For GISS, the "average" temperature anomaly is the daily maximum - the daily minimum divided by two. This has become "THE" indicator of climate change. With the satellite era, there has been some grousing that the satellite "surface" data is not the same as the land "surface" data. They should be different, they are measuring different "surfaces". While they are measuring different "things", the trend relationship between the two should mean something.
The only thing that changes differently between the two is the amount of CO2, H2O and "other" stuff floating around in the atmosphere. Since GISS is at "THE" surface and UAH is near "THE" atmospheric boundary layer, the biggest differences should be clouds, aerosols and CO2 in that order.
In the Chart above I have ploted GISS NH land only, the one with the scary linear regression, UAH NH land only, the one with the not as scary linear regression and the difference. You should notice that the chart says Energy not temperature. I converted the temperature anomaly to approximate Energy anomaly using 11 C as the approximate "surface" temperature for both.
There is considerable uncertainty in this comparison, but the difference should roughly approximate the atmospheric changes influencing temperature. That 3e-02 is about 0.3 Wm-2 per decade or 3 Wm-2 per century. If 3.7 Wm-2 produces 1C of warming, the 3 would produce less, ~ 0.8 C of warming. Not a very accurate estimate, but in the general ballpark of the "no feedback" climate sensitivity for whatever that is worth.
The common trend would likely be due to something else. Remember there is that whole tropospheric hot spot thing that is supposed to happening that is not. If this trend difference is meaningful, then is should be approximately equal to the ocean energy imbalance.
Now think about the differences between comparing the land to ocean rate and the land to lower troposphere rate. Two different frames of references with atmosphere as the conduit.
I could do the same with the oceans, but there is such a small difference because SST has less variation that it is not of much use. That is were the Stratosphere comparisons have a large advantage.
The values are still too coarse for much precision, but it is a neat way to make better use of the available data.