Guest Post by Willis Eschenbach
So once again, I have donned my Don Quixote 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, such as cosmic rays, the solar wind, changes in heliomagnetism, changes in extreme ultraviolet (EUV), or changes in total solar irradiation (TSI), all vary in phase with the sunspots. As a result, if there is no sunspot cycle visible in the terrestrial surface weather datasets, then we can assume that none of those phenomena are affecting the dataset.
To date I’ve looked for an 11-year cycle in local sea level datasets, river flow datasets, beryllium datasets, global surface temperatures, lower tropospheric temperatures, global sea levels, Nile River levels, and a bunch more surface datasets. I have looked at literally dozens of measures of the weather, and I’ve found … well … nothing.
Now, does this mean that the sunspot cycles have no effect on the weather? Absolutely not, you can’t prove a negative in any case. It just means, I haven’t found the evidence to support the claims that some weather phenomena somewhere follows the sunspots. It may still be there, and so I continue looking.
I freely admit that I entered upon this quest with a preconception of what I’d find … but not the preconception I’m often accused of having. What many people may not realize is that I thought this quest would be easy. I believed that the evidence of a sunspot-climate connection wouldn’t be hard to find. Back in 2008 I wrote:
There are strong suggestions that cloud cover is influenced by the 22-year solar Hale magnetic cycle.
And given the number of places I’d read the claim that sunspots influence the climate, I figured it would be no problem to find surface weather datasets that showed some correlation with the 11 or the 22-year sunspot cycle. But to my great surprise, I’ve not been able to come up with a single surface weather dataset of any kind which shows such effects.
In this quest, I have relied mostly on a couple of methods to determine if the sunspot cycles are present. One is the cross-correlation analysis. This shows how well correlated two time series are at a variety of time lags. It helps in distinguishing real from random resultsāfor example, if the correlations are as great for sunspots lagging the climate as they are for sunspots leading the climate, you’ve got problems.
Note that there is an inherent problem when looking for correlations with a signal like sunspots, which has a strong cyclical nature and high autocorrelation. This is that you will get what look like real correlations when you correlate sunspots with a wide range of random data. In addition, again because of the strong cyclical nature of sunspots, whatever correlations you find you’ll also find around eleven years later. As a result, you have to include an adjustment for autocorrelation when you calculate the statistical significance of your results, or you need to do a Monte Carlo analysis of the question.
The other method that I rely on is the periodogram. This is an analysis of the underlying cycles in the data itself. For example, here are the sunspots since 1749:
Figure 1. Monthly sunspots since 1749. Pre-1947 values have been increased by 20% to bring them into line with the modern counts, as has been repeated discussed here at WUWT. It makes little difference for the analysis.
And here is the periodogram of the sunspots shown above:
Figure 2. Periodogram of monthly sunspots. A periodogram shows the strength of each of the component cycles. The units are the percentage of the range of the data (range of individual cycle divided by range of the data. Due to popular demand, the data is windowed with a Hanning window.
Note that there is a fairly broad peak with the largest value at 11.2 years, and a secondary narrow peak at 10 years. So if we find these kinds of signals in any surface weather dataset, that would be good evidence that there is a connection to either the sunspots, or to some phenomenon that varies in phase with the sunspot cycle, such as cosmic rays or TSI.
With that as prologue, let me show the latest dataset I’ve been investigating. This is US station records showing the percentage of clouds. The cloud data are from 192 locations scattered around the US. As is my custom, I started out by looking at each and every one of the 192 records in the dataset. Here is a sample of ten of them:
Figure 3. Ten of the individual cloud records in the dataset.Ā
When I looked at that, I was surprised by the trends. To be sure, the scale is different for each panel, which emphasizes the trends. But the same results are visible in the average of the dataset.
Figure 4. Average of all of the 192 cloud datasets. Unfortunately, the data ends in 1987. The data is calculated as the cumulative sum of the means of the first differences (monthly changes) of all available datasets. Note that in this case, this first-difference method shows little variation in the period 1900-1987 from a simple mean of the data (not shown).
Now this is what I call an interesting result. I would never have guessed that the cloud cover in the US was about 10% greater in the 1980s compared with the 1920s. I do find it interesting that in response to the rapid US surface temperature rise during the 1930s and 1940s, the cloud cover acted to minimize the change by increasing quite rapidly.
However, that’s just evidence in support of my hypothesis that the clouds (and other emergent phenomena) act to minimize changes in temperature. It says nothing about whether there is a solar signal there or not. To determine that, I calculated the periodogram for the average cloud percentages shown in Figure 4.
Figure 5. Periodogram, average of US station data.
As you can see, there is very little in the way of power in the range from five to twenty-five years … no sign of a solar signal.
The situation with the individual datasets is much the same. There are a variety of signals, but again nothing in the range from five to twenty-five years exceeds about 5% of the total signal. Here is a typical set of four:
Figure 6. Periodograms showing the strength of the various cycle lengths, for four individual stations.
As you can see, there is quite a bit of variation in the individual records. Some have strong annual cycles, while others have equal-sized annual and six-month cycles. And some show only a six-month cycle, with no clear annual cycle. This is interesting, because it allows us to identify those stations which have strong thermally-driven summer thunderstorms. They show strong six-month cycles.
But to return to the sunspots, none of the stations shows any significant strength in the range from five to twenty-five years, it’s all down in the weeds, and each of them is different.
So I had to conclude once again that the solar signal is simply not visible in these cloud datasets.
Once I was done looking at the solar question, I got to thinking about the fact that the ground station cloud dataset ends in 1987. I realized that the ISCCP satellite dataset started in 1983 … giving a 53-month overlap. So I decided to see what the ISCCP dataset had to say about the US clouds. Since the ISCCP dataset is gridded, it is a matter of extracting just the land data in the area covered by the US stations. I started by looking at the two datasets during the period of the overlap:
Figure 7. Overlap of the two cloud coverage datasets, one being the average of 184 ground station records (green), and one from the ISCCP satellite data (blue). In the raw form, the two are offset by only about one percent. As a result, I have rebaselined the ground station data upwards by about one percent to match the ISCCP data during the period of the overlap.
Now, I have to say that I was very impressed by the agreement between the two datasets. I had expected to find much poorer correlation. But in fact, by and large the two datasets are in surprisingly good agreement.
As a result, I think that we are justified in splicing the two datasets together, so that we can get a long-term record of the US clouds. Here is that record.
Figure 8. Spliced data, with the early data from the ground stations and the late data from the ISCCP satellite. I used the average of the two datasets during the period of the overlap.
Now, this is a fascinating graph containing lots of unanswered question. It puts the ISCCP data (black and red) into a most interesting long-term context. When looking at just the ISPPC data it seems as if the cloud coverage was falling. But in the longer context, it can be seen that this kind of variation has been going on since about 1960.
I draw no great conclusions from this, except that overall, as the US warmed post-1930, the US cloud coverageĀ rose as well. Go figure …
One reason I’m reluctant to draw many conclusions about this graph of the clouds is a curious one. In science, it is very difficult to demonstrate causality. However, a very smart man named Granger realized that we can demonstrate a weaker form of causality, called “Granger causality”.
The essence of the idea of Granger causality is that if having historical information about phenomenon A allows us to significantly improve our predictions of phenomenon B, then A is said to “Granger-cause” B. Makes sense to me. If knowing historical cloud cover allows us to improve our predictions of temperature, then cloud cover would be said to “Granger-cause” temperature.
However, there’s an oddity of Granger causation, which is that there are not just three, but four possible states, viz:
1) Neither variable A nor variable B Granger-causes the other, or
2) Variable A Granger-causes variable B, or
3) Variable B Granger-causes variable A, or finally
4) Variable A Granger-causes variable B AND variable B Granger-causes variable A.
So … care to guess the Granger relationship between temperature and clouds? Yep, you’re right, it’s choice 4) … each one Granger-causes the other one. Clouds affect the temperature, and temperature affects the clouds. Knowing the history of either one helps us to predict the next step of the other one. It’s just another demonstration of Murphy’s Corollary, which is that “Nature always sides with the hidden flaw.” Which definitely makes it hard to draw lots of conclusions.
In any case, that’s all the fun I’m legally allowed to have in one twenty-four-hour period. So to keep the future full of interest, let me put out a requestĀ for people to send me a link to a dataset. What dataset? Why, of course, the surface weather dataset that you think most clearly shows the sunspot-related solar influence.
A few notes about the kinds of datasets I’m looking for.
First, I’m asking what you think is the veryĀ best dataset, the dataset that actually contains the clearestĀ evidence for the solar signal. Please don’t send me links to ten datasets. I don’t have graduate students or assistants and I do have a day job pounding nails. So please, choose your best evidence to make your best case.
Note that I’m not asking for studies, I’m asking for observational data. I’m not interested in any of the hundreds of studies out there that claim to find a solar signal in someĀ surface climate dataset, like my favorite study. That was the oneĀ that found the solar signal ā¦ in the ring widths of one single treeĀ in Chile. Seriously, one single tree, the study is out there, and somebody sent it to me claiming it was evidence.
Here’s the issue. Every study that I’ve looked at has had serious and often horrendous problems, and I’m sick of digging through the trash looking for a diamond. In any case, no matter what the study may claim, I’m gonna have to do the analysis myself. And to do that, I don’t need the studyāall I need is the link to the dataset.
Next, please provide a link to the exact dataset itself. I don’t want to guess which dataset you are referring to, or choose from among a dozen datasets on some web page, or have to google some obscure name.
Next, please no “reanalysis data” from NCAR or NCEP or anywhere. Let me explain the problem with using what is laughably called “reanalysis data” from NCAR or NCEP in any analysis of solar effects. The “reanalysis data” is not data at all. Instead, it is the output of a computer model. Now, I’m not an opponent of computer models, I’ve both written and also used lots of computer models. The problem is that the current generation of climate models are quite linear … but nature is antilinear.
Now, for some kinds of analyses this is not a big problem, but this flaw makes model output useless for some specific applications. In particular, since the model outputs are some kind of lagged linear transformation of the inputs, in general whatever you put in the front end of a climate model will be visible in the output … and downward solar radiation flux is most assuredly an input to the reanalysis models. Since the models are linear, this means that there is a very good chance that we will find a solar signal in the output of the reanalysis models, that we’ll see a sunspot-related wiggle in the so-called “reanalysis data” … but unfortunately finding that signal in the computer output means absolutely nothing about the extremely non-linear real world. So please, if it says NCEP or NCAR or “reanalysis” anywhere, don’t bother sending it.
Finally, as a radio amateur I’m well aware of the solar effect on the ionosphere. Up there where there is one molecule per cubic ohmmeter or somesuch miniscule number, and it’s not atmosphere as we know it but a plasma of ionized gases, yes, I agree that the sun has effects. However, I can find no evidence that those effects make it down to affect the weather where we live, at the surface. Might do so, but I haven’t seen it yet. This is why I’ve asked for surface datasets, because I’ve never found the claimed 11-year cycles down here at the bottom of the gravity well.
So with those exceptions, you’re free to send any of a host of surface datasets that you think have a sunspot signalāsea level, rainfall, river flow, lake levels, temperature, atmospheric pressure, river flow and many more, the choice is up to you.
Now, there is a commenter who objects strongly whenever I ask people to send me their best evidence. He claims I’m asking him to do the research that I should be doing. But all I’m asking for is a datasetāI am the person who has to do the research. The problem is, there are hundreds and hundreds of datasets out there, and my time is short. I don’t have time to analyze them all. So I’m asking for your assistance in winnowing the wheat from the chaff. If you don’t like my asking for assistance in this process, please, just ignore the post. Raising the same objectionsĀ once again won’t burnish your reputation.
In addition, I see this as a chance for you guys to definitively show everyone that there actually is a sunspot-related signal that makes it down here to the surface where we live. So I invite youābring out your best dataset. I’ve just looked at the US cloud record and found nothing ā¦ your turn.
Regarding the clouds, I walked this evening with the gorgeous ex-fiancee along the western edge of Bodega Bay. The cold front that brought us welcome rain earlier in the day was just past, and so we got to look east across the Bay at the back side of the cold front. Mostly it was kind of a wiggly stratus, an unusual formation. Out to the west, there was clear sky, and the late evening sun from behind us lit up the clouds above and the hills below. Looking at that blue, clean-washed piece of sky, I was reminded of the scientifically accurate words of the poet Ā about clouds:
Ā I am the daughter of Earth and Water,
And the nursling of the Sky;
I pass through the pores of the ocean and shores;
I change, but I cannot die.
For after the rain when with never a stain
The pavilion of Heaven is bare,
And the winds and sunbeams with their convex gleams
Build up the blue dome of air,
I silently laugh at my own cenotaph,
And out of the caverns of rain,
Like a child from the womb, like a ghost from the tomb,
I arise and unbuild it again.
Best regards to everyone, keep on unbuilding,
w.
MY REQUEST: If you disagree with someone (highly unlikely, I know, but work with me here), please QUOTE THE EXACT WORDS YOU DISAGREE WITH. That will let everyone know just what you think is incorrect.
MY POSITION: Note that I am NOT saying that the sun doesn’t affect the earth. It does affect the earthĀ in a host of ways, and we can see that every dawn.
What I AM saying is that I have never found a significant sunspot-related ~11 year signal in any surface weather-related dataset. Doesn’t mean it’s not there somewhere, just means I haven’t found it yet. If you think it’s there … this is your chance to identify the dataset which contains the signal.
DATA: the ISCCP D2 cloud coverage data is here, the US ground station cloud coverage data is here, and the sunspot data (unadjusted) is here.
CATALOG: all of the ISCCP data is indexed here. Took me a while to find it, because it’s on the NOAA website.
CODE: R computer code, along with some of the collated data, is here as a zipped folder. The code is all there. However,Ā I’d describe it as user-aggressive … and like all of my code, it’s not written to run from top to bottom. It also contains a lot of other things that don’t make it into the final report, various analyses and graphs. I believe that all of the code and functions are there to produce all of the graphics and analyses above. If not, let me know.
Why is one looking for a 11 year cycle?.
No one seriously suggests that temperature fluctuates on a 11 year basis, or that sea level rises and falls (or the rate of chabge thereof varies) on a 11 year basis.
The fact that one cannot find some correlation in some data set (which in any event would not establish causation) with an 11 year cycle sheds all but no light on whether solar may be a major driver of earth’s climate. The absence of such correlation, certainly does not establish that solar is not a significant player.
richard verney November 1, 2014 at 1:58 am
Thanks, Richard. Sadly, there are a host of people who seriously suggest exactly what you say. For someone seriously suggesting that “sea level rises and falls on an 11-year basis”, see here. And for someone saying “temperature fluctuates on a 11 year basis”, see here.
As to why I’m looking for an 11-year cycle, it is because all of the relevant phenomena, such as galactic cosmic rays or TSI, vary on an ~11-year cycle. So if one or more of those phenomena are actually affecting the weather, we’d expect to find an 11-year cycle in some weather dataset.
Finally, you say not finding an ~11-year cycle “sheds all but no light on whether solar may be a major driver of earthās climate”. I’m not sure what you mean by a “major driver”, but regardless, I’d say that the sun is a major climate driver.
However, that sheds no light on whether cosmic rays affect the weather ā¦ for that, we need to look for the 11 year cycle, despite the fact that the sun is indeed a major climate driver.
In other words, you are right that not finding the ~11-year cycle means nothing about whether the sun is a major climate driver ā¦ but it means a whole lot about whether cosmic rays are a major climate driver.
All the best to you,
w.
Energy storage systems tend to act as low pass filters to the input. ‘Nuff said.
ShrNfr November 1, 2014 at 6:06 am Edit
Thanks, ShrNfr, but without an actual example and the math to back up your claim you’re just blowing wind ā¦
w.
I agree with Richard. Willis, I think your premise is that the system is simple, so that variations in the input signal should be visible in the output. However, the system is extremely complex. Also, your approach is mathematical (like CA), instead of scientific.
For example, what if we were looking at datasets for Lake Ontario’s water level, and found no correlation to rain storms in the upper great lakes region? The system is too complex for that to show up.
Viking, you and others seem to think that I invented the idea that there are ~11-year sunspot cycles in the climate. In fact, the idea goes all the way back to the claims (since refuted) by William Herschel that there were sunspot cycles visible in wheat prices. So I’m just trying to refute that idea, but I make no assumptions about whether the system is simple or not.
If you truly want me to consider that example, please provide links to the datasets that support your claim.
w.
Willis, it’s a good exercise in the scientific method to attempt to falsify every idea, especially our own. So, good on you.
However, you may have missed my point. My point is that when systems are complex enough, input variations may not be detectable in the output. Therefore, your search for this type of correlation falsifies nothing.
I presented the great lakes system not as a data set related to the 11 year sunspot cycle, but as a system too complex to see variations in upper great lakes region rain storms (input) in the Lake Ontario water level (output).
Willis
It’s very interesting that nothing is detectable anywhere that connects the sunspot cycle to climate. Although when searching for exoplanets one of the things looked for is if they are in a habitable zone. It just suggests to me that two things are preventing establishing a link between the Sun, in general, and Climate (other than there isn’t one which isn’t likely)
1. Not enough data
2 Looking in the wrong place.
I prefer to look at periods of not less than 30 years or about three solar cycles because the single cycle variations swamp the longer term trends. and to use data from the Earthshine project because that deals with global albedo and not just regional cloudiness changes.
Thus far the data from Earthshine is very limited historically but I do note that the Earthshine trend changed around 2000 as did the global temperature trend.
I think it will be found in due course that an active sun reduces global cloudiness, allows more energy into the oceans, skews ENSO in favour of El Nino relative to La Nina and causes warming overall.
The opposite for a quiet sun.
As for the mechanism I have set that out elsewhere.
Well, good luck with your tilling at 11 year windmills. 0.8C in 150 years? No way you are going to find it like that.
As I have said many times before – āIf you donāt understand how the sun heats the oceans, you cannot understand climateā.
Given that you have shown no understanding of the multiple SW selective surface effects of liquid water, including –
1. SWIR/SW/UV absorptivity higher than IR emissivity.
2. SW/UV absorption at depth, not surface.
3. Intermittent illumination.
4. Internal convection.
– Just how do you expect to claim no discernible solar effect? Dr. S has worked it out. That’s why he is trying to stamp not just TSI flat, but UV variance as well.
Everybody is watching. Many are laughing š
(seriously, if you cannot understand how the sun heats the oceans, how can you determine the effect of cloud cover variance?)
Konrad. November 1, 2014 at 2:40 am
Thanks, Konrad, but I have to askāwas there some part of QUOTE THE EXACT WORDS YOU DISAGREE WITH that escaped you? Because I can repeat the explanation if you wish.
If you can quote me saying somewhere that SW/UV is NOT absorbed at depth, for example, we could understand what your point 2 is about.
Or take number 4, that I don’t understand the internal convection of the ocean ā¦ see here for a discussion of that exact question, which I have also described elsewhere. If there is some part of that you think is wrong, please QUOTE THE EXACT WORDS that you disagree with.
A scattergun attack like the one you’ve delivered above is meaningless, unpleasant, and detrimental to both your reputation and further discussion. No one knows what you are talking about.
Finally, I note that in all of that you have NOT provided any dataset that shows the ~11-year solar cycle.
w.
PSāas far as I know, I’ve never commented on the ratio of IR emissivity and SW absorptivity. Let me remedy that. IR emissivity of the ocean is about 0.96.
SW absorptivity is 1 – albedo. The albedo of the ocean varies from about 0.13 (rough water, low sun) to .02 (calm water, low sun). Typical mid-day conditions (relatively calm, sun within two hours of overhead) have an albedo of somewhere around 0.06. Data from my bible, Geiger’s “The Climate Near The Ground”.
So I’d say that SW absorptivity is about the same as IR emissivity, maybe a bit less. I’ll take a look at the CERES data, I should be able to figure the average albedo of the ocean ā¦ hang on ā¦ OK, CERES data puts the average ocean albedo at 0.06. Dang, go figure, my back-of-the-envelope calculation was right on.
This means that for the ocean, IR emissivity is 0.96, and SW absorptivity is 0.94.
As a result, it appears that your claim that SWIR/SW/UV absorptivity is higher than IR emissivity is not true. And more to the point ā¦ so what? I fail to see how my understanding of that question is supposed to disqualify me from looking for an ~11-year cycle.
Again, please, if you disagree with my analysis of absorptivity and emissivity, please QUOTE MY EXACT WORDS. It saves heaps of time, and gives substance to your arguments.
Willis Eschenbach
November 1, 2014 at 3:33 am
////////////////////////////////////////////////
āThanks, Konrad, but I have to askāwas there some part of QUOTE THE EXACT WORDS YOU DISAGREE WITH that escaped you? Because I can repeat the explanation if you wish.ā
I can cut and paste the exact words if you desire. Remember the āUsoskin Et Al. Discover A New Class of Sunspotsā thread? The Internet does, forever.
It matters not if you understand that UV/SW is absorbed at depth or whether you understand the diurnal overturning cycle. You have shown you don’t understand the effect of this on average ocean surface temps. You are still stuck in oceans=near blackbody nonsense land. The oceans are convecting, SW translucent and intermittently illuminated. Here are the 5 rules –
For SW translucent / IR opaque (material A) compared to SW opaque / IR opaque (material B) with both materials having equal IR emissivity and total watts for both constant or intermittent SW illumination being equal, the results of empirical experiment are clear –
1. If materials are solid, constant SW illumination will result in close surface temps for A & B with average temp of A higher than B
2. If materials are solid, intermittent SW illumination will result in surface temps for A higher than B, with average temp of A also higher than B.
3. If materials are liquid and convect, constant SW illumination will result in surface temps for A higher than B, with average temp of A higher than B.
4. If materials are liquid and convect, intermittent SW illumination will result in higher temperature differential (both surface and average) between A & B than condition 3.
5. If materials are liquid and convect, intermittently SW illuminated and deeper than condition 4, temperature differential between A & B will be greater again than condition 4.
Those 5 rules are verified by empirical experiment. Resistance is useless. You will be assimilated.
And as to the asymmetry between SW absorptivity and IR emissivity? Your referencing in situ measurements don’t count. Those are just measurements of apparent (not effective) emissivity within the Hohlrumn of the atmosphere. The simplest empirical experiments –
http://i61.tinypic.com/24ozslk.jpg
– show apparent emissivity dropping below 0.8 at 15 degree viewing angle when background temp is dropped below -40C.
Smarter people than me have measured BRDF using a modulated source and a synchronous detector for still water. Answer? Emissivity dropping below 0.05 beyond 70 degrees from vertical.
E=0.96 for water? Apparent emissivity (within the atmospheric Hohlrumn) maybe. Effective emissivity? Don’t make those watching laugh any harder, they may cough up their spleens!
Willis, you were right about the āIrisā or ācloud thermostatā, your āsteel greenhouseā works (only with vacuum), I know, I built it. But you are just going to have to accept that ālukewarmersā were just as scientifically incorrect as CAGW believers.
Am I the bad guy Willis? How many times have I stated that I wanted you to be the one that presented the right answer? Face facts, you were given the clues, but your ego was the obstacle.
That is TILTING at windmills, an English idiom (from jousting) which means attacking imaginary enemies.
Are sunspots just a figment of the imagination?
While “tilting” indeed does come from jousting, the phrase “tilting at windmills” derives from Cervantes’ novel “Don Quixote”, referenced by Willis.
Dennis,
Tilting at windmills is one thing. Re-ploughing āthe sun does nothingā soil (tilling) is quite another š
Willis has not yet descended to the depths of Dr. S, but he is getting dangerously close……
Konrad. November 2, 2014 at 3:24 am Edit
Konrad, this is why I say QUOTE THE EXACT WORDS YOU DISAGREE WITH. My exact words in this were not “the sun does nothing”, as you falsely claim. My words were:
I put that in there specifically because I knew that some jerkwagon like you would try to misrepresent my position ā¦ and as I foresaw, you’ve tried just exactly that.
I never said “the sun does nothing”, that’s just your ugliness surfacing. Your attempt to put words in my mouth, and then attack me for what you falsely claim I said, is pathetic. Either QUOTE MY WORDS or go play somewhere else. Your unpleasant, uncited, and mendacious attacks are getting old.
w.
āI never said āthe sun does nothingā, thatās just your ugliness surfacing. Your attempt to put words in my mouth, and then attack me for what you falsely claim I said, is pathetic.ā
Willis,
I am most assuredly not attempting to put words in your mouth. I am therefore not attacking you for any false claims about what you said. My concern is your approach to the question of solar influence. While you do say –
āNote that I am NOT saying that the sun doesnāt affect the earthā
You also say –
ā It is based on the fact that all of the phenomena commonly credited with affecting the temperature, such as cosmic rays, the solar wind, changes in heliomagnetism, changes in extreme ultraviolet (EUV), or changes in total solar irradiation (TSI), all vary in phase with the sunspots. As a result, if there is no sunspot cycle visible in the terrestrial surface weather datasets, then we can assume that none of those phenomena are affecting the dataset.ā
What I and many others are saying is that just because there is no discernible 11 year cycle in current data sets, it does not necessarily follow that the phenomena listed (and the addition of TSI component variation in UV bands other than EUV) are not having longer term climate effects. In fact, given the way the sun heats the oceans, and the complexity of ocean circulation, I would consider it unlikely that a clear 11 year signal would be manifest.
This means I am not so much objecting to your words, but actions. This is one of a string of articles on not being able to find an 11 year cycle in messy data. Why do you persist with these? The lack of a 11 year signal in no way rules out a long term solar influence. Further, because the most plausible mechanisms (cloud cover variation and TSI component variation) effect energy accumulation below the ocean thermocline, looking for an 11 year signal would be looking in the wrong place.
Such a signal may in time develop in the ARGO data, but currently we have insufficient data. If two extra sensors were included on the new āARGO deepā probes, we may have some chance of early detection before the full run of SC25. Those would be optical ocean turbidity sensor and multi frequency solar penetration sensor.
All that can be said at this time is we have insufficient data to rule out a long term solar influence on climate. No amount of articles on missing 11 year signals can change this, and this raises the question ā what is the point of these no 11 year signal articles?
If you recorded temperatures above a storage heater that was turned on for one hour in every six and then tried to detect the six hourly cycle you would probably fail. The decline in heat output during the intervening 5 hours would probably be swamped by other factors such as outside temperatures, the number of people in the room etc.
Trying to detect an eleven year signal in earth temperatures, which is a much more chaotic system, seems, to me, to be a similar task as the sea acts as the earthās storage heater. We know that at the end of an ice age there is an average lag of 800years before CO2 levels rise. This is because it takes that amount of time to warm the seas enough to start releasing gasses.
If on the other hand the storage heater was switched alternately on and off for a two week period the difference in temperatures would be easy to detect.
I suggest that you look for a correlation between sunspot activity and temperatures when there is a much longer period (as during the Maunder Minimum), but then this link is already well established.
Excellent point.
So that would lead me to believe that the small change in solar measures over the recent rise and pause of surface temperatures can be disregarded (essentially set at 0), because any causality under the recent past 60 years is buried in the weeds of Earth and its phenomenal ability to ignore such small solar changes and do its own thing.
There are at least two regular commentators here who ascribe to that very theory. Ignore the solar connection until a certain level of extreme solar variation is present. I dare say this premise is also ascribed to and can be found in research articles penned by several kingpin climate scientists: Only under extreme solar variations do we find possible correlations and with unsubstantiated mechanisms (some provide suggested mechanisms).
Pamela, there is a difference between a) system ignoring input variation and b) system being complex enough to prevent a mathematical correlation between output and sinusoidal input.
PaulM November 1, 2014 at 2:49 am
Oh, please. That’s just an empty claim. In many cases, yes, you would definitely be able to detect the signal.
Hey, I’m not the one making the claim that there are ~11-year cycles in the climate. There are literally dozens of people making that claim. I’m just the poor fool looking for the evidence for that claim.
No, that link is no better established than the claims of the 11-year cycles. Yes, the Maunder Minimum (MM) occurred during the Little Ice Age (LIA) ā¦ but the LIA started before the MM and continued after the MM, so the MM obviously didn’t cause the LIA. I have never seen a single scrap of evidence that the MM was colder than the periods immediately before or after. If you have such evidence, now would be a good time to present it.
Until you come up with said evidence, you might enjoy reading this …
w.
“I have never seen a single scrap of evidence that the MM was colder than the periods immediately before or after. If you have such evidence, now would be a good time to present it.”
I was amazed that you could not see it for yourself, but to ignore it after it was pointed out to you several times is inexcusable:
http://wattsupwiththat.com/2014/06/23/maunder-and-dalton-sunspot-minima/#comment-1668036
Ulric, the problem is that we have so little data for the time of the Maunder Minimum. About the best we have might be the “Winter Severity Index” of the famous H. H. Lamb. If you could point out the Maunder and Dalton minima on this, I’d be obliged ā¦
ā¦ did you get it right? Because they are here:
ā¦
ā¦
ā¦
Aw, heck, I got my dates mixed up. Here’s the actual data:
As you can see, the dates of the various “minima” are not immediately obvious, because ā¦ well, because they seem to make little impression on the temperature.
Ulric, I’d said:
Regarding the graphic you directed me to, that was this one:
Unfortunately, that graph is uninformative as to whether the temperature was warmer or colder before the MM, since the data starts after the start of the MM. It does, however, show the Dalton minimum. According to the CET (Central England Temperature), England warmed over the period of the Dalton Minimum.
The problem is that there is a lack of actual data to back up the claims of cold times during the various minima.
There is a deeper problem, however. This is that the changes in sunspots/TSI/GCR/whatever between say the Dalton Minimum and the period on either side is much smaller than the 11-year swings in the data. Why on earth should the planet be sensitive to and respond quickly to daily and annual swings in the sun’s strength, and be insensitive to 11-year swings in the sun’s strength ā¦ and despite all of that, be very sensitive to even tinier swings in the long-term activity of the sun?
