Guest Post by Willis Eschenbach
I thought I was done with sunspots … but as the well-known climate scientist Michael Corleone once remarked, “Just when I thought I was out … they pull me back in”. In this case Marcel Crok, the well-known Dutch climate writer, asked me if I’d seen the paper from Nir Shaviv called “Using the Oceans as a Calorimeter to Quantify the Solar Radiative Forcing”, available here. Dr. Shaviv’s paper claims that both the ocean heat content and the ocean sea surface temperature (SST) vary in step with the ~11 year solar cycle. Although it’s not clear what “we” means when he uses it, he says: 
“We find that the total radiative forcing associated with solar cycles variations is about 5 to 7 times larger than just those associated with the TSI variations, thus implying the necessary existence of an amplification mechanism, though without pointing to which one.” Since the ocean heat content data is both spotty and incomplete, I looked to see if the much more extensive SST data actually showed signs of the claimed solar-related variation.
To start with, here’s what Shaviv2008 says about the treatment of the data:
Before deriving the global heat flux from the observed ocean heat content, it is worth while to study in more detail the different data sets we used, and in particular, to better understand their limitations. Since we wish to compare them to each other, we begin by creating comparable data sets, with the same resolution and time range. Thus, we down sample higher resolution data into one year bins and truncate all data sets to the range of 1955 to 2003.
I assume the 1955 start of their data is because the ocean heat content data starts in 1955. Their study uses the HadISST dataset, the “Ice and Sea Surface Temperature” data, so I went to the marvelous KNMI site and got that data to compare to the sunspot data. Here are the untruncated versions of the SIDC sunspot and the HadISST sea surface temperature data.
Figure 1. Sunspot numbers (upper panel) and sea surface temperatures (lower panel).
So … is there a solar component to the SST data? Well, looking at Figure 1, for starters we can say that if there is a solar component to SST, it’s pretty small. How small? Well, for that we need the math. I often start with a cross-correlation. A cross-correlation looks not only at how well correlated two datasets might be. It also shows how well correlated the two datasets are with a lag between the two. Figure 2 shows the cross-correlation between the sunspots and the SST:
Figure 2. Cross-correlation, sunspots and sea surface temperatures. Note that they are not significant at any lag, and that’s without accounting for autocorrelation.
So … I’m not seeing anything significant in the cross-correlation over full overlap of the two datasets, which is the period 1870-2013. However, they haven’t used the full dataset, only the part from 1955 to 2003. That’s only 49 years … and right then I start getting nervous. Remember, we’re looking for an 11-year cycle. So results from that particular half-century of data only represent three complete solar cycles, and that’s skinny … but in any case, here’s cross-correlation on the truncated datasets 1955-2003:
Figure 3. Cross-correlation, truncated sunspots and sea surface temperatures 1955-2003. Note that while they are larger than for the full dataset, they are still not significant at any lag, and that’s without accounting for autocorrelation.
Well, I can see how if all you looked at was the shortened datasets you might believe that there is a correlation between SST and sunspots. Figure 3 at least shows a positive correlation with no lag, one which is almost statistically significant if you ignore autocorrelation.
But remember, in the cross-correlation of the complete dataset shown back in Figure 2, the no-lag correlation is … well … zero. The apparent correlation shown in the half-century dataset disappears entirely when we look at the full 140-year dataset.
This highlights a huge recurring problem with analyzing natural datasets and looking for regular cycles. Regular cycles which are apparently real appear, last for a half century or even a century, and then disappear for a century …
Now, in Shaviv2008, the author suggests a way around this conundrum, viz:
Another way of visualizing the results, is to fold the data over the 11-year solar cycle and average. This reduces the relative contribution of sources uncorrelated with the solar activity as they will tend to average out (whether they are real or noise).
In support of this claim, he shows the following figure:
Figure 4. This shows Figure 5 from the Shaviv2008 paper. Of interest to this post is the top panel, showing the ostensible variation in the averaged cycles.
