Why Ireland Is Green

Guest Post by Willis Eschenbach

There’s an interesting study over at Climate of the Past entitled A 305-year continuous monthly rainfall series for the island of Ireland (1711–2016) by Conor Murphy et al. Unfortunately, their link to their dataset was broken. So I got in touch with the Copy Editor, Anja Krzykowski, and she was fantastic. Within 24 hours she got back in touch with me to clarify exactly which paper I was referring to; got in touch with the author; got the right link from the author; fixed the link; and finally, she got back to tell me about the new link. All that since yesterday! Gold stars for Ms. Anja, give that woman a pay raise! The data is located here.

I was amused to see that there are no dry months in Ireland. In fact, the dryest month has an average rainfall of 68 mm (2.7″), enough to keep the Isle Emerald …

Now, I like to look at all kinds of datasets to see if I can find any trace of any solar signature. I do that by noting that all of the solar phenomena such as solar wind, solar magnetic field, TSI, and the like all vary in synchrony with the sunspot cycle. So my method has been to look for an approximately eleven-year signal in a variety of surface datasets.

In this case, if Svensmark’s theory about cosmic rays affecting the climate were true, we should see some kind of an eleven-year cycle in the Irish rainfall. Svensmark’s theory is that cloud formation is affected by cosmic ray levels, which in turn are affected by the variations in the sun’s magnetic field that are synchronous with the 11-year sunspot cycle.

With that as prologue, here’s the annually-averaged monthly rainfall in Ireland:

Annually Averaged Irish Rainfall.png

Figure 1: Annual average of monthly rainfall amounts for the island of Ireland.

Not much happening over time, no big changes over the centuries … and what about any solar signature? Here is the Complete Ensemble Empirical Mode Decomposition (CEEMD) analysis showing the various underlying signals that make up the Irish rainfall data:

CEEMD periodogram Irish rainfall 1711 2016.png

Figure 2: Periodograms of the various intrinsic empirically determined signals that when combined add up to the Irish rainfall signal.

I see absolutely no sign of any solar signature in the CEEMD. There is no significant signal at 11 years, which we would see if cosmic rays were affecting the rainfall. So we can add this to the long list of phenomena that per theory should show some solar signature, but in reality they show nothing of the sort. I append a number of them to the bottom of the post, and I note it here in the head post so that Dr. Roy and others don’t bust me because they missed them …

Finally, be clear that I am not saying that the surface climate is not affected by the sunspot cycle. You can’t prove a negative. All I’m saying is that once again, I’ve examined yet another surface dataset that doesn’t contain any indication of any effect of sunspot-related solar variations.

Best regards to all, I’m headed outside to revel in the sunshine. For a conversation on things other than science, you’re all welcome to join me over at my blog or follow me on Twitter @WEschenbach

w.

The Usual Polite Request: When you comment please quote the exact words you are discussing, so that we can all understand what you are referring to. I ask politely. I may not be so polite if you persist in ignoring the polite request.

My Previous Inquiries Into Surface Climate and Solar Variations:

Congenital Cyclomania Redux 2013-07-23

Well, I wasn’t going to mention this paper, but it seems to be getting some play in the blogosphere. Our friend Nicola Scafetta is back again, this time with a paper called “Solar and planetary oscillation control on climate change: hind-cast, forecast and a comparison with the CMIP5 GCMs”. He’s…

Cycles Without The Mania 2013-07-29

Are there cycles in the sun and its associated electromagnetic phenomena? Assuredly. What are the lengths of the cycles? Well, there’s the question. In the process of writing my recent post about cyclomania, I came across a very interesting paper entitled “Correlation Between the Sunspot Number, the Total Solar Irradiance,…

Sunspots and Sea Level 2014-01-21

I came across a curious graph and claim today in a peer-reviewed scientific paper. Here’s the graph relating sunspots and the change in sea level: And here is the claim about the graph: Sea level change and solar activity A stronger effect related to solar cycles is seen in Fig.…

Riding A Mathemagical Solarcycle 2014-01-22

Among the papers in the Copernicus Special Issue of Pattern Recognition in Physics we find a paper from R. J. Salvador in which he says he has developed A mathematical model of the sunspot cycle for the past 1000 yr. Setting aside the difficulties of verification of sunspot numbers for…

Sunny Spots Along the Parana River 2014-01-25

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…

Usoskin Et Al. Discover A New Class of Sunspots 2014-02-22

There’s a new post up by Usoskin et al. entitled “Evidence for distinct modes of solar activity”. To their credit, they’ve archived their data, it’s available here. Figure 1 shows their reconstructed decadal averages of sunspot numbers for the last three thousand years, from their paper: Figure 1. The results…

Solar Periodicity 2014-04-10

I was pointed to a 2010 post by Dr. Roy Spencer over at his always interesting blog. In it, he says that he can show a relationship between total solar irradiance (TSI) and the HadCRUT3 global surface temperature anomalies. TSI is the strength of the sun’s energy at a specified distance…

Cosmic Rays, Sunspots, and Beryllium 2014-04-13

In investigations of the past history of cosmic rays, the deposition rates (flux rates) of the beryllium isotope 10Be are often used as a proxy for the amount of cosmic rays. This is because 10Be is produced, inter alia, by cosmic rays in the atmosphere. Being a congenitally inquisitive type…

The Tip of the Gleissberg 2014-05-17

A look at Gleissberg’s famous solar cycle reveals that it is constructed from some dubious signal analysis methods. This purported 80-year “Gleissberg cycle” in the sunspot numbers has excited much interest since Gleissberg’s original work. However, the claimed length of the cycle has varied widely.

The Effect of Gleissberg’s “Secular Smoothing” 2014-05-19

ABSTRACT: Slow Fourier Transform (SFT) periodograms reveal the strength of the cycles in the full sunspot dataset (n=314), in the sunspot cycle maxima data alone (n=28), and the sunspot cycle maxima after they have been “secularly smoothed” using the method of Gleissberg (n = 24). In all three datasets, there…

It’s The Evidence, Stupid! 2014-05-24

I hear a lot of folks give the following explanation for the vagaries of the climate, viz: It’s the sun, stupid. And in fact, when I first started looking at the climate I thought the very same thing. How could it not be the sun, I reasoned, since obviously that’s…

Sunspots and Sea Surface Temperature 2014-06-06

I thought I was done with sunspots … but as the well-known climate scientist Michael Corleone once remarked, “Just when I thought I was out … they pull me back in”. In this case Marcel Crok, the well-known Dutch climate writer, asked me if I’d seen the paper from Nir…

Maunder and Dalton Sunspot Minima 2014-06-23

In a recent interchange over at Joanne Nova’s always interesting blog, I’d said that the slow changes in the sun have little effect on temperature. Someone asked me, well, what about the cold temperatures during the Maunder and Dalton sunspot minima? And I thought … hey, what about them? I…

Changes in Total Solar Irradiance 2014-10-25

Total solar irradiance, also called “TSI”, is the total amount of energy coming from the sun at all frequencies. It is measured in watts per square metre (W/m2). Lots of folks claim that the small ~ 11-year variations in TSI are amplified by some unspecified mechanism, and thus these small changes in TSI make an…

Splicing Clouds 2014-11-01

So once again, I have donned my Don Quijote armor and continued my quest for a ~11-year sunspot-related solar signal in some surface weather dataset. My plan for the quest has been simple. It is based on the fact that all of the phenomena commonly credited with affecting the temperature,…

Volcanoes and Sunspots 2015-02-09

I keep reading how sunspots are supposed to affect volcanoes. In the comments to my last post, Tides, Earthquakes, and Volcanoes, someone approvingly quoted a volcano researcher who had looked at eleven eruptions of a particular type and stated: …. Nine of the 11 events occurred during the solar inactive phase…

Early Sunspots and Volcanoes 2015-02-10

Well, as often happens I started out in one direction and then I got sidetractored … I wanted to respond to Michele Casati’s claim in the comments of my last post. His claim was that if we include the Maunder Minimum in the 1600’s, it’s clear that volcanoes with a…

Sunspots and Norwegian Child Mortality 2015-03-07

In January there was a study published by The Royal Society entitled “Solar activity at birth predicted infant survival and women’s fertility in historical Norway”, available here. It claimed that in Norway in the 1700s and 1800s the solar activity at birth affected a child’s survival chances. As you might imagine, this…

The New Sunspot Data And Satellite Sea Levels 2015-08-13

[UPDATE:”Upon reading Dr. Shaviv’s reply to this post, I have withdrawn any mention of “deceptive” from this post. This term was over the top, as it ascribed motive to the authors. I have replaced the term with “misleading”. This is more accurate…

My Thanks Apologies And Reply To Dr. Nir Shaviv 2015-08-17

Dr. Nir Shaviv has kindly replied in the comments to my previous post. There, he says: Nir Shaviv” August 15, 2015 at 2:51 pm There is very little truth about any of the points raised by Eschenbach in this article. In particular, his analysis excludes the fact that the o…

Is The Signal Detectable 2015-08-19

[UPDATE] In the comments, Nick Stokes pointed out that although I thought that Dr. Shaviv’s harmonic solar component was a 12.6 year sine wave with a standard deviation of 1.7 centimetres, it is actually a 12.6 year sine wave with a standard deviation of 1.7 millime…

The Missing 11 Year Signal 2015-08-19

Dr. Nir Shaviv and others strongly believe that there is an ~ 11-year solar signal visible in the sea level height data. I don’t think such a signal is visible. So I decided to look for it another way, one I’d not seen used before. One of the more sensitive …

23 New Papers 2015-09-22

Over at Pierre Gosselin’s site, NoTricksZone, he’s trumpeting the fact that there are a bunch of new papers showing a solar effect on the climate. The headline is Already 23 Papers Supporting Sun As Major Climate Factor In 2015 “Burgeoning Evidence No Longer Dismissible!…

The Cosmic Problem With Rays 2016-10-17

Normal carbon has six neutrons and six protons, for an atomic weight of twelve. However, there is a slightly different form of carbon which has two extra neutrons. That form of carbon, called carbon-14 or ’14C’, has an atomic weight of fourteen. It is known to be formed by the …

 

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161 thoughts on “Why Ireland Is Green

  1. With thousands of miles of open ocean to the west of Ireland and prevailing winds from that direction, I would find it hard to recognize any short term signal in temps or precipitation. Why don’t you run it against the NAO which would make more sense I would think.

