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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About richard telford

Ecologist with interests in quantitative methods and palaeoenvironments
This entry was posted in Peer reviewed literature, solar variability and tagged , , , , . Bookmark the permalink.

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