Sunny Spots Along the Parana River

Guest Post by Willis Eschenbach

In a comment on a recent post, I was pointed to a study making the following surprising claim:

Here, we analyze the stream flow of one of the largest rivers in the world, the Parana ́ in southeastern South America. For the last century, we find a strong correlation with the sunspot number, in multidecadal time scales, and with larger solar activity corresponding to larger stream flow. The correlation coefficient is r = 0.78, significant to a 99% level.

I’ve seen the Parana River … where I was, it was too thick to drink and too thin to plow. So this was interesting to me. Particularly interesting because in climate science a correlation of 0.78 combined with a 99% significance level (p-value of 0.01) would be a very strong result … in fact, to me that seemed like a very suspiciously strong result. After all, here is their raw data used for the comparison:

parana streamflow fig 1Figure 1. First figure in the Parana paper, showing the streamflow in the top panel, and sunspot number (SN) and total solar irradiance (TSI) in the lower two panels.

They are claiming a 0.78 correlation between the data in panel (a) and the data in panel (b) … I looked at Figure 1 and went “Say what?”. Call me crazy, but do you see any kind of strong 11-year cycle in the top panel? Because I sure don’t. In addition, when the long-term average of sunspots rises, I don’t see the streamflow rising. If there is a correlation between sunspots and streamflow, why doesn’t a several-decade period of increased sunspots lead to increased streamflow?

So how did they get the apparent correlation? Well, therein lies a tale … because Figure 2 shows what they ended up analyzing.

parana streamflow fig 2

And wow, that sure looks like a very, very strong correlation … so how did they get there from such an unpromising start?

Well, first they took the actual data. Then, from the actual data they subtracted the “secular trends” (see dark smooth lines Figure 1). The effect of this first one of their processing steps is curious.

Look back at Figure 1. IF streamflow and sunspots were correlated, we’d expect them to move in parallel in the long term as well as the short term. But inconveniently for their theory … they don’t move in parallel. How to resolve it? Well, since the long-term secular trend data doesn’t support their hypothesis, their solution was to simply subtract that bad-mannered part out from the data.

I’m sure you can see the problems with that procedure. But we’ll let that go, the damage is fairly minor, and look at the next step, where the real destruction is done.

They say in Figure 2 that the sunspot data was “smoothed by an 11-yr running mean to smooth out the solar cycle”. However, it is apparent that the authors didn’t realize the effect of what they were doing. Calling what they did “smoothing” is a huge stretch. Figure 3 shows the residual sunspot anomaly (in blue) after removing the secular trend (as the authors did in the paper), along with the 11-year moving average of that exact same data (in red). Again as the authors did, I’ve normalized the two to allow for direct comparison:

normalized sunspot anomaly and 11 yr running meanFigure 3. Sunspot anomaly data (blue line), compared to the eleven-year centered moving average of the sunspot anomaly data (red line). Both datasets have been normalized to a mean of zero and a standard deviation of one.

Talk about a smoothing horror show, that has to be the poster child for bad smoothing. For starters, look at what the “smoothing” does to the sunspot data from 1975 to 2000 … instead of having two peaks at the tops of the two sunspot cycles (blue line, 1980 and 1991), the “smoothed” red line shows one large central peak, and two side lobes. Not only that, but the central low spot around 1986 has now been magically converted into a peak.

Now look at what the smoothing has done to the 1958 peak in sunspot numbers … it’s now twice as wide, and it has two peaks instead of one. Not only that, but the larger of the two peaks occurs where the sunspots actually bottomed out around 1954 … YIKES!

Finally, I knew this was going to be ugly, but I didn’t realize how ugly. The most surprising part to me is that their “smoothed” version of the data is actually negatively correlated to the data itself … astounding.

Part of the problem is the use of a running mean to smooth the data … a Very Bad Idea™ in itself. However, in this case it is exacerbated by the choice of the length of the average, 11 years. Sunspot cycles range from something like nine to thirteen years or so. As a result, cycles longer and shorter than the 11 year filter get averaged very differently. The net result is that we end up with some of the frequency data aliased into the average as amplitude data … resulting in the very different results from about 1945-60 versus the results 1975-2000.

Overall? I don’t care what they end up comparing to the red line … they are not comparing it to sunspots, not in any way, shape, or form. The blue line shows sunspots. The red line shows a mathematician’s nightmare.

How about the fact that they performed the same procedure on the Parana streamflow data? Does that make a difference? Figure 4 shows that result:

normalized parana anomaly and 11 yr running meanFigure 4. Parana streamflow anomaly data (blue line), compared to the eleven-year centered moving average of the streamflow anomaly data (red line). Both datasets have been normalized to a mean of zero and a standard deviation of 1.

As you can see, the damage done by the running mean is nowhere near as severe in this streamflow dataset as it was for the sunspots. Although there still are a lot of reversals, and turning peaks into valleys, at least the correlation is still positive. This is because the streamflow data does NOT contain the ± eleven-year cycles present in the sunspot data.

Conclusions? Well, my first conclusion is that as a result of doing what the authors did, comparing the red line in Figure 3 with the red line in Figure 4 says absolutely nothing about whether the Parana river streamflow is related to sunspots or not. The two red lines have very little to do with anything.

