Dr David Evans, 8 July 2014
At the introduction to this series of blog posts, we said we’d release the spreadsheet containing all the data, model, and calculations. All in one file for Microsoft Excel. Thanks for your patience.
The model, data, code and calculations are here: Climate.xlsm (20Mb).
Containing 44 datasets, 33 sheets, 90+graphs, and 15,000 lines of code
New Here? See this summary of posts. Evans looked at TSI (total solar irradiance) and Earths temperature, and discovered a mysterious notch filter. That implies some kind of solar effect occurs with an 11 year delay – or one solar cycle after the TSI. He built a model. See the hindcasts, and the prediction of imminent cooling. See the replies to critics.- Jo
(Click to download the Climate.xlsm file. 20Mb)
I chose to do all the work for this project, right from the beginning, in a single Microsoft Excel spreadsheet for pc. It’s not the fanciest or the fastest, but an Excel spreadsheet is the most ubiquitous, and one of the friendliest programming environments as well. It runs on most computers—any Windows computer with Excel 2007 or later, and possibly on Macs with Excel 2011 or later (in principle it should work, but could someone who tries it let me know if there is anything that definitely does not work on Mac please?)
The models use VBA code, the BASIC programming language that is part of Microsoft Office. There are buttons on the sheets to make models run and so on. You can inspect and run or step through the code; it is all totally open.
The main, long discussion paper is still to come. There is more to this series of blog posts. We don’t want to preempt what is coming, and it’s useful to keep the discussion focused.
Some random screenshots for those who want to oogle without the 20Mb download. (No, it doesn’t begin to capture the sea of data.)
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The optimization process by which we found the range of parameter values for the notch-delay solar model, and their most likely values, is complicated by the presence of many local minima. It is lengthy and was guided at hand at some stages, to trim the burgeoning number of possibilities in sensible ways. So at this stage we are also releasing excerpts from the main paper that define and describe the model, the total climate model, and the finding of the solar model parameters.
The spreadsheet was written for doing research—it is not a production version intended for consumers. It assumes the user knows generally what is going on. There are some descriptions and help, mainly in text boxes and comments (the red triangles in the upper right of cells—hold the mouse over the cell and the comment pops up).
People are welcome to make changes, but the only authorized copy of the spreadsheet will be at the download location above. Please send corrections or suggestions for changes to me at email@example.com, and I’ll try to incorporate them (no promises about timeliness though, because it has been extremely busy around here since starting the blog posts, with a mountain of comments and so on to read and respond to).
An Open Source Software Project?
If there is sufficient interest, the spreadsheet can be turned into an open source software project. Does anyone know if GitHub is suitable for large Excel files? Software is usually built as many small text files but we have one large non-text file, so it is not clear that version tracking and differencing will work meaningfully. Also, if we go open source there is an administrative overhead for everyone.
Please note that any results you generate using the spreadsheet are not endorsed by me, and if you make graphs other than what is obviously intended in the spreadsheet, please take the “sciencepeak.com” label off them. (Please provide links back to credit the original work, without any endorsement implied, see below.)
Journalists and data?
By the way, this spreadsheet started life as an aid for journalists. The idea was just to have all the main datasets, with instructions on how to download them, and some pretty graphs as examples—to show journalists and news producers how to get the data for themselves.
Soap box time: True authority in science comes from the data. That was the point of the Enlightenment: reason and empiricism triumphed over superstition and abuse by church and state. People learned to trust data ahead of any human authority, and science was born. Empirical data became the highest authority in physics, chemistry, and biology.
However in modern climate science the mainstream media and most politicians go to the government climate scientists as their highest authority, not to the data. Sure the climate scientists show them some data, but only their favorite data—and for a theory to be true it has to agree with all the data. With the Internet it is easy to bypass the authorities and go directly to the data itself, but the old media isn’t doing that yet. Can downloading a file of numbers, reading it into a spreadsheet, and graphing it really be too hard for the media? Come on media people, I’ll show you how.
I was preparing this spreadsheet for journalists in late 2012 when David Stockwell convinced me to look for a low pass filter in the empirical transfer function, assuming the climate was mainly driven by solar radiation (TSI). All the data was there, so I built the Fourier transforms and analysis software right into the same spreadsheet, and got distracted from the journalist project.
By the way, I couldn’t find the low pass filter we were expecting, and I twice gave up on the project because the data analysis was definitely not finding the transfer function of a low pass filter (perhaps the TSI assumption was way wrong?). Then one day, on the point of abandoning it again, I realized there was a notch instead, which was unexpected and interesting.
Sharing and using the model
- Attribution — Please give appropriate credit to Dr David Evans, provide a link to the Notch-Delay Solar Project Home Page, and indicate if changes were made, with a brief description of the nature of all changes. You may do so in any reasonable manner, but not in any way that suggests David Evans or ScienceSpeak endorses you or your use.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- Permission -- To use any part of this work in a for-profit project, please email firstname.lastname@example.org.
- Cite as: Evans, David M.W. “The Notch-Delay Solar Theory”, sciencespeak.com/climate-nd-solar.html, 2014.
The world needs more independent science
This large work is offered freely, and has been entirely self-funded and funded through donations to Jo Nova’s blog. There are no government grants, and no UN programs paying for analysis like this. To all those who help make it possible we are more grateful that you can imagine for assistance and contributions of all shapes and sizes.
You can help support more independent scientific research at the Paypal Tip Jar or by direct deposit or cheque.
Click for details
|*Due to strange Nanny-state rules, you can’t donate freely to me, but you can buy me quantities of $1 emergency chocolate. (No. I can’t believe it works this way either.)
Jo notes: I dislike group emails, and have not been able to thank every one personally, though I wish I could — I know most people would rather I write and research for public consumption instead. There are some direct depositors who deserve a mention: so thank you to Rodney, David M, Jules, Tom, Fay, Keith, Max, Bartels, Aaron, Phil, Fred D., W.E.B, Peter H, Peter K, Keith, Jim, Lawrie, M.J.B, Black Duck, W.B.C., Reed, I also owe one Peter C a letter in reply. Special thanks to MC, SB, BM, PF, GJ, PM, JD, DE, GB, VM, JP, TL, HC. There are too many good people to name. We’re grateful to every one of you. I still owe many emails!
Skeptics are winning, against all the odds, but there is still a lot to do, and if you’ve enjoyed the latest revelations, we’d be delighted to get your help so we can do more.
About Dr David Evans:
David Evans, PhD, M.S. (E.E.), M.S. (Stats) [Stanford Uni], B.Eng, M.A., B.Sc. [Syd Uni] worked with Fourier analysis and signal processing, and trained with Professor Ronald Bracewell late of Stanford University. Evans main focus is researching mathematics (Fourier analysis, calculus, the number system, multivariable polynomials, and related topics). He consulted full-time for the Australian Greenhouse Office from 1999 to 2005, and part-time for the Department of Climate Change from 2008 to 2010, and was the lead modeler in developing FullCAM, the carbon accounting model that Australia uses for the Kyoto Protocol. Evans also produces the GoldNerds excel sheets that have become the industry standard for investors in precious metals on the ASX. He is available for contract work.
UPDATE: New version 1.15 posted Wednesday 2pm Perth time, hopefully fixes “clock” compilation problems on 64-bit Excel. Should now run on 32 bit and 64 bit Excel on Windows, and on Mac. Thanks to Mark Gutzwiller, DT Christensen, and Don Jordan in the comments below for a fix.
Sabra Lane interviews Bernie Fraser, Chairman of the Climate Change Authority on the ABC 7:30 report. She only had time for a few questions. Shame then, to only ask one’s everyone knows the answer to.
Instead of asking Fraser how many dollars each Australian will have to spend to lower global temperatures by one degree Celsius, Sabra Lane asks him about global psychology instead: “On the Renewable Energy Target, there’s a lot of talk about the Government watering it down or getting rid of it. What impact is that having on Australia’s reputation?
What does she think the head of any “authority” dependent on the fear of a carbon-crisis for its existence was going to say? Not much — Sabra, no one overseas cares a lot about what we do?
Keep reading →
Palmer is offering to vote for Tony Abbott’s Direct Action Plan as long as he gets “his” Emissions Trading Scheme as well (the one he didn’t want eight weeks ago, to solve a problem he didn’t believe existed).
None of it makes sense on its face. Clive Palmer, the coal miner and die-hard unbeliever, appears to “want” an ETS, the Climate Change Authority, the Clean Energy Finance Corporation and direct action to reduce CO2 as well as the RET. (And some say that Gore lost?)
Is Palmer just playing games with both the Coalition and the media, holding cards for negotiation-sake, and messing with journalist’s heads? It could be. But until we see the fine print on the legislation (and all the other deals), we can assume the loser of the Gore-Palmer paradox was neither Gore nor Palmer, but the Australian taxpayer.
Abbott will find it hard to knock back a deal to bring in “Direct Action”, after having campaigned for so long to get it working. Especially if the ETS is sold as a dead duck at zero dollars and only on the condition that Japan, South Korea, China, Australian and the US all start emissions trading. How could he turn that down?
It could be a painful squeeze: he said he wants Direct Action, but he won his position as leader of the Coalition by opposing an ETS in 2009, and Coalition voters hate the carbon imposts in every guise. An ETS is just a carbon tax in a form where the bankers scoop up the cream, instead of it being “redistributed” to special interests and marginal seats. The brokerage fees on a 2 Trillion dollar market greases a lot of wheels. The Money-Go-Round that market powers, sets up an Industry of industries clamouring for green gravy.
For Gore, being able to claim that Australia had legislated an ETS (however meaningless) is a momentum win in the public relations campaign. It would also be a sweet lever to use on Canada and Japan and everywhere elsewhere. Gore would love to turn up to the UNFCCC in Paris in 2015 with conditional agreements from a range of countries. Could he get away with it? If the Australian ETS was a joke (down to the fine print) and known to be a joke, it would not be worth a thing. But given how patsy the media are, dutiful journalists will report that “even Australia’s conservative government has signed up to an ETS” as if it means something. Do we want to rely on investigative impartial reporting? Exactly.
Clive is playing hard ball every which way, and seemingly getting away with it. But his facade of ploys would turn around and bite him if the media would only do their job and portray him as the fake man he is who transparently believes in nothing but games. His voters didn’t vote for him to keep the RET, the Climate Change Authority, or an Emissions Trading Scheme. But the Love Media are enjoying watching him play Abbott, and have no problem with the insincerity as he panders to their green religion.
When will someone pin Clive down and ask him to explain his “climate beliefs”? We’d need a real media…
Funny things happen on the Internet sometimes. Rather spectacular claims were made that 900 days of data “were fabricated”. This claim was described as not just speculation, but “a demonstrable fact”, and worse, the crime was apparently even “admitted to” by the man himself! Except that none of it was real, and three tiny misunderstood dots were not fabricated, not data, and not important. Welcome to a Bermuda-Triangle-moment in blog-land, where facts vanish, ships full of misquotes appear from nowhere, and ghosts-of-malcontent and misunderstanding roam freely. This post here is to slay the last loose ghosts, lest anybody think they might still have life in them, or indeed, think they ever did.
Usually a live debate is a brilliant way for spectators to learn. But in that particular science thread, the main lesson is not science but manners. Common courtesy may seem a quaint anachronism, but without it, logic and reason die on the sword of uninformed passion. A simple polite email and an open mind could have saved the world from a cloud of nonsense.
Thanks to the many valiant souls who fought for common sense.
It’s rare in a complex situation that the answer is so simple. (You won’t believe how small and irrelevant it all was.) The short answer is that the 900 days of fabrication was a fuss about three dots covering three years of data at the end of a 400 year graph. The tiny blue dots were described on the graph as “assumed as average” and added to the end of a solid red line. In other words, they were obviously not actual data, the description made it clear they were estimated, they were colored differently, and nothing was hidden. What’s more, their presence or absence made little difference to the arguments or the predictions. (So there was no incentive to fake them up.) It was kind of like a handy-hint was misinterpreted as a constitutional law and the trial went on for days before anybody noticed. Time for a cup of tea instead, then? We think so.
In round one, Leif Svalgaard said Evans was “blatantly wrong” about the big TSI drop (Willis Eschenbach said “wildly incorrect”) — so we explained how the fall was 11 year smoothed and was right there even in Leif’s own data. Both men read our reply (citing it here or on WUWT) and both men can comment freely here.Yet neither was willing to admit they were wrong, apologize, or correct their claims. Does accuracy matter? It does to us. This is round two, where their second mistake is as wrong as the first. We remain baffled at their behavior. We can but point to the data.
If you’ve come here for the science, the graphs and details of datasets come first. If the bloodsport competition is more your thing, the accusations and “highest” criticisms of our critics are printed at the bottom. You won’t want to miss those.
