NEW An update to this has been made here:
NOTE: This essay represents a collaboration over a period of a week via email between myself and Basil Copeland. Basil did the statistical heavy lifting and the majority of writing, while I provided suggestions, reviews, some ideas, editing, and of course this forum. Basil deserves all our thanks for his labor. This is part one of a two part series. -Anthony
Evidence of a Significant Solar Imprint in Annual Globally Averaged Temperature TrendsBy Basil Copeland and Anthony Watts
It is very unlikely that the 20th-century warming can be explained by natural causes. The late 20th century has been unusually warm.
So begins the IPCC AR4 WG1 response to Frequently Asked Question 9.2 (Can the Warming of the 20th Century be Explained by Natural Variability?). Chapter 3 of the WG1 report begins:
Global mean surface temperatures have risen by 0.74°C ± 0.18°C when estimated by a linear trend over the last 100 years (1906-2005). The rate of warming over the last 50 years is almost double that over the last 100 years (0.13°C ± 0.03°C vs. 0.07°C ± 0.02°C per decade).
Was the warming of the late 20th century really that unusual? In recent posts Anthony has noted the substantial anecdotal evidence for a period of unusual warming in the earlier half of the 20th century. The representation by the IPCC of global trends over the past 100 years seems almost designed to hide the fact that during the early decades of the 20th century, well before the recent acceleration in anthropogenic CO2 emissions beginning in the middle of the 20th century, global temperature increased at rates comparable to the rate of increase at the end of the 20th century.
I recently began looking at the longer term globally averaged temperature series to see what they show with respect to how late 20th century warming compared to warming earlier in the 20th century. In what follows, I’m presenting just part of the current research I’m currently undertaking. At times, I may overlook details or a context, or skip some things, for the sake of brevity. For example, I’m looking at two long-term series of globally averaged annual temperature trends, HadCRUTv3 and GHCN-ERSSTv2. Most of what I present here will be based on HadCRUTv3, though the principal findings will hold true for GHCN-ERSSTv2.
I began by smoothing the data with a Hodrick-Prescott (HP) filter with lambda=100. (More on the value of lambda later.) The results are presented in Figure 1.
Figure 1 – click for a larger image
The figure shows the actual data time series, a cyclical pattern in the data that is removed by the HP filter, and a smoothed long term low frequency trend that results from filtering out the short term higher frequency cyclical component. Hodrick-Prescott is designed to distinguish short term cyclical activity from longer term processes.
For those with an electrical engineering background, you could think of it much like a bandpass filter which also has uses in meteorology:
Outside of electronics and signal processing, one example of the use of band-pass filters is in the atmospheric sciences. It is common to band-pass filter recent meteorological data with a period range of, for example, 3 to 10 days, so that only cyclones remain as fluctuations in the data fields.
(Note: For those that wish to try out the HP filter, a freeware Excel plugin exists for it which you can download here)
When applied to globally averaged temperature, it works to extract the longer term trend from variations in temperature that are of short term duration. It is somewhat like a filter that filters out “noise,” but in this case the short term cyclical variations in the data are not noise, but are themselves oscillations of a shorter term that may have a basis in physical processes.
For example, in Figure 1, in the cyclical component shown at the bottom of the figure, we can clearly see evidence of the 1998 Super El Niño. While not the current focus, I believe that analysis of the cyclical component may show significant correlations with known shorter term oscillations in globally averaged temperature, and that this may be a fruitful area for further research on the usefulness of Hodrick-Prescott filtering for the study of global or regional variations in temperature.
My original interest was in comparing rates of change between the smoothed series during the 1920’s and 1930’s with the rates of change during the 1980’s and 1990’s. Without getting into details (ask questions in comments if you have them), using HadCRUTv3 the rate of change during the early part of the 20th century was almost identical to the rate of change at the end of the century. Could there be some sense in which the warming at the end of the 20th century was a repeat of the pattern seen in the earlier part of the century? Since the rate of increase in greenhouse gas emissions was much lower in the earlier part of the century, what could possibly explain why temperatures increased for so long during that period at a rate comparable to that experienced during the recent warming?
