I have made a new calculation of global temperatures using 7300 NOAA/NCDC V3c station data combined with HadSST3 ocean temperature. For the ocean data I use cell locations only where measurements exist for a given month. I then make a (lat,lon) triangulation of all combined station/ocean locations for that month to form a global irregular grid structure. Then I use the IDL irregular griding routine GRIDDATA to interpolate this triangulation onto a regular grid and thereby calculate global monthly and annual anomaly averages normalised to 1961-1990. Anomalies for each V3C station data are independently calculated relative to their monthly averages over the 30 year period. The end result of this procedure is essentially a full global integration of irregularly interspersed measurements for each month. The annual average shown is then simply the 12 month average.
How does this compare to other ‘kriging’ methods which supposedly remove the coverage bias of Hadcrut4? What I discovered is that the end result depends critically on what grid spacing you interpolate onto. If you chose a fine grid spacing, such as the 1 degree used by Berkeley Earth, then you get an enhanced warming trend over recent years. If however you chose the same grid size as Hadrut4 (5 degrees) then you get a reduced trend. This implies that a systematic error is introduced by the methodology. Here is the comparison.
The 2 degree results are very similar to Berkeley Earth but give a slightly larger warming trend. However by using the same 5 degree target grid size as Hadcrut4 the result gives a much reduced warming trend. Cowtan and Way use the HADCRUT4 station data rather than V3C and lies somewhere in the middle. Here is a detailed comparison of results for one month – September 2016.
The 2 degree resolution extends the expanse of each warm zonal area.
The 5 degree resolution is in line with that of HADSST3 and HADCRUT4
This is Cowtan and Way version 2 which reconstructs ocean and land separately and then blends them during the time period shown.
Does kriging actually improve the accuracy of global temperatures? While it is probably correct that Hadcrut4 has a ‘coverage bias’ over polar regions, what is even clearer is that interpolation to remedy this can itself introduce a systematic warming bias dependent on method and target grid size. The other temperature series all use data infilling based on ‘kriging’ type techniques.