediments, I can create a “hockey stick” too… ;)

      But, should we really abandon our basic requirements just so that we can be “part of the gang”? We don’t think so. We’re more interested in trying to figure out what the data is really telling us, even if it means going through it piecemeal… What do you think, yourself?

      I think that your claim that you have “opened up a third region” reflects a total misunderstanding of what you are about.

      Does that background give you a better insight into our approach?

      It explains what you are doing wrong …

      Also, does it explain why this assumption you made is wrong:

      Willis: “I take a lot of grief because I make obvious assumptions. In this case, I assume that if you’d been able to find the desired correlations with the datasets containing UHI you would have used them and not bothered with making your own … but you didn’t use them.”
      Michael & I weren’t even considering solar variability when we started this – our main goal was (and still is) simply to do the best we can with the currently available data to minimise UHI and other non-climatic biases, etc.
      Willie, on the other hand, had been using the standard temperature datasets all along, and probably would have continued to do so. But, once he realised that there were potentially serious problems with them, he decided this wasn’t good enough!

      My point was that if Willie had found the correlations he seeks in the usual datasets, he wouldn’t need a new dataset. In that, I was correct. However, as you point out, I was 100% wrong that that this was the reason for creating your dataset, because clearly you wanted to create a dataset that was free of UHI.

      I guess this brings us to the other half of our paper, i.e., the solar variability debate, but this comment is also very long, so I’ll continue in yet another comment…

      My final question would be this: the satellite temperature datasets track the surface temperature datasets quite closely, although they differ somewhat in the overall trends. And presumably the satellite datasets are NOT affected by UHI.

      So why don’t your results track the satellite data, since you claim your results are also free of UHI?

      w.

  60. …With regards to the solar variability debate, in some senses, Michael & I have a fairly similar perspective to you. When we first started reading the literature and analysing the data on this topic, we found (like you!) that most of the “It’s the Sun, stupid” studies were one-sided and often relied on cherry-picked and/or incomplete analysis.
    However – and perhaps we diverge from you here? – we also found that the same applied to most of the “It’s NOT the Sun, stupid” studies!!!

    We found that – on both sides – a lot of the studies seem more interested in researchers ‘defending their turf’ and ‘affirming the consequent’, and less interested in trying to genuinely establish what role (if any) the Sun has played…

    Importantly, as we discussed on our blogpost assessing the IPCC process (http://globalwarmingsolved.com/2013/11/what-does-the-ipcc-say/), the CMIP5 hindcasts which the IPCC relied on for their “Detection and Attribution” studies seemed to have taken a very one-sided approach to modelling “natural forcings” by only focusing on the low solar variability TSI reconstructions – see in particular Section 3 of that blog post (i.e., the ‘How was this “extremeley likely that…” conclusion reached?’ section).

    So, when we were discussing with Willie the various TSI reconstructions and what we actually know (and don’t know!) about how solar activity has varied since the 19th century, we all decided that it was time that somebody did a balanced and comprehensive review of the literature (and data) presenting all perspectives.

    We tried our best to be as non-judgemental as we could in our review (Section 2), and wherever we had strong opinions on a particular aspect, to flag them as such to the reader. Of course, we have to keep reminding ourselves that if you think you are unaffected by confirmation bias, then it’s quite likely that you are affected! ;)
    But, we tried to do our best…

    At the end of our review, our main conclusion was that… this is actually a very tricky problem, and we still don’t really know for sure!!! :(
    See Section 2.4 for our summary of the main points of general agreement and the main points of debate currently in the literature.

    So I actually completely agree with you when you say,
    Willis: “Mmmm … my preference would be to study the eight individual TSI reconstructions first, and see if I agreed with ANY of them. Next, I’d be clear and say that none of them might be right … and that at a maximum, one of them might be right. Given that, if you consider them all, the same would be true … bad odds.”
    and
    Willis: “Not in the slightest. As I pointed out, and as you agree, there are a number of differences in all of the ~ 20 solar proxies. This means that at most only one is right, and 19 are wrong. The idea that if we just consider all of them we’ll be closer to the truth seems … well … optimistic.”

    In our opinion, neither the “It’s the Sun, stupid” nor the “It’s NOT the Sun, stupid” groups have satisfactorily resolved the issue.

    However, there are several conclusions we think we can confidently draw from our analysis:
    1. The CMIP5 hindcasts (which formed the basis for the latest IPCC report’s main conclusions) only considered a small (and one-sided) subset of the currently available TSI reconstructions (see Figure 8). Therefore, their treatment of “natural forcings” in their detection and attribution studies is very limited, and as such, should be treated with considerable caution.

    2. While the CMIP5 hindcasts with “anthropogenic + natural forcings” seemed to do a reasonable job of hindcasting the standard temperature datasets, they seem to have done a rather poor job of hindcasting our new mostly rural dataset.
    We can say that the standard temperature datasets are – at least to some extent – significantly affected by urbanization bias, e.g., see the evidence of urbanization bias in the China data over the 1951-1990 period (Figure 10), since only 1/4 of the Northern Hemisphere stations (in the GHCN at least) are fully rural. On the other hand, our new Northern Hemisphere reconstruction is mostly rural, with 86.4% of the stations being fully rural… and it is constructed from a large chunk of the fully rural NH stations that are available in the GHCN (42.2%) – see Table 6.
    Also, there are problems with the automated Menne & Williams homogenization algorithm which the groups using the homogenized GHCN dataset relied on, e.g., see Sections 3.1 and 3.3.

