Willie Soon at Heartland: “The sun is big”

Dr Willie Soon is in the news again. His recent paper with Monckton et al ended with the conflict of interest statement

The authors declare that they have no conflict of interest.

A statement seeming at odds with his long and well-documented industry funding. Well, one thing led to another, and now the New York Times has taken an interest in Soon’s activities and his funding sources have been exposed (again). The agreements with the Southern Company Services contains a clause forbidding naming the funder in any publications and demanding advanced written copy of publications. These are both dubious, no reputable funder would impose these clauses (and no sensible institution would agree to them).

Soon lists his publications and presentations in his final reports to funders. I suppose they were part of the service offered. If the funders wanted uncertainty and doubt, Soon delivers. But on the off chance that they were really interested in the science, did they get their money’s worth?

Time to look at Soon’s presentation at the Heartland mock climate conference in July last year, which followed on from two other solar-climate presentations. He was also awarded a prize – I haven’t watched that presentation yet.

Soon begins his presentation with the observation that “the sun is big” [0:36] ensuring that his presentation includes at least one true statement.

He spends over three minutes [1:33 – 4.48] of his 16 minute presentation discussing the absolute calibration of total solar irradiance (TSI). Different satellites have measured TSI but the measurement time series don’t all overlap and there is instrumental drift, so there is some uncertainty (basically 1365±5 Wm-2). Soon objects to the IPCC AR5 WG1 statement

8.4.1.1. However, the few tenths of a percent bias in the absolute TSI value has minimal consequences for climate simulations

but neglects to complete the sentence

because the larger uncertainties in cloud properties have a greater effect of the radiative balance.”

He complains at length that “nobody is making any attempt to tell me where is the absolute exact value” while showing various attempts to determine the value (which like all measurements will always have some uncertainty).

Why does the uncertainty in TSI matter? Soon claims that it “allows climate modellers to adjust their climatology” [3.10]. He seems to think that modellers are adjusting TSI in their model until the model produces the correct answer. And what evidence does he provide?

He offers Barsugli et al (2005) [4.48] who vary the insolation in their model between 260 and 340 Wm-2 (multiply by four to get TSI) and find that at 340 Wm-2 the mean global temperature is 35 °C. Only when the insolation was reduced to 260Wm-2 does the mean global temperature resemble the 15 deg C of Earth. So Soon alleges that modellers reduce insolation  by 90-100 Wm-2 to match climatology. What he omits to mention is that Barsugli et al 2005 are studying an aqua-planet with a slab ocean to study the impact of varying insolation on tropical climates. There is no claim in the paper that this planet represents Earth, and the high temperatures are explained by the low albedo and lack of ocean heat transport of the watery planet. The value of TSI that modellers actually use is easily checked, for example GISS model E uses 1367 Wm-2.

If the audience was not already confused by Soon’s account of Barsugli et al, his claim that if they had included the land the temperature would be 38 °C, will have done it. There is no land on an aqua-planet.

Next, Soon shows [5:58] a long pre-industrial climate model control run Liang et al (2013) that has a strong 11-year cycle. Soon alleges that this cycle is

of course artificially imposed. It is a terrible thing that these people are playing with their model in that way.

He offers no evidence to support this claim. Liang et al do not discuss this cycle, they are more interested in the amount of low frequency variability.

A video of Gavin Schmidt (who is a far, far better presenter) discussing the skill of climate models follows [6:48], which Soon describes as “arrogance” [7:39].

Soon’s counter example is the proxy-model comparison of Jiang et al (2012) which shows a large mismatch for winter temperatures with climate models suggesting it was cooler and palaeoecological evidence suggesting is was 5-8 °C warmer.

Soon assumes that this discrepancy means that the models must be wrong. He does not consider the alternative hypothesis that the palaeoecological evidence might be wrong. I have previously argued that the palaeoecological evidence for large winter warming in China is not robust and the model result is not unreasonable given the orbital and greenhouse gas forcing.

The use of the mid-Holocene pollen-based reconstruction from Bartlein et al (2011) by the IPCC is discussed next [9:32], and dismissed as bogus because there are “no data in China” (actually there is at least one site). This analysis is of the northern extratropics, China is not so large that the relative lack of data from there will hugely bias the results.

