Just take the ensemble mean of the sats. That solves the divergence issue. Works so well with the GCMs.
Do Cowtan and Way know about the problems with the sats?
5. Dr. Mears says: “As a data scientist, I am among the first to acknowledge that all climate datasets likely contain some errors. However, I have a hard time believing that both the satellite and the surface temperature datasets have errors large enough to account for the model/observation differences. For example, the global trend uncertainty (2-sigma) for the global TLT trend is around 0.03 K/decade (Mears et al. 2011). Even if 0.03 K/decade were added to the best-estimate trend value of 0.123 K/decade, it would still be at the extreme low end of the model trends. A similar, but stronger case can be made using surface temperature datasets, which I consider to be more reliable than satellite datasets (they certainly agree with each other better than the various satellite datasets do!). So I don’t think the problem can be explained fully by measurement errors.”
The surface temp datasets agreeing with each other is not surprising. How does that make them more reliable than the sats? The coverage of the sats and uncertainty of 0.03/K decade ain’t bad. If the sats are good enough for Cowtan and Way, they are good enough for me. And I don’t care what did or didn’t happen, before 1979.