Total Solar Irradiance (TSI) Data
Dr David Evans, 4 July 2014
We need to clear up some confusion over TSI data and the Solar Model. Sorry, there is no big new “News” here, but the details matter and allegations as serious as fraud or fabrication deserve a proper response. Plus there’s a sort of useful lesson in how a silly mistake can get magnified and live on for days. Much of what follows will be obvious or covered previously. (An early reviewer said it cemented some things in his mind and he liked that anyway). We’d rather be pushing the scientific ideas forward. Soon.
1 The Context: Why there is a fuss over a fall in TSI?
The notch-delay solar model predicts a sharp global cooling and the turning point is soon (see Post VIII). It’s widely known the current solar cycle is a lot lower than the one before, but the notch-delay model predicts a sharp turn. An obvious question arises: is there some other way, apart from using the model, to see there is going to be a sharp cooling soon? (Assuming the notch-delay theory is right.)
Figure 1: Climate model driven only by solar radiation, with no warming due to carbon dioxide. Predictions shown by dotted lines. See Post VIII for explanation and context.
The model includes a delay, a low pass filter, a notch filter, and parallel paths. For a move as gross as the projected imminent cooling, we can dispense with the subtleties of the last three elements and just focus on the dominant driving element—the delay. This is just a simple check. The model is of course very “aware“ of the sunspot cycle, so any corresponding fall in TSI is not of the usual sunspot cycle variety, but is a fall after taking into account the usual ups and downs of the sunspot cycle.
The obvious and simplest way to remove most of the sunspot cycle and reveal the underlying trend is to apply an 11-year smoother to the TSI. The sunspot cycle varies from 8 to 14 years, but averages 11 years. The goal is only to crudely mimic the model’s behavior in order to get more understanding of why it predicts an imminent cooling.
2 TSI in Post VIII
Here is the TSI graph presented in Post VIII: [Jo says: look out, this is the graph that generated the Bermuda-Triangle moment.]
Figure 2: The recent fall in TSI is the steepest and one of the largest ever recorded (records go back to 1610). (There is a trivially different original before the PMOD data was updated a few days ago, linked to in Post VIII.)
Which Lean 2000 dataset was that? It’s reasonably clear:
This TSI graph shows the composite TSI data used in our project, which is described in its bare bones on the graph itself (top left). Direct measurements of TSI only started in late 1978, by satellite. The reconstruction used for most of the data in Figure 2 is from Lean 2000, which is the main, standard reconstruction. Anyone familiar with the TSI datasets can also see that the Lean 2000 data used here is the newer version with the Wang, Lean, & Sheeley background correction (2005), because the level during the Maunder Minimum is about one W/m2 below the average level since 1940, whereas in the original Lean 2000 data the difference was over two W/m2—see the first graph here.
We did mention that smoother in Post VIII: “We put an 11-year smoother through it to give us the red line, which shows the trends in solar radiation.” We then commented on the three big falls in the red line, and made the point that the third fall, which started around 2004, will lead to a corresponding fall in temperature sometime around 2014 to 2017 (but more likely 2017) according to the notch-delay solar theory.
A close up of those misunderstood blue dots
Notice the blue dotted line (circled) at the end of the red line. Here it is, blown up:
Figure 3: Enlargement of the fall in the 11-year-smoothed TSI around 2004, in Figure 2 above.
The text on Figure 2 explains the dots: “Composite TSI for Sep 2013 to Dec 2015 assumed as average TSI value from Sep 2012 to Aug 2013, to extend smoothed curve (dotted line).” That period is roughly 900 days.
The extension was made to give us an idea of where the TSI fall might bottom out. If the data stops in August 2013, as in Figure 2, then the 11-year-smoothed values stop 5.5 years earlier, in January 2008.* We are close to a solar maximum in sunspots now, so the values of TSI for the rounded top will probably be about the same. You could reasonably disagree with that extrapolation, but the method was stated clearly on the graph.
The extension was noted in the explanatory text, dotted, and a different color to the data. It is described as assumed and used to extend. It is difficult to confuse with the data. (Apologies for stating the glaring obvious. It’s odd having to point out things this simple. We describe the fracas below. Who would have thought?)
The same dots are more obvious (and useful) on a close-up graph:
Figure 4: As per Figure 2, but from 1950. Notice how the extension of the data shows that the fall in 11-year smoothed TSI will likely end soon, and thus indicates the size of the fall in 2004, so it can be more easily compared to the falls in the 1600’s and in Napoleon’s time.
What are the differences in the TSI datasets?
TSI measurements come from satellite-based instruments. There are three main datasets. PMOD starts in late 1978, is the dataset Judith Lean used to reconstruct TSI back to 1610 from the sunspot data, and is the dataset we use predominantly. ACRIM had some troubles in the 1980s, but we use it from 1992. SORCE started in 2003. See footnote.**
Lief Svalgaard made it clear that he prefers his own reconstruction and the SORCE/TIM reconstruction (a reconstruction until 2003, then the SORCE/TIM data) to PMOD/Lean-2000:
Figure 5: The SORCE/TIM and Svalgaard reconstructions both show the three big drops in their 11-year-smoothed curves, including a recent fall. Compare to Figure 2.
Their 11-year smoothings both show three sharp declines – in the 1600’s, in the time of Napoleon, and recently — just like our composite TSI in Figure 2. However the timing of the most recent fall is different:
Figure 6: The start of the recent fall in the 11-year-smoothed trends of the SORCE/TIM and Svalgaard reconstructions occur earlier than in the PMOD/Lean 2000 data. Compare to Figure 4.
If the SORCE/TIM and Svalgaard reconstructions are to be believed, the recent fall in TSI started back in 1995. This is a significant difference. If TSI fell from 1995 then the corresponding fall in temperature should have been evident from about 2006 — but since it didn’t happen that would mean the solar influence is weak. (Toss out that theory eh?) But if the sharp fall started around 2004, the corresponding temperature drop is yet to impact Earth.
See the graph posted and discussed here. It shows that all the TSI estimates show a recent fall in their 11-year smoothed trends, and all the falls are of a similar magnitude. All show a TSI peak in about 1986. The only substantial differences (relevant to this work) are in the timing of the start of the recent fall.
Basically it comes down to a choice between the sunspots and reconstructions based on those sunspots, or the measured TSI. As Svalgaard himself said, “All so-called ‘reconstructions’ of TSI are Guesses. Most of them bad.” The only measured data covering the relevant period from the late 1980s (required to construct an 11-year mean of the early 1990s) to the current day is PMOD.
4 The Accusations (Aka science as a “bloodsport”?)
Comments below come from the post “A Cool Question, Answered?” (which turned out to be a Hot Question, Unanswered). Don’t Svalgaard and Eschenbach protest just a little bit too much?
There are basically three accusations that Svalgaard and Eschenbach repeat over and over:
1. That my claim of “TSI dropping” around 2004 is false.
They argue against a straw man, as if I had claimed that monthly or daily TSI readings have dropped since 2004. However Post VIII , linked to in the article at WUWT, makes it abundantly clear that I was talking about the trend, as established explicitly by 11-year smoothing and implicitly by the filtering action of the notch-delay solar model. See Figures 2 and 5 above. Svalgaard links to a graph or the SORCE/TIM measurements since 2003 as support for his position that there is “no such drop” — but his graph is of TSI, not ll-year smoothed TSI or any trend measure of TSI. They never acknowledge either that there was a recent fall in the 11-year smoothed TSI or that I was referring to it.
Svalgaard repeats his misunderstanding here. He links here to the Figure I used in Post VIII, which is labelled “Solar radiation (TSI) 11-year smoothing“. Here Eschenbach even pastes our graph of 11-year smoothed TSI estimates, and attacks our use of 11 year smoothing! So they knew. I talked about three big falls — which are clearly visible in the 11-year-smoothed red line, but not the brown line of 1-year smoothed TSI with many falls. It is hard to explain how they missed it.
Note that this is a separate issue from Svalagaard’s position that all the past reconstructions and recent measurements are wrong except his reconstruction and the SORCE/TIM measurements from 2003, which he explains here and here.
2. That I fabricated data in my TSI graph, which is Figure 2 above.
The extension is not data. I described it as an extension on the graph itself : “to extend smoothed curve (dotted line)”, the method used to obtain it is given on the graph and that makes it clear that is not data, it is presented in a different color from the data, and is dotted, not solid, like the data. See above. The general principle is you can put anything on a graph, so long as you explain what you are doing and it is not deceptive. The extension isn’t so useful on the 400 year graph, but on the 60 year graph (Figure 4 above) it shows the likely extent of the fall.
3. That I am hiding something by not releasing my data and calculations yet.
A reminder of what we said in the introductory post: “All the data, model, and computations are in a single Microsoft Excel spreadsheet. It runs on any pc with Excel 2007 or later; it runs at least partly (and maybe fully) on any Mac with Office 2011 or later. This is completely open science—every bit of data and every computation is open for inspection. We will be releasing this towards the end of the series of blog posts.” The reasons for this—so as not to preempt the blog posts, and to engender a more focused conversation with useful feedback –were given several times, and elaborated upon here. The spreadsheet would already have been released by now, but some people prepared to comment publicly on it still don’t know the basics, and it takes time to correct their mistakes.
Here are some of the , er, highlights:
Svalgaard 1. “The TSI used by Evans is totally wrong“. | Lean 2000, PMOD, and ACRIM are mainstream datasets. The datasets for the critical period from the mid 1980s on are basically the PMOD and ACRIM measurements. Svalgaard implies these measurements are “totally wrong”, while putting forward only reconstructions to cover the period before 2003. So, this is a case of measurements vs reconstruction.
Svalgaard 2. “The most blatant error is the statement that TSI has had a sharp unprecedented drop starting in 2003-2005 to now. This is complete nonsense. There is no such drop.“ | Straw man. A drop in 11-year smoothed TSI has clearly occurred, even in his own reconstruction (doesn’t he see it?).
Svalgaard 3. “As far as I am concerned, the model is already falsified. Not by the observations but by the [almost fraudulent - as there clearly is an agenda here] use of invalid input to begin with.” | Fraud implies lying with intent to deceive. See Figures 5 and 6: who lied? Svalgaard prefers his own reconstruction or the the IPCC reconstruction that recently replaced the one I used: who’s got an agenda?
Svalgaard 4. “The data is not slightly wrong, but verry wrong, and hence the prediction [...] is wrong, which was my point.” | The prediction is based on measurements of TSI since the mid1990s, but mainly around 2004, made by PMOD and ACRIM. Svalgaard only offers a reconstruction for most of this period. Again, we use mainstream measurements while he uses reconstructions, both of which show a trend drop anyway.
Svalgaard 5. “On the contrary he has shown that Mr Evans used wrong TSI data. This is either incompetence [I will allow for that hence my 'almost'] or a deliberate act [you made that call].” | Again, we use mainstream measurements while he uses his reconstruction.
Svalgaard 6. “The SORCE/TIM data is correct since 2003 and contradicts Mr Evans demonstrably false assertion that there was a sharp drop in TSI in the 2003-2005 time.” | Straw man. See accusation 1 above.
Svalgaard 7. “On the contrary, TSI is now higher than at any time in the SORCE/TIM record, so Mr Evans has spliced the SORCE/TIM data incorrectly to the observations covering 1978-2002.“ | Huh? How would he know? As it happens, I didn’t use the SORCE/TIM data.
Svalgaard 8. “That the 2000 Lean reconstruction is invalid is well-known [even Lean agrees with this] so Mr Evans is either incompetent or deliberately using invalid ‘data’ without having done his due diligence. The Krivova reconstruction suffers from the same problem as Lean’s obsolete one: invoking a background based on the flawed Group Sunspot Number.“ | My prediction of an upcoming fall relies on PMOD and ACRIM data from the critical period from the mid1980s, not from any reconstruction. Perhaps those making claims of incompetence ought first be competent readers?
Svalgaard 9. “Mr Evans made a horrible mistake [deliberately or out of ignorance - your call] making his prediction worthless; one cannot scientifically disagree with such nonsense. Disagreement requires substance and there is none in Mr Evans’ work.” | Straw man. See accusation 1 above.
Svalgaard 10: In response to something Christopher Monckton said, “You are correct that nothing can rest on Mr Evans’ incorrectly doctored dataset.“ | Oddly enough he refers to me as “Mr Evans” but accuses me of doctoring. Funny man.
Svalgaard 11: “I will agree that Mr Evans did not intend to have anybody discover his little ‘trick’. [One is reminded of Mann's 'Nature Trick' of Climategate fame].” | The extension is plain to anyone. There was no “trick”, nor anything to gain from the dots—they limit the downward trend. See accusations 2 and 3 above.
Svalgaard 12: In response to “what’s all the hubub about?” Svalggard says “It is about scientific honesty [or rather lack thereof]“. | Dishonesty? I didn’t misquote Svalagaard or say he did things he didn’t, did I?