    Therefore, even if you dispute the reliability of our new Northern Hemisphere reconstruction (and we have stressed that this is just our current best attempt to deal with the problems!), there are also problems with the “standard temperature datasets”. So, the apparent goodness of the CMIP5 fit used by the IPCC to conclude that “it’s NOT the Sun, stupid” is no longer valid.

    It also means that, if “it’s still not the Sun, stupid”, then the CMIP5 models are leaving out at least one key driver that seems to have played a major role on temperature trends since the 19th century – and it doesn’t seem to be proportional to either GHG or man-made aerosols (i.e., the CMIP5 models’ “anthropogenic forcings”). If it’s not the Sun, then identifying this apparently “missing” factor should become a top research priority. It is plausible that this could be a “natural forcing”. This would concur with the arguments people like Roy Spencer have been making for years, i.e., climate scientists should be actively encouraged to investigate the role natural variability has played on recent climate change.

    3. One of the current TSI reconstructions which the CMIP5 modellers ignored (i.e., ACRIM’s update of the Hoyt & Schatten, 1993 reconstruction) does seem to show a remarkable fit to our new Northern Hemisphere composite… and also to each of our four regional components (if you still dispute the representativeness of our composite). So, if the updated Hoyt & Schatten, 1993 TSI reconstruction is at all accurate, then this suggests that solar variability has been a primary driver of temperature trends since at least 1881 for much of the Northern Hemisphere – regardless of whether you think it was just limited to the four specific regions in Figure 19, or for the entire hemisphere.

    This suggests that the “it’s the Sun, stupid” argument is at least a plausible one, and more research should be carried out into establishing which TSI reconstructions (if any) are most representative of the actual TSI trends. If it transpires that none of them are reliable, then more research should be carried out into developing new TSI reconstructions.

    Do you agree that those specific conclusions are reasonable ones to draw from our analysis?
    If so, then I think we’re probably mostly in agreement on this, after all…

    On a separate issue, while I think Leif Svalgaard (who often comments here) and the rest of the Clette et al. group have put a lot of valuable work into their new version of the sunspot record, and I acknowledge that Leif (and others) are quite vigorously arguing that the “old” versions are “outdated” (as you claim), not everyone in the solar community agrees with them!

    Remember, as we discuss in Section 2.2.1, the “sunspot number” is rather confusingly not “the number of sunspots”, but is actually an index which combines information on the number of sunspot groups, the number of individual sunspots and a factor based on who measured the sunspots. This means that compiling the dataset is surprisingly subjective. Indeed, this is why Clette et al. are arguing that several of the subjective decisions made for the other two versions are invalid. But, these arguments (while certainly plausible) are themselves somewhat subjective, and I know that several researchers disagree with some/all of their adjustments!

    So, we think it is premature to conclude that the other datasets are “outdated”. This is part of the reason why we discuss all three versions in our review, not just “the latest”.

    More importantly, we are not convinced that sunspot numbers are necessarily the best proxy for TSI. It’s true that they provide one of the longest datasets, and they certainly seem to be capturing an important aspect of solar variability… but they are a very indirect measure, particularly if the ACRIM group’s estimate of TSI is accurate.

    • Ronan Connolly October 4, 2015 at 12:58 pm

      …With regards to the solar variability debate, in some senses, Michael & I have a fairly similar perspective to you. When we first started reading the literature and analysing the data on this topic, we found (like you!) that most of the “It’s the Sun, stupid” studies were one-sided and often relied on cherry-picked and/or incomplete analysis.
      However – and perhaps we diverge from you here? – we also found that the same applied to most of the “It’s NOT the Sun, stupid” studies!!!

      I haven’t seen many of the latter, in part because it’s very difficult to show a negative … which studies are you referring to?

      We found that – on both sides – a lot of the studies seem more interested in researchers ‘defending their turf’ and ‘affirming the consequent’, and less interested in trying to genuinely establish what role (if any) the Sun has played…

      And? …

      Importantly, as we discussed on our blogpost assessing the IPCC process, the CMIP5 hindcasts which the IPCC relied on for their “Detection and Attribution” studies seemed to have taken a very one-sided approach to modelling “natural forcings” by only focusing on the low solar variability TSI reconstructions – see in particular Section 3 of that blog post (i.e., the ‘How was this “extremeley likely that…” conclusion reached?’ section).

      I’m sorry, but whether they use high or low reconstructions is unimportant. The models are tuned, which makes their hindcasts meaningless.

      So, when we were discussing with Willie the various TSI reconstructions and what we actually know (and don’t know!) about how solar activity has varied since the 19th century, we all decided that it was time that somebody did a balanced and comprehensive review of the literature (and data) presenting all perspectives.