That’s the conundrum I don’t understand. The temperature of the earth’s surface responds very quickly to small changes in solar input. The thinnnest hazy cloud comes over the sun and the surface cools right away. But the changes from the 11-yer cycle are not visible. I say that’s because they are so small that they get lost in the weeds ā¦ but the changes to/from the Dalton Minimum are even smaller than the 11-year cycles.
So how do you explain the fact that we can’t find the 11-year cycles, and yet you claim the much weaker cycles to/from the solar minima make some easily visible change in the temperature? That’s my problem, I can’t figure out physically how that would happen.
w.
Willis,
You don’t have to rely on the Winter Severity Index. Others & I have repeatedly linked here to the reconstructed CET going back to AD 800.
http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&ved=0CC0QFjAD&url=http%3A%2F%2Fwww.climateaudit.info%2Fpdf%2Fothers%2Flamb.ppp.1965.pdf&ei=7HVWVIaBMJeaoQTHmIL4DQ&usg=AFQjCNGMR8vloZcVY6Gt2RF9TUw0jvSK4w&sig2=6UEVn4JJDKaeMJxE6W4s-A&bvm=bv.78677474,d.cGU
Table II (PREVAILING TEMPERATURES (Ā°C) IN CENTRAL ENGLAND, Annual Averages) clearly shows the effect of minima. These 50-year averages don’t correspond directly to the usual dates for the minima, & the longer periods include rebounds from low temperature decades. But the effect of the minima is still visible, despite the imprecise match, with the periods straddling warm & cold intervals or including both.
Tony could provide the annual data for greater resolution, if you’re interested, since the effect shows up more dramatically using shorter periods.
800-1000 9.2
1000-1100 9.4
1100-1150 9.6
1150-1200 10.2
1200-1250 10.1
1250-1300 10.2
1300-1350 9.8 Wolf
1350-1400 9.5
1400-1450 9.1
1450-1500 9.0 Spƶrer
1500-1550 9.3
1550-1600 8.8
1600-1650 8.8
1650-1700 8.7 Maunder
1700-1750 9.24
1750-1800 9.06 Dalton
1800-1850 9.12
1850-1900 9.12
1900-1950 9.41
Here is Tony’s own extension back to 1538:
http://wattsupwiththat.com/2013/08/16/historic-variations-in-temperature-number-four-the-hockey-stick/
milodonharlani November 2, 2014 at 11:37 am
Say what? That link goes to Lamb’s work that includes the very Winter Severity Index that I listed.
w.
Willis said:
“Unfortunately, that graph is uninformative as to whether the temperature was warmer or colder before the MM, since the data starts after the start of the MM. It does, however, show the Dalton minimum. According to the CET (Central England Temperature), England warmed over the period of the Dalton Minimum.”
The graph without question shows the Dalton Minimum as one of the three coldest periods through CET, the other two being during the Maunder and Gleissberg minima, and it also shows that the MM was *colder than any period after it*.
The weaker sunspot cycles of the MM began around 1650, and we have better records than than Lambs winter index for the 1650’s. Apart from two noted cold winters, it was generally warm, and very warm in the first 5 years:
http://wattsupwiththat.files.wordpress.com/2011/09/weather1.pdf
Then CET takes over from 1659.
“There is a deeper problem, however. This is that the changes in sunspots/TSI/GCR/whatever between say the Dalton Minimum and the period on either side is much smaller than the 11-year swings in the data.”
Not really so for SSN, SC4 SSN = >140, SC’s 5&6 SSN = 145. The most curious thing though is that the period 1836-1845 in CET is as cold as the coldest part of Dalton; 1807-1817. The most pertinent clue to the coldest part of Dalton that I can see, is the dearth of Aurora that Silverman notes (page 11):
http://www.leif.org/EOS/92RG01571-Aurorae.pdf
“Why on earth should the planet be sensitive to and respond quickly to daily and annual swings in the sunās strength, and be insensitive to 11-year swings in the sunās strength ā¦ and despite all of that, be very sensitive to even tinier swings in the long-term activity of the sun?”
I would not say an El Nino response to slow solar wind is insensitive. The changes in the aa index in the last 100 years were not tiny:
http://www.ngdc.noaa.gov/stp/geomag/aastar.html
p.s. I don’t agree with the popular dating of the Spƶrer Minimum, there should be two minima there, one from the 1430’s, and a second from the 1550’s, as confirmed by CET reconstructions and proxies.
Some of my text got lost there, it should have read:
SCās 5&6 SSN = 145.
Same problem, I’ll spell it this time. SC’s 5&6 SSN was less than 50, and in SC8 SSN was greater than 145.
Good point! I still think that Svensmark’s theory is valid, but Lief will chop my head off for saying that!
http://iopscience.iop.org/1748-9326/7/4/044004/pdf/1748-9326_7_4_044004.pdf
Persistent solar signatures in cloud cover: spatial and temporal analysis
Voiculescu & Usoskin 2012
You forgot to provide a direct link to the data set they used in that study.
Pamela, I didn’t forget. There is a link in the link and even if there weren’t a link he has the link already.
Okay. I see that you have not quite understood Willis’ directions. But I’ll bite. I linked on the link and got this:
http://isccp.giss.nasa.gov/products/onlineData.html
Lots of choices here. Now tell us which one is the data set used in the study you cite so we can all know that that one is the data you think best to use.
Personally I’d go with the D2 data since that’s what they specified. If you doubt you can find the right data perhaps writing the author at the email address he provided for instructions would help you. I would except I have no intention of doing anything with the data.
Thanks, Steven. To begin with, it’s not at all clear exactly which dataset they used. You blithely say that “Personally Iād go with the D2 data since thatās what they specified.” ā¦ but it appears that you are totally unaware that there are no less than nineteen D2 datasets, viz: MnCldAmt.nc, MnCldAmt-St-Liq.nc, MnCldAmt-St-Ice.nc, MnCldAmt-Sc-Liq.nc, MnCldAmt-Sc-Ice.nc, MnCldAmt-Ns-Liq.nc, MnCldAmt-Ns-Ice.nc, MnCldAmt-Mid.nc, MnCldAmt-Low.nc, MnCldAmt-Hi.nc, MnCldAmt-Cu-Liq.nc, MnCldAmt-Cu-Ice.nc, MnCldAmt-Cs.nc, MnCldAmt-Ci.nc, MnCldAmt-Cb.nc, MnCldAmt-As-Liq.nc, MnCldAmt-As-Ice.nc, MnCldAmt-Ac-Liq.nc, MnCldAmt-Ac-Ice.nc.
All of those are D2 datasets ā¦ care to tell us which ones they used, and let us know how you determined which ones they used?
In any case, that is a classic example of a data dredge. Let’s look at one of their results:
ORIGINAL CAPTION: Correlation map between high cloud cover and CRII (negative correlation: blue dots, positive correlation; red dots, 90% confidence) and average high cloud cover for 1984ā2009.
Now that all looks very impressive ā¦ until you consider a few things. First, their threshold for statistical significance is 90% ā¦ which means that with random data, we should find “significant” results 10% of the time.
But wait, it gets worse. That 10% would be what we would find if we looked at one single dataset. But according to them, their data dredge has extended to no less than three target datsets (some kind of low, middle, and high clouds) and two putative forcings (cosmic rays and ultraviolet). With that many datasets and forcings, you are almost guaranteed to find a plethora of results at the 90% level.
Let me give you an example. If you pull out a coin and flip it six times, the odds of getting six heads is one in 2^6, or one in 128. That would be a statistically significant result, it might indicate a weighted coin.
But suppose you did the six-flip sequence a hundred times. Somewhere in there you are almost guaranteed to get six heads ā¦ but so what?
Similarly, when they look at 2,500 gridcells in each of three datasets and they compare them to two different forcings, somewhere in there you are almost guaranteed to get something like Figure 1 ā¦ again, so what?
But wait, it gets worse. These naifs seem completely unaware of the fact that when you use autocorrelated data, like say the ISCCP dataset and the cosmic rays dataset and the ultraviolet dataset, you find many more “significant” correlations than you would find with random data. As a result, you need to allow for autocorrelation
But wait, it gets worse. Even though the dataset only covers a pathetic two solar cycles, the authors say:
They state that, but they never seem curious enough to wonder why they are getting spurious correlations.
But wait, it gets worse. For unknown reasons, they have downsampled the ISCCP data from the original 2.5Ā°x2.5Ā° grid to a 5Ā°x5Ā° grid, which increases the spatial autocorrelation. They make no mention of why they have done this or what effect it has on the statistics.
Short answer? Sorry, Steven, but that study is garbage. When you mine that much data and you ignore the issue of repeated sampling and you don’t deal with autocorrelation, you are guaranteed to find the kind of results they glowingly report ā¦ but unfortunately, that means nothing.
w.
Willis, You seem fairly confident in your rebuttal. Would you object to my emailing your response to the author and invite him here to defend his work? I think it would be interesting if we could get him here since both he and his coauthor have several solar studies published.
Steven, please do invite the authors to defend their work. I would be very interested in their comments.
Best of luck in your mission,
w.
Ok, I’ll see if I can get a response.
Willis, actually I’m going to hold off for now. The paper states that the best correlations were with low cloud cover and it was obvious from looking at the correlation maps that was the case. Right away attacking what appears weak instead of what appears strong makes you appear biased. I’ll take a look at some of the other issues you brought up even though statistical processes aren’t really something that interests me. I just assume that people reviewing the paper can pick out things that only take a few minutes to pick out but maybe this isn’t the case. As far as the data sets go, you are right I didn’t know there were 19. I didn’t really expect there to be only one since there were different cloud types but 19 was a bit of a suprise.
http://journals.ametsoc.org/doi/pdf/10.1175/1520-0442(1997)010%3C2147%3AAMTETS%3E2.0.CO%3B2
They state they used methods for plotting their maps that they used in the 2007 paper. In that paper they processed the data for autocorrelation using the method from the Ebisuzaki 1997 paper. A bit egotistical to think everyone is familiar with the 2007 paper perhaps but maybe those in the field are.
So far you aren’t batting too well. I think finding out why they used the grid size they did would require asking the author. Feel free.
Steven, why would serious scientists respond to questions which are posed in such a nasty, disrespectful manner? I have worked with many actual scientists, and I can say for certain that they would not deem criticism of this level worthy of response.
steven November 2, 2014 at 4:04 am Edit
Actually, they didn’t say they used methods from the 2007 paper. They said they used methods SIMILAR to the 2007 paper. This is why quoting the words you are talking about is so important. What they actually said was:
A “similar manner” could mean anything. Perhaps it means that they did adjust for autocorrelation ā¦ but perhaps it just means that they used the same mapping methods. You are starting to understand why we need the code as used and the data as used to understand a study. Saying they did something in “a similar manner” means nothing.
steven November 2, 2014 at 4:06 am Edit
Not batting too well? You have not been able to falsify a single one of my claims about the paper. You certainly haven’t shown that they adjusted for autocorrelation. You haven’t shown that they are aware of the various statistical problems I listed related to data dredging. Where am I not “batting too well”?
As to “asking the author”, after a number of refusals over a number of years, I’ve simply given that nonsense up. I tried being nice, and it got me nowhere. The most common response is just silence ā¦ which has the (perhaps intended) effect of preventing me from doing anything for a number of weeks. Sorry, but I no longer go down that path.
More to the point, this is the 21st century. Even the journals are starting to require data as used and code as used in order to avoid all of those problems. The main issue is that no matter how well you can describe in English what you THINK your computer program is doing, the program may well be (and indeed often is) doing something entirely different. They may have adjusted for temporal autocorrelation, for example, but not for spatial autocorrelation. Without the code, we’ll never know.
But heck, Steven, you seem to think it will work ā¦ so how about you write to the author and ask them to send you their code and data as used. I’m looking forward to seeing how that plays out ā¦ although if (as I suspect) they read WUWT, or at least this thread, they may give it to you just to spite me. In any case, let us know what results you get. Me, as I said, I don’t enter that maze any more. Far too often, there’s no cheese at the end …
w.
Willis, no you aren’t batting too well. It is clear they did correct for autocorrelation regardless of if they felt the need to spell it out for the reader. It is clear you picked a map that you felt would be easy to critique when there were other maps available which showed a great deal more correlation. Tell me why comparing 6 pairings for possible correlations in the same paper is worse than using the same data for 6 different papers. If you can’t that’s 3 and you have struck out. I agree with you on code so we have no argument there. Willis, I would have written him but I decided just sending your comments would reflect poorly on me. Seriously.
steven November 2, 2014 at 1:28 pm Edit
Since you are so sure they did it, perhaps you can tell us whether they adjusted just for temporal autocorrelation, or just for spatial autocorrelation, or both?
In fact, you have ASSUMED that they did it, but the entire paper doesn’t contain anything about autocorrelation ā¦ only that it is “similar to” a previous paper. And if they did adjust, we still don’t know how they adjusted You are free to interpret “similar to” as you wish, and you have done so ā¦ but that doesn’t provide any evidence. That’s why we need the code.
In fact, it was the first map I came to, and I stopped there because it was ludicrous. Once again, you have ASSUMED that you knew what I was doing, when you had no clue. Your assumption is wrong.
In any case, whether specially selected or picked because it was first, so what? It is a figure in the paper that, as you mention, is “easy to critique”. And why is it easy to critique? Because of all of the numerous problems with it that I mentioned. How it was selected is immaterial, it’s still junk.
Because they only show the successful pairings, obviously. Otherwise, they’d have to write an entire paper about a map where nothing is happening ā¦
If you agree about the code, then why are you defending the autocorrelation question? The only reason it even comes up is because the code isn’t archived.
Finally, you said upthread that you were going to invite the authors to defend their work. Then you walked away from what you had agreed to do, saying:
Say what? Of course you start out by attacking what appears weak. You don’t attack the parts that are robust and solid, why would you? Look, my task here is to point out the flaws in the paper ā¦ but when I do point out the flaws you say oh, willis, you can’t focus on that part of the paper, that part is weak and has flaws, you should focus your search for flaws on the parts that aren’t weak and don’t have flaws ā¦
Words fail me.
Regardless, now you say:
Seriously? Sorry, not buying that reason any more than the previous one. You don’t have to send my comments to him, why would you believe you had to do that? Nor did you say you were going to do that. You said you were going to invite him to defend his work. How inviting him to defend his work would reflect badly on you is a mystery to me.
Frankly, I don’t know why you’ve chosen not to invite the authors to defend their work, nor do I particularly care, but those excuses just don’t hold water.
Regards,
w.
Willis, strike 4 unless you can explain why a paper that is testing for these 6 possible correlations is as weak as the weakest correlation. I think we are done.
steven November 2, 2014 at 7:37 pm Edit
Steven, leaving aside the fact that you haven’t shown that I’ve made any strikes, the issue is the statistics of repeated testing. Here’s the explanation I gave above.
I can see that you don’t understand that, but I’m not sure I can explain it any more clearly. However, I’ll try.
The issue is, if you keep looking in enough places, eventually you will find something that LOOKS significant. As I mentioned, if you flip six coins one time, six heads is a very unusual outcome.
But if you flip six coins a hundred times, somewhere in there you are almost guaranteed to get six heads ā¦ which means that finding six heads in that test is MEANINGLESS. In fact, with a hundred flips of six coins, your odds of getting six heads on one of the flips is about 80% ā¦ so it would be unusual if you didn’t find six heads.
The same thing is true when looking at six datasets. If you look at one dataset, and you find a result that exceeds your threshold of the 90% level (as in this study), you have a one-in-ten chance that it’s just a random result, what’s called a “false positive”.
But if you repeat that same investigation on six different datasets, just like with the coins, your odds of finding an “unusual” result go way up. How far up? Well, they go up to (1 – 90% to the sixth power), which is (1 – .96), which is 0.47 ā¦ so when you look at six datasets instead of one, your chances of finding a false positive are almost 50/50. In other words, with six datasets, finding a result at a 90% level means nothing, there’s a 50% chance of that happening.
And that, my friend, is why looking for a result in six datasets is statistically very different than just looking in one dataset.
I must say, given the level of your understanding of these basic statistical concepts, your arrogance is somewhat surprising ā¦
w.
There’s a fun discussion of the odds of strings of 6 consecutive heads or tails in 200 flips here. They end up figuring a 96.5% probability of this using a technique I didn’t recognize.
Willis,
Strike 4: Your attacking a weak correlation instead of a stronger correlation. CR and the correlation between high clouds is possibly, and quite likely, independent of UV correlation with low clouds. If you to critique the correlations you would pick out the strongest one not the weakest. JUst like if you had 6 different papers. You wouldn’t say this paper shows a really poor correlation between CR and high clouds so that paper showing a correlation between UV and low clouds is also poor.
Strike 3: You say a data dredge produced a map with lots of dots. I am blind and missed that map. Give me a figure number. I remember seeing the 6 that represented each of the possible correlations they were testing for.
Strike 2: It doesn’t matter if you stopped as soon as you saw something you could critique or if you looked and then picked it out. Your bias either made you stop or made you select. I’m not sure how you could start a critique of a paper without even taking the time to look at the pretty pictures first but if you say you did that’s fine doesn’t change my diagnosis of your actions.
Strike 1: You assumed they didn’t do their statistics properly because you didn’t see where it had been done. This was a no lose situation for you. You could say I don’t see where they corrected for autocorrelation and the paper isn’t likely to be correct unless they did. Instead you said they didn’t and were rather insulting about it. Now you have made it a win lose situation. Either you are right or you look foolish. I have made a declarative statement that they did. It is every bit as supported as your statement they didn’t. When I start making disparaging remarks along with my assertions we will be on the same level field.
You can make the 11 year cycle go away by averaging it – for example, if you assume a 400 sample average (roughly 30 years of smoothing (plausibly caused the by thermal inertia of the oceans?), the 11 year cycle is barely a ripple.
http://woodfortrees.org/plot/sidc-ssn/mean:400/plot/sidc-ssn
You can also play other games with the sunspot count. For example, if you subtract 40 from the sunspot count, then integrate it (so sunspot becomes a proxy for rate of heating or cooling, rather than a direct heating or cooling proxy – with less than 40 assumed to correspond to cooling), you get a graph which looks a lot like the instrumental temperature record.
http://woodfortrees.org/plot/hadcrut4gl/from:1850/mean:50/normalise/plot/sidc-ssn/from:1850/mean:50/offset:-40/integral/normalise
I don’t know whether this means anything or is just mathturbation, but I think its early to write off a solar influence – though I agree the solar argument would be a lot more compelling if there was a detectable 11 year cycle.
BTW have you looked at Willie Soon’s research into solar vs temperature? I remember a claim a while ago by Soon that he had found a correlation between solar activity and daytime maximum temperature.
http://quadrant.org.au/opinion/doomed-planet/2013/03/changing-sun-changing-climate/
The whole temperature record is hourly averages into daily average into station averages into monthly averages and then averaged with nearby stations to then on to national and world averages. Filters of proven and unproven worth are applied to the averages to get a huge pile of average data that has lost all meaning.
The WFT normalized curves are amusing. Wiggle-matching, of course. There seem to be sinusoidal components which are in opposite phase to each other…except when they aren’t. The periodicities are different, as near as I can tell. Take another look and see if anything will isolate the sine functions.
Note that sunspots are not evenly distributed between solar hemispheres. Perhaps an analysis taking this into account might be more productive. http://www.sidc.be/silso/datafiles. The 40 figure might need adjustment, or it might need to be tossed out altogether.
Monthly Data:
http://www.sidc.be/silso/DATA/monssnns.dat
Daily Data:
http://www.sidc.be/silso/DATA/ISSN_D_hem.txt
Eric Worrall November 1, 2014 at 2:52 am Edit
Wills, some years ago I wrote a post about the temperatures in Echuca, and how there was no global warming at Echuca. I also noted that the peak maximums appeared to coincide with the odd solar cycles. You poored rain down on this, in your usual manner. Maybe it is worthwhile looking at that maximum data again, along with the ENSO cycles.
Regards,
Ian
Ian, no link to dataset, no answer. Sorry.
w.
Took seconds to find this:
http://wattsupwiththat.com/2012/06/25/an-interesting-graph-showing-solar-cycle-el-nino-and-surface-temperature-correlation-in-australia/
Milodon, I’m sorry, but I asked for a link to a dataset, not to a WUWT post.
w.
And Ian, my objections at the time still hold. The data you point to shows no sign of being connected to sunspot cycles.
w.
Willis,
Have you studied the Uppsala and Stockholm temperature series provided by the Swedish Meterological and Hydrological Institute?
These datasets show a decadal-scale cyclical variation, and have the additional benefit of being two of the worlds longest accurate temperature measurement series starting in the 1700:s.
http://www.smhi.se/klimatdata/meteorologi/temperatur/uppsalas-temperaturserie-1.2855
http://www.smhi.se/klimatdata/meteorologi/temperatur/stockholms-temperaturserie-1.2847
Thanks, Johan, I do love good datasets. I had looked at the Stockholm but not the Uppsala temperatures. Here’s the periodogram of the Stockholm dataset:
As you can see, there in no cycle from 5 to 50 years that exceeds a few percent of the range of the data. I don’t see the “decadal-scale cyclical variation” you mention.
My best to you, thanks for the data,
w.
If you ever want to be taken seriously by very serious climate scientists you should ditch looking at datasets and start running computer models. I’m sure you can come up with a computer model that spits out something resembling an 11-year cycle, which then becomes proof that your hypothesis must be correct.
It doesn’t have to hindcast the past, either. If you suddenly find a brand new computer generated 11-year cycle where one has never existed in any similar historical datasets, you’ve stumbled onto solid proof that manmade CO2 has made the earth more susceptible to sunspots, causing “more extreme weather”.
Love the spoofing of the so called climate scientists and their methods. I’m glad Willis is honest. It would be very helpful to know what has caused the global temps to stop rising, but no one really knows. What we do know is that CO2 isn’t the driver the so called climate scientists thought it was, and they should start looking elsewhere. But that doesn’t scare people into funding them.
I have a data set with some 11 periods over a 120 years. Not perfect spaced with 11 years interval .
It is annual see-level rise from Sweden- SMHI. You donĀ“t get it if You look at the numbers on a yearly base. You have to treat the numbers gently, by a five Year equalization method . More on that on the site.
Hope Your Swedish is good!
http://www.klimatupplysningen.se/2013/10/13/angaende-historiska-havsnivaer-vad-doljer-sig-i-siffrorna/
Lasse, I looked at the sea level rise in Sweden (among other places) here. However, I didn’t display the Stockholm results at the time, so I’ve done them up anew:
As you can see, there is a 1-year and a 6-month cycle. However, there is nothing solar-related that is visible.
w.
Thanks!
It is a long way between the sun and the Baltic see-level. The currents draw energy from the Pacific that is the main reserve of energy. But anyhow I got a nice periodic pattern with now acceleration in recent years.
Willis,
A simple question. Given that “The climate system is a coupled non-linear chaotic system” (TM IPCC)
Would you expect inputs to a coupled non-linear chaotic system to provoke the same response each time they were applied? This is as foolish as creating linear projections based on the past behavior of a coupled non-linear chaotic system.
What one would expect is that each periodic input generates some effect but that the exact effect would vary dependent on the orbits around the multiple interactive attractors and the state of other coupled inputs and effects. In other words the response of the coupled non-linear chaotic system will be different each time. That does not mean that there is no effect, there will always be an effect from (response to) an input, but it does mean that looking for simplistic responses to that input in one or more metrics is a nugatory effort.
An effect that is so small it cannot be detected in a noisy signal can thus be essentially set at 0 then? For example, when the equatorial trade wind is blowing from East to West, we get a nice walker cell circulation and a very predictable cloud formation in Indonesia. But when it calms down or even goes the other way in the form of Westerly Wind Bursts, we get another kind of less organized and less cloudy cloud pattern. It’s a big effect and models match observations. One can input solar metrics into the mix or not. It doesn’t change the outcome. Therefore, no matter what is happening with solar input, that part of the equation is so miniscule that one can ignore it. In chaotic systems that are being transformed into calculations, models, etc, one can do that mathematically and be safe in doing so. It’s the kiss principle.
The correlation between increased CO2 and increased water vapor is one of those types. It has not been observed to match the model. Why? Because when switching out CO2 for things like ENSO, the correlation with water vapor change is strengthened to a much greater degree than when using just the CO2 data. This particular well-known and stronger correlation is not mentioned in paper after paper proposing that we are all going to die from CO2.
>> An effect that is so small it cannot be detected in a noisy signal can thus be essentially set at 0 then?
Pamela, this is incorrect. There are plenty of systems that are complex enough that variations in input are not found in the output. This doesn’t mean that the input can be ignored or doesn’t matter.
I already gave you the great lakes / rain example. There are many examples of this. People eat in 3 discrete pulses per day, but we shouldn’t expect to find evidence of those pulses in weight measurements. However, it’s obvious that increasing the amplitude of those pulses will add weight.
Other examples include anything that uses pulse-width modulation. Electric cookers use a thermal oscillator running at approximately two cycles per minute, while the duty cycle varies according to the knob setting. The thermal time constant of the heating elements is several minutes, so that the input signal variations cannot be found in the output temperature.
Yet, even though the same attribute is present for this very simple thermal system, some commenters here seem to consider the lack of output variation in the much more complex thermodynamic system of the Earth (core/crust/land/sea/atmosphere) as evidence of something.
Viking, your eat more weigh more simile bears no relationship to solar-temperature cause and effect. The percent change in solar output at the Earth’s surface is nothing like the power of a single calorie on a human body. However I will concede that oceans have the unique capacity to hold onto heat hence build up heat, much like a sedentary lifestyle can hold onto and build fat from calories that are not burned off.
Pamela, I wasn’t implying that the cause-effect mechanism was the same, although both are definite and undeniable periodic injection of Joules. My analogy was that both systems are complex enough that one should not expect input variation to be visible in the output. Wouldn’t you agree that one would be laughed at if one claimed that food intake was irrelevant to obesity because there was no meal signature in weight data?
VikingExplorer November 1, 2014 at 11:20 am:
+++++
What you have been writing makes good sense. I appreciate your input here. Thank you!
Right you are, but .. how do you propose to detect such a quasi-random response?
It is necessary to understand the systems and how they interact and in a coupled non-linear chaotic system of coupled non-linear chaotic subsystems when many of the parts of the system and their relationships are unknown, or misunderstood and their effects less so usually measured in the wrong units. So you can not detect that quasi-random response because you don’t even know what response you need to look for or when/where you need to look for it.
Ian W November 1, 2014 at 3:30 am
Hey, I’m not the one making the claims that the sunspot cycle affects the weather. I’m just the poor fool that is trying to find the evidence.
w.
“It is necessary to understand the systems and how they interact..”. Exactly. Let me quote from Rex Stout’s The Mother Hunt: There must be traces – letters or telegrams, check stubs and canceled checks, a hair from your head in her car, a hair from her head in your car – the possibilities are innumerable, now that you have been named.
Using statistics blindly won’t necessarily result in an insight. Once you have an insight, statistics becomes a powerful tool to support – or to disprove – it.
“…to seek, to find, and not to yield…”
‘ if the correlations are as great for sunspots lagging the climate as they are for sunspots leading the climate, youāve got problems.’
If the sunspots lag the climate by eg six months, isn’t that the same as saying the climate lags the sunspots by ten years and six months, presuming that the peaks and troughs of the solar cycle are similar each time?
It is what I call a āsometimeā theory
http://www.vukcevic.talktalk.net/SSN-UKsh.gif
However Iāve been working on something I call Geo-Solar cycle (solar + earthās magnetic field)
http://www.vukcevic.talktalk.net/GSC-UKsh.gif
It does indicate that might be something to it, but delay of 13 years is very hard if not next to impossible to explain, possibly to do with the two N. Atlantic gyres, since UK gets its clouds from there.
Just a thought.
.
GeoSolar cycle does a good job of reproducing the de-trended NA SST with an extra couple of years delay
http://www.vukcevic.talktalk.net/GSC.gif
However, delay is the proverbial āelephant in the roomā
>> but delay of 13 years is very hard if not next to impossible to explain
Seems easy to explain, given that ocean currents in 3 dimensions are involved.
http://svs.gsfc.nasa.gov/cgi-bin/details.cgi?aid=3827
as an aside, it would be interesting to see that graphed against the north atlantic oscillation index
reply to your comment went in wrong place, see above above
thank you for taking the time to post that,interesting result . a reasonably constant delay could imply there is constant worth looking for ?
KTM’s “contribution” at 3.17am is one of those that annoy me when I am looking for meaningful contributions to the debate. Why bother “contaminating” the thread with such nonsense? I wonder why he/she even did not add “sarc off”? Did KTM actually mean what he/she wrote?
It is sarcasm, John š
Hi Willis. Thanks for another well-presented argument.
BTW, the Don Quijote armor looks good on you.
Cheers.
Forget about the solar cycle.
The gold mine here is the cloud cover dataset that might actually be worth using. Clouds are supposed to be going down as it gets warmer. One of the great questions remaining in the debate is the feedback response of clouds.
Nobody really believed the ISCCP cloud dataset before (even climate scientists) but maybe matching up the two different datasets so closely provides some backing that both are reliable.
>> Clouds are supposed to be going down as it gets warmer.
Wouldn’t warmer temperatures result in more evaporation, which leads to more clouds?
Nope, not in this theory.
Low cloud cover goes down, high cloud cover goes up but the net impact is a reduction in the degree of cloudiness producing a positive feedback as it gets warmer (less solar reflection). There is more water vapor in the atmosphere but less net cloud cover somehow.
[This implies that during the ice ages or during snowball Earth, for example, there was a huge increase in cloud cover which, as you note, makes no sense whatsoever].