As I examined the data in more detail, I was surprised by what I found. When working with a smoothed but non-linear “trend” like that shown in Figure 1, we compute the first differences of the series to calculate the average rate of change over any given period of time. A priori, there was no reason to anticipate a particular pattern in time (or “secular pattern”) to the differenced series. But I found one, and it was immediately obvious that I was looking at a secular pattern that had peaks closely matching the 22 year Hale solar cycle. The resulting pattern in the first differences is presented in Figure 2, with annotations showing how the peaks in the pattern correspond to peaks in the 22 year Hale cycle.
Besides the obvious correspondence in the peaks of the first differences in the smoothed series to peaks of the 22 year Hale solar cycle, there is a kind of “sinus rhythm” in the pattern that appears to correspond, roughly, to three Hale cycles, or 66 years. Beginning in 1876/1870, the rate of change begins a long decline from a peak of about +0.011 (since these are annual rates of change, a decadal equivalent would be 10 times this, or +0.11C/decade) into negative territory where it bottoms out about -0.013, before reversing and climbing back to the next peak in 1896/1893. A similar sinusoidal pattern, descending down into negative annual rates of change before climbing back to the next peak, is evident from 1896/1893 to 1914/1917. Then the pattern breaks, and in the third Hale cycle of the triplet, the trough between the 1914/1917 peak and the 1936/1937 peak is very shallow, with annual rates of change never falling below +0.012, let alone into the negative territory seen after the previous two peaks. This same basic pattern is repeated for the next three cycles: two sinusoidal cycles that descend into negative territory, followed by a third cycle with a shallow trough and rates of change that never descend below +0.012. The shallow troughs of the cycles from 1914/1917 to 1936/1937, and 1979/1979 to 1997/2000, correspond to the rapid warming of the 1920’s and 1930’s, and then again to the rapid warming of the 1980’s and 1990’s.
While not as well known as the 22 year Hale cycle, or the 11 year Schwabe cycle, there is support in the climate science literature for something on the order of a 66 year climate cycle. Schlesinger and Ramankutty (1994) found evidence of a 65-70 year climate cycle in a number of temperature records, which they attributed to a 50-88 year cycle in the NAO. Interestingly, they sought to infer from this that these oscillations were obscuring the effect of AGW. But that probably misconstrues the significance of the mid 20th century cooling phase. In any case, the evidence for a climate cycle on the order of 65-70 years extends well into the past. Kerr (2000) links the AMO to paleoclimate proxies indicating a periodicity on the order of 70 years. What I think they may be missing is that this longer term cycle shows evidence of being modulated by bidecadal rhythms. When the AMO is filtered using HP filtering, it shows major peaks in 1926 and 1997, a period of 71 years. But there are smaller peaks at 1951 and 1979, indicating that shorter periods of 25, 28, and 18 years, or roughly bidecadal oscillations. There is a growing body of literature pointing to bidecadal periodicity in climate records that point to a solar origin. See, for instance, Rasporov, et al, (2004). A 65-70 year climate cycle may simply be a terrestrial driven harmonic of bidecadal rhythms that are solar in origin.
In terms of the underlying rates of change, the warming of the late 20th century appears to be no more “unusual” than the warming during the 1920’s and 1930’s. Both appear to have their origin in a solar cycle phenomenon in which the sinusoidal pattern in the underlying smoothed trend is modulated so that annual rates of change remain strongly positive for the duration of the third cycle, with the source of this third cycle modulation perhaps related to long term trends in oceanic oscillations. It is purely speculative, of course, but if this 66 year pattern (3 Hale cycles) repeats itself, we should see a long descent into negative territory where the underlying smoothed trend has a negative rate of change, i.e. a period of cooling like that experienced in the late 1800’s and then again midway through the 20th century.
Figure 2 – click for a larger image
Figure 2 uses a default value of lambda (the parameter that determines how much smoothing results from Hodrick-Prescott filtering) that is 100 times the square of the data frequency, which for annual data would be 100. This is conventional, and is consistent with the lambda used for quarterly data in the seminal research on this technique by Hodrick and Prescott. I’m aware, though, of arguments for using a much lower lambda, which would result in much less smoothing.