    • rbabcock March 28, 2018 at 11:19 am

      Why don’t you run it against the NAO which would make more sense I would think.

      Thanks, r. Run exactly what “against the NAO”?

      Regards,

      w.

      • Willis,
        (Just a curious question)
        Looking at the Solar Cycle Records only 7 cycles were 11.6 years long
        4 cycles were 9.N years long (9, 9.3, 9.7, 9.9)
        7 cycles were 10.N years long (10.1, 10.1, 10.2, 10.4, 10.5, 10.5, 10.5)
        7 cycles were 11.N years long (11.3, 11.3, 11.3, 11.4, 11.5, 11.8, 11.8)
        4 cycles were 12.N years long (12.3, 12.3, 12.4, 12.8)
        1 cycle was 13.6 years long (beginning of Dalton minimum)
        Regardless of if the 11.6 year cycle average is present (very few cycles were that length) do the peaks, more or less align with the Solar Cycle Peaks?

      • Well if you want to know what causes the rainfall patterns in Ireland to make it green, I would turn to the Atlantic Ocean. Since it takes a while to heat or cool vast amounts of water, an 11 yr Sunspot cycle is way too short on the face of it.

        Storm patterns in the North Atlantic (NA) follows the jet streams, which are influenced by the pressure patterns in the atmosphere. Ireland is so far north convective storms are of influence only a few months a year, so most of the rain comes from storms off the NA.

        Pressure patterns change from High north/ Low south to Low north / High south. These flips are the NAO.

        The water temperature of the NA also oscillates where the northern part of the NA is cold and the southern is warm to warm north and cold south, known as the Atlantic Multidecadal Oscillation (AMO).

        https://en.wikipedia.org/wiki/Atlantic_multidecadal_oscillation#/media/File:Atlantic_Multidecadal_Oscillation.svg

        So my guess is a positive NAO vs a negative NAO effects precipitation patterns in Ireland, especially since there have been quite a few studies indicating that.

        I also have a good feeling the AMO also effects Ireland’s precipitation pattern since some studies indicate this ; however the cycle time is so large we don’t have that much data to correlate it.

        So maybe running the precipitation dataset against the AMO and the NAO might yield a better correlation than against the running Sunspot numbers?

      • I believe there are multivariate reasons for any change in the weather and this exercise is useless because of that fact!

      • Henry,

        Eishhh…Bryan
        there is no 11 year SC

        Exactly
        there is no 11 year cycle so why would one expect to see an 11 year blip or an 11 year cycle represented in any other record indicating potential solar influence?

      • Bryan and Henry,

        Schwabe solar cycles average around 11 years, but, as Bryan shows, range from nine to almost 14 years. Some claim that they come in two standard forms, a short one averaging around ten years and a long one around 12, for a net net of about 11 years. The odd and even ones also differ.

        In any case, the Hale Cycle is some 22 years, whether (9+13), (10+12) or (11+11) years.

        The heliospheric Hale cycle over the last 300 years and its implications for a ‘‘lost’’ late 18th century solar cycle

        https://www.swsc-journal.org/articles/swsc/pdf/2015/01/swsc150038.pdf

      • Bryan A March 28, 2018 at 2:14 pm

        Henry,

        Eishhh…Bryan
        there is no 11 year SC

        Exactly
        there is no 11 year cycle so why would one expect to see an 11 year blip or an 11 year cycle represented in any other record indicating potential solar influence?

        Sorry, gents, but this is what the CEEMD analysis of the sunspots looks like:

        As you can see, there is a lot of power in the 11-year cycle, along with lesser power in the ten and twelve year periods. And that is what I’m looking for and not finding in the rainfall data. So yes, Virginia, there is an 11-year cycle in the solar data …

        w.

      • Here is a map of the North Atlantic Oscillation.

        Ireland is almost on the null cusp of the warm/cool oscillation. One would expect the weather patterns there to be relatively unaffected while the NAO swings back and forth.

    • The Central England Temperature record as both monthly and annually correlates well to Northern Hemisphere teleconnections such as the AO, NAO, EA etc, as well as the AMO. It also correlates to annual CO2 levels, at a rate of 0.006C per extra ppm of CO2 in the atmosphere.

      https://mynaturaldiary.wordpress.com/2018/03/03/whither-the-weather-2/

      I would be surprised if the Irish monthly rainfall didn’t do the same, given the relative proximity to the CET stations.

      • What is surprising from this data is the difference between October

        which has shown a rising signal since 1897, compared to June

        which is essentially unchanged since 1850.

        It would be interesting to see the rainfall data month by month, as seasonal weather patterns must underlie the pattern.

        The CET datasets in the link above are more or less normally distributed, suggesting a degree of stochastic behaviour, year to year.

    • Also, Ireland lies between ~51-56 degrees north latitude. According to a recent Svensmark presentation, the cloud effect may be most observable between 40 N and 40 S. He theorizes ionization of air circulating up and away from equator. Ireland may not be the best place to observe the 11-yr cloud cycle.

    • Best subsets regression against a cluster of teleconnections since 1950, CO2 and AMO gives the following

      Irish Rainfall = 96.5 + 14.7 EA + 5.15 SCA – 2.50 NAO
      S = 36.32 R-Sq = 17.9% R-Sq(adj) = 17.4%

      With no other teleconnection, CO2 or AMO significant.

      The trend with the East Atlantic pattern is here

      which is the most important of the three variables. The relationship to the NAO is the weakest.

  2. Willis,
    You remarked, “Not much happening over time, no big changes over the centuries…” It seems to me that there is at least a hint of an increase starting in the last quarter of the 20th C. But, nothing to get excited about.

    • Clyde,
      Well, yes, a short term hint but it only gets you back to where it’s been several times before, right back to the early 1700s.
      So the point is – no long term climate trend and, in particular, no CO2 concentration or warming correlation.
      Looks like NOAA and friends need to work their magic on this graph ‘cos it ain’t following the narrative.

      • Their main problem is that “narrative” doesn’t contain the letters CO2. Yes, I’m being facetious.

  3. Ireland is smack in the middle of the temperate belt where any small variation is likely to swamped by the normal distribution of rainfall in the temperate zone. Would it be more obvious at the outer edges of the temperate zone, do you think?

    • Stephen Richards March 28, 2018 at 11:46 am

      Ireland is smack in the middle of the temperate belt where any small variation is likely to swamped by the normal distribution of rainfall in the temperate zone. Would it be more obvious at the outer edges of the temperate zone, do you think?

      Give me a link to what you think is a better dataset, I’m glad to analyze it.

      Thanks,

      w.

  4. Conor Murphy in Ireland I understand, but how did Anja Krzykowski get to Ireland? Bloody Vikings!

  5. Willis
    I am not always sure I understand your logic at the end of your post. It is always as if you want to say: please don’t argue with me?

    So, I am asking: Do you want to argue or not? I thought that was the point of doing a post, i.e. making an argument and hearing the counter proposals.

    The way you work out things, is wrong, because you have been trained to look at it wrongly. There is no [complete] 11 year solar cycle. You must look at it in terms of the Hale cycle. 4 Hale cycles in succession make one complete GB cycle. Each Hale cycle is like one quadrant of the sinewave for incoming energy.

    [in itself, that sine wave might not be going absolutely straight, strictly speaking, it might be going a bit up or down, depending on the more longer SC’s]

    I looked at rainfall here at a place in South Africa and indeed, just looking at the initial data, the amount of rainfall does not make sense. However, if you class the results into the relevant Hale cycles, it starts to make a lot of sense:

    Now, I can understand that you might find this evidence not convincing. However, I did check same rainfall patterns at two other places on earth and I found similar results [although in the case of Wellington, I think the curve was parabolic, not hyperbolic]

    How about it, if you classed the Ireland results into the Hale cycles as I specified, and report back to me what you find?

    • henryp March 28, 2018 at 11:54 am

      Willis
      I am not always sure I understand your logic at the end of your post. It is always as if you want to say: please don’t argue with me?

      So, I am asking: Do you want to argue or not? I thought that was the point of doing a post, i.e. making an argument and hearing the counter proposals.

      And I’m asking you, as I have done many times before, to QUOTE WHATEVER YOU ARE BABBLING ABOUT!! I see nowhere that I said anything remotely resembling “please don’t argue with me”.

      Henry, you’re on your last legs with me because of this kind of nonsense. I’m this close to just skipping over your posts entirely to save my blood pressure.

      w.

      • Willis,
        surely you must agree with me that the evidence is compelling to ignore the 11 year SC but not to ignore the 87 year GB cycle which consists of 4 Hale cycles [but note that not all Hale cycles are exactly 22 years….]
        Anyway, I guessed that you would not be interested in my comment as you seem to be having some kind of attitude. How about it if you give me the original Ireland data so that I can see if I can correctly hind cast and forecast the rainfall patterns just from looking the last GB cycle?

      • Henryp, start by quoting what you meant when you claimed I’d said “please don’t argue with me”. Until you do that, you’re just trying to slide your bovine waste products past the assembled masses, and I have no interest in discussing anything with you.

        You’re really fast at trying to fool people about what I said, and really slow in cleaning up your lies. Why should I discuss anything with someone like you?

        w.

      • Willis
        sorry
        I missed that you had given the data inside the post
        with a link shortly marked
        ‘here’
        let me look at it just for my interest’s sake.
        Clearly you and others here are not interested in my opinion.

    • Henryp,

      “The Usual Polite Request: When you comment please quote the exact words you are discussing, so that we can all understand what you are referring to.”

      If such a simple request is beyond your grasp, why should anyone have any faith in anything you might post regarding science?

      • The point Henry is making is that he asserts that Willis is looking at the wrong cycle, and a more pertinent cycle would be that based upon the Hale cycle.

        Let us assume for one moment (merely for the sake of argument) that Henry is correct that the Hale cycle is pertinent, and the so called 11 year cycle is not. How could Henry quote Willis and make his correct point?

