This is part of my critical review of the palaeoenvironmental evidence for the influence of solar activity on climate.
Di Rita (2013) A possible solar pacemaker for Holocene fluctuations of a salt-marsh in southern Italy. Quaternary International, 288, 239–248
Di Rita (2013) investigates the pollen assemblages in a sediment core from a coastal wetland next to the Adriatic Sea. The paper reports large fluctuations in pollen percentages of the salt marsh indicators Salicornia and Ruppia maritima and uses wavelet analysis to find periodicities of 130 and 260 years, which are consistent with solar forcing. Further, the minima in the Salicornia record coincide with minima in the GISP2 10Be record.
It is not immediately obvious why one would a priori select the percentages of salt-marsh herb pollen as a proxy likely to be sensitive to solar activity (Helianthus annuus, I would understand). Reading the introduction, it turns out that the proxy was not selected a priori, but that the cycles were first observed in the proxy, and the cycles were then tested for possible solar influence. A major problem with a post hoc analysis like this is that the Type I error rate — the probability of incorrectly rejecting the null hypothesis — is greatly inflated: random but interesting looking patterns are much more likely to appear statistically significant than other random data. This alone should make us cautious about accepting the conclusions of Di Rita (2013). Post hoc analyses are far from the only doubt-inducing issue with Di Rita (2013).
Di Rita (2013) uses wavelet analysis on the Salicornia pollen curve. Wavelet analysis is a useful tool to visualise the time and frequency distribution of cycles in the data. The data are interpolated to ten year intervals, greatly in excess of the actual data resolution. This will greatly increase the apparent number of degrees of freedom available, making cycles appear more significant than they are, and induce strong temporal autocorrelation in the data. It is not discussed how these issues will affect the wavelet analysis, the reader is not even told if the 95% significance level in the wavelet plot is based on a red noise or a white noise null model. Nor is the reader told which software was used. The wavelet analysis is not applied to the entire Salicornia record, nor even the whole period when the wetland was a lagoon, instead it is applied to a shorter section where “fluctuations are especially pronounced” between 4800 BP and 6350 BP (the base of the core). Regular might have been a more appropriate word than pronounced, as the fluctuations after 4800 BP are as large as those before.
So the wavelet results are from a post hoc analysis of a carefully selected section of a selected pollen profile using methods that might be prone to exaggerate the evidence for strong cyclicity. All this could be ignored and I would still be sceptical about the significance of solar-frequencies in the pollen data. When discussing Chen et al (2011; 2013) I argued that an 11 year cycle in noisy proxy data cannot be considered evidence of solar forcing of climate as such cycles are not evident in analyses of long instrumental records. The same argument cannot be used against the longer cycles found by Di Rita (2013), the instrumental record is just not long enough to detect periods this long. Instead the problem is that there are too many possible solar frequencies. From Di Rita (2013):
Ogurtsov et al. (2002) demonstrated that the DeVries–Suess cycle shows a variation with a period of 170–260 years, while the Gleissberg cycle is even more complex presenting a wide frequency band with a double structure that consists in 50–80 year and 90–140 year periodicities.
The majority (80%) of periods between 50 and 260 years fall within either the DeVries–Suess or Gleissberg frequency bands, so the majority of proxy cycles, whether real or otherwise, whether solar-forced or otherwise, will appear consistent with solar frequencies. As cycles of any cause are likely to have a frequency within the solar frequency bands, finding a cycle within these bands in scant evidence for solar forcing.
To make a spectral analysis more credible, we need to demonstrate that the proxy not only has the right frequencies, but that these are in phase with solar forcing. Di Rita (2013) attempts this, finding that the minima in the pollen profiles match minima in the 10Be GISP2 record within 50 years. But the 10Be record is spiky: there are many minima between 6350 and 4000 BP, a minority of these coincide with minima in the pollen record. Maxima in the pollen data match the main maxima in the 10Be data rather poorly. No null model is attempted to demonstrate that the observed minima matching is better than expected by chance, and no consideration is given to the chronological uncertainty in both records (indeed the presence of the 260 year cycle is used in Di Rita et al ( 2011) as evidence that the age-depth model is appropriate).
A salt marsh is a dynamic environment. Over the 2300 years investigated by Di Rita (2013), sediment accumulated (four metres in Lago Salso), sea level rose by about 2 m and tectonic and isostatic subsidence lowered the land by about a metre, the bar separating the lagoon from the Adriatic would have been more open at some times that others, affecting the salinity and tidal regime of the lagoon. We are asked to believe that Salicornia and Ruppia ignored all this and other noise, to focus on the Suess and Gleissberg cycles. If such a noisy environment could reliably record solar cycles, then we should expect solar cycles in proxies with less non-climatic noise to be as clear as a bell. They are not.
Di Rita (2013) contains no credible evidence of a solar climate link.
But even if Salicornia cannot reconstruct solar activity, there is at least one thing that it can be used for, even if it doesn’t taste very nice.