Without the positive feedback from clouds (let’s say it is actually net zero), the total temperature increase from global warming falls from 3.0C per doubling to 2.0C per doubling. Hence, they need to keep it in the positive illogical range to keep the disaster scenarios going.
I’ve been meaning to do this chart for awhile. A depiction of how the feedbacks (and their assumptions) are crucial to the global warming theory. Change the feedback assumptions by modest amounts and the whole amplification process falls apart.
http://s29.postimg.org/5x7dto207/Global_Warming_and_Feedbacks.png
Bill, different kinds of clouds have different effects. For example, high thin cirrus has low space oriented albedo to income solar but high earth oriented OLR albedo because comprised of ice rather than water. Total cloudiness per se would not be expected to yield much. And Willis composite suggests that indeed it doesn’t, at last for the geography charted, which is basically CONUS. Probably too regional to detect any global climate signals, most of which are likely ocean oriented, as that is where Earths heat sink and water vapor feedback sources originate.
Bill Illis November 1, 2014 at 4:47 am
I couldn’t agree more. The models say that cloud feedback is positive. My hypothesis that emergent phenomena control the temperature says that cloud feedback is negative.
This dataset strongly supports my hypothesis.
w.
Willis said:
“I draw no great conclusions from this, except that overall, as the US warmed post-1930, the US cloud coverage rose as well. Go figure ā¦”
The US cooled from the 1930’s. Cloud then cover reduced from the 1970’s as the US warmed again:
http://wattsupwiththat.files.wordpress.com/2011/10/palmer_figure2.png
And cloud cover increased again in the last decade as US temperatures have fallen:
The observed trend in cloud cover is the most interesting result. The fact that the ISCCP data agrees so well with the ground observations is very striking.
Agreed, I was quite surprised by how well the ground data matched the satellites.
w.
I have often postulated that the clouds around the equatorial belt may have far more predictive power on ocean heat than global cloud data. This is because of the potential depth to which shortwave infrared is absorbed when the angle is perpendicular versus glancing. It would be of interest to compare equatorial cloud data versus upper oceanic heat measures all centered solely around this equatorial belt. The more poleward the data, the less it is useful in making predictions in terms of future oceanic heat increase.
I speculate that the amount of SWIR, and the depth of it, that bore upon the equatorial belt during the last substantial La Nina is to blame for current North Pacific SST increase. Therefore, equatorial La Nina and possibly long lived La Nadas could be useful metrics in forecast future SSTs. Combined with oceanic currents, it is possible to even forecast where and when these equatorial sourced increases in sea surface temperature will surface.
Wouldn’t albedo in the equatorial regions be the most significant measure?
Robert of Ottawa November 1, 2014 at 5:20 am
Seems like it to me. I plan to look at the ISCCP dataset vis-a-vis the CERES dataset, but it’ll take a bit of programming.
w.
Willis, svensmarks cloud cover would be upper troposphere cirrus and alto stratus – please cleanse your data set of all low altitude cloud types and see what you get
James of the West November 1, 2014 at 5:27 am Edit
I would if I could, James, but the problem is that the ISCCP dataset only goes from 1983 to 2009, which is only a couple of sunspot cycles. As a result, we can’t conclude much about the effect of GCRs, there’s simply not enough data.
w.
Willis, the mystery isn’t that there is a 11 year signal in the climate there MUST be, the mystery is why there is not! There should be, and based on summer winter cycles it should be about half a degree, but uniquely for sunspots its not. It works for earth and solar system geometry but seemingly not for Emission. Why not! That is the question to be solved. Non linear feedbacks are a possible answer but why doesn’t that cancel geometric forcing changes?
The answer may lay in other data, there are saturation effects in climate, perhaps you need to look beyond (min + max)/2 which is about as dumb a view as you could get, perhaps the sunspot cycle shows up in the rate of warming at dawn or dusk or relationships between summer and winter. Perhaps there is an ionospheric or stratospheric effect that counters warming due to the increased insolation, eg such as say increased jet stream velocity. Maybe the increased insolation reduces cloud cover at night which allows more radiative loss. Could be anything. Clearly though (Min + max)/2 isn’t going to solve that puzzle.
One hint though, geometric forcings changes the distribution of sunlight over the earth, sunspots do not – this difference might account for somthing. Non linear feedbacks might affect sunspot warming differently to that caused by geometric changes.
Bobl
Imagine kicking someone’s ankle once every 11 seconds – almost every time you do it there is no effect but by chance if you give the ankle a light tap just before the foot hits the ground they fall over then you carry on fruitless kicking out of a few hundred attempts one fall. So you would say all those times the ankle was kicked no reaction one random time something happened – there’s no such thing as an ankle throw. You would be mathematically/statistically correct but factually wrong. You just do not understand the system that you are measuring
I don’t see your point here, if you increase insolation you must increase temperature or the earth would be a frozen mass at 0 K, this is known to be true. The question then remains why can’t this signal be extracted over 20 cycles (even with niose reduction of some 20 times). The obvious answer is that there is a counter forcing that applies to insolation but not geometric forcings, that could he a feedback or a non-linearity ( forcing is too weak to shift the climate to a new attractor), but it could also be a related effect, say solar wind changes, UV or magnetism resulting in some atmospheric effect.
I agree with you in the sense that the sunspot cycle mystery shows we don’t understand the system we are measuring.
By the way the issue you point out pervades medicine, results are based not on biochemical response to drugs but rather the statistics of a double blind trial. Confounding factors (say side effects) can and do affect outcomes, efficacious treatments are being elliminated all the time by the mechanism you mention, the few percent it helps are hidden by the morbidity of the side effects. It also turns up in cancer nomograms. The doctor will cite a nomogram that says half the people who contract your cancer will be dead in five years! That sounds really bad, except when you realise that this figure is all cause mortality and that older people (80 and up) are overrepresented in the population, most of them actually die from something else! The wrong statistic is being measured, what you really need to know is disease specific mortality, not all cause mortality.
“you canāt prove a negative in any case”
BS. Evidence of absence proves a negative.
From Wikipedia:
If someone were to assert that there is an elephant on the quad, then the failure to observe an elephant there would be good reason to think that there is no elephant there.
By the same token: If someone were to assert that there is an temperature signal in the clouds based on sunspot cycles, then the failure to observe a temperature signal there would be good reason to think that there is no temperature signal there.
Always be careful when quoting Wikipedia. The elephant in the quad has no boundaries set on it. You may be able to eliminate large elephants, but tiny toy elephants in the bushes might be missed. It depends on how thorough you are. It also depends on what the application you’re studying elephants for. If you are in an area where elephants naturally live, its helpful to know where they are to avoid being trampled by one.. One observation won’t prove they aren’t there all the time, only the time the observation was made.
The so called climate scientists may be trying to associate the current CO2 rise with current temps. But if CO2 is rising from both man made causes and natural causes, there may be a lag of a few hundred years. If the oceans are responsible, the cause of the current warming may have ended a long time ago, and Trenberth’s missing heat may have long ago been emitted to outer space, and the cycle is about to turn cold. They may be searching for a current cause when there isn’t one because they are looking in the wrong place.
Willis writes:
“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, such as cosmic rays, the solar wind, changes in heliomagnetism, changes in extreme ultraviolet (EUV), or changes in total solar irradiation (TSI), all vary in phase with the sunspots.”
If only it were that simple. The solar wind for the last 50 years did not follow the sunspot cycles very well at all:
http://snag.gy/vM2dv.jpg
The previous 6 cycles have a fairly regular low value around a year after each sunspot minimum, but there is often a low in the solar wind speed at sunspot maximum too, which is strictly out of phase with sunspots, and radically alters the periodicity of lows in the solar wind speed.
http://www.leif.org/research/Ap-Monthly-Averages-1844-Now.png
Cloud cover in the US will be effected by the AMO mode, which appears to switch phase in relation to sunspot cycles depending whether it is in its cold or warm phase. That would tend to cancel out a solar cycle length cloud response signal in data sets longer than 3 solar cycles:
http://www.woodfortrees.org/plot/esrl-amo/mean:25/plot/sidc-ssn/from:1850/normalise
There is also the problem that warmer in summer can be less cloudy, but warmer in winter can be more cloudy. And what about the regional differences in change in cloud across the US, by averaging many stations you may be losing the signal, as some regions may get cloudier while cloud reduces in other regions.
Ulric, what is it with you and links? Without a link to your solar wind data, there’s no way to determine anything about your claims.
In any case, you claim:
In fact, a periodogram of the OMNI solar wind data clearly shows the sunspot cycles:
Solar Wind Data Source: ftp://spdf.gsfc.nasa.gov/pub/data/omni/low_res_omni
As you can see, the strongest cycle in the solar wind (red), twenty percent of the range of the variations, is at around ten years. This of course is clearly related to the solar cycle (blue) for the same period, as you can see by the correspondence between the two periodograms.
In other words, your claim that the solar wind doesn’t follow the sunspot cycles is simply not true.
w.
“Without a link to your solar wind data, thereās no way to determine anything about your claims.”
The graph is there in my comment, a visual analysis immediately shows a poor match. And your periodicity result of only 20% confirms that the solar wind speed does not follow the sunspot cycle well during the last 50 years.
“In other words, your claim that the solar wind doesnāt follow the sunspot cycles is simply not true.”
With the solar wind speed in sunspot cycles 19 & 20 being lower at sunspot maximum than at the following sunspot minima, it is decidedly true.
Ulric: The problem here is that the solar wind data does have a strong solar cycle dependence, but that the shape of the cycle is different from that of the sunspots:
http://www.leif.org/research/Solar-Wind-Climatology.png
The curves show the solar cycle behavior through the cycle based cycles 13 through 23. The average cycle is shown at the left, and then repeated five more times to the right to make the cyclic behavior more clear.
So, if you look for periods, you’ll find the 11-yr cycle.
Leif, as you know there are large drops in the solar wind at the sunspot maximum of some cycles, e.g. SC’s 20 & 21, that radically alters the periodicity of major drops in solar wind speed through the last 50 years. Can we pay attention to what has actually happened at these events rather than meaningless averages thanks.
Ulric: Can we pay attention to what has actually happened at these events rather than meaningless averages thanks
Perhaps you should do that. The average behavior during the space age is very closely the same as for last ten solar cycles. Averages are not meaningless, they dampen out the noise and let the truer signal through showing what is really repeatable and stable. You can see that from this comparison of the spacecraft area and the full dataset since cycle #13:
http://www.leif.org/research/Averages-Space-Climate.png
You could redeem yourself by acknowledging here that you agree and understand his.
“Averages are not meaningless, they dampen out the noise and let the truer signal through showing what is really repeatable and stable.”
They dampen out the noise in your graph, the real world responds to all the noise.
“You could redeem yourself by acknowledging here that you agree and understand his.”
I already knew about the averages, you could redeem yourself by acknowledging the noted lack of coherency of the solar wind with the solar cycle through the last 50 years.
noted lack of coherency of the solar wind with the solar cycle through the last 50 years.
The Ap-index [you showed it yourself] is a good measure of solar wind speed. The last 50 years show very clear solar cycle period dependence with a peak every ~11 years during the declining phase of the cycle [as is also shown in the average curve]:
http://www.leif.org/research/Ap-index-peaks.png
So, you will note that there is no lack of ‘coherency’ in the data. Perhaps in your persistent claim instead.
The solar wind speed the last 100 [and 50] years show a very strong ‘coherence’ with the solar cycle always maximizing during the declining phase of the cycle with peaks spaced the cycle-length apart:
http://www.leif.org/research/Solar-Wind-Speed-Last-100-Years.png
This should put the matter to rest, although I doubt you will admit it.
I am well aware of the peak in solar wind speed on the declining phase of the sunspot cycle, the lack of coherency that I had noted above was the frequency of slow SW periods in the last 50 years.
The last 50 years are not any different than the rest.There are 23 years with an average [404 km/s] less than the average speed [430 km/s] compared to 18 years before the space age with an average [401 km/s] less than overall average. Hardly a significant difference. Said in a different way: the average speed the first 50 years was 444 km/s vs. the average of 441 km/s for the last 50 years. The last 50 years are not any different from the first 50 years over the 100-yr period. And both show a strong ’11-yr’ cycle with wind speed peaking during the declining phase in all cycles.
lsvalgaard
November 3, 2014 at 1:52 pm
“You are more than usually dim today.”
Given how long it has taken you to address the low SW points that I am discussing, I can only regard your ad hominem as a projection.
“If anything, the recent period has fewer low-speed Ap periods.”
There were unusually deep SW lows for sunspot maxima at 1969 and 1979/80, but not at 1989 and 2000, trashing any regularity in the last 50 years:
http://snag.gy/hSqT4.jpg
There are lows and highs of short duration all the time for someone [like you] to cherry pick what he likes. The fact is that the general character of the variation follows the solar cycle very well with a peak on the declining branch in almost every cycle. The last 50 years are no different. You have painted yourself into a corner and can’t afford to creep out. This is clear to everybody so no more demonstration is needed.
No, the point is that short dips in any quantity occur rather at random and thus do not influence the fundamental solar-cycle variation of solar wind parameters. The sooner you realize this, the sooner you are to enlightenment
“The last 50 years are not any different than the rest.”
Again, for the frequency of the major periods of slower solar wind speed, they do differ, as can be readily seen on the Ap index series above.
You are more than usually dim today. For you convenience I re-show the Ap-index so you can see that there is no real difference. If anything, the recent period has fewer low-speed Ap periods.
http://www.leif.org/research/Ap-index-peaks.png
Note the low speed around 1900, 1913, 1924
lsvalgaard said
āYou are more than usually dim today.ā
Given how long it has taken you to address the low SW points that I am discussing, I can only regard your ad hominem as a projection.
āIf anything, the recent period has fewer low-speed Ap periods.ā
There were unusually deep SW lows for sunspot maxima at 1969 and 1979/80, but not at 1989 and 2000, trashing any regularity in the last 50 years:
There were unusually deep SW lows for sunspot maxima at 1969 and 1979/80, but not at 1989 and 2000, trashing any regularity in the last 50 years:
Not at all. Note the red arrows spaced a solar cycle apart:
http://www.leif.org/research/Ap-index-peaks.png
or will you persist being dim?
There is nothing unusual about 1969 [left red oval]:
http://www.leif.org/research/Solar-Wind-Ulrics-Folly.png
There were also several such dips around the maximum of 1990 [right red oval] and many more besides. The solar cycle variation of the solar wind speed is clearly shown by the blue ovals spaced a cycle apart. Willful dimming is not helping you much. Hope this final demonstration makes closure possible for you.
Your red oval encompasses 8 years, and even includes the low in the solar wind near the start of SC 21. The point is that the lows tend to happen at roughly alternate 3.5 and 6.5 intervals, in and out of phase with the solar cycle.
Thanks Willis. I like data, rather than simulations. As a complete novice, could you answer a couple of points.
If the cloud cover varied in cycles from 9 years to 13 years (ie: +\- two years), would your analysis detect it?
If the controlling factor was Sunspot cycle length, rather than Sunspot amplitude, would your analysis detect it? (Cycle length would be multi-decadal, rather than the decadal amplitude cycle.)
Thanks,
Ralph
ralfellis November 1, 2014 at 6:29 am
Yes. Look at Figures 1 and 2. The cycles vary, but the periodogram shows them nonetheless.
I think so, but If you have a dataset where the controlling factor is the length of the cycles in forcing we can test it. Me, I don’t understand the physics of how such a cycle-length based control would work in the climate system, so any information about that would be helpful as well.
w.
>> I donāt understand the physics of how such a cycle-length based control would work in the climate system
w, how could you not understand the concept of pulse width modulation? All of our homes are heated this way.
Why don’t you take a break from searching for evidence of solar variation and try a system a million times less complex. Measure the temperature in your house periodically and look for evidence of the input pulse frequency. If you can’t find it, then I’m wrong about how our homes are heated.
Wait…
er…
I thought that logic was so good…
Electronics math works for real systems’ frequency responses. I think engineers tend to see the climate as very complex inductors, capacitors and resistors, hence RLC circuits. PWM uses a given frequency of digital pulses, where the percentage of on-time is varied. Light dimmers are done this way… That allows you to excite the system with the correct voltage but limits the overall power from entering the system. Using a variable resistor would limit the the voltage, which can cause problems for systems designed to run at a certain voltage.
I understand very well how pulse width modulation works in electronics circuits and in warming the house. What I don’t understand is how it would work in climate, given the evidence we have.
That is to say, we can find no evidence of the sunspot related cycles in the surface weather records using e.g. a cross-correlation analysis. Now, this analysis specifically includes the variable cycle lengths ā¦ so IF the variable cycle lengths are regulating the temperature/sea level/rainfall/whatever, the CCF analysis should show it.
But it doesn’t ā¦ so again I ask, how is this supposed to work in the climate? As Viking says:
That’s great. So let me answer back.
w.
VikingExplorer November 2, 2014 at 10:04 am
Why donāt you take a break from searching for evidence of solar variation and try a system a million times less complex. Measure the temperature in your house periodically and look for evidence of the input pulse frequency. If you canāt find it, then Iām wrong about how our homes are heated.
Now, there have been several comments in this thread that purport to ‘prove’ that there is a solar cycle variation in any number of parameters. So, by your ‘logic’ all those peer-reviewed papers are just plain wrong. It would be good for you to state that explicitly here and now rather than just sit silently and less all those correlations pass you by.
Bob Weber said:
“Todayās solar flux dropped to 121. My analysis of SST and solar flux from 1960-now indicates the oceans on average drop in temperature when flux reaches 120 sfu/day, and the temps increase above that level of solar flux.”
That would fit with my hypothesis that a higher solar flux reduces ozone above the poles in the stratosphere. That cools the stratosphere above the poles which causes tropopause height to rise and the climate zones and jets to shift poleward.
The result is lower global cloudiness and more solar energy into the oceans.
The reverse when the solar flux is low.
I would be very intrigued if Bob or anyone else could show a rapid short term response since I have been focusing on much longer timescales.
It would be very useful if Bob has identified the switchover point at 120sfu/day
Stephen thanks for your vote of confidence. The rapid short term response is most readily observable in a single solar rotation when one half of the rotation has little to no activity, followed or preceded by a much higher activity level, such as what happened over the past 27 days, and will most likeky happen again in the next 27 day rotation.
It’s doubtful anyone else has made this connection as I’ve not heard anyone here or elsewhere talk about this even after I’ve mentioned it here several times over the course of the year.
My answer here also applies to Bruce Ryan’s comment further down the line here.
Solar influences the weather and climate two ways: variable radiative output in sunlight; and the particle effects from solar plasma outflows that operate electrodynamically, what I call “electric weather effects”.
Stephen I’ve been building a case all year long for what I’m saying, and working towards first publishing blog post articles here and elsewhere if our host will post them, and hopefully later science paper(s) for submission to a journal for peer-review. There’s so much more to say but I want to save it for those articles and papers for now.
Since I value the input from people across the spectrum I look forward to getting your feedback and from others when that day arrives.
The Sun causes warming, cooling, and extreme weather events, not CO2.
Good luck to you (and me) š
And so much for the unnecessary and ostentatious comment about “hand wringing”.
Bob, you haven’t put forward a single link to allow us to investigate your claims, and despite that, already you’ve gotten a “vote of confidence” ā¦ must be nice to get people to believe you without a scrap of evidence.
Me, I’m not that foolish …
w.
Willis I’ve been out cutting wood all day and I just got home. Chill.
Except Stephen, you have cause and effect reversed. It is ENSO conditions that shifts the polar jets. We now know that ENSO conditions have both short and long term oscillations. You need to complete your study by determining whether or not there is a lag between what you think is the cause and what you think is the effect. I can tell you upfront that researchers have already established that ENSO changes first, which THEN shifts the polar jets. You must therefore focus your speculations on causes at the equator, not the poles.
http://www.srh.noaa.gov/srh/jetstream/tropics/enso_impacts.htm
There are clouds, and there are clouds. High level ones interact with temps differently than mid and low level ones. So this throws three more variables in the mix.
Does increased solar activity result in more t-storms giving more high level clouds? Or does it cause more or fewer low level clouds to form in the mid and upper latitudes?
There really are so many variables in all this. it seems a little fruitless to try to find patterns in just one or two.
Wouldn’t Fourier Analysis reveal major cycles?
The periodograms I show above are done using Fourier analysis, which will indeed reveal major cycles.
w.
I wasn’t sure how you constructed them, but they looked quite laborius. So i tried a shortcut version of your analysis with a single station with, was stunned that it really showed the 10.9 year cycle on the first try and was planning more sober tests on the other station later.
Now i realized that it was not reproducible even for the same station with other years than 1922-1987.
So i don’t even try the other stations anymore, especially since you already did it.
Thanks
I would think one would find solar influence in the ocean temperatures over time rather than land if it existed.
Ozone breakdown, stratospheric cooling and mixing atmospheric layers and influencing jet streams and ultimately tradewinds etc. Unfortunately, no data set that I can find.
forgive me the concept, would the relative position of the earth in its orbit have influence on the strength of whatever the sun is doing? iow would you need to have a chart for every month of the year, or a chart for each of the summer months of each hemisphere?
Trouble is the position of the sun on the earth is so short lived at any orbital position the fluctuations in the suns spots would have to be perfectly nil in order to witness a correlation.
This is all academic. I can’t see why people can’t accept that the climate is a very chaotic system, and does not really respond to any of our little pet theories. As such, you’ll get this beautiful correlation and model results … “sometimes”, and at others, complete divergence. Hold the data just right … shake it appropriately, and use the “appropriate” mathmatical analysis, and something will always fall out …. or not.
CO2 is not the culprit
Solar is not the culprit
Aliens are not the culprit.
Math can’t explain it …. nor can “People Magazine”, or Time or any other journalistic endeavor.
BUT .. .alas .. our little egos just won’t let it go.
Yes and no. We are making small steps toward understanding atmospheric/oceanic teleconnections at the local and regional level. Of even more importance we are making small steps toward understanding the bridges between one area of the globe and another area of the globe (IE ENSO 3.4 and say…the Atlantic Multidecadal Oscillation, or upward propagating Rossby Waves and Jet Stream behavior). What models get wrong and Earth seems to care little about is long term forecasting 30 to 50 years out. All such forecasting endeavors that make a difference to us at local and regional levels get washed out in the error bars. Most are wrong and the ones that are right got there by sheer chance.
The IPCC does consider warming as caused by aliens. Since they separate human activity from what they refer to as “natural” we become unnatural, and thus alien.
Willis, I was hoping you would show proof of the absence the sun spot period in the cloud data. Instead of highlighting the the location of the required 11 and 10 year periods, and showing their absence, you leave the reader to struggle with the peak locations of your oddly labeled graph. And arguing that “nothing in the range from five to twenty-five years exceeds about 5% of the total signal” is not equivalent to proving that there is nothing there. And when was the last time that anybody here accepted the argument that trends less than 5% of the seasonal variation can be dismissed as irrelevant. Indeed, the alarmists make the opposite argument.
On the other hand, if there is something there, that would also not be proof of causation. I would also like to see a comparison of your averaged cloud data with averaged temperature data. My hunch is that there is only superficial similarity. I don’t see the 70’s cold spell nor the recent plateau or pause in your cloud data.
I can see that as a loop. If A then B, if B then A. In programming this is called a run-away loop and can freeze a computer. Plugging in clouds and cooling for the variables, is it reasonable to think clouds cause cooling and cooling causes clouds? Probaly not, it is more likely clouds causes cooling and cooling causes some number of variables in a some squence to cause clouds.
In PC operating systems the idea of linear processing went out the window a while ago, operating systems are event driven. Nature is similar to this in that it is one big loop constantly reacting to and creating events. Events occur concurrently, can be cascading, are not predictable (the operating system does not know when user A might start program B), and can clash. Within the big loop there are smaller nested loops doing the same thing, shooting events outwards eventually reaching the outer loop.
Causation is relatively easy, If A then B. Understanding causation has shot civilization forward. However, in terms of understanding how climate works, causation is like learning the alphabet, the first step but still far away from making meaningful stories. Earth and it’s atmosphere is a complex, multi-layered, event driven system with randomness and unpredictability a core component. At this point working on climate models helps understand the events and event handlers, claiming models simulate Earth and it’s atmosphere is science-fiction, claiming to be able to predict climate for centuries into the future is both childish and a God complex.
@Willis
Your hypothesis that “tropical clouds and thunderstorms actively regulate the temperature of the earth” suggests you should look at the rate of cloud cycling vs.sunspot number. What you are currently doing is looking at total cloud cover. I suspect all the interesting stuff has been averaged out of the dataset. Why not try again eating your own dogfood, as they say? š
Who on earth says “eating your own dogfood”, and what does it mean?
w.
It specifically means “using the software one sells to other people”, or more generally, doing as you tell other people to do.
Thanks, Viking, but I’ll wait for JDN to answer my question about what he means by it. Your interpretation doesn’t make a whole lot of sense ā¦ what have I been “tell[ing] other people to do”?
w.
Wow. I didn’t know that was going to be controversial.
The phrase “eating your own dogfood” is usually used by programmers to imply that they get their work done using the tools that they themselves made. It’s stretching the metaphor a bit, but the tool you made is an insight that storms seem to regulate temperature. Since that’s your thing, I though you might look for correlation of the global thunderhead area with sunspot number, or something to that effect.
I’m just as puzzled as you are about why there is no periodicity.
Thanks, JDN. I’d just never heard that “dogfood” expression.
I’d love to but as far as I know, there is no measure of global thunderhead area, although I could be wrong. I’ll have to look deeper into the ISCCP data, since that information may be there ā¦ so many datasets, so little time.
All the best, thanks for the suggestion, and for clearing up the confusion.
w.
Would a global lightning database serve as a proxy for thunderstorm severity?
See http://thunder.nsstc.nasa.gov/data/data_lis-otd-climatology.html
The cloud cover data that you have ‘uncovered’ for the US seems much more interesting. If one looks at this from what might be called a ‘gaia’ perspective, it’s mightily logical that cloud cover would be the thermostat. I can’t believe that nobody has taken a good global look at this, but even local data ought to yield some very interesting results without too much torture. There is likely good, long-term cloud reporting in the UK to link with the very long CET temperature record, although as the UK is subject to very rapidly changeable weather patterns on the edge of the continent this may limit what we can learn.
Part of the problem is using the Average SFU to represent the Solar Output energizing the Earth. One needs to use the “area under the curve” or the integral. This gives one number that more accurately represents the total energy for that 10~11 year period.
To be more precise than that, one needs to take into account the oceans and their heat storage and release.
To that end, I made a model, based on physics, to merge the Solar UV and Ocean Temperatures. I ran this model from 1650 until now.
Simply stated:
1) EUV > 120 warming.
2) EUV = 100 to 120 holding steady
3) EUV < 100 cooling.
Note that the EUV values must be averaged [area under the curve] over the 10~11 year solar cycle.
Now, if the Sun is in a reduced output period, then there will be less EUV resulting in less Ozone. This will allow the Ozone holes to expand. Since Ozone blocks heat flow into space [absorbs infrared and re-radiates 1/2 to earth], the holes will allow heat to escape at an increased rate. The effect could be similar to falling off a cliff, Global Temperature wise.
We are now into "only" 15 years of reduced Solar Output [starting at 2000]. Look at the results in the Northern Pacific and Northern Atlantic:
http://weather.unisys.com/surface/sst_anom.gif
Also, one can see a signal of the EUV in
http://ocean.dmi.dk/arctic/meant80n.uk.php
Notice the temperature "peak" at day 300. This was due to the huge EUV output from the giant Sunspot.
Also, one can track the Ozone holes via:
http://ozonewatch.gsfc.nasa.gov/monthly/NH.html
You have identified what I have identified. Congratulations! In fact, I also use Unisys!
jlurtz November 1, 2014 at 7:26 am
Part of the problem is lack of links ā¦ exactly what are you calling “Average SFU”, and where is the link to the “SFU” dataset you are using?
Sorry, but that is reanalysis data. In any case, what is the dataset which you are using to determine the variation in EUV during that period? I ask because as far as I know, the EUV didn’t hit the earth until October 24th ā¦ but the reanalysis data you show has the temperature starting to rise on October 20th. So I’m curious about how a temperature rise on the 20th can be caused by a rise in EUV on the 24th.
w.
w.
There are no data sets. I make and analyze my own. I use
http://www.solen.info/solar/
as my inputs. I did this because the centroid of a triangle is 1/3 the way from the base, but the average is 1/2 the way from the base! The amount of energy is strongly related to the centroid not the “instantaneous average”.
As per the rise in temperature related to EUV, the EUV is radiation and it absorbed by the ozone layer and remitted [we measure the remitted as SFU]. The amount of radiation absorbed is a function of the path length: short path length less absorbed; long path length more absorbed. The Arctic this time of year has a long path length.
So, the EUV does not need the peak to affect the ozone, it is more a function of the “path length time” that the ozone is exposed to the EUV.
What is wrong with “reanalysis of data”? All data is analyzed and then reanalyzed, etc.!
Progress is made by new ideas and better analysis; not in rehashing the existing muck.
You will get ‘updated’ UNISYS interpretation, note that I am NOT saying it is correct, by using the proper link. It is now SSt anom_new as opposed to SST anom, which you show above.
http://weather.unisys.com/surface/sfc_daily.php?plot=ssn&inv=0&t=cur
Sigh. No, we cannot. All that we can state is that there is no direct univariate correlation, and since correlation is not causality that is insufficient to justify the conclusion, just as it would be insufficient to draw the conclusion that they are “affecting” (the cause of) observed variations in the dataset if there were correlation.
. Bayes theorem comes into play.
is uninteresting and not predictive, but this does not suffice to show that 
is not causal until you show, or argue, that 
is independently uninteresting, that 
is uninteresting, etc.
th dimensional?