Now, I’ve used this technique myself. However, if I were to do it, I wouldn’t do it the way he has. He has aligned the solar minimum at time t=0, and then averaged the data for the 11 years after that. If I were doing it, I think I’d align them at the peak, and then take the averages for say six years on either side of the peak.
But in any case, rather than do it my way, I figured I’d see if I could emulate his results. Unfortunately, I ran into some issues right away when I started to do the actual calculations. Here’s the first issue:
Figure 5. The data used in Shaviv2008 to show the putative sunspot-SST relationship.
I’m sure you can see the problem. Because the dataset is so short (n = 49 years), there are only four solar minima—1964, 1976, 1986, and 1996. And since the truncated data ends in 2003, that means that we only have three complete solar cycles during the period.
This leads directly to a second problem, which is the size of the uncertainty of the results of the “folded” data. With only three full cycles to analyze, the uncertainty gets quite large. Here are the three folded datasets, along with the mean and the 95% confidence interval on the mean.
Figure 6. Sea surface temperatures from three full solar cycles, “folded” over the 11-year solar cycle as described in Shaviv2008
Now, when I’m looking for a repetitive cycle, I look at the 95% confidence interval of the mean. If the 95%CI includes the zero line, it means the variation is not significant. The problem in Figure 6, of course, is the fact that there are only three cycles in the dataset. As a result, the 95%CI goes “from the floor to the ceiling”, as the saying goes, and the results are not significant in the slightest.
So why does the Shaviv2008 result shown in Figure 4 look so convincing? Well … it’s because he’s only showing one standard error as the uncertainty in his results, when what is relevant is the 95%CI. If he showed the 95%CI, it would be obvious that the results are not significant.
However, none of that matters. Why not? Well, because the claimed effect disappears when we use the full SST and sunspot datasets. Their common period goes from 1870 through 2013, so there are many more cycles to average. Figure 7 shows the same type of “folded” analysis, except this time for the full period 1870-2013:
Figure 7. Sea surface temperatures from all solar cycles from 1870-2013, “folded” over the 11-year solar cycle as described in Shaviv2008
Here, we see the same thing that was revealed by the cross-correlation. The apparent cycle that seemed to be present in the most recent half-century of the data, the apparent cycle that is shown in Shaviv2008, that cycle disappears entirely when we look at the full dataset. And despite having a much narrower 95%CI because we have more data, once again there is no statistically significant departure from zero. At no time do we see anything unexplainable or unusual at all
And so once again, I find that the claims of a connection between the sun and climate evaporate when they are examined closely.
Let me be clear about what I am saying and not saying here. I am NOT saying that the sun doesn’t affect the climate.
What I am saying is that I still haven’t found any convincing sign of the ~11-year sunspot cycle in any climate dataset, nor has anyone pointed out such a dataset. And without that, it’s very hard to believe that even smaller secular variations in solar strength can have a significant effect on the climate.
So, for what I hope will be the final time, let me put out the challenge once again. Where is the climate dataset that shows the ~11-year sunspot/magnetism/cosmic rays/solar wind cycle? Shaviv echoes many others when he claims that there is some unknown amplification mechanism that makes the effects “about 5 to 7 times larger than just those associated with the TSI variations” … however, I’m not seeing it. So where can we find this mystery ~11-year cycle?
Please use whatever kind of analysis you prefer to demonstrate the putative 11-year cycle—”folded” analysis as above, cross-correlation, wavelet analysis, whatever.
Regards,
w.
My Usual Request: If you disagree with someone, myself included, please QUOTE THE EXACT WORDS YOU DISAGREE WITH. This prevents many flavors of misunderstanding, and lets us all see just what it is that you think is incorrect.
