Guest Post by Willis Eschenbach
[See also the new Update at the end of the post.]
I see that there is a new paper from China causing a great disturbance in the solar force … as discussed here on WUWT, the claim is that the El Nino Modoki Index, which is an index of sea surface temperatures, is significantly affected by some sunspot-related solar variable.
The first problem with their study is that the sea surface temperature (SST) “data” they have used to establish the relationship is not data as we understand it. It is not observations. It is not measurements of the actual sea surface temperature (SST). It is not stout-hearted men of oak going out and dipping up a bucket of water and inserting a thermometer.
Instead, their sea surface temperature “data” is the output of a climate model, a type called a reanalysis model. This reanalysis model is “tested” and adjusted by comparing it with the output of another climate model. That model is called the GFDL CM2.1.
So, we’re not looking at observed SST. Instead, we’re looking at the output of a couple of climate models.
This means the Chinese have found a correlation between sunspots and climate model output.
Now, having considered the OUTPUT of the climate models, would you care to guess what is used as the INPUT to the climate models?
According to one of the cited underlying documents, the inputs include variations in forcings by greenhouse gases, aerosols, and the “best available estimates of solar radiation changes”.
That means that the authors claim to have found statistically significant evidence of sunspot-related variations in the output of a climate model whose input includes sunspot-related variations … sorry, not impressed even if it were true.
However, there is a much deeper problem, which is that the claim of statistical significance is not true. Their results are not statistically significant, it’s just statistics gone bad. Let me see if I can explain the problems using mostly pictures. I’ll start by clarifying their underlying hypothesis.
Their basic claim is that the small ~11-year variations in the sun affect the sea surface temperature in some unspecified manner and by means of some unspecified solar phenomenon (TOA, solar wind, sunspots, heliomagnetism, etc.). And to their everlasting credit, and unlike far too many climate science authors, they have provided links in the paper to the datasets used in the study.
So, being a data guy, I went and got the ERSST sea surface temperature (SST) data they were using. At least when I got it I thought it was data, and I’m sure some of it is real data … but I digress. They used it, so we’ll use it.
Now, the obvious first step in this is to compare the global sea surface temperature to the sunspot record. Being a graphics-oriented guy, I calculated the correlation between each and every 1°x1° gridcell on the surface of the ocean, and the sunspot record. However, as Figure 1 shows, there is basically no correlation between the sunspots and the global average ERSST sea surface temperature (0.008).
Figure 1. Gridcell by gridcell correlation, monthly sunspots and monthly sea surface temperatures. Colored boxes from left to right are the west, central, and eastern Pacific areas used in calculating the El Nino Modoki Index. The El Nino Modoki Index is calculated as the red box sea surface temperature anomaly minus half the temperature anomaly in each of the blue boxes (all values detrended).
Now, when I saw that graphic, I didn’t much believe it. At this point I’ve looked at this exact kind of map displaying dozens and dozens of different variables—rainfall, SST, atmospheric absorption, cloud reflection, correlation of albedo and temperature, the list goes on and on. As a result, I’ve grown used to the shapes and the forms of real relationships.
And Figure 1 is not much like any of the global climate-related maps I’ve seen. There’s no trace of the usual suspects like the inter-tropical convergence zone (ITCZ) or the typically bi-polar nature of the North Pacific. Instead, it’s just peculiar, too random.
Now as Figure 1 shows, there is basically zero global correlation of sea surface temperature with sunspots. So they are looking at correlation of the sunspots with the sea surface temperatures in the three El Nino Modoki boxes shown in blue and red.
According to the paper the “El Nino Modoki” index is defined as:
In this work, the El Niño Modoki Index (EMI) is defined as (Ashok, Behera, and Rao 2007)
EMI = [SSTA]C − 0.5 × [SSTA]E − 0.5 × [SSTA]W, (1)
where the square bracketed terms [SSTA]C, [SSTA]E, and [SSTA]W represent the area-averaged SST anomalies in the central Pacific region (C (10°S–10°N, 165°E–140°W)), eastern Pacific region (E (15°S–5°N, 110–70°W)), and western Pacific region (W (10°S–20°N, 125–145°E)), respectively.
The problem with Figure 1 is that those individual El Nino Modoki areas don’t particularly match up with the variations in correlation. As mentioned above, the El Nino Modoki index is the red box temperature anomaly minus half of each of the blue box anomalies. So ideally, to see the greatest correlation you’d want the blue boxes in areas of negative correlation and the red box to be in positive correlation … not happening.
In addition, the overall global correlation of SST and sunspots is basically zero (correlation of 0.008). In such a situation we’d expect to find individual areas with small positive or small negative correlation … and due to the high spatial autocorrelation in ocean temperatures, we’d expect the positive and the negative gridcells to be grouped into large areas.
In other words, Figure 1 looks about like what we’d expect if there is no connection between the ~11-year solar variations and the ocean temperatures. So to determine whether the pattern is representative of some real enduring sunspot–>SST relationship that persists over time, I repeated the exercise using only the first half of the data, and then using only the last half of the data. Figures 2 shows the early data up to 1935, and Figure 3 shows the more recent half of the data.
Figure 2. As in Figure 1, but for only the first half (976 months) of the data.
In this earlier half of the dataset, the pattern is very different from that of the full dataset. This is a clear sign we’re not looking at a stable enduring relationship In addition, the El Nino Modoki areas are even more poorly placed than in the full dataset. There’s almost no correlation between the Index and the sunspots during this period. The red box, which was in the hot spot, is now in the cold spot.
Compare those first two with the recent half of the data.
Figure 3. As in Figure 1 and 2, but for only the last half (976 months) of the data.
As you might expect by now, the pattern of positive and negative correlations in this one is once again totally different from both the full dataset (Figure 1) and the early data (Figure 2). However, presumably by coincidence, the variations in correlation happen to line up well with the El Modoki areas … blue boxes where there’s negative correlation and the red box where there’s positive correlation.
So which of these time spans have the authors used? Well … none of them. Instead, they’ve picked 1890 as their starting date. Here are the correlations for the data from 1890 to the present
Figure 4. As in Figures 1, 2, & 3, except starting in 1890 and continuing to the present.
So now, we have a fourth different and distinct pattern of positive and negative correlation. In this case of this particular pattern, the blue and red boxes line up pretty well with the negative and positive sections of the correlation map.
And as a result, the authors of the study found a statistically significant correlation between the El Nino Modoki index and the sunspots. But only if you include a two-year lag from sunspots to El Modoki variations.
And to be fair, my own standard statistical analysis of these results says that they are indeed significant at the 95% level. The usual statistical tests give a p-value of 0.03, which is less than their significance level of 0.05.
However, that usual statistical analysis is wrong for four reasons. First, they’ve looked for results in more than one place, so they need to divide the desired p-value (0.05) by the number of places they looked. Second, they have not allowed for autocorrelation. Third, they have not allowed for the strongly cyclical nature of the sunspot data. Finally, they have not shown that the correlation they’ve demonstrated is stable over time.
Looking in lots of places – the effect of repeated trials
Consider. If you pick up ten coins, flip them all in the air, and every one comes down heads, you’d suspect that the coins were weighted. Why? Because the odds of that happening by chance are less than one in a thousand. So it would be a statistically significant occurrence, with a p-value less than 0.001.
But suppose you flipped that batch of ten coins a thousand times, and you find one flip that ends up with ten heads. Is that still significant? No, because the more trials, the more chance you have of finding something unusual. If you look in lots of places, you’re likely to find odd things … but that does NOT make them statistically significant.
As a result, when you look in more places, your criteria for significance has to become more stringent. The usual correction is called “Bonferroni’s correction”. What you do is to divide the initially required p-value (e.g 0.05) by the number of trials, and that’s the p-value you need to find for it to be significant in that many trials.
So if you look in five places for results, and you desire significance at a p-value of less than 0.05 (the usual standard in climate science), you need to find something with a p-value of less than 0.05 / 5 trials = 0.01. Not so easy.
And therein lies the first problem. We’ve already looked for significance in the whole ocean for the whole time period, and for the whole ocean for the first half and last half of the time period. No joy in any of those. So now, we’re looking at three small boxed-in areas out of the whole ocean, in a period limited to only the time since 1890 … and those three boxes represent just under eight percent of the ocean area.
How many places in time and space have we searched the ocean so far for the elusive solar signal?
Whatever the appropriate Bonferroni correction number might be for this calculation, at this point we’ve established that a “significant” correlation (p-value of 0.03) can be found if we turn our sights to a specially selected 8% of the ocean during a particular time period … you’ll forgive me if I find that less than significant.
The first problem is, no Bonferroni correction, and we’ve already looked a lot of places …
The pernicious effect of autocorrelation
Autocorrelation is a measure how much today is like yesterday. For example, the time of sunrise is constantly changing, but it never changes much. So today is a lot like yesterday in that regard. With air temperature there is less regularity than with sunrise times. But it is rare to have a sweltering hot day followed by a freezing day. So air temperature is less autocorrelated than is sunrise time.
Ocean temperatures, however, are very highly autocorrelated, because the thermal mass of the water means that today is very much like yesterday. And this is a problem for statistics, because autocorrelation increases the uncertainty. How much? Well, in the case of highly autocorrelated datasets, the answer is, a shocking amount.
For example. The El Nino Modoki/sunspot correlation has a p-value of 0.03. But adjusted for autocorrelation (using the method of Koutsoyiannis, see note at end) the p-value goes up to 0.37, a long, long ways from significant.
So the second problem is, no adjustment for autocorrelation.
Statistics of a cyclical signal
There are special problems and special procedures needed when looking at correlations with a signal with a strong varying-length, varying-amplitude cycle … like say sunspots. Again let me explain this with pictures. First, here is the cross-correlation of the post-1890 El Nino Modoki and the sunspots. It shows how well the Index and the sunspots correlate at a variety of lags. In it you can see the best correlation at a two-year lag (El Nino Modoki Index responding two years after the sunspots) that the authors discuss.
Figure 5. Cross correlation, El Nino Modoki Index and sunspots. The blue lines show the correlations at various time lags between the sunspots and the El Nino Modoki Index.
So Figure 5 looks convincing, it looks like it represents a real correlation … as far as it goes. And again according to standard statistics it is supposed to be significant. But is it? Let’s expand the boundaries of our same analysis out to say thirty years …
Figure 6. The exact same analysis as in figure 5, but this time with a wider time window.
I’m sure that you can see the problems. First off, there’s a better correlation at 13 years than at two years. And there’s a strong negative correlation out eighteen years … warming sun now means cooling eighteen years from now? Say what?
The difficulty is two-fold. First, if there are a couple of bumps or dips of any kind in the data, doing a cross-correlation with a cyclical signal like sunspots will give you alternating correlations as seen above. Second, the result is likely to appear significant at the peaks, without actually being significant.
Let me demonstrate this problem with pseudo-data. Here are nine instances of pseudodata modeled on the actual El Nino Modoki Index, along with the actual El Nino Modoki Index itself.
