On a whim, I’ve been looking at sorting surface station data for quality according to UAH satellite data. It’s a process which, if done right, could have either validity or be used as a method cherry picking – [sarc]an advantage unique to climatology[/sarc] . In messing around I sorted ground data against the final UAH global average, throwing out stations below a correlation threshold. As expected, I was able to generate beautiful UAH curves with every wiggle and shape by selecting the ‘right’ temperature stations. Only somewhat surprisingly the trends returned were double the UAH trend – below.



Anyway, the results above are nothing but junk that happens to have the same shape as UAH. I began to look at sorting according to a gridded UAH series. On NicL’s suggestion, I correlated each stations gridcell to the surrounding gridcells in order to account for weather patterns. If the prevailing winds on a sea shore station predominantly carry inland air across the station, you might correlate better to a gridcell which has more inland area. The next graph is the gridded GHCN global trend since 1978 – no sorting involved. One thing I noticed is that the blue color stations predominantly sit along shorelines. The second plot below is the same data sorted by removing anything with lower than 0.6 correlation.

Most of the light blue stations correlated poorly to UAH. There is almost a band of stations across the equator +/- 20 degrees which lost all stations based on poor correlation to UAH.

Remember, in a recent post NicL also noted the poor correlation of UAH to sea surface temperatures.

This makes some sense since while SST affects the lower troposphere, the satellites measure a substantial air thickness and it would take time to conduct/convect the heat from the sea surface through the whole thickness.

I then plotted the correlation vs latitude.

Regulars here may remember John Christy’s amplification factor between UAH and surface measurements. He recommends a value of 1.2 times ground for everything except the tropics +/- 20 degrees where the value is 1.3. This represents the dampening factor of the decadal variance. If you’re familiar with the effect, I now wonder if the tropical difference in amplification factor is a result of the difference in proximity of the tropcial stations to the ocean. IOW, looking at the geography in equatorial regions, many of the stations are positioned closer to the ocean such that trade winds would cause additional dampening of surface level measurements in comparison to the lower troposphere sat measurements. The increased tropical surface temperature dampening effect may be due to nothing other than geography.