In Part 2, we will look at the effect of filtering with a lower value of lambda. The results are interesting, and surprising.
Part 2 is now online here
NEW An update to this has been made here:
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Hi Basil, Looking forward to your part 2. I note that referenced the same Fisheries Advisory paper as Dr Gerhard Loebert – totally independently, I might add. It is well worth a read, and bears little relationship to the above Dr’s theory.
Here are the relevant parts of the abstract:
Klyashtorin, L.B.
Climate change and long-term fluctuations of commercial catches: the possibility of forecasting.
FAO Fisheries Technical Paper. No. 410. Rome, FAO. 2001. 86p.
“It was found that the dynamics of global air surface temperature anomaly (dT), although in correlation with the long-term dynamics of marine fish production, is of poor predictive significance because of high inter-annual variability and a long-term trend. The Atmospheric Circulation Index (ACI), characterizing the dominant direction of air mass transport, is less variable and in closer correlation with the long-term fluctuations of the main commercial stocks (r = 0.70-0.90).
Spectral analysis of the time series of dT, ACI and Length Of Day (LOD) estimated from direct observations (110-150 years) showed a clear 55-65 year periodicity. Spectral analysis of the reconstructed time series of the air surface temperatures for the last 1500 years suggested the similar (55-60 year) periodicity. Analysis of 1600 years long reconstructed time series of sardine and anchovy biomass in Californian upwelling also revealed a regular 50-70 years fluctuation. Spectral analysis of the catch statistics of main commercial species for the last 50-100 years also showed cyclical fluctuations of about 55-years.”
The features of interest are the detrended time series, not only of dT, but of various climate indices and proxy data relating to the catch history of several commercial fish species.
The web version contains some transcription omissions. The PDF versions are complete at: ftp://ftp.fao.org/docrep/fao/005/y2787e/
Anthony, I am dissapointed you snipped my post. I think you are doing a great job and appreciate all the time you put into this site.
REPLY: Bob I’m not aware that I snipped any post from you. What was it about? I just looked at the SPAM comment que but didn’t see anything, but I also emptied it this AM. I get dozens to hundreds of spams overnght, so it is possible that it went there.
Anthony, it was post #98 I think. I had linked to Tanmino and asked if you or Basil had a look at that.
REPLY: OK well I checked, it’s gone now. Probably deleted with other SPAM. Sometimes links combined with certain keywords get nailed by the SPAM filter. Comments with links get automatic scrutiny.
I’ll point it out to Basil, and I’ve pointed it out to Jim Goodridge. I’ve already responded over there. It wouldn’t matter what anyone says to Tamino about the short essay from Jim Goodridge. He’s decided it is wrong, end of story. Tamino never comes out of his comfort zone, and won’t participate in dialog elsewhere. He sends others to do that. He also won’t defend criticisms of his work elsewhere, such as on Climate Audit, where McIntyre takes him to task several times. So instead of defending that, he picks the low hanging fruit by beating up on Jim Goodridge’s “accumulated sunspot departure” graph. Now he’s even setting up pass/fail grades on comments and says he won’t respond to “idiots” that make comments about it anymore.
The sound of crickets in Taminoland regarding McIntyre’s smackdown of Tamino’s defense of MBH98 is deafening.
On the subject of graphs; one person’s pattern recognition is another’s mathematical heresy. What you get from them depends on what you are trying to demonstrate. Interpretation is subjective, which is why so many people get into nasty fights over graphs.
Funny you should mention the polarity changes. I have been thinking about how we measure cycles and change from one to another. I thought about this polarity switch thing and wondered if a true cycle is first to last sunspot (which I have mentioned in one of my posts) and from same polarity to same polarity (kind of like comparing apples to apples).
My thought about measuring from first to last and the length of overlap is that it would have much better predictive value than the current definition of new cycle spots being more in number than old cycle spots.
@jim Arndt
Dr Gerhard Loebert is indeed a quack.
This has already been uncovered at German website http://www.oekologismus.de/?p=538
and other sites (and lack of sites thereof).