        Henry is obviously correct that sometimes, one may have a relevant point to throw into the climate mix, and it is not possible to quote Willis verbatum since you are not per se disagreeing with anything said, merely that there is something amiss through omission.

        As Henry says, either one wishes to debate the issue raised ie., do variations in solar irradiance, if there be any at all, impact on climate, or climate factors, or one does not wish to debate that issue.

        As I see it, when one seeks to define the limit and scope of arguments, then one is acting as a politician not a scientist.

        What i find outrageous in this is Willis’ response of March 28, 2018 at 12:46 pm. Willis states:

        Henryp, start by quoting what you meant when you claimed I’d said “please don’t argue with me”. (my emphasis)

        This is a typical strawman response, where someone deliberately misstates what was actually said so as to frame the debate. Henry never said that Willis said “please don’t argue with me”

        Henry said It is always as if you want to say: please don’t argue with me? So, I am asking: Do you want to argue or not? I thought that was the point of doing a post, i.e. making an argument and hearing the counter proposals. The use of conditional phraseology makes Henry’s statement rather different.

        And then Willis goes on talking about bovine waste which is akin to an ad hom accusation. Completely unnecessary and adds nothing of substance to the exchanges.

        Now I understand that Willis gets frustrated that his posts are often under attack. But that is the point of debate, and one has to step up to the plate with a thick skin. Willis is sticking his head up above the parapet. that takes courage and is to be applauded, but one must also expect that coming with the territory is that slings and arrows will inevitably be thrown his way. He should be man enough to take it on the chin, and confine his response to the science. I know that that is expecting more of him than is expected of various commentators on this site,

        Finally, I would point out that Henry appears to be raising an interesting issue which issue is as relevant to this debate as are the comments about the latitude of Ireland, and the possibility that the Atlantic and prevailing winds dominate or at any rate obscure response etc, Of course, I did not like his comment The way you work out things, is wrong, because you have been trained to look at it wrongly... That comment ought not to have been made, but it is one that Willis ought to have been man enough to ignore.

      • Richard
        Thanks for your interest.
        I looked at the rainfall in Ireland now, as reported by Conor Murphy et al, and analyzed the data the same way as I did it in several previous cases, as per the last GB cycle, which, as you know, consists of 4 Hale cycles. Based on various parameters that I looked at, I know the exact length of each of the last 4 Hale cycles.
        http://tinypic.com/view.php?pic=35a9lw1&s=9#.Wry55zgUna8

        From the observed pendulum equation which applies to the last GB cycle, I was able to hind cast the rainfall for the period 1907 – 1926 to 1135, in actual fact it was 1129. I think that error of 6/1130 is acceptable? Likewise, I can predict that rainfall will come down, to close to an average of 1152 mm/year for the next period.

    • henryp: Please let me add to Mr. E’s answer. NO, he does not want to argue; and no, that’s not the point of doing a post. As he says, the word “argue” isn’t there, perhaps you saw that the post was about Ireland and assumed an argument was bound to happen. Maybe you enjoyed your little potshot, but your approach didn’t get you an answer. Try a different approach and see how that works.

      • Henry, Willis is like his buddy Lief. They resort to insults when they’re contradicted, or proven wrong. Bad sports. :-)

    • In 1987, Labitzke showed that solar cycle influences emerge more strongly when data are grouped properly. In 2006 she and her colleague added more recent data and the signal in the Quasi-Biennial Oscillation was reconfirmed.

      Sunspots, the QBO, and the Stratosphere in the North Polar Region – 20 Years later

      http://strat-www.met.fu-berlin.de/labitzke/moreqbo/MZ-Labitzke-et-al-2006.pdf

      Abstract

      “We have shown in earlier studies the size of the changes in the lower
      stratosphere which can be attributed to the 11-year sunspot cycle (SSC).
      We showed further that in order to detect the solar signal it is necessary
      to group the data according to the phase of the Quasi-Biennial Oscillation
      (QBO). Although this is valid throughout the year it was always obvious
      that the effect of the SSC and the QBO on the stratosphere was largest
      during the northern winters (January/February).

      “Here we extend our first study (Labitzke 1987) by using additional data.
      Instead of 30 years of data, we now have 65 years. Results for the entire
      data set fully confirm the early findings and suggest a significant effect of
      the SSC on the strength of the stratospheric polar vortex and the mean
      meridional circulation.”

      • Thanks, Chimp. The effect of the sunspot cycle on the upper atmosphere has been known for a while. I fear, however, that your link rests entirely on reanalysis “data”, which is not data at all. It is the output of a climate model which is constantly “nudged” to keep it from running off the rails. As you may or may not recall, I specifically said:

        And please, no studies using reanalysis “data”, which is not data in any form, it is the output of a climate model.

        Please note that I’m not interested in studies and data about the upper atmosphere. I’m a ham operator, I know that sunspots affect the highest layers of the atmosphere. I’m looking for any evidence that there is any effect here on the surface where it might actually make a difference to us.

        Thanks,

        w.

      • Willis,

        As I asked below, what do you find wrong with the reanalysis?

        As you know, the stratosphere expands and contracts and in other ways affects climate in the troposphere and at the surface. Among the solar effects is the production or destruction of ozone by higher-energy UV, the flux of which varies much more greatly than does TSI.

      • No reanalysis data whatsoever. The authors find solar cycles in paleoclimatological data from the Arabian Sea:

        Decadal resolution record of Oman upwelling indicates solar forcing of the Indian summer monsoon (9–6 ka)

        https://www.clim-past.net/13/491/2017/cp-13-491-2017.pdf

        Abstract. The Indian summer monsoon (ISM) is an important
        conveyor in the ocean–atmosphere coupled system on a
        trans-regional scale. Here we present a study of a sediment
        core from the northern Oman margin, revealing early to midHolocene
        ISM conditions on a near-20-year resolution. We
        assess multiple independent proxies indicative of sea surface
        temperatures (SSTs) during the upwelling season together
        with bottom-water conditions. We use geochemical parameters,
        transfer functions of planktic foraminiferal assemblages
        and Mg / Ca palaeothermometry, and find evidence corroborating
        previous studies showing that upwelling intensity
        varies significantly in coherence with solar sunspot cycles.
        The dominant ∼ 80–90-year Gleissberg cycle apparently also
        affected bottom-water oxygen conditions. Although the interval
        from 8.4 to 5.8 ka BP is relatively short, the gradually
        decreasing trend in summer monsoon conditions was interrupted
        by short events of intensified ISM conditions. Results
        from both independent SST proxies are linked to phases
        of weaker oxygen minimum zone (OMZ) conditions and
        enhanced carbonate preservation. This indicates that atmospheric
        forcing was intimately linked to bottom-water properties
        and state of the OMZ on decadal timescales.

        Just one of the many papers finding solar influence on the ISM, other Asian monsoons and still more on other continents.

      • Chimp March 28, 2018 at 2:54 pm

        Willis,

        As I asked below, what do you find wrong with the reanalysis?

        Sorry, Chimp, I hadn’t seen the question. Thanks for asking again.

        Reanalysis “data” is not data, but people would like you to think it is. Why do you think it has such a deceptive name? In fact, it is the output of a climate model just like the ones that have failed us so badly. Do you trust them?

        In addition, the temperature outputs of these climate models are merely linear or semi-linear transforms of their inputs … and among the inputs to the reanalysis models are the solar data … and this virtually guarantees that you will find a solar signature in the reanalysis output. Garbage in, garbage out, solar in, solar out … it’s how the models work.

        So no, I want actual observations, I’m not interested in any kind of climate model output.

        Regards,

        w.

      • Willis,

        I didn’t mind asking again.

        IMO not all reanalysis is unreliable.

        But the solar cycle effect on climatic phenomena was observed long before there was such a thing as reanalysis, back when there was only data. The effect (and lunar effects) was discovered simply in the record books of the Raj in India and in basic meteorological data around the world in the 19th and 20th centuries.

      • Chimp March 28, 2018 at 3:13 pm

        No reanalysis data whatsoever. The authors find solar cycles in paleoclimatological data from the Arabian Sea:

        Decadal resolution record of Oman upwelling indicates solar forcing of the Indian summer monsoon (9–6 ka)

        https://www.clim-past.net/13/491/2017/cp-13-491-2017.pdf

        As I said:

        Please, no studies using the 14C data as a proxy for the sun, as it is known to be affected by the weather.

        Your study uses reconstructed TSI data based on 14C variations.

        I also note that in the abstract they say they’ve found Gleissberg cycles in their data … but in the body of the study they say:

        Turner et al. (2016) found, however, that periodicities within the range of the Gleissberg cycle are also common in random-walk simulations and could be statistical artefacts from the sampling resolution and the age model applied. In fact, the overall error of the reservoir correction of ±31 years is close to the observed periodicities, which might give an indication of why we found the Gleissberg cycle but not the longer-period (∼ 200–210 years) de Vries solar cycle.

        Very important, that …

        Next, various scientific studies have claimed that the Gleissberg cycle is anywhere from 65 to 110 years in length … which is very convenient when trying to fit it to data, since any cycle in that length counts. Heck, even in this one paper they say the Gleissberg cycle is 85, 88, and “80 – 90” years in length …

        Finally, I need a link to the data used before I can analyze anything, and I don’t find anything like that in the study.

        So for all of those reasons, your study is less than useful.

        I ask again: please give me two links, one to the study and one to the data as used, for what you think is the best study showing a relation between the solar variability and a surface dataset.

        Let me close by asking this—IF, and it is a very big IF, there is some kind of Gleissberg cycle seen in comparing proxy temperatures with proxy sunspots five thousand years ago, why aren’t you pointing me to modern studies with real temperatures and real sunspots over the last couple hundred years that show such a relationship?

        And how do you explain this?

        As I said, where we have real data and real sunspots the claimed relationship falls apart …

        Regards,

        w.

      • Willis,

        As I said the last time you posted that graph, the apparent divergence is easily explained.

