"I didn't see you address the issue of Mann's deducing temperature from ice cores, tree rings, etc for the 1000 to 1900 part of the graph and using measurements for 1900 onwards"
Give me a break!! I am only one person and each of my replies is composed by me (except as noted). This is more time consuming than just cutting and pasting from a website. For example, post number 83 is a cut and paste directly from CO2 Science. I had to actually try to dig out the paper and when that failed I had to try and see what other work the authors had done so I could get an idea of what they said as opposed to how CO2 Science put the spin on it. Going back to source papers is time consuming but I like to do my own thinking.
In regards to Mann, the way he matched up the records was (and this is a direct quote from Mann's paper):
"We first decompose the twentieth-century instrumental data into its dominant patterns of variability, and subsequently calibrate the individual climate proxy indicators against the time histories of these distinct patterns during their mutual interval of overlap."
The method he used to decompose the instrumental data is the Principle Component Analysis (that is the current area of discussion between McKitrick and Mann). Again, from the paper:
"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’."
I do not agree with your last comment. TO me (from what I understand) it looks like fairly solid science to me. However it does appear (to me anyway) to be fairly complex. You might have the math necessary so that this appears simple, I do not. However I am always willing to learn.
However it does appear (to me anyway) to be fairly complex. You might have the math necessary so that this appears simple, I do not. However I am always willing to learn. 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)?