Svalgaard 13: “So Mr Evans fabricates out of thin air about 900 days of TSI and tags that to the end of the curve.” | The extension was clearly explained on the graph itself, and is visually very different from the data. See accusation 2 above.
Svalgaard 14: “Both Willis and I have shown that Mr Evans invented the decline of TSI since 2003-2005.“ | All the estimates and datasets show a recent fall in 11-year-smoothed TSI, even Svalgaard’s own reconstruction. See the first figure here.
Svalgaard 15: “And the fabrication [of data] is a fact as I showed above by Mr Evans’ own words.” | It’s a “fact” now? And wait… it’s in my “own words”, but you said I was hiding it? So which is it? See accusation 2 above.
Svalgaard 16: “Even the data he claims is Lean 2000 has been tampered with and doctored into shape.” | The TSI in the TSI graph in Post VIII is a composite of Lean 2000 and other sources, so it will not exactly match Lean 2000. As noted above, anyone familiar with the TSI datasets can immediately see that the Lean 2000 data used here is the version with the Wang, Lean, & Sheeley background correction. Odd that he didn’t notice.
Svalgaard 17: “Mr Evans does indeed fabricate and invent data. End of discussion.” | Since there is no fabrication or invention, where does that put Svalgaard and Eschenbach? See accusation 2 above.
Eschenbach 1: “I begged David Evans, begged him please, please, to release the hidden code, to stop keeping the model equation a secret, to reveal the data, to expose the numbers of tunable parameters, to show the results of the out-of-sample tests that Jo says he’s already done …” | Really? Begged? I don’t recall ever having talked with Willis or exchanging emails with him. And I’ve searched through all the comments Eschenbach left on the blog posts here about the notch-delay solar project…and no “beg”. No asking even. Certainly no “please”. Just lots of repetitive berating for not releasing material immediately, and he did not even address our clearly stated reasons given for introductions-before-material.
So how about you quote yourself Willis: where is this begging you keep said you did?. I’ll quote you — this is what you typically say at the bottom of one of your articles: “USUAL REQUEST: …please quote the exact words you disagree with. That way, everyone can understand your point of reference and your objections.”
Eschenbach 2:“I begged Jo and David to publish, and I got the same answer we’ve gotten from every other pseudo-scientist, that for me to ask was wrong, wrong, wrong, and that they’d publish the code and data and out-of-sample tests when they damn well felt like it … science at its finest.” | Yep, definitely said “begged”; see accusation 3.
[Jo adds: I note that Willis raised the “Mann and Jones” false equivalence on this blog on June 21, and my answer to this was not quite the “same answer we’ve gotten from every other pseudo-scientist”. Willis asked: “And why on earth do I have to ask you pretty please if you’ll release your results as if you were Phil Jones or Michael Mann?” Jo replied: “Because Phil Jones and Michael Mann get your taxes. We don’t. That’s why.” This from the man who insists people quote him exactly?]
Eschenbach 3: “…and admit that (at least according to their graph) they have made a wildly incorrect claim that the TSI has fallen precipitously since about 2004. It is on the basis of this supposed fall that they are predicting falling temperatures.” | Straw man. See accusation 1 above.
Eschenbach 4: “But neither of us owe David Evans an apology. He’s the one that made the horrendous newbie mistake, not us.” | Ummm, you didn’t notice it was 11-year-smoothed TSI and trends in TSI we were talking about?
Eschenbach 5: “That quote from the graph itself clearly says that they have invented the data from March of 2013 to December of 2015, which is the 900 days of data that Leif mentions. Now, I’ve used the word “invented” for that data. The graph itself uses the word “assumed” for that data. And Leif used the word “fabricated” for that data.” | “Invented data” now? Not so. (It’s like Chinese whispers: assumed means invented means fabricated. Go Directly To Jail!). It was clearly explained and marked on the graph itself. See accusation 2 above.
Eschenbach 6: “Next, David Evans has not released the data, the model, the model results, the equations, the out-of-sample tests, or any of the details. This is the same garbage we got from Michael Mann and Phil Jones. And now, here you are cluttering up WUWT with the same kind of garbage. There is no transparency. There is no data. There is no code. In what alternate universe does this pass for science?” | Didn’t read the introductory post perhaps? Don’t believe the answers we gave you? See accusation 3 above.
Eschenbach 7: “Christopher, I have a simple rule that has never failed me. When a man is hiding something, it’s because he’s got something to hide.” | I have a simple rule too: when a man attacks a scientific argument with accusations about motives, there is something else going on. See accusation 3 above.
Eschenbach 8: “I’m sad to see you and David Evans and Joanne taking up the habits of Mann and Jones, David. I’d thought y’all were scientists. Ah, well, live and learn.” | We are sad to see a skeptic taking up the habit of character attacks, as is commonly used by unskeptical people. See accusation 3 above.
And on and on and on.
We are looking forward to releasing the spreadsheet, and are grateful that Eschenbach and Svalgaard have made it clear they have made their conclusions already. ; -)
Otherwise, we remain baffled. The comments by Svalgaard and Eschenbach at WUWT are inexplicable. Svalgaard says that “science is a bloodsport”, but Joanne notes that it “doesn’t have to be… You could use logic and reasoning instead.” We offer no speculation on the reasons for their repetitious, tendentious, and aggressive comments. It doesn’t look like truth-finding to us when someone uses fallacies, fails to quote exactly, and fails to acknowledge polite responses pointing out their misunderstandings. We see little hope that their attitude will change, so we expect more of the same as we roll out the project.
A big thank you to Christopher Monckton and the others who objected at WUWT and pushed back. Thank you! They sensed that a crime was being committed and they did what they could. And thank you also to those who have emailed us, or left comments on this blog about the matter, or donated. (BTW Joanne spoke to Anthony Watts at length yesterday in a friendly exchange. He had arrived late at the “Bermuda Triangle”, and did what he could. Please keep comments constructive below. This post is about commenters and a new theory, not Anthony.)
We are still rolling out the introductory blog posts. It is taking much longer than we had anticipated partly because of the need to respond to unwarranted and inaccurate criticisms and statements. We very much want feedback, good and bad, and appreciate the well informed, polite sort the most. We will resume the series as soon as we can, other commitments, notwithstanding.
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Ten awards will be given to prominent global warming skeptics at the Ninth International Conference on Climate Change (ICCC-9), taking place in Las Vegas on July 7-9.
It’s great to see people who have put their careers and reputations on the line for scientific progress get the recognition they deserve. Awards are not as exciting as “new science” I know, but they are an important way to say thank you for some exemplary dedication. There are some giants here who I very much admire.
Sherwood B. Idso, Arthur B. Robinson, Roy Spencer, Viscount Monckton of Brenchley,
S. Fred Singer, Willie Soon, Patrick Moore, Tom Harris, Alan Carlin, E. Calvin Beisner .
There is still time to get tickets to go to the conference. We, unfortunately, can’t be there, but had fabulous, rewarding experiences in the past. It is a great credit to Heartland that they put on better science conferences than The Royal Society. The truth shall not be suppressed (but only because some people put in the effort to get it out there). Don’t discount how useful it is to make the effort to say thank you.
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Filed under: Light relief (for a moment til we get back to The solar model)
Not only will your air conditioner make little fish more reckless, but other fish might seriously not be able to find their friends for coffee. I did not make up that headline. Your taxes did.
Note the carefully phrased results:
“Whilst fish kept under normal conditions consistently chose the familiar school, fish reared under high CO2 conditions showed no preference for either the unfamiliar or familiar school.”
If increasing CO2 was a politically-correct achievement, would that same result carry a headline telling us that “Climate Change makes Fish More Confident with Strangers”?
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Leif Svalgaard claims “TSI has not fallen since 2003″. It’s technically true in a sense, but demonstrably false when discussing 11 year smoothed trends (which is written on the graph he was criticizing). Willis Eschenbach sadly was carried along. This post is in response to an overheated thread at WUWT. Both men owe David Evans an apology.
The fuss is over the big fall in TSI. Leif Svalgaard said it was “almost fraudulent” that we claimed there was a fall in TSI since 2003 since there wasn’t a fall in this dataset. He says: “There is no such drop.” I say, look at the graph below, it’s even in your own data. Svalgaard provided the link to his TSI set, and we’ve included that line in the graph below. It’s the light-purple line. (Has he paid attention for the last ten years?)
In his rush to call it “totally wrong” and to declare “the model is already falsified” he didn’t notice we were talking about a trend in 11 year smoothed TSI, and the fall is evident in whole cycles (but takes some wisdom to find in daily or monthly data). I guess that’s a mistake that could happen to anyone — but some of us might ask politely before we started calling “fraud”, and saying things like “Mr Evans assertion is false [and I maintain seems to be agenda driven..." Likewise, Willis Eschenbach unskeptically follows: "as Leif points out, he’s using a bogus set of TSI data." If skeptics toss out careless accusations, it rather cheapens the real ones.
Obviously the 11-year smoothed effect is news to Svalgaard, perhaps it's news to a lot of people. It's something David found because his Fourier work suggested a notch, and the Solar Model that was made with a notch filter predicted a big fall to come. From that David inferred there must have been a corresponding large drop in TSI and then he created the 11 year smoothed graph and found it (in response, it must be said, to an email from Lubos in April asking if there was an easier way to see there was going to be a big fall in temperature than through the model output).
The comments at WattsUp has been unseemly, and entirely unnecessary. (I'm sure it doesn't help Anthony.) We will deal with other misunderstandings from the same thread (yes there were more) in a future post. The uninformed ad homs are a waste of time. What happened to common courtesy?
Compare the major datasets of TSI or proxies:
The major TSI datasets all agree there has been a large fall since 2003, in terms of 11 year smoothing (which is obviously required to remove the sunspot cycle and reveal the underlying trend). The SORCE/TIM reconstruction shows the fall starting in 1994. The “composite TSI” is that used by David to drive the model, averaging Lean 2000 (to the end of 2008), PMOD, and ACRIM (from the start of 1992).
As to whether the SORCE data should have been used in the Notch-Delay Solar Model — it’s rather trivially clear that since it starts in 2003 it’s not very useful for 11 year smoothed graphs, because there is only a single point of 11-year-smoothed data. It’s no use for finding the model parameters, because the delay of about 11 years means it cannot be used to check predicted temperatures against observed temperatures yet. And SORCE might be wonderful but it isn’t useful for Fourier analysis of long term climate cycles either (it’s hard to find an 11 year delay in only 11 years of data).
Strangely too, for a commenter who I hear is familiar with solar data, Svalgaard seems to forget that the last peak of solar cycles was 2001-2002, which is not visible in the graph he linked to (SORCE wasn’t operating then). Svalgaard compares data that starts after the peak with the next peak and says “they are the same” as if it means something. It’s a tad misleading (to be polite). I’m sure he didn’t mean it that way.
The graph below pretty clearly shows how TSI from the 2003 to 2012 fits — at least in the larger PMOD scheme of things (SORCE data only covers this short era). Yes, it’s technically accurate to say that TSI now is the same as 2003. Svalgaard declares ” If anything TSI is now higher than it were in 2003.” But it is obvious that the peak of the latest cycle is a lot less than previous ones.
In PMOD data (like SORCE data) obviously TSI now is similar to 2003. Equally obviously, that’s a meaningless comparison. The current peak is nothing like the last one.
Svalgaard thinks science is a bloodsport
Svalgaard emailed me this morning saying “science is a bloodsport”.
I replied that it “doesn’t have to be… You could use logic and reasoning instead.”
All the facts could be uncovered faster by honest enquiring minds without malice. People who brought preconceived assumptions about “motivations” and bad-will into a science debate failed to read what was put before them. We knew David’s work was going to be difficult, and that’s why we’ve released it bit by bit. They aren’t the only ones who have not read carefully enough.
Svalgaard admits reconstructions are “guesses”
Noteworthy is Svalgaard’s honesty about reconstructions. Commenter Brad, here asked why Leif used the term “TSI-guess” in his file label, Leif responded saying: “All so-called ‘reconstructions’ of TSI are Guesses. Most of them bad. The TSI-Guess.xls file is my guess.”
TSI varies because the magnetic field of the Sun varies, and the field varies as the Sunspot Number does, so the variation of TSI is essentially that of the sunspot number [which is known] on top of a fixed background that does not vary. Based on the past decade of SORCE/TIM precise measurements of TSI, we can calibrate the sunspot number in terms of TSI. That gives me a Guess, which will not be correct in details, but will capture the gross features of the variation.