But in a highly multivariate system this isn’t just an idle repetition that can safely be ignored. It is serious stuff. The climate is “probably” orbiting in poincare cycles in some high dimensional space in association with a strange attractor. The temperature, cloudiness, etc etc are all projections of this orbit. The orbit itself is perhaps not some simple hyperplanar single loop in two effective dimensions, because that would produce precisely the kind of signal you are looking for the projective dimensions of all of the variables contributing to the hyperplane and you’re not finding it, but then, why would it be?
Even in the simplest 2D systems, predictive modelling with 2D outer-product logistic regression fails because sometimes the relationship between the 2 variables and the outcome is of the exclusive or type, not the independent logistic type. In statistics-speak, the relationship is not separable, the joint probability distribution does not factor,
All you are showing is that independently,
I think that you’ve already made assertions for causal relationships of precisely the form that would confound this yourself in the past. Water warming because of e.g. seasonal changes produces more water vapor (a potent greenhouse gas). But more water vapor makes more clouds (a potent high-albedo covering and latent heat transportation system). The greenhouse warming is offset by the albedo cooling and the climate is (likely to be) far less sensitive to increases in water vapor content caused by warming or cooling variations than one expects by considering only the direct relationship “more water vapor means more warming”, or the direct opposite relationship “more water vapor means more cooling”. This is further compounded by the fact that this same phenomena could (still) be net warming or net cooling systematically, but only in a way that depends on average latitude, or a way that depends on average latitude and whether one is over certain kinds of surface, or a way that depends on average latitude, whether one is over certain kinds of surface, and time of year/local mean surface temperature!
Seriously. Water vapor and consequent clouds and cooling might well dominate heavily over otherwise dry tropical or subtropical deserts, be irrelevant (on average) in the temperate zone, and be net warming in the polar regions, but only over ice where the increase in albedo is invisible. You might see a very clear signal if you look at these regions independently, but by the time you average the warming and cooling globally the signal all but vanishes. The systems dynamically redistributes many things, to be sure, but perhaps the average temperature, the average total cloudiness, and so on, do not change (much) as it does so.
This is why the climate system is such a bear. We have inadequate data to do a particularly good job of trying to untangle the joint distribution a mere two or three variables at a time, let alone the five or six visible in the example, and that data becomes more inadequate rapidly as one goes into the past, to the point where for more than thirty to fifty years ago, one isn’t even modelling the data, one is modelling a model that is supposed to reconstruct the data we do not have for that era subject to some assumptions, which then become additional unstated Bayesian priors for models that try to fit the model data that fills in the real data we know rather imprecisely where we know it at all but is missing. Yet we rather expect that causal relationships in the climate could be what, ten dimensional? Fifty? Ten thousand?
Do we even have an argument to place a plausible limit on the nontrivial dimensionality of the strange attractor we are checking in projection one or two axes at a time?
Actually, probably we do. Take the climate data in the various dimensions and analyze the noise (or rather, the variance), not the mean. Perhaps when the sun is active, the projective radius of the attractor on a variable’s dimension produces a signal of some sort that isn’t buried in other “noise” that is really causality in still other subspaces of the system. Perhaps the spectrum of this “noise” shifts in tune with the solar data, even though the integral over the noise rarely accumulates into a macroscopic average signal. If one examines in particular things like “discrete” events — volcanic explosions, say — that have a clear spike followed by a smooth decay, perhaps the rates (time constants) of those decays are functional on solar state. Solar state could be leaving its causal fingerprints all over the place, but not having much total effect on global averages of variables taken one at a time.
There is also the second approach. Start looking not for univariate correlation, but for multivariate correlation. Much more difficult, much more subject to the curse of dimensionality. But perhaps useful.
rgb
Peanuts. The string theory struggles with a 10^{500}th dimensionality.
It struggles with much more than that!
rgb ‘ Take the climate data in the various dimensions and analyze the noise (or rather, the variance), not the mean. Perhaps when the sun is active, the projective radius of the attractor on a variableās dimension produces a signal of some sort that isnāt buried in other ānoiseā that is really causality in still other subspaces of the system. Perhaps the spectrum of this ānoiseā shifts in tune with the solar data, even though the integral over the noise rarely accumulates into a macroscopic average signal. ‘
When I look at the NH sea ice anomaly on wuwt’s ref page http://arctic.atmos.uiuc.edu/cryosphere/IMAGES/seaice.anomaly.arctic.png there seems to be distinct phases where the variability shifts from one range to another. With the eye of faith, it’s also possible to see a portion of a cycle centered on 2010, though that would be a lot longer than the solar cycle so perhaps not relevant here. Hope rgb that I haven’t misinterpreted what you said above.
rgb, “Solar state could be leaving its causal fingerprints all over the place, but not having much total effect on global averages of variables taken one at a time.” You are correct.
The solar activity effects are layered and time dependent and sorting them out IS a bear!
Equatorial cloud data can and does correlate strongly with trade winds. The models reproduce this phenomenon rather nicely. Walker cell circulation and disruption factors hugely. And all of that has to do with physics: the shape of our Earth, its inclination towards the Sun, tidal forces due to gravitational pulls of the Sun and moon, and the coriolis effect as Earth spins on its axis. The trick is that one must add inertia and fluid dynamics to the mix which then causes a breakdown in predictability the further away you get from 0 degrees latitude. In other words, you get chaos. Hard to forecast using models because they must simulate chaos as well.
Really hard to forecast using the average of model averages as in the MME mean of CMIP5, because the average of a chaotic trajectory is not a chaotic trajectory at all, and is nearly useless for the purpose it is being used for.
What might be interesting is looking for models that produce trajectories that correspond well with observation, but with chaos and the Kolmogorov scale mismatch, even that is more like looking for chance correlation rather than anything that could conceivably be predictively robust.
rgb
And as I have put in a simpler manner, the climate modelers are trying to model something they imperfectly understand.
Another well argued piece, Willis.
I know it’s not scientific evidence but just anecdotal evidence, but astronomer William Herschel was supposed to have won many a bet on the price of wheat & corn, by counting sunspot numbers. The more sunspots, the lower the price of wheat & corn, the fewer the number, the higher the price, etc. Piers Corbyn hasn’t posted here for sometime but he always talked about the 22 year cycle of full reversal of Solar magnetic field back to it’s previous state as having influence. As they say, absence of evidence is not evidence of absence!
Any ideas?
I’ve analysed his data, the correlation doesn’t exist. I’ve never read that he won bets at it. There’s a good analysis of the question here.
w.
Great stuff Willis,
Something else to consider might be the work of Dr Tim Patterson, professor and director of the Ottawa-Carleton Geoscience Centre, Department of Earth Sciences, Carleton University. He studies sediment, diatom and fish-scale proxy records in British Columbia and postulates longer period marine cycles, all correlating closely with other well-known regular solar variations. In particular, he sees marine productivity cycles that match well with the sun’s 75-90-year “Gleissberg Cycle,” the 200-500-year “Suess Cycle” and the 1,100-1,500-year “Bond Cycle.” The strength of these cycles is seen to vary over time, fading in and out over the millennia. He proposes that the variation in the sun’s brightness over these longer cycles may be many times greater in magnitude than that measured over the short Schwabe (11 yr) cycle and so are seen to impact marine productivity even more significantly. An example of Dr Pattersonās work can be seen here (2007):
http://www.sciencedirect.com/science/article/pii/S0037073804002507
More recently (2012), Kern et al, (High-resolution analysis of upper Miocene lake deposits: Evidence for the influence of Gleissberg-band solar forcing) show similar correlation results for European lake sediments. I donāt know if this is the type of data set and longer period cycle correlation you are interested in studying (one thing is that the resolution is too low to sort out the 11 yr cycle) – two data tables are included at the end of the article linked here: http://www.sciencedirect.com/science/article/pii/S0031018212006748
Thanks, Kirby. What is the evidence for your claimed “1,100-1,500 hundred year cycle” in the sun?
w.
The Bond/Dansgaard-Oeschger semi-periodic climate cyclicity in the North Atlantic area is well established for the Late Pleistocene, less well for the Holocene. There is no clear evidence that it is solar-related.
Willis,
This is proxy evidence of course. The āevidenceā is the paleoclimatic interpretation of oxygen-isotope (and other isotopes) records derived from pollen, diatoms, fish scales, etc. from cores extracted from marine sediments. Both isotopic abundance and scale, pollen, and skeletal counts are processed to extract the periodicity. As usual, correlation is not causation.
ā¦
Late Holocene variability in pelagic fish scales and dinoflagellate cysts along the west coast of Vancouver Island, NE Pacific Ocean (2005)
Patterson et al
(abstract): āCorrelation of the marine paleoproductivity records observed in Effingham Inlet with solar influenced climate proxy cycles observed in the North Atlantic region indicates that solar forcing at different scales might have influenced the climate in the northeast Pacific as well. In particular an 1100- to 1400-year cycle in regional climate is well represented in the fish productivity proxy and sedimentological record.ā
ā¦
Solar Forcing of Climate. 2: Evidence from the Past (2005)
Gerard J. M. Versteegh
(Abstract) āThe noisy character and often insufficient temporal resolution of proxy records often exclude the detection of high frequency decadal and bi-decadal cycles. However, on multi-decadal and longer time scales, notably the 90 years Gleisberg, and 200 years Suess cycles in the 10Be and 14C proxy records of solar activity are also well presented in the environmental proxy records. The additional 1500 years Bond cycle may result from interference between centennial-band solar cycles.ā
Thanks, Kirby. As you point out, these are paleoproxies. My problem with many such proxies is that the authors almost never establish their claims using modern data.
For example, you show a study of “variability in pelagic fish scales” and dinoflagellate cysts. Well, we have pelagic fish and cysts scales right up to the present. Their study is here ā¦ but unfortunately, as near as I can tell they make no attempt to link up pelagic fish scales with solar variations in the modern period. In fact, they say:
That’s great ā¦ but where is the putative solar effect? Or are they claiming that somehow the solar variations affect the “nutrient supply and salinity”?
It is for me a recurring problem with many, perhaps most of these kinds of studies. Despite the fact that the processes involved are going on right up to the present, far too often there is no attempt made to see if e.g. the modern levels of fish scales correlate with the modern, known variations in solar.
To me, it’s like calculating the temperature from the tree rings. If tree rights were as good as they claim, able to discern Ā± 1Ā°C variations in temperature five hundred years ago ā¦ then where are the modern records to bear this out?
And if you want a good laugh, take a look at their Figure 2.
Notice the top line, showing the 14C variations (supposedly the index of solar activity) versus what they identify as the warm and cold periods. Now you tell me ā¦ do you see any correlation between the putative solar activity level supposedly shown by the 14C variations, and the warm and cool periods?
Also, the variation of the fish scale counts seems only very vaguely connected to the cool and warm periods, and I see virtually no correspondence between the fish scales and the solar activity.
Unfortunately, this is far too common in the paleo worldāno bringing it up to the present, and vague handwaving presented as though it were actual evidence. And bizarrely, for their statistical significance calculations they cite that well-known expert on statistics ā¦ Michael Mann. Anyone citing M. Mann on statistics desperately needs to go back to basics and start over.
Net result? When I find that kind of nonsense in a study, I toss it in the circular file. And unfortunately, the paleoproxy community has produced reams and reams of those kinds of studies ā¦ you need to start from a position of total disbelief. Far too many people google something like “millennial solar cycles” and grab whatever comes up that fits their beliefs. Me, I’m cynical as hell. I start from the assumption that the authors haven’t done a good job, and that they are trying desperately to make something out of nothing ā¦ and most of the time, I find that that’s not far from the truth.
Best regards,
w.
I’ve run an Inverse Fourier Analysis on the data from ftp://cdiac.ornl.gov/pub/ndp021/ndp021r1.f13 with MS Excel.
For station 3812 NC ASHEVILLE there’s a period of 10.9 years for every months(januaries, februaries, marchs…) i analyze for the data from 1922 to 1987 since Excel needs a power of 2 (like 64) as number of inputs.
Interesting. Pick another station at random and see if it works. Given the sheer number of stations with data, you may be as right as a broken watch.
Thanks, Pamela, couldn’t have said it better. In addition ā¦ how strong is the cycle? I find all kind of cycles down at the “lost in the weeds” zone.
w.
Heh
i don’t understand why you would look for an 11 year cycle, unless you think the cloud of ejecta shielding us dissipates in an 11 year cycle.
Not only that but many flares are directional .. so the shielding will be uneven, and so more effective
if between us and the denser sources of cosmic rays … in addition to the particle density of the solar win generally.
you would need to calculate flight time, distance vs density, and you might have some idea
how long the sun must be quiet for the cloud to thin out ,,, the further out, the more distance
between particles
a very quiet sun after a very active period, if the activity picks up again before cloud from the previous cycle dissipates, would it even show in the record ?
Normal a quiet periods and feeble active periods over more than one cycle, then we might notice.
But would we be able to separate that from PDO or a couple volcanoes or any other factor ?
I doubt you will until we actually go thru another mini ice age … and notice the dang sun has been a wimp for the past 3 cycles …. otherwise your looking historical accounts and no better evidence than correlation
Either that, or get to calculating the particle density gradient of the ejecta cloud in the solar system.
What, you thought it was the “near earth” material that was being talked about ?
Its not the density of the solar wind passing by .. its the density of the material years past us.
Other can feel free to bop me over the head, and point where I am wrong …
But it seems so clear to me, and I assumed others had the same understanding
The density of any solar wind passing by at the moment simply cannot be what
that theory was about,,,, that ,, would be flatly absurd .. that will intercept next to nothing
Its the accumulation, its dissipation rate, is it being replenished, how often, how much etc.
You wont see any 11 year cycle, you would see only the effect of the average over several cycles
How dense is the cloud .. how much material ,,, how long will it be effective if it is not replenished
what is its speed of expansion etc.
If there has been estimates and math on that, Ive missed it, and yet i assumed that was the mechanism
Willis, your hard work is certainly appreciated. You have explored many approaches and the climate does not respond as we would expect. Can you run an analysis of how the earth’s temperature responds to it’s elliptical orbit around the sun so we can have a more robust signal and see how the climate system responds to it. Perhaps, it would give a clue as to what is happening.
But aren’t we looking for anomalies? The solar effect on atmospheric temperature on a cyclic basis has been calculated. The problem is that Earth’s own variability can and does bury that effect. On a clear dry-air day with no reflectance back to space, we know what happens to the solar heating effect. On a fully “veiled” day (clouds, ash, or sulfur dioxide in the atmosphere), we know what happens to that solar heating effect. The problem is that the mercurial Earth slaps that effect all over the place in-between those two extremes in such a chaotic way that the signal cannot be detected anymore.
A number of Willis’s posts have show the possibility that the Earth’s climate system is a closed loop system. The analysis I suggested would help clarify the dynamic structure of a closed loop system.
Willis what you don’t seem to be able to understand.
The historical climatic record shows a good correlation between extremes in solar variability and the climate. The problem for so many is they just do not understand or do not want to understand that at times when solar variability is limited solar/climate relationships are going to become more obscure. Nevertheless post Dalton solar activity had been on a steady overall rise(especially the magnetic component) and overall global temperatures once again move in an upward fashion which was to be expected. Post 2005 solar activity has switched from an active to an inactive state and correspondingly the temperature rise has already ended and soon it will be down.
As I have requested many times show me the data which shows a long period of time when prolonged minimum solar conditions corresponded to a global temperature increase over several years and when prolonged maximum solar conditions corresponded to a fall in global temperatures over several years. I have yet to see such data.
Now going forward in order for solar variability to overcome the noise in the climate system it has to reach certain low value parameters for a sufficient length of time in order to show a significant solar /climate relationship. I have outlined those parameters and believe they will be coming into play as this decade advances.
One condition has already been satisfied which is several years of sub-solar conditions in general now it is just a matter of waiting for the maximum of solar cycle 24 to end and then seeing how deep and long in duration the depth of solar minimum conditions become and see how this translates to the climate.
As far as AGW theory CO2 is increasing year in and year out and temperatures are not responding in an upward fashion ,if that same situation in a reverse way happens with my theory I would admit to being wrong instead of standing by something (agw theory) which will soon be obsolete.
History has shown that the climate of the earth is NOT static and that it has gone abrupt and significant changes from time to time and I say the the most likely route cause for this is the item that drives the climate that being the sun . When something that drives something changes it, it will change what it is driving to a varied degree.
The essence of all my post is that if solar variability is long enough in duration and strong enough in degree of magnitude change it will have a significant impact on the climate but absent this solar/climate connections will be very obscure.
This decade offers a great chance to see if extreme solar conditions will impact the climate.
Finally, someone who actually understands how sun spot activity=climate rises and falls!
I have grown quite irritated by people wanting to prove that the sun, the #1 driver of climate, has little or no effect! Even very slight changes in overall solar activity leads to huge changes on our planet.
And all ice ages end very abruptly due to the sun suddenly becoming more active and the fact that we are now in a cyclical ice age system that began only 3 million years ago shows that something has very much changed over time and not in a good direction and certainly not towards ‘global warming forever’ but the exact opposite.
Perhaps our local star is much less energetic as previously.
My question is where was your climate governor prior to 10000 years ago?
Also the evidence is there in that weak solar/geo magnetic fields will result in a trend (jig saw pattern) to a cooler climate (but to different degrees read below)and this is EXACTLY what is going to take place now going forward.
I want to add this, thresholds, lag times the initial state of the climate(how close to glacial/interglacial conditions climate is), land/ocean arrangements, earth magnetic field strength , phase of Milankovitch Cycles ,random terrestrial events ,concentrations of galactic cosmic rays within 5 light years of earth due to super nova or lack of for example, the fact that the climate is non linear is why many times the solar/climate correlation becomes obscured, and why GIVEN solar variability(with associated primary and secondary effects) will not result in the same GIVEN climate response.
What is needed is for the sun to enter extreme quiet conditions or active conditions to give a more clear cut solar/climate connection which I outlined in my previous post.
The solar criteria I suggested needed to impact the climate to make it more likely to become colder, which I suggest can happen if the prolonged solar minimum continues and becomes more established going forward.
Which you would then ascribe the recent and now paused global warming to something else?
Willis Eschenbach: November 1, 2014
āSo 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. ā¦. [snip] ā¦.. As a result, if there is no sunspot cycle visible in the terrestrial surface weather datasets, then we can assume that none of those phenomena are affecting the dataset.
In addition, I see this as a chance for you guys to definitively show everyone that there actually is a sunspot-related signal that makes it down here to the surface where we live. So I invite youābring out your best datasetā.
——————-
I donāt have a ādata setā to offer, but I will, after a couple hours of thinking about it, offer my critique of your quest to solve a problem, to wit:
Now I will agree there is a per se ā11-year sunspot-related solar cycleā ā¦. but I really donāt think that the max number (quantity) of Sunspots, ā¦.. or the lack thereof, ā¦. that occur during said 11 year cycle can be used as a āsignalā for pointing out ā¦.. or determining some surface weather/climate related dataset or event. If said Sunspots only originated on the Sunās earth-facing equator then their ānumbersā might be used as the aforesaid āsignalā.
Now the above does not negate the possibility that the presence or absence of Sunspots is in actuality a āsignalā, ā¦. and given the observed correlation between Sunspot numbers and climate events on earth , ā¦ā¦ then said āsignalā is potentially one that only defines a Solar event for which, ā¦ that I am aware of, ā¦. there has never been a detailed ādatasetā generated for.
My above conjecture is based on the following, to wit:
———–
Excerpted verbiage from: http://en.wikipedia.org/wiki/Sunspot
Sunspots were rarely recorded during the second part of 17th century.
sunspots all but disappeared from the solar surface
observations of aurorae were absent at the same time.
the lack of a solar corona was noted prior to 1715.
a renewal of sunspot cycles starting in about 1700.
The period of low sunspot activity from 1645 to 1717 is known as the “Maunder Minimum”.
===================
Excerpted verbiage from: http://imagine.gsfc.nasa.gov/docs/science/mysteries_l1/corona.html
One of the most puzzling features of the Sun is what has been dubbed “the solar corona problem.” There is a region around the Sun, extending more than one million kilometers from its surface, where the temperature can reach two million degrees. This region, called the solar corona, is where the solar wind originates.
======================
And the ādatasetā I made reference to was one that denoted the presence, absence, surface height and/or temperature of the āsolar coronaā.
If the lack of sunspots is a āsignalā of the potential decrease in/of the āsolar coronaā ā¦.. then it should also be a āshadow signalā of a potential decrease in Solar irradiance that the earth is subjected to, ā¦.. which appears to be confirmed by the surface temperatures during both the Maunder Minimum and the Little Ice Age, ā¦ to wit:
http://i.huffpost.com/gen/1578821/thumbs/o-MAUNDER-MINIMUM-570.jpg?1
I now invite yaāll to āhaveattitā with my conjecture.
Wow!
All my life I’ve been told, like many others, that the sunspot cycle influences the earth’s weather.
Willis, rightly so, says we should be able to prove that. Except, despite trying, he can’t. And, apparently, neither can anyone else.
But ideas do not go quietly into that good night and the reasons why are interesting;
a) the earth’s climate is too complicated (i.e. it does influence it, but we can’t see it)
b) the earth’s atmosphere is a giant heat sink that modulates the sunspot influence into the noise level (but, trust me, it does have an effect)
c) you’re looking at it all wrong … you need to be looking at x or y or z
a) and b) smack of belief without data which, given the usual skepticism about climate change, is ironic. c) is just a distraction.
So why can’t we accept that if the impact is almost immeasurable it is likely a third order effect and we can ignore it? Then, maybe, we can stop all the navel gazing over sunspots.
The connection has been found over & over again in study after study for 200 years.
More verbiage without any link to a dataset. Color me unsurprised, milodon.
w.
I most certainly did provide a link. The same one that you have been shown over & over yet continue to ignore.
The precision of the data are a function of being a 50 year average. Is that really all you have to say?
More blatant ignorance of any data that don’t fit your preconceived notion. No surprise there.
That’s a reply to your comment below about the Central England Temperature reconstruction.
At this point the proper reply is that I have shown you dozens of such papers, but you always studiously ignore them, demanding instead just a single one, which you then find some excuse for not considering.
>> says we should be able to prove that
Can you mathematically prove that rain causes wet sidewalks?
We know for a fact that a solar maximum inserts into Earth about an additional 2.75 x10^22 Joules of energy. To put this into perspective, if only 37% of this energy were in the atmosphere, it would be 2 degrees warmer.
And the vagaries of Earth’s atmosphere and churned up or glass smooth ocean can smack that around like a WWF free for all.
Pamela, agreed. It’s like a rogue wave on the ocean. For a long time, some people speculated that they were possible, when by chance, all the various waves at different frequencies might all sum in the same direction. In the last decade, with increased technology, we now know that at any given time, one or more rogue waves is happening somewhere on earth.
I provide my calculation not as a prediction of what will happen at any certain time, but rather, what should we expect as the limits of natural variability. The point is, clearly, nothing extraordinary has yet happened that calls for a new hypothesis.
I’m glad you brought this up, VikingExplorer. Since 1900 the solar constant has a range of ~2.6 W/m^2 from min to max. We know from Trenberth, et al. that only about 17.7% of incoming flux is absorbed (240/1,362 = 0.177), the range of change in downwelling LWR is ~0.46 W/m^2. Multiply that by the canonical value of 0.8 C/W*m^-2 for equilibrium sensitivity and we get 0.37 C for an approximate upper limit temperature change due to solar cycle. Least squares linear regression of TSI only against temperature yields a slope of 0.29 C/W*m^-2, which is both pleasingly close to and predictably lower than the theoretical upper bound. My linear regression model spits out 0.1 C min to max against the residuals of ENSO, AMO, etc., so there’s about 0.2 to 0.3 C of variability I can’t account for — understandable given the noise and observational resolution/uncertainty already much discussed in this thread — as well as the fact that equilibrium and fast transient response to perturbations are two very different animals.
Regardless, my back of napkin theoretical calcs are within striking distance of a linear regression model. Not exactly QED, but a promising result methinks.
https://drive.google.com/file/d/0B1C2T0pQeiaSTndfczY2N0tBWWc/view
Note that prior to 1960, the clear correlation between TSI and residual breaks down, but just eyeballing the graph the effect still jumps out in places.
VikingExplorer, Errata:
Aack, I mixed units. The proper check of model against theory is: 0.29 C/W*m^-2 * 0.46 W/m^2 = 0.1334 C. Regression of TSI against the model residuals gives a range of ~0.1 C, so I’ve got ~0.03 C of variation I cannot explain, a much stronger result.
The SSN [when calibrated correctly] is an excellent measure of the variation of solar output, as is the F10.7 flux. First you have to realize that both measures by their nature are already a 14-day average as they refer to the signal integrated over the visible disk of the Sun. The R-squared measure for the correlation is in excess of 0.97. This is as good as it gets.
We have a good measure of the F10.7 flux back to 1840 [and possible back into the 18th century as well, as the Earth itself is a good instrument for measuring the variable [the EUV flux] causing the F10.7 flux variation: http://www.leif.org/research/Reconstruction-Solar-EUV-Flux.pdf
Willis is asking for data sets, not your hand wringing. So here is my data set with the F10.7 flux
http://www.leif.org/research/F107-Flux-Since-1840.xls
http://www.leif.org/research/F107-Flux-Since-1840.png
And F10.7 flux and SSN follow each other very closely on daily values too:
http://www.leif.org/research/TSI-SORCE-Latest.png
as they should as they are proxies for the same thing: the solar magnetic field.
Here’s your F10.7 data link http://www.ngdc.noaa.gov/stp/space-weather/solar-data/solar-features/solar-radio/noontime-flux/penticton/penticton_observed/listings/listing_drao_noontime-flux-observed_daily.txt
Here’s the daily F10.7 link for recent activity http://www.swpc.noaa.gov/ftpdir/indices/quar_DSD.txt
and further back you can use these http://www.swpc.noaa.gov/ftpdir/indices/old_indices/
This isn’t hand-waving – I have a spreadsheet and model and the calculation for my determination of 120 sfu/day is very easy and straightforward.
However I’d like to see if someone else can figure this out too, given all the hints I’ve dropped over the summer. I even gave Willis another major clue in August. Of course he wants it spoon fed to him.
Here is that clue: http://climate4you.com/images/SunspotsMonthlySIDC%20and%20HadSST3%20GlobalMonthlyTempSince1960%20WithSunspotPeriodNumber.gif
By the way, I’ll give you all even one more clue, and we’ll see who’s smart enough to figure it out: I substitute F10.7 for SIDC number.
When the time comes I’ll relate it for you wrt the revised GSN.
The only real reason I can say this with confidence is I’ve been paying real close attention to the “system” for some time now and have seen that it works out pretty well. Have at it!
You are missing the point: your F10.7 flux is a 14-day running mean.
Typo – Quijote should be Quixote. Much time spent in Spanish?
No, Willis is correct. The original text (1605) used the Quixote spelling. The English spelling probably dates from then, but Spanish has moved on and it is now ‘Quijote’. Although I would not want to make any claims that Spanish is totally unified.
ENSO effects US cloud cover, and El Nino episodes/conditions tend to happen in the same place in most solar cycles. That doesn’t mean that you can reduce it to an 11yr signal though, as the solar wind speed has more than one major drop through the sunspot cycle.
http://www.esrl.noaa.gov/psd/people/tao.zhang/UShydrocycle-9.pdf
The same place? Really? Prove it. And cite “significant”, not “tend” statistics.
The most regular major drop in the solar wind speed through the solar cycle is typically around one year after sunspot minimum. El Nino episodes (conditions*) occurred in 1902, 1913/14, 1925, 1936*, 1946, 1957 (late), 1965, 1976, 1986, 1997, and 2009.
There is a similar frequency of El Nino episodes/conditions at sunspot maxima, exceptions with higher solar wind speed and La Nina episodes at sunspot maximum instead occurred in 1938, 1989, and 2000.
https://www.longpaddock.qld.gov.au/products/australiasvariableclimate/ensoyearclassification.html
There seems to be a lot of belief in the comments that the signal would be obscurred by other aspects of the climate system. I disagree.
The diurnal cycle is obvious and short. The temperature of the atmosphere changes rapidly due to changes in input. Any effects over a yearly cycle would be detectable. I have camped outdoors in many climate zones, one may freeze in the desert at night and fry during the day. a shift of 40 degrees C.
There is a governor in this climate system and it is very powerful, identifying it is a bit of a problem however. I believe that Willis is on the right track with the moisture system ie. clouds.
Thereās an emergent phenomena in the afternoon of the solar cycle thatās pretty obvious in this graphic from Joe Bastardiās recent post:
http://wattsupwiththat.files.wordpress.com/2014/10/clip_image003.png
Perhaps like thunderstorms being feature of the diurnal cycle, El Ninoās might be a feature of the solar cycle; its precise arrival timing and strength depending upon the conditions of the solar cycle.
Please read Joe Bastardi more carefully. The red crosses are carefully cherry-picked El Ninos; most are not depicted.
Thanks, George. That graph is very misleading.
w.
There are two errors, in SC 19 the El Nino was from early 1957, three years after the sunspot minimum, and the last red cross is placed too early, it should be on the 2009/10 El Nino. That major El Nino episodes occur with regularity at the dominant drop in the solar wind speed in most sunspot cycles around one year after each sunspot minimum, is profound evidence for a solar cycle influence on weather and climate.
I’m not sure it’s correct to splice the two cloud datasets this way. The two signals appear to lie nicely on top of each other but amplitudes of the two signals are clearly different – there seems to be a systematic shift where at lower clouds, satellites are picking more cloud cover than humans and the other way around for high cloud cover. By proper recalculating of human observations to satellite measurements, the 1940 rise might decrease in magnitude significantly. Sadly the overlap is very short. It would be probably better to do much more detailed correlation analysis, down to the level of individual stations to have some decent certainity it’s done right.