My second conclusion is, NEVER RUN STATISTICAL ANALYSES ON SMOOTHED DATA. I don’t care if you use gaussian smoothing or Fourier smoothing or boxcar smoothing or loess smoothing, if you want to do statistical analyses, you need to compare the datasets themselves, full stop. Statistically analyzing a smoothed dataset is a mug’s game. The problem is that as in this case, the smoothing can actually introduce totally false, spurious correlations. There’s an old post of mine on spurious correlation and Gaussian smoothing here for those interested in an example.

Please be clear that I’m not accusing the authors of any bad intent in this matter. To me, the problem is simply that they didn’t understand and were unaware of the effect of their “smoothing” on the data.

Finally, consider how many rivers there are in the world. You can be assured that people have looked at many of them to find a connection with sunspots. If this is the best evidence, it’s no evidence at all. And with that many rivers examined, a p-value of 0.05 is now far too generous. The more places you look, the more chance of finding a spurious correlation. This means that the more rivers you look at, the stronger your results must be to be statically significant … and we don’t yet have even passable results from the Parana data. So as to rivers and sunspots, the jury is still out.

How about for sea level and sunspots? Are they related? I can’t do better than to direct you to the 1985 study by Woodworth et al. entitled A world-wide search for the 11-yr solar cycle in mean sea-level records , whose abstract says:

Tide gauge records from throughout the world have been examined for evidence of the 11-yr solar cycle in mean sea-level (MSL). In Europe an amplitude of 10-15 mm is observed with a phase relative to the sunspot cycle similar to that expected as a response to forcing from previously reported solar cycles in sea-level air pressure and winds. At the highest European latitudes the MSL solar cycle is in antiphase to the sunspot cycle while at mid-latitudes it changes to being approximately in phase. Elsewhere in the world there is no convincing evidence for an 11-yr component in MSL records.

So … of the 28 geographical locations examined, only four show a statistically significant signal. Some places it’s acting the way that we’d expect … other places its not. Nowhere is it strong.

I haven’t bothered to go through their math, except for their significance calculations. They appear to be correct, including the adjustment to the required significance given the fact that they’ve looked in 28 places, which means that the significance threshold has to be adjusted. Good on them 1980s scientists, they did the numbers right back then.

However, and it is a very big however, as is common with such analyses from the 1980s, I see no sign that the results have been adjusted for autocorrelation. Given that both the sunspot data and the sea level data are highly autocorrelated, this can only move the results in the direction of less statistical significance … meaning, of course, that the four results that were significant are likely not to remain so once the results are adjusted for autocorrelation.

Is there a sunspot effect on the climate? Maybe so, maybe no … but given the number of hours people have spent looking for it, including myself and many, many others, if it is there, it’s likely very weak.

My best regards to all,

w.

NOTA BENE! If you disagree with something I said, please quote my exact words, and then tell me why you think I’m wrong. Telling me things like that my science sucks or baldly stating that I don’t understand the math doesn’t help me in the slightest. If I’m wrong I want to know it, but I have no use for claims like “Willis, you are so off-base in this case that you’re not even wrong.” Perhaps I am, but we’ll never know unless you specify exactly what I said that was wrong, and what was wrong with it.

So if you want me to treat you and your comments with respect, quote what you object to, and specify your objection. It’s the only way I can know what the heck you are talking about, and I’ve had it up to here with vague unsupported accusations of wrongdoing.

DATA: Digitized Parana streamflow data from the paper plus SIDC Sunspot data and all analyses for this post are on an Excel spreadsheet here. You’ll have to break the links, they are to my formula for Gaussian smoothing.

PS—Thanks to my undersea contacts for coming up with a copy of the thirty-year-old Woodworth study, and a hat tip to Dr. Holgate and Steve McIntyre at Climate Audit for the lead to the study. Dr. Holgate is well-known in sea level circles, here’s his comment on the sunspot question:

Many people have tried to link climate variations to sunspot cycles. My own feeling is that they both happen to exhibit variability on the same timescales without being causal. No one has yet shown a mechanism you understand. There is also no trend in the sunspot cycle so that can’t explain the overall rise in sea levels even if it could explain the variability. If someone can come up with a mechanism then I’d be open to that possibility but at present it doesn’t look likely to me.

If you’re interested in solar cycles and sea level, you might look at a paper written by my boss a few years back: Woodworth, P.L. “A world-wide search for the 11-yr solar cycle in mean sea-level records.” Geophysical Journal of the Royal Astronomical Society. 80(3) pp743-755

You’ll appreciate that this is a well-trodden path. My own feeling is that it’s not the determining factor in sea level rise, or even accounts for the trend, but there may be something in the variability. I’m just surprised that if there is, it hasn’t been clearly shown yet.

I can only agree … 

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Ken Mitchell

Wow! Talk about applying Finagle’s Infinitely Variable Constant to the raw data!

pdtillman

Willis, that’s quite a story! Thanks for chasing it down.
Cheers — Pete Tillman

The generation of random numbers is too important
to be left to chance.

justsomeguy31167

Cool! Do you read this amazing blog WUWT where he shows correlation is irrelevant.
You are right, try, just try.

justsomeguy31167

Great work! an analysis that you even you do not understand.