Subject: This post is about the quest for the 11-year solar cycle. It is not about your pet theory about 19.8 year Jupiter/Saturn synoptic cycles. If you wish to write about them, this is not the place. Take it to Tallbloke’s Talkshop, they enjoy discussing those kinds of cycles. Here, I’m looking for the 11-year sunspot cycles in weather data, so let me ask you kindly to restrict your comments to subjects involving those cycles.
Data and Code: I’ve put the sunspot and HadISST annual data online, along with the R computer code, in a single zipped folder called “Shaviv Folder.zip“
Chris Beal (@NJSnowFan) says:
June 9, 2014 at 6:57 am
First, on my planet, the only dumb questions are the ones I don’t ask, ’cause then I won’t find out the answer …
Regarding your question, I’m not sure it can be answered because we first need to define “hits the earth”.
In terms of CMEs, the index usually used is the solar wind. You’d think that if you multiply the energy in the solar wind by the cross-sectional area of the earth, that’s how much hits the earth. However, much of this energy is captured in the upper atmosphere … so do you include that as “hitting the earth”?
In addition, the sun also puts out energy in the form of a complex time-varying magnetic field. THis interacts with the magnetic field of the earth, and since the earth is rotating, there is a net transfer of energy … but I don’t know if you could call that “energy hitting the earth”. Not sure how you’d measure it, either … you need someone like Leif Svalgaard for that question.
w.
CME’s are monitored via the Neutron Monitor Database. Plus there are lots of interesting articles about the energy from CME’s that hit the Earth.
http://www.nmdb.eu/?q=node/148
The ocean is cold because it’s full of big ice cubes. –AGF
Willis: “…. given your instant knee-jerk disagreement with almost anything I say.”
No, Willis that’s in your head. Much of what you produce is fine work. Sometimes very insightful. That does not mean I’m not going to say when I think something is wrong. As I’ve explained before my aim is improve what you have to move it forwards, not to tear it to bits to dismiss it as you seem to enjoy doing with the work of others.
Maybe you’re projecting your motivations onto me. In either case you’re mistaken about what drives me. Now let’s look again at what you have to say about the data.
Willis: “Nope. It’s a simple fact. If you don’t understand why you would detrend a pair of ~150 year datasets when looking for an 11 year cycle, I’m quite sure I will have no success explaining it to you, particularly given your instant knee-jerk disagreement with almost anything I say. Ask your favorite statistician, Greg, because truly, I can’t help you.
Since your results are meaningless when looking for 11-year cycles (due to the fact that you did not detrend your data), there is no way to calculate their statistical significance.”
Ah, you’re a classic Willis. This is what your pretence at objective science has come to? Any twist and devious argument rather that accept what the data shows. So you can soldier on : “can’t find solar here, can’t find there….. well try as I might … I’m waiting for someone to show… blah blah”.
So there’s a clear correlation between hadISST and SSN but you refuse to calculate whether your ‘program’ says it significant because it’s not tuned to look only for an “11y” signal at the exclusion of all else, even though the first peak is at about 11y, the second about 22y and there is an overall correlation showing a peak at 11y lag.
Well let me help you out Wiilis. Your post showed 0.2 ( figure 2) when you wanted to take out Shaviv’s paper. From figure 1 we see you used the full data as I did except that it clearly was not monthly resolution, it’s annual. That means I have about 12x as many degrees of freedom as you had.
To cut a long story short that will notably reduce significance threshold but to avoid further argument lets just say it will be 0.25
So, after months of tireless work we have finally found something.
====
kadaka (KD Knoebel) says:
June 9, 2014 at 3:12 am
Many thanks, KD. I’ll make that correction.
Mmm … I’ll have to look into that. I’ve assumed (without actually checking it, always dangerous) that even if you only took observations one day in three or four, that the annual sunspot average would not be affected much. Hang on … … … OK, here’s what I did. I took the daily sunspot data and averaged it annually.
