How well do temperature indices agree?

In comparing TempLS integration methods, I was impressed by how RMS differences gave a fairly stable measure of agreement, which was quite informative about the processes. So I wanted to apply the same measure to a wider group of published temperature indices, which would also put the differences between TempLS variants in that context.

There are too many pairings to show time series plots, but I can show a tableau of differences over a fixed period. I chose the last 35 years, to include the satellite measures.

It is shown below the fold as a table of colored squares. It tells many things. The main surface measures agree well, HADCRUT and NOAA particularly. As expected, TempLS grid (and infilled) agree well with HAD and NOAA, while TempLS mesh agrees fairly well with GISS. Between classes (land/ocean, land, SST and satellite) there is less agreement. Within other classes, SST measures agree well, satellite only moderately, and land poorly. This probably partly reflects the underlying variability of those classes.

As an interesting side issue, I have now included TempLS variants using adjusted GHCN. It made no visible difference to any of the comparisons. The RMS difference between similar methods was so small that it created a problem for my color scheme. I colored according to the log rms, since otherwise most colors would be used exploring the differences between things not expected to align, like land and SST. But the small differece due to adjustment then so stretched the scale, that few colors remained to describe the pairings of major indices. So I had to truncate the color scheme, as will be explained below.

I am now including the adjusted version of TempLS mesh in the regularly updated plot, from which you can also access the monthly averages.

To recap, I am calculating pairwise the square root of the mean squares of differences, monthwise. I subtract the mean of each data over the 35 years (to Sep 2015) before differencing. Colors are according to the log of this measure. The rainbow scheme has red for the closest agreement. The red end of the scale finishes at the closest pairing involving at least one non-TempLS set. Pairings beyond that red end are shown in a brick red. Later I'll show color schemes with this cut-off relaxed. So here is the pairwise plot, with key in °C. If you want the numbers, they are here html, csv

Abbrev Dataset name Link HadCRUT HadCRUT 4 land/sea temp anomaly link GISSlo GISS land/sea temp anomaly link NOAAlo NOAA land/sea temp anomaly link UAH6.beta UAH lower trop anomaly link RSS-MSU RSS-MSU Lower trop anomaly link TempLSgrid TempLS grid weighting link BESTlo BEST Land/Ocean link C@Wkrig Cowtan/Way Had4 Kriging link TempLSmesh TempLS mesh weighting link BESTla BEST Land link GISS Ts GISS Ts Met stations temp anomaly link CRUTEM 4 CRUTEM CRU global mean Stations link NOAAla NOAA land temp anomaly link HADSST3 HADSST3 Sea Surface link NOAAsst NOAA sea temp anomaly link

Some points to make, in no particular order:

• TempLS interactions are bottom right. Adj means variants using adjusted GHCN. You can see that the differences in integration method makes much less difference than the variation elsewhere between different indices/datasets.
• The difference due solely to adjustment is even less - this will be quantified better below.
• The main global surface indices are top left. NOAA and HADCRUT are particularly close. I'll show comparisons with TempLS in a later plot. BEST agrees moderately with the others; C&W (Cowtan and Way kriging) notable better with GISS and worse with NOAA, and only moderately with HADCRUT, which it sought to improve (meaning probably that it succeeded). The agreement with GISS makes sense, since both improve coverage by interpolation.
• The troposphere indices RSS and UAH agree only moderately with each other, and with others not much at all.
• The land indices agree not much with each other, and BEST and NOAA diverge widely from other measures. CRUTEM and GISS Ts less so. Of course, GISS T2 is land data, but weighted to try for global coverage.
• SST data agree well with each other, and not so much with global (about as well as UAH and RSS). Some agreement is expected, since they are a big component of the global measures.

Here is a plot of just the global surface measures. It shows again how there is a GISS family and a HADCRUT/NOAA group. The distinction seems to be on whether interpolation is used for complete coverage, upweighting polar data.

And here are the plots with the color maps extended. On the left the cut-off level is the minimum of the TempLS plots with different methods. It emphasisees how little difference integration method makes compared with differing indices. And on the right is the map with no cut-off. You can see that it is now dominated by the four cases where only adjustment to GHCN varies. Otherwis, same data, same method. Adjustment makes very little difference. It also shows why I originally restricted the color range. In this new plot, everything else is blue or green.