General P. Malaise

I guess on top of everything else there are precious few people or articles we can trust unless we have the knowledge to deconstruct the message ourselves ..or in this case have Willis.deconstruct it for us.
thanks again for shining the light on the vermin.

braddles

Another conclusion might be that the good old eyeball is an underrated way of spotting correlation, or lack of correlation. Fancy statistics might tease out correlations that are not obvious, but they seem to produce spurious artefacts all too often. And the fancier the statistics, the more sceptical we should be about claimed results. The history of Mannian and Steigian stats should tell us that.

David L. Hagen

Compare WJR Alexander et al. 2007
Linkages between solar activity, climate predictability and water resource development
JOURNAL OF THE SOUTH AFRICAN INSTITUTION OF CIVIL ENGINEERING Vol 49 No 2, June
2007, Pages 32–44, Paper 659

This study is based on the numerical analysis of the properties of routinely observed
hydrometeorological data which in South Africa alone is collected at a rate of more than
half a million station days per year, with some records approaching 100 continuous years
in length. The analysis of this data demonstrates an unequivocal synchronous linkage
between these processes in South Africa and elsewhere, and solar activity. This confirms
observations and reports by others in many countries during the past 150 years.
It is also shown with a high degree of assurance that there is a synchronous linkage
between the statistically significant, 21-year periodicity
in these processes and the
acceleration and deceleration of the sun as it moves through galactic space. Despite a
diligent search, no evidence could be found of trends in the data that could be attributed
to human activities.
It is essential that this information be accommodated in water resource development and
operation procedures in the years ahead.

Alexander’s life long effort was to compile all hydrology related data for the Southern African region. He is making all the data available on disk to whoever requests it to the address given.

David in Cal

My wife worked as a biostatistician at a medical school. She was involved in lots of research, because the medical journals require that any article involving statistical analysis include a qualified statistician as a co-author. I wish the climate journals had the same requirement.

u.k.(us)

justsomeguy31167 says:
January 25, 2014 at 4:43 pm
Great work! an analysis that you even you do not understand.
==============
Which leads me to the conclusion, that you understand what was not understood ?
Care to enlighten us ?

Willis:
Why don’t you read my paper
Strong signature of the active Sun in 100 years of terrestrial insolation data
in Annalen der Physik 552,6, p.372 (2010)
http://onlinelibrary.wiley.com/doi/10.1002/andp.201000019/pdf
It is also discussed in the book of Vahrenholt & Luening
Sincerely,
Werner Weber

scf

I have two grandfather clocks. The timing of the chimes is very strongly correlated, to over a 99% level. Also, one of the two always chimes first, so that one must be causing the other one to chime.
When the first one of them stops chiming so does the other, so this confirms the causation (whenever a power failure occurs).
This comment is no sillier than the Parana river study.

“Stream flow corrolated with sunspot activity”, Sorry, but I stopped being interested after that much nonsense.

John F. Hultquist

I thought about seeing what and how they approached the Sun Spot numbers and even looked at this:
http://www.leif.org/research/CEAB-Cliver-et-al-2013.pdf
. . . so maybe Leif will comment.
Regardless of whether they used “International” or “Group”, the smoothing and processing seems to make it meaningless. I also thought of the William M. (Matt) Briggs series and maybe it will be useful to post those links:
#1 Do not calculate correlations after smoothing data __Note p=86
http://wmbriggs.com/blog/?p=86
#2 Do not smooth times series, you hockey puck! __Change p to 195
#3 Do NOT smooth time series before computing forecast skill __Change p to 735

James Strom

Willis, it’s got to be a thankless job, documenting poor science.
What strikes me in your figure one is the high correlation between graphs (b) and (c), which appears to be between sunspots and solar insolation, unless I am missing an inversion somewhere. This seems at odds with Dr. Svalgaard’s assurances that sunspots reduce the output of solar energy.

KR

“NEVER RUN STATISTICAL ANALYSES ON SMOOTHED DATA” – Mmmm, sometimes it’s appropriate.
I would disagree in one situation only – a 12-month running mean can average out the (fixed length!) seasonal cycle for the purposes of looking at longer term trends. Not 13 months (which some people for unknown reasons seem to prefer, but which introduces spurious beat frequencies due to the phase difference between 12 and 13 months), not 60 months, but 12.
Aside from that fairly minor quibble, excellent post. There are entirely too many papers that apply high, low, and bandpass filtering – and then claim extraordinary results from what’s left when their filtering has thrown out the dominant data, leaving only minor side frequencies that just happen to match their preconceptions.

Paul Westhaver

It kills Willis to acknowledge that I posted the paper link in a comment here:
http://wattsupwiththat.com/2014/01/24/how-scientists-study-cycles/#more-102111
I found the paper elsewhere but also found a 2010 guest posting by David Archibald right here at WUWT:
http://wattsupwiththat.com/2010/07/22/solar-to-river-flow-and-lake-level-correlations/
Willis never mentioned that either.
I suggest you all read the comments about the original posting of the peer reviewed and published paper, not by Scafetta, rather, Mauas, P.J..D., A.P.Buccino and E.Flamenco, 2010, Long-term solar activity influences on South American rivers, Journal of Atmospheric and Solar-Terrestrial Physics on Space Climate, March 2010.
But strangely NOW 3.5 years later, I guess things have changed.
I blew the dust of that paper for a reason. I detect that Willis’ disproportionate and a little obsessive assault on the Scafetta was to do a little more than the content of the paper. Gosh know what else! Here I found a paper that was EXALTED in the comments here at WUWT 3.5 years ago and now Willis must again assert that there is no correlation (0.78 is no correlation I guess) and protest that now this paper is junk science.
You all be the judges.
I don’t know the authors of either paper. I seems to me that Scafetta is involved some political battle and now Willis must toss Mauas under a brand new bus, just so he can be consistent
Whatever the strange politics that are driving this odd situation, I can only speak for myself adn I say there, in both cases seem to some form of relationship that begs analysis.
Have a look here at the original WUWT post by David Archibald.
http://wattsupwiththat.com/2010/07/22/solar-to-river-flow-and-lake-level-correlations/
Read the highly contrasting assessments compared with those of this page.
I don’t know quite what to make of it.