        First off, HadCRU “data” are thoroughly cooked to a crisp and totally unfit for any scientific purpose, only politics. Secondly, the solar effect on temperature naturally lags, as it takes time for our water planet to gain and shed heat.

        And in any case, I was on about the solar cycle and rainfall, not temperature. Although there is abundant evidence to that effect, too.

      • Nor should 14C data be dismissed out of hand. The manner in which weather and climate affect the data is well understood and compensated for in using the data.

        Archaeologists and paleoclimatologists have been at pains to calibrate the data.

      • Chimp commented on Why Ireland Is Green.

        Willis,

        As I said the last time you posted that graph, the apparent divergence is easily explained.

        First off, HadCRU “data” are thoroughly cooked to a crisp and totally unfit for any scientific purpose, only politics. Secondly, the solar effect on temperature naturally lags, as it takes time for our water planet to gain and shed heat.

        And as I pointed out at the time:

        1) HadCRUT agrees very well with the UAH MSU satellite data over the period. Don’t try that nonsense twice, it wasn’t true the first time. Here is the graph that I posted the last time you tried it.

        2) LOOK AT THE GRAPH OF SUNSPOTS AND TEMPERATURE! I don’t care how much you lag the signal, it still doesn’t match up.

        w.

      • You’re more impressed with the correlation between HadCRU and UAH than I am. Have you analyzed its statistical significance?

        OTOH, to me the lag in air temperature response to the time integral of solar output is obvious and statistically significant.

      • Chimp March 28, 2018 at 4:36 pm Edit

        You’re more impressed with the correlation between HadCRU and UAH than I am. Have you analyzed its statistical significance?

        Really? Correlation? We’re discussing the trend, in case you didn’t notice.

        OTOH, to me the lag in air temperature response to the time integral of solar output is obvious and statistically significant.

        The “time integral of solar output” can trend up, down, or have no trend at all depending on what you take as the zero point. It also differs markedly depending on the chosen starting point. Here are three time integrals of solar output that differ by nothing more than a different zero point:

        You could use that to prove that the temperature is rising, falling, or staying the same. As a result, claiming a correlation is meaningless. It’s nothing but a fitting exercise.

        w.

      • Chimp, I think all reanalysis is indeed unreliable. The reason is that no climate model deploys a valid theory of climate.

        Even where reanalysis is of the known climate, for which the model has been parameterized to reproduce certain observables, the uncertainty remains in the reanalysis because the parameters merely are tuned to have offsetting errors. Other sets of parameters, reflecting different physical relationships, will reproduce the same set of observables.

        That is, the underlying physical theory is incomplete or wrong or both, no matter whether the tuned parameters reproduce known observables, or not. Therefore large uncertainties remain in the calculational product. The uncertainties are merely hidden because of the parameter tuning.

        No one in the modeling community seems to pay attention to these absolutely critical details of scientific rigor. By excluding proper physical error analysis, climate modelers are claiming to know what they manifestly do not.

      • Pat,

        Quite right for climate models.

        But the instance I cited was an attempt, with scientific rigor IMO, to merge “objective estimates of global daily precipitation from gauge observations, satellite estimates, and numerical predictions”.

        Maybe it would be better just to analyze the rain gauge observations and/or satellite estimates, without adding numerical predictions to the mix.

      • Hi Chimp, from the paper at your link, “The NCEP–NCAR reanalysis (hereafter, NCEP) (Kalnay et al., 1996) precipitation data included short-range forecast accumulations from the model based on physics and parameterizations,…

        Here’s what Kalnay, 1996 say about the climate model they used for the reanalysis: “T62 model (equivalent to a horizontal resolution of about 210 km) with 28 vertical levels. The model is identical to the NCEP global operational model implemented on 10 January 1995, except for the horizontal resolution, which is T126 (105 km) for the operational model (Kanamitsu 1989; Kanamitsu et al. 1991).

        The numerical simulations/predictions in the paper were climate model runs. All reanalysis products involve climate model projections and/or infills using observations to constrain the model. They all have large hidden uncertainties.

      • Pat Frank March 28, 2018 at 6:41 pm

        By the way, extrapolating Christopher Essex’ demonstration that a global temperature does not exist, physical analogy supports that neither does a global precipitation.

        Pat, I don’t think that you can extrapolate Christopher’s demonstration that way. The reason is that rainfall measures a volume, which is an extensive property, while temperature is an intensive property.

        The difference, which you likely know but others may not, is that extensive properties change with the “extent”, meaning the area or the volume or the length, of what is being measured. Intensive properties do not.

        Mass, for example, is an extensive property. If you put two cups of water together, you get water with twice the mass of one cup.

        But if you put two cups of water at a certain temperature together, you don’t get water of twice the temperature. You can’t add temperature like you can add mass. And since an average is a sum divided by a count, and you can’t sum up temperatures … I’m sure you can see the problem

        This is why the idea of an “average” temperature is so slippery. It’s an intensive property, like density, so it’s very hard to get a meaningful average. In fact, you can think up several equally logical and defensible ways to average temperature … and they will all give you different answers.

        Rainfall, on the other hand, is extensive. If you get 10 mm of rain and then 5 mm of rain, you got 15 mm of rain. If it rains 20 inches at my house this year, and then I get 40 inches of rain the next year, twice the amount of water has fallen.

        So I would say that while I agree with Christopher that a global average temperature doesn’t exist, a global average rainfall does exist.

        Thanks for all of your excellent work on all of these questions and more,

        w.

      • Hi Willis, I agree with your discussion and the distinction you make between intensive and extensive variables.

        I think Chris’ point, though, is that a global average temperature is not a temperature in any thermodynamic sense. It’s a statistic.

        In the same way, a global average precipitation is not precipitation in a physical sense. It is a statistic. Average precipitation here plus average precipitation there does not add like the volumes of glasses of water.

        As with temperature, a wide variety of precipitation states and distributions can produce the same global average; necessarily all very uninformative.

        Let me return your thanks in spades. You’ve done enormous good in the debate about climate and CO2. If I might add, moral good as well as scientific good. Just recently, by the way, I downloaded the fine paper you wrote on extinction with Craig Lohle.

      • Pat Frank said:

        Hi Willis, I agree with your discussion and the distinction you make between intensive and extensive variables.

        I think Chris’ point, though, is that a global average temperature is not a temperature in any thermodynamic sense. It’s a statistic.

        In the same way, a global average precipitation is not precipitation in a physical sense. It is a statistic. Average precipitation here plus average precipitation there does not add like the volumes of glasses of water.

        Pat, you are correct that “Average precipitation here plus average precipitation there does not add like the volumes of glasses of water”. However, consider this graphic:

        Because of the total coverage of the area surveyed, it does add like volumes of water.

        You go on to say:

        As with temperature, a wide variety of precipitation states and distributions can produce the same global average; necessarily all very uninformative.

        Averages are both less and more informative than individual data. We use averages all the time because they are informative. From the average GDP of different countries, to the average murders committed by people over fifty, to the average food available in the developing world, to the average response of a group of people to a particular drug, the averages give us information which is quite useful and not obtainable by just examining individual data points.

        However, as you point out, averages can also be totally misleading. My favorite example of this is the relative humidity of the atmosphere in areas of thunderstorms. The water content in and under the thunderstorm goes through the roof, while the descending air around the thunderstorm gets dryer … of what use is an average relative humidity in such a situation, particularly when we can’t measure it inside the storm?

        Best to you, and thanks for your kind words.

        w.

      • Pat Frank March 29, 2018 at 6:30 pm

        Hi Willis, thanks. :-)

        I don’t see the additivity in your chart, but will let it go.

        Thanks, Pat. The additivity works like this. We know the annual rainfall for each 1°x1° gridcell. And we know the area of each gridcell. So for each gridcell, we can convert rainfall rate into the total volume for that gridcell.

        Since these are volumes, an extensive quantity, we can add them and divide by the total area of all of the gridcells. This gives us the average rainfall for the area under examination.

        Regards,

        w.

  6. Given that cosmic rays can get deflected by solar processes (as I understand it), is the cosmic ray flux homogeneous across all latitudes?

    • philincalifornia March 28, 2018 at 11:57 am

      Given that cosmic rays can get deflected by solar processes (as I understand it), is the cosmic ray flux homogeneous across all latitudes?

      Good question, Phil. Leif Svalgaard is likely the person to answer it, as I certainly don’t know the answer.

      w.

      • No, it is not. The cosmic ray flux is smallest at the [magnetic] equator and largest at the [magnetic] poles. And depends on the energy of the particles. Lower-energy particles cannot penetrate the Earth’s magnetic shield at low latitudes.

      • Wow, I’m honored to be in the company of you two deep thinkers but, the origin of my question was – even if the Ireland data doesn’t pan out, could it still work as a hypothesis in, for example, cloud cover in the ENSO region as I’ve read such speculation? Sounds like maybe no – the opposite, unless there are other factors such as cloud seeding involved.

      • …. and of course, what I meant by that was an anisotropy of other factors such as ocean-derived chemicals that would assist cosmic ray seeding

  7. A sampling of recent instances of the thousands of scientific papers finding robust correlation between the basic solar cycle and climatic and meteorological phenomena:

    Solar cycle modulation of the ENSO impact on the winter climate of East Asia

    https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/jgrd.50453

    Robust Response of the East Asian Monsoon Rainband to Solar Variability

    https://journals.ametsoc.org/doi/full/10.1175/JCLI-D-13-00482.1

    Amplification of the Solar Signal in the Summer Monsoon Rainband in China by Synergistic Actions of Different Dynamical Responses

    http://www.cmsjournal.net:8080/Jweb_jmr/EN/abstract/abstract1736.shtml

    Solar cycle effects on Indian summer monsoon dynamics

    https://www.sciencedirect.com/science/article/pii/S1364682614001370

    28 New Papers: Solar, Ocean Cycles Modulate Rainfall Trends

    http://notrickszone.com/2017/10/02/28-new-papers-solar-ocean-cycles-modulate-rainfall-trends/#sthash.2wInPedn.dpbs

    Some of the papers in this survey deal with ENSO without factoring in the sun, while others do deal with the effect of the solar cycle. All show rainfall of the past century to be well within normal limits of natural variability. IOW, no anthropogenic signal in trends.

