An informal look at:

Hansen et al. 2012
Hansen, J., Mki. Sato, and R. Ruedy, 2012: Perception of climate change. Proc. Natl. Acad. Sci., 109, 14726-14727, E2415-E2423, doi:10.1073/pnas.1205276109.

Specifically this graph:


I, like many people, found interesting the broadening and flattening of the bell curve in the latter decades.  As the distribution can also be expressed as  it's standard deviation, I decided to plot the standard deviations for all 11yr moving periods.  To do this, I downloaded the Monthly Anomaly: 250km smoothed land temperature data from this page:

Next I selected only those grid boxes with a full complement of data over the entire period.  This resulted in 1451 of 4768 reporting grid boxes being selected.  I then calculated the standard deviation of the combined June, July, and August anomalies for each 11yr period and plotted the results as follows:    

This appears to match up nicely with Dr. Hansen's graph as the lowest standard deviation is in the 1960's and the highest is in the latter decades.

Next I performed the same exercise for the record starting at the year 1900.  This resulted in selecting 895 grid boxes.  Again, the post 1950 data matches up with Dr. Hansen's graph, but the pre-1950 data shows a period of high variability similar to that of the latter period.

I also plotted the number of stations that make up GISTEMP for any given year with an inverted axis.  The reason for this is that if we averaged a number of time series consisting of standard uncorrelated random white noise, the standard deviation would drop number as the sample size increased.  The standard deviation would be related to the sample size by a factor of 1/sqrt(n).

In this case, however, I'm not convinced that the number of stations had a material impact.  Within each grid box, the station data should be highly correlated and the sample size should have little impact on variability.  Additionally, I can't think of any obvious reason why the number of stations would be both a leading and a trailing indicator of variability.  It could be just a coincidence that the period of low variability had a high number of stations.  I would be interesting, however, to see a temperature record consisting only of long running station data to eliminate this possibility.  Perhaps separate reconstructions for the periods 1930-1960 and 1960-2000.

P.S.  My code also calculated the standard deviations on detrended anomolies.  This flattened the trends, but the general shape was the same.

<<Insert code here after commenting and additional QA>>