      We tried our best to be as non-judgemental as we could in our review (Section 2), and wherever we had strong opinions on a particular aspect, to flag them as such to the reader. Of course, we have to keep reminding ourselves that if you think you are unaffected by confirmation bias, then it’s quite likely that you are affected! ;)
      But, we tried to do our best…

      At the end of our review, our main conclusion was that… this is actually a very tricky problem, and we still don’t really know for sure!!! :(
      See Section 2.4 for our summary of the main points of general agreement and the main points of debate currently in the literature.

      So I actually completely agree with you when you say,
      Willis: “Mmmm … my preference would be to study the eight individual TSI reconstructions first, and see if I agreed with ANY of them. Next, I’d be clear and say that none of them might be right … and that at a maximum, one of them might be right. Given that, if you consider them all, the same would be true … bad odds.”
      and
      Willis: “Not in the slightest. As I pointed out, and as you agree, there are a number of differences in all of the ~ 20 solar proxies. This means that at most only one is right, and 19 are wrong. The idea that if we just consider all of them we’ll be closer to the truth seems … well … optimistic.”

      In our opinion, neither the “It’s the Sun, stupid” nor the “It’s NOT the Sun, stupid” groups have satisfactorily resolved the issue.

      Ronan, people have been looking for the effects of the ~11-year solar fluctuations for two centuries now … and there is still no clear, unassailable evidence that they do anything at all.

      If you interpret that as being equality between the two propositions, I’m not sure what I can say.

      However, there are several conclusions we think we can confidently draw from our analysis:
      1. The CMIP5 hindcasts (which formed the basis for the latest IPCC report’s main conclusions) only considered a small (and one-sided) subset of the currently available TSI reconstructions (see Figure 8). Therefore, their treatment of “natural forcings” in their detection and attribution studies is very limited, and as such, should be treated with considerable caution.

      Perhaps you care what the Tinkertoy models say or why they say it. I don’t.

      2. While the CMIP5 hindcasts with “anthropogenic + natural forcings” seemed to do a reasonable job of hindcasting the standard temperature datasets, they seem to have done a rather poor job of hindcasting our new mostly rural dataset.

      TUNING! TUNING! They could just as easily be tuned to your dataset, but they weren’t.

      We can say that the standard temperature datasets are – at least to some extent – significantly affected by urbanization bias, e.g., see the evidence of urbanization bias in the China data over the 1951-1990 period (Figure 10), since only 1/4 of the Northern Hemisphere stations (in the GHCN at least) are fully rural.

      Not sure what you mean by “at least to some extent significantly effected”. Either they are significantly affected, or they are not. What do you mean by “extent”? Temporal? Spatial? Conceptual?

      Nor am I clear how you are measuring the effect of the UHI, other than by using only stations that by some criterion or another are “rural” … when you don’t even know whether they are “rural” or not, and you don’t have any clear definition of “rural” that applies to all the stations.

      On the other hand, our new Northern Hemisphere reconstruction is mostly rural, with 86.4% of the stations being fully rural… and it is constructed from a large chunk of the fully rural NH stations that are available in the GHCN (42.2%) – see Table 6.

      Yes, and it doesn’t agree with the satellite data, which has no significant UHI component … the part that you don’t seem to get is that simply because you call a station “rural”, that doesn’t mean that it doesn’t have problems, perhaps serious ones.

      Also, there are problems with the automated Menne & Williams homogenization algorithm which the groups using the homogenized GHCN dataset relied on, e.g., see Sections 3.1 and 3.3.

      I dislike that automated homogenization algorithm, but then I’m not fond of doing anything automated to climate data.

      Therefore, even if you dispute the reliability of our new Northern Hemisphere reconstruction (and we have stressed that this is just our current best attempt to deal with the problems!), there are also problems with the “standard temperature datasets”. So, the apparent goodness of the CMIP5 fit used by the IPCC to conclude that “it’s NOT the Sun, stupid” is no longer valid.

      It never was valid. It was always just the result of tuning. So your analysis doesn’t help there.

      It also means that, if “it’s still not the Sun, stupid”, then the CMIP5 models are leaving out at least one key driver that seems to have played a major role on temperature trends since the 19th century – and it doesn’t seem to be proportional to either GHG or man-made aerosols (i.e., the CMIP5 models’ “anthropogenic forcings”). If it’s not the Sun, then identifying this apparently “missing” factor should become a top research priority. It is plausible that this could be a “natural forcing”. This would concur with the arguments people like Roy Spencer have been making for years, i.e., climate scientists should be actively encouraged to investigate the role natural variability has played on recent climate change.

      The models leave out or poorly parameterize the emergent phenomena which regulate the temperature to within a very narrow range (e.g. ± 0.3°C over the 20th century). So nothing they say has any more meaning than a monkey at a typewriter … and that means that all of your conclusions based on the fact that they are wrong are likely wrong …

      3. One of the current TSI reconstructions which the CMIP5 modellers ignored (i.e., ACRIM’s update of the Hoyt & Schatten, 1993 reconstruction) does seem to show a remarkable fit to our new Northern Hemisphere composite… and also to each of our four regional components (if you still dispute the representativeness of our composite). So, if the updated Hoyt & Schatten, 1993 TSI reconstruction is at all accurate, then this suggests that solar variability has been a primary driver of temperature trends since at least 1881 for much of the Northern Hemisphere – regardless of whether you think it was just limited to the four specific regions in Figure 19, or for the entire hemisphere.