Then I took the same daily data, and knocked out 90% of it randomly, leaving only 10% of the observations, on average only one every ten days. I then annually averaged that decimated data, and plotted it. I did that 100 times. Here are the results:
As you can see, even if we knock out 90% of all the daily observations, it doesn’t affect the annual averages all that much.
Much appreciated,
w.
So how might this be explained?
You will recall one of your excellent posts about how models were just smoke and mirrors wrapped around a linear. You produced a graph that showed what happened in response to a constant increase dRad: it created a constant ramp in temperature, but only after a certain delay related to the time-constant tau of the system.
The system integrates the change and if the change is constant, after a few time-constants the system settles to a fixed response. Usual e-folding rules: after five tau it’s within 1% of its final value. After 3 tau 95%.
Back shortly.
Following on. So in general there is a lagged response of a different form to the changes in the input. So it’s not a simple fixed time offset, but its of the same order as one or more tau. One way to calculate it is convolution with suitable exponential decay kernel, another a simple one-step recursive calculation.
see:
http://climategrog.wordpress.com/?attachment_id=884
http://rankexploits.com/musings/2013/estimating-the-underlying-trend-in-recent-warming/
PaulK ( generally knowledgeable on O.D.E. stuff ) :
http://rankexploits.com/musings/2013/estimating-the-underlying-trend-in-recent-warming/#comment-116405
“Tau typically has a value of between 3 and 4.5 years to emulate the forced response of AOGCMs over the instrumental period, but this needs some severe health warnings. There are many instances where it is not appropriate to assume a constant heat capacity model.”
Now due to various negative feedbacks in the system and the depth that the effect penetrates into the ocean there will be different time constants in a more realistic model. Daily and even annual excursions will have large amplitude and affect just the mixed layer.
Decadal cycles will, by diffusion, start to penetrate below the thermocline, invoking a much greater mass of water and longer time constants.
This will integrate changes in the magnitude of the solar cycle severely attenuating the circa 11y variations but retaining a long term rise or fall in solar activity. Clearly any naive attempts at regression against monthly or annual SSN will totally fail to match this response curve. However, it may manifest in a lag correlation plot.
What it would look like is something like this:
http://climategrog.wordpress.com/?attachment_id=959
This does not mean this correlation IS solar, such a bump could be some other signal like an internal oceanic oscillation but it shows long range correlation with SSN.
From Willis Eschenbach on June 9, 2014 at 12:29 pm:
Actually, my problem with it was the pre-1749 is whole numbers, the rest from SIDC is one decimal place. That had messed up my spreadsheet comparisons.
If you’re worried about significant digits, then they are separate datasets, do not commingle without appropriate notations and caveats. I had to use different rounding. Different equations for different results in the same column is often frowned upon.
BTW, you and Leif are both guilty of claiming too much significance, as you take numbers to the tenths, multiply by a constant, then give results to the hundredths. The non-multiplied post-1748 stays at tenths.
From Willis Eschenbach on June 9, 2014 at 11:14 am:
If that is true then Jacques Cousteau was a lying Frenchman as he never showed me any deep sea ice and talked of liquid water straight to the bottom.
http://www.onr.navy.mil/focus/ocean/water/temp3.htm
(Note Ocean Temperature Profile graphic)
Even the Challenger Deep in the Mariana Trench, the deepest known spot in all the oceans, is not noted for having ice at the bottom, but instead water is there. Note the pressure is reported to be 111 MPa (mega pascals).
Now water ice has 15 known solid phases. However, if you look at the log-lin pressure-temperature phase diagram at the link, you’ll see that while higher pressures will force a change to ice, 111 MPa is actually in the range where pure water can exist as liquid while below 0°C. While there are some solid phases that would be dense enough to stay at the bottom, none of them form in the deep ocean conditions, not enough pressure.
If you’re going to pull the ‘But it’s not fresh water’ line, given the lowered freezing temperature to about -10°C (by zoom and eyeball), I’d like to see supporting documentation please.
