Evidence of a Significant Solar Imprint in Annual Globally Averaged Temperature Trends – Part 1

NEW An update to this has been made here:

evidence of a lunisolar influence on decadal and bidecadal oscillations in globally averaged temperature trends

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.

essifigure1

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.

essifigure2

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:

evidence of a lunisolar influence on decadal and bidecadal oscillations in globally averaged temperature trends

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Basil
Editor
March 26, 2008 6:18 am

This truly has been a collaborative effort. Anthony should have changed all the first person singular references to a first person plural. His role in this becomes even more critical in Part II, when it comes to explaining some of the science we are seeing in all this. If he wants, he can put that part in the first person singular for himself. That way, if “we’re” wrong, he gets the blame, not me! 🙂
Maybe, when we’re done, we’ll put the two parts together into a single integrated PDF that can be hosted somewhere for easy download. http://www.icecap.us, maybe?
I suspect there will be lots of questions about the use of HP filtering. I mainly plan to let the results speak for themselves, and that includes the results in Part II. So I’m not going to expend a lot of effort defending the use of HP filtering. It is just a tool, like spectral or wavelet analysis to help us extract signals from complex data. It just happens to be one that is particularly useful for a certain kind of time series analysis (time series that are not stationary when differenced, but rather show non random first differences — like we see in Figure 2). Again, the results will speak for themselves, and I would encourage commenters not to get too distracted by the mere novelty of HP filtering until we see what it produces in Part II.
Basil

MattN
March 26, 2008 6:23 am

Wow.
A question I have is why didn’t the cooling of the 1940s-1960s cool us back to where we were in the early 20th century? Or is that what the whole surface record/UHI effect is about? We did, but we don’t see it because of an urban effect GISS/CRU refuse to admit exists?

terry
March 26, 2008 6:37 am

you guys should publish this in a journal. E&E may take it if no one else will.
One question though–why did you use the particular filter that you used? I am merely curious, and I think your detractors will pick up on that and ignore the rest, merely saying that “you need to learn some climate science,” when clearly I think you know what you’re talking about here.
REPLY: When you are looking for a signal of a particular frequency, a bandpass filter excludes other frequencies outside of the range you specify. That is essentially what the HP filter does. As referenced in the text, it has other uses in meteorology, so it’s use here is not without precedence. -Anthony

Bob Tisdale
March 26, 2008 6:56 am

Anthony: Are we allowed to get ahead of you and post links to graphs of, say, the PDO, which also illustrate (near to) 22 and 44 year cycles? Or would you prefer us to wait?
Here’s a graph, though, that won’t get the cart before the horse. It’s of the number of posts at Real Climate since it opened. The opening month with its 41 posts is excluded, since it was anomalous. Note the drastic decline in the trend over the last year that seems to coincide with the drop in global temperature.
http://tinypic.com/fullsize.php?pic=eupwg8&s=3&capwidth=false
REPLY: Lets wait, it is never good to spoil the end of the movie or book for others.

Pamela Gray
March 26, 2008 6:57 am

I too have used filters: to increase the slope of frequency bands surrounding the one I wanted in fast onset tone pips used in auditory brainstem research. (Side note: There are so many better uses for filters than in cigs.) But back to my thought about your use. I have been cogitating on the pattern of cycle change and sunspot overlap during normal periods and its definition, versus cycle change in “minimal” periods. Would this filter work on actual sunspot data between normal cycles versus historic “minimals” to discern a predictive pattern? Maybe even a mathematical pattern? By the way, all these pictures of waves have sent me back to my days of research in audiology with a great deal of sentiment. Unfortunately, I found the Ivory tower environment to be less than pure, as in “We only fund research that supports our belief”, kind of like the current crop of global warmers.

Alan Chappell
March 26, 2008 7:02 am

I thank you both, if you were politicians the world would be a much better place.
German News this morning is full of ” The Extreme Weather Conference” in Hamburg which started today, with apparently, more than 700 participants, it will be interesting to see if they produce any ‘extreme’ results.

Evan Jones
Editor
March 26, 2008 7:40 am

A question I have is why didn’t the cooling of the 1940s-1960s cool us back to where we were in the early 20th century
Maybe it did. Just a thought.
(Or would you rely on the adjusted surface station metadata?)