Figure 7. Nine instances of pseudodata. The real El Nino Modoki Index data is Series 7
And here’s what you get when you run a cross-correlation of that selection of pseudodata with the highly cyclical sunspot data:
Figure 8. Cross correlations, sunspots with the pseudodata shown in Figure 7. As before, the actual cross correlation of the El Nino Modoki Index and the sunspot data is Series 7.
Not a pretty picture … as you can see, the results of the pseudodata are indistinguishable from those of the real data. We know the pseudodata has no connection with the sun but it still gives strong correlations that appear to be significant. So the third problem is that have not considered the effect of the cyclical nature of the sunspot data.
Duration Over Time
Natural climate-related datasets are maddeningly tantalizing because they appear to contain stable natural cycles, but the dang things have an ugly habit of suddenly appearing and disappearing without warning. After some period where there is no correlation, a correlation with say sunspots will suddenly pop up, and it will last and last, sometimes for as long as five full sunspot cycles … and then it will just vanish. Gone. Perhaps it will be replaced by a cycle with some other longer or shorter period, perhaps not, perhaps we’ll just get random noise for a while before another cycle pops up.
This is visible in the examination of the maps of the early (Figure 2) and late (Figure 3) halves of the data. In each one there are things that seem to be strong, significant correlations. And since each half is eighty years long, you’d sure think that over that time the random fluctuations would have evened out …
Fuggeddaboutit. The two halves of the one single dataset are widely different, the differences have not averaged out. The observational climate datasets tend to be self-similar at all scales. Daily data is no less chaotic and full of appearing and disappearing cycles than is monthly data, which has as many apparent but evanescent cycles as does yearly data, which in turn is no less chaotic than a century or a millennium. At all time scales, apparently real and regular cycles appear and disappear at unpredictable times.
Now, are there long-term, enduring relationships in there? Definitely … but you cannot assume you are looking at such a stable relationship, as the examination of this dataset shows. Even averaged over eighty years the relationships are not stable. Their fourth problem is, they provide no verification of the stability of the purported relationship.
Conclusions:
The paper claims that a climate model that is fed solar variations will reflect those variations in its output. However, whether or not that is the case, the paper has four much more serious issues with their statistical analysis:
• They have not used the Bonferroni correction to adjust for the number of places that they have looked at in order to come up with the 8% of the ocean’s surface that they find “significant”.
• They have not allowed for autocorrelation in their calculations of claimed significance.
• They have not considered the effect of the strongly cyclical nature of the sunspot data on the calculation of significance.
• They have assumed that the signal they found is stable over time, where in fact it is very different in the early and late parts of the same dataset.
The effect of any single one of these statistical errors is enough to invalidate their results. The combined effect of all four errors is … well, words fail me.
Here’s the odd part. Look, I’m no statistician. As with all of my scientific knowledge, I’m entirely self-taught. I never took one single class in statistics. What are the odds of that?
My question is, if a self-tutored man like myself knows about the Bonferroni correction and the need to adjust for autocorrelation … what’s up with these PhD folks all across the climate landscape who apparently never heard of those concepts?
Final Reflections
Many folks seem to misunderstand my position in all of this. I started out a firm believer in the “It’s The Sun” mantra. I thought the sunspot cycle truly did affect wheat prices as Herschel had speculated in the 1700’s. I thought that there were a number of climate phenomena that were affected by something related to the sunspot cycle. I didn’t know whether that “something” was the solar wind, or the changes in total solar insolation, or changes in the far UV, or variations in the heliomagnetic field, but I sincerely believed that there was a clear sun/climate connection related in some manner to the sunspot cycle. I thought that all I had to do to verify that solar connection was look up in the daytime.
And as a result, I thought it would be a piece of cake to find solid scientific evidence to back up what I took to be a fact—that the small ~ 11-year variations in the sun’s output would leave their mark somewhere on the earth’s climate. I had no doubt that was true.
But then I encountered something strange. None of the studies I found had any more solidity than does the Chinese study I just analyzed above. Almost all of them had some or all of the same four huge problems with the statistics exhibited above—no Bonferroni, no correction for autocorrelation, no allowance for strong cycles in the sunspots data, and no investigation of the long-term stability of the claimed relationship.
So I looked and looked, and found nothing. To try to winnow through the literally hundreds of sunspot-related claims, I asked people who thought they had a solid scientific study establishing the connection between the ~ 11-year solar variations and some surface climate variable to send me two links—one link to the study, and a second link, to the actual data used in the study.
Many, perhaps most folks couldn’t seem to grasp the “two links” concept of the request. But it’s essential to have a link to the data as well. I was able to do the above analysis only because I had access to the data that they used. Without that link to the dat there is nothing to analyze. So for people who sent in one link, it went straight to the circular file …
But even when I got two links to some piece or other of research that I was assured was the best, their claims melted like snowflakes in the Sahara when examined closely. Like this study above, it wasn’t just small flaws. It was huge problems, and often the very same four problems I listed above
And as a result, as an honest man I have to say that despite looking for something that I started out truly and completely believing existed, and despite examining a long string of solar-related studies, to date I have not found convincing evidence of such a connection between the ~11-year solar cycles and the climate here at the surface where we live. Now, if the facts change I’ll change my mind, but as it stands I haven’t yet found the requisite evidence.
We had condensing fog last night. When I woke up the deck was soaked and the ground wet. Cold and overcast all day. Where is global warming when I need it?
My best to all,
w.
Please: If you disagree with someone, have the courtesy to quote the exact words you disagree with. This gives all of us clarity on the exact nature of your objection.
Autocorrelation: My discussion of the Koutsoyiannis method for calculating effective N is here.
[Update]: For those of you who still think that the correlation of sunspots with the El Nino Modoki Index is significant, after reading the comments I realized that there was a simple test I could apply. This was to compare the El Nino Modoki Index, not with the sunspots, but with the time-reversed sunspots. I just swapped the full sunspot record end for end, and then I compared that reversed record to the individual gridcells as in Figures 1 to 3.
As you can see, despite the “sunspot” data in the Figure above being meaningless because it is time-reversed, the correlations are very similar to those of the actual sunspots.
Which of course means that their results are as meaningless as those from time-reversed sunspots …
High-Altitude Effects: As an erstwhile ham radio operator (H44WE), I’m well aware that the sunspot cycle affects long-range radio transmission (DXing) by messing with the beautifully named “Heaviside Layer” … what I can’t find is any solid evidence of any corresponding 11-year variation down here on the ground where we live. And yes, I do know that Heaviside is someone’s name, but I still think it’s a great name.
Further Reading: As I said above, I’ve investigated a lot of these claims. Here is a list in chronological order of my previous posts on the subject …
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.…
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…
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…
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 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.
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…
Changes in Total Solar Irradiance 2014-10-25
Total solar irradiance, also called “TSI”, is the total amount of energy coming from the sun at all frequencies. It is measured in watts per square metre (W/m2). Lots of folks claim that the small ~ 11-year variations in TSI are amplified by some unspecified mechanism, and thus these small changes in TSI make an…
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…
The New Sunspot Data And Satellite Sea Levels 2015-08-13
[UPDATE:”Upon reading Dr. Shaviv’s reply to this post, I have withdrawn any mention of “deceptive” from this post. This term was over the top, as it ascribed motive to the authors. I have replaced the term with “misleading”. This is more accurate…
My Thanks Apologies And Reply To Dr Nir Shaviv 2015-08-17
Dr. Nir Shaviv has kindly replied in the comments to my previous post. There, he says: Nir Shaviv” August 15, 2015 at 2:51 pm There is very little truth about any of the points raised by Eschenbach in this article. In particular, his analysis excludes the fact that the o…
The Missing 11 Year Signal 2015-08-19
Dr. Nir Shaviv and others strongly believe that there is an ~ 11-year solar signal visible in the sea level height data. I don’t think such a signal is visible. So I decided to look for it another way, one I’d not seen used before. One of the more sensitive …
Is The Signal Detectable 2015-08-19
[UPDATE] In the comments, Nick Stokes pointed out that although I thought that Dr. Shaviv’s harmonic solar component was a 12.6 year sine wave with a standard deviation of 1.7 centimetres, it is actually a 12.6 year sine wave with a standard deviation of 1.7 millime…
23 New Papers 2015-09-22
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!…
This seems like valid criticism to me. Probably the article by Huo and Xiao should never have been published in this form, but since it has been, why don’t you write up your your main points up in a 3 page paper and send it to “Atmospheric and Oceanic Science Letters”? This should not be much work for you, and I believe that the points you bring up are so serious that the journal would have to react.
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. read papers about the subject and thought about the subject).
Sun spot count (closed magnetic flux on the surface of the sun) is a rough measure of one of two solar phenomena that create solar wind bursts. The solar wind bursts remove cloud forming ions from the high latitude regions and the tropics changing the amount of cloud cover and the properties of the clouds, by creating a space charge differential in the ionosphere.
The process where solar wind bursts remove and add ions to the clouds is called electroscavenging. Electroscavenging is what amplifies or inhibits El Niño events.
Coronal holes, open magnetic flux regions on the sun, also cause solar wind bursts. What causes coronal holes to form is not known.
Comment: For some unexplained reason the sun is now covered with coronal holes. The coronal holes of course cause solar wind bursts which explains why the planet has not cooled due to the increase in high speed cosmic particles that are now striking the earth.
Coronal holes can persist for months and have for some unknown reason occurred late in the solar cycle in low latitude regions thereby causing solar wind bursts to occur when there are few sun spots on the surface of the sun or no sunspots. Coronal holes make it appear that the solar magnetic cycle is not the primary modulator of the earth’s.
Comment:
The solar magnetic cycle also modulates the amount of high speed particles (called cosmic ray flux (CRF) or galactic cosmic rays (GCR) for historical reasons, the discoverers thought the phenomena was caused by a ray rather than a particle and the misleading name stuck) that strike the earth’s atmosphere creating cloud forming ions. Solar wind bursts remove and change the ions in the atmosphere, so solar wind bursts change make it appear that an increase in CRF/GRF does not cause there to be an increase in cloud cover in high latitude regions.
This peer reviewed paper notes planetary temperature changes closely correlates with solar cycle changes when solar wind bursts and GCR changes are both taken into account.
http://sait.oat.ts.astro.it/MmSAI/76/PDF/969.pdf
The following is a review paper that discusses some of the mechanisms by which solar changes modulate planetary climate.
http://www.klimarealistene.com/web-content/Bibliografi/Tinsley2007,GlobalElectricCircuit.pdf
Solar wind bursts cause the planet to warm by creating a space charge differential in the ionosphere which in turn causes an electric current flow from high latitude regions of the planet to the equator. The return path for the current is in the ocean. This process is called electroscavenging.