      I’m sorry, but I didn’t see that in your paper … and it is not clear what you are calling a “remarkable fit”. I also don’t know how “ACRIM” could update anything, since it’s a satellite dataset. Some links and examples would be very useful here.

      This suggests that the “it’s the Sun, stupid” argument is at least a plausible one, and more research should be carried out into establishing which TSI reconstructions (if any) are most representative of the actual TSI trends. If it transpires that none of them are reliable, then more research should be carried out into developing new TSI reconstructions.

      Is it “plausible”? People since Hershel have thought so … but we still have no definitive data showing anything going on, despite two centuries of trying. If a failure to find any strong data in two hundred years makes something “plausible” on your planet, then it is indeed plausible.

      Do you agree that those specific conclusions are reasonable ones to draw from our analysis?

      Dunno … but I do know that I still haven’t heard anything about how the Bonferroni correction applies to the “remarkable fit” of one among twenty solar reconstructions …

      If so, then I think we’re probably mostly in agreement on this, after all…

      I still don’t have any idea what you are calling the “remarkable fit”, how it was measured, or why you think it is “remarkable” …

      On a separate issue, while I think Leif Svalgaard (who often comments here) and the rest of the Clette et al. group have put a lot of valuable work into their new version of the sunspot record, and I acknowledge that Leif (and others) are quite vigorously arguing that the “old” versions are “outdated” (as you claim), not everyone in the solar community agrees with them!

      Well, there’s a surprise.

      Remember, as we discuss in Section 2.2.1, the “sunspot number” is rather confusingly not “the number of sunspots”, but is actually an index which combines information on the number of sunspot groups, the number of individual sunspots and a factor based on who measured the sunspots. This means that compiling the dataset is surprisingly subjective. Indeed, this is why Clette et al. are arguing that several of the subjective decisions made for the other two versions are invalid. But, these arguments (while certainly plausible) are themselves somewhat subjective, and I know that several researchers disagree with some/all of their adjustments!

      First, while the compilation of the data was subjective, that does NOT mean that removing historical errors in the dataset are subjective. That makes no logical sense.

      However, since you haven’t said what either you or the others think is wrong in the Clette et al. changes, I fear I can’t respond to those allegations.

      So, we think it is premature to conclude that the other datasets are “outdated”. This is part of the reason why we discuss all three versions in our review, not just “the latest”.

      I say it’s premature to say that the new reconstruction is wrong without letting us in on your specific objections to their changes, along with quotations of what they said that you think is wrong.

      More importantly, we are not convinced that sunspot numbers are necessarily the best proxy for TSI. It’s true that they provide one of the longest datasets, and they certainly seem to be capturing an important aspect of solar variability… but they are a very indirect measure, particularly if the ACRIM group’s estimate of TSI is accurate.

      Regarding the “ACRIM group’s estimate”, all I find in your paper is this:

      When Scafetta & Willson updated the Hoyt & Schatten reconstruction using the ACRIM composite (Scafetta & Willson, 2014 – see their Appendix B), they noted an apparently strong correlation with Manley’s Central England Temperature (CET) dataset (Manley, 1974), as updated by Parker & Horton (2005). This dataset is not part of the Global Historical Climatology Network, and so was not considered in our analysis.

      Scafetta is not a scientist, in that he has refused repeatedly to make his calculations public despite repeated requests. If Scafetta and Willson’s update is what you are calling the “ACRIM group’s estimate”, then the odds that it is unbiased, valuable, or accurate just went down stupendously … and that may be related to the fact that I don’t see a single citation to his work other than yours …

      There’s a post of mine about Scafetta’s goofy claims here. In addition he was up to his ears in the infamous Copernicus-PRP affair. See Anthony’s post here, and don’t miss Anthony’s response to Nicola’s comment here. Depend on Scafetta at your own risk … and if his TSI reconstruction shows “correlations”, I’d be damn sure to examine his “correlations” with a microscope to see if they are real. Fortunately, not my problem, I don’t read anything of his these days. My rule is simple—no code, no data, no science.

      In any case, the physical reasons that TSI is related to sunspots are well-known, and the historical sunspot records are available and detailed … so while no proxy is perfect, I’m not sure what you think might be a better proxy for TSI than sunspots.

      Let me close by saying that I am greatly troubled by your lack of comment about the Bonferroni corrections, despite my explanation of why they are important. You keep saying things like that you considered ten or so different TSI reconstructions, and found one of them significant … well, duh, when you look at ten datasets, the odds of significance go through the roof … and you seem totally innocent of the consequence of that.

      I am also distressed by the lack of any discussion of the effects of autocorrelation on the significance of the results. All of the datasets you are using are highly autocorrelated, and you appear to not know that this seriously changes your statistics.

      Finally, I saw no discussion of the fact that an asymmetric cyclical dataset like sunspots or TSI will show totally bogus “significant correlations” with random red-noise data at an amazing rate. You need to do a Monte Carlo analysis to say anything at all about the statistical significance of results when you are using such a dataset … and the term “Monte Carlo” doesn’t appear anywhere in your paper.