Curious George

Do the Physical Review Letters have the same exacting requirements for a peer review as the Copernicus Publishing? BTW, can we know who reviewed this gem?

Willis Eschenbach

David L. Hagen says:
January 25, 2014 at 5:14 pm

Compare WJR Alexander et al. 2007
Linkages between solar activity, climate predictability and water resource development
JOURNAL OF THE SOUTH AFRICAN INSTITUTION OF CIVIL ENGINEERING Vol 49 No 2, June
2007, Pages 32–44, Paper 659

Alexander’s life long effort was to compile all hydrology related data for the Southern African region. He is making all the data available on disk to whoever requests it to the address given.

I took a look at his paper. I can’t understand his method. It appears that every alternate sunspot cycle has been recorded as a negative number, in order to kinda sorta convert it to a sine wave …
I gotta say, once someone starts doing calculations using the claim that in 1930 there were minus 63 sunspots or the like … my urban legend alarm starts to go off. What is a negative sunspot? There is indeed a 21=year “Hale cycle” of the solar geomagnetic activity, but if you are claiming a correlation with that, then you should use that and not some hacked-up version of sunspots.
And indeed, the annual sunspot data does not lend itself easily to flipping. Consider the following annual average sunspot counts:
1963 27.9
1964 10.2
1965 15.1
Now if you are going to “flip” the cycle starting in 1964, do you flip the 1964 data, or do you start the cycle by flipping the 1965 data?
As near as I can tell, he doesn’t answer that question in his paper, but whichever way it is done, it is bound, guaranteed, to change his results significantly. This is because he then accumulates the number of sunspots … so if the flipping points are all moved back or forwards by one year, what he identifies as critical points (which allegedly line up with changes in flow) move back or forwards by one year … and if they move forwards by one year, we’re left with the paradox of the effect happening before the cause.
Sorry, David, but my inability to be able to figure out either why or how he is flipping sunspots, along with the sensitivity of the results to totally arbitrary flipping decisions, along with the fact that is is using a crude and arbitrary measure like flipped sunspots instead of actually measuring the strength of the 21 year cycle … well, all of that combined makes my hair stand on end. I fear I will give Mr. Archibald’s work a miss.
w.

Now I get it!
In Climatology the term “Correlation does not imply causation”
is a Koan!

Manfred

NIce to see that paper debunked so quickly.
Though, I don’t think it is helpful to make more general conclusions on the basis of such a poor paper. Please keep focussed on the best evidence and most influential papers.
I am also not impressed by Holgate’s statement, which essentailly says, he doesn’t believe his own data, because he cannot explain it, and because it can’t explain something else (a long term trend), and then, instead of analyzing, he comes up with an old paper from his boss…

Keith Minto

James Strom.
I believe that the faculae make for the loss of sunspots.

Variations in TSI are due to a balance between decreases caused by sunspots and increases caused by bright areas called faculae which surround sunspots. On the whole, the effects of the faculae tend to beat out those of the sunspots.

Willis,
Hmmm, wow. I have to agree with you on this one.
Can I be so bold as to make a request? You spend an incredibly amount of time going over these papers. You are obviously very dedicated and patient. You have proved beyond a shadow of a doubt that there is a lot of bad science out there. Kudos. My request is, could you focus more on interesting papers that have merit? I can’t speak for others, but for me personally that would be much more enlightening and entertaining. As fun as it is to point at others and laugh together with a shared sense of intellectual superiority, it’d be more entertaining (to me anyways) to hear about actual discoveries. Or not, whatever. You’re the one that puts in the time, so of course, whatever you find the most rewarding. It’s just a suggestion.

Willis Eschenbach

Paul Westhaver says:
January 25, 2014 at 7:07 pm

It kills Willis to acknowledge that I posted the paper link in a comment here:
http://wattsupwiththat.com/2014/01/24/how-scientists-study-cycles/#more-102111
I found the paper elsewhere but also found a 2010 guest posting by David Archibald right here at WUWT:

I didn’t even consider acknowledging you, Paul. My bad, I didn’t realize you were that starved for approbation. Let me fix that right now.
Folks, Paul posted the link to the Parana paper, so if you see him, give him a big pat on the back from me. That’s PAUL WESTHAVER, if you were wondering how to spell it. He posted the link, and I can’t possibly tell you what a difference his posting that link has made in my life. If I were to pick one link-poster to be awarded the Kennedy Medal of Freedom, it would be PAUL WESTHAVER, no one’s even close.
Seriously, Paul, do you believe I thought about you enough to deliberately leave your name out of the post? I assure you, your name never crossed my mind, nor am I that petty.
Care to know what my real mental process was regarding the link?
You might note that not only did I not link to you, but contrary to my usual practice I didn’t even link to my own post. This was deliberate, because people on that post wanted to bust my chops over the Copernicus issue, and I wanted to move on. I’m tired of getting abused by handwaving vague fools simply because I’m calling for scientific transparency, and because I hold that if you break the pool rules you can’t complain when the lifeguard kicks you out.
So I didn’t mention the post by name nor discuss it in any fashion.
THAT was why I neither linked to your comment, nor to my own post. I wanted this post to be separate and unconnected.
Sorry you weren’t the first thing on my mind when I made the choice … but heck, you weren’t the last thing on my mind either. I made the decision on entirely different grounds than your imagination provided, grounds that I fear had nothing to do with you.
Regards, and in seriousness, thanks for pointing me to the paper.
Please note, however, that while you provided the pointer to the Parana paper, I provided the work, the insights, the analysis, the thoughts, the math, the graphs, and the writing regarding the Parana paper… and since you haven’t acknowledged me for doing that, I fear your complaint that I didn’t acknowledge your paltry contribution, well, that don’t impress me much.
w.

Willis Eschenbach

Ian Schumacher says:
January 25, 2014 at 7:39 pm

Willis,
Hmmm, wow. I have to agree with you on this one.
Can I be so bold as to make a request? You spend an incredibly amount of time going over these papers. You are obviously very dedicated and patient. You have proved beyond a shadow of a doubt that there is a lot of bad science out there. Kudos. My request is, could you focus more on interesting papers that have merit? I can’t speak for others, but for me personally that would be much more enlightening and entertaining. As fun as it is to point at others and laugh together with a shared sense of intellectual superiority, it’d be more entertaining (to me anyways) to hear about actual discoveries. Or not, whatever. You’re the one that puts in the time, so of course, whatever you find the most rewarding. It’s just a suggestion.

Thanks, Ian. Unfortunately, I’m a climate heretic. I hold that the temperature of the earth is NOT determined by the forcing. Instead, the temperature is held within narrow bounds (±3°C over the 20th Century) by the action of emergent climate phenomena including thunderstorms, El Nino, and the PDO.
As a result, there’s not a whole lot of work out there that actually relates to my work. As a result, I generally either work on my own scientific research, or I try to keep bad science under control. Not for reasons of “intellectual superiority”, but simply so that people don’t get led astray.
It’s a long slog …
Thanks again for the good thoughts,
w.

Willis Eschenbach

Manfred says:
January 25, 2014 at 7:27 pm

NIce to see that paper debunked so quickly.
Though, I don’t think it is helpful to make more general conclusions on the basis of such a poor paper. Please keep focussed on the best evidence and most influential papers.

Quotations, Manfred, quotations let us know what you are referring to. Exactly what “general conclusion” of mine are you disagreeing with?
w.

John West

So, when will PRL be canceled?

They miss what the data truly shows with all of the mathematical gymnastics. I see very high correlation with the flood cycle of the Pacific NW. An example is the 1996/97 high water, which is the second highest on the graph. That was a semi biblical flood event in No California. These flood events occurred on the ascent after a solar minimum. The year 1964/65, a big year for the Parana. In the Pacific NW, a huge rain event that stretches from SF/Bay Area through to British Columbia. The floods occur shortly after the solar minimum and on the ascent side.The year 1955/56 shows the Parana River at a high level, and moving higher over time. In the Pacific NW, there is a massive flood, although it did not impact as large of an area as 1964/65. The 1955/56 floods occur on the middle of the ascent after the solar minimum and before the max. In 1975/76, the Parana has a high flow. In the Pacific NW there is a drought in No California, and the climate shifts at this point. This is the first year since the 20s where the 9 year flood cycle breaks. It seems to now be between 11 and 12 years between high water events. The years 1975/76 are just prior to a solar minimum. In 1984/85, the Parana River is flowing strong, and the years 1983/84 mark the highest peak recorded in the chart. The Pacific NW had strong rains in areas of the coast and the new cycle of over 11 years per flood is in place, with the following flood cycle in 1996/97. There is some high water in the Pacific NW around 2007/08. It would be interesting to see an updated graph to see if the Parana had a spike at that time. That is on the way down to the solar minimum. The correlations stretch back into the 20s from what I can see and mesh with what I know.
So once again straightforward observations and historical data would have served them well, in seeing deeper into these charts.

Streetcred

Willis, I have enjoyed reading your stuff over the years and have rooted for you against the warmista. But, Mate, do us a favour, please, less of the sanctimonious BS … you’re better than that; and you’re coming across like a bit of a Mikey Mann.

Willis: As I recall the MOTHER of all “correllation errors” has to be the wolve and moose population on Isle Royal, Lake Superior. “Closed system” obviously, and fairly good tracking of the number of wolves and moose (fly overs, good spotters, consistent methods) from the 30’s through the ’80’s AND, paper after paper after PAPER showed this WONDERFUL correlation showing the wolves controlling the number of moose, etc, yada, and so on… BUT some HERETIC like yourself, took a GOOD statistical look, and said, “PURE NONSENSE”, force a re-evaluation. Eventually a plant with a 7 year cycle of abundance and retreat, provided a vital nutrient, which controlled the fertility of the meese (haha, I know MOOSE!) …and the wolve population would more or less correlate with the amount of moose to loose…and all the preceding scholarly work became WORTHLESS. Again, the “prima facia” example of year of “academics” fooling themselves. Delicious. All I have to say is:
KEEP THROWING THOSE MONKEY WRENCHES IN THE WORKS!