Sunspots and coronal holes both affect the strength and extent of the solar heliosphere which is the name for the tenuous solar ionized 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 blocks 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 no 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 affects both high latitude regions and the equator while strength of the solar heliosphere which in turn affect GCR amounts only high latitude regions. This comment is true as long as the geomagnetic field is not strongly tilted or is in an excursion.
http://sait.oat.ts.astro.it/MmSAI/76/PDF/969.pdf
http://gacc.nifc.gov/sacc/predictive/SOLAR_WEATHER-CLIMATE_STUDIES/GEC-Solar%20Effects%20on%20Global%20Electric%20Circuit%20on%20clouds%20and%20climate%20Tinsley%202007.pdf
This peer reviewed paper notes that has been an astonishing increase in coronal holes late in the solar cycle when then there are no or few sunspots. The solar wind bursts from the coronal holes of course remove cloud forming ions which makes it appear that increase in
http://www.agu.org/pubs/crossref/2009/2009JA014342.shtml
http://www.ann-geophys.net/27/2045/2009/angeo-27-2045-2009.pdf
Lots going on in the sun/earth system William.
Thanks for showing us some of those complexities.
When Willis goes off on one of his debunkers, it’s through a single paramenter, such as SSN or TSI and supoosedly having a single effect on something or another.
This is because of the different authors which use a single parameters for their studies.
Multiple solar parameters occur over a solar cycle.
The suns output is not just SSN which result in a number of CME super blasts which hit our planet, but coronal hole high speed streams and TSI eeek (TSI) and HCS crossings, solar wind speed dips, increases and decreases in Galactic Radiation GCR.
There is a waxing and waning in rotation, geomagnetic effects, electric field variations etc…
ALL these things together need to be considered over a time period
There are multiple effects, over a given period, on the Earth system, some of which you have mentioned above.
Lots that go on..over the rising and falling of solar cycles in time .. and in just the 11 and 22 year cycles. lol
The suns output is not just SSN which result in a number of CME super blasts which hit our planet, but coronal hole high speed streams and TSI eeek (TSI) and HCS crossings, solar wind speed dips, increases and decreases in Galactic Radiation GCR.
There is a waxing and waning in rotation, geomagnetic effects, electric field variations etc…
And all of these things vary together in the regular cycle. So if they have any effects, the effects will follow the same cycle.
As usual, most of what you say is muddled or nonsense. E.g. we do know how and why and when in the cycle coronal holes form. It is quite normal that coronal holes form at this point in the declining cycle [and we know why] as they do in every cycle, as I have explained elsewhere.
lsvalgaard: As usual, most of what you say is muddled or nonsense.
Was that addressed to William Astley?
Obviously, yes.
lsvalgaard: Obviously, yes.
I am mistaken about much of what is “obvious”. So I had to ask.
From https://wattsupwiththat.com/2016/09/10/chinese-scientists-claim-peak-solar-activity-drove-201516-el-nio/
lsvalgaard September 12, 2016 at 10:24 am
Coronal holes form from the magnetic debris from decaying sunspots. For a hole to open up, the magnetic areas with uniform polarity must be large enough. If there are many sunspots, they will inject mixed polarities [sunspots are bipolar] and so destroy the uni-polarity of an area. So, as the cycle declines this destruction will also decline [the survival rate will increase] so the chance of a hole will increase. On the other hand, as the cycle declines, so will the number of sunspots and hence the amount of magnetic debris, so that the chance of a hole will decrease. You can see the result of those two opposing trends here:
http://www.leif.org/research/CH-Chances.png
There will be a ‘sweet’ spot [oval] somewhere during the declining phase.
Geomagnetic activity will maximize during that sweet spot.
For the Maunder Minimum, the situation is interesting. Some people claim that the sun was one big coronal hole and other people claim that there were no coronal holes. We know from cosmic ray proxies that the modulation of the GCRs was strong and healthy during the MM. Also that comet tails [produced by the solar wind coming from coronal holes] were clearly present, so perhaps coronal holes were common. If so, geomagnetic activity should have been significant.
lsvalgaard September 13, 2016 at 10:40 pm
From https://wattsupwiththat.com/2016/09/10/chinese-scientists-claim-peak-solar-activity-drove-201516-el-nio/
lsvalgaard September 12, 2016 at 10:24
————————————————————
Thanks Dr. S., we call that post a ‘keeper.’
Which solar hemisphere will be leading in spot production for cycle 25?
we call that post a ‘keeper.’
Who are ‘we’?
Which solar hemisphere will be leading in spot production for cycle 25?
Usually, the hemisphere changes in the middle of the cycle.
For the past several cycles, the North was the most productive in the beginning of the cycle.
See http://www.leif.org/research/ApJ88587.pdf
Some people think there is a regularity in this, see
Slide 33 of http://www.leif.org/research/Asymmetric-Solar-Polar-Field-Reversals-talk.pdf
I am generally leery of cyclomania, so am sitting on the fence on this one.
Short: I don’t know.
lsvalgaard September 14, 2016 at 8:44 pm
The most important words a scientist can utter, and a mark of a true scientist.
w.
lsvalgaard September 14, 2016 at 8:44 pm
we call that post a ‘keeper.’
Who are ‘we’?
__________________________________
Thanks for the reply Dr. S., the solar hemispheric asymmetries are a bit of a conundrum.
A ‘keeper,’ is a fishing term around these parts indicating that this one gets cleaned and eaten and not thrown back in the lake to grow up.
‘We,’ is the frog in my back pocket. lol ribbit ribbit
In my sadly uniformed opinion, the hemispheric asymmetry during the Maunder Minimum is a big clue. To what, I dunno.
==============
lsvalgaard September 14, 2016 at 8:44 pm
Which solar hemisphere will be leading in spot production for cycle 25?
Usually, the hemisphere changes in the middle of the cycle.
For the past several cycles, the North was the most productive in the beginning of the cycle.
See http://www.leif.org/research/ApJ88587.pdf
Some people think there is a regularity in this, see
Slide 33 of http://www.leif.org/research/Asymmetric-Solar-Polar-Field-Reversals-talk.pdf
I am generally leery of cyclomania, so am sitting on the fence on this one.
Short: I don’t know.
————————————————————
Another question if I may…
Is there a relationship to the hemispheric extent of the HCS ?
The neutral line sometimes extends more southward or northward of the equator bringing downward/upward either more positive/negative flux to a given hemisphere..
The neutral line sometimes extends more southward or northward of the equator bringing downward/upward either more positive/negative flux to a given hemisphere..
Causality flows the other way: it is an already existing hemispheric asymmetry that causes warps and asymmetries in the ‘neutral line’.
One more quick question before I start getting ready for work..
Why can’t the Interstellar pressures (wind/magnetic/density) have an effect on the on the HCS (helio current sheet)?
The answer is the same as always: because the solar wind is supersonic. and because the pressure in the inner solar system is MUCH larger than in interstellar space.
???? ‘supersonic’. What is the meaning of supersonic in this medium. And ‘much larger’ seems to me to mean that any effect may be very small and difficult to detect.
I know I’m pretty ignorant, but there seems to be a logic fail here. Moar, please.
============
In a plasma there is a speed limit to how fast magnetic changes can propagate [called the Alfven speed]. The solar wind moves away from the sun ten times faster [near the Earth] than the Alfven speed. Its ‘Alfvenic Mach’ number is thus about 10, meaning that the plasma moves away from the sun 10 times faster than any external changes can move towards the sun.
MUCH means something like this: The solar wind that flows past the Earth has a certain density. As the interstellar medium begins 100 times further out, the wind out there [which is the same as was near the Earth a year ago] has now been diluted 10,000 times.
That’s helpful, thanks. I now understand the meaning of ‘supersonic’ as used.
=========
kim September 15, 2016 at 8:04 am
————————————————-
Don’t stop wondering about how something might propagate inward through the “supersonic” solar wind.
An inspirational video for you and everyone else. Baffled scientists when it happened, tooooo…
https://youtu.be/YxPehCj-ouU
Dec. 16, 2011
Comet Lovejoy Plunges into the Sun and Survives
http://www.nasa.gov/mission_pages/sunearth/news/comet-lovejoy.html
This morning, an armada of spacecraft witnessed something that many experts thought impossible. Comet Lovejoy flew through the hot atmosphere of the sun and emerged intact.
“It’s absolutely astounding,” says Karl Battams of the Naval Research Lab in Washington DC. “I did not think the comet’s icy core was big enough to survive plunging through the several million degree solar corona for close to an hour, but Comet Lovejoy is still with us.”
The comet’s close encounter was recorded by at least five spacecraft: NASA’s Solar Dynamics Observatory and twin STEREO probes, Europe’s Proba2 microsatellite, and the ESA/NASA Solar and Heliospheric Observatory. The most dramatic footage so far comes from SDO, which saw the comet go in (below) and then come back out again (above)……………………………………………………………………………..
tick tock goes the clock, bye bye
Don’t stop wondering about how something might propagate inward through the “supersonic” solar wind.
And our spacecraft also propagate inwards. The difference is that the comet and our spacecraft are not conducting plasma. You still have not understood anything.
lsvalgaard September 15, 2016 at 9:37 am
Don’t stop wondering about how something might propagate inward through the “supersonic” solar wind.
And our spacecraft also propagate inwards. The difference is that the comet and our spacecraft are not conducting plasma.
————————————————————
Turbulence Dr. S., think turbulence..
Turbulence in the interstellar regions where two or more interstellar clouds converge (an interaction region whose size may last several solar cycles in depth) or interstellar size corotating super shells. The size of which would exceed the puny little heliosphere.
The supersonic solar winds are more swiss cheese like at distance containing subsonic regions.
But this is more interesting for now.
“Warning” this video contains a water pistol.
Sept. 1, 2016
Images From Sun’s Edge Reveal Origins of Solar Wind
http://www.nasa.gov/feature/goddard/2016/images-from-sun-s-edge-reveal-origins-of-solar-wind
…”””Both near Earth and far past Pluto, our space environment is dominated by activity on the sun. The sun and its atmosphere are made of plasma – a mix of positively and negatively charged particles which have separated at extremely high temperatures, that both carries and travels along magnetic field lines. Material from the corona streams out into space, filling the solar system with the solar wind.
But scientists found that as the plasma travels further away from the sun, things change: The sun begins to lose magnetic control, forming the boundary that defines the outer corona – the very edge of the sun.
“As you go farther from the sun, the magnetic field strength drops faster than the pressure of the material does,” said Craig DeForest, lead author of the paper and a solar physicist at the Southwest Research Institute in Boulder, Colorado. “Eventually, the material starts to act more like a gas, and less like a magnetically structured plasma.”
The breakup of the rays is similar to the way water shoots out from a squirt gun. First, the water is a smooth and unified stream, but it eventually breaks up into droplets, then smaller drops and eventually a fine, misty spray. The images in this study capture the plasma at the same stage where a stream of water gradually disintegrates into droplets.”””…
https://youtu.be/QYM2_ytkjQo
Turbulence Dr. S., think turbulence
Not needed, as it does not aid inward propagation. On the contrary, turbulence helps make the heliosphere more difficult to traverse, e.g. for cosmic rays. One can only admire your steadfast devotion to your delusion.
Have a look at this video from IRIS.
