Unadjusted GHCN V3 and V4 global differences due to coverage, not content.

In yesterday's post I did a graphical study of the differences of GHCN V3 and V4 anomalies for June 2019. The idea was to try to pin down what caused a small discrepancy in the global average for that month. Clive Best has been looking at this, and thinks there may be a systematic change in reported temperatures between versions. My conclusion was that the difference was not because V4 was reporting higher temperatures, but that the greater coverage fixed a problem in V3 that in sparsely covered areas, sea temperatures would have an influence on land. I should add that this is a potential problem in TempLS and in Clive's similar code based on triangle mesh. I think these are very good methods, but the main codes do have one feature that they process land and sea separately, which should reduce this effect.

Clive sought to establish a link by finding a common set of V3 and V4, and seeing whether the difference persisted. I wasn't sure of this, because while there may be a common inventory, the stations in any one month won't correspond. I proposed a test in which the locations of GHCN V3 stations reporting in a month would have values interpolated (by mesh) from the V4 result. But they would then be analysed using the V3 mesh. I suspected that the result would be closer to V3 than V4, which would indicate that the difference was due to the reduced coverage, rather than a discrepancy between the temperature levels of the measures.

I'm reporting on that here. To summarise what I did - I had results from a run of the mesh method for both V3 and V4, reported here. From the V4 directory, I copied the node locations Z4, the anomalies A4, the weights W4, and I rebuilt the mesh M4. TempLS does integration by calculating weights, so the global average is the scalar product W4.A4. From the V3 directory, I copied the locations Z3 and weights W3. Then I linearly interpolated A4 onto Z3, using the mesh M4, giving result Aa. The "hybrid average" is then the scalar product W3.Aa, and it is being compared with W4.A4 and W3.A3.

So here is the first plot. I am presenting them as difference plots, because, as you can see from the plots here, the differences are small and hard to see against the full range of variation of the anomalies. Here is a plot of the last 30 months, with unadjusted data.





The V4 plot seems to jump around more, and clearly the V3 plot is a better match. In reality, the V3 anomaly fluctuates just as much as V4, if not more, but the hybrid tracks better with V3. I'll show another plot of the last 60 Junes:



There are more interesting features here. back to about 1990, the hybrid fairly closely tracks V3, with quite a lot of variability between V3 and V4, mostly with V4 warmer. But before 1990 V4 calms down, and only occasional spurts from V3, now not tracked by the hybrid. It's hard to know what to make of this; except for V3 excursions, both V4 and V3 seem in agreement with the hybrid, and so with each other.

The story is slightly different with adjusted data, V3 and v4. Here is the plot of the last 30 months:



The V3 difference still jumps around a lot less, but there is a fairly persistent offset of up to 0.02°F. This might reasonably be attributed to an effect of homogenisation. Finally, here are the adjusted data averages differenced over the last 60 Junes.



The adjusted pattern reflects the same interesting features as the unadjusted, with big differences between V4 and V3, and the hybrid mostly tracking V3, but with a departure during the last decade. Before 1990, less variation generally, with no obvious tendency for the hybrid to preferentially track V3.

The purpose of this post was to give some quantitative backing to the effects on the average that might be expected from the patterns shown in the last post. The adherence of the unadjusted data hybrid to v3 does not support the idea that there is a recent decades bias in the V4 measured data relative to V3. The performance of adjusted data does suggest a small effect arising from the V4 data, with some rather interesting behaviour going back before 1990 (for Junes).