If there was ice formation, then wouldn’t there be brine rejection, moving it to a higher freeze point? With the lower salinity water freezing at the bottom, would the resultant higher salinity water go under the ice, leading to ice over water under water? Or would the excess salinity precipitate out and accumulate on the bottom? Now that would be silly, as the salts would wind up sequestered along the bottom, leading to decreasing ocean salinity over time if land runoff and minerals dissolved from higher-up marine deposits couldn’t replace what was sequestered.
Little progress can be made in examining the empirical relationship between
SST and SSN time-series–or most geophysical data–by clinging to
conceptualizations based on little more than linear regression. The latter
assumes INDEPENDENT trials of a dependent variable, whose outcomes are
plotted as a function of the perfectly known independent variable. It is
only there that any correlation between trials needs to be accounted for,
as mistakenly suggested in the caption to Figure 3, to prevent
OVERESTIMATION of the statistical reliability of derived trend and
intercept.
With noisy time-series that may have only concommitant variability, instead
of direct linear dependence, there will always be sample acfs for both
series, each reflecting their S/N ratios. Those noise levels have a
profound degrading effect upon the sample ccf, invariably UNDERESTIMATING
the actual level of coherence between signal components in nature. These
are fundamental matters of ANALYTIC insight, for which no tedious Monte
Carlo simulations are required for those equipped to do serious geophysical
signal analysis and physical interpretation.
Greg, I said that you had made a mistake in your calculations by not detrending the data before you did the cross-correlation. In response, you said, inter alia:
and
and
and
and
Thanks, Greg. It’s clear that you think that your results, with a “correlation of 0.25 at 10y and 22y”, establish the case for the existence of a an ~11-year solar cycle in the HadISST sea surface temperature data. And you invite me to do my own cross-correlation.
Well, I’m not the man to shrink from such a challenge, so I’ve done my own cross-correlation. And to my surprise, it’s almost identical to yours. I used annual data because that’s what I’ve been using all along in this thread, but other than that they are quite similar. First, here are your results …
And here are my results:
As I said, I was kinda surprised by that result. But I’m a scientist, I follow where the data leads, and just like yours, my results show peaks at 11 and 22 years, with a correlation of about 0.25. I gotta admit, you were right about that.
So, in your words, does this mean that “after months of tireless work we have finally found something”?
Unfortunately … no. To explain why the results are meaningless, let me show you the two datasets that I actually used for the cross-correlation shown immediately above:
NOW do you understand why I said that your procedure is meaningless, and that it does NOT establish your case for either an 11-year or a longer term solar effect? I get results indistinguishable from yours when the “temperature data” is a few straight lines and there is absolutely no 11-year cycle in the data anywhere.
Which is what I told you above, over and over. Test your procedure by running it against red noise, I said. Anything with a prominent peak or valley will give that kind of result with peaks at 11 and 22 years, I told you, so test your method against something like that.
But noooo, Greg is a brilliant man, he don’t need no steenkin’ tests, he just wants to tell me that I’m wrong.
w.
Now that’s funny right there!
kadaka (KD Knoebel) says:
June 9, 2014 at 2:54 pm
KD, a quotation of whatever you accuse a man of being “guilty” of is not optional. If you want to call a man “guilty”, then you need to have the stones to quote what the hell you are accusing him of. What you have done is just slimy mudslinging.
An apology would be appropriate. Calling a man “guilty” of anything, no matter how small, without any attempt to provide even a scrap of evidence is … is … well, I won’t say it, this is a family blog, but it is not the act of an honorable man.
w.
kadaka (KD Knoebel) says:
June 9, 2014 at 5:01 pm
Sorry, that’s my lack of clarity. What I meant was that at the salinity of the ocean, sea water keeps contracting all the way down to freezing temperature. I didn’t mean that there is a layer of ice on the bottom of the ocean, because there isn’t one.