I purposely stopped this at 5 seconds. It LOOKs as though this magnetic field segment was out in the outer corona and then reattached it self to the surface? Of forming out there and not coming from the surface at all?
https://youtu.be/sfKZHto7acw?t=5s
Aug. 5, 2016
IRIS Spots Plasma Rain on Sun’s Surface
http://www.nasa.gov/feature/goddard/2016/iris-spots-plasma-rain-on-suns-surface
…”””As the video continues, solar material cascades down to the solar surface in great loops, a flare-driven event called post-flare loops or coronal rain. This material is plasma, a gas in which positively and negatively charged particles have separated, forming a superhot mix that follows paths guided by complex magnetic forces in the sun’s atmosphere. As the plasma falls down, it rapidly cools – from millions down to a few tens of thousands of kelvins. The corona is much hotter than the sun’s surface; the details of how this happens is a mystery that scientists continue to puzzle out. Bright pixels that appear at the end of the video aren’t caused by the solar flare, but occur when high-energy particles bombard IRIS’s charge-coupled device camera – an instrument used to detect photons.”””
My bold above
I purposely stopped this at 5 seconds. It LOOKs as though this magnetic field segment was out in the outer corona and then reattached it self to the surface? Of forming out there and not coming from the surface at all?
Almost everything that is thrown out from the sun [e.g. a CME] actually falls right back in, because the sun’s gravity is strong [27 times stronger than the Earths at their surfaces]. Often it is difficult to see how the material is connected because its temperature determines what we can see, so a change in temperature [e.g. when the stuff balloons outward and cools] will make the material invisible.
Willis’s arguments are strongly statistical in nature and yet many of the contradictory replies are anecdotal or rely on authority or popularity (literature citations). I don’t believe Willis has proven or attempted to prove that there is no correlation between solar activity and significant climate events on Earth such as ENSO. Trying to prove a negative is generally a fools game anyway. Wills has however very effectively shredded an attempt to document a solar-ENSO correlation by using strong statistical testing. In a battle of wits, statistics beats anecdote and reputation. If Willis is self-taught he had an excellent teacher.
The article referenced violates a research integrity – it is a fishing expedition. Proper research methods in the actual scientific method requires researchers to plan everything ahead including every comparison and correlation they will conduct. And set degrees of freedom, confidence levels … You don’t just grab data and sift through it, as they did here. Think about it – that introduces researcher discovery bias. These people have had inadequate instructors or they have deliberately violated the principles of research which separate the searchers from the data discoveries – i.e. they had no hypothesis. This is shameful behavior in “research” methods. I have had many undergraduates who would have known better.
Thanks, Willis, although I am aghast. You did your part exposing this.
Javier September 13, 2016 at 6:49 am
Thanks, Javier. Unfortunately, the paper you suggest is mis-titled. An accurate title would be “The impact of modeled solar variability on a climate model”. The error is very revealing regarding the headset of the climate modelers. After while, they start to conflate “climate” with “my whiz-bang climate model ….
And that is exactly what Haigh has done in the paper. But wait, it gets weirder. Consider this from the paper:
If you believe that will produce significant results, I have a bridge to sell you … in any case, it now appears the Haigh paper should have been titled “”The impact of modeled solar variability on a climate model that is eternally stuck in January”.
Actually, that’s what I said. It is a modeled admixture of modeled output and observations, so yes, as I said, it is the output of a type of climate model called a reanalysis model. I also said I was sure there was real data in there … but it is not clear how much. However, you seem to miss my point. Part of the input is solar variations … so we would expect to find solar variations in the output. Which is why so many solar studies use reananlysis “data”, as it is called—it’s pretty much guaranteed to contain a solar signal in the output because it has a solar signal in the input.
HOWEVER: None of this touches what I clearly identified as the real problem with the study, which was bad statistics.
w.
Willis,
The only way you can propose a physical mechanism on the atmosphere is through a model. Ask, Leif. He can confirm that they use models to try to understand what happens in the inside of the Sun. That is what models are for, to try to understand things, not to predict the future. So there is no problem that Haigh uses models for that, as there is no other way.
Once the hypothesis is established, then it is the time for evidence gathering to support the hypothesis. That is the stage now, and that is why it is getting so many citations. The support is coming not only from atmospheric studies, but also from paleoclimatology.
You can say the same from the climate, as solar variations are part of the input there. Obviously they are not going to omit solar variations from the input, as they do not omit anything. Perhaps the answer that you are working so hard to disprove is that we should expect to find solar variations in the climatic output. That you did not find them does not say that they are not there, perhaps you did not look at the right place and time.
Javier September 14, 2016 at 8:08 am Edit
According to that theory, there could have been no physical mechanisms proposed for the atmosphere until the late 20th century when computer models were developed … you sure you want to stake your reputation on that claim?
In fact, people proposed all kinds of physical mechanisms regarding the atmosphere for centuries before there were models, so your claim is nonsense.
Next, you seem to think that all computer models are the same, so if say Leif uses one somewhere they all must be justified everywhere. Perhaps a short explanation is in order. In the way of credentials, I wrote my first computer program over a half century, never stopped writing code, and have written programs in a bunch of computer languages (Visual Basic, C, Pascal, R, Mathematica (2 languages), Lisp, Hypertalk, and back in the day, Fortran, Algol, and Cobol. Oh, and 68000 assembler language.)
So, to models. There are two basic kinds of models—direct, and iterative. Direct models take all of the variables, run some complex calculation or other on the numbers, and give you an answer.
Iterative models, on the other hand, are an entirely different animal. An iterative model takes a survey of the current situation, and outputs what the programmer thinks the situation will look like in some short time, say half an hour. Then, it repeats that step—it takes where the programmer predicted the situation will be in a half-hour as INPUT, and using the same method it predicts where the programmer thinks the situation will be in an additional half hour. Then it repeats that step—it takes that programmer’s prediction as INPUT, and calculates where it will be a half hour after that … lather, rinse, and repeat.
Repeat that process oh, just under two million times, and you’ll be able to tell us just what the situation will look like a hundred years from now. And if you believe that, you don’t understand iterative models.
Now, iterative models suffer from some huge problems that direct models don’t have. I’m sure that those following the story will already have noticed the largest achilles heel of the the iterative model … you are feeding the output of the model back in as input, so tiny errors will very quickly become huge errors. This is why so many iterative models go off the rails entirely. Of course the modelers don’t like to show you that, but here’s a bit that slipped out …
Notice how many of them went entirely off the rails and plunged into either thermageddon or snowball earth?
There is a further difficulty with iterative models. It is very hard to understand just how an iterative model gets its answers, as it is the result of literally millions of steps. This makes it a struggle to understand, hard to diagnose, harder to fix, and hardest to test … getting a right answer for a wrong reason doesn’t inspire confidence.
Now, I’m not saying that iterative models are useless. Far from it, I’ve made and used some myself. But the very best weather models (which are iterative) can only get good results a short time into the future … and a climate model is just a weather model driven where it should never go,
No, we can’t “say the same from the climate”. Ask David Evans, he’s got a whole theory developed about why, despite the ~ 11-year sunspot variations being clear in the sun, there is no trace of them in the climate. And that is the problem … despite literally centuries of looking, we still haven’t found any actual evidence that is definitive and bulletproof that connects the tiny ~11-year variations with anything down here on the surface. Sure, if you look at a selected period of time and only consider 8% of the ocean you might find something that would convince a blind man or an alarmist scientist … sorry, not impressed.
Best regards,
w.
Ask, Leif. He can confirm that they use models to try to understand what happens in the inside of the Sun
No, the model is a summary of what we have learned from observations.
Sure, and Galen made a lot of discoveries in medicine without modern methods, but if you want to push an atmospheric hypothesis nowadays you will be required to provide evidence from respected models before being accepted for publication in any decent journal. Your combination of naivety and nonsense accusations is startling. You may disagree, but that is the way things are and outsiders like you and me should not come telling them how to conduct their research. I am sure they are doing the best they can. If you think you can do better, you are welcome to try.
As I am an empiricist, I am so lucky as to have never needed a model, so I won’t get into a discussion about them. I understand that they are tools and, as long as you use them properly, useful. The output of a model should never be treated as real evidence, and models should be validated before too much credit is placed on them, but some people get enthused over their toys.
As you say the tiny 11-year variations are hard to see because the effect they produce is small and the noise of the system is high. However regarding the effect of solar variability on climate, the longer the solar cycle the stronger the climatic effect. The effect of the ~ 2400 and ~ 1000 year solar cycles on the climate is huge and well recorded in paleoclimatology. Once the effects are past, the recovery takes centuries. The effects are consistent with Haigh’s hypothesis. We know that the Hadley cells are expanding and that it is not due to GHGs, but probably ozone. Proxies indicate the expansion has been taking place for more than 100 years and probably since the end of the LIA. The expansion of the Hadley cells, about 1-2° since 1979 means the tropical areas expand and the entire atmosphere and climate of the planet reorganizes towards a warmer state with a change in wind and precipitation patterns.
As the atmosphere changes quite a lot with the seasons, the effect of the 11-year cycle is just too small compared to seasonal changes. The experts think that the effect is more noticeable during the winter months, and point to a higher frequency of high pressure blocking days over the North Atlantic with an increased frequency of very cold winters over Western-Central Europe with a 1-2 year delay with the solar cycle minimum like the 2010 cold winter after the 2009 solar minimum. Perhaps cold winters coming to Europe for 2019-20.
The effect of the ~ 2400 and ~ 1000 year solar cycles on the climate is huge and well recorded in paleoclimatology.
There is very little evidence that those climate cycles are solar-related. The cosmic ray record is contaminated by climate and have uncertain calibration [e.g. different ice cores disagree].
Exactly what climate scientists say about their models. And then models are used to test new ideas. If the new idea does not agree with the model one has a problem. Before the idea is accepted the model has to be proven wrong.
You missed the point: what we know about the solar interior is not derived from models, but from observations of solar pulsations [like oil prospecting uses seismic waves to learn about the interior of the Earth] and from observations of the neutrino flux.
14C is not recorded in ice-cores but tree rings and there is an entire archeological field based on its calibration.
The 14C record is derived from modelling the atmospheric circulation [due to the long residence time of the isotope]. Here are some raw data:
http://www.leif.org/research/INTCAL13.png
where are the 2400-yr cycle [and the 1000-yr to boot]?
And what we know about the atmosphere is not derived from models but observations. I fail to see the difference.
That you fail to see the difference does not mean there isn’t any.
The knowledge of the atmosphere [which by the way we were not discussing – it was the interior, remember] is derived from observations of limb darkening and strength of spectral lines.
The knowledge about the interior is derived from measurements of the sound speed in the interior, which in turn is derived from direct observation of the travel time of seismic waves.
We do construct models to compare with the observations and find excellent agreement.
Javier September 14, 2016 at 2:21 pm
I always love it when someone points to unidentified “experts”. It gives just that extra soupçon of authenticity to a vague legend.
