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The Present Status of Understanding of the Long-Period Spectrum of Radiocarbon

  • Charles P. Sonett

Abstract

Libby’s (1952) discovery of the ubiquitous distribution of radiocarbon in the biosphere provided the basis for a new means of dating of archaeological and paleontological finds. His principle was based upon the then reasonable supposition that the atmospheric reservoir from which radiocarbon was drawn was uniform and unchanging. But with Suess’ discovery of the dilution of the atmospheric reservoir from burning of fossil fuel, evidence for variability began to solidify. For a synopsis, see, eg, Walker (1977). In 1958, some six years after Libby’s discovery, de Vries (1958) and Barker (1958) disclosed variations on the order of 1% during the past 400 years in the atmospheric reservoir. Shortly thereafter, Willis et al (1960) showed that over the past 1200 years the ‘fundamental assumptions’ of radiocarbon dating were empirically correct to ~ 1.5%, but they surmised more. Their results suggested that a residual variability was related to solar physics through modulation of cosmic-ray (CR) activity. Willis et al (1960) also recognized this, as well as the possibility of 150–200-year and 1000-year periods in the atmospheric inventory of radiocarbon. If the CR flux were constant, the atmospheric 14C inventory would be in secular equilibrium. Then radiocarbon would be an absolute archaeological clock, but it would be of lesser interest geophysically, though certifying Libby’s (1952) thesis of a constant atmospheric inventory. Today, a major challenge of radiocarbon research is understanding the spectrum of the radiocarbon variability. The apparently periodic spectral features are suggestive of the presence of natural oscillators in the terrestrial environment, in the Sun or both.

Keywords

Solar Activity Maunder Minimum Sunspot Activity Geomagnetic Dipole Gleissberg Cycle 
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

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  • Charles P. Sonett

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