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. 2016 Apr 5:6:23961.
doi: 10.1038/srep23961.

Solar cycles or random processes? Evaluating solar variability in Holocene climate records

T Edward Turner  1 , Graeme T Swindles  1 , Dan J Charman  2 , Peter G Langdon  3 , Paul J Morris  1 , Robert K Booth  4 , Lauren E Parry  5 , Jonathan E Nichols  6
Affiliations

Solar cycles or random processes? Evaluating solar variability in Holocene climate records

T Edward Turner et al. Sci Rep. .

Erratum in

Abstract

Many studies have reported evidence for solar-forcing of Holocene climate change across a range of archives. These studies have compared proxy-climate data with records of solar variability (e.g. (14)C or (10)Be), or have used time series analysis to test for the presence of solar-type cycles. This has led to some climate sceptics misrepresenting this literature to argue strongly that solar variability drove the rapid global temperature increase of the twentieth century. As proxy records underpin our understanding of the long-term processes governing climate, they need to be evaluated thoroughly. The peatland archive has become a prominent line of evidence for solar forcing of climate. Here we examine high-resolution peatland proxy climate data to determine whether solar signals are present. We find a wide range of significant periodicities similar to those in records of solar variability: periods between 40-100 years, and 120-140 years are particularly common. However, periodicities similar to those in the data are commonly found in random-walk simulations. Our results demonstrate that solar-type signals can be the product of random variations alone, and that a more critical approach is required for their robust interpretation.

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Figures

Figure 1
Figure 1
(A) Normalised water-table reconstruction from Ballyduff, Derragh, Dead Island, Slieveanorra (Ireland), Butterburn and Malham (England), Minden and Sidney (USA). The record from Great Heath (USA) is Sphagnum/Vascular Ratio based on ratios of leaf wax compounds. A loess smoothing function is illustrated (red line). The chronologies have been modelled using a Bayesian statistical approach (Supplementary Fig. S2). Reconstructed sunspot numbers (Solanki et al.27) and sunspot counts (blue line; source: SILSO data/image, Royal Observatory of Belgium, Brussels), and the combined CO2 record from Mauna Loa, the Law Dome and EPICA Dome C ice cores (See refs in Supplementary Method S1). (B) An example random walk simulation for each site (sampled to the same chronological spacing as the real data) is also shown.
Figure 2
Figure 2. Histograms of significant periodicities present in the data and random walk simulations.
(A) All periodicities in the random walks over 90% false alarm level; (B) All periodicities in the proxy climate records over 90% false alarm level; (C) Highest power periodicities in the proxy climate records over 90% false alarm level; (D) Periodicities with a period ≤500 years in random walks over 90% false alarm level; (E) Periodicities with a period ≤500 years in the proxy climate records over 90% false alarm level; (F) Highest power periodicities in the proxy climate records over 90% false alarm level ≤500 years. Solar cycle bands commonly reported in palaeoclimate literature are illustrated.
Figure 3
Figure 3. Continuous wavelet analysis of (A) the sunspot reconstruction of Solanki et al.; (B) normalised water table reconstruction from Dead Island; (C) Cross-wavelet analysis of (A,B); (D) Random walk simulation sampled to the same chronological spacing as Dead Island; (E) Cross-wavelet analysis of (A,D).
The black lines signify 95% significant levels against a lag1 (red noise) background. Dead Island is given here as an example: for other sites refer to Supplementary Fig. S5.
See this image and copyright information in PMC

References

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