      All of this indicates to me that if I could say one thing to you, it is that you, like many others, are radically overestimating the significance of the things you are finding. From here, it appears that you desperately need a crash course in statistical corrections for autocorrelation, and in the Bonferroni correction, and in Monte Carlo analysis. You are getting badly fooled by what appear to be significant results, but which are nothing of the sort.

      Best regards, and thanks for continuing the discussion.

      w.

      • Hi Willis,

        It’s quite late here and there’s quite a lot of points. I’ll try to address the main points, but apologies if my replies to individual sections end up a bit terse, or if I haven’t answered something on this round!

        Willis Eschenbach October 4, 2015 at 10:45 pm

        Oooh, you are omitting data from your [Connolly & Connolly, 2014 – Urbanization bias III] study because it doesn’t do what you expect … this is egregious data snooping.

        Here’s my question, which all of this hasn’t answered … why didn’t you a) put out ex-ante criteria for which stations you would use, and then b) use EACH AND EVERY station that fit your ex-ante criteria, whether or not they did what you expected it to do.

        No, it was not a matter of what we “expected” them to do. Rather it was the case that the stations didn’t meet our initial ex-ante criteria. In our “Urbanization bias III” 2014 study, when we tried to identify those stations which met our a priori-defined selection criteria for that paper, i.e., fully rural stations with at least 95 years of data for the last 100 years, there were only 8 stations outside of the U.S. – 5 in Europe, 1 in Greenland, 1 in Canada and 1 in the Southern Hemisphere (New Zealand) – see Section 3.2 in that paper. Although it might be argued that the 5 European stations might possible be close enough for a regional estimate, we found a lack of consistency between all of the station records, and we concluded that – without access to the station histories for these stations it was difficult to determine their reliability (something which you apparently agreed with below).

        We therefore recommended the collection of station histories for these (and other) stations should be carried out before using them for estimating regional temperature trends.

        In January, 2015, we followed our own recommendation by personally collecting the station history for one of the 5 European stations, i.e., Valentia Observatory. Therefore, we were able to use that data for our new paper…

        Willis Eschenbach October 4, 2015 at 11:02 pm

        As I said above, if the solar analysis had proven fruitful with the standard temperature datasets, Willie wouldn’t have needed a new dataset to compare with. You objected strongly to this characterization, but it appears to be true.

        Where is your evidence that he “needed a new dataset to compare with”? He was using the standard datasets all the time, and didn’t seem to be running out of research ideas…

        Same objection. You are stuck on the idea of “regions”, and as a result you are not using all of the data.

        I’m sorry, but I simply do not believe that the only valid stations for your research are in the US, China, Ireland, and the Arctic. That makes no sense at all.

        In the end, between our 4 regions, although we only used 10% of the Northern Hemisphere stations, we used nearly half of the fully rural stations in the Northern Hemisphere, and nearly 2/3 of the stations with data for the early 20th century.

        These were the regions which had the greatest density of fully rural stations with relatively long records and/or useful station history/information. I agree that with careful work and/or

        My final question would be this: the satellite temperature datasets track the surface temperature datasets quite closely, although they differ somewhat in the overall trends. And presumably the satellite datasets are NOT affected by UHI.

        So why don’t your results track the satellite data, since you claim your results are also free of UHI?

        Here’s the comparison with UAH’s land only Northern Hemisphere data:

        Unfortunately RSS don’t seem to do a land only estimate specifically for the Northern Hemisphere. However, as we discuss in Section 4.2, if we rescale our land-only estimate by 0.73 to reflect the dampened nature of ocean temperature trends, we can treat this as a crude “land+oceans” estimate. When we do this, we can compare our rescaled estimate to both RSS and UAH:

        I agree they’re not exact matches, particularly after about 2005, but to me they look fairly similar.

        Willis Eschenbach October 5, 2015 at 12:10 am

        I haven’t seen many of the latter, in part because it’s very difficult to show a negative … which studies are you referring to?

        There’s quite a lot of literature for both the former and the latter. Michael & I spent several weeks carefully reading & studying 100s from either side (Willie had already read most of them over the years, as that’s one of his main fields of expertise). In the paper, we tried to summarise the most representative ones, but if you have a look at the references section, you’ll see that’s still quite a lot!

        Ronan, people have been looking for the effects of the ~11-year solar fluctuations for two centuries now … and there is still no clear, unassailable evidence that they do anything at all.

        Agreed! The main focus of our review paper was on “… the role of solar variability on Northern Hemisphere temperature trends since the 19th century”. So, we were mostly discussing possible influences on multidecadal time-scales, e.g., possible secular trends between solar cycles.

        I’m sorry, but whether they use high or low reconstructions is unimportant. The models are tuned, which makes their hindcasts meaningless.

        Perhaps you care what the Tinkertoy models say or why they say it. I don’t.

        I’m also unimpressed by the current Global Climate Models. So, I don’t personally think much of their hindcasts either. But, apparently the IPCC do care! The results of the hindcasts were the main justification for their claim that the global temperature changes since the 1950s are mostly man-made.

        Nor am I clear how you are measuring the effect of the UHI, other than by using only stations that by some criterion or another are “rural” … when you don’t even know whether they are “rural” or not, and you don’t have any clear definition of “rural” that applies to all the stations.

