From the Journal of Younger Dryas Impact Hypothesis Studies

The Journal of Younger Dryas Impact Hypothesis Studies (formerly the prestigious journal known as the Proceedings of the National Academy of Sciences; PNAS) has just published Kennett et al, yet another paper promoting the hypothesis that the Younger Dryas, mass extinction of the megafauna, and the demise of the Clovis culture was due to a cosmic impact event.

Kennett et al use OxCal to fit Bayesian models to radiocarbon and other dates on and around sediment layers containing (disputed) evidence (for example, nano-diamonds) of the purported impact. They find that all the candidate layers, from locations in North and South America, Europe, and the Middle East have the same date (with large uncertainties at some sites), and that this is compatible with the start of the Younger Dryas recorded in ice cores and other archives, within the chronological error of the dates.

The copious supplementary material includes a description of all the dates and the OxCal models used. These could easily be rerun and modified to test the effects of assumptions that the authors have made. Since Kennett et al acknowledge assistance from Christopher Bronk Ramsey and others, I’m going to assume that the models are reasonable. If anyone suggest that one or more of the models are problematic, I’ll have a closer look at them. Since many of the sites were known from archaeological evidence to span the initiation of the Younger Dryas, that OxCal confirms that many of the impact layers to date to this time this is not too surprising. That all of the sites have this date is fairly impressive.

I’m still not impressed by the cosmic impact hypothesis as a whole though. Objections arising from the absence of crater or of shocked quartz have been raised before. I want to raise two objections that I have not seen discussed before.

1) The cosmic impact is hypothesised to have generated a huge fireball that trigged forest fires across North America and Europe. A fireball intense enough to trigger mass forest fires should also melt ice. This should be evident as a melt layer in the Greenland ice cores – Greenland is not so far from the Canadian Laurentide Ice Sheet where the object is supposed to have landed. The initiation of the Younger Dryas in the Greenland ice cores has been closely studied, for example by Steffensen et al (2008) at NGRIP. A melt layer would be obvious, but Steffensen et al (2008) don’t mention one.

Nor do Petaev et al (2013) who report a platinum spike in the GISP2 ice core that is attributed to an Ir-poor iron meteorite synchronous with start of the Younger Dryas (but not with the evidence of biomass burning).

It could be argued that the shockwave from the fireball eroded any melt layer, this should leave a detectable erosive contact overlain by a disturbed ice. This is not observed.

2) Part of the climate impact ascribed to the cosmic impact is due to dust released by the impact. This dust should be recorded as a spike in the dust record in ice cores from Greenland that is simultaneous with (or slightly leading) the initiation of the Younger Dryas. Steffensen et al (2008) present high resolution records of ice core proxies across the Younger Dryas initiation and report no dust spike simultaneous with the transition. Instead, the increase in dust lags behind changes in deuterium and oxygen isotopes.

Multiple-parameter records from the NGRIP ice core 11.0 to 15.5 ka. (A) d (red) and δ18O (dark blue) at 20-year resolution over the entire period and details of the transition zones: (B) from GS-1 into the Holocene at 11.7 ka, (C) from GI-1a into GS-1 at 12.9 ka, and (D) from GS-2 into GI-1e at 14.7 ka. [Left part of (B) to (D)] NGRIP records of d (red), and δ18O (dark blue) and logarithmic plots of dust content (yellow), calcium concentration ([Ca2+], light blue), sodium concentration ([Na+], purple), and annual layer thickness (λ, green) at annual resolution. Bold lines show the fitted ramp functions; gray vertical bars represent the 95% (2σ) confidence intervals of the ramp point locations. [Right part of (B) to (D)] Bars representing the locations of the fitted ramp functions for the NGRIP records shown to the left and for the corresponding results obtained using DYE-3, GRIP, and GISP2 data, where these are available at sufficient resolution.