In any case, the question you should be asking is, did the experts consider the Bonferroni correction? I mean, they’re looking at only one season out of four, meaning they couldn’t find the signal in the other three seasons. And they’ve limited it to the “North Atlantic”, about ten percent of the earth’s surface area, so once again we’re dealing with a small subdivision of the planet’s surface …
Looking season by season through the four seasons, while examining the world 10% by 10% … you can find 95% significance all day long that way, but after looking in only five places that turns into a need for 99% significance … and they’ve looked in many more than five places.
Regards,
w.
What long residence time? The atomic bomb pulse of 14C that duplicated the concentration of 14C in the atmosphere has taken a mere 60 years to 90% decay. The changes due to solar variability are much smaller and decay in just a few years. And we are talking centuries here.
https://cams.llnl.gov/cams-competencies/forensics/14c-bomb-pulse-forensics
You don’t need any modelling of 14C to see the cycles. This is detrended raw data:
http://www.euanmearns.com/wp-content/uploads/2016/05/Figure-4.png
http://euanmearns.com/periodicities-in-solar-variability-and-climate-change-a-simple-model/
You clearly have a bad case of cyclomania. There are enough ‘cycles’ to go around to fit almost any wild ideas.
An FFT of the raw data shows none of your peaks:
http://www.leif.org/research/FFT-INTCAL13.png
I was comparing the models that climatologists make about Earth’s atmosphere, with the models that you make about the Sun. I don’t see much difference. They are both based on observations and physics laws. Obviously you believe that solar physicists make much better models, but after all nobody is asking you to predict solar climate. I don’t believe you would be very successful predicting solar storms.
but after all nobody is asking you to predict solar climate.
On the contrary, that is precisely what I am asked to predict.
I don’t believe you would be very successful predicting solar storms.
As successful as weather forecasters are in predicting lightning strikes.
Solar storms are weather, and can be predicted to occur quite well if a big, complex active region is on the solar disk.
I don’t think so. The 2400 year solar cycle is confirmed by terrestrial climate evidence and reported multiple times since 1971, Cyclomania is believing in cycles not supported by evidence. Perhaps you have a bad case of cyclophobia, not believing in cycles that are real and supported by evidence.
They are not my peaks and they were reported multiple times based on non-modeled data. This one is from:
Damon, P. E., & Sonett, C. P. (1991). Solar and terrestrial components of the atmospheric C-14 variation spectrum. In The Sun in Time (Vol. 1, pp. 360-388).
http://i1039.photobucket.com/albums/a475/Knownuthing/DamonampSonnet1991_zpsgx24rhpj.png
I am sure you have a copy of that book somewhere. You can go an accuse Damon and Sonnet of cyclomaniacs.
Your link mentions peaks at 65 87 105 130 148 208 350 510 708 976 1126 1301 1768 and 2310 years. That is far too many for my taste. Now, the question is whether these peaks are solar or climate peaks. You claimed a 2400-yr period in detrended 14C data, and with some good will, one can see hints of peaks in the detrended version:
http://www.leif.org/research/INTCAL13-All-Data.png
The FFT power spectrum looks like this:
http://www.leif.org/research/INTCAL13-14C-FFT.png
And does show power in the 1500-4000 year range, but note that the amplitude of the peaks in the time series varies in a curious way, being much larger during the glaciation before 14K years. The Sun does know about the glaciation, so it would seem that the climate had a large influence on the 14C. Perhaps all the peaks in the 1500-4000 year range are simply climate-related, in which case it is no wonder that there is a correlation with the paleoclimate record.
It is curious that some people will not accept that the climate has natural variation of its own, but happily claim that the Sun has.
The Sun does know about the glaciation,
The Sun does not know about the glaciation,
Most peaks in a frequency analysis do not represent real cycles. They need to be confirmed by other type of evidence.
Damon and Sonnet state the following:
To that I add the ~ 1000 yr cycle. Those periodicities come up repeatedly in cosmogenic proxies and climate proxies.
It has already been noted. 14C data can be relied on for the Holocene. During the glacial period and specially during deglaciation the CO2 exchange with the oceans was very much changed and makes the interpretation a lot more difficult as our understanding of the carbon cycle during that time is poor.
Experts say they represent changes in solar variability, and since the overlap period when we have data both for solar variability and 14C changes agrees, I have no reason to doubt it.
Most peaks in a frequency analysis do not represent real cycles
Correct. One has to cherry pick the ones that match one’s ideas.
Experts say they represent changes in solar variability…I have no reason to doubt it
I am an expert, and I doubt it.
That’s not how it should be done. One has to let the data speak. See if the lows or the highs of the cycles match known lows or highs in the climate. If the climate has the same periodicity then the cycle has a chance of being real.
I have a great deal of respect for you and your scientific capability, but on this one I will go with the consensus. Not personal.
I’ve got over 20 papers on my hard disk on the effects of solar variability on the atmosphere and this is just a small sample of probably hundreds on the issue. I could put the list up with little effort, but why bother? If they use reanalysis they are no good because solar signal is an input. If they use models they are no good because models can’t be trusted. If they use real data they will always miss one of your favorite statistical tests. Why should I bother? The evidence available doesn’t convince you. That’s ok. You set the bar for that. But don’t say that there is no evidence. It is published.
There is a field of scientists where there is a huge consensus that solar variability plays an important role in climate change, and that is paleoclimatology, because they are faced with the evidence all the time. So we have this curious disconnect between solar physicists and paleoclimaologists that will have to be resolved one day, and I am pretty sure that it will be resolved to the side of those that have the evidence, not to the side of those that have the theory.
Javier September 14, 2016 at 7:04 pm
I ask if the studies used the Bonferroni Correction, and you announce proudly how many studies you have on hard disk … say what?
I will pass over your curious idea that the number of papers somehow establishes their veracity, and instead remind you that I’ve invited you (and others) to send me a link to the one study you (or they) think is the best. You know, the study that even Willis can’t find errors in. Send me a link to that study AND a link to the data used in the study, and I’m happy to take a look at it.
Before selecting the study, however, please consider the four issues listed above. Have the authors properly applied the Bonferroni correction? Have they allowed for autocorrelation, and if so, how? Have they allowed for the cyclical nature of the sunspot data? And have they shown that their claimed pattern persists in time?
I suggest you consider those aspects before sending me the links because it’s your reputation on the line. You’re the one attesting to the quality of the study, and I am known for telling the blunt truth about the quality of a study. So I’d be cautious and take a hard look before you send the links.
But THAT’S WHAT YOU SHOULD BE DOING IN ANY CASE!! You should be going back to your desk and sifting through the “20 papers” you say you have there, armed with your new information about how to correct for autocorrelation and repeated trials, and finding out if your idols have feet of clay.
Because I would be astounded if even one of your papers uses proper statistics in all of those regards listed above.
Anyhow, I’m happy to examine the one study of your choice, so please, send me the TWO links (study and data).
Best regards,
w.
PS—Let me note that in the face of my asking for your one best study, you start complaining about your fantasy of what I will do if you provide one …
So in place of actually putting your best paper to the test, you’d rather whine about your imagination of what I would do with your best paper …
Next, asking that when climate scientists calculate significance they include the known effects of repeated tests and of autocorrelation is just bog-simple statistics. It’s not something I dreamed up. It has nothing to do with my “favorite statistical tests”. It is what you must do when you are doing repeated testing of autocorrelated data if you want the correct answer.
Next, finding solar influences in models is meaningless, whether they are climate models or reanalysis models. It doesn’t demonstrate anything at all except that the models are linear machines whose input shows up in the output. We have no evidence that the real earth operates in such a linear fashion, and much evidence that it does not.
So I’m sorry, but you can’t claim that you’ve shown that the minor 11-year fluctuations of the sun affect the earth by showing that modeled solar fluctuations affect climate models. That’s not science, that’s magical thinking where you take the map for the reality.
w.
Javier we know the data show clearly that there is a solar /climate relationship when the sun enters extreme periods of activity. The studies lend further evidence that this is indeed the case.
Willis,
That is your criteria, and clearly not the only valid one. Scientific journals with professional editors and scientists reviewers set their own criteria.
I am not going to review those papers to see what statistic tests they report in their methods, and it is not possible to know what tests they did apply but are not reporting. But mainly because they are not “my idols” because as I said:
I am interested in the long cycles where the climatic effect is clear. In the last five lows of the 2400-yr cycle there has been a very strong climatic effect that is very well established and that coincides very precisely with the reductions in solar activity. That is 5 out of 5 and a global effect.
In the last five lows of the 2400-yr cycle there has been a very strong climatic effect that is very well established and that coincides very precisely with the reductions in solar activity.
It is very likely that those variations a simply climate variations and not solar related. You assume that they are solar, but there is evidence [as I have shown you] that they are not necessarily so.
In the last five lows of the 2400-yr cycle there has been a very strong climatic effect
as shown [NOT] here:
http://c3headlines.typepad.com/.a/6a010536b58035970c01b7c7c6a5cb970b-pi
This is some type of joke, right Leif? I mean, you don’t seriously believe that polar ice cores adequately represent Earth’s climatic variability during the Holocene, do you?
For a start Greenland and Antarctic ice cores profoundly disagree with each other regarding Holocene variability. This agrees well with the fact that the poles appear to be doing the opposite of each other during the modern warm period.
http://static.skepticalscience.com/pics/Bond-events2.png
The Holocene has quite a few significant cooling events between the Pre-Boreal Oscillation and the LIA. They are very well known to paleoclimatologists and registered in proxies and glacier advances all over the world.
Willis thanks again for your work.
I’m in the same camp as the logic behind the sun did it is so compelling, it is doubles its energy output we cook, if it halves it energy output we freeze.
But the energy passes through essentially a low band filter, in the oceans, that I personally think obscure / filter out the relationships. As a consequence, I do not think you will see any direct correlation between the sun and climate temps.
I’m starting to see the logic behind Salvatore Del Prete position around multiple points of influence over a longer time period.
The need to have the large amount of energy change over a period of time to essentially get beyond the system noise.
The more I read, the less I realize I know. – L
““if a self-tutored man like myself knows about the Bonferroni correction and the need to adjust for autocorrelation … what’s up with these PhD folks all across the climate landscape who apparently never heard of those concepts?”
It would take several courses in statistics to gain real proficiency, but most advanced degrees do not require even one statistics course, even though many accept statistics as an elective. Where I work the Statistics Department offers a course to new graduate students and newly hired science faculty. I have no idea why the Statistics Department offers this course, but maybe they saw it filled a need.
Willis
Does your requirement for Bonferroni correction assume that climate data at different places on the earth’s surface are independent of eachother?
This would make it an unduly stringent requirement since there is no such independence.
Thus spatial autocorrelation could undermine your Bonferroni argument.
ptolemy2 September 13, 2016 at 10:34 pm
Mmm … thanks, ptolemy, an interesting thought. My considerations in return are:
1. The spatial autocorrelation can’t be very large if you can only find the desired putative effect in a small part of the surface.
2. In most cases the claimed “significant” effect is weak, with a p-value not far below 0.05. As a result, either the Bonferroni correction OR the autocorrelation correction is usually enough to push it over the edge.
3. In some cases, there’s no clear mathematical solution, so you need to use Monte Carlo methods.
Doesn’t really answer your questions, but there it is,
w.
