One of the most vexing things about climate change is the endless debate about temperatures. Did they rise, did they fall or were they pushed? At times it seems like a Monty Python sketch following either the Dead Parrot or the 5 or 10 Minute Argument.

However it is possible to see some of the issues by looking at the correlation of the five temperature series that are advanced by the uppers or the downers.

The five groups are:
1. GISS, The Goddard Institute, home of James Hansen,
2. NCDC, The National Climate Data Center, a part of NOAA (as is GISS), the National Oceanographic and Atmosphere Administration.
3. BMO/UEA, The British Meteorological Office and the University of East Anglia.
4. UAH, The University of Alabama, Huntsville, home of Roy Spencer with his colleagues including John Christy of NASA and
5. RSS, Remote Sensing Systems in Santa Rosa, California, a company supported by NASA for the analysis of satellite data.

The first three groups use ground based data where possible with a degree of commonality. However since 70% of the surface of the earth is ocean and it is not monitored in a detailed manner, various recipes are followed to fill the ocean gap, if that is the best way of putting it.

The last two groups use satellite data to probe the atmosphere and with the exception of the Polar Regions which are less than 10% of the globe, they get comprehensive coverage.

One question is of course are the two groups measuring the same temperature? After all the satellite looks down through the atmosphere, while the ground stations are exactly that.

One of the ways to probe this is to look over time at the degree of correlation achieved in the measurements of the “global temperature anomaly

The results of such a comparison are given in Table 1 for the monthly time series from 1979 to 2008. There is the Pearson correlation coefficient extracted from the data. A value of 1.00 shows the compared values move in step with each other while a value of 0.00 would give complete independence. (A value of-1.00 is also possible.) “Commonality”, the square of the correlation coefficient is interpreted as showing what proportion of one measurement series is covered by the other series. Note that correlation does not imply connection or causality except that we know there is some commonality with ground based measurements.

Table 1.

First a check of the land based measurements shows that two groups are closely aligned, the difference reflecting the different processing to get the global result.

GISS is more problematic with less commonality which must be a reflection of quite different processing assumptions to that of NCDC or BMO/UEA.

For land based measurements we are faced with a “Judgement of Paris” and it is not clear who gets the Golden Apple.

Finally the satellite measurements have a high internal commonality but a commonality of some 50% with the land based measurements.

None of this should be surprising. The land measurements are on the land and subject to a number of uncertainties, such as heat island effects and lack of extensive ocean measurements while the satellites probe the atmosphere but not ground level.

So for the last 8 years the results are in Table 2

Table 2.

It is surprising to see the agreement achieved by two quite independent approaches.

However we should be aware that none of this is simple.   Source

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