The way it is done, amplitude of features to the left of the splice is uncomparable to the amplitude of features to the right.
Thanks, Kasuha. I made a conscious decision not to try to adjust the variance (what you call the “amplitude of features”, because I wasn’t really interested in the variance. Instead, I was interested in the trends over time.
Your suggestion, that the reason for the difference in variance, is that humans and satellites see the high and low cloud cover in different ways, is very interesting.
However, I suspect that the reason is actually in the density and regularity of the sampling. An average of individual stations is almost guaranteed to have greater variability than the result of a satellite-style regular sampling of the entire area. From memory, the underlying ISCCP data is something like a 25 kilometer square pixel, and there are a whole lot of those over the US.
I was most encouraged by the fact that the means of the raw ISCCP data and the raw station data in the overlap period (53 months) were within one percent of each other. That plus the high correlation of the two datasets are what convinced me that splicing is justifiable.
All the best,
w.
rgbatduke
November 1, 2014 at 7:36 am
Much more difficult, much more subject to the curse of dimensionality.
Not only in space but in time.
Two of the major indices run on clocks āsetā at different frequencies, a bit like two cars, one travelling at 50 and the other 55 mph. Last re-starting point was sometime before or around 1900, there are some indications that both may soon run out of petrol (gasoline), if so I suspect another re-starting point may not be far off.
While we are at ‘pseudoscience’, the three mayor highly respected scientists McCracken, Beer and Steinhilber have moved over to ‘astrology’
http://link.springer.com/article/10.1007%2Fs11207-014-0510-1
Desperate times require desperate measures !
Iāll try another analogy
Two cars on a racing circuit at 150 and 165 mph, no re-start more likely ālappingā over. Unfortunately, good data may not be even one ālapā long.
While we are about solar cycles, SSN is 27 points down at the SIDC’s October count see graph here , making the second SC24ās peak definitely over, is there peak no. 3 coming (?), no idea, but Dr. S some time ago suggested it may be even more.
I hope not. I want those warmists to eat some crow and only the Sun can make that happen!
I hope yes. Longer the cycle, stronger the cooling.
Willis is also showing the lack of any other short term cycles.
Willis Eschenbach wrote shortly after Figure 8:
“I draw no great conclusions from this, except that overall, as the US warmed post-1930, the US cloud coverage rose as well. Go figure ā¦”
I consider this comment as a somewhat cherrypicked sideshow to claim that clouds are a negative feedback to global temperature. However, I notice Figure 8 having cloud reporting peaking in the late 1960s, and most global warming in the time record covered by Figure 8 occurred afterwards.
The way I see it, to the extent Figure 8 shows clouds having a negative feedback on global temperature, I think there is no need to narrow down to a decade – I see that as motivating suspicion of cherrypicking more than it explains a point.
Also, I see this point as a distraction from low rate of visible presence of a ~11-year or ~22-year in surface weather datasets. If your point is that Earth is simultaneously self-regulating and acting randomly, I suggest avoidance of appearance of cherrypicking, of things related to negativity/positivity & chicken/egg of cloud coverage with global (or US) temperature. It appears to me that being more strictly focused on lack of correlation with solar cycles would be better for you to get your point across.
There are different types of clouds no? To me, what’s important is the type of cloud… does the cloud cover give us any indication of that.?
Willis, why not plot you composite clould graph figure 8 with SSN ?
I’ll do this later but I’m going out and don’t have time.
It looks, by eye, a lot like that those bumps in post 1940 cloud data match SSN peaks, ie negative cloud feedback to solar max.
If that is as it appears this raises the question of the notable dip in 2000 and flat earlier period. Perhaps as RGB suggests there are confounding varaibles at work here.
You should also try plot the first derivative of the temperature trend line against the cloud cover.
A first derivative of noisy data is a palmist’s (or climastrologist’s) dream.
RGB – I’m struggling trying to comprehend what you mean by “The climate is āprobablyā orbiting in poincare cycles in some high dimensional space in association with a strange attractor. “
You should read “Chaos” by James Gleick (the better of the brothers) and/or “Does God Play Dice – The New Mathematics of Chaos” by Ian Stewart. Then you will understand a little more about ‘strange attractors’ and PoincarĆ© Cycles.
Right, although you can also read nice short review articles, with pictures, on wikipedia (e.g. here):
http://en.wikipedia.org/wiki/Attractor
and make a small contribution to keep this incredible resource going. Personally, I think of wikipedia as one of the all time great achievements of humankind, right up there with open source software collections. Note well that the picture on the right is a projective view of a very simple attractor in low dimensionality. Now — and this will hurt a bit, or at least it does when I try to stretch my own brain to do it — try to visualize just a ten dimensional version of this same kind of structure, where this entire figure might just be a projection of the trajectory domain from ten down to two with a third sort-of-visible with imagination or a pair of 3D glasses.
A climate model with only ten dimensions would still be trivially oversimplified.
Note well that a trajectory like the one portrayed isn’t static. The system moves from one state to the next on the attractor. The attractor itself could be stable — a limit cycle where the system is constantly pulled towards a fixed point (or fixed cycle, or fixed hypersurface as it needn’t be stable in all dimensions) or it could be unstable. The attractors themselves can rearrange themselves, the fixed points can move, whole orbital regions can appear or disappear in systems with multiple locally stable attractors. Points leading to stable attractors can be mixed infinitely finely so that two distinct real numbers separated by infinitely small boundaries can go to completely different limit cycles or stable points.
Note well Lorenz’s original portrayal of a limit cycle from one of the original climate models (where chaos was discovered) is a figure. Note also the absurdity of averaging over pitifully short fragments of the trajectory bundles from neighboring initial points and asserting that this average is the “most likely trajectory”. Most of the “most likely trajectory” is as like as not to pass through completely unoccupied, inaccessible parts of the actual phase space of possible trajectories! See, for example the bifurcation diagram (Feigenbaum “tree”) showing the period doubling route to chaos for simple nonlinear oscillators. The “mean” trajectory is nearly completely meaningless — one might as well linearize the system and pretend that the chaos does not happen, extending the original unbifurcated line.
rgb
Willis Re: āa . . .surface weather dataset that . . . shows the sunspot-related solar influence.ā
I recommend searching for the LAG of the global ocean temperature behind the integral of the solar cycle, using the ACRIM Composite TSI against the ocean temperature data sets. Posted at: ACRIM Data Products
This lag was predicted by David R.B. Stockwell to be 2.75 years (90Ā° or Pi/2) and found by TSI (Lean 2001) (and sunspot count) against each of HadCRUT3vGL, HadSST2GL, CRUTEM3vGL, and GISSTEMP. CO2 warming cannot produce this lag.
Nicola Scafetta,Ā Richard C. Willson, ACRIM total solar irradiance satellite composite validation versus TSI proxy models, Astrophysics and Space Science ( 2014) Volume 350, Issue 2, pp.421-442 ; Scafetta @ Duke; arvix.org; Supplement.
Alternatively use the revised sunspot area, based on the revised sunspot number.
David R.B. Stockwell, āKey evidence for the accumulative model of high solar influence on global temperatureā 4 August 23, 2011 http://vixra.org/pdf/1108.0032v1.pdf
See especially Fig. 3 http://vixra.org/pdf/1108.0032v1.pdf
David R.B. Stockwell shows that the direct correlation of solar irradiance with temperature R^2 is only 0.028 while the cumulative solar irradiance has a correlation R^2 of 0.72 and solar + volcanic has R^2 of 0.78. See Fig. 4 in
David R.B. Stockwell āOn the Dynamics of Global Temperatureā August 2, 2011 http://vixra.org/pdf/1108.0004v1.pdf
David R.B. Stockwell, āAccumulation of Solar Irradiance Anomaly as a Mechanism for Global Temperature Dynamicsā 9 Aug. 2011
http://vixra.org/abs/1108.0020
Stockwell further shows a 2.75 year Phase Shift in Spencerās Data
Model “data”? No.
Pamela
Satellite TSI, Sunspots, Sea Surface Temperatures are all “DATA”.
Scafetta & Wilson provide an interpolation between missing satellite data to form the “composite”. The lag should show up with/without the interpolation.
David L. Hagen November 1, 2014 at 12:38 pm
Many thanks for that link , David, I do love datasets. Here is the actual data for the SST and the ACRIM TSI, along with the integral of the TSI data. I got the HadSST data from KNMI.
First, I don’t see much correspondence between the integral of the TSI and the SST, at any lag. So I’m not sure what you are referring to. I looked at the cross-correlation of the integral and the SST, and the correlation was much better for sea temperatures LEADING the TSI than for the physically possible case where sea temperatures LAG the TSI. So the correlation is not apparent there either.
However, there’s a deeper issue. We have run into an unsurmountable problem, which is the incredibly high autocorrelation of the integral of the TSI. Lag_1 autocorrelation is 0.998, and it remains above 0.9 out to lag_12. And this, combined with the short length of the data (35 years), means that we will not find any statistically significant correlations to the integral of the TSI.
Regards, and thanks for the link to the ACRIM dataset
w.
Thanks Willis for checking and identifying the autocorrelation issues. I thought the satellite data might be better. Stockwell used about 4x longer data sets with about 10 solar cycles.
If you are seeing a lag similar to about pi/2 of the main frequency component, you should probably be looking at the derivative or integral for the direct correlation.
This is like phase shift between SST and CO2, once you compare d/dt(CO2) there is no phase shift and the overall correlation is better since it is not just the principal frequency that is now in phase.
Also, since temperature is a measure of thermal energy and the solar proxies are considered in W/m2 , ie power, this would again suggest looking for at either the integral of the solar proxy or d/dt(temp).
anyone who is looking for a single variable to explain the weather (CO2, sunspots) is on a fools errand … good work if you can get it but a waste of time for the most part …
Indeed, unless you are out to show that there’s no detectable solar signal, in which case you will get some reassuring bias confirmation.
The flat pre-WWII section of Willis’ graph and Ulrich’s graph in comments both indicate some other variable that is drifting in phase with respect to solar. In Ulrich’s we clearly see two peaks per solar cycle and lesser peak-to-peak from about 1920-1950.
The two seem to come into phase and be additive in the later warming period. Several studies finding a solar signal have concentrated on a restricted period where it shows a strong signal. This leads to a falsely strong solar attribution.
Conversely others take the lack of correlation in the earlier period to be proof that there is zero detectable solar signal.
All this shows is that solar is not the producing the vast majority of the 11y variability. It does not tell us whether there is or is not a longer term correlation. This is more difficult to determine. There is a rough correlation with the integral of SSN as there is with CO2. Neither is clearly identifiable and both have discrepancies that require other factors to be brought in.
In short, single variable analysis does not really tell us much at all, except that climate is not slave to one driving force.
JeffC November 1, 2014 at 12:40 pm
Reply
Greg November 3, 2014 at 8:04 am
I must have missed it. Exactly who is it that is “looking for a single variable to explain the weather” on this thread? Assuredly not me.
Me, I’m just looking to see if there is a detectable sunspot-related signal in various datasets, and if so, how large it is. To date, the answers are “not really”, and “very tiny”.
w.
Willis, a couple of questions about the observation data set:
Are some of the apparent trends in the cloud cover over time down to a switch between ‘tenths’ to ‘oktas’ ?
I think that they changed around WWII and there is a big step change in the Wichita cover ~1940 .
Showing that the annual signal is much larger than any 11/22 year signal is not quite the same as showing there isn’t much of a solar signal. Might it be worthwhile to look at the annual-average cloud covers?
Splicing your two graphs on top of each other suggests some correlation between 1950 and 2000 but of course lack of correlation before and after that makes that correlation very questionable. I’m definitely not defending the idea that there is any causation between them. It however also suggests that the periodogram analysis might be completely unsuitable tool for this purpose.
http://i.imgur.com/vMDSknc.png
Willis You are looking over too short a cycle frequency over too small an area to see the solar effects on a global scale. The 11 year cycle is more or less noise on top of the longer term cycles.
You need to look at the approximate 60 and 1000 year temperature cycles to see the relationship.
The best proxy for solar activity is the 10Be and neutron count data. Check the NGRIP Be flux data at Fig 11
at http://climatesense-norpag.blogspot.com/2014/07/climate-forecasting-methods-and-cooling.html
Expand the view to 400%.
Look at the trends from about 1870 – 95 ,1895- 1940,1940-1965,1965 -1991.
There is about a 12 year lag from this driver input to Global Temperatures.
see Fig3 in Usoskin et al
http://adsabs.harvard.edu/full/2005ESASP.560…19U.
Therefore e.g on the Had SST3 temperature NH 5 year moving average curves on Fig 15 (on link to my site)
First note the approximate 30 year segments of the 60 year cycle.
Then Check 1882-1907, 1907-1952,1952- 1977,1977 – 2003
These provide very reasonable matching trends considering the real world variation in the lag times and the problems of the exact dating of the 10Be Data.
For further ” proof” note the obvious Dalton and Maunder minimums ie higher flux on the NGRIP 10BE flux data.
The solar driver ” activity peak” is also nicely illustrated at 1991-2 in the Oulu neutron data in Fig14.
with the corresponding temperature peak or plateau expected 2003-4.
Relative to he sun climate connection the same post says
“NOTE!! The connection between solar “activity” and climate is poorly understood and highly controversial. Solar “activity” encompasses changes in solar magnetic field strength, IMF, CRF, TSI, EUV, solar wind density and velocity, CMEs, proton events etc. The idea of using the neutron count and the 10Be record as the most useful proxy for changing solar activity and temperature forecasting is agnostic as to the physical mechanisms involved.
Having said that, however, it is reasonable to suggest that the three main solar activity related climate drivers are:
a) the changing GCR flux – via the changes in cloud cover and natural aerosols (optical depth)
b) the changing EUV radiation – top down effects via the Ozone layer
c) the changing TSI – especially on millennial and centennial scales.
The effect on climate of the combination of these solar drivers will vary non-linearly depending on the particular phases of the eccentricity, obliquity and precession orbital cycles at any particular time”
It is of interest to note that the cloud trends from 00-30 N and 00-30S in fig 10 at McLean
http://www.scirp.org/journal/PaperInformation.aspx?PaperID=50837#.VE9LlFfivOU
would fit very nicely both the temperature and 10Be trends discussed earlier. I suggest that this area would be the one best suited for investigating the sun- clouds – temperature relationships.
The idea of using the neutron count and the 10Be record as the most useful proxy for changing solar activity and temperature forecasting is agnostic as to the physical mechanisms involved.
No, it is not, as the long-term 10Be record is mostly controlled by the Earth’s magnetic field and by the climate itself [as the 10Be in ice cores depends on the deposition rate as well which is controlled by atmospheric circulation].
The Flux measurement adjusts for deposition rate as you well know. I agree that the earths magnetic field is an influence but it doesn’t destroy the 10Be solar activity trend signal. If I had only the 10Be NGRIP flux data and some estimate of the lag time ,in which I follow Usoskin , at about 12 years ,as discussed in my post I would have a reasonable notion of what the temperature trends and temperature high and low points were over the last several hundred years. That is why the 10Be data is the most useful proxy. It is very clear that we are on the downtrend from the Holocene maximum which was 7 -8000 years ago. It is very clear that we are close to ,at or just past the peak of a millennial cycle. The recent sharp drop in solar activity seen in the Ap Index ,neutron count ,euv etc coupled with the weak solar cycle 24 is likely indicating the we are past the solar millennial activity peak. The temperature and climate effects of this change lag the driver change by varying amounts in varying regions. eg the Arctic and Antarctic react differently.I think it should show up noticeably especially in the NH temperature data in the 2017-20 time frame. We will see.
FTA: “As a result, if there is no sunspot cycle visible in the terrestrial surface weather datasets, then we can assume that none of those phenomena are affecting the dataset.”
No, you can only assume that none of those phenomena in that specific frequency range significantly affect the dataset.
No, you can only assume that none of those phenomena in that specific frequency range significantly affect the dataset in linear fashion.
Bart – exactly what I said above.
Yes.
Added note – if you detrend the 60 year cycles you are then left with the millennial cycle which is the main control for forecasting multi decadal and centennial trends . See the last millennial cycle Fig 9 and the cooling forecasts at the link given in the 2:11pm comment
Willis, I might suggest that trying to find patterns in the monthly “average” US cloud series could be somewhat counterproductive. I took your cloudts variable and formed my own version of the mean monthly levels using the offset method which I devised for temperatures several years ago. The result was visually quite similar to the version in the head post. However, I thought that looking at the results by month would possibly be informative given that cloud levels could be expected change seasonally at a given location. Note that I have added a red line for reference purposes at the 50% level.
You will notice that there is a substantial difference in the levels of the time series for the various months. Not only that, the late spring and early summer months appear to have a pattern which has a sudden increase around 1940 that is not reflected as dramatically in the other months. I suspect that there may be regional differences with the data as well.
My point is not that there is necessarily a “solar signal” in this data, but rather that averaging over the entire set with these diverse properties might make it much more difficult to find.
The image included in my comment did not appear:
RomanM, that’s most fascinating. Any chance you could link to the code?
I find it interesting that the step changes are in the summer months, as this is the season of thunderstorms.
w.
I have uploaded the code for calculating the combined series and for creating the above graph:
https://statpad.files.wordpress.com/2014/11/cloudcombine.doc
The file is a simple text file which can be saved to your computer and renamed as .R or .txt if so desired. The reason that I used the .doc extension is that WordPress does not allow the uploading of ordinary text files to a blog site.
RomanM November 1, 2014 at 2:27 pm
Roman, always good to hear from you. Glad to see the work that you have done. Obviously, mine is just a first cut, there are always more analysis and different investigations.
However, I would disagree about averaging. If there is a solar effect, presumably it would be a global effect. Yes, such a hypothetical effect would be stronger in some places than in others, just like sunshine, but it would be a general effect.
As such, averaging should actually enhance the solar signal, not reduce it. This is because the random differences would tend to average out, but the general effects would not.
In any case, that’s why I looked at the periodogram of each of the 184 individual stations. Slow work, boring, but it’s gotta be done. As you might expect, a few of them have a peak at about 11 years ā¦ but most of them don’t. And in no case is the peak anything approaching statistical significance.
w.
rgbatduke November 1, 2014 at 7:36 am
Robert, as always I’m glad when you weigh in, your posts are invariably thought-provoking. However, I’m not sure your conclusions are correct. Or perhaps they are correct, but they don’t apply to this particular situation. You are correct, however, that I’ve overstated the case slightly in the quote you provide. Perhaps I should have said:
It would be helpful if you could give a real-world example of a climate situation where there is a cyclically varying input which does NOT result in a cyclically varying output, but where nonetheless the input is actually affecting the output.
For example, we see that the sun’s radiation varies daily from zero to 1360 W/m2, and the temperatures vary with the same 24-hour period. Now suppose that there were some kind of change in the sun’s radiation or in the earth such that the sun still varied in the same way, but the temperature stayed the same day and night ā¦ what conclusions would we draw from this?
Wouldn’t we reasonably and correctly say “something has happened such that the temperature is no longer affected by the variations in the sun”? Would that not be a reasonable conclusion?
And how does that differ from what I am saying about the longer cycles? The oddity is that solar variations on the daily, monthly, and annual cycles have corresponding cycles in the temperature. But the longer 11-year cycles show no such corresponding cycles.
As to whether correlation is or is not causation, if we see a correlation between daily temperatures and daily variations in sunshine, there is only one possible conclusion. Why? Because it is obvious that variations in earthly temperatures don’t cause variations in solar input to the system. So while I agree with you that in general correlation is not causation ā¦ sometimes it is.
I just thought of a great example to illustrate my point. We see an 11-year cycle in ham radio reception, which is strongly correlated with the sunspot cycle. Unless you are a really strong believer in long-term coincidence, that correlation in itself is sufficient to establish that something associated with the sunspot cycles is causing the variations in radio reception. So yes ā¦ while the general rule that “correlation is not causation” is generally true, sometimes correlation actually is enough to establish causality.
Finally, recall that I have repeatedly stated that I’m NOT saying that the ~11-year sunspot cycles have no effect on the weather. I’m just saying, I have not been able to find any actual evidence that it is doing so. You are correct that absence of evidence is not evidence of absence.
My best to you,
w.
It would be helpful if you could give a real-world example of a climate situation where there is a cyclically varying input which does NOT result in a cyclically varying output, but where nonetheless the input is actually affecting the output.
For example, we see that the sunās radiation varies daily from zero to 1360 W/m2, and the temperatures vary with the same 24-hour period. Now suppose that there were some kind of change in the sunās radiation or in the earth such that the sun still varied in the same way, but the temperature stayed the same day and night ā¦ what conclusions would we draw from this?
I think (fwiw) that your analogy and argument are appropriate. If something in the sunspot cycle affects Earth climate, then despite the fact that the Earth climate is a high-dimensional non-linear dissipative system, the overall evidence is that it responds rapidly enough (at least at the surface and troposphere) that the 11-year period of the driving ought to show up in the Earth climate measurements somewhere. The magnitude of the day-night effect, and the magnitude of the annual effect, plus the lack of an 11-year effect to date, suggest that the sunspot effect if it exists at all must be very weak.
I think that absence of evidence (when looked for where it ought to be) is evidence of absence — it jsn’t conclusive proof, but practically nothing empirical ever is.
Surely correlation is one of the first things that twigs our interest in a phenomenon and has led to fundamental discoveries. Perhaps it’s more exact to say that correlation isn’t guaranteed to lead us to causation. However it is essential to have it in relation to actual causation. Are there exceptions to this?
Willis Eschenbach, I appreciate your systematic hypothesis-directed search through these data sets. Many thanks.
It remains possible that some data set somewhere, transformed somehow, will display a relationship between sun and Earth, but the posited 11 year cycle in Earth climate measures has not shown up so far. Maybe some day.
maybe monkeys will fly . the cycle peak to peak is small. with such a small change in forcing, it would be shocking if it did show up.
Thats why people have to invent supposed amplifications.
the Physics to a first order say the effect will be undetectable.
the belief is that the sun MUST control things
therefore an amplification is posited out of thin air
Without amplifications, what are we left with? Milankovitch cycles, Winter-Summer cycles, Day-Night cycles.
you are left with radative physics.
Change the atmosphere and you change the climate.
It must be a tranquil summer dayās thing
http://www.vukcevic.talktalk.net/CET-JJAvsSSN.gif
Well, most of the time; nothing is for ever.
Brings the thought from “correlation does not imply causation” but in this case…. Causation does not always imply correlation?
Surely that is smoothed CET data ā¦ and where is the correlation, and the significance adjusted for autocorrelation? And why does the CET move BEFORE the sunspots about half the time? And what are the “Aerosols” and “CO2” mentioned in your graph?
Vuk, as I’ve mentioned before, posting pretty pictures with no provenance, no explanation, no data sources, and no statistical analysis is overwhelmingly unconvincing.
w.
Hi Willis
Some of us ālaid to restā the climateās 11 year cycle some time ago, so ārevivingā it next day after very late Halloween night, not everything is what it appears to be.
Yes, it is an LP filter of CETās balmy summer days.
Giving some āstreet credā to the distressed climate model designers, by suggesting that the aerosols caused cooling in the 1960s, despite the strongest SSN, and the CO2 warming in the 2000ās despite the SSN going down, canāt do much harm, could it?
California boys like their sun so much, the idea that it may ‘fade’ even a little bit it is just not on.
I enjoy your story telling, do more of it, time is relentlessly moving on, get a book done, might be even some money in it. All the best to you.
p.s. by the way in the above “not everything is what it appears to be”, it wasnāt 11 year, it wasnāt even 22 year, by Jove (I should say it twice) it was 24 year cycle. While I am at all mighty Jove, next paper in need of a scalpel knifeās attention, just published, is
āEvidence for Planetary Forcing of the Cosmic Ray Intensity and Solar Activity…ā
http://link.springer.com/article/10.1007%2Fs11207-014-0510-1
K. G. McCracken, J. Beer, F. Steinhilber,
all gone crackers.
Willis you experience matches mine.
I went looking in AIRS because it has cloud cover by pressure level.. low clouds to high clouds..
from 1018 hPa to 22
what did I find?
Nothing
Zip
Nada.
But GCRs.. GCRs.. its the sun I tell you..
Since we are near a solar max, its time for people to make a prediction.
Based on their understanding of the climate what will happen to cloud cover at every pressure level for the next 6 years.
the answer?
crickets. because they have no understanding of the climate
I wish that there were some general way – I mean, at WUWT outside of a post by Willis – to indicate to those who skip Willis’s posts, that he has hit his stride, found his balance, in terms of reducing the ‘arrogance’ etc. of which some have accused him. In fact I’m worried that he’ll eventually over-correct and become too uncolorful.
dont know, the ssn looks good to me –
http://i931.photobucket.com/albums/ad158/mobihci/average-change-us-clouds-1900-1987-rebuildssn.png
thats the wood for trees ssn over the US cloud changes
Hmmm.
48 month on temps, 12 month on Sun spots.
http://www.woodfortrees.org/plot/sidc-ssn/mean:12/from:1850/plot/hadcrut4gl/mean:48/offset:1/scale:100
there are obviously more influences than just the one factor. eg if you look at the AMO AND the PDO between 1940 to 1970 they go from max to the min, so one could expect the cloud cover to be different with warm water availability or not. more recently the AMO is different than the PDO, maybe because of the combined effect of water in the atlantic being more affected by (not so)global warming 1980-2000 and the southern oceans not being so.
it is probable that there is more than just the ssn modulating the ocean cycles that determine ocean temps, in fact it would be ridiculous to suggest that all possible effects are accounted for when thinking about how many variables there are in our atmosphere.
Look further up under Leif’s comments for your F10.7 links that you could have easily found yourself in far less time than it took you to write your comments to me.
So, is there something missing from the data?
Example being that we just had the largest spot in some 25 years. It produced 6 X class events. 5 of the 6 produced no CME, none, zip, nada, and the other a minor one. Why?
How long does our relavent data go back relating to CME recognition and intensity as part of a flare event?
To Moshers point, what happened to the climate the 6 years following 1859’s event?
Just wondering if we are missing something.
Willis: “As a result, if there is no sunspot cycle visible in the terrestrial surface weather datasets, then we CAN assume that none of those phenomena are affecting the dataset.”
RGB : “No, we CANNOT.
All that we can state is that there is no direct univariate correlation, and since correlation is not causality, that is insufficient to justify the conclusion ; just as it would be insufficient to draw the conclusion that they are āaffectingā (the cause of) observed variations in the dataset ( if there were correlation).”
Nice . We got that sorted “toot sweet”
So tilting at windmills it is. Carry on. š
He was right, I overstepped, but only slightly. Once again, I’m NOT saying that there is no sunspot related effect. I’m saying that I can’t find the evidence. Since lots of folks have claimed to have found the evidence, and since many people believe such evidence exists, this is a significant finding.
w.
O.K. I will let you off just this one time š
Thanks, amigo. I needed that.
w.
If sunspot cycles did affect cloud cover at all, I don’t think you’d expect the strength of that effect to be anywhere near that of yearly or seasonal cycles. In fact, it seems like you’d pretty much expect it to be well under the 5% threshold you mentioned a few times. Why didn’t you put the data through a low pass filter before looking for periods? I don’t have an opinion on whether it would change anything, but it seems like it would give you a better look at the range of periods you’re interested in.
Thanks, Nancy. The key is that people keep claiming that there is some significant effect. The problem is that when you get down to the low levels of a few percent of the signal, those small “cycles” are NOT statistically significant. You will find them in random “red-noise” data, and we have no reason to believe that they are caused by anything but chance.
So it’s not the fact that it only affects say 4% of the data that is an issue ā¦ it’s that an effect that small is not reliably distinguishable from random results.
Regards,
w.
IITM Pune published the rainfall data for 32 subdivisions [monthly, seasonal] for 1871 to 1994. You simply take the data of annual Southwest Monsoon data series of All India level and just plot 10 year averages. You get exactly 60 year cycle. Can we call this a random cycle. The same cycle is seen in global temperature, Hurricanes. We must be cautious that each cycle may not be of the same magnitude. It is also true with sunspot cycles, solar flares cycle.
Dr. S. Jeevananda Reddy
Agreed. A blocking high or a low pressure system during a stormy decade running off a storm producing Pacific ENSO scenario will slap the hell out of such tiny solar signals. So why bother. And there are plenty of solar cycles to look at and compare with ENSO conditions to see that no solar cause to ENSO effect correlation exists. And it is rather comical to see both sides of the which-tiny-cause-is-greater debate (solar versus CO2) call upon the same god of induced oceanic conditions to further their argument. Both cannot be right but both can be wrong. Buyer beware.
Dr Norman Page November 1, 2014 at 2:11 pm Edit
Dr Page, thanks for the comment.
I fear I have no trust in the 10Be data as a proxy for solar activity. I discuss the question here. The problem is, there is no sign of an 11-year cycle in the 10Be data.
I’m no happier with the 14C data as a solar proxy either. Although there is an 11-year cycle visible in the 14C data, it only represents about 5% of the swing of the 14C data. This means that 95% of the wanderings of the 14C data occur for an unknown cause, with 5% of the variation due to solar variations ā¦ sorry, but that don’t impress me much. So I place no stock in those kinds of hypothetical long-term solar reconstructions.
At the other end of the scale, the problem with looking at 60 year cycles is that our current datasets are far too short to show any cycles that long. We do have some individual datasets, see the Stockholm data above (which doesn’t show a 60-year cycle). But the global datasets are only about 150 years long, and for a 60-year cycle that’s not even three complete cycles.
And I assure you, a man would be wildly optimistic to base anything in climate on finding a mere two and a half cycles of some purported sine wave. Heck, there’s a dataset where we see five sunspot cycles that line up nicely with sea level cycles ā¦ but when we add the data before and after that time, there’s no correlation at all.