GaryM

Were any of the co-authors statisticians? I googled them and found nothing suggesting any of them were. Professional journals ought to require a statistician as a co-author of these papers that conflate statistics with science. But I suspect there aren’t enough statisticians to go around.

Paul Westhaver

Wow…

Paul Westhaver

911

The correlation coefficient is r = 0.78, significant to a 99% level.

Thanks Willis. That mediocre correlation and unbelievable significance triggered my BS meter too. But unlike you, I didn’t have the faintest idea what to do about it. Well done.

Willis Eschenbach

goldminor says:
January 25, 2014 at 8:12 pm

They miss what the data truly shows with all of the mathematical gymnastics. I see very high correlation with the flood cycle of the Pacific NW. An example is the 1996/97 high water, which is the second highest on the graph. That was a semi biblical flood event in No California. These flood events occurred on the ascent after a solar minimum. The year 1964/65, a big year for the Parana. In the Pacific NW, a huge rain event that stretches from SF/Bay Area through to British Columbia. The floods occur shortly after the solar minimum and on the ascent side.The year 1955/56 shows the Parana River at a high level, and moving higher over time. In the Pacific NW, there is a massive flood, although it did not impact as large of an area as 1964/65. The 1955/56 floods occur on the middle of the ascent after the solar minimum and before the max. In 1975/76, the Parana has a high flow. In the Pacific NW there is a drought in No California, and the climate shifts at this point. This is the first year since the 20s where the 9 year flood cycle breaks. It seems to now be between 11 and 12 years between high water events. The years 1975/76 are just prior to a solar minimum. In 1984/85, the Parana River is flowing strong, and the years 1983/84 mark the highest peak recorded in the chart. The Pacific NW had strong rains in areas of the coast and the new cycle of over 11 years per flood is in place, with the following flood cycle in 1996/97. There is some high water in the Pacific NW around 2007/08. It would be interesting to see an updated graph to see if the Parana had a spike at that time. That is on the way down to the solar minimum. The correlations stretch back into the 20s from what I can see and mesh with what I know.
So once again straightforward observations and historical data would have served them well, in seeing deeper into these charts.

Goldminor, while that is an interesting theory, so far it is nothing but anecdote. Do you have a record of the rainfall in the “Pacific Northwest”, whatever that might mean to you? I’m always interested in real world data and teasing out relationships, but a list of floods doesn’t help. Humans are great at seeing connections that aren’t there … faces in clouds and constellations in the stars.
So if you’d provide a link to the data I’m happy to take a look.
w.

george e. smith

I always thought filters throw away information. Don’t see how they can add information.
If you do enough low pass filtering, you end up with a single value.
Then anything correlates with anything else.
It also seems to me that if you actually have two phenomena that really do have a physical cause and effect relationship, then the one that is the effect will generally be delayed from the one that is the cause (changes). Doing a correlation for various values of time offset, should enable that physical delay to be determined.
Funny thing Willis, is those Piranhas didn’t seem to do any such analysis.
If CO2 lags behind temperature by 800 years, How would a correlation at zero delay reveal any connection ?

Willis Eschenbach

Paul Westhaver says:
January 25, 2014 at 8:58 pm

Wow…

Paul, you falsely accused me in a quite unpleasant manner of deliberately not acknowledging you for pointing out the Parana paper, an accusation which was laughably far from the truth.
What did you expect in response? That it would make me feel all warm and fuzzy towards you? That I’d ask you to go steady?
You seem surprised that when you bite me, I bite back … in future, you might save time and avoid further shocks to your belief system by simply assuming that when I’m falsely accused, that’s what I’ll do.
Wow indeed … in any case, as I’d like to avoid this unpleasantness next time ’round, a simple request for acknowledgement would have a much different outcome. I like acknowledging people, I do it all the time, and I try to do it as a matter of course for those who inspire my posts. As I indicated in my reply, there were other considerations in this case, and as a result I never even thought about it for this paper.
But deliberately doing not acknowledging you for your contribution? Not my style and never has been.
w.

Leonard Lane

Willis. You mentioned some information on how smoothing introduced spurious correlations. Here is the good place to start.
Loynes, R. M. 2005. Slutzky–Yule Effect. Encyclopedia of Biostatistics.
Abstract
Smoothing a time series by forming a moving average is a commonly employed approach. In this article, some of the problems that arise are discussed, in particular, the introduction of correlations even when the observations in the original series were independent.
Your work with independent or random series supported what was said in this paper.
Take care.

Willis Eschenbach

george e. smith says:
January 25, 2014 at 9:56 pm

I always thought filters throw away information. Don’t see how they can add information.

A poorly designed filter, or a filter that is poorly chosen for a given task, can indeed add spurious information to a signal, and this one is a great example. In this filter the frequency information is being aliased into the amplitude information. Other than getting aliased information from the frequency, I suspect you are right that the filter can’t add information … but it can move it from one location to another and generally screw with the signal.
w.