Gary
March 26, 2008 7:41 am

Anthony, your comment about analog circuitry reminds me of a course I took in ecosystem modeling many years ago before desktop computer existed. The professor first trotted out a simple circuit board with a couple of voltmeters and potentiometers and demonstrated how fiddling with the input parameters (resistance settings) affected the output (voltage readings on the meters). Although it only modeled a simple prey-predator relationship, it sure made the point in an easily grasped way.

terry
March 26, 2008 7:41 am

Thanks for your reply Anthony, I appreciate the work you and Basil did on this piece.

Evan Jones
Editor
March 26, 2008 7:44 am

I thank you both, if you were politicians the world would be a much better place.
Consider that the Rev wound up making all these wonderful discoveries as a result of being hounded out during a local election.

GeneT
March 26, 2008 8:14 am

If we are going to use analog circuits as an analogy, would a hurricane be a short circuit in our weather machine? 🙂

Phil
March 26, 2008 8:21 am

Anthony,
Yes, I think you could model the weather system as a complex electical circuit
– with inductance, capacitance, resistance etc
– the weather system will have it’s own set of reasonances – like the El Nino/La nina cycle
– and (we postulate) it is being ‘pumped’ or stimulated somehow by the sun’s period oscillations
– which will produce reasonances with strong amplitudes when the cycles coincide & less strong, small reasonances when the cycles are anti-phase.
But I disagree that an analogue computer is better at modelling this than an digital one
– digital systems are perfectly capable of simulation these systems
– in fact their more reliable than analogue computers, and more accurate
– provided they’re properly programmed, and given good input data
– Garbage In – Garbage Out – is true if you’re talking about any sort of computer – analogue or digital!

AGWscoffer
March 26, 2008 8:25 am

Alan,
Tell me about about it! Every time I turned the radio on in the car, that’s what I heard. Not to disappoint you, they’ve produced lots of “extreme” results, while snow falls outside! On NDR radio I heard a report that they’ve been running every hour all day long with the following contents:
1. Global warming is continuing and accelerating.
2. A big 450 sq. km piece of ice broke off Antarctica this morning – another sign of global warming.
3. One scientist, I don’t recall his name, says Greenland will melt in the next 700 years. Causing sea levels to rise 1 meter per century, which he states will be catastrophic.
4. Global warming is going to be very expensive for Germany, costing up to 800 billion euros in the next 50 years.
5. Global warming will cause more severe droughts, more floods and more weather extremes.
6. Water shortages in Germany will lead to power outages,
7. and agricultural losses, etc. etc. etc.
And on it went for the entire day, and most likely the rest of the week.
The German weather service, DWD, is filled with Hansens and Schmidts.
The Germans have certainly taken a few pages out of Goebbel’s playbook. Apparently they’ve learned nothing from their previous follies.

pablo an ex pat
March 26, 2008 8:30 am

Excellent work Gentlemen !
Thank you.
It is amusing to me that after all the noise that the AGW lobby have made while attempting to dismiss the solar effect over a period of years that they suddenly appear to have found merit in it as a partial explanation for why their predictions are so far out of whack with observed data.
They now have the nerve to postulate that natural variation has temporarily overridden CO2 forcing.
Hello this is Gaia calling NASA/GISS, come in NASA/GISS do you read me ?
Sorry to burst your bubble NASA/GISS but the changes we’ve seen have all been naturally driven, CO2 was merely along for the ride.
You need to find a free lunch counter somewhere else !
Over and out.
Gaia

Peter Hartley
March 26, 2008 8:31 am

Anthony — from the first time I heard the hypothesis that variations in the sun’s activity could alter cloud cover it struck me that it made the sun-earth system like a transistor. Small changes in cloud cover correlated with variations in the sun’s activity are like small signals applied to the transistor gate. They modulate the much larger flow of sunlight getting to the earth’s surface. The fact that the sun’s radiation output (especially UV but also TSI) is also varying in sync adds to the effect — but looking only at those small fluctuations missing the much larger “transistor amplification” mechanism. In that context, the ocean oscillations (PDO, AMO, NAO etc) help to “tune” the climate response to the amplified solar signal. The natural frequency of response of the ocean systems would, however, likely differ by ocean basin depending on the shape and size of each basin, the relative exposure to solar radiation, relative depths etc.
REPLY: Excellent analogy. Cloud cover is indeed like the gate of a PNP transistor. Given that the temperature chnage has been about .7 rather than .3, I’ll wager it is a silicon rather than a germanium transistor. 😉 I think there’s another blog post in this. “Earth as Electric Circuit” perhaps.
Lets just hope no silicon controlled rectifiers (SCR) exists in the system.

