A previous post looked at the how the number of stations used in reporting climatic temperature data through the NOAA GHCN database has varied since 1880. Here, before starting to examing the effects of adjustment, I’m simply looking at how much of the data is adjusted. It’s a lot actually. I’m not going to do too much discussion here; I just really want to let the graphs speak for themselves.
(black line) percentage that are adjusted by year.
are expressed as a percentage of the total number of stations.
A guest post at Die Klimazwiebel today from Reinhard Böhm of the Central Institute for Meteorology and Geodynamics (ZAMG) in Austria, discusses the need for adjustment (homogenization) and he is concerned that access to unadjusted data can result in its ‘misuse’ by those who do not understand the inherent biases that require adjustment. What he says is important and I agree with his reasoning for a lot of it. He then says:
“I can advise everyone to use original data only for controlling the quality or the respective homogenization attempts but not for analysis itself if the goal is a timeframe of 20 years or more – a length usually necessary to gain statistically significance at the given high frequent variability of climate.”
Just one thing to point out here, there are adjustments and adjustments.
The NOAA GHCN ‘Raw’ data is already adjusted (for time of observation, station history etc.). There is then a further set of homogenisation done by either GHCN or GISS and these adjustment have been the focus of our analysis. [Update 23rd Jan. After checking NCDC documentation here I can see I was wrong – v2.mean is ‘raw’ data.]
Adjustments are an integral part of temperature station data and climate analysis, but they should be necessary and appropriate. So far in our analysis we have found a lot of adjustment that seems to be neither, or at least it is not clear how some of the adjustements we see can be justified as either. However, what our approach allows us to do is to isolate sub-sections of data very rapidly for comparison and analysis. So far what we have found is interesting….. (but you’ll still have to be patient, Andy).