Here’s a problem with the Bonferroni correction in settings of multiple tests with low power. Say the tests (which may be in multiple studies) have 10% power at the nominal 5% alpha level. You only expect one study out of every 10 to produce a statistically significant result, p = 0.03, say, with R^2 about 4%. The Bonferroni correction requires the nominal p-value to be 0.005 for an overall significance level of 0.05, so your p = 0.03 is not statistically significant. It continues with 2 results out of 20 at p=0.03 or so, 3 out of 30, and so on. Even though you get the expected result, Bonferroni corrections essentially reduce the power of the sequence of studies to 0.
It is about the same if power is 25%. Then you expect about 1 out of 4 tests to be statistically significant at a nominal level of 5%; a p-value of a single study say p=0.03, is compared to a Bonferroni-corrected 0.05/4 = 0.0125.
If this is the case with solar effects on climate, many low power studies pursuing effects that have R^2 = 0.04 or less, then the Bonferroni corrections prevent any set of expected results (expected under the alternate hypothesis) from confirming the alternate hypothesis. Something better is needed. In laboratory research you can arrange conditions to increase the statistical power of studies. With purely observational research, that is not possible.
What to do? Right now, I think the best approach is the empirical Bayes approach of Prof Bradley Efron of Stanford, who has used the approach in genome-wide association studies. Best single reference is probably his book “Large Scale Inference”, published by the Institute of Mathematical Statistics, 260 pp. But he has papers in journals: Annals of Applied Statistics, Annals of Statistics, Journal of the American Statistical Association. As applied to solar effects on climate, many more studies are required before the method can be used.
If the null hypotheses are really true, then about 5% of tests will produce apparently statistically significant results, if not Bonferroni-corrected. The Bonferroni correction reduces that to 0, the appropriate thing to do if in fact all the null hypotheses are true. Whatever you might think you believe, if you are using the Bonferroni correction you are placing a strong bet that there are no small effects. That might not be the best thing to do if there are small effects with significant consequences on large time or space scales — say something that might over a century raise the global mean temperature about 1C.
something that might over a century raise the global mean temperature about 1C.
As the Earth is open to space, it also radiates away the solar input. If the temperature goes up, the Earth radiates even more.
Meh, matt shows a possible bias to understanding.
======
lsvalgaard: If the temperature goes up, the Earth radiates even more.
A complementary theory to that one is that the temperature has to increase before the outbound radiation can increase, pretty much in accordance with the Stefan-Boltzmann law.
Also, there are claims that the Earth mean temperature has in fact increased about 1 C over the past century or so. Something might have caused it. If the lukewarm position with regard to CO2 is correct, as asserted for example by ristvan, might the cause have been an increase in solar output of some kind? To me, each attempted explanation is full of holes (Willis Eschenbach has gone good work exposing holes in the solar case, as here), so I consider the cause unknown.
A complementary theory to that one is that the temperature has to increase before the outbound radiation can increase
I think that is what I just said.
matthewrmarler September 15, 2016 at 8:23 am
Regards, Matt, always good to hear from you. If I understand you, what you have described is very different from the current situation. To begin with, I assume that by “low power” you mean low hit rate (correct positive results / total positive predictions), which describes some kind of “yes/no” test. We’re not doing that here. We’re just measuring correlation and deciding if it is significant.
I also don’t understand why you’d ever use a test with a 10% hit rate as you describe. This means it has a 90% false alarm rate … sounds like the tests the climate alarmists use.
Here’s the underlying reality I’m pointing to. The Chinese study covered 6% of the planet. The correlation is weak (p ~ 0.03). And to get that correlation, they had to throw away about forty years of data.
What are the odds of finding that kind of correlation somewhere on the planet, given those parameters? Quite good, I’d say. But as I showed in the head post, the purported relationship falls apart when we divide the data into two halves, each of which has a spatial pattern which is remarkably different from both each other and the full dataset … no bueno. Means we’re looking at random fluctuations. Which is just what the Bonferroni correction says …
w.
Willis Eschenbach: I also don’t understand why you’d ever use a test with a 10% hit rate as you describe. This means it has a 90% false alarm rate …
Low power at a 5% (or other given) significance level does not imply a high false alarm rate. It only implies a low power, and a 90% false negative rate. “you’d use a test with a 10% hit rate” probably because of not considering power in the first place, and then working with the data as have been collected. When you reanalyze someone else’s data, as you and the Chinese here, you are using a test with a low power.
We’re not doing that here. We’re just measuring correlation and deciding if it is significant.
Your analysis and decision of whether it is significant is a procedure with low power against weak alternative hypotheses (e.g. R^2 = 0.04). If you then perform the Bonferroni correction, you reduce the power to 0, as I wrote.
Is there some reason why you think your procedure has high power (> 0.25, for example) against reasonable alternative hypotheses?
This is why I call it “limbo”: the statistical methods available to us now do not lend confidence to any conclusions. “Rejecting the null hypothesis” without considering the multiplicity of tests is not very good evidence that the null hypothesis is false; accepting the null hypothesis with a procedure (data collection plus analysis) that has low power is not very good evidence that the null hypothesis is true.
matthewrmarler September 15, 2016 at 9:08 pm
I’m not understanding your definition of “power” and “low power”. I thought you meant “hit rate”, which to me is hits divided by (hits plus false alarms). It seems you mean something else, but I’m not sure what.
I recognize four categories of true/false test result—hits (test says true, result is true), false alarms (test says true, result is false), correct rejections (test says false, result is false), and misses (test says false, result is true).
Using these, what is the definition of what you are calling the “power” of a test?
What we are doing is measuring the correlation and seeing if the calculated p-value of the correlation exceeds some pre-set level (e.g. less than 0.05).
In making such a measurement, what would constitute e.g. a “false alarm”?
Sorry, still not clear. It might make more sense if we think of flipping coins. The chance of ten coins coming up heads in a single throw is one in 210 = one in 1024. This gives a p-value of just under 0.001.
Now, what would be a “reasonable alternative hypothesis” to that calculation?
I still don’t understand why you think the Bonferroni correction “reduce[s] the power to 0”. All it means is that the more places you look, the more unusual a result must be in order to be considered statistically significant.
Best regards,
w.
PS—Have you ever derived the Bonferroni Correction? I calculated the actual values a couple years before I ever heard of the Bonferroni Correction. The Bonferroni Correction is actually just an approximation of the actual values, but it is quite close. The actual p-value required is given by
(1 – (1 – p_value)1/n)
where n is the number of trials and p_value is the original level of significance desired (typically 0.05).
This is very close to p_value / n, which in turn is the first (and only significant) term in the power series expansion of the formula above.
Willis Eschenbach: I’m not understanding your definition of “power” and “low power”.
That might be true. The power of the procedure (data collection plus analysis) is the probability of rejecting the null hypothesis (at the given alpha level) if the alternative hypothesis is true.
If in this case the alternative hypothesis is true, the Chinese authors have a lucky hit from using a test with an inflated alpha, and the Bonferroni correction turns that into a false negative.
If in this case the alternative hypothesis is false, the Chinese authors have a false positive, and the Bonferroni correction turns that into a true negative.
If, as a Bayesian, one had prior probabilities on H0 and Ha, this evidence would produce posterior probabilities nearly equal to the priors. I only do Bayesian inference when there is good evidence supporting the prior probabilities, and in this case there isn’t any such evidence.
In medical terminology, the posterior probability that the diagnosis is true, given that a condition (e.g. cancer) is judged present is called the “positive predictive power” (PPV) of the test. The posterior probability that the diagnosis is true given that the condition (e.g. cancer) is judged absent is called the “negative predictive power” (NPV) of the test. In your phrase “hits divided by hits plus false alarms” you might be referring to the positive predictive power. In the famous case of breast cancer screening, the procedure has a low PPV in women under 40 because the actual (and well estimated) base rate of the disease, taken as the “prior”, is very low — and they have large counts of actual true and false positives.
In this case, I think it is undecidable whether H0 or Ha be true.
Willis Eschenbach: I still don’t understand why you think the Bonferroni correction “reduce[s] the power to 0”. All it means is that the more places you look, the more unusual a result must be in order to be considered statistically significant.
If you are studying a lot of phenomena with small effect sizes, say relationships with small R^2 value, and if your tests have low power at the given alpha level, then the obtained p-values will never be small enough to be statistically significant after the Bonferroni corrections, even when the relationships are there.
The tests have low power because the effects are small and the time series are not long. So if there are a lot of solar effects awaiting discovery, say things like a rainfall pattern in the Indian Ocean that is somewhat responsive to changes in the intensity of a particular band of UV light, those relationships will never be revealed by Bonferroni-corrected null hypothesis testing. The problem is analogous to looking for quantitative trait loci in genome-wide association studies, where it is known a priori that most genes are unrelated to the trait at issue, and each gene that is related has a weak effect — say genes related to clinical depression, resistance to pneumonia, obesity, or type 2 diabetes.
I realize I am repetitive: lots of hypotheses, low power tests, weak effects and relationships.
lsvalgaard:
I think that is what I just said.
oh, sorry. I read an implication that wasn’t there.
The thought that if extreme solar conditions prevail long enough in duration will result in a global cool down are supported through the historical climatic record .
I think as we move forward the global temperature response will be down if prolonged minimum solar conditions meet my criteria.
Thus far I see no arguments or data to suggest my thoughts may be wrong.
We have been asked to quote the exact words we disagree with. Here is the quoted summary.
“And as a result, as an honest man I have to say that despite looking for something that I started out truly and completely believing existed, and despite examining a long string of solar-related studies, to date I have not found convincing evidence of such a connection between the ~11-year solar cycles and the climate here at the surface where we live. Now, if the facts change I’ll change my mind, but as it stands I haven’t yet found the requisite evidence.”
I disagree with the many comments that claim Willis denies any connection between sun changes and climate changes. Read the quote. He does not find any strong evidence for any of the claimed connections. Reading between the lines, I might conclude he is starting to doubt that any evidence will be found, or perhaps that he is confident no such evidence will ever be found. But that would be my imagination or fantasy, not his words or claim.
Thanks, Chuck. You are correct. I have always been very careful about what I claimed. All I can say is that I set out to find evidence for a relationship that I thought would be easy to find … and despite looking at all of the studies listed in my 23 posts above, plus a number more that I discussed in the comments to those posts, I’ve not found anything solid. I’ve not found a single claimed connection to stand up to close inspection, from Hershel’s claims about wheat yields to the Chinese paper discussed above.
Here’s the thing. I do my best to avoid making any scientific claims that I cannot defend with math, logic, observations, statistics, whatever it takes.
And sadly, I have not found any evidence that I would be prepared to defend that claims to show such a sunspot/climate relationship. It’s all of the same type, with claims that boil down to “In this obscure corner of the climate world, if you take this temperature and subtract half of that temperature, and you use only the winter months, and you restrict it to years since 1942, it lines up perfectly with sunspots” … I can’t defend that kind of claim.