Soon now returns to his main research focus of astronomy and leaves my area of understanding.

At [10:15] he declares that he is “very bothered” by a statement in IPCC WG1 AR5 8.4.1.2.

Concerning the uncertainty range, in AR4 the upper limit corresponded  to the reconstruction of Lean (2000), based on the reduced brightness of non-cycling Sun-like stars assumed typical of a Maunder minimum (MM) state. The use of such stellar analogues was based on the work of Baliunas and Jastrow (1990), but more recent surveys have not reproduced their results and suggest that the selection of the original set was flawed (Hall and Lockwood, 2004; Wright, 2004); the lower limit from 1750 to present in AR4 was due to the assumed increase in the amplitude of the 11-year cycle only.

Soon, who has piled on the crowd-pleasing invective throughout his presentation (the IPCC is “gangster science”, “misrepresenting results”, “misleading everybody” “anti-scientific”, and includes “bogus results” and “pseudo-scientific claims”) now gets upset that the IPCC “suggests” that Baliunas and Jastrow’s work was “flawed”. “Very strong words”, he moans, a “matter of really amazing injustice”, before labelling the IPCC statement as “pseudo-scientific”.

I didn’t understand much of the argument that followed. As this was supposed to be a talk to a non-scientific audience, you can be fairly certain that few, if any, of the audience understood it either. Soon is really not a good science communicator. Perhaps he doesn’t aim to be.

Soon had apparently run out of science to mangle at [14.26] so played a clip of Larry the Cable Guy making lame jokes about climate change for the last 90 seconds of his presentation.

Posted in Fake climate sceptics, Silliness, solar variability | Tagged , | 3 Comments

Solar science Heartland style

[I wrote this some time back, but got distracted by Lord Monckton’s inability to use a scroll bar, and then lost momentum. Given Willie Soon’s return to media attention, I thought I should give his presentation a little loving, but for starters, here is an account of Sebastian Lüning’s presentation]

No fake climate skeptic conference would be complete without a session on the sun and the Heartland’s recent jolly did not disappoint, at least in this regard, holding a session on  Solar Science and Climate (video) with Dr. Sebastian Lüning, Dr. Habibullo Abdussamatov, and Dr. Willie Soon discussing the latest and best evidence. The session was a bit of a mix, the speakers got some thing right – for example the title of this post – some things wrong, and some things weird.

Lüning thought it necessary to remind the audience that 99.98% of the energy in the climate system comes from the sun, and then goes through the list of periodicities present in solar activity, suggesting that changing the solar energy “slightly” will have “an effect” on the climate system. He then moves to Soon’s recent paper which identifies “fundamental solar modes at 2300-yr (Hallstattzeit), 1000-yr (Eddy), and 500-yr (unnamed) periodicities” in palaeoclimate proxy data. Unfortunately it appears that Soon et al got some of their proxies upside-down so their claim that the reconstructions are in phase is dubious. I wrote to Soon to inform him of this problem months ago but have not received a response.

Moving swiftly on, Lüning compares the IPCC figures on radiative forcing from greenhouse gases and solar variability and argues that because he wrote a book with “sixty pages of references” collating lots of dubious examples of sun-climate interactions (it even cites the absurd di Rita 2013), the IPCC numbers should be doubted. That’s not exactly how he phrased it.

He claims that search “solar forcing” gets 22683 hits on science direct (the most recent of these is “Phenolic profiles in leaves of chicory cultivars (Cichorium intybus L.) as influenced by organic and mineral fertilizers” – perhaps not so relevant – only 1717 include the string “solar forcing”) and complains that these papers are not covered by the mainstream media – “the scientist, they write they papers but they don’t go public because then they fear their funding will be cut” (6:58). This is unevidenced conspiracy-theory feeding nonsense.

Enough with the quantity of evidence, what about the quality. Lüning cites four papers as evidence of the importance of solar forcing.

