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Solar Forcing of Global Climate Change?

  • Paul E. Damon
  • John L. Jirikowic

Abstract

Precise analysis of the carbon isotopes in dendrochronologically dated tree rings demonstrates that atmospheric 14C has varied ± 5% during the past nine millennia. These data have been summarized in the “Calibration Issue” of Radiocarbon (Stuiver & Kra 1986). Discrete Fourier Transform (DFT) power spectral analysis of the atmospheric 14C variations, after removal of the longterm geomagnetic field-induced trend, reveals three fundamental periods. By using other independent spectral methods, Damon & Sonett (in press) corroborated these periods — the 2250-year (Hallstattzeit), the 210-year (Suess), and the 88-year (Gleissberg). Collectively, these are known as the de Vries Effect or “Suess Wiggles” (see Damon 1987, for review). Houtermans (1971) first reported the two longer periods, with the exception of the Gleissberg cycle, from Fourier analysis of the La Jolla 14C data set. Suess (1980) has repeatedly stressed the persistence and solar origin of the 210-year period. Stuiver and Braziunas (1989) observed all of these lines in both the DFT and Maximum Entropy Method (MEM) spectra of the high-precision 14C data, but considered the two short periods to be harmonics of a near 425-year fundamental. However, even if this hypothesis is correct, both DFT and MEM power spectra show the resolved hypothetical fundamental to be weak in comparison to its harmonics. A weak 11-year period (Schwabe cycle) emerges from spectral analysis of high-precision 14C data of single annual tree rings. This cycle is usually obscured, however, by solar-flare-produced 14C, which tends to be out of phase during the Schwabe cycle. The 14C component is produced by galactic cosmic rays modulated by the solar wind (Damon, Cheng & Linick 1989).

Keywords

Solar Activity Solar Cycle Discrete Fourier Transform Tree Ring Camp Century 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science+Business Media New York 1992

Authors and Affiliations

  • Paul E. Damon
  • John L. Jirikowic

There are no affiliations available

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