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 results—for 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.
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 coverage 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 request 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 very best dataset, the dataset that actually contains the clearest 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 some surface climate dataset, like my favorite study. That was the one that found the solar signal … in the ring widths of one single tree 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 study—all 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 signal—sea 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 dataset—I 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 objections 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 you—bring out your best dataset. I’ve just looked at the US cloud record and found nothing … 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 about clouds:
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 earth 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, 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.
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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 don’t 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 … so IF the variable cycle lengths are regulating the temperature/sea level/rainfall/whatever, the CCF analysis should show it.
But it doesn’t … 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 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.
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:
“Today’s 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) 🙂
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” … 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? 🙂
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 … 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 … 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 … 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 … 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 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. ‘
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 Patterson’s 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 don’t 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 “evidence” 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.
…
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): “Correlation 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.”
…
Solar Forcing of Climate. 2: Evidence from the Past (2005)
Gerard J. M. Versteegh
(Abstract) “The 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.”
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 … 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 … 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 ± 1°C variations in temperature five hundred years ago … 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 … 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 world—no 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 … 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 … 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 … 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 … 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
“So once again, I have donned my Don Quijote armor and continued my quest for a ~11-year sunspot-related solar signal in some surface weather dataset. …. [snip] ….. 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 you—bring out your best dataset”.
——————-
I don’t have a “data set” 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 ”11-year sunspot-related solar cycle” …. but I really don’t think that the max number (quantity) of Sunspots, ….. or the lack thereof, …. that occur during said 11 year cycle can be used as a “signal” for pointing out ….. or determining some surface weather/climate related dataset or event. If said Sunspots only originated on the Sun’s earth-facing equator then their “numbers” might be used as the aforesaid “signal”.
Now the above does not negate the possibility that the presence or absence of Sunspots is in actuality a “signal”, …. and given the observed correlation between Sunspot numbers and climate events on earth , …… then said “signal” is potentially one that only defines a Solar event for which, … that I am aware of, …. there has never been a detailed “dataset” 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 “dataset” I made reference to was one that denoted the presence, absence, surface height and/or temperature of the “solar corona”.
If the lack of sunspots is a “signal” of the potential decrease in/of the “solar corona” ….. then it should also be a “shadow signal” of a potential decrease in Solar irradiance that the earth is subjected to, ….. which appears to be confirmed by the surface temperatures during both the Maunder Minimum and the Little Ice Age, … to wit:
http://i.huffpost.com/gen/1578821/thumbs/o-MAUNDER-MINIMUM-570.jpg?1
I now invite ya’ll to “haveattit” 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.