A brief history of TSI datasets
David Evans looked at all the major TSI datasets he could find in 2013, and downloaded the data available on 8 Aug 2013 when he froze the data used by the project. It’s harder to measure the sunlight than you might think, because there is a wide spectrum of light from UV to infra red. Everyone thought there was essentially no variation to measure up ’til late 1978 when people started observing it with satellites — it’s telling that TSI used to be called “the solar constant”. But there is really only one observed record that runs through the last 35 years, namely PMOD. ACRIM provides data from 1978, but before 1992 or so its results disagreed with PMOD and Lean (which is a reconstruction guided by PMOD). Like everything in climate, there is a war going over the adjustments and reconstructions and no one can agree. Fans of the IPCC now say the TSI was falling for decades, while others think TSI pretty much stayed high til the 23rd cycle and the 24th has been strikingly small.*
The PMOD dataset is the longest running continuous TSI record. ACRIM data got compromised by the Challenger Shuttle exploding, and then its results apparently didn’t quite make sense until the early 1990s. Bring on SOURCE, a new hot tool in 2003, which seems to work well. Lean and co used the PMOD data with sunspots during the same period and figured out a kind of calibration to use so we could estimate TSI from the old sunspot data. Yes, it’s difficult and we all wish Napoleon had fixed his satellite program, but it was not to be. The data is what it is. We are all doing the best we can.
Hmm. That’s a sudden adjustment in the reconstruction of TSI dataset?
The steep fall shown in the graphs for the predictions in part VIII is from 11 year smoothing of the PMOD and ACRIM data. And it’s still there in the updated data (see the update in post VIII). But a funny thing happened to the SORCE/TIM dataset. Anthony Watts covered the strange rearrangement of TSI reported on Feb 6 2014. The SORCE / TIM data changed rather a lot overnight. Previously there were four high peaks in the late 20th Century, but now there was only one, and it was the earliest.
Note the dramatic change in the last three peaks. (It’s a 3 second slow blink)
The animation makes it clear the shape of the last few peaks is quite different.
The next data battleground is going to be when the fall in TSI occurred. If it occurred in 2003 as per the PMOD and ACRIM data, then a corresponding fall in temperature is on the cards for about 2017. If it started in 1995 as per the new SORCE/TIM reconstruction now favored by the IPCC suggests, then the corresponding fall in temperature should have been evident from about 2006 — but since it didn’t happen that would mean the solar influence is weak. In the return of a previous theme, the measured data favors the former, while the later relies on reconstructions (the SORCE/TIM data only starts in 2003, and is not relevant to the 11 year smoothed values in the mid 1990s) that flatly disagree with the measured data.
The bottom line here is that perhaps SORCE/TIM is a better guesstimate of TSI than PMOD, but whatever PMOD is measuring seems to be a better predictor of Earth’s temperature. So in future if we can elucidate what makes PMOD useful and the new SORCE/TIM reconstruction not so much, then we may get clues as to the mystery force that operates with an 11 year lag. Obviously temperature is not following the SORCE/TIM reconstruction with an 11 year lag, but it does seem to follow PMOD.
Keep reading →
Some people are claiming that the transfer function is meaningless because you could use white noise instead of temperature data and get the same notch. It’s true, you could. But the argument is itself a surprisingly banal fallacy. It looks seductive, but it’s like saying that it is meaningless to add 3 oranges to 3 oranges because you could add 3 oranges to 3 apples and you’d still get six!
It is trivially obvious that the transfer function will find a relationship between entirely unrelated time series, as any mathematical tool will when it’s misapplied. The question that matters — as with any mathematical tool — is has it been misapplied? What matters is whether the base assumption is valid, and whether the results will be a useful answer to the question you’ve asked. If the assumption is that apples and oranges are both pieces of fruit, and the question you ask is “how many pieces of fruit do we have”, then it is useful to add apples and oranges. But if you are trying to compare changes in fruit consumption, adding the two is mindless. So let’s look at the assumptions and the question being asked.
Two assumptions were made before computing the transfer function. And before anyone complains that the whole project was a circular tautology — pay attention — the assumptions are temporary. They are a “what if” used to see if we get a meaningful answer. Later the assumptions are dropped and tested.
1. Recent global warming was associated almost entirely with TSI.
2. The climate system is linear and invariant
…then, the transfer function from TSI to temperature is of great interest and sinusoidal analysis is appropriate.
David Evans has been explicit about both right from the start, but not all commenters seem to realize the implications.
The transfer function between TSI and Earth’s (surface) temperature will be meaningless if there is no causal link between TSI and Earth’s temperature. (Some people may need to read that twice).
This “link” could be an indirect one. It doesn’t mean that TSI itself is causing the change in temperature. It could, for example, mean that TSI is a leading indicator of other solar events that lag it by 11 years. It could be that those other events — say magnetic fields, solar wind, UV or other spectrum changes — are the ones actually causing the albedo changes that cause the temperature to change 11 years after the TSI changes.
By all means, if you have definitive evidence that changes in TSI cannot possibly be directly or indirectly associated with changes in Earth’s temperature, do let us know. It will save us a lot of time. Likewise, if you know of any reason why TSI can not possibly be a leading indicator for some other solar factor which acts with an 11 year cycle, please let us know. Some people are willing to declare they know that TSI cannot be associated with changes in Earth’s temperature. Some of us have an open mind. The solar dynamo is not completely worked out. Fair?
What about the question we are trying to answer?
As to the second part, what question was David Evans asking, and are the results useful? He made it explicit.
The initial aim of this project is to answer this question: If the recent global warming was associated almost entirely with solar radiation, and had no dependence on CO2, what solar model would account for it?
So is the discovery of a notch filter useful, and does it help to create a solar model? It certainly looks that way so far.
The model was constructed in the frequency domain. The main feature in the transfer function is the notch, so we tried building a similar notch in the model. The existence of the notch implies there has to be an accompanying delay (the timing seems unnaturally perfect, people are understandbly having trouble wrapping their heads around that). The delay was later found to likely be 11 years, which is not only the length of the major cycle of the solar dynamo but is borne out by other independent studies– such as Usoskin, Soon, Archibald, Friis-Christensen and Lassen, Solheim, Moffa-Sanchez, etc. (see post III). Later, a model based on an 11 year delay was found to produce reasonable results (see that hindcasting).
So the notch turned out to be very useful in building the model, giving two of the five elements in the model (the other three are the low pass filter, the RATS multiplier, and the immediate path for TSI, which were deduced by physical reasoning).
It would be better if there was a known mechanism. Of course, but Rome wasn’t built in a day, steady on. We are working on it. If people already knew what force X was then presumably they would have noticed its correlation with temperature and the climate problem would already have been solved, wouldn’t it? Some commenters, (those not focused on fallacies like argument from incredulity or the mechanics of publication time-tables) are being very helpful in gathering clues on force X — thank you!.
PS: The release?
And for those who are impatiently waiting the full working model, we’re working on it. There are a few last-minute things to sort out. The spreadsheet used data to August 2013 in the investigation, and was frozen months ago with that data. That’s the copy that is available to people who got advance notice. Now that we are releasing it, it would be nice to update the data, while preserving the original calculations. David is copying the Aug 2013 data and updating all the data. We are also figuring out the creative commons conditions that would be workable, and deciding how to manage suggestions, adaptations, and modifications. We suspect the normal open source software sites don’t deal with 20Mb Excel files which people can modify, but which are very difficult to track changes on (does anyone know of a similar project?). Right now the sciencespeak legal department, open science support team, human relations division and marketing arm are working flat tack. (That’s both of us. )
The biggest impediment at the moment is that some people still haven’t read the first posts we put up carefully enough. Even though we answered their questions personally in comments they still keep repeating the same points. Should we have kept the whole project secret until we had solved all these questions? Perhaps, but it’s been immensely helpful to get some feedback and help from some readers, and we didn’t know who would be the most useful beforehand. They have made themselves known.
On the other hand we’re being compared to Phil Jones and Michael Mann by one commenter, which we think is a tiny bit over-the-top, given that Jones and Mann are funded by the taxpayer and they spent years and used legal means to prevent their data being made public. To put a fine point on it, we got no income from taxes, and we owe the critics nothing. We also ask nothing of them (except, implicitly, patience and manners). Maybe that looks equivalent to a few people – we can’t see it. All the fuss, seriously, is flattering (if counter-productive).
Manners makes no difference to the scientific method, but ultimately the human practice of the Scientific Method is only ever advanced by … humans, and manners do matter. Science is never advanced by namecalling, misquoting, strawmen and personal attacks. Please quote us exactly, eh?
I’m sure some people must be tired of discussing the solar model
Nils‐Axel Mörner has a new paper out (his 589th). For 60 years he has been tracking the coastlines close to him, and carefully isolated the exact part which appears to be the most stable. From that he shows that the real sea-level rise in Northern Europe is less than 1 millimeter a year since 1890. This is less that the 1.6mm trend in 182 NOAA tide gauges, and far below the estimates of the IPCC reports.
There is also no sign of acceleration in sea-levels for the last 50 years. (How much should Europeans spend to stop a 1mm annual rise that was already going in 1890 and has not changed much since then?) If anything, Nils work shows how difficult it is to measure true sea-level rise on land that shifts.
In this graph below, he compares the rise of most tide gauges with the Kattegatt region, and the IPCC results. This is only one result from one place, but it is based on thousands of readings from sites all around Kattegatt. His painstaking attention to extreme detail and empirical data stands in stark contrast to the IPCC where the trend depends heavily on adjustments. (Those adjustments appear to be based on a tide gauge in Hong Kong that is subsiding compared to the four other records nearby). Nils notes that people once thought true eustatic sea level changes would be the same all over the world, but this is not so. He remarks that the search for a meaningful mean global rate has become “illusive”.
Nils explained that the superb thing with the Kattegatt region is that we have both a perfect control on the crustal movements, as well as a number of fine tide gauges, so, we can separate the two factors in a way hardly possible anywhere else.
FIG. 1. SPECTRUM OF RATES OF SEA LEVEL CHANGES IN RELATION TO THE DISTRIBUTION OF RATE VALUES OF THE NOAA TIDE GAUGE STATIONS [18, 22, 24]. ESTIMATES OF RISE BY THE IPCC FOR YEAR 2100 (GREEN ARROWS) , SATELLITE ALTIMETRY (+3.2 mm/yr) , MEAN OF 182 NOAA TIDE GAUGE STATIONS (+1.6 mm/yr) , THE NEW DATA FROM THE KATTEGATT SEA HERE PRESENTED (+0.8‐0.9 mm/yr), AND THE VALUE FROM SOME KEY SITES (±0.0 mm/yr) [22, 24].
The rise since 1890 is consistent, slow, and linear.
FIG. 5. TIDE GAUGE RECORDS OF KORSÖR, NYBORG AND AARHUS AS PRESENTED BY NOAA . KORSÖR LIES RIGHT AT THE ZERO ISOBASE OF UPLIFT, AND THE SEA LEVEL RECORD (+0.81 ±0.18 mm/yr) SHOULD HENCE REPRESENT REGIONAL EUSTASY. (His graph includes two other areas, not shown here) THIS IMPLIES THAT ALL THREE RECORDS GIVE A CONGRUENT RECORD OF A REGIONAL EUSTATIC RISE IN THE ORDER OF 0.8‐0.9 mm/yr (THE MEAN BEING +0.87 ±0.15 mm/yr). THIS TREND HAS REMAINED STABLE OVER THE LAST 125 YEARS.
To show how much work goes into analyzing land masses for their tilt and change in height, here is one graph of norther Europe. The boxed area (Kattegatt) lies on the edge in between areas which are moving in opposite directions.
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To recap — using an optimal Fourier Transform, David Evans discovered a form of notch filter operating between changes in sunlight and temperatures on Earth. This means there must be a delay — probably around 11 years. This not only fitted with the length of the solar dynamo cycle, but also with previous independent work suggesting a lag of ten years or a correlation with the solar activity of the previous cycle. The synopsis then is that solar irradiance (TSI) is a leading indicator of some other effect coming from the Sun after a delay of 11 years or so.
The discovery of this delay is a major clue about the direction of our future climate. The flickers in sunlight run a whole sunspot cycle ahead of some other force from the sun. Knowing that solar irradiance dropped suddenly from 2003 onwards tells us the rough timing of the fall in temperature that’s coming (just add a solar cycle length). What it doesn’t tell us is the amplitude — the size of the fall. That’s where the model may (or may not) tell us what we want to know. That test is coming, and very soon. This is an unusual time in the last 100 years where the forecasts from the CO2 driven models and the solar model diverge sharply. Oh the timing!
Ponder how ambitious this simple model is — the complex GCM’s only aim to predict decadal trends, and have failed to even do that. Here is a smaller simpler model proffering up a prediction which is so much more specific. The Solar Model has not shown skill yet in predictions on such short time-scales, though it hindcasts reasonably well on the turning points and longer scales. It cannot predict ENSO events, and obviously not aerosols, nor volcanoes. But if the notch-delay theory is right, the big drop coming is larger than the short term noise.