    • Chimp, I’m aware that there are a bunch of studies out there that claim to show such a link. I’ve never found one that will stand up to close examination.

      If you think that one of these will do that, please give me two links, one to what you consider to be the best paper showing the effects of the sun on the surface, and another to the data used in the study. I’m happy to take a look at it.

      Please, no studies using the 14C data as a proxy for the sun, as it is known to be affected by the weather. And please, no studies using reanalysis “data”, which is not data in any form, it is the output of a climate model.

      w.

    • Strong, statistically significant correlations between the solar cycle and monsoons around the world have been observed for over a century.

      What do you have against reanalysis products in general, such as to reject them out of hand?

      From this site, you can download the full text .pdf for this paper, which uses both unreanalyzed and reanalyzed (GPCC) data sets, comparing the results, both of which are highly statistically significant. The unreanalyzed data are from the UEA’s CRU, but rainfall is less likely to be fudged than temperature. The authors describe in detail their statistical treatment of the data.

      Amplification of the Solar Signal in the Summer Monsoon Rainband in China by Synergistic Actions of Different Dynamical Responses

      http://www.cmsjournal.net:8080/Jweb_jmr/EN/abstract/abstract1736.shtml

      Links to the rainfall and sunspot data are included in the paper.

      “The CRU and GPCC products are freely available from http://www.cru.uea.ac.uk/ and http://gpcc.dwd.de, respectively. Both of these two independent datasets are produced based on
      long-term in-situ rain-gauge observations and are long enough to study decadal and interdecadal precipitation variations during the past 100 years.

      “The relative sunspot number (SSN) data in this paper are from the Sunspot Index and Long-term Solar Observations (SILSO) data/images, Royal Observatory of Belgium, Brussels (http://sidc.oma.be/silso/datafiles).”

      You won’t like the other data sources, but they weren’t critical to the rainfall analysis:

      “Wind and temperature data are from the US National Oceanic and Atmospheric Administration-Cooperative Institute for Research in Environmental Sciences (NOAA-CIRES) 20th century reanalysis, version 2, from 1871 to 2012, with a 2.0° spatial resolution for 1000 to
      10 hPa (Compo et al., 2011).

      “The ozone data are from the ERA 20th century (ERA-20C) product of the ECMWF (Poli et al., 2013). ERA-20C is a 10-member reanalysis of the 20th century (1899–2010), only assimilating surface pressure, mean sea level pressure, and marine wind observations from the International Surface Pressure Databank and the International Comprehensive Ocean–Atmosphere Dataset.
      Its atmospheric data are available on the native 91 model levels and 37 pressure levels (as in ERA-Interim).”

      The same conclusion has been repeatedly reached using a variety of precipitation data sets around the world.

  8. I am no expert in climatology. But why would you ex ante expect that you would find more clouds in a place where you have 100% cloud coverage at all times? As I said, I have no expertise whatsoever, but reading that someone looks for a variation in clouds in a place where cloud coverage is dense all the time is surprising. For similar reasons I would not expect to detect much of a “Svensmark signal” in the driest parts of the Sahara desert.

      • So when I write that I don’t see much sense in either looking in the places that are always 100% wet or in the places that are always 100% dry, then you interpret that to mean that I am saying that one should not look anywhere?

        Of course, you can check also in Ireland. But then I would expect a comment of the kind “Well, its probably not the best place to look for the Svensmark signal, but hey let’s try anyway given that we have the data. If we find something then that would really be a hit for my position. But if we don’t find anything then that would probably not mean much.”

        That is how I would proceed in my field and how I would expect my colleagues to proceed. But then again, I am no expert in climatology and I have no clue how they deal with such issues.

    • Cloud height is still important despite the nominal 100% cloud cover. The nucleation may impact this.

      The region does enjoy a level of sunshine, that being 1200 plus hours or so and we all know that 100 hours up or down would be transformational in the surface climate outcome.

      But do get the point.

      One other thing is that a dry region will often achieve 100% humidity as the temperature drops at night and should nucleation actually be enhanced then this could be where it happens. I seem to remember that the bulk of warming has been a function of low temperature increases.
      Could it be that the cosmic ray hypothesis may be that the temperature actually lifts on average in arid regions.

  9. Willis writes

    In this case, if Svensmark’s theory about cosmic rays affecting the climate were true, we should see some kind of an eleven-year cycle in the Irish rainfall.

    Actually I would have thought Svensmark’s signal, if it exists, would be low in Ireland. The effect enables cloud formation where it otherwise wouldn’t have occurred due to low nucleation site count. Ireland is always raining so nucleation and cloud coverage is never a problem pretty much by definition.

      • My opinion on the Svensmark effect is that it’s likely to be a true climate effect that you can’t see in weather and could only detect in a global signal. Our cloud data is neither long nor accurate enough. All IMO of course.

      • And I should also mention that IMO the Svensmark effect is also probably going to be creating clouds that are barely able to form rather than rain clouds. So I wouldn’t count on seeing an effect in rainfall at all

      • “And I should also mention that IMO the Svensmark effect is also probably going to be creating clouds that are barely able to form rather than rain clouds. So I wouldn’t count on seeing an effect in rainfall at all”

        …this is where my money is

      • Guys, saying that Ireland is the wrong place to look is immaterial. I have to check anyhow or I’m not doing it right.

        In addition, how about if you tell me the right place to look? Since y’all seem to know so much, how about a link to the right dataset?

        w.

      • Willis,

        You could start by looking in those places where the signal has been repeatedly found, such as in monsoons and wet-dry tropical climates. Also of course ozone-related warming and cooling in synch with the solar cycle, since EUV fluctuates so widely during it, much more so than TSI. And the effect on the Pacific Ocean of sun-driven warming, air pressure and winds. Small changes in TSI add up. Again, more UV matters, as it penetrates water more deeply.

      • I linked to an Indian monsoon (ISM) paper above, plus the Chinese rainbelt study previously posted, both just two among the many.

      • Here are recent raw data from India, which appear to show a ~17-year period between monsoon rainfall peaks. Longer series* have found an 18.6 year Luni‐Solar cycle and a 10–11 year solar cycle in rainfall in North‐West and North Central India and the plains of Uttar Pradesh.

        *Rainfall data available in books published by the India Meteorological Department for 115 rain-gauge stations in north-west India, the Plains of Uttar Pradesh, and north-central India for the period 1901–1950.

        These data were subjected to maximum entropy spectral analysis (MESA) in:

        https://www.researchgate.net/publication/229926783_Some_indications_of_186_year_LUNI-Solar_and_10-11_year_solar_cycles_in_rainfall_in_North West_India_the_plains_of_Uttar_Pradesh_and_North-Central_India

      • Chimp March 28, 2018 at 3:46 pm

        Here are recent raw data from India, which appear to show a ~17-year period between monsoon rainfall peaks.

        Sorry, but that is not “recent raw data”. Raw data is numbers. You’ve given me a picture and a link to an abstract.

        Pass,

        w.

      • Willis,

        That’s a graphical representation of raw data. The data can be read off that graph easily.

      • Chimp March 28, 2018 at 4:26 pm

        Willis,

        That’s a graphical representation of raw data. The data can be read off that graph easily.

        Chimp, I’ve digitized data off of lots of graphs. And no, the data cannot be digitized off of that graph. The lines are too thin, the time period is too long, and the background graph

        But OK, I’ll bite … what was the rainfall in May 1976? And if you can tell me that, how about you just tell me about all the months from start to finish, so that I can actually analyze it?

        You may be happy with pretty pictures, but my computer requires actual numbers before I can analyze the data.

        w.

      • Willis,

        The monthly increments are plainly visible, but of course for purposes of digitization the numerical data would be handier.

        However, for the purpose of observing the repeating peaks, the pretty picture is far easier than crunching the numbers.

      • Chimp March 28, 2018 at 4:41 pm

        Willis,

        The monthly increments are plainly visible, but of course for purposes of digitization the numerical data would be handier.

        However, for the purpose of observing the repeating peaks, the pretty picture is far easier than crunching the numbers.

        Gosh, Chimp, that’s brilliant! I wonder why no scientist ever thought about that before? We should give up all this difficult number-crunching, it’s far too hard. Science will be so much easier with your new plan, where we all just sit around and look at the pretty pictures and simply observe the repeating peaks …

        w.

      • Willis,

        Where did I say that’s all we need to do?

        I said that for the purpose of observing the repeating peaks, the graph is better. How do you infer from that that I think that’s all one needs to do with numbers?

      • Willis, you yourself often find graphical representations of data to be useful. So I don’t know why you think my presenting Indian rainfall data in that manner should indicate that I have no use for numerical data.

      • Chimp March 28, 2018 at 6:21 pm

        Willis, you yourself often find graphical representations of data to be useful. So I don’t know why you think my presenting Indian rainfall data in that manner should indicate that I have no use for numerical data.

        Seriously?

        OK, I’ll give it to you slowly. The scientific process is that you take numbers representing measurements, observations of the real world. You put them through some kind of generally computer-aided analysis, and you present the results. Often, the results are presented as graphics. Here’s a simplified picture:

        Data –> Computer –> Graphics

        I asked you for DATA so I could do an analysis. You hand me a graphic and tell me I can get the data from that.

        I challenged you to look at the graphic and give me the data for May 1976, from memory. You ran for the door, didn’t answer the question, and said you’d rather just observe the peaks of the data … here’s your response:

        Willis,

        The monthly increments are plainly visible, but of course for purposes of digitization the numerical data would be handier.

        However, for the purpose of observing the repeating peaks, the pretty picture is far easier than crunching the numbers.

        (In passing I note that your claim that “for purposes of digitization the numerical data would be handier” is just goofy. If I have the numerical data there’s no need to digitize a graphic … but I digress.)

        Seriously?

        In short, data is not graphics, graphics are not data. When I ask for data, I mean data, not graphics.

        w.

      • Willis writes

        I have to check anyhow or I’m not doing it right.

        As you say keep looking…. But to make myself clear, with the data were have my opinion is that you won’t find it.