Multiple-parameter records from the NGRIP ice core 11.0 to 15.5 ka. (A) d (red) and δ18O (dark blue) at 20-year resolution over the entire period and details of the transition zones: (B) from GS-1 into the Holocene at 11.7 ka, (C) from GI-1a into GS-1 (Younger Dryas) at 12.9 ka, and (D) from GS-2 into GI-1e at 14.7 ka. [Left part of (B) to (D)] NGRIP records of d (red), and δ18O (dark blue) and logarithmic plots of dust content (yellow), calcium concentration ([Ca2+], light blue), sodium concentration ([Na+], purple), and annual layer thickness (λ, green) at annual resolution. Bold lines show the fitted ramp functions; gray vertical bars represent the 95% (2σ) confidence intervals of the ramp point locations. [Right part of (B) to (D)] Bars representing the locations of the fitted ramp functions for the NGRIP records shown to the left and for the corresponding results obtained using DYE-3, GRIP, and GISP2 data, where these are available at sufficient resolution.

Neither the melt layer nor the dust records support the Younger Dryas impact hypothesis. Nor, as I’ve pointed out before, does the Older Dryas, a short cold interval between the Bølling and the Allerød. The Older Dryas proves that the climate at the time could flip from a warm to a cold state without cosmic intervention.

Kennett et al (2015) Bayesian chronological analyses consistent with synchronous age of 12,835–12,735 Cal B.P. for Younger Dryas boundary on four continents

Posted in Peer reviewed literature | Tagged , , | Leave a comment

Incidence of papers reporting spurious correlations with solar variability correlates with solar variability

From the price of wheat (Hersche 1801) to childhood mortality (Skjærvø et al 2015), there seems to be no end to papers reporting spurious correlations with solar variability. As both these examples are published at solar maxima (±5.5 years), I conclude there is a correlation between the incidence of such publications and solar activity. Further evidence for this revolutionary hypothesis is provided by the publication of Wing et al (2015) near a solar maxima. Wing et al find “highly significant” correlations between the incidence of two types of arthritis and solar variability.

I don’t really need to do any more than link to XKCD to show that Wing et al is almost certainly spurious.


But since this paper is picking up some media attention, I thought it might be worth pointing out why solar activity is unlikely to become a tool for diagnosing arthritis.

Wing et al analyse the incidence of giant cell arthritis (GCA) and rheumatoid arthritis (RA) with in Olmsted County, Minnesota over five decades. They correlate the 3-year smoothed incident data with the F10.7 index (solar radiation at 10.7 cm wavelength) and the AL index (a proxy for the westward auroral electrojet), allowing for lags of up to 14 years.

Wing et al Fig 2. Rheumatoid arthritis (RA) incidence (1955–2007) and lagged correlation with F10.7 and AL . Yearly average and 3-year moving average of RA incidence rates are plotted as dotted and solid lines, respectively, in panels (A and C, left scale). F10.7 and AL are plotted as dashed lines (A / C, right scale). The lagged correlations between (yearly average and 3-year moving average RA incidence rates) and (F10.7 and AL) are plotted as dotted and solid lines in panels B and D, respectively. The grey lines in panels (B and D) indicate the p=0.05 significance threshold. The dashed horizontal grey line in panels (B and D) indicate r=0.

The first problem is in interpreting the p-value. It is a measure of how likely a correlation as large as that observed is under the null hypothesis of no correlation. It does not indicate how likely the alternative hypothesis, that there is a relationship, is. To know that we have to have some idea of how plausible the alternative hypothesis is. As the XKCD cartoon above shows, if the hypothesis is unlikely to be true, it is more likely that a highly significant correlation is a fluke than a genuine finding.

Since Wing et al is a single study without a strong theoretical expectation of a relationship between solar variability and arthritis, even a highly significant p-value is not strong evidence. Even if there were no other problems, this would be enough to be fairly certain that the correlations in Wing et al are spurious. And there are other problems.