  • Steinhilber et al (2012) combine records of the cosmogenic isotopes 14C and 10Be to make a Holocene length reconstruction of solar activity and correlate this with the oxygen isotope record from Dongge cave, China. The correlation is remarkably good for a solar-palaeoclimate correlation (r=0.3), but this record is not chosen by accident, it was selected because it was known to have a good correlation with 14C. Does anyone see any potential problems with that? Haam and Huybers (2010) show that the correlation between the Dongge cave records and 14C record is not significant. The significance levels of the spectral analyses in Steinhilber et al seem to be against a white noise null hypothesis. Given that the spectra look red, this is going to give a huge risk of type 1 errors.  Contrary to what Lüning claims (8:05), the solar-palaeoclimate correlation is announced in the abstract of this paper.

    (A) solar activity (blue) and δ18O from Dongge cave, China (green). Both records have been detrended. (B) Wavelet of solar activity (TSI). (C) Wavelet coherence of solar activity (TSI) and δ18O. De Vries cycle at approximately 210 y and Eddy cycle at approximately 1,000 y are marked with horizontal, grey dashed lines. Arrows pointing to the right indicate that the records are in phase. Black boundaries mark the 95% significance level.

  • Junginger et al (2014) includes excellent and innovative work reconstructing the lake level of palaeo-lake Suguta in the Kenyan rift valley, but then has a severe attach of weasels, correlating the lake level curve with the solar activity record from Solanki et al. (2004). The correlation is not particularly persuasive, especially when the chronological uncertainty is considered. The 14C dates are mostly on mollusc shells and need to be corrected for an old carbon effect estimate from the offset between 14C dates on contemporaneous charcoal and shells. The offset varies between 1700 and 2200 years in the four samples where it was estimated. The mean offset is used, with no allowance for uncertainty. I would argue, therefore, that the uncertainty on their dates has been underestimated by about 200 years, which is, coincidentally, about the length of the wiggles they were trying to correlate. Not impressed.Lake level and solar activity.  Junginger et al (2014)
  • Moffa-Sanchez et al (2014) link salinity changes in the Atlantic  over the last thousand years with solar variability. I find their attribution of the variability to solar rather than volcanic forcing less than convincing, but otherwise it is a good paper . There remains however, the problem of publication bias.
  • Ogurtsov and Oinonen (2014) report that “Greenland and Antarctic nitrate correlate at least fairly significantly with the Gleissberg cycle”. The authors argue that cosmic radiation produces nitrate in the atmosphere – no climate link – so this paper is perhaps not the most convincing evidence of a solar-climate link.

Of Lüning’s list of selected papers, only one is good evidence for a solar-climate link. Is this the best he could do?

Lüning tries to argue that current climate change is all part of a cycle and that the sun is now in an active phase so must be responsible. Of course the Medieval Climate Anomaly is discussed, showing figure 5.7 from the IPCC AR5 and arguing that models cannot reproduce the MCA. If only he had read as far as figure 5.8.

Somehow Lüning manages to avoid the details of how small changes in solar activity can be responsible for large changes in climate, but that radiative forcing from CO2 is almost irrelevant. Perhaps the next speakers will grasp the nettle.

Abdussamatov‘s talk is awful. Watch it if you don’t believe me (it is stuffed full of irrelevant and long-refuted fake climate sceptic talking points). I’m going to skip over it and go directly to Willie Soon’s talk in the next post.

 

Posted in climate, Fake climate sceptics, Peer reviewed literature, solar variability | Tagged , | 1 Comment

Diatoms, running correlations and solar variability

Almost two years ago, I wrote a post about running correlations and their problems. It is still a well read post. I wish it was better read.

It is not that running correlation cannot be be a useful tool. If a good correlation has been found between two variables, it can be useful to test how consistent this correlation is over time. But if the correlation between two variables is weak and non-significant, then running correlations risks being a data dredging technique.

Case in point: Jiang et al (2015) who reconstruct Holocene sea-surface temperatures (SST) just north of Iceland using diatoms and relate the variability in SST to cosmogenic isotopes (an indicator of solar variability) using running correlation.

The abstract starts

Mounting evidence from proxy records suggests that variations in solar activity have played a significant role in triggering past climate changes.

As readers of my critical evaluations of papers reporting of solar-palaeoecology links will know, much of this “mounting evidence” is weak. How robust is Jiang et al?