        We defined the “fully rural”/”intermediate”/”fully urban” categorisation in Section 3. We used the exact same categorisation as we did in our 2014 papers – see the last 2 paragraphs before Section 3.1.
        See Section 3.1 for a discussion of urbanization bias in China. We discussed urbanization bias in the U.S. in considerable detail in our 2014 “Urbanization bias III” paper, so we just briefly discussed it in this new paper.
        Note that it’s not so much “the effect of the UHI” that we’re interested in – it’s the effect of the expansion/growth of UHI at stations that have become urbanized.

        I’m sorry, but I didn’t see that in your paper … and it is not clear what you are calling a “remarkable fit”.

        See Section 5. We think the fits between the updated Hoyt & Schatten TSI reconstruction and our new mostly-rural Northern Hemisphere temperature reconstruction are ‘worthy of attention’. If the Hoyt & Schatten TSI reconstruction and our new temperature reconstruction are even partially accurate, then it implies long-term solar variability has played a substantial role in temperature trends since the 19th century.
        However, see also Section 1.1 and Section 2, where we stress that “correlation doesn’t imply causation” and that there are a lot of uncertainties about the TSI reconstructions which have not been satisfactorily resolved!!! This is why we repeatedly tell the reader to treat this result with considerable caution.

        First, while the compilation of the data was subjective, that does NOT mean that removing historical errors in the dataset are subjective. That makes no logical sense.

        However, since you haven’t said what either you or the others think is wrong in the Clette et al. changes, I fear I can’t respond to those allegations.

        Personally, I think their proposed changes are quite plausible and their rationale for the changes seems quite reasonable. Indeed, we noted that their adjustments seemed to slightly reduce the apparent differences between sunspot numbers and the solar cycle length proxies. But, as I explained yesterday, in our review, we were trying to accurately summarise the various perspectives among the community, and avoid forcing our own personal opinions down the reader’s throat!

        Regarding the “ACRIM group’s estimate”, all I find in your paper is this…


        Scafetta is not a scientist, in that he has refused repeatedly to make his calculations public despite repeated requests. If Scafetta and Willson’s update is what you are calling the “ACRIM group’s estimate”, then the odds that it is unbiased, valuable, or accurate just went down stupendously … and that may be related to the fact that I don’t see a single citation to his work other than yours …

        I also don’t know how “ACRIM” could update anything, since it’s a satellite dataset. Some links and examples would be very useful here.

        We discuss the debate between the three satellite TSI groups (ACRIM, PMOD and RMIB) in Section 2.1. It’s worth reading carefully – in my opinion, it’s quite representative of the whole debate. In particular, have a look at the quotes at the end of the section, e.g.,

        The fact that some people could use [the upward TSI trend of the ACRIM composite] as an excuse to do nothing about greenhouse gas emissions is one reason we felt we needed to look at the data ourselves.
        – Judith Lean (of the PMOD group), 2003

        vs.

        It would be just as wrong to take this one result and use it as a justification for doing nothing as it is wrong to force costly and difficult changes for greenhouse gas reductions per the Kyoto Accords, whose justification using the Intergovernmental Panel on Climate Change reports was more political scence than real science.
        – Richard Willson (of the ACRIM group), 2003

        Clearly, both groups have very strong views on the relative importance of greenhouse gas emissions vs. solar.

        All three groups base their TSI composites predominantly on the ACRIM satellites. Willson is the P.I. in charged of these satellites.
        The ACRIM group use the data mostly unadjusted. Their TSI estimate implies TSI increased between cycles from 1980-2000 and then decreased. The PMOD group apply several “corrections” to the ACRIM data first. Their TSI estimate implies a fairly continuous decrease in TSI between cycles since 1978.

        To me it is unclear which (if any) of the satellite estimates is most reliable.

        The ACRIM group also recently updated the Hoyt & Schatten, 1993 TSI reconstruction (http://www.atmosp.physics.utoronto.ca/people/guido/PHY2502/articles/solar-activity/Hoyt-Schatten.pdf) using the ACRIM composite for the satellite era component instead of the NIMBUS dataset which Hoyt & Schatten had used.
        Hoyt was the P.I. for the NIMBUS satellite, which was the first satellite to systematically record TSI – it started 2 years before the first ACRIM satellite.

        Scafetta joined the ACRIM group a few years back. I also found several of his studies (particularly the ones on his theories about barycenters) to be problematic. A lot of them seemed to me to be little more than curve-fitting.

        Let me close by saying that I am greatly troubled by your lack of comment about the Bonferroni corrections, despite my explanation of why they are important. You keep saying things like that you considered ten or so different TSI reconstructions, and found one of them significant … well, duh, when you look at ten datasets, the odds of significance go through the roof … and you seem totally innocent of the consequence of that.

        If you read Section 2, one of our major conclusions is that there are still many unknowns, and we still don’t know which (if any) of the TSI reconstructions are most reliable. So, I totally agree with you there. Yes, researchers who are simply picking the reconstruction which gives them “the best fit” are artificially inflating their P values, and in such cases, as you say, they should apply the Bonferroni correction, for instance. Simmons et al., 2011 (http://pss.sagepub.com/content/22/11/1359) provide a great example of a similar phenomenon in the field of psychology.

