Jolliffe (2002) describes many issues in the use of principal component analysis, including principal component regression, as it is used in many areas of science.

The principal components contain maximum information in the sense that the full set of proxies can be reproduced as closely as possible, given only the values of the new variables (Johnson and Wichern 2002, Suppl. 8A). In general, one should judge the set of principal components taken together as a group because they are used together to form a reconstruction. Comparing just single principal components between two different approaches may be misleading. For example, each of the two *groups* of principal components may give equally valid approximations to the full set of proxies. This equivalence can occur without being able to match on a one-to-one basis the principal components in one group with those in a second group.

McIntyre and McKitrick (2003) demonstrated that under some conditions the leading principal component can exhibit a spurious trendlike appearance, which could then lead to a spurious trend in the proxy-based reconstruction. To see how this can happen, suppose that instead of proxy climate data, one simply used a random sample of autocorrelated time series that did not contain a coherent signal. If these simulated proxies are standardized as anomalies with respect to a calibration period and used to form principal components, the first component tends to exhibit a trend, even though the proxies themselves have no common trend. Essentially, the first component tends to capture those proxies that, by chance, show different values between the calibration period and the remainder of the data. If this component is used by itself or in conjunction with a small number of unaffected components to perform reconstruction, the resulting temperature reconstruction may exhibit a trend, even though the individual proxies do not. Figure 9-2 shows the result of a simple simulation along the lines of McIntyre and McKitrick (2003) (the computer code appears in Appendix B). In each simulation, 50 autocorrelated time series of length 600 were constructed, with no coherent signal. Each was centered at the mean of its last 100 values, and the first principal component was found. The figure shows the first components from five such simulations overlaid. Principal components have an arbitrary sign, which was chosen here to make the last 100 values higher on average than the remainder.

Principal components of sample data reflect the shape of the corresponding eigenvectors of the population covariance matrix. The first eigenvector of the covariance matrix for this simulation is the red curve in Figure 9-2, showing the precise form of the spurious trend that the principal component would introduce into the fitted model in this case.

This exercise demonstrates that the baseline with respect to which anomalies are calculated can influence principal components in unanticipated ways. Huybers (2005), commenting on McIntyre and McKitrick (2005a), points out that normalization also affects results, a point that is reinforced by McIntyre and McKitrick (2005b) in their response to Huybers. Principal component calculations are often carried out on a correlation matrix obtained by normalizing each variable by its sample standard deviation. Variables in different physical units clearly require some kind of normalization to bring them to a common scale, but even variables that are physically equivalent or