Raven
March 26, 2008 8:37 am

DNorris says:
“I think the answer lies in the increased solar output during the 20th Century. ”
The graph you linked to is old data. Most solar scientists feel the sun’s radiative output has been stable for a long time.

March 26, 2008 8:45 am

I really appreciate this site and the level of discussion – I have just completed a review of climate science and the role of the IPCC (in house – my group advises a lot of conservation organisations who are frankly badly advised by computer modelling climatologists) – on the issue of why the 1940s dip did not take us back – the answer may lie in a combination of ocean dynamics and solar effects on cloud coverage (both overall percentage and spatial distrubution) – these are more easily followed in the recent warming/cooling period because of more extensive monitoring – of ocean heat content changes and sea surface changes, as well as ISCCP data – the period 1980-2001 shows quite clearly a 4% drop in cloudiness, and NASA GISS pick up the flux of SW radiation to the surface – my sense is that the oceans stored the previous heat wave, as with this last one – but not for long – they lose heat more rapidly than currently modelled following some kind of phase change – I can see such a change at the solar max of 2001/2002. We need to think cross-disciplinary, and I have to say, though IPCC try to, they don’t really get it – nor does Hadley – and until they do, all will refer to blips in general trends, rather than cycles and phase changes.
Thanks again,
Peter

Raven
March 26, 2008 8:50 am

Basil,
The science that says CO2 should cause some warming is well understood and not really questioned. Can your analysis be used to estimate how much CO2 related warming has occurred?

Jeff C.
March 26, 2008 9:00 am

“A question I have is why didn’t the cooling of the 1940s-1960s cool us back to where we were in the early 20th century
Maybe it did. Just a thought.
(Or would you rely on the adjusted surface station metadata?)”
Good point, the US surface network, despite the the obvious problems documented at Surfacestations.org, is still the best in the world. It shows our current temperatures almost match those of the 1930’s (GISS USHCN). Perhaps this is a local phenomena isolated to the US, but it I suspect it is a closer match to the true global trend that the HadCRUT plot.
Regarding the TSI plot referenced by DNorris above, use caution as the author (Lean 2001) has since backtracked. This has been a point of discussion in the latest Svalgaard thread over at Climate Audit.
Basil and Anthony – thanks for the great write-up, very understandable and compelling. I suspect many of the readers/commenters are engineers like myself, not scientists. The comparisons to analog electrical circuits are very helpful to quickly grasp the concepts (as opposed to getting lost in the math).

March 26, 2008 9:12 am

Raven & Jeff C.
Can I get sources? If I am wrong, I need to change my opinion.
Thanks

March 26, 2008 9:35 am

OK. Let’s see. The climate system can be seen as an circuit system, complete with inputs and outputs. The Sun’s input is split unto 2 components: 1) Low freq rectified for baseline power, and 2) Higher frequency components filtered for input into a signal processing network. So far, the AGW folks have been concentrating on the power supply part of the input, and have smoothed out the signals that may be modulating the system output in major ways.
But, I speculate.
Great posts. Thanks.

Jeff C.
March 26, 2008 9:41 am

DNorris,
Here is a link from the Climate Audit Svalgaard thread regarding TSI:
http://www.climateaudit.org/?p=2868#comment-227038
This is also discussed elsewhere in the thread. Note the Judith Lean trace (brown) is significantly out of family with the others. I don’t recall where I read she had retracted the data. I’ll see if I can find it and post a link.

kim
March 26, 2008 9:42 am

Raven, see Pete’s latest graph, comment #207 in the Svalgaard #4 thread at climateaudit.org
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