The Moyhu TempLS mesh index was down, at 0.657°C, compared with 0.707°C in August. This was at variance with the reanalysis index, which was up by 0.06, and with TempLS grid, which was steady at a rather higher value of 0.749°C. This is all based on 4168 stations reporting; we can expect 2-300 more reports to come.
The warm areas were Russia W of urals, E Canada and US mid-west, and Brazil. Also E Pacific (but not SE). This is the same pattern as with the NCEP/NCAR reanalysis. The very cold place was Antarctica.
I was curious about the reason for discrepancy, especially with the TempLS versions, which just integrate the same data with different weights. Each month, I publish in the Mesh report a plot of attributions, described here. This shows the breakdown of the contributions to the weighted average. You can see, for example, that the contribution from Antarctica was large negative, nearly 0.1. That means that the global average would have been 0.1°C higher if Antarctica had been average instead of cold.
So I made a similar plot comparing the contributions for both grid and mesh, just in September.
You can see that Antarctica made a very small negative contribution to the grid average. That is because the same data has much smaller weight. Each station there sits in just one cell, and is weighted with that area, which is further reduced by converging longitudes. Most of the area has unoccupied cells, which get the default global average - ie don't reflect measued Antarctic cold. But the mesh weighting weights the few Antarctic cells by the whole land area.
You can see other effects; particularly that the sea contributes less with mesh. This again reflects Antarctica. Some of the big triangles there terminate in the sea, and those cold points get upweighted too. But the accounting assigns that part to the sea total. And these two negatives explain why in Sept, the grid mean was almost 0.1°C higher than the mesh. Actually, in the same way, other warm sparse regions, like Arctic, Africa and S America, made a greater warm contribution with mesh, which brings the total down a bit.
I'm developing new integration methods, Grid with Infill, and Spherical Harmonics based. I'm hoping these will give more consistency. In September, I think the mesh is formally more accurate, but with increased uncertainty, because of the high dependence on a few Antarctic stations. If more Antarctic data comes in, the average could change.