Hypothesis tests are only fully valid if they are designed before the data are observed. According to the press release, it was the observation of a 10-year cycle in the incidence data that inspired the study. If data have a 10-year cycle, they are virtually certain to correlate with solar variability with a lag of 0-14 years. This is data-snooping and inflates the risk of finding a “significant” p-value when there is no relationship. A better strategy would be to use these data to help develop a hypothesis and then use independent data from another region to test this hypothesis.

The p-value is valid if a single correlation is analysed. If multiple correlations are analysed, there are multiple chances of finding a significant p-value, just as buying several lottery tickets increases your chances of winning a prize. Wing et al test the correlation between the incidence of arthritis and two solar proxies at lags 0-14 years. This does not get them 30 tickets to the p-value lottery because, for example, the solar proxies at lag 0 and lag 1 are highly correlated, but it does give them several chances to win. It is possible to correct for multiple testing, and at least the paper should have shown that the authors are aware of the problem. I wouldn’t dream of suggesting that the authors might have examined other solar variability proxies before settling on the two they report as the westward auroral electrojet is such an obvious place to start.

The incidence of both types of arthritis is temporally autocorrelated: if one year has a high incidence, the next year is likely to, and vice versa. The statistical test used by Wing et al assumes that the observations are independent, that there is no autocorrelation. Violating this assumption makes the statistical tests more liberal, more likely to report a significant result than is justified by the data. The autocorrelation inherent in the incidence data is enhanced by the 3-year smooth used, making the problem worse. Wing et al should have corrected for the autocorrelation in the (smoothed) data. There are several strategies that could be used, all would result in a less impressive p-value.

Even though I don’t find this paper in the least plausible, I do agree with the authors’ conclusions that those afflicted by arthritis should move to lower latitudes. I’ll start packing now.

(Andrew Alden @aboutgeology alerted me to this paper.)

Posted in Peer reviewed literature, solar variability | Tagged | 2 Comments

Alea iacta est

I have been carrying a burden in the shadows. I carry it no more. It has been sent to those with broader shoulders to bear forth into the light. And yet I weep at what will come to pass.

Forgive me if you know not to what I allude: in time you will know more than you desire to, as I do now.

Posted in Uncategorized | 3 Comments

Dissembling with graphs: Murry Salby edition

Perhaps the easiest way to mislead your audience, or indeed yourself, is to generate deceptive graphics. Murry Salby is to be saluted for his mastery of this art, with several fine examples in his recent London lecture. Here are the best two.

Bomb 14C by Salby

Fig 1. Bomb 14C by Salby (0:30:31)

The first example is a figure showing the changes in atmospheric 14C due to atmospheric nuclear bomb testing and the subsequent absorption of this 14C into the oceans and biomass. Salby is arguing that this occurs quickly.

“Within two decades the nuclear surplus of 14C was history”

Bomb-radiocarbon is a useful dating technique over the last 60 years, so I am familiar with the general shape of the curve and something is wrong with Salby’s figure – it shows atmospheric 14C to have declined to its original value within 20 years of the 1963 test ban treaty. In reality, 14C concentrations remain above background 50 years later. Comparing Salby’s version with the correct data, it becomes obvious (OK, Cathy worked it out first) what he has done.

Bomb 14C curves for different geographical regions from Hua et al (2013)

Fig 2. Bomb 14C curves for different geographical regions from Hua et al (2013)

Rather than showing the whole curve, Salby starts his curve in 1958 after many bomb tests have been conducted. His background level of 14C is therefore much higher than the true background level. This makes it appear that the bomb 14C moved out of the atmosphere much faster than it really did. These shenanigans would have been obvious had he extended his figure to the present day as he would have had negative 14C values, and the poor fit of his exponential curve would have been exposed.

Salby’s claim that 14C levels declined to background within 20 years is bogus, his graph is exceptionally deceptive.

The second example is a figure Salby uses to argue that emissions are a function of population as the two curves overlie each other since 1860. If we accept this claim, it implies that population control rather than emissions abatement is needed.