As we head to the UNFCCC meeting in Paris 2015 where global bureaucracy beckons, a sharp cooling change appears to be developing and set to hit in the next five years. Yet consortia of five-star politicans are not preparing for climate change, only for global warming. Around the world a billion dollars a day is invested in renewable energy, largely with the hope of changing the weather. Given that 20% of the world does not even have access to electricity, history books may marvel at how screwed priorities were, and how bureaucratized science cost so much more than the price of the grants.
As Bob Carter has been saying for a long time, politicians need to prepare for everything the climate may throw at us — see Climate the Counter Consensus.
Global Cooling is Imminent
Dr David Evans, 27 June 2014
Cite as Evans, David M.W. “The Notch-Delay Solar Theory”, sciencespeak.com/climate-nd-solar.html, 2014.
If the Sun mainly controls the temperature on Earth, a turning point is almost upon us. (In the second part of this series of blog posts we will demonstrate that carbon dioxide is responsible for less than 25% of the global warming of the last six decades, so presumably the Sun is mainly responsible.)
1 Why It’s Going to Cool
The reason for the cooling is the dramatic fall in solar radiation that started around 2004. Here is a graph of solar radiation since 1610, when sunspots were first recorded. The brown line is the solar radiation, and it peaks every 11 years or so because of the sunspot cycle. We put an 11-year smoother through it to give us the red line, which shows the trends in solar radiation.
Figure 1: The recent fall in TSI is the steepest and one of the largest ever recorded (records go back to 1610).
UPDATE: This graph has been updated after the PMOD revisions to TSI in late 2013 or 2014. It makes very little difference. Compare the new graph to the original here.
There have been three big, steep falls in solar radiation in the last 400 years.
The first was in the 1600s. It led to the depths of the Little Ice Age, and the Maunder Minimum. This was the coldest period during the last 400 years. There used to be fairs on the ice in the Thames River in London, because it would freeze over for weeks at a time.
The second fall is around the time of Napoleon and it preceded the second coldest period in the last 400 years, called the Dalton Minimum.
The third fall occurred recently, starting in about 2004. This recent fall is as big as the fall in Napoleon’s time, almost as large as the fall in the 1600s, and it seems to be steeper than either of those falls. But the temperature hasn’t fallen … yet.
The timing for the cooling is indicated by the delay, which was deduced from the observed notch but has been independently corroborated to varying extents several times in the last decade (see Post III). The delay is most likely 11 years, though definitely between 10 and 20 years.
2004 + 11 = 2015.
Eleven years after 2004 is 2015, suggesting the cooling will start in 2015. However, 11 years is only the average delay, and the physical interpretation of the delay (see Post IV) suggests the delay is actually the length of the solar cycle—which has varied from 8 to 14 years, but averages 11 years. The current solar cycle is a long one, probably running around 13 years:
2004 + 13 = 2017.
So the cooling is most likely to begin in 2017.
The delay could be as much as 20 years, in which case the drop could be as late as 2024. Or it could occur as soon as 2014. An El Nino or La Nina could affect the timing too. At this stage, we don’t know. But by the end of 2018 seems fairly likely.
(Notice that so far we have only applied our physical understanding of the delay, and its implication of a powerful solar influence that is signaled by changes in solar radiation but acts after a period of time equal to the delay.)
3 How Much Cooling
How much cooling and how quickly? For that we turn to the notch-delay solar model, which hindcasts the last 240 years of temperatures reasonably well simply from the total solar irradiance (TSI). This model was developed earlier in this series of blog posts; see here for an overview and links.
The changes in solar radiation are tiny, and have an almost insignificant immediate effect on Earth’s temperatures. However the physical interpretation of the notch and delay (see Post IV) show that these little changes foretell the changes in a newly detected climate influence from the Sun, which we are calling “force X” for now. The effect on temperatures of changes in force X is 10 to 20 times as great as the immediate effect of changes in solar radiation (see Post VI). Force X works by modulating the albedo of the Earth, or the amount of solar radiation reflected straight back out to space without changing the heat of the planet, by clouds and ice and so on. Force X turns the tap that controls how much sunlight pours into the Earth’s climate system. This could be through UV, magnetic field effects, solar wind, or some form of electrical field.
Force X lags TSI by half of a full solar cycle of 22 years, which is to say, by 11 years on average. Therefore the changes in solar radiation over the last 11 years tell us what force X is going to do soon. It’s already baked in the cake; we can see a few years into the future.
Figure 2: Climate model driven only by solar radiation, with no warming due to carbon dioxide. See Post VII for explanation. Predictions shown by dotted lines. This instance of the notch-delay solar model used a constant delay of 10.7 years and shows cooling beginning in 2014.
UPDATE: The predictions have been updated with new TSI data from PMOD revisions in late 2013 or 2014. Again, there is little difference, compared to the original.
If the temperature on Earth is entirely controlled by solar effects, the cooling will return us to the temperature levels of the 1950s or even the 1920s, undoing the last 50 or 100 years of global warming in just a few short years.
The temperature data from land thermometers from 1850 to 1978 may have exaggerated past temperature rises. The solar model here trained on that data so it may be too sensitive, in which case the imminent cooling will not be as large as shown in absolute terms.
At least a small portion of the recent global warming was due to rising carbon dioxide, so the fall will not be as large as shown in Figure 2.
4 Solar versus Carbon Dioxide
Both the carbon dioxide and notch-delay solar theories agree with the warming observed during the 1900s, because carbon dioxide levels and solar radiation levels were both generally rising. So we cannot tell the models apart on recent performance.
However, over the next 10 years the theories strongly diverge. Carbon dioxide levels will continue to rise at much the same rate, so the carbon dioxide models predict warming over the next decade of about 0.2°C, plus up to 0.3°C of previously-committed warming not reflected in the temperature “pause” of the past 15+ years. Owing to the fall in solar radiation from around 2004, and making allowance for rising carbon dioxide, the notch-delay solar model predicts cooling of 0.2°C or more.
Figure 3: Comparing the CO2and solar models. They show general agreement from 1900 to 2000, because carbon dioxide and solar radiation levels were generally rising, but they diverge sharply soon.
5 Theories and Falsifiability
Science is about testable hypotheses. Over the next decade, the changes in temperature will reveal which theory is more correct, the carbon dioxide model or the notch-delay solar model.
Here’s the criterion: A fall of at least 0.1°C (on a 1-year smoothed basis) in global average surface air temperature over the next decade.
If the criterion does not occur: Then the notch-delay solar model is falsified and it should be thrown away.
If the criterion does occur: Then carbon dioxide driven models are falsified, and they should be thrown away. (Note that the carbon dioxide theory predicts only warming over longer periods such as a decade, and we’ve already had a pause in warming for 15+ years.)
6 Old Temperatures
The Maunder Minimum from about 1645 to 1715 and the Dalton period from about 1790 to 1830 are generally reckoned to be the two coldest times in the last 400 years.
There was no global thermometer network before 1850, so for a global picture we have to rely on proxy data (ice-cores, pollen, marine sediments, lake sediments, tree-rings, etc.). The most comprehensive study is Christiansen and Ljungqvist’s huge proxy study in 2012, which used 91 proxies scattered around the world. We smoothed it by 25 years in Figure 1 because proxy data is uncertain and hazy.
Even the IPCC thought those two periods were the coldest in the last 400 years, before they went all hockeystick:
Fig 5 (updated) IPCC Second Assessment Report, Fig 3.20 page 175, The SAR WGI first pdf). Decadal summer temperature index for the Northern Hemisphere, from Bradley and Jones (1993), up to 1970-1979. The record is based on the average of 16 proxy summer temperature records from North America, Europe and eastAsia. The smooth line was created using an apporximately 50-year Gaussian filter. Recent instrumental data for Northern Hemisphere summer temperature anomalies (over land and ocean) are also plotted (thick line). The instrumental record is probably biased high in the mid-19th century because of exposures differing from current techniques (eg Parker, 1994b)
UPDATE: We’ve replaced the graph originally posted (copied here) which was sourced via here. Thanks especially to William Connolley for the proof reading and to ThinkingScientist for accurately finding the actual SAR IPCC Graph above. It is entirely incidental to the Solar Model or its predictions, a mere side note here, not included in the main paper or the model, but we always appreciate feedback. The point remains that it has been long accepted that solar minima correspond to cooler temperatures on Earth. — Jo
7 What’s Next
This almost finishes the first part of this series of blog posts. The second part is about finding whether the carbon dioxide or solar model is dominant, from the evidence to date. This develops a method for computing the extent of causation, and finds that rising carbon dioxide levels were responsible for less than 25% of the global warming of the last 60 years.
The next post in this series is of the spreadsheet that contains all the data, code and the model behind the notch-delay solar theory. We have delayed releasing it so as not to preempt the blog posts, and to engender a more focused conversation.
The home page for the entire notch-delay solar theory is here. It includes links to all these blog posts, with summaries.
The Fairfax press say the improbable Gore-Palmer play was a win for alarmists. The Australian calls it for skeptics and says Gore is a fool. I’m not calling anything until I see the fine print. Palmer says he’s met P.M. Abbott and he was ‘encouraged’ by his climate plan.
The only thing I can say for sure is that the science of CO2 is irrelevant to both Gore and Palmer. Everything else is a paradox. We’re not being told everything.
It seems now that Palmer’s amendments to repealing the carbon tax do not include an Emissions Trading Scheme (even the Fairfax press agrees). That makes it look like a skeptic win, but keeping the $10b Clean Energy Finance Corporation is a win for Gore, and so is keeping the RET (Renewable Energy Target) and the Climate Change Authority — it’s another government funded advertising unit for the carbon scare campaign. The more patrons who are dependent on the carbon-subsidies, the more pro-carbon lobbyists there are. And they lobby like their livelihood depends on it — because they have nothing if the government policies don’t prop up their pretend free market.
Why would Gore have any interest in standing next to Clive-Palmer-the-coal-magnate as he axes Australia’s carbon tax? Some suggest Gore was paid for the event, but the man got $100m from Big-Qatari-Oil selling his TV Channel — even a few million to be there yesterday (and we don’t know he got anything) would not make it worth his while. I don’t think Gore was here for anything bar the big game. He wants a global trading scheme (which might be worth more than the global oil market — we’re talking a $2T annual turnover). The rest is little biccies. A million here, a million there, so what? And Gore sheds no tears over the death of the coalition’s Direct Action Plan, because it was never really about actually reducing carbon emissions, was it? It’s about keeping Green Gravy flowing and window dressing.
More inexplicable is what Palmer gains from standing next to Gore. Part of Palmer’s appeal at the last election was that he wouldn’t support “carbon action” of any kind. Palmer, surely, is not aiming to win semi-Green voters to his voter base? Clive risks burning off more voters than he gains. This is not about the environment, and it’s not about voters, so what is it about?
Gore’s motivations seem easier to understand than Palmer’s. The election of Tony Abbott on a blood oath to get rid of the carbon tax is a devastating break in the global PR story about the so-called rise of “carbon trading”. It popped the bubble — and it’s no accident Gore is here just before it goes to the new Senate. He wants to limit that damage and rescue the narrative that a global carbon scheme is inevitable. It’s all about momentum, or rather the semblance of such. Gore wants to go to Paris in 2015 being able to say “Australia wants carbon trading”. Perhaps he can finangle weak agreements from all the countries named, which each nation thinks is never going to happen, then present them as a fait accompli at the UNFCCC and embarrass them into meeting their agreements?
The latest developments are that Senator Nick Xenophon is leaning on two of Palmer’s senators — Ricky Muir and Jackie Lambie — to support the Coalition’s Direct Action plan. A political consultant of Muir says he hasn’t decided. Lambie says “no way” to Xenophon’s suggestion.
This is not what PUP voters thought they were voting for… but the Big-Bankers will be happy.
Really? Clive Palmer holds the balance of p0wer in the new Australian Senate, due to start on July 1. He’s the coal magnate who made it clear he would get rid of the carbon tax. Now he’s palling up with Al Gore, and saying he’ll vote the tax down but only if we add a clause for an emissions trading scheme that is conditional on China, the US, the EU, Japan and Korea joining in too. Is this a meaningless dead-duck promise that is unlikely to happen, or is this the long softening up for the UN convention in Paris next year, when weak schemes (like China’s, where lots of permits are free) are used as leverage to call in the sub-clauses? I don’t think Gore would be flying out here if there was no chance this legislation would matter. At the very least he will use it to lean on other countries, as evidence that “Australia wants in”. At the very least this is about keeping the illusion of momentum going.
What is going on behind the scenes for this extraordinary turn-around? The man said only two months ago that he thought global warming was natural and 97% of carbon emissions came from nature. Clive the-coal-miner suddenly cares about carbon?