  10. Ireland is like the opposite of CAGW. The climate stayed the same and everything else was a disaster!

    • When humans first arrived in Ireland around 9000 years ago (or earlier), the entire island was predominantly covered by thick oak and pine forests. It didn’t take the newcomers long to deforest this primeval woodland.

      Same thing happened millennia later when the Norse colonized Iceland, which was then forested by birch, rowan and willow trees and shrubs.

      • Chimp
        March 28, 2018 at 12:57 pm: IIRC, it has cooled since then, and bogs replaced trees, as elsewhere in this biome. The ‘holocene thermal optimum’ has long gone, I fear. Trees are less-easily disappeared by farmers than some think (obviously not farmers, are they?). And generally they are too useful not to encourage as part of the system, where they will grow usefully. Bogs. Lowland and Highland, are more about reduced evaporative drying on level land. The bogs themselves, came to be useful for fuel, luckily…..Brett

  11. Looking at the graphs I am not convinced that there is no evidence for the effect of solar cycles on
    the rainfall data. Firstly there is a non-zero component of the Fourier transforms at 11 years. It is
    not strong but again why should it be strong? Secondly the fact that the climate is likely to respond in
    a nonlinear fashion to any forcing means that it will respond at a different frequency to the input one and
    so again the lack of a 11 year signal can be thought of as showing that the climate is nonlinear.

    Of course if it is nonlinear then you might expect the the solar cycle to show up as a difference frequency
    and looking at the graphs there is a strong peak in c4 at 13.5 years which can be explained as the difference
    frequency between a solar period of 11 years and the 59 year period present in c6. And you can then explain
    the 59 year cycle as a signal from the atlantic ocean multidecadal oscillation. And all of a sudden you can
    see “evidence” that the 11 year solar cycle combines with the atlantic ocean cycle to produce the 13.4 year signal that is clearly present in the data.

    Alternatively there is sufficient noise in the system to allow any amount of bullshit cycle mania none of
    which should be taken seriously.

      • rko, I only program in R. The CEEMD function is found in the “hht” package. You can call Python from R, and if you can go the other way by calling R from Python, that might be an option. I doubt you’ll find it in Excel, as it requires repeated iterations of a number of calculations.

        You need to be a bit careful, as before there was CEEMD, there was EEMD, with the “C” standing for “Complete” … meaning that when you add the empirical modes together you exactly reproduce the signal being analyzed.

        If you want, I could email you the underlying R script for the CEEMD function. In R that’s easy, you just type the function name (“CEEMD”) and it gives you the actual script. Let me see how long it is … OK, not too long. I’ll append it to the bottom here in case you can use it. The CEEMD function documentation is here.

        Best regards,

        w.

        function (sig, tt, noise.amp, trials, verbose = TRUE, spectral.method = "arctan", 
            diff.lag = 1, tol = 5, max.sift = 200, stop.rule = "type5", 
            boundary = "wave", sm = "none", smlevels = c(1), spar = NULL, 
            max.imf = 100, interm = NULL, noise.type = "gaussian", noise.array = NULL) 
        {
            if (!(noise.type %in% c("uniform", "gaussian", "custom"))) {
                stop(paste("Did not recognise noise.type option", noise.type, 
                    "Please choose either ''uniform'' or ''gaussian''"))
            }
            if (noise.type == "custom") {
                if (!is.null(noise.array)) {
                    if ((dim(noise.array)[1] != trials) | dim(noise.array)[2] != 
                        length(tt)) {
                        stop("You requested a custom noise array but either the number of rows did not equal the number of CEEMD trials or the number of columns did not equal the signal length, or both.")
                    }
                }
                else {
                    stop("If noise.type = \"custom\", then you must set noise.array equal to an array with the same number of rows as CEEMD trials and the same number of columns as signal samples.")
                }
            }
            if (noise.type == "uniform") {
                noise <- t(array(noise.amp * runif(length(sig) * trials), 
                    dim = c(length(sig), trials)))
                noise <- noise - mean(noise)
            }
            else if (noise.type == "gaussian") {
                noise <- t(array(noise.amp * rnorm(length(sig) * trials), 
                    dim = c(length(sig), trials)))
            }
            else if (noise.type == "custom") {
                noise <- noise.array
            }
            if (verbose) {
                print("Extracting IMF 1 from each noise/signal realization...")
            }
            imfs <- rep(0, length(sig))
            for (k in 1:trials) {
                imfs <- imfs + Sig2IMF(sig + noise[k, ], tt, spectral.method = spectral.method, 
                    diff.lag = diff.lag, tol = tol, max.sift = max.sift, 
                    stop.rule = stop.rule, boundary = boundary, sm = sm, 
                    smlevels = smlevels, spar = spar, max.imf = 1, interm = interm)$imf[, 
                    1]
                if (verbose) {
                    print(paste0("Trial ", k, " complete."))
                }
            }
            imfs <- imfs/trials
            if (verbose) {
                print("IMF 1 extracted.")
            }
            noise.imfs <- NULL
            if (verbose) {
                print("Decomposing noise series...")
            }
            for (k in 1:trials) {
                noise.imfs[[k]] <- Sig2IMF(noise[k, ], tt, spectral.method = spectral.method, 
                    diff.lag = diff.lag, tol = tol, max.sift = max.sift, 
                    stop.rule = stop.rule, boundary = boundary, sm = sm, 
                    smlevels = smlevels, spar = spar, max.imf = max.imf, 
                    interm = interm)$imf
                if (verbose) {
                    print(paste0("Noise trial ", k, " complete."))
                }
            }
            r <- sig - imfs
            n.i <- 1
            noise.imf <- rep(0, length(sig))
            escape <- FALSE
            while (n.i  2) {
                imf.avg <- rep(0, length(sig))
                for (k in 1:trials) {
                    if (dim(noise.imfs[[k]])[2] < n.i) {
                        warning(paste0("Attempted to extract more IMFs from the signal than are present in the noise series for trial ", 
                          k, "."))
                        noise.imf <- rep(0, length(sig))
                    }
                    else {
                        noise.imf  2) {
                        imf.avg <- imf.avg + Sig2IMF(r + noise.imf, tt, 
                          spectral.method = spectral.method, diff.lag = diff.lag, 
                          tol = tol, max.sift = max.sift, stop.rule = stop.rule, 
                          boundary = boundary, sm = sm, smlevels = smlevels, 
                          spar = spar, max.imf = 1, interm = interm)$imf[, 
                          1]
                    }
                    else {
                        imf.avg <- imf.avg + r + noise.imf
                    }
                    if (verbose) {
                        print(paste("IMF", n.i + 1, "TRIAL", k))
                    }
                }
                imf.avg <- imf.avg/trials
                imfs <- cbind(imfs, imf.avg)
                r <- r - imf.avg
                n.i <- n.i + 1
            }
            imfs.arr <- array(imfs, dim = c(length(sig), n.i))
            ceemd.result <- NULL
            ceemd.result$original.signal <- sig
            ceemd.result$residue <- r
            ceemd.result$tt <- tt
            ceemd.result$max.sift <- max.sift
            ceemd.result$tol <- tol
            ceemd.result$stop.rule <- stop.rule
            ceemd.result$boundary <- boundary
            ceemd.result$sm <- sm
            ceemd.result$smlevels <- smlevels
            ceemd.result$spar <- spar
            ceemd.result$max.imf <- max.imf
            ceemd.result$interm <- interm
            ceemd.result$hinstfreq <- array(0, dim = c(length(ceemd.result$original.signal), 
                n.i))
            ceemd.result$hamp <- ceemd.result$hinstfreq
            ceemd.result$imf <- imfs.arr
            ceemd.result$nimf <- n.i
            for (i in 1:n.i) {
                imf = imfs.arr[, i]
                aimf = HilbertTransform(imf)
                ceemd.result$hinstfreq[, i] = InstantaneousFrequency(aimf, 
                    tt, method = spectral.method, lag = diff.lag)
                ceemd.result$hamp[, i] = HilbertEnvelope(aimf)
            }
            invisible(ceemd.result)
        }
  12. Hi Willis, thanks for a look at this study. It seems that the Emerald Isle and my home province of Manawatu here in New Zealand, have more in common than just the green & white of our representative rugby football teams (the current Irish coach, Joe Schmidt is from here BTW). The Manawatu is renowned in New Zealand for it’s lush, verdant pasture land, not that we don’t often endure droughts. We do but they are short lived affairs (much cherished by us non-farmers BTW) and we recover quickly from them. Our driest month from records that go back just 90 years is March at 66.7mm. Very close to the Irish ‘dry’ month! Our wettest month is June with a long term mean of 96.8mm. The long term annual mean is 986mm.

    With 90 years of data available, and only looking at the decadal aggregates we see a climb from the late 1920’s to a peak wet decade in the 1940’s, followed by a slow decline to the driest decade of the 1980’s. The 90’s saw increased levels of rain with a dip in the first decade of this century. The current decade, despite a lengthy wet period recently, is vying for the driest on record total with the 80’s. There appears to be very little correlation at all with the sunspot cycles over that period.

    I think the answer is closer to home in the form of a connection with ENSO and other long term phenomena such as the PDO and our SAM. The 70’s & 80’s were a period of strengthening El Nino events that saw an increase in the strength of the prevailing westerly wind, an increase in cloud cover on our side of the divide, but not for us an increase in rainfall as one might suspect with all of that moisture laden cloud rolling in off the sea!

    Trying to make sense of a chaotic system, a fascinating journey.

  13. “You can’t prove a negative”.

    Anyone care to explain what this means ?, especially the “negative” part.
    Sounds like semantics to me.
    Thanks in advance.

    • u.k.(us) March 28, 2018 at 2:30 pm

      “You can’t prove a negative”.

      Anyone care to explain what this means ?, especially the “negative” part.

      Sure. Suppose someone makes a negative claim, like say “There are no orange sheep”.

      How can you prove that? You can look everywhere you can think of … but that doesn’t prove that an orange sheep might exist somewhere you haven’t thought of.

      I’m in the same position. I can’t prove that there is no solar influence on climate. It might exist out there somewhere … all I can do is look every place I can think of.