However, it appears you don’t believe my claim. If so, please take a look at this document. The crossover point for density, salinity, and freezing point is shown in Fig. 5.1. It occurs at a salinity of 0.25. Since the ocean is saltier than that, sea water continues to increase in density until it freezes.
All the best,
w.
1sky1 says:
June 9, 2014 at 5:10 pm
Gotta love 1sky1, my favorite random anonymous internet popup. The thing about him is that he has a perfect record. Every single comment of his is 100% bafflegab, not one citation, never one quotation, not one fact … just a lot of claims about how brilliant he is and how wrong everyone is who disagrees with him.
And this comment of his continues his flawless performance. This is good, it means the world is unfolding as usual, no surprises.
w.
Good work Willis. So what you’ve demonstrated is the long term variation in ISST shows correlation with SSN that peaks somewhere between 11 and 22y lag (ignoring the short term bumps) that is significant according to the test you adopted in figure 2. This it what you said I “forgot” to remove when looking for correlation.
Then I did remove it and reported peaks at +0.2 and -0.2 correlation remain, contrary to what you showed in your figure 2. That too is presumably “meaningless” now that I’ve done what you suggested I should have done.
“Which is what I told you above, over and over. Test your procedure by running it against red noise, I said. ”
Well unless I’m mistaken ( because you have not stated where your 0.2 comes from or what it is supposed to represent ) that is a 95% confidence test against a random noise model. But I agree, I’m not even sure that test which is usually applied to auto-correlation is valid here. If you think some other test against red noise is needed then you should have done yourself instead of drawing a 0.2 line. Either it’s valid or it isn’t.
So what you are now saying is that the central test you used in figiures 2 and 3 to attack the work of Shaviv is “meaningless”. Or it only matters when it suits the point you wish to make.
Anyway, it’s a beautiful summer’s day which I don’t wish to spend on yet another Pythonesque arguement with Willis.
Have a good one.
BTW, if we have to detrend to remove the long term correlation and any peaks that may appear at 11 or 22 are spurious artefacts that would equally be produced by random noise, why did you choose to do cross-correlation at all to test the validity of Shaviv?
If there’s no peaks it proves he’s wrong , if there are peak they’re spurious.
The poor guy really didn’t have a chance did he?
Greg. says:
June 10, 2014 at 12:30 am
Please provide a quotation to show that I claimed that “any peaks that may appear at 11 or 22 are spurious artefacts that would equally be produced by random noise”. I have never made that claim that I know of. That’s why we have tests of significance, to separate signals from random noise.
Let me ask again, since you seem resistant to the idea. Please quote the stuff you disagree with, so we can understand what it is you are on about. You’ve just accused me of something I never said. How can I defend something I never said, something that you made up?
QUOTE MY WORDS!!! Your fantasies about what I’ve said are getting very old.
w.
Ultimately this is pointless.
Any attempt to claim that because an 11 or 22 year cycle cannot be determined in SST records is a dead end as to disproving solar influence on climate or saving WUWT from the embarrassment of attacking those who were right at Talkshop.
You did it. You can’t undo it. You just have to wear it.
If you incorrectly treat the oceans as a “near blackbody” instead of a UV/SW selective surface, you cannot possibly understand solar influence on climate.
The effect that needs to be detected is vanishingly small. 0.8C in 150 years. We just don’t have the data. No amount of bleating about 11 year cycles can erase WUWT’s premature “real climate” style dismissal of our variable star’s influence on climate.
To determine solar influence on climate two things are needed –
1. Long accurate records of ocean temps below the thermocline.
2. Long accurate records of incident UV-A at the sea surface.
We have neither.