Lets start with a detour into the transfer function that Jiang et al use to reconstruct SST from their fossil diatom assemblage. For reasons I don’t understand, they cite me (Telford and Birks 2009) when reporting that they test six transfer function methods. We didn’t and testing so many methods this risks a model selection bias (Telford et al 2004). In a previous paper, Jiang et al (2005) cite Juggins and ter Braak (1992) for an identical phrase about six methods.

Jiang et al (2015) settle on a four component weighted average-partial least squares model. They claim this is a parsimonious choice having used a five component model in Jiang et al (2005). I suspect using so many components (rare to need more than two) means that they have a spatial autocorrelation problem although the modern analogue technique (which normally does well in such cases) performs surprisingly badly relative to the other methods. It would have been good if they had tested if there was a spatial autocorrelation problem – code is available.

The choice of a four component WAPLS model won’t bias the results, but it might make the model performance appear better than it really is and make the reconstruction more variable. As it is, almost all the high frequency variability in the SST reconstruction is less than ±1°C, about the same at the root mean square error of prediction, so potentially a lot of this variability is just noise.

The chronology is based on tephra layers, circumventing any problems with a variable radiocarbon reservoir effect, and the sedimentation rate is fairly linear over most of the Holocene. The chronology is as good as chronologies get for marine cores, but still the chronological uncertainty on the pre-settlement tephras is about 100 years, enough to matter for a high resolution correlation.

What about the relationship between the reconstruction and solar activity? Jiang et al start by showing that the long term trends match the orbitally driven decline in summer insolation, as do many proxy records of summer temperature in the North Atlantic region. Next they compare the reconstruction with cosmogenic isotopes, detrending both records with a 6th order polynomial and then using a 50 year lowpass filter to remove high frequency variability. (There must also be an undocumented interpolation step to even temporal spacing.)

Jiang et al Figure 4. Figure 4. Comparison of the 14C production rate (Muscheler et  al., 2005; Reimer et al., 2009) and the reconstructed summer  sea-surface temperature (SST) data from core MD99–2275 (50 yr  averages). Both records were detrended by removing a 6th order  polynomial fitted to the data and low-pass filtered to remove  high-frequency variations on time scales shorter than 50 yr.

Jiang et al Figure 4. Figure 4. Comparison of the 14C production rate (Muscheler et
al., 2005; Reimer et al., 2009) and the reconstructed summer sea-surface temperature (SST) data from core MD99–2275 (50 yr averages).

For the period 9500-4500 BP, there is no obvious correlation between SST and the solar proxy. For the period 4500-0 BP at least some of the wiggles align, as would be expected for smoothed data. Jiang et al don’t report or test the overall correlations between the solar activity proxy and the SST reconstruction, instead they proceed directly to a running correlation. Jiang et al base their significance level of the running correlation on a Monte Carlo test using surrogate time series with the same temporal autocorrelation as the SST reconstruction (they use Ebisuzaki’s (1997) phase randomisation method). This is good: often correlations are either not quantified or autocorrelation is ignored (eg Jiang et al 2005). However, Jiang et al. (2012) do not take account of the multiple testing inherent in a running correlation.

Jiang et al Figure 5 Comparison of proxy records of solar forcing and reconstructed summer sea-surface temperatures (SSTs) from core MD99–2275. A: Direct comparison of  band-pass filtered (1/1800 yr to 1/500 yr) and linearly detrended 14C and SST data. B: The same comparison between summer SST and 10Be fluxes to Summit, Greenland.  C: Running correlation coefficient between 14C production rate and SST reconstruction shown in Figure 4 (2000-yr long windows moved in steps of 100 yr). D: Result of a significance analysis indicating highly significant negative  correlations for the past ~4000 yr. The analysis included  a random phase test that takes into account the autocorrelations present in the time series (Ebisuzaki, 1997).

Jiang et al Figure 5 Comparison of proxy records of solar forcing and reconstructed summer sea-surface temperatures (SSTs) from core MD99–2275. A: Direct comparison of band-pass filtered (1/1800 yr to 1/500 yr) and linearly detrended 14C and SST data. B: The same comparison between SST and 10Be fluxes to Summit, Greenland. C: Running correlation coefficient between 14C production rate and SST shown in Fig. 4 (2000-yr long windows moved in steps of 100 yr). D: Result of a significance analysis indicating highly significant negative correlations for the past ~4000 yr.