His long-awaited declaration on climate policy clears the way for Mr Abbott’s signature carbon tax abolition, but throws into doubt other aspects of the Coalition’s climate policies.
In a blow to the Abbott government, Mr Palmer said his Palmer United Party would use its decisive four votes in the Senate to block the proposed abolition of the money-making CEFC and would also move to legislate an emissions trading scheme with a starting price of zero dollars. – Sydney Morning Herald.
Keep the CEFC? The Clean Energy Finance Corporation
There would be enough support for the government to abolish the 20 per cent Renewable Energy Target, despite figures showing consumers would be better off if the target was kept, but the CEFC, which has turned a $200 million profit on investing in renewable energy projects, is likely to be retained on current numbers.
– also, Sydney Morning Herald.
Financial institutions benefit from trading schemes, but they don’t benefit from taxes (and they certainly don’t want “Direct Action”).
From Andrew Bolt h/t TonyfromOz and Bobl
Keep reading →
The Solar Series: I Background | II: The notch filter | III: The delay | IV: A new solar force? | V: Modeling the escaping heat. | VI: The solar climate model | VII — Hindcasting (You are here) | VIII — Predictions
All models are wrong, some are useful. That’s how all modelers speak (except perhaps some climate scientists).
The barriers to making a good climate model are many. The data is short, noisy, adjusted, and many factors are simultaneously at work, some not well described yet. Climate modeling is in its infancy, yet billions of dollars rests on the assumption that CO2 will cause catastrophic warming and the evidence that most recent warming was due to CO2 comes entirely out of models. It’s important to focus on the pea:
“No climate model that has used natural forcing only has reproduced the observed global mean warming trend” (IPCC 2007)
It is a crucial plank that modelers say “we can’t explain the current warming without CO2″. Current climate models assume that changes in solar radiation have a small immediate effect and solar magnetic fields have no significant effect on Earth’s temperature. They do not consider the possibility that a solar effect may occur with an 11 year delay (equivalent to one solar cycle), despite the independent studies that suggest this. These GCM models cannot use CO2 to predict modern temperatures without amplifying feedbacks, for which evidence is sparse or even contradictory. They don’t predict the pause, or the upper tropospheric temperatures, or the Medieval Warm Period.
The total climate model described below can reproduce graphs based on a CO2 model, such as one used by GISS, but it can also produce graphs using the solar model developed in these posts, or a mix of both CO2 and solar. (This is the point where the solar assumption is dropped and tested.) The point here is simply to see if there is a viable alternative model to the CO2 model. It appears there is, which is not to say it’s finished, or can’t be improved, or cannot be presented better, or tweaked. At this stage it’s crude, but it exists.
There are 23 well funded ambitious global climate models that have been developed by international teams over the last 30 years, and a huge effort has been made by PR teams to make those models look good. The model below is one person’s work over 18 months with the aim of asking only, “is this possible” and “what can we learn?” The results are displayed with bare honesty and many caveats about how much (or how little) can be read out of them.
No model, much less one whose predictions have not been tested, is proof of any hypothesis. But they are sometimes good tools to tell us where to look. The notch-delay solar model is a viable alternative to the current CO2 models. It matches most major turning points of temperature (something CO2 models have struggled to do), and is used here back to 1770 — 100 years earlier than most. There’s a definite weak period with the 1950-1980 era where the atmospheric bomb test line resolves to have an improbably large effect. You might think the idea that nuclear tests cooled the planet in the 60′s and 70s is ridiculous. I certainly did. It’s something fans of CO2-theories have used to explain the cooling that Co2 based models can’t explain. Does it have legs? Hard to say, and worthy of a post on its own. But before you write it off, see John Daly’s site which has an interesting discussion page that compares bombs to Pinatubo eruptions, and points out atomic bomb testing went on in the atmosphere, despite the 1963 test ban treaty, until 1980 (thanks to the Chinese and French). Nuclear bombs contribute aerosol dust, but moreso, it’s radioactive too (a bit of a cosmic ray effect?). You might think it will rain out quickly, but bombs of 1Megaton reach up to the stratosphere above the clouds that rain. All up, 504 atmospheric nuclear explosions occurred between 1945 and 1980. A total of 440 MT were detonated (Fujii). It hangs around, C14 Radiocarbon levels in the atmosphere peaked in 1963 but the isotope stayed above natural levels for years – into the mid 1980s. (Edwards 2012). Fujii (2011) suggests atmospheric tests caused the “global stagnation“of that era and say it should be included in GCM’s. Maybe it isn’t as mad as it sounds?
The model has no aerosol component, which may or may not offset the cooling theoretically attributed to atmospheric bombs, nor does it have the Pacific Decadal Oscillation or Lunar cycles. The anomaly may be resolved if the model is expanded, or maybe it just means the delayed force from the sun is not the major driver — as we’ll explain in the next post, we’ll probably all have a good idea in a few years.
Solar TSI appears to be a leading indicator for some other (probably solar) effect, that we are calling “force X” for now. If that factor, quantified by TSI, was fed into current climate models, then those models would work with less forcing from CO2. Perhaps they would have produced better long term graphs, as well as fitting the recent pause and not requiring such a pronounced tropospheric hotspot. It might solve a lot of problems at once. Presumably projections of catastrophe would be subdued.
Lastly, compounding the many hindcast inaccuracies is the problem of inexplicable adjustments to temperatures (every skeptic will be wondering). It’s possible a model trained on raw temperatures curves (or older published datasets) may produce quite a different fit (which might be better or worse). For instance, if the land thermometer data from 1850 to 1978 exaggerates the general temperature rise then the solar model will be too sensitive — because it trained (or computed its parameters) on this data and “thinks” the TSI changes caused that amount of temperature change. Ultimately we won’t know for a few years whether it is right. (Bring on those independent audits please!)
The theory of the “delay” will be tested soon. It is falsifiable. We’re putting it out there for discussion. We have issued no press release, we aren’t selling a product (we’ll give it all away soon), nor do we demand your tax money. Judge it accordingly.
The bottom line is that modern climate models do not include any delayed force from the Sun. Saying that models don’t work without CO2, and no natural factors can explain the modern warming, is and always was, a fallacy known as argument from ignorance. — Jo
Dr David Evans, 24 June 2014
Cite as Evans, David M.W. “The Notch-Delay Solar Theory”, sciencespeak.com/climate-nd-solar.html, 2014.
In the previous posts we built the notch-delay solar model. Now we are going to test it.
The solar model is given the TSI record from 1749 (the start of monthly sunspot records), and it computes the corresponding temperature in each month from 1770 from just the TSI data for the current and previous months. Then we compare this “hindcast” with the measured temperatures. We also test the CO2 model to compare how it performs, and we test a mix of the CO2 and solar models to show that they play together well.
Finally, we look at the significance (or not) of the solar model so far.
1 Our total climate model
The total climate model* includes the notch-delay solar model, a standard CO2 model (two-compartments, with transient and equilibrium responses, computing temperature changes from the observed CO2 levels), a CFCs model (based on Lu 2013), and an atmospheric nuclear bomb tests model (based on the megatons exploded in the atmosphere, from UN reports). It can also apply all the forcings from the GISS model E, a mainstream climate model that released its forcings publicly in 2011—notably volcanoes, black carbon, snow albedo, and land use.
All these models can be switched on or off in any pattern within our total climate model. The total climate model has an optimizer to fit the model’s temperature output to measured temperatures, thus finding a set of optimal parameters.
We use composite TSI and temperature records for the measured TSI and temperature, as in the previous post. The composite temperature was put together from the main temperature datasets, instrumental back to 1880 then increasingly reliant on proxies, mainly the huge proxy study by Christiansen and Ljungqvist in 2012. Similarly the composite TSI record was constructed out of the main TSI datasets, using measured data where possible.
2 What if CO2 was the main driver?
To show how our “total climate model” works, let’s first fit a CO2 model to the observed temperatures, assuming there is no delayed link between TSI and temperatures (that is, the mainstream assumption).
Let’s run the CO2 model with solar input as per the GISS model (that is, the immediate, direct effect of changes in TSI), with the volcanoes, black carbon, snow albedo and land use also from GISS, and the CFCs. The CO2 model was fitted to the measured temperatures and found to have an equilibrium climate sensitivity (ECS) of 3.4°C, agreeing with the IPCC’s central estimate of 3.3°C. The carbon dioxide theory fits the measured temperatures since 1800 fairly well in a very smoothed sense:
Figure 1: Total climate model without the solar model. It includes immediate warming due to changes in TSI as per the mainstream “GISS Model E” climate model. Thus, most of the warming must come from carbon dioxide. The estimated equilibrium climate sensitivity is 3.4°C, close to the central estimate of 3.3°C by the IPCC
The CO2 model produces a smooth increase in temperature, echoing the smoothly increasing CO2 concentration. Carbon dioxide by itself cannot begin to explain the jiggles in temperature on time scales from one to 10 years, so the carbon dioxide theory calls these jiggles “natural variability”—essentially meaning the bits they cannot explain.
3 What if solar effects were the main driver?
Now let’s run the notch-delay solar model, without any contribution from CO2 or CFCs. In other words, we are running the solar model under the solar assumption, that the recent global warming was associated almost entirely with solar radiation and had no dependence on CO2. As explained at the start of these posts, we set out to build a solar model that could account for the recent global warming under that assumption.
So, we are now testing the proposition that the recent global warming could have been mainly associated with TSI rather than CO2.
There is monthly TSI data from 1749, when the SIDC monthly sunspot records start—they are a decent proxy for TSI, and along with Lean’s yearly reconstruction of TSI from sunspots are the only components of the composite TSI from 1749 to 1882. The step response of the notch-delay solar model takes about 15+ years to fully respond, so the model takes 20 years or so to spin up, and we begin the simulation in 1770. (During the Maunder minimum, from about 1660 to 1705, there were almost no sunspots, so the solar model has no way of estimating force X. Thus it cannot really be expected to work before about 1720 at about the earliest.)
Each monthly temperature computed by the solar model is computed only from the TSI data for previous months and the current month. This is the only data input to the solar model. We then add temperature changes due to volcanoes and so on from the other models, to form the temperature record computed by the total climate model.
The solar model computes the temperature for a given month by adding together all the step responses of the TSI steps of the previous months (that is, by convolution). The change in TSI from one month to the next is a “step” in TSI, and the temperature response to that step is as shown in the step response of the solar model in Figure 4 of Post VI, appropriately scaled by the size of the monthly TSI step. Yes this method is a little slow and there are faster methods, but this way makes it clear that we are using the step response and previous TSI data only—and anyway computers are faster these days, and the data series here have only a few thousand points.
We previously found the parameters of the solar model by fitting the model’s computed temperatures to the observed temperatures (and simultaneously fitting the model’s transfer function to the empirical transfer function). Therefore, so long as the temperature record computed by the solar model basically has the right shape, then of course it is going to fit the measured temperatures reasonably well. The question of how well it does is mainly going to depend on whether the model predicts the right shape of temperature curve, such as getting the turning points about right, because the fitting is going to ensure that the computed temperatures match the measured temperatures in a general sense.
The notch-delay solar model fits the measured temperatures reasonably well:
Figure 2a: 1770 – 2013 Total climate model when driven only by solar radiation, with no warming due to carbon dioxide. The solar model output is not explicitly shown here because having three lines close together (solar model, climate model, and observed temperatures) is too confusing, but it can be inferred by subtracting the other constituent models from the total climate model.
Figure 2b: 1900 – 2013: As for fig 2a, but for the last century.
The major temperature trends are all reconstructed, with major turning points about right, and the sizes of the reconstructed changes are roughly as observed. Therefore the notch-delay solar model could provide an entirely solar explanation for recent global warming, without any significant warming due to rising CO2 or CFC levels.
The solar model reproduces a lot of jiggles, but gets the timing of them wrong as often as not, especially further back in time. This might simply be due to the fairly uncertain nature of the TSI data, which is reconstructed from sunspot numbers. Sunspot numbers themselves are uncertain because standards of what counted as a sunspot have varied over the years. And, as indicated by the physical interpretation of the delay in Post IV, the delay presumably is not constant but instead it is probably the length of the prevailing sunspot cycle, which averages 11 years but varies from 8 to 14 years. The solar model here is using a constant delay of 11 years. It doesn’t take much timing error to put an up-jiggle where there should be a down-jiggle. So there is some hope that, with better solar radiation data in future from satellites and a more complicated model with variable delay (the subject of future research perhaps, if there is sufficient interest), the solar model could explain some portion of “natural variability”.