Any attempt to disprove solar influence on climate without such records would be clearly disingenuous.
willis,
my involvement in this thread is not about individual efforts to find 11 year cycles or 22 year or whatever, i dont really have any position on the subject of solar min/max. i do however believe that SSNs and the sun in general influence the climate, just not through the methods you have so far covered.
i quoted the problem i have with this thread, and it wasnt in the post itself, but the comments. but if you must have something from the post it is this-
“What I am saying is that I still haven’t found any convincing sign of the ~11-year sunspot cycle in any climate dataset, nor has anyone pointed out such a dataset. And without that, it’s very hard to believe that even smaller secular variations in solar strength can have a significant effect on the climate.”
you leave this as an implication that the subject is resolved, and of course mosher makes it a statement. but i find this statement just amazing in terms of how complex the system actually is.
the truth is that it is VERY likely that the variations hide in noise. i dont understand why you believe they need to be visible in the noise. this just does not make sense unless you have perfect records for everything (including the various cycles of the sun, the oceans, the atmosphere, the ‘currently unknown influences’ etc) over thousands of years in which case you could tease out longer modes and calculate the higher frequencies from that. we dont have perfect records, we dont understand even a fraction of the biospheres influences or how their cycles may overlap etc, so this just will not happen.
my point is that when you say-
“Let me be clear about what I am saying and not saying here. I am NOT saying that the sun doesn’t affect the climate.”
you should mean it, because it may be that it is impossible to ever see this in data as we know it/have collected it in the last few decades. it may be that the only way to work out the influence the sun has on climate is to work on the mechanism. the how, not the if. the if as far as i am concerned is broadly answered by history and multiple proxies- yes the sun does affect the climate.
willis, my tag is not anonymous. it may be to you, but a lot of people i know personally know it and i have only ever posted/replied etc under that one tag, so keep your abuse to a minimum please. my response to this thread in general that you replied to was me being sick and tired of snark comments from mosher which you seem to applaud and seeing a red flag. perhaps you are seeing too many red flags too, but if you truly are interested in finding the WAY the sun CAN influence the climate, then i apologise for implying that you may not be.
From Willis Eschenbach on June 9, 2014 at 10:51 pm:
Excuse me! I thought it was clear because that was the small thing we were talking about.
To quote myself: “BTW, you and Leif are both guilty of claiming too much significance, as you take numbers to the tenths, multiply by a constant, then give results to the hundredths.”
We were talking about the SIDC SSN data. The only spot where Leif and you were multiplying by a constant was the +20% correction range, multiplying by 1.2, a constant.
So in your data, “Shaviv Folder.zip” -> “Shaviv Folder” -> “SIDC Sunspots.csv”, there is for example:
1749,97.081750,100.08
1751,57.24
1752,57.36
1753,36.84
where SIDC in “yearssn.dat” has:
1749.5 80.91750.5 83.4
1751.5 47.7
1752.5 47.8
1753.5 30.7
There it is. SIDC reported tenths, you gave results to the hundredths. Leif in “SSN-HMFB-TSI.xls” did the same.
Quoting myself again: “The non-multiplied post-1748 stays at tenths.”
And indeed, near the end of “SIDC Sunspots.csv”, you can see the transition:
1944,11.641945,39.84
1946,111.12
1947,151.6
1948,136.3
1949,134.7
Leif’s Excel file shows the same transition but starting in 1945. Thus as I succinctly said, the non-multiplied post-1748 stays at tenths.
Thus both you and Leif have a significance issue, where you are both guilty of claiming too much significance, by taking numbers to the tenths, multiplying by a constant (1.2), and reporting hunderdths.
However, as not quite a correction but more like an addition, I see I may have limited the scope of the charge too far. Pre-1749 from SIDC had a zero tenths, which a human should notice means very likely those are whole numbers as that is a special range and they all have zero tenths, thus it’s a record format issue.
Following the pattern you and Leif share would yield a zero hunderdths for the results of that range, which would be supressed on output as with other trailing zeroes. Thus the significance issue likely did happen pre-1749 as well, but for two orders of magnitude, but the evidence has conveniently disappeared.
konrad, you also need top of the atmosphere solar metrics. If all you have are surface solar indices, no matter how long or accurate, you will not be able to rule out Earth’s own atmospheric source of variation in solar input measured at the surface over land or sea.