Fossil fuel emissions and population growth since 1850

Fig 3. Fossil fuel emissions and population growth since 1850 (0:40:42)

There is just the little problem of the scale of the two curves. The emissions scale starts at zero, the population curve starts at about one billion. The two curves only overlie because of this scaling trick. A fair plot would show that emissions have risen much faster than population, over this time period. Does Salby really think that per-capita emissions in 1860 (horse-drawn ploughs, etc) were equal to those of 2010? The data suggest otherwise.

Is Salby aware of his skill in constructing deceptive figures? Certainly his audiences are not.

Posted in Fake climate sceptics, Silliness | Tagged , | 10 Comments

The most interesting part of Murry Salby’s lecture

I watched Murry Salby’s London lecture: it was awful. Salby addresses what he calls the core issue of climate change (0:2:30) “Why is atmospheric CO2 increasing?” The answer is obvious – because of CO2 emissions from fossil fuel burning and land-use changes – but Salby does not like the answer so repeats oft rebutted fallacies in a hopeless attempt to prove the increase is almost all natural.

First he shows that the annual increase in atmospheric CO2 concentrations is correlated to temperature. This is the well-known effect of El Niño which induces global temperature increases, drought over south-east Asia and changes in Pacific Ocean productivity. this relationship explains the year-to-year variability in the increase in atmospheric CO2, not the trend. This is not a novel error.

Salby’s second argument is that the atmospheric life-time of a CO2 molecule is short, less than five years. This is true but irrelevant. What matters is how long a pulse of CO2 stays in the atmosphere, even though the individual molecules may be exchanged between the atmosphere and ocean or vegetation. This crucial difference has been explained many times: Salby is wantonly ignoring facts that refute his mad hypothesis, or hope that his audience is ignorant.

This brings me to the interesting part of the lecture – the first questions from the audience (1:13:57) and its answer. Our favourite Viscount, Christopher Monckton, offers this fulsome praise, demonstrating that he either does not realise or does not care that the lecture was nonsense.

Professor Salby, I think we all want to start by just saying thank you. You are one of a tiny band of immensely courageous genuine scientists who have had their livelihood and their professional career stolen from them, not because their science was bad, but because it was socially inconvenient, politically uncongenial, and financially unprofitable to the governing class. Your bravery with persisting with your research for so many years after this was done to you is commendable. The clarity, breadth and depth of your presentation, which has grown since I last saw it only a year ago, and grown exponentially , is breath-taking, and my question therefore is this: when are you going to publish in a journal that they cannot ignore?

Salby replies

Thank you for your gracious remarks. I am not worthy of them, but thank you nonetheless. The immediate answer to your question is that this material will not be published, until the material from which it is derived is published. That won’t be published until I have recovered my research files and been reinstated in the field.

If Salby really believed that his work proved that CO2 emissions were natural, he would rush to publish, saving the world from unnecessary action to abate climate change, and receive the accolades not only of a lunatic lord but the entire population. A Nobel Prize awaits.

Yet it would seem that Salby prefers to play the martyr to a tiny audience of climate sceptics (perhaps 12) than to submit his research to scrutiny. His conditions for publication are pathetic. He does not need his research files. None of the material Salby presented was based on his own data: the atmospheric CO2 concentration, global temperature and other datasets he used can all be downloaded within an hour. None of the analyses Salby presented were complicated: it should be possible to repeat them within a few days. He also does not need to be reinstated: if his research is valid he would not want for employment at any institute of his choice.

By refusing to publish, does Salby believe he is holding the World to ransom to get his job back or is he too embarrassed to face the reality that his errors are not even novel?

Posted in Fake climate sceptics, Silliness | Tagged , | 17 Comments

Expressions in R

expression() and related functions including bquote are powerful tools for annotating figures with mathematical notation in R. This functionality is not obvious from their respective help files. demo(plotmath) nicely shows the huge potential of expression(), but does not help that much with getting the code need for many real cases.

