Guest post by Clive Best

Perhaps like me you have wondered why “global warming” is always measured using temperature “anomalies” rather than by directly measuring the absolute temperatures ?

Why can’t we simply average the surface station data together to get one global temperature for the Earth each year ? The main argument to work with anomalies (quoting from the CRU website) is: ”Stations on land are at different elevations, and different countries estimate average monthly temperatures using different methods and formulae. To avoid biases that could result from these problems, monthly average temperatures are reduced to anomalies from the period with best coverage (1961-90)….” In other words although measuring an average temperature is “biased”, measuring an average anomaly (deltaT) is not. Each monthly station anomaly is actually the difference between the measured monthly temperature and so-called “normal” monthly values. In the case of Hadley Cru the normal values are the 12 monthly averages from 1961 to 1990.

The basic assumption is that global warming is a universal, location independent phenomenon which can be measured by averaging all station anomalies wherever they might be distributed. Underlying all this of course is the belief that CO2 forcing and hence warming is everywhere the same. In principal this also implies that global warming could be measured by just one station alone. How reasonable is this assumption and could the anomalies themselves depend on the way the monthly “normals” are derived?

Despite temperatures in Tibet being far lower than say the Canary Islands at similar latitudes, local average temperatures for each place on Earth must exist. The temperature anomalies are themselves calculated using an area-weighted yearly average over a 5×5 degree (lat,lon) grid. Exactly the same calculation can be made for the temperature measurements in the same 5×5 grid which then reflect the average surface temperature over the Earth’s topography. In fact the assumption that it is possible to measure a globally averaged temperature “anomaly” or DT also implies that there must be a globally averaged surface temperature relative to which this anomaly refers. The result calculated in this way for the CRUTEM3 data is shown below:

Fig1: Globally averaged temperatures based on CRUTEM3 Station Data

So why is this never shown ?

The main reason for this I believe is that averaged temperatures highlight something different about the station data. They instead reflect an evolving bias in the geographic sampling of the station data used over the last 160 years. To look into this I have been working with all station data available here and adapting the PERL programs kindly included. The two figures below show the location of stations used dating from 1860 compared to all stations.

Fig 2: Location of all stations in the Hadley Cru set. Stations with long time series are marked with slightly larger red dots.

Fig 3: Stations with data back before 1860

Note how in Figure 1 there is a step rise in temperatures for both hemispheres around 1952. This coincides with a sudden expansion in included land station data as shown below. Only after this time does the data properly cover the warmer tropical regions, although there still remain gaps in some areas. The average temperature rises because gaps for grid points in tropical areas are now filled. (There is no allowance made in the averaging for empty grid points neither for average anomalies nor temperatures). The conclusion is that systematic problems due to poor geographic coverage of stations affects average temperature measurements prior to around 1950.

Fig 4: Percentage of points on a 5×5 degree grid with at least one station. 30 % is roughly the land surface of Earth

Can empty grid points similarly affect the anomalies? The argument against this, as discussed above, is that we measure just the changes in temperature and these should be independent of any location bias i.e. CO2 concentrations rise the same everywhere ! However it is still possible that the monthly averaging itself introduces biases. To look into this I calculated a new set of monthly normals and then recalculated all the global anomalies. The new monthly normals are calculated by taking the monthly averages of all the stations within the same (lat,lon) grid point. These represent the local means of monthly temperatures over the full period, and each station then contributes to its near neighbours. The anomalies are area-weighted and averaged in the same way as before. The new results are shown below and compared to the standard CRUTEM3 result.

Fig5: Comparison of standard CRUTEM3 anomalies(BLACK) and anomalies calculated using monthly normals averaged per grid point rather than averaged per station (BLUE).

The anomalies are significantly warmer for early years (before about 1920), changing the apparent trend. Therefore systematic errors due to the normalisation method for temperature anomalies are of the order of 0.4 degrees in the 19th century. The origin of these errors is due to the poor geographic coverage in early station data and the method used to normalise the monthly dependences. Using monthly normals averaged per lat,lon grid point instead of per station causes the resultant temperature anomalies to be warmer before 1920. Early stations are concentrated in Europe and North America, with poor coverage in Africa and the tropics. After about 1920 these systematic effects disappear. My conclusion is that anomaly measurements before 1920 are unreliable, while those after 1920 are reliable and independent of normalisation method. This reduces evidence of AGW since 1850 from a quoted 0.8 +- 0.1 degrees to about 0.4 +- 0.2 degrees

Note: You can view all the station data through a single interface here or in 3 time slices starting here. Click on a station to see the data. Drag a rectangle to zoom in.

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