Finally, I fail to see how an eleven-year cycle is “too short” to see the solar effect. The surface changes temperature quite rapidly on daily, monthly, and annual scales. Obviously, temperature responds very quickly to short-term solar variations. Why would you then assume that eleven years is too short to see a solar effect?
w.
The problem is, there is no sign of an 11-year cycle in the 10Be data.
There is, but it is noisy. See Slide 30 of http://www.leif.org/research/Keynote-SCOSTEP-2014.pdf
The 10Be data is due to McCracken and Beer.
Dr Norman Page November 1, 2014 at 4:23 pm
The Flux measurement adjusts for deposition rate as you well know.
I don’t think that is so. Provide a link if you can. If we could adjust for deposition rate, that rate would be wonderful climate proxy and be debated all over the place. where is that debate?
Thanks, Leif. The graph compares two things, but it’s not clear at all which is what. One is presumably HMF-B calculated from 10Be ā¦ but what is the other line, labeled “B13”? It’s not clear what you are comparing.
In any case, you say there is a sign of a solar cycle in 10Be but it is “noisy”. As I pointed out above, suppose the “noise” is three-quarters of the data, and the solar effect is a quarter ā¦ since that means that three quarters of the variation in the 10Be record comes from unknown causes, I fear it doesn’t give me a warm fuzzy feeling about the accuracy of a 10Be based reconstruction.
All the best, thanks as always for your comments,
w.
The B13 is also HMF B, but derived from the geomagnetic record as described in the link.
There is general agreement that the 10Be record is contaminated by climate [and other things] as commented on by Webber et al. http://arxiv.org/ftp/arxiv/papers/1004/1004.2675.pdf
this problem becomes more severe the further back we go as we have little else to compare with.
Leif
For Flux calculation see
http://www.eawag.ch/forschung/surf/publikationen/2009/2009_berggren.pdf
“The flux is the 10Be deposition rate at the surface, and is calculated by multiplying each sample concentration with the snow accumulation of that specific year.
NGRIP 10Be data show high inter-annual variations which are superposed on wider fluctuations of an irregular nature. To some extent periods of high 10Be values correspond to grand solar activity minima, most prominently during the Maunder (1645ā 1715 AD) and Dalton(1790ā1830 AD) solar minima, while less distinctly during the Sporer minimum (1415ā1535 AD). Since snow accumulation varies over time, the 10Be flux and concentration curves differ somewhat. The long term variations are similar in both parameters, except during parts of the Dalton and Maunder minima.”
Matching the 10Be production flux and concentration rates within and between cores and at the top of each core is very problematic. I think it may have something to do with different compaction rates and also different core relaxation rates due to differences in core handling. However as Berggren says in the quote above there is a usable relation between solar activity 10Be data and temperature when looking at longer frequencies and times longer than the 11 year cycle which does turns up sometimes however as you yourself point out.
For the Sporer Dalton and Maunder temperature minima see the NGRIP flux at Fig 1 at the Berggren link above – This is Fig 11 at
http://climatesense-norpag.blogspot.com/2014/07/climate-forecasting-methods-and-cooling.html
See also the later Be10 – temperature trend correlations in the same time series discussed in my 2:11 pm post above .
For another example correlating 10 Be and temperatures see also Steinhilber in Fig 10 C and D at my post linked above.
The snow-accumulation is computed from a model relating the thickness of the annual layers to snow-depth. This, however only work for the past ~300 years where the annual layers are clear enough, and does nothing for the 10000-yr record. So your 1000-yr variation is NOT based on actual measured flux corrected for deposition. Furthermore, there is strong evidence that the whole notation of 10Be being a reliable measure is shaky: e.g. http://arxiv.org/ftp/arxiv/papers/1004/1004.2675.pdf
“We have made other tests of the correspondence between the 10Be predictions and the ice core measurements which lead to the same conclusion, namely that other influences on the ice core measurements, as large as or larger than production changes themselves, are occurring. These influences could be climate or instrumentally based.”
Dr, Svalgaard The 10Be data is due to McCracken and Beer.
McCracken and Beer now say that not only cosmic rays but the solar activity itself is result of forcing by Jupiter, Saturn etc
āEvidence for Planetary Forcing of the Cosmic Ray Intensity and Solar Activityā¦ā
http://link.springer.com/article/10.1007%2Fs11207-014-0510-1
If this is serious science fine, and if it is not, are we suppose to doubt the results on 10Be etc as quoted in their previous publications?
‘Unwashed masses’ including myself, as you put it elsewhere need to know!
Leif – The 1000 year +/- periodicity comes initially from the temperature data not the 10 Be data see Figs 5 and 9 at
http://climatesense-norpag.blogspot.com/2014/07/climate-forecasting-methods-and-cooling.html
However the persistence on this periodicity during the Holocene – based on the10 Be data and in the Miocene – millions of years ago based on lake sediment sequences is well illustrated in Fig 6 in my post – taken from
Fig.6 Kern et al http://www.sciencedirect.com/science/article/pii/S003101821200096X
I think this reference would be well worth your time for a careful read.
WMO in its 1966 manual on “climate change” discussed the length of the data required to assess the presence of cyclic variation. The authors of the report were top meteorologists from several countries — one from India Meteorological Department. They suggested minimum of two cycles. If not use other techniques like moving average or auto-correlation. I used to identify cyclic variation in the onset dates of monsoon, moving average technique. Here the filter is important — 5 or 10 year moving average, etc. Recent report by US academy of Sciences and British Royal Society presented a figure — global temperature march, 10, 30 and 60 year moving averages. With the 60-year moving average, the trend is clearly evident.
In the case of Sunspot, there is long time series [more than 400 years, with China] but in meteorological parameters it is around 15 years only.
Dr. S. Jeevananda Reddy
small correction, it is not 15 but it is 150 years.
Dr. S. Jeevananda Reddy
I think the main climate driver is the in radiation balance at the intra-tropical ocean- atmosphere interface. You don’t see generally the 11 year cycle because of the thermal inertia of the oceans . Also regional land responses are very variable because of geography – distribution of continents, elevations ,mountain ranges deserts -forests etc The main trends will be best seen in the ocean data – because we should really be looking at enthalpy which varies widely on land while SST trends approximate enthalpy trends much more uniformly.
Thus as a suggestion while the lag in driver – temperature trends may be as little as 12 years or so- I would expect OHC lag to be longer – 20 – 25years+?
This appears logical. The oceans have immensely greater energy then the atmosphere, and we are talking about small decadal changes in atmospheric
GAT, (.1 to .2 per decade) manifesting in an annual seasonal flux about twenty to forty times that, being influenced by many disparate factors with disparate signs, fluctuating on different time cycles from hourly to daily to seasonal to decadal to multiple centurion. On top of this our capacity to measure said factors and GAT responses to said disparate factors is stretched to the max, and influenced by political agendas.
The fact that the only actual atmospheric GAT change that is clearly and easily observed is of the OPPOSITE in sign to a massive input change (the January cooling of atmospheric GAT despite a plus 90 watt per square meter increase in insolation) is testimony to both the complexity of the system in looking for a consistent signal from one input, and to the great power of the oceans to modulate the timing of energy flux into earths atmosphere. ERB data indicates that despite the atmospheric cooling during this seasonal increase in insolation, the earth is gaining energy. Thus our measurement of a reduced atmospheric GAT , during this time of increased insolation, is exactly an incorrect assessment of what is actually happening to earths energy budget.
So we are measuring the wrong phenomena (atmospheric GAT) when we need to be measuring true GAT including the energy entering the oceans and modulated, regulated and collected by the oceans, and only released to the atmosphere at the interface between the oceans and atmosphere, the surface.
Only at this interface from the oceans to the atmosphere, is a clear signal in atmospheric GAT observed.
What we need to understand and measure is the energy flux that goes into the oceans, and the timing of said release to the atmosphere. Every disparate W/L of insolation entering the oceans has a different residence time, (thus a different capacity to alter the earth’s energy budget depending on the change to background) dependent on how deeply it penetrates the ocean, and where it enters the oceans, and said release timing back to the atmosphere dependent on ocean currents in that area.
As the seasonal cycle demonstrates, measuring atmospheric GAT is using a
rubber ruler to measure the wrong metric, if one wishes to determine the earths energy budget, especially when considering solar energy entering the oceans, where the lag in signal to the atmosphere has great flux.
Dr. Page, as well as the observations indicate multi decadal cycles in the oceans energy release, which may be operating within far longer cycles. (As indicated by each apparent warm period, Roman, to ME to now, possibly being reduced, indicating long term cooling)
End of rant, with the caveat of one question. Do the PDO and AMO flip on different cycle times, and could this timing be regulated by the size of the ocean basins and the shape of the continents around them?
Willis, regarding these changes to surface T. You said, “The surface changes temperature quite rapidly on daily, monthly, and annual scales.” Would it not be logical to add multi decadal scales as well? (A warm blob of water rising from several hundred meters depth, may well impact the surface GAT, but the timing of when said energy entered under the ocean surface, well away from the atmosphere, and how long it took for said energy to form, is not known.)
For more thoughts along this line please consider my post in this thread here…
http://wattsupwiththat.com/2014/11/01/splicing-clouds/#comment-1777170
A general comment: many people here swear to the Svensmark hypothesis that cosmic rays modulate the cloud cover. Svensmark’s ‘evidence’ was the coincident solar cycle variation of temperature and cosmic rays for some period in the past. Many of those same people seem to argue that the climate system acts a low-pass filter so that a sunspot cycle variation is not to be expected [easy to say as none is observed]. They ignore the disconnect between what they say and what they swear to. This is typical of the level of discourse one sees here.
I like option #4:
4) Variable A Granger-causes variable B AND variable B Granger-causes variable A.
===
If I may repeat a comment by Genghis on another thread that seams pertinent:
Quote:
“Yes, but it is circular logic. Clear skies > warmer temps > more wind > more evaporation > cloudy skies > cooler temps > less wind > less evaporation > clear skies, rinse and repeat. Pick whichever variable you prefer as the driver they are all equally valid.”
The figure showing sunspot cycle presents two peaks, namely one peak at 10 years [smaller] and another at 11 years [larger]. My study on solar radiation presented a principal peak at 10.5 years cycle and its multiples 21 & 42 years. Here solar radiation is a function of sunshine hours. and Sunshine hours is a function of cloud cover.
Dr. S. Jeevananda Reddy
Willis,
“It would be helpful if you could give a real-world example of a climate situation where there is a cyclically varying input which does NOT result in a cyclically varying output, but where nonetheless the input is actually affecting the output.”
= = = = = = = = =
ENSO – the chaotic capacitor:
http://www.srh.noaa.gov/jetstream/tropics/images/soi_nino34.jpg
The Murray Darling Basin covers a large area of SE Australia and floods on a regular basis even though there have been a of of flood mitigation works. It all funnels through the Murray river in SA. Not exactly cyclic but the years are 1870, 1906 (1909), 1917,1921, 1931, 1956, 1973-1975, http://www.murrayriver.com.au/about-the-murray/murray-river-timeline-1951-to-2000/, 2011. They don’t quite line up with minimums in the sunspot numbers but are close.
There is a correlation with SOI, along with droughts http://www.bom.gov.au/climate/ahead/soirain.shtml.
Perhaps a large positive SOI with a low Sunspot Number is the correlation for a large rainfall in the basin?
lsvalgaard
The climate system isn’t the low pass filter ,,, the dissipation rate of the ejecta is …
It isn’t the solar wind passing by, .. its the quantity of material already past us,
How dense, how deep, and as it continues, how long to expand outward losing density
leaving earth more exposed.
The shielding particles are of more than one solar cycle, and it requires more than one wimpy
or short peak to have an effect.
How much material, how dense, and for how long, without replenishment, the cloud of ejecta
will continue to expand outward becoming less dense, less able to intercept anything.
This “low frequency filter effect” has nothing to do with earth, it may effect earth, but it
does not require there to be an earth present.
That’s my understanding of the mechanism.
The solar wind changes all the time on short time scales and the solar cycle and even solar rotation is well observed, so the low-pass filter in not in the ‘shielding’ variables. It takes the ‘ejecta’ about a year to travel through the solar system to finally merge into the interstellar medium, so they don’t hang around long enough to filter anything longer than that in any event.
lsvalgaard
At the heliosphere …
…
The forces acting on a neutral hydrogen atom approaching the Sun are gravity and the radiation pressure. In addition, atoms are lost, mainly as a result of charge exchange with solar wind protons which converts a fast solar wind proton into a fast hydrogen atom.
….
Returning to the interstellar pick-up ions, they have an interesting fate. After entering the heliosphere initially as neutral atoms, being ionized, and then picked up, they are carried out to the termination shock. There, a small percentage are accelerated to cosmic ray energies and then propagate back into the inner heliosphere where they are observed as “anomalous cosmic rays.” This process has recently been confirmed by the observervation on AMPTE and Ulysses that anomalous cosmic rays are, indeed, singly ionized.
http://web.mit.edu/space/www/helio.review/axford.suess.html
So you assume that 100% of the material just keeps going at 400km/h and by then its
not thick enough to affect anything … and on out into interstellar space it all goes.
Well then any cosmic ray interception to be measured at all, or what ever modulation measured
would be in real time with solar wind density going past earth at that moment. or as you
say, effects lingering a year at most … And no lingering cloud of material between us and the heliopause, to the bow shock. et al
so.. singly ionized anomalous cosmic rays are spontaneous events then, and the
heliosheath is an empty void
Heh
No, Ray, that is not how it works. Once the solar wind meets the interstellar medium the wind comes to a screeching halt. The anomalous cosmic rays are such a small part of the cosmic ray flux that they don’t matter in the greater picture.
Bob Weber November 1, 2014 at 5:17 pm
Bob, I’ve given up guessing, and I’m incapable of reading your mind. There is nothing in what you wrote that would lead me to think that you were using Leif’s dataset, whatever that might be.
I note that you still haven’t provided the link to either the solar flux dataset, or to whatever temperature dataset that you think is affected by the solar flux, and I’m not going to guess. If you’re not interested enough to provide the links to your own work, I’m not interested enough to waste any time on it. I provide direct links to the data and code that I use in my work. If you are unwilling to do the same, I’m gone.
w.
Halts … and then spontaneously ceases to exist ?
No, it begins to mix with the interstellar medium and actually piles up in front of the Sun [the hydrogen wall], but is so turbulent that it loses all structure. The solar cycle modulation of cosmic rays takes place inside the solar wind ‘bubble’. The important thing tor you to grasp is that there is a lot of structure of short time scales in the solar wind and we can and do observe these.
Those cosmic rays simply say “something there” .. as in not empty .. of course particles
of that energy level are not going to be hanging around anyway, but . not all the material is that energetic
So .. where does it go after this “halt” (yeah i know it don’t come to a complete stop, its slowed
changes direction,, buffeted around .. more incoming from both sides … its eventually going to be necessarily squeezed out like a fluid pressed between two plates .. between the two pressures of the shock wave ….. so it wont be there forever .. its being pushed on, by more arrivals, so it will all escape eventually, but that also gives rise to thoughts of an accumulation of atoms, a stalled particle
that is no longer energetic isnt going to stay solitary very long. not when there are so many sexy electrons and protons wanting companionship …
Yes i know about the slow particles and fast one bunching up, creating wave like structures ..
Im saying there is a lot of material out there, at there at the halt ,, the slowest moving
portion has the most mass, and at 400km/h it takes about a year to get there
Slow it down and it will accumulate ,, sure, it wont stay around forever, and when the solar wind goes feeble the heliopause would necessarily collapse inward .. how much material … the interstellar pressure isnt going to be equal so it will eventually be lost to the “heliotail.” as they call it …
There is a signal though it is a local one. Look at the data the eleven cities run has been held in Holland. They are practically only organized during sunspot minima.
Egads, is my writing that unclear? Please give me a LINK to the “data for the eleven cities run”, I’m not going to guess what dataset you are talking about.
Thanks,
w.
Willis, Henk is referring to the Elfstedentocht ice skating ‘race’ or tour along ~200Km of frozen canals in the Netherlands. The race can (rather obviously) only be run when the canals are frozen sufficiently for skating.
See: http://en.wikipedia.org/wiki/Elfstedentocht
There may be a better dataset of dates that it was run than the Wikipedia table,
Thanks, Ian. In that case, the evidence is quite weak:
The real problem is the small size of the sample. In any case, Henk’s claim that
is definitely falsified by the facts. A bootstrap analysis of the results shows that they are not even significant at the 90% level. To do the analysis, I repeatedly selected 15 years at random from the same time interval, and looked at the average value of the sunspots on those dates. Out of 1,000 trials I get 135 which have a lower mean than the mean of the actual data. This shows that we cannot conclude anything from those dates.
w.
er .. thats 400km/s … welp the model i had in my head told me there was a cloud in the “heliosheath”,
it takes a few years for our leaving probes to cross it, one of them might have, we think.
It would be a lot of material ,,, not enough to register as heat on voyager but enough to act as a brake
if you put out a sail, the velocity change was one of the methods proposed i herd about to detect it.
under a hail of particles from both sides and with so much pressure modulation, not a a calm day
to be found, but the matter should be there …. and a lot of it .
Very enjoyable exchange, thanks.
Iām no scientist, but would Sunspot 2192 be an example worthy of investigation? Surely station records would show a weather pattern correlation of some kind. Or not.
Ok … now everybody can take a breather by relaxing and watching the Danish documentary (52 minutes in English) titled “The Cloud Mystery” about the serious research of Dr Henrik Svensmark:
Willis,
I did a regression analysis on HADCRUT 4.3 using CO2, PDO, AMO, NAO, MEI and SOI. TSI pops out of the residuals quite nicely:
https://drive.google.com/file/d/0B1C2T0pQeiaSX1JUa0lYQmN1bVE/view
Brandon,
Excellent work. It’s pretty clear that you’ve finally put this issue to bed. I’m sure that w will color himself unimpressed and come up with even more excuses why this doesn’t pass muster. Now, if we could only show a mathematical correlation between rain and wet sidewalks.
Thanks. Not surprisingly, the response thus far has been crickets and the issue remains wide awake. Besides, everyone knows that cosmic rays cause wet sidewalks, lack of correlation proves it.
RH November 2, 2014 at 5:59 am
Oh, I see, RH. You think that something like “I refuse to answer your questions because you didn’t ask them in the RH approved way” is a legitimate scientific response.
That’s right up there with Phil Jones’s famous response, which paraphrased from memory was “Why should I give you my data, when you only want to find fault with it”? Ummm ā¦ because this is science, Phil, not a childhood game of keep-away …
Having the scientist as the gatekeeper for the code and the data is a very bad idea. Maybe the scientist hates women, so he deems all their questions as being posed in some unacceptable manner. Maybe the scientist doesn’t want to give any answers to skeptics because he thinks they are disrespectful. The possibilities for the misuse of power are endless.
Or on the other hand, maybe he quite innocently (or not so innocently) loses the data, like the UEA did. Heck, they were able to convince their Freedom of Information minder that it was justifiable to refuse to answer questions from anyone who posted at Climate Audit! Seriously, that was accepted by the FOI person as a legitimate excuse.
Clearly they were followers of the RH theory at its finest, which seems to be “It’s my data and code, and if I don’t like the way you eat your waffles, I don’t have to give it to you, so there.”
The part you seem to be missing is that if the authors had actually archived the data as used and the code as used, as I and other scientists around the world do as a matter of routine, even for blog posts, I wouldn’t have to ask a single question.
Heck, I’m lucky. I speak the author’s language. What about some kid in Africa who reads the paper after it’s translated into Adangbe, and doesn’t speak English? How is he supposed to ask, politely or not?
The main reason for archiving the data and the code as used is so that nobody has to ask the authors anything, because anybody, anywhere, at any time of the day, has access to exactly what the authors used and the information about exactly what they did. It is nothing but simple scientific transparency, without which ā¦ well, you see the results in this thread.
Best regards,
w.
Now it appears to me that there is a correlation between the āplotted dataā that is depicted on these two (2) graphs, to wit:
—————–
NASA – Figure 9. Solar irradiance (1975-2010) from composite satellite-based time series.
http://www.aip.org/history/climate/images/solar_irradiance.jpg
Source: http://data.giss.nasa.gov/gistemp/2011/
——-
Dr Leif Svalgaard has this plot comparing the current cycle 24 with recent solar cycles. The prediction is that solar max via sunspot count will peak in late 2013/early 2014:
http://wattsupwiththat.files.wordpress.com/2013/04/solar_region_count.png
Source: http://wattsupwiththat.com/2013/09/13/like-the-pause-in-surface-temperatures-the-slump-in-solar-activity-continues/
=================
Now unless the aforesaid ācorrelationā is just a figment of my poor eyesight and/or my imagination ā¦. then I have to assume there is a direct correlation between the maximum quantity of Solar Irradiance (W/m2) ā¦ā¦ and the maximum Sunspot Active Region Count
When the Solar Irradiance is āhighā (W/m2 ) ā¦. the Sunspot Active Region Count is also āhighā, ā¦ and vice versa, ā¦ when the SI is ālowā ā¦.. the SARC is also ālowā……. (no pun intended)
Therefore, if the plotted ādataā is reasonably correct then one has to assume that āSunspot numbersā is the driver of Solar Irradiance quantity (W/m2) ā¦.. or ā¦.. the quantity of the āSunspot Active Region Countā is the āsignalā that defines the Solar Irradiance quantity (W/m2).
Now, IMO, the decrease in Solar Irradiance during any given ā11+- year Solar Cycleā will have little to measurable effect on earthās climate.
But now I am reasonably sure that a 100 to 300 year āslowā decrease in the average āmaximumā Solar Irradiance will have an observable and measurable effect on earthās climate.
Thus, if a long-time decrease in Sunspot ānumbersā is a āsignalā that defines a long-time decrease in Solar Irradiance ā¦ā¦ then the hearsay evidence and/or facts about the LIA can not be totally ignored or discredited, to wit:
==================
ā1. Sunspots were rarely recorded during the second part of 17th century. [1645 to 1699]
1a. sunspots all but disappeared from the solar surface
1b. observations of aurorae were absent at the same time.
2. the lack of a solar corona was noted prior to 1715.
3. a renewal of sunspot cycles starting in about 1700.
4. The period of low sunspot activity from 1645 to 1717 is known as the āMaunder Minimumā.
5. The Little Ice Age (LIA) was a period of cooling that occurred from about 1350 to about 1850
6. NASA defines the LIA as a cold period between 1550 and 1850 ā
===================
And one should not ignore or discredit the effects on earthās climate ā¦.. that the scientific claims about the Solar Corona or the Solar Winds that are generated by said Solar Corona, to wit:
——————–
āThe solar wind is a stream of plasma released from the upper atmosphere of the Sun. It consists of mostly electrons and protons with energies usually between 1.5 and 10 keV. The stream of particles varies in density, temperature, and speed over time and over solar longitude.
In the mid-1950s the British mathematician Sydney Chapman calculated the properties of a gas at such a temperature and determined it was such a superb conductor of heat that it must extend way out into space, beyond the orbit of Earth.
Observations of the Sun between 1996 and 2001 showed that emission of the slow solar wind occurred between latitudes of 30ā35Ā° around the equator during the solar minimum (the period of lowest solar activity), then expanded toward the poles as the minimum waned. By the time of the solar maximum, the poles were also emitting a slow solar wind.
The wind is considered responsible for the tails of comets, along with the Sun’s radiation. [Robin Kerrod (2000)]ā. http://en.wikipedia.org/wiki/Solar_wind
————————–
According to the above, one can not separate the changes in earthās climate from the changes in Solar Irradiance ā¦.. and one can not separate the Solar Irradiance from the Solar Corona, the Solar Wind and/or the Sunspot numbers.
But one can ignore any or all of it iffen it doesnāt āturn their crankā to what pleases them.
Willis you have never presented any hard data to support all the many claims you make about the climate system, nor have you shown any of my claims are not correct.
For example explain why your climate governor appeared some 10000 years ago and where as it prior to that time?
Show some data that shows an increasing global temperature trend over a long period of time with a prolonged solar minimum condition.
For example explain why your climate governor appeared some 10000 years ago and where was it prior to that time? correction to my previous post
Salvatore,
The nature of the climate governor is it is effective within certain limits only.
1) At the hot end, the equatorial region clouds up and shades the surface (cooling), and convection quickly raises the warm moist air up into the upper troposphere where, essentially punching through the zone where CO2 LWIR absorption would otherwise take place, it condenses releasing latent heat of vaporization and this heat is high enough that much of it escapes out into space. This causes the heating to be interrupted
2) With cooling, the clouds clear exposing the sea more to the sun and evaporation and convective heat loss under the cooler conditions is reduced thereby allowing heat to build up again. However, the cool end is where the limitation exists. If through some mechanism such as a Milankovich cycle, the cooling continues, with all the clouds already gone and convective heat loss reduced, there is nothing else to bring into play to save the planet from continued cooling. Bingo, we go into an ice age.
I hope this helps you understand how the ‘governor’ works and its limitation.
Gary Pearse
======
Your comment may be much more important than many realize with regards to Willis’ thermostatic hypothesis.
milodonharlani November 2, 2014 at 11:37 am
Other than the fact that you haven’t provided any provenance or link to the source of these claims, anyone who seriously says that they can tell us the temperature in Central England from 1100 to 1150 to the nearest tenth of a degree is off their meds ā¦
w.
How did you miss the link given right before that table? Were you in such a hurry to dismiss the data that you couldn’t be bothered to read the whole comment?
But as I’ve commented before, had you ever actually studied climatology, you’d have recognized that series right away (besides which it has been posted in this very blog many times before). Same as you would not have imagined that you discovered tropical cloud formation, as some here have been led to believe falsely, which Roy Spencer showed you from old studies describing the effect.
The time you spend trying to reinvent the wheel could IMO be better spent learning basic climatology before commenting on a discipline that some here have spent decades studying.
sturgishooper November 3, 2014 at 3:14 pm Edit
Actually, no. I went to that link and searched on “prevailing temperatures” and a few other phrases. In fact, in reply I pointed out that I had gone there and not found the information. Thanks to you, I’ve now finally located it.
I never claimed I “discovered tropical cloud formation”, nor did Dr. Roy think that I had claimed that.
Instead, he falsely claimed that I had not cited Ramanathan, when I actually had cited him when discussing a part of the climate system covered by his study.
I lay out the whole thing here, I’m happy to leave it to the reader to decide if Dr. Roy’s accusations were off the mark, I have nothing to be ashamed of in any of it.
And yet, despite all of your ad hominem accusations, I am published (peer-reviewed) on climate science in Nature magazine, and you’re not ā¦ seems like that must gall you badly.
w.
Not ad hominem when stating relevant facts.
You seriously misrepresent the record when you claim to have been published in Nature, implying a peer-reviewed paper. Nature printed a comment by you. It’s pathetic that you equate that with having had a research article accepted for publication. It’s akin with Mann claiming a Nobel Prize. You should be ashamed.
No article of mine has ever been published in Nature, I’ll grant you, but then neither has one by you, and I’ve never submitted one. I have however published dozens of scientific articles, so jealous, I’m not. Amused, yes.
sturgishooper November 3, 2014 at 6:45 pm
In a scientific discussion, the only relevant facts are the scientific claims and objections. Attacks on the author’s perceived qualifications or claimed lack of understanding, such as you have been making, are never relevant to science. The only relevant question is, are my claims correct? My publication history, my level of education, and the size of my johnson are all totally irrelevant to the question of whether my claims are true or not.
Dang, it does gall you indeed. In fact, what was published was not a “comment”. It was what Nature calls a “Communication Arising”. It is a short form of a full article, limited to 500 words. More to the point, unlike a “comment”, it is indeed peer-reviewed, and strictly so.
So no, I misrepresented nothing. In this world of the internet, anyone can look up my Communication Arising. And obviously, Nature magazine felt it was solid, worthwhile climate science, regardless of the rather average dimensions of my johnson, even combined with my obvious lack of formal education.
I didn’t say “jealous”, that’s your fantasy. I said the fact that I have published a peer-reviewed piece (called a “Communication Arising”) on climate science in Nature magazine must gall you ā¦ and given your response as evidence, I’d say I was right.
And of course, all of this is an ad hominem attack on my qualifications, on my presumed state of knowledge, and the like. You have totally given up any pretense of discussing the science ā¦ and when a man does that, as you have done, everyone realizes that you’re out of scientific ammunition, and starts to laugh. Game over.
w.
Everyone knows the metaphor for futile causes; few have read the book. “Don Quijote” is a fantastic book for those who have the patience to read a very long book. Part One is a tragedy/comedy about what we would now call noble cause corruption. Don Quijote wants to be noble and do good, but being rather naive, he always makes it worse for those he is trying to help and doesn’t realize it.
I read the book many years ago and enjoyed every page.
Willis,
I think that everyone would agree that any climate signals associated with solar cycles would probably be small and buried in noisy data. Further, only data since the satellite era would be likely to capture a global climate signal. Earlier data might well be too sparse or too noisy to tell us much. So if you want to find signals correlated to solar cycles you probably don’t have many reliable cycles to work with.
That said, in an article that I posted here a while back http://wattsupwiththat.com/2014/07/26/solar-cycle-driven-ocean-temperature-variations/ I used your SFT technique and showed that the hadsst3 sea temperature data ( http://woodfortrees.org/plot/hadsst3gl ) showed a small, but significant variation at the solar cycle period for the data of the last 60 years. I had first demonstrated that such weak signals could be extracted from noisy data.
You commented on my findings that “I suspect the problem may be that you have subtracted a cubic polynomial from the data, which is a technique fraught with problems. If not, Iām not sure what you did ā¦ my analysis shows, for example that there is a 5-year cycle nearly as large as the 9-year cycle, and a 3.8 year cycle that is larger than the nine year cycle. In addition, your graph is much smoother and less detailed than mine. Again, Iām not sure why.”