Like that bloddy bee story, his story had me going for awhile.
PG, you can go to solarcycle24.com to see the whole discussion on sunspots. Generally, there is no such thing as a clear first sunspot to last as cycles overlap each other. Nor is the minimum established in the abscence of sunspots. The minimum and thus the start of the new cycle is usually determined by new cycle spots out numbering the old cycle. As you may know there is a polarity difference between the old and new spots, however in addition, new cycle spots appear around the 30 (+ or -) degree latitude area (shown by the Maunder butterfly diagram), whereas the fading cycle spots appear within 10 (+ or -) degrees of the equator. Currently, even though we have had one bonified SC24 spot in January, since then only lots of SC23 spots have been appearing.
Thanks for the feedback. I enjoy brainstorming and cogitating on generally accepted definitions and beliefs/theories. I know about the current definition and the polarities as well as the latitudes and butterfly pattern. I was just trying on something new. A new definition. A new place along the data stream to look at for possible useful information. Just thinking outside the box. I am considering that cycle 24 is already two years old if new definitions are used.
I think Pamela has a point, actually. Maybe it is difficult to dtermine when the new cycle “starts” and the old one “ends”, but it is undeniable that there is an overlap.
Sure, the outnumbering measure is a useful and perfectly legit, but why limit ourselves? I agree with Pamela that the”first-to-last” spot measurement is also, in the current circumstances, quite relevant.
Anthony, Have you considered that the Hadcrut is actually a series of harmonic waves? That each year is merely an addition and subtraction of numerous sine waves of varying frequencies and strengths? Is there a way to dissemble a complex series of harmonic waves?
Folks,
I don’t know if this is useful, but I’m a much better programmer than statistician or climate scientist, so I thought I’d try to help out in that way… I’ve written a little C++ utility which will read the raw HADCRUT3 data and generate data suitable for gnuplot. It will also do some basic running means of different lengths (at least, as far as I understand it, which isn’t very far). The interesting thing is that an 11-year (132 month) running mean generates something very like the graph in the main article above.
Some example output graphs and a tarball of the code are at:
http://www.box.net/shared/c58tdkccgs
I’ve included the latest HADCRUT3V global mean data but you should be able to substitute any of their averaged files.
The basic code should run on any GNU-based system (e.g. Linux): Just type ‘make’. If you want the plots you need the gnuplot package, and if you want to display them easily (using ‘make display’), you need imagemagick.
Let me know if there’s anything else I could usefully make it do.
Enjoy!
Paul
REPLY: Thank you Paul, your effort is appreciated.
Update on my C++ tools: I’ve refactored it so you can run multiple algorithms in sequence, and also added differentials and year selection and a few other things. It can now pretty much replicate Basil’s differential graph above – although still using 11-year running means, not a complex filter:
http://www.box.net/shared/8xn81l7y8s
The command to do this graph is:
$ ./climate hadcrut3 mean 132 derivative mean 132 hadcrut3.mean132-d.out
In other words: Read HADCRUT3 format data, do 132 month running mean, then take first derivative, then do 132 month running mean again. What this means in detail I’m not competent to judge, but it does generate a nice graph!
The new package is at: http://www.box.net/shared/jan0st3wgc along with all the graphs I’ve done so far.
There are now switchable input formats, so if anyone wants another format added and can point me at the spec and an example data file, let me know here. I’ll find a more sensible place to store and maintain this soon!
Paul
Oops, wordpress mangles the input and output redirection of the Unix command, but you get the idea.
Why do you post part of the work like this? You’re prone to showing sloppy analysis because you haven’t finished your thoughts. And you reply to people who criticize it that they shouldn’t and need to wait for the other post instead. And we’re only talking a few days to wait to finish your work. This is just bizarre, rinky dink crap. Like kids on a playground or something.
REPLY: The part 2 is done. But I’m circulating it for some peer review to several specialists to check it’s accuracy and catch any mistakes. If that’s “bizarre, rinky dink crap” to you then so be it.