How serious a problem is multiple testing for Jiang et al? I’ve repeated their analysis as well as I can (they leave several details undocumented – how was SST interpolated (sampling resolution varies from 2 to >50 years), what filter did they use for the low pass). I find the absolute maximum correlation in a running correlation with a window width of 2000 years, step size 100 years, for 1000 phase-randomised detrended-SST surrogates. The 95th percentile of this null distribution is 0.44. Almost exactly the same as the absolute maximum correlation of Jiang et al’s running correlation. Rather than suggesting a strong link between solar activity and SST over the last 4000 years, Jiang et al’s result is on the cusp of statistical significance at the p=0.05 level. Not the worst result possible, but it makes their story less persuasive. My choice of methodological details may have affected the significance threshold somewhat.

Jiang et al also run a spectral analysis that finds several peaks that are close to some of the solar cycle frequencies, but not others.

Of course Jiang et al have an explanation of why their reconstruction is only sensitive to solar variability some of the time (more sensitive in cool climates). However plausible these explanations are, without supporting evidence we have to ask whether a more parsimonious explanation is that the on-off correlation between solar activity and the SST construction is due to chance.

(hat tip to Kaustubh Thirumalai @holy_kau)

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

New version of palaeoSig

A new version of my R package palaeoSig has been approved by CRAN! It always feels like a major achievement to get everything working and pass all the tests.

The package provides some diagnostics to test whether quantitiative palaeoenvironmental reconstructions based on microfossil assemblages are likely to be robust.

So what’s new in version 1.1-3?

  • Bug fixes. Partialling reconstructions out now works again.
  • A new function for plotting the coverage of the fossil assemblages by the modern calibration set based on this blog post, and  a centipede plot showing the weighted average and tolerance of the species in the calibration set.
  • The package now includes slightly modified versions of the age-depth modelling procedure from Heegaard et al 2005 as I didn’t have a working version. I would generally be inclined to use a Bayesian sedimentation model (for example in OxCal or Bacon) as these are more powerful when there is some prior knowledge, but my tests show that these perform about as well as the mixed-effect model in Heegaard et al.
  • Functions for simulating species assemblages along environmental gradients based on Minchin’s (1985) COMPAS are included. These are useful for generating realistic looking species sets with known properties to test statistical methods. I used an earlier version of these functions to test the effect of uneven sampling on transfer function. Gavin Simpson’s coenocliner is similarly motivated.
  • Due to advances in the gstat library, in particular this package working with geodesic distances, some functions in palaeoSig for simulating surrogate autocorrelated environmental variables are now redundant and have been removed.

I meant to include functions for calculating segment-wise RMSEP, but forgot. That will have to go in the next version. If I get inspired, I’ll give some examples of how the package can be used over the next month or so.

Posted in R, transfer function | Tagged | 1 Comment

Have Lüdecke et al made a solar-cycle generator?

H.-J. Lüdecke, a member of a German climate skeptic group who had an interesting paper published in Climate of the Past last year has a paper in Climate of the Past Discussion awaiting peer review. As with the previous paper, it uses spectral analysis to make inferences about the Earth’s climate.

Lüdecke et al 2015 seek to detect de Vries’s ~200-year solar cycle in three annually resolved palaeoclimate time series that are in excess of 2000 years long, and the reconstruction of solar activity from Steinhilber et al (2012). Of course they find de Vries’s cycles, so what matters is whether their methods are reasonable. I may have misinterpreted what Lüdecke et al have done, so please correct me if I have got it wrong.

Rather than simply running a Fourier transform on the raw reconstructions, they first process the data. They fit a linear regression to a window containing the first 100 years of data to find the slope and multiply this by 100. They move their window along by one year and repeat the analysis. This gives them the moving rate-of-change of the proxy in units per 100 years.

This moving rate-of-change is a filter that alters the spectra of the data: high frequency variability is suppressed. It can be reasonable to use a filter before spectral analysis, but there are good filters and bad filters with generate lots of spurious spectral signals. Which is Lüdecke et al’s?