Over the period of better TSI data from 1610, the TSI was clearly at a maximum from about 1950 to 2000. However the temperature kept increasing during this period, even though TSI plateaued. The delay in the solar model is 11 years, which pushes back that plateau from 1960 to 2010, but that is not enough to explain why the total climate model reconstructs rising temperatures throughout this period when it is based on the solar model and omits the CO2 and CFC models. Here the output of the solar model is explicitly shown:
Figure 3: The solar model from 1900 as in Figure 2, but with the solar model output explicitly shown (in pink). From the 1950s through the 1990s (but mainly the 1960s), the solar model alone computes temperatures significantly warmer than actually occurred. In the total climate model this is counteracted by global cooling due to the atmospheric nuclear bomb tests, which put fine reflective dust into the atmosphere and apparently caused a mini-nuclear winter.
The answer found by curve fitting the total climate model to the observed temperatures is that global cooling caused by the atmospheric nuclear bomb tests may have counteracted the warming associated with the stronger TSI. This initially came as a great surprise to us, because the nuclear data had only been added as a bit of a joke and for completeness, but after a bit of research it started to look kind of plausible. The tests, conducted from 1945 to 1980 but mainly before 1963, put up fine dust that stayed high up in the atmosphere for years, reflecting sunlight back into space and lowering the incoming radiation [Fujii, 2011], and also dropping down radioactive nuclei that might seed clouds. Because the nuclear dust is in the stratosphere, there is no rain to wash it out. The required cooling from the tests is about 0.5°C at its peak in 1963, the year that the USA and the USSR agreed to discontinue atmospheric testing. (If the solar model is too sensitive because the warming of the land thermometer records is exaggerated, then less cooling is required.)
While this is only an answer found by numerically piecing together the test yield data with the output of the solar model and the observed temperatures, it fits. Maybe the nuclear winter hypothesis is partly correct. We feel it is likely to overestimate the effect.
Alternative causes for a cooling influence during the 1950s to 1990s could be pollutant aerosols and/or whatever caused global dimming, or even the Pacific Decadal Oscillation (PDO). With no data that quantifies their effects, the total climate model only had the nuclear bomb yield data to work with, but it is remarkable that the piece that fits the puzzle quite well is the atmospheric nuclear bomb test data.
4 Mix of CO2 and solar
There are now two solutions to the climate question:
- If we assume global warming is mainly due to CO2 then we get the CO2 theory, and it fits the measured temperatures from 1800 (though not before).
- If we assume that global warming is mainly associated with changes in TSI then we get the notch-delay solar model, which also fits the measured temperatures from 1800.
Obviously both assumptions cannot be true, but it may be that the true solution is a mix of both models—such as 40% of one model and 60% of the other. If both solutions fit the measured temperatures on their own, then any linear mix will also fit the data. Here is an example:
Figure 4: Total climate model when driven by a mix of solar radiation and carbon dioxide. The temperature changes computed by the solar model were multiplied by the solar factor of 70%, then the CO2 and other models were fitted. This mix was arbitrarily selected for illustration; do not read any significance into it.
This illustrates that the CO2 and solar models play together nicely. Assuming the climate system is linear for the small perturbations of the last few hundred years, the two solutions can operate almost independently and their temperature changes add (that is, they superpose).
If the optimizer is given both the CO2 and solar models to work with, it finds a solution that is mainly the CO2 solution and only a little of the solar solution. However this is only because the jiggles in the solar solution are wrong as often as not (Figure 2), which the optimizer finds worse than simply ignoring the jiggles and getting them right on average (Figure 1). So there doesn’t appear to be any significance in this, and we will have to find other means of determining the true balance between the CO2 and solar solutions.
5 Significance of the solar model
We have developed a solar model that accounts for the recent global warming, if that warming was almost entirely associated with solar radiation and had no dependence on carbon dioxide.
This is a viable solution to global warming, because:
- It’s quantifiable, with a model that approximately hindcasts the observed temperatures. It is not just a concept with handwaving, or a rough one-off computation.
- It’s got physical interpretations for all the parts. This is a physical model, not just curve fitting or an unexplained correlation.
In short, we have demonstrated that the global warming of the last two centuries could have been mainly associated with TSI rather than CO2. This overcomes one of the bedrock beliefs of anthropogenic global warming, namely that the recent global warming could not plausibly be due to anything other than CO2.
The most important element of the solar model is the delay, which is most likely 11 years (but definitely between 10 and 20 years). The delay was found here as a necessary consequence of the observed notch, but it has been independently corroborated to varying degrees several times over the last decade, apparently without its significance being noticed.
A major objection to substantial solar influence is the finding of Lockwood & Froehlich in 2007, who showed that four solar indicators including TSI peaked in about 1986 then declined slightly. However temperature continued rising for several years after 1986. This has been widely interpreted to mean the recent warming cannot have been due to the Sun. However, the delay can explain this: 1986 + 11 = 1997, about when global warming ended. Thus the delay overcomes another of the bedrock beliefs of anthropogenic global warming.
Conversely, without the delay, the objection of Lockwood and Froehlich appears solid and it is hard to see how a substantial solar influence is possible.
The weakest points of the notch-delay solar theory are:
- The assumption of sufficient linearity of the climate system,
- The need for the nuclear winter hypothesis to counteract the early part of the TSI plateau from 1950 to 2000, especially the 1960s.
- The inability to precisely identify force X (see Post IV).
Some may challenge the discovery of the notch, but the notch implies a delay and the delay receives support from several independent findings.
What we have not shown so far in these posts is that the notch-delay solar model is true, or to what extent it is true. There is nothing in the posts so far to support the assumption that the recent global warming was almost entirely or even partly associated with solar radiation. On the material presented so far, the CO2 and solar solutions are both viable and no reasons have been given to suppose that either one is more influential.
The notch-delay theory provides a second, alternative solution to the climate problem, with a physical model and a plausible interpretation. No longer is climate a “one horse race”, where you are limited to either supporting the CO2 theory or focusing on its deficiencies. We are now in a “two horse race” (though one horse is very new to the world and not fully introduced or fleshed out yet).
Regular readers of this blog are well aware that the CO2 solution has a lot of problems. Soon we will be turning to the second part of this series, where we will look at reasons for believing that the solar model is dominant and the CO2 solution is only a small part of the overall solution.
In the next post on this topic, we will use the notch-delay solar model for forecasting. This is where it gets interesting.
Notch-delay solar project home page, including links to all the articles on this blog, with summaries.
* Our climate model is in a spreadsheet that we will be releasing shortly. We chose to do all the work for this project, right from the beginning, in a single Microsoft Excel spreadsheet for pc. It’s not the fanciest or the fastest, but an Excel spreadsheet is the most ubiquitous and one of the friendlier programming environments. It runs on most computers (any pc with Excel 2007 or later, maybe on Macs with Excel 2011 or later), can hold all the data, makes nice graphs, and all in a single file. The models use VBA code, a form of the basic programming language that is part of Microsoft Office. The spreadsheet is professionally presented, and you press buttons on the sheets to make models run and so on. You can inspect and run or step through the code; it will be all totally open. Thank you for your patience, but giving away the spreadsheet early would preempt the blog posts and disrupt a focused discussion.
IPCC, Assessment Report 4, 2007, Working Group 1 Understanding and Attributing Climate Change, Chapter 9. Executive Summary. [IPCC site] Page 665
Edwards (2012) Entangled histories: Climate science and nuclear weapons research, The Bulletin of Atomic Scientists,
Fujii, Y. (2011). The role of atmospheric nuclear explosions on the stagnation of global warming in the mid 20th century. Journal of Atmospheric and Solar-Terrestrial Physics, Volume 73, Issues 5-6, April 2011, Pages 643-652. [PDF]
Lockwood, M., & Froehlich, C. (2007). Recent oppositely directed trends in solar climate forcings and the global mean surface air temperature. Proceedings of the Royal Society, 10.1098/rspa2007.1880.
Lockwood, M., & Froehlich, C. (2008). Different reconstructions of the total solar irradiance variation and dependence on response time scale. Proceedings of the Royal Society, 464, 1367-1385.
Lu, Q. (2013). Cosmic-Ray-Driven Reaction and Greenhouse Effect of Halogenated Molecules: Culprits for Atmospheric Ozone Depletion and Global Climate Change. International Journal of Modern Physics B, 27.
Pinker, R. T., Zhang, B., & Dutton, E. G. (2005). Do Satellites Detect Trends in Surface Solar Radiation? Science, Vol. 38, 6 May 2005, 850 – 854.
Jennifer Marohasy has been very involved in looking at Australian temperature data this year. She is speaking in Sydney on Wednesday about what she’s found. She’s talking about the new temperature dataset the BOM uses called ACORN, which they built after we asked them for an independent audit of their High Quality set.
Modelling Global Temperatures – What’s Wrong. Bourke & Amberley – as Case Studies
From Jennifer’s site: “The most extreme example that Ken found of data corruption was at Amberley, near Brisbane, Queensland, where a cooling minima trend was effectively reversed, Figure 1.” Jennifer has also raised her concerns (repeatedly) with Minister Greg Hunt.
Venue: The Gallipoli Club, 12 Loftus Street (between Bridge Street & Alfred Street), Sydney Time: 5.30 for 6pm
Additional Information: **Bookings from 11 June only ** BAR OPENS AT 5 PM – LIGHT REFRESHMENTS
Click here for info on how to book
Sorry…we’ve been busy in the comments
The Solar Series: I Background | II: The notch filter | III: The delay | IV: A new solar force? | V: Modeling the escaping heat. | VI: The solar climate model (You are here) | VII — Hindcasting | VIII — Predictions
Open Science live — The story so far: Dr David Evans is building the O-D notch-delay solar model. It’s a much simpler big-picture approach than Global Climate Coupled Models. They use an ambitious bottom-up system where the models add up every small aspect in every small cell of the Earth’s climate atmosphere and oceans and try to predict everything, but the trap is the errors — small errors in 10,000 calculations add up to big-mush. David’s approach is top-down. He looks at the whole system from the outside, and doesn’t try to understand or predict each individual part. It’s a way of starting at the start — to shed light on the big forces and processes that happen as energy arrives on Earth, gets reflected, or blended, and eventually changes the surface temperature. His model won’t tell us what happens to rainfall in Sudan in 2050, but it might do what current models don’t and that is predict the global temperature.
The important development here is to complete the path of the energy flow in the most brutally simple way from Sun –> Earth –> Space. We know the sun provides heat through TSI or Total Solar Irradiance. But this is almost constant — it produces heat for sure, but possibly not much of the variation in temperature on Earth that we are interested in. The discovery of the notch filter means some other force (yet to be specified) from the sun acts with a delay of probably 11 years. This delayed force turns out to cause a lot of the variation in temperature. But Earth is not going to immediately warm or cool with every change. Energy collects in all kinds of pools and buckets before it ends up warming the atmosphere. So the effects of both incoming paths — immediate solar and delayed solar — get combined and run through a “low pass” filter — which blends and smooths the bumps.
Having discovered the pattern in the way TSI is tranformed into temperature, David builds the model with the filters to produce the same “transfer function” as he found in empirical data. Hopefully the model will mimic the overall processes without needing to know the details of all the parts. In a sense all models have to do this at some level. No climate model tracks each molecule or follows each photon. Will it work? It does a good job of hindcasting (and we’ll talk about that soon), but the real test will take a few years. Enjoy the quest to figure it out.
By the way, one of my favourite graphs is below — Figure 4 — some curves are intrinsically beautiful. – Jo
Building a new solar climate model
Dr David Evans
Cite as Evans, David M.W. “The Notch-Delay Solar Theory”, sciencespeak.com/climate-nd-solar.html, 2014.
This is the last of the three posts in which we build the solar model. We assembled a notch filter, a delay filter, and a low pass filter in cascade in part III, in part IV we took a diversion to physically interpret the notch and the delay, and in part V we added the RATS multiplier to model the atmosphere on the yearly timescales of the TSI datasets.
In this post we assemble these four elements in their correct order, and add the immediate path for the TSI changes that obviously warm the Earth directly. This will complete the model. We finish by examining the step response of the model.
The Order of the Filters
The notch-delay solar model so far is simply a computational path from TSI to (surface) temperature that contains a notch filter, a delay filter, a low pass filter, and the RATS multiplier (which is a trivial “filter” whose transfer function is a constant). There are no other filters we can discern from the empirical transfer function, or from elementary physical theory. So with no more to add, let’s put these four in order.
The transfer functions of these four filters, when multiplied together, form the empirical transfer function. The transfer function of two filters in cascade is the products of their two transfer functions, so these four filters must be in cascade (that is, the output of one is the input of the next). But multiplication is commutative, so the empirical transfer function does not indicate their order. For that we turn to physical reasoning.
The filter whose place is most obvious is the low pass filter. It models the Earth as a bucket of heat with unreflected TSI pouring in the top, and its output is the radiating temperature. We can now place the other filters around it.
In the flow of computation the RATS multiplier goes immediately after the low pass filter, because its input is the radiating temperature and its output is the surface temperature. We then have the computational path covered from the unreflected TSI all the way to the output of the entire model.