From Willis Eschenbach on June 9, 2014 at 11:02 pm:
Don’t twist my words after the fact. You said:
I showed at the ocean depths the water is not continuing to contract until it freezes. I didn’t believe your claim, I showed why. End, period, full stop. Now you clarified your statement, you changed the conditions, and implicitly say my rejection of your original claim means I appear to reject the revision.
You don’t get to write up a new contract with new terms and say my rejecting of the old contract means I apparently reject the new one. I’ll evaluate it on its own merits.
Ack! 4.5MB at 11 minutes estimated dial-up time for a single graphic. That’s a slick move worthy of Crypt-Mosh or Stokes, ‘Just look inside this giant tome for the proof you could examine in just seconds’.
And what is it with you crazy people who keep sticking spaces into folder and file names. I’ll need an escape sequence for the URL in a browser and a “backslash-space” for the command line. Sanity, people, sanity!
Oh dear God, not another M$ PowerfullyPointless presentation crammed into a pdf. Which my simple non-Adobe reader doesn’t like, that I have to look at with the browser. Just post the dang ppt file, LibreOffice can handle it!
(Psst, you’re a decimal point off, see Fig 5.1 caption.)
But just one slide back it says:
It’s a variation of the isothermal column of air. Warmer and less dense above, colder and more dense below, energy evenly distributed per volume of sea water.
So increasing density at depth won’t do it, not in the “mild” Earth deep sea pressure ranges. Energy needs to be removed from the entire column, until every horizontal slice of the column is at the freezing point relative to its depth, then the column freezes.
But in the deep water regions you can’t freeze the whole column from surface to the bottom, likely as the ocean is stratified. So you’d likely have a break at the main thermocline to deep water layer transition.
You had said:
and
Perhaps a sample of seawater subjected to mega-pressures in the lab might yield ice, far higher than in the ocean depths.
But at those pressures, I have doubts the seawater would remain as it was. Checking up on precipitation fouling, I see that, depending on the substance, increasing temperature may increase or decrease solubility, likewise for decreasing temperatures.
Thus I strongly suspect pressure, within the giant range variations between merely deep sea to center of the Earth levels, could have a considerable effect on solubility on some of the solutes, leading to precipitation and a decrease in salinity, leading to more “normal” freezing with brine rejection.
Willis,
I believe you and the author are deceived by the sunspots because it is not exactly the sunspots that would have the effect on earth. Sunspots are the precursor to CME’s. I strongly believe that if you looked at CME’s during the past 3 cycles which were directed toward earth… you will find your answer.
Cheers
kadaka (KD Knoebel) says:
June 10, 2014 at 4:08 am
From Willis Eschenbach on June 9, 2014 at 10:51 pm:
Obviously not …
You said that I “claim too much significance”. I don’t see anyplace in there that I “claim” anything. My practice is to never truncate the results of the individual calculations leading up to a result. So for example, if I’m multiplying, then dividing, then taking the square root, I don’t truncate to significant digits after each operation. Do you?
Instead, I only truncate to significant digits the numbers that I am reporting as part of my results. Because people would be using it for further mathematical operations, the file you refer to is data rather than results, so it was not truncated to significant digits,.
Now, that may not be your practice, and that’s fine.
But don’t say I’m “guilty” of something just because it doesn’t fit your fancy, particularly for such trivial nonsense. What difference does an extra digit, not in the results but in the data, make in any case? Saying I’m “guilty” is an emotionally loaded word. It implies that I know better and I did it anyways, and it is an ugly and unpleasant accusation.
Now, if you had just posted up the actual data you were concerned about and asked about the number of digits, we could have had a conversation about significance, and whether and when to truncate. Instead, you skipped the questions and went straight to the accusation …
Sadly,
w.