1) Three indices of global climate have been monitored in the record of the past 450,000 years in Southern Hemisphere ocean-floor sediments. 2) Over the frequency range 10(-4) to 10(-5) cycle per year, climatic variance of these records is concentrated in three discrete spectral peaks at periods of 23,000, 42,000, and approximately 100,000 years. These peaks correspond to the dominant periods of the earth's solar orbit, and contain respectively about 10, 25, and 50 percent of the climatic variance. 3) The 42,000-year climatic component has the same period as variations in the obliquity of the earth's axis and retains a constant phase relationship with it. 4) The 23,000-year portion of the variance displays the same periods (about 23,000 and 19,000 years) as the quasi-periodic precession index. 5) The dominant, 100,000-year climatic [See table in the PDF file] component has an average period close to, and is in phase with, orbital eccentricity. Unlike the correlations between climate and the higher-frequency orbital variations (which can be explained on the assumption that the climate system responds linearly to orbital forcing), an explanation of the correlation between climate and eccentricity probably requires an assumption of nonlinearity. 6) It is concluded that changes in the earth's orbital geometry are the fundamental cause of the succession of Quaternary ice ages. 7) A model of future climate based on the observed orbital-climate relationships, but ignoring anthropogenic effects, predicts that the long-term trend over the next sevem thousand years is toward extensive Northern Hemisphere glaciation.

http://science.sciencemag.org/content/sci/194/4270/1121.full.pdf

Variations in the Earth's Orbit: Pacemaker of the Ice Ages

Assembly, configuration, and break-up history of Rodinia: A synthesis

Major and trace element composition of the depleted MORB mantle (DMM)

Least squares fitting of a straight line with correlated errors

The revolution that wasn't: a new interpretation of the origin of modern human behavior.

A detailed discussion of the calculation of the "best straight line" by the method of least squares is given. The most general solution is found and the conditions under which certain previously de...

Least-squares fitting of a straight line.

It has long been recognized that the least-squares estimation method of fitting the best straight line to data points having normally distributed errors yields identical results for the slope and intercept of the line as does the method of maximum likelihood estimation. We show that, contrary to previous understanding, these two methods also give identical results for the standard errors in slope and intercept, provided that the least-squares estimation expressions are evaluated at the least-squares-adjusted points rather than at the observed points as has been done traditionally. This unification of standard errors holds when both x and y observations are subject to correlated errors that vary from point to point. All known correct regression solutions in the literature, including various special cases, can be derived from the original York equations. We present a compact set of equations for the slope, intercept, and newly unified standard errors.

Derek York

Papers

Least squares fitting of a straight line with correlated errors

Least-squares fitting of a straight line.

Unified equations for the slope, intercept, and standard errors of the best straight line

Late PlioceneHomoand Oldowan Tools from the Hadar Formation (Kada Hadar Member), Ethiopia

Geothermometry from 40 Ar/ 39 Ar dating experiments