I’m going to generate some white noise and apply Lüdecke’s method to it.

library(gtools)
z<-1:100
x<-rnorm(2000)
#x<-arima.sim(list(ar=.7), n=2000)

x2<-running(x, fun=function(a){coef(lm(a~z))[2]*100}, width=100)


spectrum(x)#Flat
sx2<-spectrum(x2)

x11(4,height=6)
par(mfrow=c(2,1), mar=c(3,3,1,1), mgp=c(1.5,.5,0))
plot(x, type="l", xlab="Time", ylab="Value")
lines(x2, type="l", col=2)
plot(sx2$fre, sx2$spe, xlim=c(0, 1/10) , type="l" , log="y", 
  xlab="Frequency", ylab="Spectrum")
abline(v=1/200,col=4)
Upper panel: white noise (black) with Lüdecke-filter (red).  Lower panel: Periodiogram of Lüdecke-filtered white noise.

Upper panel: white noise (black) with Lüdecke-filter (red).
Lower panel: Periodiogram of Lüdecke-filtered white noise. Vertical blue line indicates 200 yr cycle.

In the time domain, Lüdecke-filtered white noise shows considerable centennial variability. In the spectral domain, Lüdecke-filtered white noise shows a spectral peak near 200 yr. A de Vries cycle in white noise? With red noise (ar(1) = 0.7) the result is similar.

Lüdecke et al construct confidence intervals for their spectra from analysis of 10 000
random time series with the same lengths and Hurst exponents as the proxy series. In principal this ought to correct for any artefacts from the filter. However, it is not clear that Lüdecke et al apply their filter to the simulated data. I say that first because they do not state that they do, and second because their confidence intervals lack the oscillating structure that is apparent in the periodiogram of Lüdecke-filtered white noise (this structure becomes much clearer when several periodiograms on Lüdecke-filtered white noise are averaged together).

The spectra of the four time series analysed by Lüdecke et al.

The spectra of the four time series analysed by Lüdecke et al.

z<-1:100
n100<-replicate(100,{
  x<-rnorm(2000)
  #x<-arima.sim(list(ar=.7), n=2000)
  x2<-running(x, fun=function(a){coef(lm(a~z))[2]*100}, width=100)
  sx2<-spectrum(x2)
  sx2$spec
})

x11(4,4);par(mar=c(3,3,1,1), mgp=c(1.5,.5,0))
plot(sx2$fre, rowMeans(n100), type="l", log="y", xlim=c(0,0.1), 
  xlab="Frequency", ylab="Spectrum", ylim=c(10E-4, 22))
lines(sx2$fre, apply(n100,1,quantile, prob=.95), col=2)
abline(v=1/200,col=4)
legend("topright", legend=c("Mean spectra", 
  "95% of distribution"), col=1:2, lty=1, bty="n")
Mean periodiogram of 100 Lüdecke-filtered white noise time series (black) and the 95th percentile of this distribution.

Mean periodiogram of 100 Lüdecke-filtered white noise time series (black) and the 95th percentile of this distribution.

If the spectra of the surrogate time series have not been treated in the same way as the proxies, the confidence intervals will be invalid.

Another curiosity in Lüdecke et al’s analysis is the padding of the data with 25000 zeros. This will allow them to have a much higher spectral resolution for the figures, but will not give more information.

The paper goes on to fit sine curves to the filtered data, and make projections for the next century (cooling of course), but if the first part of the paper falls, the whole paper falls.


Lüdecke, H.-J., Weiss, C.O. & A. Hempelmann, A. (2015) Paleoclimate forcing by the solar De Vries/Suess cycle. Clim. Past Discuss., 11, 279–305.

Posted in Peer reviewed literature, R, solar variability | Tagged | 8 Comments

Applying for a job abroad? Explain your degree grading system

Hermione Granger: “So top grade’s O for ‘Outstanding,’ and then there’s A —”
George Weasley: “No, E. E for ‘Exceeds Expectations.’ And I’ve always thought Fred and I should’ve got E in everything, because we exceeded expectations just by turning up for the exams.”