Willis Eschenbach

Leonard Lane says:
January 25, 2014 at 10:19 pm

Willis. You mentioned some information on how smoothing introduced spurious correlations. Here is the good place to start.
Loynes, R. M. 2005. Slutzky–Yule Effect. Encyclopedia of Biostatistics.
Abstract
Smoothing a time series by forming a moving average is a commonly employed approach. In this article, some of the problems that arise are discussed, in particular, the introduction of correlations even when the observations in the original series were independent.
Your work with independent or random series supported what was said in this paper.
Take care.

Thanks, Leonard. I knew the effect had a name, Steven McIntyre referred to it but I couldn’t remember. Appreciated.
w.

Were any of the co-authors statisticians? I googled them and found nothing suggesting any of them were. Professional journals ought to require a statistician as a co-author of these papers that conflate statistics with science. But I suspect there aren’t enough statisticians to go around.

Can somebody direct me to the data of that river, s flow? Henry

Joe Bloggs

Streetcred says:
January 25, 2014 at 8:38 pm
Willis, I have enjoyed reading your stuff over the years and have rooted for you against the warmista. But, Mate, do us a favour, please, less of the sanctimonious BS … you’re better than that; and you’re coming across like a bit of a Mikey Mann.
Well said that man, Willis gets all twitchy with the “If you disagree with something I said, please quote my exact words, and then tell me why you think I’m wrong.”
I’m sure that most here understand that game, but some here are trying to point out to you that the ‘attack dog’ writing style really does you no favours.
I’m in the same boat as Streetcred above, the lack of humility is outstanding. I have no argument with the way you want to ‘do your science’ but you could show a little less aggression in your ‘tone’ when writing your ‘science’ essays.
Maybe I should just stick to reading your well crafted life experiences and avoid the ‘shove it down your throat’ science articles.
I’m sorry Willis but the schizophrenic writing style is not so nice to digest.
yours in honest disappointment
Joe B

@Willis…I always thought of the Pacific Northwest as SF/BayArea to southern British Columbia. Large storms that cross through this boundary then go on to affect states well to the east. I first heard about the 9 year flood cycle in 1971, when I moved up to the Klamath River. I knew of two of the 9 year floods from personal experience, the 1955/66 and 1964/65. In the summer of 1965 I took a Greyhound bus up to Seattle to stay with cousins for the summer. That was the summer without sun in Seattle. The bus ride took 38 hours from SF to Seattle.It was supposed to be an 18 hour route. The devastation of the flood stretched all the way to Seattle.
I just looked at a revised ssn chart that was produced by Dr Svalgaard. It has a much higher resolution than most, and I can see that the connection between ssn and high water events is not as clear. The connection with the Pacific NW high water events and the Parana River is right on, though. I saved a link for San Francisco rainfall, 1849 to present. The Parana and SF share some years 1996/97, 1982/83, 1972/73, 1941/42, 1930/31, 1911/12 with peak rain years, but there are some SF years that are moderate to low against the Parana highs. Although, I notice that for many of the Pacific coastal heavy rain events, San Francisco had a below average rainfall. The No California/Oregon/ Washington big rains in the 40s through the 60s run counter to SF rainfall, during that period, then it changes in the 70s and synchronizes for the 70s, 80s, and 90s. Never noticed that before….http://www2.ucar.edu/sites/default/files/news/2013/rainfall_chart_orig.jpg

I just had a chance to have a good look at that figure 1 a)
which represents the flow rate.
I suspect that the curved line in that figure 1a) is a best fit of a polynomial of the third order? Or is it a running mean of some sort? What period?
Either way, looking at that curved line I conclude:
The minimum flowrate of the Parana river was in 1953 or 1954, average..
The maximum flowrate appears to be around 1990, average.
There is no data before 1905, but it seems the curve came down from a maximum flow rate at around 1895.
Now look here:
There are good records of the flooding of the Nile, for example here:
http://www.cyclesresearchinstitute.org/cycles-astronomy/arnold_theory_order.pdf
to quote from the above paper:
“A Weather Cycle as observed in the Nile Flood cycle, Max rain followed by Min rain, appears discernible with maximums at 1750, 1860, 1950 and minimums at 1670, 1800, 1900 and a minimum at 1990 predicted.
The range in meters between a plentiful flood and a drought flood seems minor in the numbers but real in consequence….
end quote
According to my table for maxima,
http://blogs.24.com/henryp/2013/02/21/henrys-pool-tables-on-global-warmingcooling/
I calculate the date where the sun decided to take a nap (that is just a figure of speech, in fact it is probably a “wake-up”), as being around 1995, and not 1990 as William Arnold predicted.
This is looking at energy-in. I think earth reached its maximum output (means) a few years later, around 1998/1999.
Anyway, either way, (a few years error is fine!), look again at my best sine wave plot for my data,
http://blogs.24.com/henryp/2012/10/02/best-sine-wave-fit-for-the-drop-in-global-maximum-temperatures/
now see:
1900 minimum flooding – end of the warming
1950 maximum flooding – end of cooling
1995 minimum flooding – end of warming.
predicted 2035-2040 – maximum flooding – end of cooling.
There is a clear and pertinent correlation with the best fit sine wave that I proposed for the observed current drop in global maximum temperatures, both for the Parana and Nile rivers.
What causes the current decrease of these rivers’ flow is this is fairly simple: As the temperature differential between the poles and equator grows larger due to the cooling from the top, very likely something will also change on earth. Predictably, there would be a small (?) shift of cloud formation and precipitation, more towards the equator, on average. At the equator insolation is 684 W/m2 whereas on average it is 342 W/m2. So, if there are more clouds in and around the equator, this will amplify the cooling effect due to less direct natural insolation of earth (clouds deflect a lot of radiation). Furthermore, in a cooling world there is more likely less moisture in the air, but even assuming equal amounts of water vapour available in the air, a lesser amount of clouds and precipitation will be available for spreading to higher latitudes. So, a natural consequence of global cooling is that at the higher latitudes it will become cooler and/or drier.
In a cooling world such as ours now,
http://www.woodfortrees.org/plot/hadcrut4gl/from:1987/to:2014/plot/hadcrut4gl/from:2002/to:2014/trend/plot/hadcrut3gl/from:1987/to:2014/plot/hadcrut3gl/from:2002/to:2014/trend/plot/rss/from:1987/to:2014/plot/rss/from:2002/to:2014/trend/plot/hadsst2gl/from:1987/to:2014/plot/hadsst2gl/from:2002/to:2014/trend/plot/hadcrut4gl/from:1987/to:2002/trend/plot/hadcrut3gl/from:1987/to:2002/trend/plot/hadsst2gl/from:1987/to:2002/trend/plot/rss/from:1987/to:2002/trend
it will simply become wetter at the lower latitudes…..
A clever farmer living at high latitude, who already experienced drought situations, would realize that it is not going to get better for the next three decades. He would now pack up his bags and move to a place of lower latitiude.