I only subtracted the arithmetic average temperature for the 60 years to help suppress the longest periodicities. I did not use a polynomial fit or otherwise alter the data or make up the correlation that I reported. I suspect that the reason that you did not duplicate my result is that there might be an error in your SFT program. If you want to do a least squares fit to sines and cosines, you have to do the sums and solve two equations in two unknowns for the amplitudes. You cannot just do a fourier sine integral and a fourier cosine integral separately and combine the amplitudes. I am not sure what you might have done, but I am pretty certain that what I did is both mathematically and numerically correct.
Now if it is true, as you say, that hadsst3 is an untrustworthy compilation that might have spuriously produced the solar cycle correlation, then there may be no significance to my calculations. However, if the data are OK, then the result is highly significant because it takes a hell of a lot more heat flux at the ocean surface to produce a few hundredths of a degree temperature variation than is available from the part of TSI variations that reaches the sea surface. If you know of anyone who has done a careful study of what might be in hadsst3 that could invalidate it, please let me know.
Stan Robertson
bones November 2, 2014 at 12:51 pm
Thanks, Stan, but sadly, you’re wrong. There are lots of folks who claim the ~11-year signal is quite visible in things like historical sea level rise, or SST data, from well before the satellite era.
w.
bones November 2, 2014 at 12:51 pm
Actually, I found a sneaky and wicked-fast way to do the calculation. I started out solving the two equations, each with two unknowns as you point out, by an iterative procedure. However, that was very slow.
Eventually, I realized that if I first generate a sine wave and a cosine wave, both with the same period of interest, I can solve directly for the sizes of the two (which is all we care about) using a simple least-squares analysis. Once I have the two amplitudes, I just add the sine and the cosine waves at those amplitudes to give me the best-fit sinusoidal wave at that frequency.
In any case, I don’t think that’s the source of the error.
w.
>āTo date Iāve looked for an 11-year cycle in local sea level datasets, river flow datasets, beryllium datasets, global surface temperatures, lower tropospheric temperatures, global sea levels, Nile River levels, and a bunch more surface datasets. I have looked at literally dozens of measures of the weather, and Iāve found ā¦ well ā¦ nothing.ā
Youāre not the only one looking.
3 papers documenting 11 yr periodicity in surface and tropospheric temperature
here (#73):
http://joannenova.com.au/2014/06/big-news-part-vii-hindcasting-with-the-solar-model/#comment-1496107
In GISTEMP here (#73.4):
http://joannenova.com.au/2014/06/big-news-part-vii-hindcasting-with-the-solar-model/#comment-1496534
Another 8 papers in a review paper here (#58.2):
http://joannenova.com.au/2014/06/big-news-viii-new-solar-model-predicts-imminent-global-cooling/#comment-1496991
Richard from NZ, I took a look at the first of your links, to the three papers. Two of them are using NCEP/NCAR reanalysis “data”, which is climate model output. Since you seem to have missed it, I will repeat the relevant section of the head post:
In addition, I specifically requested that you not bother me with lists of studies. I asked for a link to the ONE dataset (not a study but dataset) that you think contains the solar signal, viz:
Next time, please try reading the head post before bothering me with your irrelevant studies ā¦
Thanks,
w.
>”I asked for a link to the ONE dataset”
OK, what was wrong with this?
In GISTEMP here (#73.4):
http://joannenova.com.au/2014/06/big-news-part-vii-hindcasting-with-the-solar-model/#comment-1496534
āOn the relationship between global, hemispheric and latitudinal averaged air surface temperature (GISS time series) and solar activityā
M.P. Souza Echera,
E. Echera,
N.R. Rigozoc,
C.G.M. Brumd,
D.J.R. Nordemanna,
W.D Gonzaleza,
(2012)
Table 2
Significant periods of the air surface temperature.
Region
Periods in years
Global 2ā2.8;3.7ā6.6;7.7;8.3;9.1;10.4;11.5;20.6;26.3;29.6and65
Northern Hemisphere
2.1ā2.8;3.1ā6.6;7.1;8.3;10.2;11.3;20.4;26.4;54.3and70.4
Southern Hemisphere
2ā2.6;3.6ā5.3;7.7;8.3;9.1;10;11.9;14.2;17.2;20.7and30.8
241 Northā901 North 2ā2.7;3.3ā5.3;6.2ā7.7;8.3;9.9;11.1;12.4;15.2;20.5;26.5;53.1and72.2
441 Northā641 North 2.1ā2.8;3.3ā5.6;6.3ā7.4;9.1ā9.9;11.2;12.8;15.4;26.7;53.1and75.6
241 Northā441 North 2ā2.7;3ā6.4;7.8;8.3;9.1;12.4;14.4;52.7and67.1
Equatorā241 North 2.4ā2.8;3ā4.6;5.1ā7.1;8.2;9;10;11.6;13.4;19.6;25.4;38.4and58.6
241 Northā241 South 2.6ā2.9;3.2ā6.3;7.1;9;11.8;20;25.8;59.9and63.4
Equatorā241 South 2.5ā3.6;4.1ā6.3;7.6;9;11.9;20.2;58and61.4
241 Southā441 South 2ā3.7;4.2ā6.6;7.5;8.3;10.1;12.2;32.9and59.5
441 Southā641 South 2.1ā3.8;4.3ā6.7;7.7ā8.9;10.7;12.8;15.1;21.5;29.4;41.6and98.9
241 Southā901 South 2ā3.6;4.7ā6.7;11.3;12.7;14.5;17.5;21.1;28.7;34.4and108.7
http://www.sciencedirect.com/science/article/pii/S1364682611002756
PDF at Google Scholar
>”please no āreanalysis dataā from NCAR or NCEP or anywhere”
GMT: GISTEMP vs HadCRUT3 vs NCDC vs NCEP vs ERA40 vs ERAINT
http://www.realclimate.org/wp-content/uploads/globalT0.png
The 10% greater cloud cover in 1980s vs. 1920s would have cooling effect of around 1.8 W/m^2 assuming 10% cloud albedo. It’s a negative feedback. This assumes the clouds are mostly stratocumulus. If the clouds are mostly cirrus, it’s a positive feedback because of low albedo and absorption of outgoing IR. It’s not clear from cloud cover data if the clouds are causing cooling or warming.
Physical mechanism how geomagnetic storms can affect cloud temperature. The greatest increase in microwave radiation during geomagnetic storms occurs at 50-100 GHz. Dielectric heating of water occurs at 1-150 GHz. They overlap. I don’t know if the effect is significant or not.
The UHF band overlaps with the spectrum of dielectric heating of water. A 50,000 watts UHF transmitter has a microwave flux of 0.0002 W/m^2 at 4,000 m altitude. This is negligible compared to 390 W/m^2 of solar radiation and LWIR absorbed by the troposphere and clouds. The microwave reflected by the ionosphere is weaker than the transmitter source. Hence, dielectric heating of clouds by microwave radiation is insignificant.
Willis
Willis, are you a denier of the warming was caused by the sun?
How come you donāt believe what you see?
Here is the sun-climate link:
http://woodfortrees.org/plot/sidc-ssn/from:1979/scale:0.001/plot/hadcrut4gl/from:1979/mean:60/detrend:0.5/offset:0.05
That does look suggestive. However, change the time on your two plots to 1900. Here’s what I get:
http://woodfortrees.org/plot/sidc-ssn/from:1900/scale:0.001/plot/hadcrut4gl/from:1900/mean:60/detrend:0.5/offset:0.05
Not quite so impressive prior to 1979/80.
Michael
The problem with longer period is that you have to remove the multidecadal oscillation from the global mean temperature data. That oscillation has nothing to do with the solar cycle as it just caused by the redistribution of heat between the surface and deep oceans.
Thanks, Girma. FIrst, as Michael said, you need to look at the whole dataset. What you have done is simply to pick the only part of the time period when the relationship vaguely appears to be strong ā¦ sorry, but you can’t simply throw away the parts of the dataset you don’t agree with.
Second, in your WFT graph you are comparing a smoothed dataset to an unsmoothed dataset ā¦ bad researcher, no cookies.
Finally, you can’t just toss a graph up there and squint at it and declare victory. You need to look at the statistical significance of the claimed correspondence ā¦ and in this case, I just did the calculation and it is a pathetic p-value of 0.145. In other words, your claimed evidence is not statistically significant at all.
w.
PSāYour crack about “deniers”, and your comments about whether I “believe what I see”, merely demonstrate your arrogance. In fact, your claimed evidence is worthless, which makes your insults ā¦ well, less than beneficial to your reputation. Next time, take a lesson from the roostersādon’t start crowing until it is actually dawn …
Willis,
Solar-climate Link
Sorry for my remarks. I get frustrated when I argue with someone who is on my side of an issue, and I believe the Sun-climate link is robust as shown:
http://woodfortrees.org/plot/hadcrut4gl/isolate:300/mean:48/offset:0.08/from:1955/plot/sidc-ssn/from:1955/compress:12/scale:0.001
The most important point to note when trying to extract the solar cycle from the global mean temperature is that they describe different quantities. The solar cycle is an instantaneous energy input into the earth but the global mean temperature represents an accumulated energy in the earth stored in its oceans. As a result, to find the solar signal in the global mean temperature data, the secular and multidecadal oscillations (greater than the solar cycle of 11 years) must be removed from the global mean temperature data.
The stored energy signal (secular and multidecadal oscillations) in the global mean temperature can be represented by the 25-year (300 months) moving average as shown below:
http://woodfortrees.org/plot/hadcrut4gl/mean:300/plot/hadcrut4gl/compress:12/plot/hadcrut4gl/mean:300/offset:0.2/plot/hadcrut4gl/mean:300/offset:-0.2/plot/hadcrut4gl/scale:0.00001/offset:2
What is left after removing the 25-year (300 months) moving average (the stored energy signal) is given by the āisolateā function in Woodfortrees and it is in this signal the 11-year solar cycle to be found. In addition to the secular and the muldtidecadal oscillation that must be removed from the global mean temperature using āisolate 300ā, to obtain the solar cycle signal, we have to also remove the short term oscillation with in the earthās climate of ENSO which has an average period of about 4 years (48 months). The ENSO should be removed because it is due to equalisation of heat within the earth system. This can be done by using the 4-year (48 months) moving average of the āisolate 300ā data to get the following result:
http://woodfortrees.org/plot/hadcrut4gl/isolate:300/mean:48/offset:0.08/from:1955/plot/sidc-ssn/from:1955/compress:12/scale:0.001
The above is the sun-climate link since mid-20th century that IPCC claims was anthropogenic. However, as the above results show the global mean temperature changes in PHASE with the solar cycle. And this disproves anthropogenic global warming.
Girma November 3, 2014 at 5:32 pm
Looks impressive ā¦ but then you realize that the data is all heavily smoothed, the autocorrelation is through the roof, and as a result, the result is not statistically significant.
Girma, you keep getting misled by apparent correlations. You need to learn to do the statistical analysis, including adjusting for autocorrelation. Come back with your statistical analysis of the claimed relationship, including autocorrelation, and I’ll be happy to discuss it ā¦ of course, once you do that, you’ll see that it is not statistically significant, so there’s nothing to discuss.
Finally, starting in 1955 is cherry picking of the highest order. Both datasets go back to 1880, you can’t just arbitrarily throw away the data that disagrees with your hypothesis. The situation since 1880 is shown here ā¦ as you can see, no relationship.
If that’s your evidence for the “Sun-climate link”, I fear it is as far from “robust” as you can get.
w.
Willis,
How do you show only the 8 to 15 year oscillation from the global mean temperature data?
Let us see what you come with.
The temperature data appear not to be as accurate before mid-20th century
willis,
Here is my question.
How do you remove the oscillations greater than about 13 years and less than about 9 years and the secular global mean temperature to show the oscillation near the 11 year cycle? I have shown my method. Show me yours. You can do it for the data since 1950 because that seems to be more accurate.
Willis Eschenbach November 3, 2014 at 8:24 pm
Girma November 3, 2014 at 8:53 pm
I give up, Girma. You’re going to have to find someone else to discuss science with. In the face of that kind of obstinacy and lack of understanding, there is absolutely nothing I can do. As the President had it, you’re beyond my poor power to add or detract …
w.
Willis,
The probability of finding the correlation I found between the two datasets by accident is nearly zero. As a result, the result is significant. If you dispute that then show me how you can extract the variation around the 11 year cycle from the global mean surface temperature data.
Otherwise you should not ever say there is no 11-year cycle in the global mean temperature data.
I have shoown how you can the secular trend, the multidecadal oscillation from the global mean temperature data by using “isolate 300” that removes the 25-year moving average data. Then remove the ENSO by using a 4-year moving average. This procedure leaves you the variation in the global mean temperature that corresponds to the 11-year solar cycle.
If you disagree with me, then show what you get for the 11-year period variation in the global mean temperature data.
Girma November 3, 2014 at 11:51 pm
I’m sorry, Girma, but I’ve already answered your question above.
Let me expand on this a bit. Yes, it is true that if you throw away two thirds of the two datasets and smooth them both, that the remaining third shows a non-zero correlation ā¦ but THAT’S NOT SCIENCE. You do not get to throw out the data that disagrees with you and then go around proclaiming that the data agrees with you, Girma. You need to use the data that you have.
Second, LEARN THE MATH!!!! Your “robust” solar-climate link is here, and it looks good ā¦ but in fact the p-value of the relationship is 0.115, meaning it is NOT STATISTICALLY SIGNIFICANT EVEN AT THE 90% LEVEL.
As I said before, you are getting misled by pretty pictures and our ability to smooth datasets. Smoothing the data so you can judge visually what is going on is not a problem.
But smoothing datasets so that you can claim statistical significance as you have done is a Very Bad Ideaā¢, and in fact it is dangerous. The process of smoothing itself can and often does introduce entirely spurious correlations when none actually exists.
Here’s a rewrite of a post I wrote six years ago on smoothing and spurious correlation. And Matt Briggs has some great posts on the question here. Short answer? Don’t do it.
w.
Willis,
You are saying the correlation since 1955 between the sun spot count and the global mean temperature shown below
http://woodfortrees.org/plot/hadcrut4gl/isolate:300/mean:48/offset:0.08/from:1955/plot/sidc-ssn/from:1955/compress:12/scale:0.001
is not significant.
For that to happen by chance is 0.4%!!!
However, the sad part is that you have not answered my question:
show me what you get when you filtered out from the global mean surface temperature data the multidecadal oscillation, ENSO and the secular trend?
!!!!!!That is the question that you must answer!!!!!!!!
>> Yes, it is true that if you throw away two thirds of the two datasets and smooth them both, that the remaining third shows a non-zero correlation ā¦ but THATāS NOT SCIENCE. You do not get to throw out the data that disagrees with you
I can’t speak for Girma, but I’ll tell you my reaction to this. This may seem like sematics, but I say that it’s not. I would characterize your approach as “THATāS NOT SCIENCE”. Instead, I would describe your approach as MATH only, which I think is the tail wagging the dog. I see math as a tool to help understand science. I make fun of this approach by saying “can you provide a statistical correlation that shows that rain causes wet sidewalks?”. However, I think the point is lost on many.
I get the distinct impression that you don’t understand or believe: correlation does not imply causality AND causality does not imply correlation.
As such, I see the task of investigating correlations as simply helping us learn more about a phenomena that is already established scientifically.
In this particular case, Girma did an excellent job of explaining a core scientific fact (which I and many others stated early on in this thread, but was completely ignored by you). Girma said:
“The solar cycle is an instantaneous energy input into the earth but the global mean temperature represents an accumulated energy in the earth stored in its oceans.”
Once this concept is fully understood and appreciated, one wouldn’t be looking for something that in all likelihood, shouldn’t show up in the way that you’re expecting it to.
Willis
I strongly disagree with you about smoothing data-sets.
You agree that the global mean surface temperature represents stored energy in the earth’s ocean. However, the sun spot numbers represents the instantaneous energy reaching the earth. As a result, to find the correlation between the two, you must remove the stored energy component from the global mean surface temperature or integrate the sun spot numbers.
Willis, the only way to find correlation between a data-set that represents accumulated energy (global mean surface temperature) and instantaneous energy (sun spot number) is by smoothing out the accumulated energy component from the global mean temperature data.
There is no other way to do it that I know. If you know that please show me what you get for the oscillation between 10 and 12 years in the global mean temperature data.
Willis,
You have filed my real question:
How do you find only the 11-year oscillations from the global mean temperature data?
This is the only question you must answer.
When I look for the 11-year sunspot-related oscillations, whether in the global mean temperature data or anywhere else, I use both cross-correlation analysis and periodogram (Fourier) analysis.
Hope that helps,
w.
Willis says:
“Iām no happier with the 14C data as a solar proxy either. Although there is an 11-year cycle visible in the 14C data, it only represents about 5% of the swing of the 14C data. This means that 95% of the wanderings of the 14C data occur for an unknown cause, with 5% of the variation due to solar variations “
===========================
Wrong – or at best, backwards (95% known/5% mystery). Most of the swings during the Holocene have been shown to follow the the sun:
In fact, the accuracy of carbon-dating improved dramatically when we started compensating for those wiggles in the record corresponding precisely with recorded changes in solar activity:
http://www.aip.org/history/climate/Radioc.htm
This video based on the data which shows a clear cut relationship between the sun and the climate. About 32 minutes into the video it shows the graphs which show a good correlation between solar/climate. One of many data sources which all show the same kind of data over and over again. I choose to believe this data due to the fact it has been in existence for many years and reached by many independent studies.
As far as cloud cover versus solar again their is noise in the climate system and items such as the atmospheric circulation, volcanic activity, and the magnetic field strength of the earth ,in addition to primary changes on the sun itself are all going to impact cloud cover.
It is not just cosmic rays and to take it further if it were just cosmic rays the sun’s variability is not the only factor in modifying the amounts of cosmic rays entering the earth’s atmosphere. One has to consider the strength of the earth’s magnetic field and the concentrations of galactic cosmic rays out in space in the vicinity of the earth.
It is and will never be straight forward and this is why GIVEN solar changes will not give GIVEN climate results.
The ice dynamic (initial state of the climate) having a major influence on given solar variability and the effect or lack of an effect it may have on the climate.
I have listed at length al the factors that can cause given solar variability to have a different given climate result. That said if GIVEN solar variability is EXTREME enough (the criteria I have mentioned) then it will be enough to influence the climate in a general direction of cooler or warmer over the long term as was the case during the Little Ice Age as this video confirms. it will at a point be able to over come the noise in the climate system.
I will leave with this why the YD period end so abruptly? Where was the climate governor?
I say there is no such thing as a climate governor out there rather it is the initial state of the climate which determines the stability or lack of stability of the climate and how much or little it may be influenced by factors which may impact the climate.
The analysis presented in WUWT, above, is overly simplistic ā pretty much EVERYTHING needed to understand solar/cloud/warming-cooling at a fundamental level is at the following links and these factors appear to remain on-track:
http://academic.evergreen.edu/z/zita/articles/09WSM/Shirley_SPD09.pdf
http://www.researchgate.net/publication/227136874_Prolonged_minima_and_the_179-yr_cycle_of_the_solar_inertial_motion
Prolonged Minima and the 179-yr Cycle of the Solar Inertial Motion by Fairbridge & Shirley; published in ā87, everything observed & calculated appears to continue on-track as these researchers projected.
http://www.youtube.com/watch?v=63AbaX1dE7I (note, CLOUD experiments remain on-going at CERN).
Fairbridge & Shirley
Count the sunspots before making complicated hypothesis.
Maunder Minimum = zero
2013 = 70
Obvious mismatch. Or you can force fit the hypothesis by looking for correlations. Many things are correlated. Check also for correlations between solar orbits and stock prices.
RE: Count the sunspots before making complicated hypothesis.
Maunder Minimum = zero
Dalton Minimum (Little Ice Age) ~ 25ish
25 > 0
25 x > 1
70/25 = 2.8
25 x > 2.8 x
Dalton Minimum was not the cause of Little Ice Age. By 1800s the world was still cold due to thermal inertia of previous colder centuries.
The fact is many different data sources for many different parts of the world all give a similar result to the data shown below. This is why the Medieval Warm Period and the Little Ice Age are accepted by many in the scientific community. They are not here out of the thin air rather they are accepted due to the many different sources of data which supports these periods of the climate.
The video I presented in the above post being a prime example.
800-1000 9.2
1000-1100 9.4
1100-1150 9.6
1150-1200 10.2
1200-1250 10.1
1250-1300 10.2
1300-1350 9.8 Wolf
1350-1400 9.5
1400-1450 9.1
1450-1500 9.0 Spƶrer
1500-1550 9.3
1550-1600 8.8
1600-1650 8.8
1650-1700 8.7 Maunder
1700-1750 9.24
1750-1800 9.06 Dalton
1800-1850 9.12
1850-1900 9.12
1900-1950 9.41
Those data are from:
Palaeogeography, Palaeoclimatology, Palaeoecology
Elsevier Publishing Company, Amsterdam – Printed in The Netherlands
THE EARLY MEDIEVAL WARM EPOCH AND ITS SEQUEL
H. H. LAMB
Meteorological Office, Bracknell, Berks. (Great Britain)
(Received September 14, 1964)
(Resubmitted January 22, 1965)
Which seminal paper others & I have linked here repeatedly, but which Willis keeps ignoring.
They’re the data upon which the first IPCC report graph (subsequently disappeared) showing the Medieval Warm Period & LIA were based.
Girma November 4, 2014 at 10:41 am Edit
Girma, it is obvious that despite having it pointed out to you in advance, you have not adjusted for autocorrelation. As a result, your statistics are a joke ā¦ and that leaves out the fact that you’ve thrown away two-thirds of the data to get that result.
A question that I MUST answer? Girma, you are so clueless that you think you can throw away two thirds of your data because it doesn’t fit your hypothesis, and then you claim because the one-third of your data that fits your hypothesis, shockingly, actually fits your hypothesis, TA-DA ā¦
Now, that was bad. But what put you into the big leagues was that when this was pointed out to you, your excuse was that the early data was bad in some form ā¦ yeah, like you wouldn’t have used it if it agreed with your hypothesis.
Now, your arrogance has grown so great that you think you can tell me what I MUST analyze ā¦ Girma, one of the beauties of my situation is that I don’t have to take my marching orders from anyone. Die gedanken sind frei, my friend, and I plan to keep them that way.
So I fear that if you want to see what the data looks like after you’ve “filtered out from the global mean surface temperature data the multidecadal oscillation, ENSO and the secular trend” you are going to have to do it yourself ā¦ and given your obvious statistical innumeracy, I’m sure you’ll forgive me if I happen to laugh at your conclusions.
w.
PSāAre you planning to throw away two-thirds of that global temperature data as well?
Willis,
Do you agree with the following?
1) The sunspot numbers represent instantaneous energy into the earth system
2) The global mean surface temperature represents accumulated energy in the earth in its oceans
3) To find the instantaneous energy (high frequency) in the global mean surface temperate, the secular component must be removed
When you do that the following is what you get:
http://woodfortrees.org/plot/hadcrut4gl/isolate:300/compress:12/plot/hadcrut4gl/scale:0.000001/plot/hadcrut4gl/scale:0.000001/offset:0.2/plot/hadcrut4gl/scale:0.000001/offset:-0.2
From this data set is the solar signal to be found as shown below:
http://woodfortrees.org/plot/hadcrut4gl/isolate:300/mean:48/offset:0.08/from:1955/plot/sidc-ssn/from:1955/compress:12/scale:0.001
Girma, since you have not replied to even one of the objections that I’ve raised to your claims, I’m gonna pass on answering your questions. I prefer dialog, where when someone points out an error in the work the other person actually addresses the error.
You, on the other hand, nimbly jump over the issues. And when I said that you can’t just throw away two thirds of the data, and your answer was that the early data was bad, I could see that there was nothing I could say that you might actually pay attention to. You just make up a bogus excuse like claiming the data is bad, and blithely continue along the same path.
Since I can get as much response from talking to my garden plants as I get from talking to you ā¦ you’ll forgive me if go I talk to them, and leave you to find someone else to bother with your inane questions. As you have proven beyond a doubt, you can lead a horse to water ā¦ but you can’t make him think.
Regards and regrets,
w.
I agree that for the correlation not to work prior to mid-20th century is a problem. I acknowledge that. However, you have to acknowledge that it works after mid-20th century.
I don’t now know how to make the correlation work before mid-20th century. But I need peoples help in this.
I strongly believe the solar cycle is instantaneous energy and the global mean temperature represents accumulated energy and they can not be compared without either integrating the solar energy or smoothing out the secular trend from the global mean temperature.
What bothers me is your focus on the empty part of the my glass rather than on the full part.
Girma, you seem to think I’m not serious about not discussing this with you. It is far too frustrating, and as I said, I could get as much response to the issues I raise from a potato as I’ve gotten from you. It’s no fun. Debate things with VikingExplorer, he doesn’t seem to have any problems with your lack of responses. He’ll tell you how you are oh-so-right, and he won’t ever ask the impertinent questions like I’ve asked.
Plus he may not care when you pay no attention to important issues that he might raise. Me, I do care, so I’m gonna do my blood pressure a big favor, and discuss these questions with a potato.
See, the difference is, I don’t expect the potato to pay attention, so I’m not frustrated when the potato ignores me.
Regretfully,
w.
VikingExplorer November 4, 2014 at 10:51 am
Viking, I fear I don’t understand you. What do raindrops have to do with the question at hand? Girma thinks that it is perfectly fine to throw away the two-thirds of your data that disagrees with your hypothesis and claim success when the remaining one-third agrees with your hypothesis. I disagree.
Do you think that is a justifiable scientific procedure? I say no. Girma says yes. Leaving aside the raindrops and ignoring wet sidewalks for the moment, what say you?
w.
>> Viking, I fear I donāt understand you. What do raindrops have to do with the question at hand?
I guess abstract thinking is not your thing? I think what I wrote was pretty clear, so I doubt further explanation will help you understand the difference between a science approach vs. a statistical math approach.
Let’s try a different explanation: What if I said “I cannot find a correlation between rain and birth rates”. Answer: what makes you expect to find such a correlation? Response: I don’t care, but I won’t believe that rain effects birth rates until someone shows me a correlation.
You would rightly say that my obsession with statistics is blinding me to scientific thinking.
Repeat: correlation does not imply causality –AND– causality does not imply correlation.
Therefore, it’s ok to use correlation to study a known phenomena further, but not to establish causality. In this case, we know that a solar max generally injects an additional 2.7 x10^22 Joules into the earth system. We’re not sure what happens to it, but we know that this energy has to have some effect.
It’s been explained to you (but completely ignored) that in general, thermodynamic systems do not reflect input variations in the output. For example, electric stove tops are controlled by pulse width modulation. However, even in this very simple thermodynamic system, we cannot see evidence of the input frequency in the water temperature inside the tea pot.
Demanding that people need to present this correlation to you, when scientifically, it shouldn’t be found, is stupid.
>> Girma thinks that it is perfectly fine to throw away the two-thirds of your data that disagrees with your hypothesis and claim success when the remaining one-third agrees with your hypothesis. I disagree. Do you think that is a justifiable scientific procedure? I say no. Girma says yes.
You’re mischaracterizing this. It’s not like Girma is throwing away tree rings that don’t agree with an a-priori hockey stick. Girma said:
“The solar cycle is an instantaneous energy input into the earth but the global mean temperature represents an accumulated energy in the earth stored in its oceans.”
Girma is asserting (and many agree, including myself) that the signal is contaminated with noise. It’s not controversial to claim that weather contaminates the climate signal. Weather vs Climate is like the very definition of a signal to noise ratio problem.
Filtering out noise of known sources is not throwing out contrary data. It’s using known scientific information to isolate the phenomena one is interested in. Remember that there is no doubt that a solar max adds additional energy. We’re not establishing causality, we’re just trying to learn more about it.
Girma November 4, 2014 at 10:59 am
Given your record, I’m not surprised. Fortunately, the opinions of random anonymous internet popups mean nothing to me. No matter what I write, someone like you will jump up and without providing any objections to the facts that I’ve provided to buttress my case, merely say I’m wrong as if their opinion outweighs the facts.
w.
VikingExplorer November 4, 2014 at 10:51 am
I get the distinct impression that you are unable or unwilling to read what I said above:
Without a quotation to establish what you are objecting to, I have no clue what you are on about.
Without specific examples of what you might think is “already established scientifically”, that’s just hand-waving.
Mmmm ā¦ no, I don’t think that’s a fact. Energy stored in the oceans is the excuse people give for the temperature NOT rising in the last two decades ā¦ and while the explanation for the lack of warming is bogus, the underlying idea is correctāenergy used up in warming the subsurface ocean does NOT affect the surface air temperature.
Again, this is meaningless without examples and quotes. What is “this concept”? Is it that correlation is not causation? Is it the false idea that energy used in warming the ocean controls the surface temperature? And what is “the way that [I’m] expecting it to” show up?
Call me crazy, but since daily variations in solar input lead to daily variations in surface temperatures, and monthly variations in solar input lead to monthly variations in surface temperatures, and yearly variations in solar input lead to yearly variations in surface temperatures ā¦ I expect decadal variations in solar input lead to decadal variations in surface temperatures,
But they don’t. That’s the oddity that no one has been able to explain. Jo Nova and David Evans got so worked up about it that they hypothesized a totally unphysical “notch filter” to wipe out the expected 11-year signal. Me, I think it’s because like all other variations in forcing, whether from the sun, volcanoes, or CO2, the sunspot cycles get wiped out by the climate response, which involves the timing and persistence of the emergent phenomena which regulate the temperature.
Regards,
w.