        I am also distressed by the lack of any discussion of the effects of autocorrelation on the significance of the results. All of the datasets you are using are highly autocorrelated, and you appear to not know that this seriously changes your statistics.

        Finally, I saw no discussion of the fact that an asymmetric cyclical dataset like sunspots or TSI will show totally bogus “significant correlations” with random red-noise data at an amazing rate.

        Did you read Section 1.1?

        All of this indicates to me that if I could say one thing to you, it is that you, like many others, are radically overestimating the significance of the things you are finding. From here, it appears that you desperately need a crash course in statistical corrections for autocorrelation, and in the Bonferroni correction, and in Monte Carlo analysis. You are getting badly fooled by what appear to be significant results, but which are nothing of the sort.

        It’s probably important to distinguish between the terms “statistically significant”, “significant correlations” and the more general term “significant”. The use of the first two implies you have carried out some statistical tests on your result. The latter term can be used in a more casual manner, e.g., when you are merely stating something is not insignificant!
        Could you clarify exactly which “things you are finding” you are referring to? The solar variability aspect of this paper was mostly a review of the literature. It’s true that in Section 5.1, we did carry out some statistical tests of the Hoyt & Schatten

        P.S. My PhD was on Monte Carlo simulations (albeit in a different field – polymer physics

  61. Ronan, let me offer an illustration of how wrong standard statistical analysis is in the world of climate. Here is the sunspot data and the HadCRUT global temperature data. We want to determine if there is a relationship.

    Here is the bog-standard analysis of the linear relationship of temperature as a function of sunspots.

    Coefficients:
                  Estimate Std. Error t value P-value  
    (Intercept) -0.1436108  0.0109595 -13.104  < 2e-16 ***
    Sunspots     0.0004062  0.0001002   4.055 5.21e-05 ***
    ---
    Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ' 1
    
    Residual standard error: 0.304 on 1975 degrees of freedom
    Multiple R-squared:  0.008257,	Adjusted R-squared:  0.007755 
    F-statistic: 16.44 on 1 and 1975 DF,  p-value: 5.208e-05

    Now, according to that result, the relationship between temperature and sunspots is indeed very very significant, a p-value of 5E-5, so the case is clearly proven that it’s the sun … but is it really?

    There are a couple of ways to determine that. You can use the method of Quenouille to adjust your significance calculations.

    According to that method, the p-value is 0.11, far from significant at the usual significance level of p<0.05.

    Or you can do a Monte Carlo analysis by generating similar “pseudo-data" which is random, and seeing how well correlated the pseudo-data is with the sunspot data. The sunspots and HadCRUT data have a correlation of 0.09, which the standard analysis says is significant, but Quenouille says no. Here’s a sample of my pseudodata, along with the actual HadCRUT data:

    So what do we get when we look at the correlation of the sunspots with this pseudo-data? Here’re the results for 1,000 instances.



    Recall that the correlation of the sunspot data with the actual HadCRUT data is 0.09 … and as you can see, that is not unusual in the slightest.

    Finally, I do what I call a “straight line comparison”. To do that, we simply compare the correlation of sunspots with the HadCRUT temperature (cor=0.091) on the one hand, to the correlation of sunspots with a straight line on the other hand … and that straight line correlation, my friend, is 0.098. The correlation of sunspots is BETTER with a straight line than with the HadCRUT data.

    All of which demonstrates why I said that asymmetrical cyclic data like sunspots give bogus correlations with about anything … and why I said above that standard stats are not just wrong but way, way wrong in this case.

    Despite the fact that the standard cookbook math says that the relationship between sunspots and HadCRUT temperature is highly significant at a p-value of 0.00005, Quenouille and the Monte Carlo analysis and the straight line comparison clearly demonstrate that no way, it’s not significant at all …

    Now, with all of that in mind, let me suggest that you re-examine Scafetta’s claim of an “apparently strong correlation" of TSI with something or other … my guess is that you will find an apparent correlation, but not a strong correlation in any sense.

    w.

  62. Ronan, thanks for your detailed reply. I’ll answer it in pieces. First, I’d said:

    We did find a handful of fully rural stations which met those requirements in Europe, but each of these stations implied slightly different trends, and there was evidence that several of them may had been affected by non-climatic biases associated with station moves, changes in microclimate, instrumentation, etc.

    Oooh, you are omitting data from your study because it doesn’t do what you expect … this is egregious data snooping.

    You replied:

    No, it was not a matter of what we “expected” them to do. Rather it was the case that the stations didn’t meet our initial ex-ante criteria.

    Ronan, you claimed you didn’t use them because inter alia they “implied slightly different trends”. Therefore, you are looking at one of your proposed new reconstruction’s OUTCOMES (the trend of the the reconstruction) to determine which stations to include.

    And that is indeed data snooping. You are looking, not at the metadata for the station (e.g. rural, length of record, etc.) to choose your stations. You are looking at the trend of the station data in order to decide whether to use it … sorry, but you can’t do that.

    Here’s an example where it might be more obvious. Suppose I want to show that the earth has really warmed more than people say. So I look at the trends of the stations, JUST AS YOU DID, and I decide not to use some of them because of their trends, JUST AS YOU DID.