I tend to get my expressions to work by trial and lots of errors (although having put this together, I now understand them at least temporarily). I’ve just searched through my code library and extracted and annotated some examples of expression() being used. I hope someone finds it useful.

I’m going to use expression() with title(), but the same expressions can be used with any of the functions (text(), title(), mtext(), legend(), etc) used for putting text on plots.

x11(width=4, height=5, point=14);par(mar=rep(0,4), cex.main=.8)
plot(1, type="n", axes=FALSE, ann=FALSE)

The simplest use of expression is take a character or string of characters and it will be added to the plot. If the string contains spaces, it must be enclosed in quotes (alternatively, the space can be replaced by a tilde ~, which probably gives better code – see comment from Gavin below).

title(line=-1, main=expression(fish))

This use of expression is entirely pointless, but is a useful starting point. Some strings have special meanings, for example infinity will draw the infinity symbol. If for some reason you want to have “infinity” written on your plot, it must be in quotes. Greek letters can be used by giving their name in lower-case or with the first letter capitalised to get the lower or upper case character respectively.

title(line=-2, main=expression(infinity))
title(line=-3, main=expression(pi))
title(line=-4, main=expression(Delta))

Subscript or superscript can be added to a string using ^ and [] notation respectively.

title(line=-5, main=expression(r^2))
title(line=-6, main=expression(beta[1]))

If the string we want to have as sub- or superscript contains a space, the string must be in quotes. Braces can be used to force multiple elements to all be superscript.

Strings can be separated by mathematical operators.

title(line=-7, main=expression(N[high]-N[low]))
title(line=-8, main=expression(N[2]==5))

To make more complicated expressions, build them up from separate parts by either using * or paste to join them together (if you want a multiplication symbol, use %*%). The * notation gives nicer code.

title(line=-9, main=expression(Delta*"R yr"))
title(line=-10, main=expression(paste(Delta,"R yr")))
title(line=-11, main=expression(paste("Two Year Minimum ",O[2])))
#title(line=-11, main=expression(Two~Year~Minimum~O[2]))
title(line=-12, main=expression(paste("Coefficient ", beta[1])))
#title(line=-12, main=expression(Coefficient~beta[1]))
title(line=-13, main=expression(paste("TP ", mu,"g l"^-1)))
#title(line=-13, main=expression(TP~mu*g~l^-1))
title(line=-14, main=expression(paste(delta^18,"O")))
#title(line=-14, main=expression(delta^18*O))
title(line=-15, main=expression(paste("Foram ", exp(H*minute[bc]))))
#title(line=-15, main=expression(Foram~exp(H*minute[bc])))

To start an expression() with a superscript (or subscript), I use an empty string (you can also use phantom()).

title(line=-16, main= expression(""^14*C*" years BP"))
#title(line=-16, main= expression(phantom()^14*C~years~BP))

So far so good. But sometimes, you want to use the value of an R-object in plot annotation.

For example, if we wanted to label a point with its x value, this will not work.

title(line=-17, main= expression(x==x))

Instead of using expression(), we have to use bquote(), with the object we want written out inside .()

title(line=-18, main= bquote(x==.(x)))
title(line=-19, main= bquote(x==.(x)~mu*g~l^-1))
Plot annotations with expression and bquote

Plot annotations with expression and bquote

If you understand these examples, you should be able to use the remainder of the functionality demonstrated by demo(plotmath) and at ?plotmath.

Posted in R | Tagged , | 2 Comments

Is there robust evidence of solar variability in palaeoclimate proxy data?

This is my EGU 2015 poster which I am presenting this evening. Poster B25 if any readers are at EGU and want to see it nailed to the board.

With my coauthors Kira Rehfeld and Scott St George, I have done a systematic review of high-resolution proxy data to detect possible solar-signals. It is an attempt to avoid the publication bias and methodological problems in the existing literature on solar-palaeoproxy relationships. A manuscript in in preparation.

There is no prize for finding any typos.

Posted in solar variability | 9 Comments