Your earlier appeal to authority has now turned into an appeal of ignorance, an interesting change of rhetoric.
"We isolate the dominant patterns of the instrumental surface temperature data through principal component analysis (PCA). PCA provides a natural smoothing of the temperature field in terms of a small number of dominant patterns of variability or `empirical eigenvectors'."
Let's see if we can figure this out together. An vector is a measurement of each dimension of orthogonal values, such values would be measurements such as tree ring width, temperature, etc. Vectors denote a point in the space made up of those axes, that point relates the various measurements. For example a wider tree ring combined with a higher temperature means in practical terms that the tree grew more in a warmer year. As stated by Mann and his critics, the only way such relationships can be determined is by using temperature measurements made in the 1900-2000 century. Then Mann applied those relationships to the previous known measurements (tree rings, ice cores and one other that I forgot).
The potential problems with this method are numerous. Foremost, the data can be cherry picked. Apparantly Mann used just nine locations out of all the available data, just 5 for North America. Surely much more data could have been used. Second, other factors could skew the relationship between the measurements. In the case of tree rings there is certainly a man-made component of increased CO2 that would cause increased tree growth for a given temperature. Third, other factors can easily be ignored, How would sun intensity affect plant growth, how would that be measured in history and how would that relate to temperature (as we must certainly agree it does)?