Harry Potter and the Order of the Phoenix

When you have spent years in a university system and are proud (hopefully) of the grade you have achieved, it is easy to forget that people reading your job applications in another country may find your grading system as obvious as that at Hogwarts.

I’m currently evaluating three dozen applicants for a PhD position. The candidates are from over twenty countries, and several have degrees from more than one country. There are a lot of different grading systems, some I am familiar with, others not.

Should I be impressed by your GPA of 18.02? If it is a percentage, it looks awfully low!

One university uses a Latin scale for grading theses:

  • Approbatur
  • Lubenter approbatur
  • Non sine laude approbatur
  • Cum laude approbatur
  • Magna cum laude approbatur
  • Eximia cum laude approbatur
  • Laudatur

Ego autem non intellego Latin. Is Approbatur equivalent  to Outstanding or Troll? Without checking Google Translate, I have to assume (initially at least) that a candidate applying for a PhD position with a thesis graded Eximia cum laude approbatur has Exceeded Expectations rather than been labelled Dreadful.

Another university awards a grade between 6.0 and 10.0, without specifying whether high or low ends of the scale are more praiseworthy. You can be sure that another university will have the scale in the opposite direction.

The University of Bergen now has a A (high) – F (low) scale. It used to be much more interesting, with a 1 (high) – 4 (low) scale, except in the Law faculty 2.15 (high) – 3.15 (low) and Medical faculty 12 (high) – 6 (low). No scope for confusing students taking joint degrees then.

Even percent can be complicated, as different systems have different expectations for 70%.

I’ve seen other systems that rival the O-E-A-P-D-T from Hogwarts in terms of weirdness. Of course, graduates from these systems don’t realise that their grading scheme is unusual.

It is simply not possible to remember all these schemes, so applicants should help the evaluator by including an explanation of the grading system. Some universities, including Bergen, do a good job here, appending a description of the grades to the transcript. The most recent graduates even get a small barplot next to the grade for each course showing the distribution of grades. If you don’t have an official description (or maybe even if you do), please put a short comment in your CV to explain the grading system.

Note, when I evaluate applications, I do not slavishly follow the grades, but I do need to check that shortlisted candidates meet the University of Bergen criteria, and am not necessarily that interested in a student awarded a Desmond.

 

 

 

 

Posted in Uncategorized | 5 Comments

Strange spectral methods find solar signal in alkenes

Via the Club du Soleil, I’ve found another paper using spectral analyses to find a solar signal in palaeoecological data.

Working on sediment from Lake Challa, a deep crater lake near Mt. Kilimanjaro in Tanzania that has been used in several studies, van Bree et al. (2014) use the ratio of two alkenes of different lengths as a palaeoecological proxy. Such biomarkers can be easy to measure (compared to counting pollen or diatoms), but are hard to interpret as their source is not well known. van Bree et al show that they are probably formed within the lake, perhaps by green algae. How green algae will respond to environmental change is less well known than for some other taxonomic groups.

(A) Total (combined) accumulation of n-C25:1 and n-C27:1 alkenes (mg m−2 yr−1). (B) The Alkene Index, defined as [n-C27:1]/([n-C25:1] + [n-C27:1]). (C) The δDwax record (‰ vs. VSMOW) on a reversed axis to highlight negative anomalies as episodes of inferred drought; adapted from Tierney et al. (2011). (D) The BIT-index, three-point moving average ( Verschuren et al., 2009). (E) Lake Challa lake-level record derived from seismic-reflection data ( Moernaut et al., 2010). Shaded areas represent Heinrich events H1 (16.8–15.4 kyr BP) and H2 (around 24 kyr BP), LGM (26.5–19 kyr BP) and YD (13–11.5 kyr BP).

Fig 1. (A) Total accumulation of n-C25:1 and n-C27:1 alkenes (mg m−2 yr−1). (B) Alkene Index, defined as [n-C27:1]/([n-C25:1] + [n-C27:1]). (C) δDwax record (‰ vs. VSMOW) on a reversed axis to highlight negative anomalies as episodes of inferred drought; adapted from Tierney et al. (2011). (D) The BIT-index, three-point moving average ( Verschuren et al., 2009). (E) Lake Challa lake-level record derived from seismic-reflection data ( Moernaut et al., 2010). Shaded areas represent Heinrich events H1 (16.8–15.4 kyr BP) and H2 (around 24 kyr BP), LGM (26.5–19 kyr BP) and YD (13–11.5 kyr BP).