      Regards,

      w.

      • I think I finally got it.
        Lack of evidence doesn’t preclude existence.
        Geez, damn philosophers.

  14. As a sometimes yacht person I love Valentia in remote SW Ireland. Long temp record, no UHI, 0.43 degs C a century.

    • Pat Frank
      March 28, 2018 at 6:29 pm: luckily, negative -ves are positive, so we are saved! Perhaps an Easter message. So Happy Easter from the Antipodes.

  15. Always interesting. Thanks Willis.

    My grandmother said Ireland was green because angels sprinkled it with stardust. It is said to make shamrocks grow.

    Stardust may be GCRs and if angels were involved it would only have to have been once.
    I’m only sure of this: My grandmother never heard of Svensmark.

  16. https://www.met.ie/news/display.asp?ID=330

    The Influence of the North Atlantic Ocean on the Mean Temperature of Ireland

    The Atlantic multidecadal oscillation (AMO) (http://www.cgd.ucar.edu/cas/catalog/climind/AMO.html) which relates to the North Atlantic sea surface temperatures, explains over 90% of the pronounced decadal variation in annual land temperatures and summer precipitation. The correlation between the 20-year running mean of the AMO anomaly (i.e. AMO differences relative to the mean AMO) and 20-year running mean of annual average land temperature anomalies (temperature differences relative to the mean temperature) over Ireland is shown in figure 1(a). This clearly shows that the AMO and mean annual land temperatures over Ireland are in phase i.e. when decadal averages are considered mean temperatures over Ireland are warmer when the North Atlantic ocean is warmer than average. Mean summer rainfall over Ireland is also correlated to the AMO, with drier summers on average when the AMO is in a negative phase.

    As a child of the 1970s in Ireland I can remember some great summers, which I haven’t seen since. Awaiting the AMO cold phase now returning to see if we get a repeat.

    • “Mean summer rainfall over Ireland is also correlated to the AMO, with drier summers on average when the AMO is in a negative phase.”

      Same for England.

  17. I hesitate to make /any/ speculations about causes of changes is the pattern of monthly rain for Ireland. However, I have downloaded the numbers (many thanks for the link, Willis) – found two mistakes – missing values – in my download by the way – and done a bit of processing using my own methods.
    I ask whether you or anyone has noticed two very abrupt and enduring changes in the numbers. I have found that at September 1976 an abrupt change occurred, to a first approximation of about 13 mm per month, which is way smaller than regression or related methods could establish. This is the exact date of the great Pacific change, as most of you will recognise. The other step change took place at September 1859, approx size 37 mm per month, and had stable regimes both before and after the step. What could this be associated with?
    Robin (Bromsgrove)

  18. The ~55 year mode may be linked to the quasicycle in the AMO, which would make sense since the AMO is derived from SSTs in the northern Atlantic.

    The AMO though does have a clear solar cycle linked signal, although there’s an interesting lag/lead variation of a few years in the peaks. But the solar cycle temperature variation is only ~0.1 C, compared with ~0.5 C in the ~60 year AMO cycle.

    The quasicycle in the AMO isn’t a short term artefact as Mann in his 2005 paper showed it to be persistent in the paleodata. (He doesn’t like to talk about that paper these days. :) )

    So what that says about the rainfall data in Ireland I don’t know. Maybe the lag/lead variation in the solar cycle peaks in the AMO relates to variations in latitude of weather systems. Ireland is a small place, so it would be easy to have weather systems tend to miss it one cycle and hit during it another, just because of relatively minor variations in their geographical tracks. That might prevent a ~11 year mode appearing in the rainfall data.

    • As I comment above, oceanic cycles naturally lag the solar cycles which affect them, and hence air temperatures.

      It should be obvious that solar heating is in large part what drives oceanic oscillations, wind, water and eventually air temperatures.

      • And maybe a little less obvious, but repeatedly demonstrated, that fairly small changes in TSI can be amplified to affect the climate system. And that the much bigger changes in UV affect specific components of the system, like ozone.

    • “The AMO though does have a clear solar cycle linked signal, although there’s an interesting lag/lead variation of a few years in the peaks.”

      It’s a phase reversal.

      • Yogi Bear March 29, 2018 at 8:20 am

        “The AMO though does have a clear solar cycle linked signal, although there’s an interesting lag/lead variation of a few years in the peaks.”

        It’s a phase reversal.

        From The Climate Dictionary:

        phase reversal — “Cause and effect just went 180° out of phase for no reason”.

        I’m sorry, Yogi, but in this context “phase reversal” is meaningless. It simply names without explaining.

        w.

      • Of course there is a reason, it wouldn’t happen without a reason. And you have been presented with the reason previously.

      • Yogi – If you look at my original WFT link I’ve taken a 48 month average for both datasets – which are monthly data. You have averaged the AMO data over 23 months and used raw SSN. That shifts the AMO dataset by a couple years compared to the SSN dataset, which moves away from a direct apples-to-apples comparison. Also in my experience the 48 month lagging average seems to bring out the ~11 year spikes in the AMO data more clearly.

        Here’s the graph again to save you looking for it.

      • Yogi Bear March 29, 2018 at 10:03 am Edit

        Of course there is a reason [for the phase reversals], it wouldn’t happen without a reason. And you have been presented with the reason previously.

        OK, let’s dig into it. First, you have applied a 23-point boxcar filter to the AMO data, which one should never do before analysis. A boxcar filter is about the worst filter you could apply, because it will actually invert signals. Here’s an example of just how horrible a boxcar filter is:

        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 the “smoothed” version of the data is actually negatively correlated to the data itself … astounding.

        So the first problem with your graph is that you’ve used a bad, terrible, horrible filter on the data, which renders everything meaningless. You’ve converted good data into garbage.

        Next, there is no power at the 11-year band in the AMO data. Here’s the CEEMD analysis of the same AMO data:

        As you can see, there is no power in the 11-year period as we’d expect if there were a sunspot signal in the AMO data.

        However, there is a quite weak signal at nine years … and when you put up a signal with a weak 9-year cycle against a signal with a strong ~ 11-year cycle, guess what you get?

        Phase reversals …

        So yes, there is a reason for the phase reversals. It’s just not what you think it is.

        Regards,

        w.

      • “Next, there is no power at the 11-year band in the AMO data. Here’s the CEEMD analysis of the same AMO data:”

        There can’t be because of the phase change. You could of excluded that possibility just by understanding this…

      • Yogi Bear March 29, 2018 at 4:14 pm

        “A boxcar filter is about the worst filter you could apply, because it will actually invert signals. ”

        Except it hasn’t.
        http://www.woodfortrees.org/graph/esrl-amo/plot/esrl-amo/mean:23

        Actually, it has, although fortunately the filter is short enough (23 months) that it is not affecting the frequencies of interest (a decade or so). What it has done is added a lot of short-frequency garbage noise to the signal. A boxcar filter “rings”, and thus distorts the signal. Here are the periodograms:

        AMO in red, 23-month boxcar filtered AMO in blue. See all the short-period noise at the left end of the graph? That is the result of the filter “ringing”. It’s why boxcar signals are anathema in signal processing. They introduce spurious signals that don’t exist into whatever you are analyzing.

        Finally, notice that as I pointed out above, there is a 9-year signal in both the filtered and unfiltered data, although it is weak (~ 10% of the range of the data). And when you post that up against an ~ 11-year signal as you’ve done above, guess what you get?

        Phase reversals as the 9 and 11-year signals go out of and into phase …

        w.

      • “Actually, it has”

        It has not inverted the signal.

        “Finally, notice that as I pointed out above, there is a 9-year signal in both the filtered and unfiltered data, although it is weak (~ 10% of the range of the data). And when you post that up against an ~ 11-year signal as you’ve done above, guess what you get?”

        A wild goose chase, as the whole AMO signal goes in and out of phase with the fairly regular sunspot cycle. So it’s irrational to be even looking for any regular signal in the AMO. Yawn.

  19. I have a good laugh when I see the Irish Dulux paint advert for Weatherguard – the one where they say “Irish weather has always been unpredictable.” It seems that this is no laughing matter for the Irish climate alarmists.

    • Wish I could find the clip from Mrs Browns boys where Mrs Brown gives some door knocking bible bashers in their place about the 40 days and 40 nights of rain in the good book but she says we call it summer !

    • If I heard only the whisper of an anti-anecdote, should I proclaim it, or charge money to hear my wisdom.
      Talk to Al Gore.

  20. willis . you quit too early.
    look only at rainfall on tuesdays
    or weekends
    or summer rainfall..
    slice and dice until you find it.

  21. “I was amused to see that there are no dry months in Ireland.”

    Yes, you could almost say there are never any dry months in Ireland. Wikipedia says
    “The longest drought in Ireland occurred in Limerick between 3 April 1938 and 10 May 1938 (37 days).”
    Which means that once upon a time, there was an April which almost didn’t get any rain.

    It’s amazing that the locals find it amusing too. Link copied from a previous comment here:
    http://www.dailyedge.ie/irish-rain-scale-1275040-Jan2014/

    But seriously, Ireland is at the target end of the Gulf Stream. Good news, bad news. It rains a lot, but it’s 20°C warmer than it would be without the Gulf Stream. I wouldn’t expect to see a solar influence here because it’s pretty well saturated already. (By that logic, the most likely place would be some place always on the edge between rain and drought. But most likely the effect is insignificant there too).

    • Toto – April 1938 is indeed the lowest value in the Irish Monthly Rainfall dataset discussed in this post, with just 5.3 mm recorded.

  22. Willis,
    Immediate thought was “Are the numbers too good?”
    Have they all gone into a saturation zone where effects are all maximised and can show no contrast?
    E.g. If people found elsewhere a signal in the ratio of browned grass to green grass, the analysis would fail in Ireland because all the grass is always green.
    Nonetheless, I continue to be fascinated by your findings, time after time, that there is no correlation with various observations proposed by others. Geoff.