Good Vibrations: I appreciate refreshing perspective that Basil brings to the subject. You mean to tell me there is a climate cycle? Or perhaps cycles within cycles within cycles. I like the analogies as earth as an electric circuit and the one about the stock market in an earlier post. Some individual stocks are cyclical in nature and if you can show how it behaves you would know when to buy low and sell high. From eyeballing the long-term temperature numbers I am thinking it is time to sell. I am looking forward to part II.
Yes: It is bizarre and rinky dink that you posted part 1, before you even knew that post 2 made sense. Especially when you are cutting off criticism with a wait for part 2 and trying to build interest in part 2 (“mystery”) when you don’t know if it’s any good. It’s VERY rinky dink.
Getting to the post:
1. Smoothing should be done for display purposes. Smoothed data should not be used for hypothesis testing, instead the raw data should be. This is something that we would correct the AGWers for doing, so it’s fascinating that we are making the same mistake.
2. The claimed correspondances in sun cycle with temp are not shown graphically nor is any math done to verify some correspondance of turning points in temp with the cycles. Also the turning points seem to vary in direction (flat, up, down, etc.) in the temp. Does this theory account for that?
3. Based on the previous Basil posts and his failure to address criticism, I doubt that we will learn much or have good discussion. Basil can snow you fine, AW, but you’re….light.
4. The whole thing with the qualtitative statements, the grasping for various cycles (not listing all possible, etc.) just comes accross as entrail looking at. It’s really a real mess. If an AGWer did work like this, we would rip it to shreds.
REPLY: “before you even knew that post 2 made sense…” Hey newsflash TCO, Part 1 and 2 were done at the same time, the difference is that part2 has been sent out for review.
Stocks are NOT cyclical in nature. “technical analysis” (chartsmanship with head and shoulders and resistance barriers and such) is complete crap for both theoretical reasons (lack of foundation AND market efficiency) as well as never holding up in out of sample tests and having all kinds of empirical papers disproving it. Talk to Ross Mc. Seriously, it is really trashy junk. Academic Economists will tell you so.
newsflash: wait with both of them, then while you have part 2 reviewed. Sheesh.
REPLY: TCO. Griping about it won’t change things. The plan was to release both; one after the other. After part 1 was posted and we received comments I argued for a review by some others to be sure the part 2 work was readable, understandable for the layman, and well vetted with others that can spot errors. It’s taken a little time. Put simply, as a career complainer, you’d complain about if from your phantom harping position of anonymous no-risk comfort no matter what or how it was brought about. So I have nothing to lose and everything to gain by waiting a bit for the last reviewer to finish. And that is exactly what I will do.
I think the “accumulated departure” charts should be resurrected but there should be a time-limit on the accumulation factor versus the average.
Effectively, solar irradiance only accumulates for a period of between 30 days to about 80 days.
The peak of the seasons always lags the equinox/solstice by about 30 to 40 days.
– The peak of the summer on land is about July 25th versus the solstice on June 20th.
– The peak of polar ice extents lag the solstice by about 80 to 85 days.
– The peak of ocean temperatures in each hemisphere is about 80 to 85 days after the solstice.
Therefore, the planet only “accumulates” solar irradiance over a 35 day to 80 day period.
Part2 is now online
http://wattsupwiththat.wordpress.com/2008/03/30/evidence-of-a-significant-solar-imprint-in-annual-globally-averaged-temperature-trends-part-2/
TCO is a troll feigning skepticism; cynicism is neither a sufficient nor a necessary bona fides for the latter but is both for the former.
To all of you posting on this blog, a million thanks for what you do. I am a truck driver from Southern California with only a highschool education but lots of curiosity. As I watch all the lemmings run off into the nonexistant Global warming sea, I am astonished at their religious like zeal in ignoring the plain truth in front of them. My only wish is for a layman’s paper explaining all this so that I can better communicate the truth to all who will take five minutes and listen to reason. Again thank you so very much for all your effort.Sincerely,
Bob Kendall
REPLY: Thanks Bob, here is one I can recommend for you done by a friend, Warren Meyer.
http://www.climate-skeptic.com/2008/01/my-best-skeptic.html
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