So … I’ve left the invitation open, I’m happy to have people put their reputations on the line by sending me the two links to the study and the data that they think establish their case …
Regards,
w.
I’m counting on your marvelous curiosity, willis. The Feynman Nile/aurorae correlation is perhaps the main reason I cling so bitterly to the idea that the sun has a climate connection on shorter than Milankovitch time scales. See what you can do with it; I’m genuinely curious, and await your analysis with eagerness.
It’s been a long time, and I can’t be sure, but I vaguely remember Leif dismissing the study with something like ‘correlation is not causation’. Please correct me if I’m wrong, Leif.
===========
It’s Ruzmaikin, Feynman and Yung in 2006. I found it in 15 seconds searching ‘Joan Feynman NASA JPL Nile aurorae’ on Yahoo.
One critique talks of wiggle matching. I’d forgotten that an 88 and 200 year signal were found.
Go for it.
======
kim September 15, 2016 at 7:19 am
Thanks, kim. You can count all you want on my curiosity, but until you send me two links, one to the study and one to the data, I’m not going to do one thing. I don’t go googling for any man. If you want me to look at your favorite study, I’m more than happy to do so, but YOU will have to provide the links. I don’t have time to waste looking. I’ve played that “just google it” game before … not again.
w.
Heh, do it for yourself, not for me.
============
kim September 15, 2016 at 10:42 am
Heh, another anonymous internet popup who talks a good fight, but when it comes time to actually provide the two links, one to his claimed whiz-bang study and the other to the relevant dataset, the best study he knows, guess what happens?
The anonymouse gracefully declines the chance to provide any actual evidence, based on whatever excuse is handy … color me unsurprised.
w.
PS, kim, if you do plan to put up that study, you might consider the very high Hurst exponent of the Nilometer data and its effects on statistical significance. I discuss the Nilometer issue here.
w.
Hoohaw, I popped up on that thread with the same study. I would still like to see your analysis of it, but I have no idea how to send you the data.
==================
C’mon, willis, this is your chance to look at something other than the 11 year cycle for a sun climate connection. Leif has pretty much convinced me that looking there is a fool’s errand.
Again, this study suggests a sun climate connection on shorter than Milankovitch time scales. If it’s debunkable, let’s hear it.
===============
kim September 15, 2016 at 6:15 pm
kim September 15, 2016 at 6:27 pm
kim September 15, 2016 at 4:25 pm
“Peek at it”? Say what? I told you, I don’t go on a google snipe hunt for any man, woman, or child. So no, I haven’t taken a peek at it, that’s your sick fantasy.
I’ve told you that I’m more than happy to look at your study, which actually sounds interesting from your anecdotal account, once you provide the oft-requested two links, one to the study and one to the data. Are you ever going to do that, or do you plan to continue your evasion forever?
w.
PS—When I started this quest, everyone said I’d find evidence of the 11-year cycle here, or over there, or somewhere. Now, after a string of failures at finding that 11-year cycle, people like you and salvatore nod your heads sagely and say the missing signal is still there … but it won’t be found in the 11-year cycle.
Curious how that works …
You can lead a horse to water but you cannot make it look at the night sky.
==================
Look, I’m not nearly as committed to this study as you seem committed to ignore it. I believe it is a good study, with good data over a long period. I’d hoped you could explain to me why that wasn’t so, but you haven’t. My loss. I wish it weren’t your loss, too.
You are the one with the meta-argument that you’ve looked everywhere for a sun/climate connection but haven’t found it. Your lack of curiosity about this surprises me.
==============
Kim, I’m more than happy to look at your study, if you have the albondigas to send me the links to it.
Otherwise, no, I’m not interested. I don’t follow random hints and direction from anonymous internet popups. I learned my lesson about that long ago.
I provide explicit links to the claims and data and citations for my work. If you can’t be bothered to do the same, why should I be bothered to go on a snipe hunt?
w.
I’ve already told you, and you’ve repeated it, that I don’t know how to send you the data. You’ve agreed that the study ‘sounds interesting’ yet you won’t interest yourself in it unless I perform some act that I cannot do.
This is mere petulance and intellectually impoverishing.
==============
kim September 16, 2016 at 12:57 am Edit
I never asked you to send me the data. I asked you to send me a link to the data. I didn’t realize you didn’t know how to send a link, I thought you were being sarcastic. My bad.
You send a link by typing out the link on a separate line, like this link to some random post of mine:
https://wattsupwiththat.com/2015/08/19/the-missing-11-year-signal/
Then WordPress does its magic and turns it into an actual link.
So please, send me the links to the study and the data, and we can move forwards.
w.
Thanks, we are getting somewhere now. Willis, I confess that I don’t even know how to find the data, or a link to it.
Now I’ve claimed that I believe that it is good data. How can I do that without looking at it? I think the observations of aurorae and of Nile River levels is reliable enough to render good data.
================
Heh, I knew you couldn’t resist a peek at it. Go ahead, blow it up. I’m all ears.
============
My claim is the 11 year sunspot so called normal cycle and the climate will not show a relationship because the noise in the climate system obscures the slight solar changes not to mention the variations within the 11 year sunspot cycle from maximum to minimum conditions cancel each other out.
Only when the sun enters extreme prolonged periods of inactivity or activity for that matter are those two issues nullified and hence a solar /climate connection is able to be established. It is no longer obscured.
I have come up with the minimum solar parameters needed in order to accomplish this by looking at the historical climatic record and how it has responded to solar activity. It shows each and every time the sun enters a protracted period of extreme inactivity the response in global temperatures has been down.
That is fact and until data shows otherwise I think the case for a solar/climate relationship is strong.
In addition the sun drives the climate therefore logic follows that any change in solar conditions has to have an effect on the climate to one degree or another. The point is how large is the effect and is it large enough to overcome the noise in the climate system which can obscure small minor solar changes.
The other side is what are the extreme solar changes in regards to degree of magnitude and duration of time needed to change the climate through solar activity changes themselves and associates secondary solar effects?
I am sure every one agrees that if solar changes are extreme enough there would be a point where a solar/climate relationship would be obvious. The question is what does the solar change have to be in order to be extreme enough to show an obvious solar/climate relationship?
Again I have listed the solar parameters which I think satisfy this issue.
The other side is what are the extreme solar changes in regards to degree of magnitude and duration of time needed to change the climate through solar activity changes themselves and associates secondary solar effects?
By definition, ‘extremes’ are rare and thus the effects you advocate will be rare too, and thus not important in the greater scheme of things. E.g. you would not attribute the recent ‘global warming’ to the influence of solar activity.
Extremes are rare and this is why solar /climate correlations tend to be obscure over short periods of time.
Example – the sun spends much more of it’s time in a regular 11 year sunspot state in contrast to a Maunder Minimum state but the Maunder Minimum state or to a lesser degree the Dalton state does happen from time to time.
Maybe this time a Dalton type of situation evolves and if it happens I think as in the past the global temperature response will cool off to one degree or another.
W. E.: “As an erstwhile ham radio operator (H44WE), I’m well aware that the sunspot cycle affects long-range radio transmission (DXing) by messing with the beautifully named “Heaviside Layer” … what I can’t find is any solid evidence of any corresponding 11-year variation down here on the ground where we live. And yes, I do know that Heaviside is someone’s name, but I still think it’s a great name.”
Up up up
Past the Russell Hotel,
Up up up up
To the Heaviside Layer.
–“Cats”
Here is the story about the Heaviside Layer:
http://www.leif.org/research/Radio-Ionosphere-Magnetism-and-Sunspots.pdf
Thanks, Leif, good stuff.
w.
For those of you who still think that the correlation of sunspots with the El Nino Modoki Index is significant, I realized that there was a simple test I could apply. This was to compare the El Nino Modoki Index, not with the sunspots, but with the time-reversed sunspots. I just swapped the full sunspot record end for end, and then I compared that to the individual gridcells as in Figures 1 to 3.
As you can see, despite the “sunspot” data in the Figure above being meaningless because it is time-reversed, the correlations are very similar to those of the actual sunspots.
Which of course means that their results are as meaningless as those from time-reversed sunspots …
w.
Notice the sun was in a regular mode of operation from 1854- 2005.
I maintain when the sun does enter a prolonged solar minimum not only are the sea surface temperatures going to fall but a weak El NINO not La Nina might be superimposed upon the overall lower sea surface temperatures. It is speculation on my part. I am thinking it would be tied to weaker atmospheric circulation changes.
How do they dare publishing things like this in Nature Communications, where they defend that the Sun has a role in climate change? Outrageous.
Ersek, Vasile, et al. “Holocene winter climate variability in mid-latitude western North America.” Nature communications 3 (2012): 1219.
“Water resources in western North America depend on winter precipitation, yet our knowledge of its sensitivity to climate change remains limited. Similarly, understanding the potential for future loss of winter snow pack requires a longer perspective on natural climate variability. Here we use stable isotopes from a speleothem in southwestern Oregon to reconstruct winter climate change for much of the past 13,000 years. We find that on millennial time scales there were abrupt transitions between warm-dry and cold-wet regimes. Temperature and precipitation changes on multi-decadal to century timescales are consistent with ocean-atmosphere interactions that arise from mechanisms similar to the Pacific Decadal Oscillation. Extreme cold-wet and warm-dry events that punctuated the Holocene appear to be sensitive to solar forcing, possibly through the influence of the equatorial Pacific on the winter storm tracks reaching the US Pacific Northwest region.”
http://www.nature.com/article-assets/npg/ncomms/journal/v3/n11/images_hires/w926/ncomms2222-f4.jpg
(a) Detrended δ18O time series at OCNM [Oregon Cave National Monument] smoothed at 50-year resolution. (b) OCNM δ13C record smoothed at 50 years, with particularly pronounced negative excursions in the δ13C record identified as events a–h. Black diamonds below the δ13C time series represent the U-Th ages and associated 2σ uncertainties. (c) Estimated 14C production rate. (d) Estimated 10Be flux in a Greenland ice core. Both nuclide time series (c,d), inferred to reflect total solar irradiance, were filtered to remove periods >1,800 years and subdecadal noise, then interpolated at 50-year resolution.
Oh Gosh, it turns out the Sun’s variability does seem to have an effect on climate variability.
Both nuclide time series (c,d), inferred to reflect total solar irradiance, were filtered to remove periods gt 1,800 years and subdecadal noise, then interpolated at 50-year resolution.
A good example of data torture. The actual data [with 5 year resolution] is very spiky [of short duration] and does not support the ponderous ‘cycles’ claimed. And some [perhaps most] of the variation is due to climate variation in the first place:
http://www.leif.org/research/10Be-Filtered-Raw.png
You keep saying this despite the evidence not supporting your belief.
http://i1039.photobucket.com/albums/a475/Knownuthing/Stuiver14CSSN_zps5s8epeni.png
From: Stuiver, Minze, and Paul D. Quay. “Changes in atmospheric carbon-14 attributed to a variable sun.” Science 207.4426 (1980): 11-19.
http://i1039.photobucket.com/albums/a475/Knownuthing/Goslar14CSSN_zpsskwzxv52.png
From: Goslar, Tomasz. “14C as an indicator of solar variability.” PAGES News 11.2/3 (2003): 12-14.