The notch and delay filters intrinsically go together and are inseparable, and it does not matter if they go notch-delay or delay-notch. The only place left for them to go is between the input to the entire model, namely the TSI, and the input to the low pass filter, which is the unreflected TSI.
Therefore the notch and delay filters are modulating the albedo of the Earth.
Figure 1: The notch and delay filters modulate the Earth’s albedo.
The Immediate Path
The development to date only shows the delayed path from TSI to surface temperature. But obviously any changes in TSI also cause direct and immediate changes in the unreflected TSI, by changing the incoming heat from the Sun, so there is also an immediate path from TSI to the input of the low pass filter. This immediate path must therefore be in parallel with the notch-delay path from TSI to unreflected TSI.
The Notch-Delay Solar Model
Putting it all together, here is the notch-delay solar model. If the recent global warming was associated almost entirely with solar radiation, and if it had no dependence on carbon dioxide, this is how it would work:
Figure 2: Schematic of the notch-delay solar model.
Note the parallel paths:
- The immediate path is for TSI, and has no effect on albedo. This is the direct warming effect of extra TSI.
- The delayed path is for force X, which is the same as TSI but delayed and notched. Force X affects the albedo.
The parameters for the model were found by fitting the model to the observed temperatures since 1610, when yearly TSI data became available, though focused mainly on the last 100 and 200 years. Composite TSI and composite temperature records were created out of the TSI and temperature records analyzed earlier. In forming the composites, the offset of each dataset was adjusted so that the average values for overlapping datasets are the same, datasets were faded in and out of a composite gradually rather than entering the average abruptly, and instrumental data was preferred over proxy data. The fitting process found the model parameters such that the model best reproduced the composite temperature from the composite TSI and best produced a transfer function like the empirical transfer function found earlier.
The most important parameter is the delay parameter, which was found to most likely be 11 years but definitely between 10 and 20 years. The break period of the low pass filter was found to most likely be 5 years, though the possible range is from 4 to 25 years because it might be hiding over to the low frequency side of the notch. (It is very unlikely to be more than about the five years that other researchers have found, but the fitting process held open the possibility.) The most likely set of parameters is called the “P25″ set of parameters. The values in P25 were rounded off to form the “P0″ set of parameters, which has been used to illustrate the transfer functions and step responses of the filters during this development.
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In typical style skeptics love to criticize, it is our strength. Sadly, diplomacy, manners, courtesy — burned at the door on a moment’s notice. Sigh. After five years in this debate you’d think I’d know not to expect respect or goodwill from every fellow skeptic. Call me naive, I don’t expect them to agree with me, just to be polite. If someone asks you for a review before they publish, would you congratulate them privately, ask questions, ignore the answers, ignore large parts of the paper, then later post those misunderstood points, without so much as a courtesy check first? Yes, I’m baffled too.
Hey Lubos, no hard feelings, but next time let us save you from posting unnecessary innuendo, irrelevant criticisms, and not-so-informed commentary. It only takes an email.
I groan. In a highly gregarious species, where power is clawed through high-order political games, schmoozing and collaboration, some skeptics still wonder why people who are bad with numbers but good with people, control the institutions, the publications and big budgets. The mystery of it all!
Anyhow, because it is out there (or was, I’ve reproduced it here)* and is being discussed, obviously we need to correct the errors. Lubos says he spent hours reading the paper but he doesn’t seem to be aware of several of the major points (hey, it’s a very long paper). Unfortunately, because Lubos thought we were suggesting something we weren’t, he concludes it’s all unlikely and bases quite a bit of his reasoning on this misconception. Here’s Lubos saying largely what we’ve said, but he thinks he’s explaining something new:
“Natural mechanisms on Earth just won’t produce a response function that happens to vanish exactly for the 11-year periodicity!”
We explained in this public post, the big paper, the FAQ, the small summary, and David wrote in personal email answers to him (April 11th), that we don’t think the delay and notching occurs on Earth. It doesn’t seem at all likely that the actual solar rays would take 8 minutes to arrive on Earth, then wait 11 years to warm the planet. The 11 year delayed effect is very odd – dare I say “mysterious?” (Perhaps I better not, lest it’s seen as “demagogy”, eh?)
Obviously the place to look for the notch and delay is on the Sun, where internal dynamics could easily produce an 11 year cycle, so easily, it already has. I don’t think Lubos realizes we are suggesting that the 11 year delay may have something to do with the timing of the 11 year solar magnetic flips? Perhaps it’s a coincidence the notching happens at the same time the sun’s magnetic field collapses and it flips its north and south pole. Perhaps it isn’t. Surely it’s an idea worth raising?
Again, I am ready to believe that the Sun has a significant impact on the Earth’s climate. But it must be either something else than the TSI, or the effect must be such that all the wiggles shorter than 20 years or so must be universally suppressed.
The argument that it is “due” to TSI, and “it’s not a mechanism on Earth” are both strawman: “it must be something else than TSI” he says — well yes, exactly. We go out of our way to say TSI is “associated with” with temperature, but does not “cause” temperature.
As for the “wiggles”, the evidence shows that all the wiggles shorter than 20 years are not equally suppressed. That is the point. Lubos is mixing up a low pass filter with the notch. The data most definitely does not suggest a low pass filter with a 20 year break point. (If it did, the lines in the graph Lubos reposted twice would be flat lines to 20 years, then bend down with a 45 degree decline to zero from there in the shorter frequencies.) The low pass filter appears weakly with about a 5 year break point. The low pass filter is a non-controversial idea — I don’t think many people would suggest that the Earth doesn’t smooth out the sun’s effects over at least a few years.
How about some manners?
For the sake of helping the skeptic world polish up on it’s key weakness, it’s time to discuss the forgotten topic of manners and communication. They matter in science. The truth may come out eventually anyway, but bad communication makes it slower, and bad manners risks burning off the independent valuable pool of volunteers who are providing a foil for the monopolistic bureaucratic influences of science. Strategically, it’s a win for skeptics to hold the torch on other skeptics, but a failure for them to waste time doing it on inaccurate and irrelevant points.
After five years of doing my genuine damnedest to improve science and advance human knowledge one tiny sliver at a time, I’m accustomed to being accused of blind faith or shallow marketing, but not from people who I thought shared the same goals.
Hence yes, lines like these (based on zero evidence) are disappointing. False motivations? Imputed intentions? Baseless accusations? We can do better.
There are climate skeptics who will endorse any claim or idea that goes against the “consensus”.
Obviously this does not apply (sometime I disagree with skeptics, sometimes I agree with the IPCC). Why say it?
David’s goal is to claim that the whole evolution of the global mean temperature – or a big portion of it, to say the least – and especially the 20th century global warming and its various intense episodes may be due to the Sun.
David’s goal is to learn more about what drives the climate, not to make false claims. Twice he dropped this project because the data didn’t seem to support the theory that there was a low pass filter (he went looking for the low pass filter, but eventually realized there was a notch obscuring it and the notch was the big deal). It’s what a scientist does. Let’s rise above the cheap shots. We don’t need pop psychoanalysis based on bad guesses.
I think that many of you will agree that the marketing point used as the title on Jo’s blog
For the first time – a mysterious notch filter found in the climate
is pure demagogy.
So when is it accurate science communication, and when is it “marketing” for an undescribed purpose? No one knew what might drive the notch, (or even that a notch existed) so mysterious seems pretty accurate, likewise, no one has described it before — looks like a first.
C’mon Lubos. Haven’t the footsoldiers in this David and Goliath battle at least earned the right to basic respect (and the right of reply) instead of half-baked, clumsy character slurs? Are they people and researchers or just dumb bloggers…
Correcting Lubos’ Errors
by David Evans, 19 June 2014
Here we correct several errors of fact or misleading impressions about the notch-delay theory made by Lubos.
1. Changes in TSI Did Not Cause the Recent Global Warming
Lubos says “David’s goal is to claim that” … “a big portion of” the “evolution of the global mean temperature”, “especially the 20th century global warming”, may be “due to” TSI. This is incorrect.
We have explicitly stated what our aim is, and that the recent global warming is NOT principally “due to” TSI. To repeat:
- Part II: “The initial aim of this project is to answer this question: If the recent global warming was associated almost entirely with solar radiation, and had no dependence on CO2, what solar model would account for it?”
- Part III: “We are building the solar model that would account for the recent global warming if it was associated almost entirely with solar radiation (notice that we didn’t say “caused”)”.
- Part IV: We introduce the force deduced in the datasets, force X, as the main influence behind the recent global warming: “Force X has ten to twenty times more influence on temperatures on Earth than changes in the direct heating effect of TSI.” “While the effects on temperature of the tiny changes in the immediate heating effects of TSI are too small to explain the recent global warming, those tiny changes are a leading indicator of force X.”
2. Transfer functions are always output divided by input, in the frequency domain
After needlessly introducing complications such as convolution and integrals, and performing some handwavy and essentially correct math, Lubos says, as if he had uncovered something: “This frequency-based Evans response function is simply the ratio of the Fourier-transformed global mean temperature and the Fourier-transformed solar output!” (By “response function” he means “transfer function”.)
That definition of a transfer function is not only standard, it is explicitly stated from first principles in the Part II: “A transfer function tells how a sinusoid in the input is transferred through the system to the output. We are only concerned with amplitudes (that is, not phases), so its value at a given frequency is simply the output amplitude at that frequency divided by the input amplitude at that frequency. Dividing the orange line in Figure 4 by the orange line in Figure 2, we arrive at the empirical transfer function shown in Figure 5.”
3. There is no peak at 11 years in the temperature spectrum (i.e. there is a notch)
Lubos writes “What the near-vanishing of R~(f) for 1/f close to 11 years really means is that … the 11-year cycle isn’t present in the temperature data.”
Just caught on Lubos? The main point in the first substantive post is that the temperature record does not contain detectable temperature peaks at 11 years, which would corresponding to the peaks of TSI every 11 years. This is unexpected, and is the discovery. Under the heading “Spot the big clue. There is no peak at 11 years!” we said “The TSI peaks every 11 years or so, yet there is no detected corresponding peak in the temperature, even using our new low noise optimal Fourier transform!”
4. The notch is the starting clue
Lubos says about the absence of an 11 year peak in the temperature datasets: “This is a problem – potentially a huge problem – for any theory that tries to present the solar output as the primary driver even at the decadal scale and faster scales. … It makes the solar theory of the climate much less likely, not more likely. Suggesting otherwise is a case of demagogy.”
Not at all. It is the vital clue that leads us to the delay (which is corroborated at least in part by several studies), and then to the conclusion that an indirect solar force that is not TSI is potentially responsible for most of the recent global warming. This is unfolding in the blog posts already posted, and was available to Lubos in the main paper.
5. Notching originates on the Sun, caused by the synchronicity between two solar forces
Joanne has mentioned that Lubos is attacking a strawman with his arguments about “natural mechanisms on Earth”. This is a major point. We said as much so in the post on interpreting the notch and delay: “As far as we know there is nothing on Earth with a memory spanning multiple years. But there is one climate actor with an 11 year clock—the Sun.” We then proposed force X, which like TSI originates in the Sun, and showed the peaks in TSI every 11 years (on average) always exactly coincide with troughs in force X, which we propose as the notching mechanism.
Curiously, in one of his emails to me Lubos asked about exactly these “unnatural” mechanisms on Earth: “Concerning the unnaturalness, are you religious – what I really mean, do you believe in Intelligent Design?” (10 April). I replied (11 April) “No, I don’t believe in Intelligent design, but in logic, data, and reading carefully … The 11 year timing (or more likely, the solar cycle length) almost certainly originates in the sun, presumably as two signals given off by different parts of the sun and 180 degrees out of phase. See Fig. 31. Force X lags TSI by 180 degrees of the 22-year Hale cycle, presumably. Hence the timing and the notching.”
6. The predictions are due to the delay
Prediction due to ringing? No, force X lags TSI by 11 years, so knowing what the TSI did we can predict what force X will do several years in advance—not Fourier analysis, just physical principles. From the post on the physical interpretation of the notch and delay: “Because TSI indicates what force X will do in about 11 years, the TSI record is also a record of future force X.”
It was the world’s sloppiest reading job. I asked for feedback when I first emailed it to him (“I’d really appreciate some feedback, especially if you disagree with or are uncomfortable with some aspects.”). But instead of sorting this out by email, he goes silent then writes a careless blog post that misrepresents the model. Unhelpful.
*Lubos took the post down. I told him that was unnecessary, I asked him to repost it. I’m reposting it here.
PS: Sadly Lubos has not coped well with this post. He refuses to correct his obvious mistakes, or quote me directly. My emails to him were polite and logical (read them in full here). I’ve asked him for an apology. Credit to him for publishing my comment on his blog. I remain baffled otherwise.
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