    And guess what … I am able to prove what I claimed!

    But only because I snooped the data, and didn’t restrict myself to looking only at the metadata.

    Do you see the problem now? If not I can give other examples.

    w.

  63. Ronan Connolly October 5, 2015 at 4:26 pm

    Same objection. You are stuck on the idea of “regions”, and as a result you are not using all of the data.

    I’m sorry, but I simply do not believe that the only valid stations for your research are in the US, China, Ireland, and the Arctic. That makes no sense at all.

    In the end, between our 4 regions, although we only used 10% of the Northern Hemisphere stations, we used nearly half of the fully rural stations in the Northern Hemisphere, and nearly 2/3 of the stations with data for the early 20th century.

    Same objection, which you have not addressed. If you only used half the fully rural stations, why didn’t you use the rest? I simply don’t believe that every single suitable station just happened to be in a couple of countries (and you’ve already said your arctic stations were unsuitable but you used them anyway).

    w.

  64. Ronan Connolly October 5, 2015 at 4:26 pm

    I am also distressed by the lack of any discussion of the effects of autocorrelation on the significance of the results. All of the datasets you are using are highly autocorrelated, and you appear to not know that this seriously changes your statistics.

    Finally, I saw no discussion of the fact that an asymmetric cyclical dataset like sunspots or TSI will show totally bogus “significant correlations” with random red-noise data at an amazing rate.

    Did you read Section 1.1?

    Indeed I did read it … and you did not even mention, much less discuss, the effect of either autocorrelation or of using an asymmetric cyclical dataset on either correlation or the statistical significance thereof. So I have no idea what you are referring to in Section 1.1. It says nothing about either problem.

    All of this indicates to me that if I could say one thing to you, it is that you, like many others, are radically overestimating the significance of the things you are finding. From here, it appears that you desperately need a crash course in statistical corrections for autocorrelation, and in the Bonferroni correction, and in Monte Carlo analysis. You are getting badly fooled by what appear to be significant results, but which are nothing of the sort.

    It’s probably important to distinguish between the terms “statistically significant”, “significant correlations” and the more general term “significant”. The use of the first two implies you have carried out some statistical tests on your result. The latter term can be used in a more casual manner, e.g., when you are merely stating something is not insignificant!

    Could you clarify exactly which “things you are finding” you are referring to? The solar variability aspect of this paper was mostly a review of the literature. It’s true that in Section 5.1, we did carry out some statistical tests of the Hoyt & Schatten

    I assumed that when you say something like

    A close inspection of the individual satellite measurements in Figure 2 reveals that there are subtle, but significant differences in the trends between cycles.

    that you have done more than squinted at them from across the room …

    And when you say:

    In an earlier study, Chapman et al. (2001) found the facular-to- spot area ratio varied significantly over their period of analysis with the ratio increasing during cycle 22 and decreasing in the first part of cycle 23.

    I assume that Chapman has done more than squinted at the graphs.

    And I assume the same for the following:

    Livingston et al. (2012) suggested that the reason for this change in the relationship could be that the average magnetic field strength had significantly decreased during Solar Cycle 23 (Penn & Livingston, 2006).

    When I read that, I assume that they have tested the change in field strength and found it to be significant.

    This is particularly clear when you say:

    Still, if the proposed link between cosmic rays and cloud cover transpires to be insignificant, this does not preclude the possibility of other mechanisms by which cosmic rays could significantly influence the climate.

    Are we to assume that you plan to just look at the link and declare it “insignificant”? Because (perhaps wrongly) I assume that you would have TESTED the cosmic ray/cloud cover relationship to determine it is “insignificant”.

    Now, above you say that when you use the word “significant” it does NOT necessarily mean statistically significant … so what am I to make of a statement like this?

    However, in both cases, the R2 values from linear least squares fitting were almost zero, so the fits are probably not significant.

    So I fear that the distinction between the uses of “statistically significant” and “significant” is nowhere near as clear as you seem to think. In your shoes, I’d restrict the use of the claim that something is “significant” to mean “statistically significant”, and use some other word when you don’t mean that. Otherwise, we have no clue what you mean.

    Finally, consider this claim:

    When Scafetta & Willson updated the Hoyt & Schatten reconstruction using the ACRIM composite (Scafetta & Willson, 2014 – see their Appendix B), they noted an apparently strong correlation with Manley’s Central England Temperature (CET) dataset (Manley, 1974), as updated by Parker & Horton (2005).

    Again I say, you are merely parroting these claims of significance and “strong correlation” without checking them. I suggested that you re-examine Scafetta’s claim. Have you done so?

    Finally, take a look at your Figure 29, or your Figure 30 … where is the adjustment for autocorrelation? You keep saying you understand it … so why, oh why, don’t you USE IT?

    P.S. My PhD was on Monte Carlo simulations (albeit in a different field – polymer physics

    Most excellent … so why in heavens name are you not putting that knowledge to use? See my example above. This is not some insignificant problem, it makes a huge difference. You go on and on about “significance” and “statistical significance” without even a mention of autocorrelation, Bonferroni, or Monte Carlo.

    Heck, you still haven’t said one word about Bonferroni … why not?

    w.

Comments are closed.