Do you have any published evidence that indicates that 14C is not an indicator of solar variability but mainly an indicator of climate variability? If not you should stop bringing up unsupported beliefs. People here might think that you being a well respected solar scientist your opinion on carbon-14 not being an indicator of solar variability is shared within the scientific community.
carbon-14 not being an indicator of solar variability is shared within the scientific community.
You are overplaying your hand. 14C and 10B are indicators of solar variability, except that both records are contaminated by climate influence, especially for the minima values. This is also generally accepted by the ‘scientific community’ or rather by workers in the field of cosmic ray research.
Great effort has gone into calibrating C14 years with calendar years. IMO at least this isotope is well understood.
Indeed, but that does not resolve the problem of the cause of the variations of the record. Some of it is solar, and come of it is climate controlled.
A third [and much larger] cause of the observed variation is due to the varying strength of the Earth’s magnetic field. And the variation of that also has uncertainty [which grows when going back in time].
So you can’t produce any evidence of what you say and there is no quantification of the climate contamination effect of the 14C records. Yet the evidence from the overlapping period 1640-1950 (three hundred years) shows no significant climate contamination during the Maunder minimum. So we should trust your word over the evidence and a long publication record, that 14C data is not to be trusted.
I have already [several times] referred you to the literature on this. I’m sure you can google the problem and find these back. Try Berggren, Webber, Higbie, and others. One quantification is that half of the values for the minima in “are as large as or larger than the production changes themselves, are occurring. These influences could be climatic or instrumentally based.”
“Indeed this implies that more than 50% the 10Be flux increase around, e.g., 1700 A.D., 1810 A.D. and 1895 A.D. is due to non-production related increases!”
As the 14C data largely agree with the 10Be data [as shown in your own Figure in a recent comment] it would seem that we have similar problems with the 14C record. Now, you are welcome to ignore the uncertainties and indications as would be the usual reaction of people wedding to some view. But ignoring the problem does not make it go away.
No, not me. You did linked once in an answer to one of my comments to an unpublished article by Webber on the unreliability of 10Be archived in arXiv, but that is a double no: not published and not on 14C. Nevertheless I downloaded it and read it.
You tend to think the worst of people, right? I want my data and my conclusions to be as solid as possible. Despite the Webber article being unpublished I decided not to trust 10Be alone and only use 14C and combinations of both (Steinhilber 2012 method). And I have redone several of my figures. If I ask is because I am genuinely interested. I do think that you are exaggerating and that 14C can essentially be trusted, and that over 90% of the grand solar minima indicated by the 14C record are real. We have seen 3 grand solar minima in the last 1000 years, so it seems reasonable that grand solar minima are a millennial feature of the Sun.
And for the record I believed that the Sun had little relation with climate variability for a long time in part due to your comments. But then I checked the data myself and changed my view. I am not espoused to any particular view, but to what the evidence shows.
I do think that you are exaggerating and that 14C can essentially be trusted
I tend to be more skeptical of that and to have a higher bar for what to trust. This is partly because I know how the ‘sausage is made’. Not pretty. Here is recent paper But it is not my aim to convince anybody who is convinced otherwise. Only to say why I hold the view I have. This is take it or leave it thing.
You may want to read: http://www.leif.org/EOS/14C-Model-Calibration.pdf
showing how the recycling of CO2 [and hence 14C] through the ocean circulation, ice conditions, and wind pattern affects the modeled 14C production.
Thank you for the reference, but of course that refers to the Younger Dryas. Everybody knows that changes in CO2 levels affect 14C levels, obviously. But changes in CO2 levels are much smaller once you reach the Holocene. That’s why solar variability reconstructions only go 11,000 years back. You are pretty safe as long as you keep within the Holocene because according to ice cores, CO2 has only changed between 258-282 ppm within the pre-industrial Holocene, that is a 10% variation. More importantly the centennial wiggles in CO2 are at most a fifth of that, again according to ice cores. The biggest drop was from 282 to 275 ppm during the LIA, that is only a 2.5% change in CO2 levels. That is taken into account in the carbon models for solar reconstructions, but even if it wasn’t, a change of 2.5% is not going to change a grand solar minimum much.
I like the last phrase in the abstract:
12,800 years before present, the YD onset, coincides with a minimum in the ~ 2400 year cycle.
12800 / 10250 / 7800 / 5350 / 2900 / 450 (LIA)
That is as good an explanation as any.
Everybody knows that changes in CO2 levels affect 14C levels
And CO2 levels are correlated with temperature [regardless of which way causality goes], so 14C levels will be correlated with temperature, hence by climate. So, you are just comparing climate with climate with a bit of noise due to the sun thrown in.
Not really. There is a big change (compared to normal variation) in 14C, and in climate, and a small change in CO2 levels. Once the change in CO2 is deducted from both 14C and climate (it has a small effect on both), there is still a big change in 14C and in climate that cannot be explained by changes in CO2.
You are just trying to make this effect bigger without evidence for it, to dismiss the clear relationship between high cosmogenic isotope productions due to lower solar activity and profound climate worsening (decrease in temperatures and changes in precipitations) during the Holocene. This relationship is so clear that there is a consensus between paleoclimatologists that changes in solar forcing in the centennial scale have driven important climatic changes in the past 11,000 years. Their consensus is based on evidence found. The consensus of those that refute a role for solar variability in climate change is based on the lack of a theory that can explain the amplification of the solar signal variability. One is based on evidence and the other is based on our limitations. That’s why I decided to change sides and go with the evidence.
The consensus of those that refute a role for solar variability in climate change is based on the lack of a theory that can explain the amplification of the solar signal variability
And the lack of a theory that can explain the variation of the solar signal itself.
JAVIER the historical climate record shows your views are correct.
Yup. The Pineapple Express, familiar to all Pacific Northwest natives. Also causes Chinook winds.
Sorry Willis, was on one those endless pursuits, when I ran across this and thought it belonged here. lol
Did the Chinese above go to the EGU General Assembly this year?
“”This suggests that the modulation of the ENSO variability by the solar cycle originates through a modulation of the El Niño Modoki rather than the canonical El Nino“”
My bold
Who made who?
Solar cycle modulation of ENSO variability
Authors:
Kodera, Kunihiko; Thiéblemont, Rémi
Affiliation:
AA(Nagoya, Institute for Space-Earth Environmental Research, Nagoya, Japan , AB(Research Division Ocean Circulation and Climate, GEOMAR Helmholtz Centre for Ocean Research, Kiel, Germany
EGU General Assembly 2016, held 17-22 April, 2016 in Vienna Austria
04/2016
http://adsabs.harvard.edu/abs/2016EGUGA..1810702K
Abstract
Inspired by the work of Labitzke and van Loon on solar/QBO modulation in the stratosphere, Barnett (1989) conducted an investigation on the relationship between the the biannual component of the sea surface temperature (SST) in the equatorial eastern Pacific and the solar activity. He found that the amplitude of biannual component of the SST (BO) is modulated by the 11-year solar cycle: the amplitude of the BO is large during a period of low solar activity, but small during high solar activity. More than 25-years or two solar cycle has passed since his finding, but the relationship still holds. In order to get an insight into the mechanism of the solar modulation of the El Niño Southern Oscillation (ENSO), here we have revisited this problem. Solar cycle modulation of the BO in the tropical SST is discernible since the end of the 19th centuries, but the amplitude modulation is particularly clear after 1960’s. The composite analysis of the SST based on the amplitude of the BO during 1958-2012, indicates that the amplitude of BO is larger when the equatorial Pacific temperature anomalies are high in the central Pacific, but low in the eastern Pacific. Central Pacific anomalies extend to the northern hemisphere, while those in the central Pacific spread toward the southern hemisphere. In short, this anomalous SST pattern is similar to the El Niño modoki. In this connection, it should be noted that the solar signal in the tropical SST also exhibits a similar pattern. This suggests that the modulation of the ENSO variability by the solar cycle originates through a modulation of the El Niño Modoki rather than the canonical El Nino
Subtle predictions for Solar Cycle 25
Unusual Polar Conditions in Solar Cycle 24 and their Implications for Cycle 25
Nat Gopalswamy1 , Seiji Yashiro1,2, and Sachiko Akiyama1,2
Accepted: The Astrophysical Journal Letters May 6, 2016
ABSTRACT
We report on the prolonged solar-maximum conditions until late 2015 at the north-polar region
of the Sun indicated by the occurrence of high-latitude prominence eruptions and microwave
brightness temperature close to the quiet Sun level. These two aspects of solar activity indicate
that the polarity reversal was completed by mid-2014 in the south and late 2015 in the north. .
The microwave brightness in the south-polar region has increased to a level exceeding the level
of cycle 23/24 minimum, but just started to increase in the north. The north-south asymmetry in
the polarity reversal has switched from that in cycle 23. These observations lead us to the
hypothesis that the onset of cycle 25 in the northern hemisphere is likely to be delayed with
respect to that in the southern hemisphere. We find that the unusual condition in the north is a
direct consequence of the arrival of poleward surges of opposite polarity from the active region
belt. We also find that multiple rush-to-the-pole episodes were indicated by the prominence
eruption locations that lined up at the boundary between opposite polarity surges. The highlatitude
prominence eruptions occurred in the boundary between the incumbent polar flux and
the insurgent flux of opposite polarity.
This is goofing me all up now. The Northern hemisphere has been leading in sunspot production? Even though…
And Willis, don’t lose hope, for that sun/earth connection in climate variability.
The 11 year solar cycle signature on wave-driven dynamics in WACCM
Chihoko Y. Cullens, Scott L. England, Rolando R. Garcia
First published: 9 April 2016
http://onlinelibrary.wiley.com/doi/10.1002/2016JA022455/full
Abstract
This study describes the influence of the 11 year solar cycle on gravity waves and the wave-driven circulation, using an ensemble of six simulations of the period from 1955 to 2005 along with fixed solar maximum and minimum simulations of the Whole Atmospheric Community Climate Model (WACCM). Solar cycle signals are estimated by calculating the difference between solar maximum and minimum conditions. Simulations under both time-varying and fixed solar inputs show statistically significant responses in temperatures and winds in the Southern Hemisphere (SH) during austral winter and spring. At solar maximum, the monthly mean, zonal mean temperature in the SH from July to October is cooler (~1–3 K) in the stratosphere and warmer (~1–4 K) in the mesosphere and the lower thermosphere (MLT). In solar maximum years, the SH polar vortex is more stable and its eastward speed is about 5–8 m s−1 greater than during solar minimum. The increase in the eastward wind propagates downward and poleward from July to October in the SH. Because of increase in the eastward wind, the propagation of eastward gravity waves to the MLT is reduced. This results in a net westward response in gravity wave drag, peaking at ~10 m s−1 d−1 in the SH high-latitude MLT. These changes in gravity wave drag modify the wave-induced residual circulation, and this contributes to the warming of ~1–4 K in the MLT.