  23. Hi Willis,

    We went through an 18 month period – 1969/1970 – without a drop of rain which caused me to study the local rainfall charts. Springsure in Central Qld has records from 1863, and is only one small spot on the earth, but believe me we studied this record.and did find the roughly 11 year cycles – one drier and one wetter that followed each other. Some wet ones were really wet – 1950s and 1970s especially – and it was the arbitrary choice of the”Global Warming” crowd starting with the 1950s to state it was getting drier that caused me to doubt their claims.

  24. As an Irishman myself, thanks to you and Anja for getting this data posted Willis. Very interesting.

  25. Looking at the data itself, the most distinctive trend I can find is in winter rainfall, which has increased at a rate of 7.8 mm/dec over the course of the data set. Three of the four most recent decades have been the wettest on record, with the latest decade (2007-2016) the wettest to date.

    Anyone who lives in Ireland won’t find that hard to believe.

  26. In this case, if Svensmark’s theory about cosmic rays affecting the climate were true, we should see some kind of an eleven-year cycle in the Irish rainfall. Svensmark’s theory is that cloud formation is affected by cosmic ray levels, which in turn are affected by the variations in the sun’s magnetic field that are synchronous with the 11-year sunspot cycle.

    Willis, I have no dog in this and I do agree with your various and rigorous observations that there is no data to support the 11-year sunspot cycle.

    However strictly speaking, “rays affecting the climate” isn’t the same thing as rays affecting rainfall – of course! ;-)

    Nucleation is a “potential” for cloud formation, it isn’t the same thing as humidity nor is cloudiness necessarily correlated to changes in precipitation*.

    As I understand it, the Earth’s magnetic field deflects particles best from equatorial regions but provides little to no protection above 55 degrees magnetic latitude. And even the choice of hemisphere has an influence on observed measurements of total flux; apparently.

    Given that most of Ireland is above 50 degrees geographic and the North magnetic pole is around 80 degrees, it would be right in the zone of increased flux.

    Perhaps “rays” do explain all that rain ;-)

    *Precipitation might be correlated to cloud formation but the causation isn’t direct.

  27. I note Svensmark & Calder’s book “The Chilling Stars” states (p78):

    “Large patches of the Pacific and Indian Oceans, and a region of the North Atlantic between Greenland and Scandinavia, show the strongest links between low cloud cover and cosmic rays. A more obvious geographical pattern emerged when Marsh and Svensmark’s exhaustive analysis looked at the cloud-top temperatures. In this case, a belt encircles the globe, centred on the tropics, where the cloud behaviour follows the cosmic rays closely. The effect is emphatic over 30 per cent of the globe.”

    Bear in mind the prevailing wind direction for Ireland and the SW of Britain is from the SW. There doesn’t seem any particular reason to suppose that cloud in Ireland would be diagnostic or otherwise of anything in Svensmark’ theory.

    I note the C4 component in Willis’ analysis in the article has the most prominent amplitude at a period just over 13 years (with what look like corresponding harmonics evident in the subsequent components C5 and C6). What could this periodicity be?

    A quick correlation of the annual rainfall for this Irish dataset and the CET precipitation set reveals a correlation of 0.51 (note the two datasets are contemporaneous only from 1873). I find that surprisingly low considering the geographical proximity of the two areas. (The correlation to CET temperature is effectively zero, as is the correlation in CET between annual temperature and annual rainfall). What we can say though is that Ireland is wetter than Central England. For the period 1873 – 2016 mean rainfall in Ireland is nearly 70% higher than for Central England – a difference of +443mm!

    It may just be me, but it seems the purpose of this article is to specifically criticise Svensmark and Scafetta even though there is no obvious reason to connect Irish rainfall with anything in particular. This quote:

    Finally, be clear that I am not saying that the surface climate is not affected by the sunspot cycle. You can’t prove a negative.

    Quite. So what’s the point of this article then? Looks remarkably like trying to prove a negative to me.

    [The mods recommend double checking that your email address has been correctly entered upon login to WP to help avoid comments being shunted to the moderation queue. -mod]

    • Minor correction – I should have written the word rainfall rather than cloud as follows:

      There doesn’t seem any particular reason to suppose that rainfall in Ireland would be diagnostic or otherwise of anything in Svensmark’ theory

  28. I’m late to the stream and I hope you see this Willis. Thanks for taking it not only on the chin but in the nethers from those who do not adhere to formal boxing rules.

    Now the real point of this comment: the data of this dataset as you have presented it seems to me to be a prime case for re-evaluation of the definition of “standard deviation’ and how it is functions in climate analysis – both in terms of error and in terms of the reliability of results.

    Some day I would appreciate your musings on such if only a goat trail in one of your articles.

    keep up the good work.

    • Thanks for the kind words, Les. I don’t understand why you would want to re-evaluate “standard deviation”. It’s one of a number of ways that we describe the spread of the data. We also use variance and range. It’s not clear what the problem with any of these might be.

      w.

  29. I am not attempting to re-defne standard deviation. The data you analyse above is a very broadly distributed set of data, it certainly does not fit any kind of bell curve, yet standard deviation has significance within the ‘bell’ curve form of data distribution.

    Standard deviation as I understand it is a measure of deviation from a/the norm. It assigns signifigance to the amount of deviation from the norm. It seems to me that in a broadly distributed set of data, the data near the normalized average has less significance than data in a narrowly distributed set.

    In addition there is a whole body of data analysis that ‘ignores’ data points more than 3 standard deviations from the norm and assigns them a spurious meaning. This in turn affects the reliability of the consequent associations and predictions derived from the data. (I am not at all suggesting you filtered the data in this way).

    When averaging an extensive property, does the breadth of the distribution of the data affect the reliability and accuracy of the analysis, especially when it comes to determining ‘P’ values? Might it also affect the CMEED analysis – masking or exaggerating the results?

    I do not have the skill set or statistiacal training to think that one through. Thank you for your time.

    • Les March 29, 2018 at 8:59 pm

      I am not attempting to re-defne standard deviation. The data you analyse above is a very broadly distributed set of data, it certainly does not fit any kind of bell curve, yet standard deviation has significance within the ‘bell’ curve form of data distribution.

      Thanks, Les. Near as I’ve found, there is no widespread agreement as to what kind of distribution the rainfall data has. Here’s the abstract of a study, emphasis mine:

      Abstract: Choosing a probability distribution to represent the precipitation depth at
      various durations has long been a topic of interest in hydrology. Early study into the
      distribution of wet-day daily rainfall has identified the 2-parameter Gamma (G2)
      distribution as the most likely candidate distribution based on traditional goodness of
      fit tests. This paper uses probability plot correlation coefficient test statistics and Lmoment
      diagrams to examine the complete series and wet-day series of daily
      precipitation records at 237 U.S. stations. The analysis indicates that the Pearson
      Type-III (P3)
      distribution fits the full record of daily precipitation data remarkably
      well, while the Kappa (KAP) distribution best describes the observed distribution of
      wet-day daily rainfall. We also show that the G2 distribution performs poorly in
      comparison to either the P3 or KAP distributions.

      It turns out that the Pearson Type I distribution (not the Pearson Type III) fits the Irish data the best of the different Pearson distributions. And while it is not a normal (bell curve) distribution, it’s not all that far from one. For example, the empirically determined standard deviation is within about a tenth of the normally-calculated standard deviation.

      When averaging an extensive property, does the breadth of the distribution of the data affect the reliability and accuracy of the analysis, especially when it comes to determining ‘P’ values?

      Mmm … not as far as I know. What does affect it is the Hurst Exponent, with a high Hurst Exponent increasing the uncertainty of trends and correlations, and generally increasing p-values.

      However, the Ireland Rainfall data has a Hurst Exponent of ~0.5, the same as random normal (bell-shaped) data, so that would indicate that standard statistical methods are applicable.

      Might it also affect the CMEED analysis – masking or exaggerating the results?

      No, the CEEMD not dependent on the distribution of the data being analyzed.

      Regards, and thanks for the interesting question. I hadn’t looked at what kind of distribution the Irish data had, most fascinating.

      w.

  30. Willis, I’m glad you wrote “You can’t prove a negative.” Questions have been raised upthread on whether a wet place like Ireland would show any response to a Svensmark-like effect.

    But anyway, I have a different point, which is that clouds don’t always cause rain. Let me tell you a story. It was late August in 2008 or 2009 I think, so around solar minimum, and the British Met Office was forecasting a heatwave. I looked at the satellite photos, and saw a really large area of what looked like a marine layer and I wondered “Would that layer (which seemed unusual for that time of year) be present if sunspots were high, and, will it get as hot in England as they are forecasting? The answer to the second was a definite “no”, several degrees down, and not as sunny as they expected.

    That’s just an anecdote of course, and no proper data for you to chew on. Sorry about that, but perhaps soon you’ll see some from me…

    Rich.

  31. Willis, above you wrote, “With that as prologue, here’s the annually-averaged monthly rainfall in Ireland:”

    but at https://wattsupwiththat.com/2015/12/10/noise-assisted-data-analysis/ you wrote,

    “Next, in Antico2015, the authors use the annual average data. To me, this is a poor choice. If you wish to remove the annual fluctuations, that’s fine … but using annual average data cuts your number of data points by a factor of 12. And this can lead to spurious results by inflating the apparent significance. But let us set that aside as well.”

    I’m wondering why you used the annually-averaged data for Ireland? It seems an odd choice, unless the full monthly data would produce a lot of high frequency fuzz in the CEEMD analysis?

    • Doug Jones April 6, 2018 at 9:42 pm

      I’m wondering why you used the annually-averaged data for Ireland? It seems an odd choice, unless the full monthly data would produce a lot of high frequency fuzz in the CEEMD analysis?

      Good question. Two reasons. First, I used annually averaged because the purpose of my analysis was to look for an ~ 11-year cycle. As a result, I was uninterested in high frequencies, so the annual data was more than adequate.

      Second reason is that the CEEMD analysis is quite slow with larger datasets, and I don’t have a supercomputer, just an eight-year-old Mac. So to cut down on processing time, I used the annual data. In such situations, a less precise analysis is more than adequate to tell me if I need to take a closer look. At that point, if needed, I’ll invest the time to do a more detailed analysis. In the Irish case, there was nothing there, so I didn’t need to get more accuracy.

      w.

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