The rising trend in alkene concentrations towards the present is suggestive of a degradation signal. The ratio of the C27 Alkenes to C25 + C27 Alkenes (the Alkene Index) appears to have some features in common with the δD and BIT, both indicators of hydroclimate, but the authors admit that the correlations are ambiguous. The paper could have ended here, and it would have been a solid investigation of the utility of alkenes as a palaeoenvironomental proxy.

But no, the authors chose to run a spectral analysis.

Spectral analysis of the proxy data set was undertaken using AnalySeries software (Paillard et al., 1996). The Alkene Index record was detrended using a polynomial function, interpolated to a constant ∼200-yr interval and analyzed with the Blackman–Tukey method. Frequencies around ∼2.3 kyr were filtered from the record (Gaussian filter centered at 0.00044, bandwidth 0.0001) excluding superimposed low and high frequencies. REDFIT analysis was conducted with PAST software (Hammer et al., 2001) for significance estimation.

This is a strange methodology mixing together different spectral methods. It is not in the least clear how or why the Blackman-Tukey method was used together with REDFIT, which uses the Lomb-Scargle transform directly on unevenly spaced data. The Gaussian filter is presumably (hopefully) not used in the spectral analysis, despite its position in the paragraph, but for the filtered records shown in figure 2B & C.  The polynomial detrending is rather vague – it could be anything from a linear detrend to a high order polynomial. Without knowing the order of the polynomial used, we cannot tell what effect it will have on the spectral analysis. This would not be the first paper with a confusing description of the spectral methods.

(A) REDFIT power spectrum estimation of the 25 kyr Alkene Index record from Lake Challa, revealing a main frequency of 2.3 kyr (p = 0.0062). The dashed line represents the 99% significance level. Spectral analysis of resampled, detrended (gray lines) and filtered records (black lines) of (B) the Challa Alkene Index and (C) the difference of total solar irradiance (ΔTSI; in W m−2; modified from Steinhilber et al., 2009). Shaded areas represent Heinrich events H1 (16.8–15.4 kyr BP) and H2 (around 24 kyr BP), LGM (26.5–19 kyr BP) and YD (13–11.5 kyr BP).

Fig 2. (A) REDFIT power spectrum estimation of the 25 kyr Alkene Index record from Lake Challa, revealing a main frequency of 2.3 kyr (p = 0.0062). The dashed line represents the 99% significance level. Spectral analysis of resampled, detrended (gray lines) and filtered records (black lines) of (B) the Challa Alkene Index and (C) the difference of total solar irradiance (ΔTSI; in W m−2; modified from Steinhilber et al., 2009). Shaded areas represent Heinrich events H1 (16.8–15.4 kyr BP) and H2 (around 24 kyr BP), LGM (26.5–19 kyr BP) and YD (13–11.5 kyr BP).

The spectral analysis finds a strong 2.3 kyr cycle which is then linked to the Hallstattzeit solar cycle (∼2.1 to ∼2.4 kyr). Without understanding the methods, I don’t know how excited I should be about this cycle exceeding the 99% significance level. I do note, however, that the allegedly solar-driven cycle captures the Younger Dryas which is normally linked to freshwater fluxes from melting ice sheets rather than solar activity.

No correlation coefficient is given for the relationship between the filtered Alkene Index and total solar irradiance. The correlation looks good – but filtered records tend to appear to be highly correlated. Without a test of the significance of this correlation (probably involving surrogate proxies) it is difficult to get excited about this result.

Overall, I don’t find this persuasive evidence of a strong solar-climate relationship. I also start to wonder what the evidence is for the Hallstattzeit cycle. Perhaps more to come on this.


van Bree et al. 2014. Origin and palaeoenvironmental significance of C25 and C27n-alk-1-enes in a 25,000-year lake-sedimentary record from equatorial East Africa. Geochimica et Cosmochimica Acta, 145, 89–102.

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