Willis Eschenbach

goldminor, thanks for the chart of SF rainfall. I digitized it and checked it against the sunspot record … correlation 0.036, p-value 0.64, no relationship …
w.

goldminor says: January 25, 2014 at 8:12 pm
– – –
I have lived just north of the Pacific NW, in the Canadian Pacific SW for nearly 50 years. My knowledge of historic flooding is that the amount of flooding is related to both the quantity of the previous winters snow pack and the timing of the hot weather in spring. About 100 years ago there was a great flood that today would have wiped out thousands of homes including mine. Since that time dikes have been built, hundreds and hundreds of miles of them of which I have ridden my bike on a large number of. Just a few years ago there was a great scare of spring flooding which resulted in millions of additional dollars being spent to increase the height of the dikes. The flooding turned out to be a dud, with levels even below average or at least nothing to write home about. I did get a lovely extra height for my bike rides for better sight seeing, at taxpayer expense. But I suppose its insurance for any future potential flooding. A couple of years ago, the Fraser river and Pitt river were at high levels, so much so that a tower holding power lines for crossing the Fraser was knocked out of commission and there were fish on my bike path, at the location where it passes below the railroad tracks.
Perhaps not a lot of science in my comment, just anecdotal observations. However there is more to flooding in this area than just the amount of rain.

Greg Goodman

Excellent Willis. This is perfect example of the kind of garbage that can result from these ubiquetous running mean “smoothers”. In fact I’ve never seen such whole scale inversion. It would have made a ideal example for my article on running mean distortion on Judith’s Climate Etc.
http://judithcurry.com/2013/11/22/data-corruption-by-running-mean-smoothers/
I’m glad the issue is getting some coverage.This may be a bit of an odd-ball paper but this kind of filtering is de rigeur in climate science. There seems to be barely a paper that does not use it somewhere and of course the processing of the “gold standard” hadSST dataset uses it over adjacent grid cells in an iterative loop to determine their background climatology.
The other main application is our friend the monthly average which, is mathematically equivalent to using a monthly running before resampling monthly intervals, whereas correct processing would require a 2 month anti-alias filter.
Most of the current data processing being done climate science is doing more to ensure that they do not identify any natural periodic forcing than anything else. But that probably plays to the “consensus” view that it’s all stochasic ‘internal’ variation plus CO2.
Bias confimation at work.
I used SSN in my article as an example of the effect of the monthly running mean. It is used in determining the date of the “peak” of each solar cycle. In the current cycle it finds the peak to be in the month that has the lowerest SSN for the last 2.5 years !!

Willis Eschenbach

Greg Goodman says:
January 26, 2014 at 1:19 am

Excellent Willis. This is perfect example of the kind of garbage that can result from these ubiquetous running mean “smoothers”. In fact I’ve never seen such whole scale inversion. It would have made a ideal example for my article on running mean distortion on Judith’s Climate Etc.

Thanks, Greg. It has been your urging and your statements about running mean smoothers on various of my threads that gave me the insight regarding the problem with the Parana data … I was still shocked, though, to find out that the resulting “smooth” actually has a negative correlation with the data. Astounding.
Best regards,
w.

Willis Eschenbach

HenryP says:
January 25, 2014 at 11:41 pm

Can somebody direct me to the data of that river’s flow? Henry

The river flow data is in the Excel spreadsheet linked to at the bottom of the head post, Henry.
w.

Ox AO

Willis said, “I can’t understand his method.”
Neither do I. But if you look at the Normalized Sunspot Anomaly and 11-year Running Mean if you invert the blue line Sunspot Anomaly it close to me. Wills why don’t you ask them first?