āThe solar cycle is an instantaneous energy input into the earth but the global mean temperature represents an accumulated energy in the earth stored in its oceans.ā Mmmm ā¦ no, I donāt think thatās a fact.
This is a basic fact of science and reality. I would replace ocean with “land & sea” to be more precise, but the distinction between power and energy is crucial and correct. If you don’t understand this, then your lack of a scientific background is a big problem. A very basic, but crucial equation is E = Cp * m * T, where E is energy in Joules, Cp is specific heat capacity in joules/degree kelvin/kg, m is mass in kg, T is temperature in K. TSI is measured in Watts (Power), which is Joules/Sec. It’s a rate of energy transfer.
It’s energy that determines temperature, not power. To determine energy, we need to integrate Watts over time. That’s what Girma means by this statement. To confuse power and energy is like confusing speed with position. A .1% increase in speed does NOT imply a .1% change in position. To determine the effect of a .1% increase in speed, we would need to integrate speed over time to get distance.
Demanding a correlation between TSI and temperature is like demanding a correlation between speed and position. It shows a lack of understanding of basic science and calculus.
>> Is it the false idea that energy used in warming the ocean controls the surface temperature?
It’s a basic fact that solar energy is absorbed by land and sea, and that the atmosphere is a thermodynamic slave of the land & sea. The mere fact that AGW proponents say something doesn’t make it false.
>> since daily variations in solar input lead to daily variations in surface temperatures, and monthly variations in solar input lead to monthly variations in surface temperatures, and yearly variations in solar input lead to yearly variations in surface temperatures ā¦ I expect decadal variations in solar input lead to decadal variations in surface temperatures, But they donāt. Thatās the oddity that no one has been able to explain
It has been explained, but you ignore everything you don’t understand or you don’t want to hear.
Daily variation represents a change from 1362 W/m^2 to zero. Every time I stop my car at a red light, my position stops changing.
Monthly variation represents a change from 6073 kJ/m^2 to 22351 kJ/m^2, which 3.68x the minimum (Chicago). Every time I start driving 3.7 times faster, I make a lot more progress.
Annual variation? I’m not aware of this variation.
Decadal variation is only a rate change of .1%. It does add up to something over 5 years, but in no way compares to Daily or Monthly variation.
VikingExplorer
A very very big thank you. I very much appreciate your comment. Take care.
Is it the false idea that energy used in warming the ocean controls the surface temperature? And what is āthe way that [I’m] expecting it toā show up?
++++++
I agree with what Viking Explorer says about power vs energy.
Let me answer your good question Willis:
The surface temperature of water is HARD to change because of the latent heat of vaporization. The energy changes water from liquid to a gaseous state, which holds the energy as gas. You did some great work, Willis, on how clouds works to bring energy up where it condenses to release the energy which mostly radiates out our system. The condensation is the reverse state change to a lower energy state, which means it gives off heat energy. Energy can be stored in a system without elevating temperature!!!!
Anyway – Viking Explorer explains things in terms of engineering facts, which cannot be ignored. Delta T is not, I repeat, not the metric one should use to determine if the earth is storing or losing energy. Heat can and often does go into a system without raising temperature… and later it can be released through energy state transformation.
Mario Lento November 4, 2014 at 10:37 pm
VikingExplorer, on the other hand, says:
One person says heat going into the oceans DOESN’T raise the surface temperature, the other says heat going into the ocean DOES raise the surface temperature.
I’ll let you two fight this out ā¦
Mario, I will note however, that when you say that ” later it can be released through energy state transformation” you are correct ā¦ but it is released at a lower temperature, and sometimes much lower, than if it were absorbed elsewhere. For example, suppose we have two identical parcels of energy. Energy that goes into the atmosphere might raise the atmospheric temperature from 16.136Ā°C to 16.253Ā°C. Energy that goes into the deep ocean might raise its temperature to 6.173Ā°C to 6.175Ā° ā¦ but that will never magically come from that 6Ā° deep ocean to warm the 16Ā°C atmosphere.
People have the idea that the energy can just go into the ocean, and then come out to warm the much warmer atmosphere ā¦ but it doesn’t work that way.
w.
Willis Eschenbach November 4, 2014 at 10:58 pm
Mario Lento November 4, 2014 at 10:37 pm
ā¦ Heat can and often does go into a system without raising temperatureā¦ and later it can be released through energy state transformation.
VikingExplorer, on the other hand, says:
ā¦ the global mean temperature represents an accumulated energy in the earth stored in its oceans.
One person says heat going into the oceans DOESNāT raise the surface temperature, the other says heat going into the ocean DOES raise the surface temperature.
++++++
Willis:
Let this post bring up some learning please. I remain a fanboy. You are brilliant, Willis, and very well researched. And – I am not pandering to you.
I think I explained it correctly, and what I wrote is not at odds with anything Viking Explorer said. I think it’s crucial that you understand the finer engineering principles I am pointing to. The point I made is that energy can make it into the system without necesarrily leaving a temperature trace of the energy –even though that energy was stored somewhere in a changed state. If you want to talk about surface water temperature, it gets more complex, so let’s stick to principles. It’s important that you understand what I am saying and that you do not selectively twist my statements out of context.
If energy is stored in the system without showing a direct temperature increase, it can later change states and become increases in temperature elsewhere – or the increase in released energy could resist a decrease in temperatue and still not be seen by only looking at temperature. It’s extremely important that the engineering facts do not get overlooked in studies where conclusions are drawn.
I CONCLUDE: You CANNOT expect certainty of correlation between delta Energy to show up as a temperature change finger print. It might work that way and it might not… it just depends on what happens to compounds that receive that energy.
FURTHER: The integral of energy input, IF there is a sustained increase, stands a much better chance of eventually showing up as a fingerprint. If you’re not looking at the integral, especially when there are sustained substancial changes, your chances of finding answers is small.
Salvatore Del Prete’s work is based on this too, I believe. He gives metrics of sustained significant changes, that can be seen because the sustained changes accumulate enough to rise above the noise. In principle, his hypothesis is sound and falsifiable, albeit difficult to prove or falsify.
VikingExplorer November 4, 2014 at 10:51 am
Folks seem to think that this is some kind of physical law. In some arenas it is not, and we’re investigating one of those arenas. As I said to Robert Brown above:
Regards,
w.
>> Folks seem to think that this is some kind of physical law. In some arenas it is not, and weāre investigating one of those arenas.
It’s a rule of logic: Correlation proves causation (cum hoc ergo propter hoc) ā a faulty assumption that correlation between two variables implies that one causes the other.
>> sometimes correlation actually is enough to establish causality.
Never. The scientific method requires a coherent hypothesis to explain an observed phenomena. An observed phenomena like ‘rain falling on a sidewalk’ is already explained. A correlation between rain and sidewalks may help us learn more about it, but otherwise, is unnecessary. That a solar max injects additional energy is an established fact, and needs no correlation to prove it. If someone finds one, fine, but if not, oh well.
A correlation might help support an hypothesis, but is insufficient by itself. It’s a logic fallacy.
I notice you have not addressed my example. I hold that while in general you are right, it’s not a law of nature, and I hold further that in that particular case, correlation is sufficient in and of itself to establish causation. If you think not, you need to explain why not.
But instead of dealing with reality, your response is to wave your hands and talk about theory, Sorry, but when you refuse to deal with the hard reality of an example and indulge in theoretical handwaving ā¦ you lose.
w.
So, you’re saying that sometimes, a logic fallacy is still valid logic? Do you understand what a logic fallacy is?
>> We see an 11-year cycle in ham radio reception, which is strongly correlated with the sunspot cycle.
The correlation would mean NOTHING, without an underlying scientific hypothesis that solar electromagnetic radiation should have an effect on ham radio reception.
This seems to be a coherent hypothesis: http://en.wikipedia.org/wiki/F2_propagation
The correlation just provides additional information, empirical support, etc. It does not, and could not establish causality all by itself.
The scientific method is really just the rules of valid logic, applied to science.
VikingExplorer November 4, 2014 at 3:08 pm
For a body operating under a given acceleration, we have:
S= 1/2 A T2
V = A T
Substituting, we get
S = 1/2 V/T T2
S = V T/2
ā¦ Sure seems like a correlation between speed and position to me ā¦ but I fail to see what that has to do with this question.
In fact, it has nothing to do with this question, because I’m not the guy “demanding” that there is some correlation between sunspot related variations in TSI, and temperature. That would be all of the folks writing the papers that I’ve disassembled one by one. I’m not making that claim that sunspots affect the weather, they are.
I’m just the fool trying to find the evidence that such a correlation exists ā¦ without success.
Viking, this is why I insist that people quote my exact words. As far as I know, I have never “demanded a correlation between TSI and temperature”, and I defy you to find any quotation of mine saying that. And in future, let me repeat what I said above:
Your repeated attempts to put words in my mouth are pathetic. Quote me when you disagree or go play someplace else. Your unending misrepresentations of my position are laughable.
w.
>> S = V T/2
You’re assuming that velocity is a constant. The relationship between speed and position in not algebraic. You may need to learn about or review calculus.
S (speed) = dP / dt (where P is the position)
or integrating both sides: Ź S dt = P
Similarly, power is the derivative of energy. That’s why I integrated the TSI of a solar max to calculate the additional energy input into the system. This is what Girma is taking into account when analyzing the data.
Conceptually, if speed is zero, position doesn’t change. Imagine driving across the US. A solar cycle is like driving 65 mph for 5 hours, then .1% faster for 5 hours, then back to 65 for 5 hours. Would you expect that to make a huge difference in how long it takes you to get from sea to shining sea? In fact, if two cars were travelling together, one with a solar cycle like variation and the other at a constant speed of 65 mph, the first would only be .325 miles ahead after one cycle. What would happen if the time varied? In order to isolate the effect, doesn’t it make sense to subtract the 65 mph?
>> ā¦ Sure seems like a correlation between speed and position to me ā¦ but I fail to see what that has to do with this question. In fact, it has nothing to do with this question,
It’s a crucial point that has everything to do with the question. If one variable (position) is an always increasing line with slight changes in slope, and the other (speed) is a nearly constant value that alternates between 65 and 65.065, what correlation would be expected?
>> because Iām not the guy ādemandingā that there is some correlation between sunspot related variations in TSI, and temperature. That would be all of the folks writing the papers that Iāve disassembled one by one. Iām not making that claim that sunspots affect the weather, they are.
Someone is “making that claim that sunspots affect the weather” but you are demanding that in order to prove that, there must be some “correlation between sunspot related variations in TSI, and temperature” without removing noise & other known factors.
Your premise is that to establish causality, there must be a correlation. However, this is a logic fallacy. There is no doubt that adding 2.7 x10^22 Joules of energy has some effect on the system. If only 37% of that got into the atmosphere, it would be 2 degrees warmer. So, causality is already established. What we don’t know is exactly how it affects the system, because of the extremely complex thermodynamic system involved.
Girma seems to be simply removing noise, other known factors, and taking into account that energy is the integral of power. The graph from the link Girma provided looks like the effect of that additional energy has been isolated pretty well.
Mario Lento November 4, 2014 at 10:37 pm
Is that supposed to be a surprise to me? Certainly energy can be stored in change of phase, from solid to gas or liquid to solid, and recovered in the same way ā¦ and? I’m not clear what your point is in repeating this well-known fact.
Huh? VikingExplorer said the exact opposite. He said
You, on the other hand, state that heat can go into a system without raising the temperature.
Since you’ve taken opposite positions, which one is an “engineering fact which cannot be ignored”?
Finally, you say:
Help me out here, Mario. WHO do you think said that āT is, I repeat, is the metric one should use to determine if the earth is storing or losing energy, and WHERE did they say it? Because I’m very sure it wasn’t me, and a quick search of the thread shows that you are the first and only person to mention the word “delta” ā¦ so whose claim are you responding to, exactly?
I do wish you guys would start QUOTING THE EXACT WORDS YOU OBJECT TO. Your handwaving method exemplified by your attacking some vague, unknown, unspecified, unidentified claim made by someone somewhere regarding Delta T is a complete waste of everyone’s time.
Thanks,
w.
Hi Willis: You wrote: “Help me out here, Mario. WHO do you think said that āT is, I repeat, is the metric one should use to determine if the earth is storing or losing energy, and WHERE did they say it? Because Iām very sure it wasnāt me, and a quick search of the thread shows that you are the first and only person to mention the word ādeltaā ā¦ so whose claim are you responding to, exactly?”
I did not say you specifically said this. However, I am reminding people, and you, that studies which correlate temperature and some other metric cannot conclude anything without understanding what I wrote. I wish to be impeccable with my words, and apologize that it seemed like I was misquoting you.
So, by your own admission you’ve made up a position that I don’t hold. What’s more, it seems that you cannot point to a single person who holds that position ā¦ and yet you are attacking it as though I hold it.
This is called a “straw man” argument, Mario. QUOTE MY WORDS if you disagree. Making up some fantasy and acting like someone holds that position is a non-starter. If there is someone that holds that position, then QUOTE THEIR WORDS and address your comment to them. You are wasting my time with this nonsense, attacking some position that I don’t hold as if I held it.
w.
Willis Eschenbach November 5, 2014 at 9:51 am
So, by your own admission youāve made up a position that I donāt hold. Whatās more, it seems that you cannot point to a single person who holds that position ā¦ and yet you are attacking it as though I hold it.
This is called a āstraw manā argument, Mario. QUOTE MY WORDS if you disagree.
++++++
I am not attacking you Willis. I understand how patient you are with all of the debating with others. You choose to see it as an attack. Perhaps I was off topic, since your post was about clouds not temperature. But the discussion led down the path of solar / temperature.
My words were constructive to the general discussion, not an attack. I’m sure you already know that looking for a temperature signal that correlates to sunspots needs to consider what I described.
I am still not sure how to group threads, so I am reposting to Willis here:
Mario Lento November 5, 2014 at 11:13 am
Willis: Actually, you wrote alot that leads me and others to believe you needed to hear my words. Specifically, you wrote: āI expect decadal variations in solar input lead to decadal variations in surface temperatures,ā. Willis, you can look at what you said before and after this statement and see clearly why I responded with my words to advance the science.
Your statement is in fact ādelta Tā. There is no strawman. I was being more precise, but you in fact should understand why I was helping advance the discussion instead of obfuscating, which is exactly why I am responding here. What I said holds true.
Instead of advancing knowledge, you often write distracted diatribes like a lawyer trying to confuse a jury. You are an adroit writer. However, this creates unneeded contention and you often seek to create argument and win at the cost of advancing knowledge.
I want to get educated at WUWT. Iāve learned from your work, and for that I am grateful. You often invest time to win an argument, often by derogatory means, and liberal use of labels with so much fervor that nothing is gained āexcept for more hits on WUWT which is a saving grace. I do not wish to fall into the trap of getting caught up into these battles.
Willis,
Is it possible for you do your frequency plots for the following data?
http://woodfortrees.org/plot/hadcrut4gl/isolate:300/compress:12/plot/hadcrut4gl/scale:0.000001/plot/hadcrut4gl/scale:0.000001/offset:0.2/plot/hadcrut4gl/scale:0.000001/offset:-0.2
Can you please do it?
Thank you in advance.
Actually, a guy I know named “Girma” told me that the first two-thirds of that dataset are no good, so they can’t be used. As a result, there’s no point in my analyzing the dataset ā¦
w.
I should have said the smoothing value of 4 years I used to remove ENSO may not be a constant throughout the data set. If you do your frequency graph, I would be able to know the value to choose prior to mid-20 century.
I think I sould be able to find the value by trial and error method!
And I shall return with the result. So we may have two graphs, one after mid-20 century and the other before that.
Thanks for the discussion and keeping me honest.
Willis,
I agree with you.
However, we have to take acceleration of the global mean temperature into account. That means the global mean temperature per year increases with time.
So the initial global warming rate also contributes to the increase in global mean temperature, not just the external forcing only.
For a freely falling body, the displacement is given by =u*t + at^/2. The first term is due to the initial condition or the history effect. The second is due to the forcing effect.
F=ma=m*dv/dt
Fdt = m*dv
W*dt = m*dv
Integral(W*dt) = m*(v-u)
Integral(g*dt)=(v-u)
Impulse of the force = change in velocity
Solar forcing = Change in global warming rate.
Forcing is not linearly related to the global mean temperature, but the forcing is related linearly to the change in global warming rate.
Willis said:
” I think itās because like all other variations in forcing, whether from the sun, volcanoes, or CO2, the sunspot cycles get wiped out by the climate response, which involves the timing and persistence of the emergent phenomena which regulate the temperature.”
In the short term, say up to 3 solar cycles, that is probably right.
However, there is evidence that solar changes affect global cloudiness over multiple cycles which is another matter.
Changes in cloudiness mimic much greater changes in TOA insolation by regulating the proportion of that insolation that can enter the oceans.
Changes in insolation will create a long term climate response that is not cancelled by emergent phenomena.
So, in so far as the sun might force a change in global cloudiness, there would be no adequate negative response from emergent phenomena.
Changes in insolation, atmospheric mass or the gravitational field do change the baseline system energy content but every other forcing will be negated by emergent phenomena.
Dear heavens, Steven, I just looked at “cloudiness over multiple cycles” in one of the best datasets we have, and found no evidence that sunspot-related solar changes do a damn thing.
Now you come along, and without offering a single citation to back it up, you start reeling off your theory about how “there is evidence that solar changes affect global cloudiness over multiple cycles”.
If there actually were such evidence, you should have posted a link to it. Your failure to do so is sadly typical.
w.
The Earthshine project shows that cloudiness decreased during the active cycles of the late 20th century and has been recovering again since the sun went quiet.
The LIA was a time of more meridional jets as was the cooling period around solar cycle 20 and the current pause. Meridional jets give more clouds but until we have a longer Earthshine record that will not be easily demonstrable.
No, Stephen, you are severely overstating your case. The Earthshine data started in November 1998 and do not cover the ‘active cycles of the late 20th century’.
See:
http://wattsupwiththat.com/2007/10/17/earths-albedo-tells-a-interesting-story/
It is a good start nonetheless.
Interesting that the change in trend accompanied both a less active sun and more meridional jets.
Regardless, you were still grossly overstating your case.
The chart in the article covers more than 20 years which is about 2 cycles straddling the change in trend.
The LIA and MWP provide additional persuasive evidence.
I was not overstating because I accepted the shortness of the Earthshine project.
You were overstating your objection by suggesting data was only available from 1998.
You overstate by saying that the Earthshine covers several active cycles. But that is typical: bend the truth to support a weak argument.and hope that no-one notices. The reliable values only date from 1999 on http://www.leif.org/EOS/Earthshine_Palle_2008.pdf
I had said:
To which Viking replied
VikingExplorer November 5, 2014 at 7:39 am
I leave it to the reader to point out Viking’s error. HINT: I specified that acceleration was constant.
w.
PSāViking, your continued focus on the level of my education is nothing but an ad hominem. Either my scientific claims are right or they are wrong, regardless of whether or not I understand calculus.
>> I leave it to the reader to point out Vikingās error. HINT: I specified that acceleration was constant.
The problem is that in the general case, and especially this case, acceleration is not constant. It’s varying between a small positive value and a small negative value.
As such, we can only say a = f(t). Integrating, we get v = Ź f(t) dt.
Integrating to get position, we get Ź Ź f(t) dt dt
It seems clear that one should not expect a simple correlation between a variable and a complex integral of that variable.
>> Either my scientific claims are right or they are wrong, regardless of whether or not I understand calculus
I agree that credentials don’t matter (argument from authority). I wasn’t resorting to ad-hominem. I was trying to help you understand what you might want to learn more about, or review, in order to understand this issue.
I’ll try one more analogy: Imagine if we tried to correlate bank account deposits with net worth for a dozen people. A couple of the people are Bill Gates and Larry Ellison, while the others are a mix between middle class and some students. You might conclude that bank deposits do not increase one’s net worth, since there is no correlation between bank deposits and net worth. However, this conclusion is invalid, because in fact, a bank deposit does increase net worth, by definition.
Maybe I need to be more explicit. Net worth = total assets – total liabilities. A bank deposit is a delta increase in total assets, the derivative of assets. The average temperature of earth reflects net worth, or the total energy in the system. Solar TSI is like bank deposits, while the solar cycle is a variation in deposits. Energy doing Work or radiating into space are bank withdrawals. There is no such thing as negative energy, so liabilities have no place in this analogy.
Once this is understood, it doesn’t make sense to expect a simple correlation between bank deposits and net worth or the solar cycle and temperature. A dollar deposited into my account versus Bill Gates, while increasing both of our net worth’s by 1 dollar, have vastly different –percentage– effects. That’s why it’s important to know the energy levels of the atmosphere, oceans and crust, which are equivalent to the initial account balance.
If Earth is like Bill Gates (which It is), then in order to isolate the periodic small deposits, we’d need to subtract his billions. To me, that’s what Girma seems to be doing.
Viking Explorer: Correct! The problem with the idea that temperature of the earth won’t change due to delta TSI because the energy is constant, is that this idea never considers that the net outflows of energy can fall out of balance due to the changing sun (in ways that have yet to be accepted as fact)
Willis: Actually, you wrote alot that leads me and others to believe you needed to hear my words. Specifically, you wrote: “I expect decadal variations in solar input lead to decadal variations in surface temperatures,”. Willis, you can look at what you said before and after this statement and see clearly why I responded with my words to advance the science.
Your statement is in fact “delta T”. There is no strawman. I was being more precise, but you in fact should understand why I was helping advance the discussion instead of obfuscating, which is exactly why I am responding here. What I said holds true.
Instead of advancing knowledge, you often write distracted diatribes like a lawyer trying to confuse a jury. You are an adroit writer. However, this creates unneeded contention and you often seek to create argument and win at the cost of advancing knowledge.
I want to get educated at WUWT. I’ve learned from your work, and for that I am grateful. You often invest time to win an argument, often by derogatory means, and liberal use of labels with so much fervor that nothing is gained –except for more hits on WUWT which is a saving grace. I do not wish to fall into the trap of getting caught up into these battles.
Willis,
The energy has raised the surface temperature to a new temperature so it had controlled it. Any change in the surface temperature will be relative to the new temperature.
Willis,
There’s not an 11-year cycle. There’s a 9 to 14 year cycle and the current one is a long, quiet cycle very like cycles around 1906 and 1823.
The cycles could be varying according to how close Jupiter is to the sun (the higher the gravitational field, the slower time moves – which is why satellite and space-station clocks move faster than those on earth).
So it all points to one or more of the sun’s indirect effects.
richardcfromnz November 12, 2014 at 12:12 am
What’s wrong is that you have cut off your quotation of my words, which were:
ā¦ and you have sent me a study.
Here’s the problem with that. They say that they are using the GISS dataset ā¦ but which one? All of the GISS datasets are regularly tweaked, with past temperatures changed on a very short schedule. So which of the dozens of versions are they using? All they say about it is that they are using the:
I know of no “AST” timeseries put out by GISS ā¦ and that is why I asked for a link to a dataset, and not a link to a study.
Finally, the authors have not made even the simplest of tests of the significance of their findings, which is to divide the dataset in two and see if the claimed “significant” frequencies are present in both halves.
Anyhow, if you can give me a link to the GISS AST dataset in question, we can go on from there.
Thanks for your response.
w.
>”I know of no āASTā timeseries put out by GISS ā¦ and that is why I asked for a link to a dataset, and not a link to a study.”
I think AST is a misnomer. More likely this:
Table Data: Global and Hemispheric Monthly Means and Zonal Annual Means
http://data.giss.nasa.gov/gistemp/
“Zonal annual means” is this data table:
Annual mean Land-Ocean Temperature Index in .01 degrees Celsius
selected zonal means
http://data.giss.nasa.gov/gistemp/tabledata_v3/ZonAnn.Ts+dSST.txt
Although the datapoints don’t correspond to Souza Echera et al Fig. 1 graph where they haven’t aggregated zones in some of their series e.g. 44S – 64S. Not sure what they’ve done in those graphs.
I would point out that the Souza Echera et al solar spectral properties are of Sunspot Number not SC length and that, as they put it:
“From the cross correlation between Rz and surface temperature we can conclude
that 22 years solar cycle (enhanced activity on the Sun) apparently has a higher impact over temperature than the 11 year cycle for all the geographic allocations studied (Table 3).”
“The 11 year cycle” being another misnomer.
http://en.wikipedia.org/wiki/List_of_solar_cycles
SC length histogram
http://www.leif.org/research/Histogram%20Solar%20Cycle%20Lengths%201749-2013.png
No occurrences within the 10.95 +/-0.4 mean after SC 12 1878 – 1890. In other words, not much point looking for 11 year periodicity after that anyway. It’s the above cross correlation where the impact arises, and then only around 22 years.
>”not much point looking for 11 year periodicity after [1890] anyway”
Or at all. The periodicities that matter are around 65-yrs and depending on the time frame, 128-yrs to 256-yrs:
Multi-periodic climate dynamics: spectral analysis of long-term
instrumental and proxy temperature records
H.-J. Ludecke, A. Hempelmann, and C. O. Weiss (2013)
http://www.clim-past.net/9/447/2013/cp-9-447-2013.pdf
Unfortunately no solar consideration. For that there’s this paper:
Rigozo, N.R., Echer, E., Vieira, L.E.A., and Nordemann,
D.J.R. 2001. Reconstruction of Wolf sunspot numbers on
the basis of spectral characteristics and estimates of
associated radio flux and solar wind parameters for the last
millennium. Solar Physics 203: 179ā191.
http://www.researchgate.net/publication/226333495_Reconstruction_of_Wolf_Sunspot_Numbers_on_the_Basis_of_Spectral_Characteristics_and_Estimates_of_Associated_Radio_Flux_and_Solar_Wind_Parameters_for_the_Last_Millennium
Basically oscillation and secular trend in both solar and temperature, the secular trend in temperature is now starting to turn down as reported by Macias et al (2014) from the European Commission Joint Research Centre:
http://www.sciencecodex.com/last_decades_slowdown_in_global_warming_enhanced_by_an_unusual_climate_anomaly-141430
As it would given solar and temperature are inextricably linked over these periods.
richardcfromnz November 12, 2014 at 6:24 pm
Thanks, Richard. I take that to mean that like me, you can’t determine which dataset they’ve used.
I did look at the dataset you pointed to, the GISS LOTI zonal dataset. Here’s the global situation:
This illustrates perfectly why it is so critical to do what I call the “bozo test” ā¦ divide your data in two, and look for the signal in both halves. In this case, you can see that one half has a strong cycle at 10 years and a weak cycle around 25 years.
The other half, on the other hand, has a weak cycle at 10 years and a strong cycle at 20 years.
There is only one conclusion that we can draw from that ā¦ if the sun is influencing the temperature, the GISS LOTI global data never got the memo. There is no consistent influence visible there at all.
Unfortunately, the authors of your article didn’t do that simple test ā¦ no surprise, people rarely try very hard to disprove their cherished ideas.
w.
>”This illustrates perfectly why it is so critical to do what I call the ābozo testā ā¦ divide your data in two, and look for the signal in both halves.”
Willis, I pointed out in my comment that there IS NO 11 YEAR SC LENGTH AFTER 1890, therefore THERE IS NO POINT LOOKING FOR 11 YR PERIODICITY in the first half, let alone the second half. David Evans made the same mistake but wont admit it either.
>”[Global] In this case, you can see that one half has a strong cycle at 10 years and a weak cycle around 25 years. The other half, on the other hand, has a weak cycle at 10 years and a strong cycle at 20 years.”
All you are doing here is discovering the distribution of SC lengths I’ve already pointed out. DID YOU NOT READ MY COMMENT and the link to SC lengths? You could divide into quarters and get a different graph again. And global is not zonal.
>”There is only one conclusion that we can draw from that ā¦ ”
You draw a conclusion from THAT? Again, did you not read my comment re CORRELATION vs PERIODICITY – they are NOT the same. Souza Echera et al is about CORRELATION, did you not READ THEIR PAPER, METHOD IN PARTICULAR?
>”if the sun is influencing the temperature, the GISS LOTI global data never got the memo.”
Again, like David Evans’ incorrect premise, you are making a fundamental mistake as above. Of course you will not find the periodicity you are looking for – THE SIGNAL DOES NOT EXIST IN THAT FORM. Again, read any of the papers that apply signal analysis techniques to solar/temperature (go back to what you dismissed upthread) similar to how Souza Echera et al did. They ALL find the signals (weakest at the shortest oscillations, strongest at the longest) because they use appropriate techniques for the form of the signal – you and David don’t Willis.
>”There is no consistent influence visible there at all.”
Well duh! See above.
>”Unfortunately, the authors of your article didnāt do that simple test ā¦ no surprise,”
In this at least you are correct – it’s no surprise they didn’t because it’s BRAINLESS TO DO SO BY THEIR METHODOLOGY.
>”people rarely try very hard to disprove their cherished ideas.”
What “cherished ideas”? And to whom are you referring? Not me I hope if you had read my followup comment, viz.:
>ānot much point looking for 11 year periodicity after [1890] anywayā
“Or at all. The periodicities that matter are around 65-yrs and depending on the time frame, 128-yrs to 256-yrs:……..[see Ludecke, Hempelmann, and Weiss (2013) and Rigozo, Echer,, Vieira, and Nordemann (2001)]”
Those oscillations are where the solar/temperature connection is clearly evident. I don’t understand your fixation on an irrelevant, non-existent, so-called, “11-yr cycle”. And the scant possibility, as you seem to be trying to prove/disprove, it’s the sun’s major signal in temperature – it isn’t i.e. that is certainly not my “cherished idea”, it is an idiotic premise and a fools errand.
>”see Ludecke, Hempelmann, and Weiss (2013) and Rigozo, Echer,, Vieira, and Nordemann (2001)”
And Macias et al (2014). All papers linked upthread.