Firstly, some common mis-characterisations. It isn't "altering the record". The true record sits with the national met offices, but organisations like NOAA have online unadjusted records, which are as accessible as the adjusted.
Secondly, records aren't adjusted in the belief that thermometers were read incorrectly. They are usually adjusted specifically for the calculation of a regional average. Station records are used as representative of a region. When something happens that is not climate-based, then it isn't representative of the region.
Let's look at a specific case. In 1928, the station for Wellington, NZ, was moved from Thorndon, at sea level, to Kelburn, at about 100m altitude. The temperature dropped by nearly 1°C, and the NOAA algorithm picked this up and reduced the pre-1928 readings.
Now the Thorndon readings aren't "wrong". What is wrong is the 1°C drop. There is no reason to believe that the region experienced that. So the adjustment is to make the record consistent ("homogeneous"). The convention is to leave the present unchanged and adjust the past. It doesn't matter which.
AveragingThere is a lot of pixels wasted on some alleged mis-adjustment of some individual stations. That misses the big picture. When you calculate a global annual average, you take over a million readings to get a single number. The averaging drastically reduces the noise. White noise would reduce by a factor of 1000. There are various dependences, but still, the reduction is huge. Noise isn't much of a problem. What is a problem is bias. That doesn't reduce.
But there is a counter to bias too - the use of anomalies. Consistent bias goes out with the mean. So the remaining problem is inconsistent bias. Inhomogenieties.
We saw this with TOBS in the US. There is a bias depending on which time of day you read. And there is no "correct" time. The bias doesn't matter unless the time of observation changes. And still, for trend, that wouldn't matter unless there was a bias in the direction of changes. Otherwise they would cancel. But there is a bias. In the US, volunteers used to ne enjoined to observe in late afternoon. That weakened, and morning obs became popular. That created a cooling bias.
Correction introduces noiseSo NOAA and others try to recognise inhomogenieties and correct them. Sometimes that goes wrong. A real change is wrongly corrected. Or a real but temporary change gets corrected but by a wrong amount, or not fixed when the cause goes away.
But this comes back to the tradeoff between noise and bias. Correcting bias is important. If you create noise in the process, that may be acceptable. And a fixed algorithm can be tested with synthetic data to see if it introduces bias.
Resisting ad-hoccery.Most of the fusses about adjustment are totally spurious. "Look, they are altering the record! data must not be touched!". But sometimes a definite error shows up. There may have been one at Reykjavik. Part of a temperature dip was wrongly considered an inhomogeneity. So should it be fixed?
No! As noted above, a good algorithm has been tested for lack of bias. If you start intervening, it loses that property. Noise won't hurt the average, but taking out the bits that displease naysayers certainly will.
So does it matter?In TempLS, I use unadjusted data. I satisfied myself a while ago that there was little difference in outcome. But I could only do that because someone had identified and corrected the inhomogeneities, to give me something to check against. And it may just be good luck that this time the inhomogeneities cancelled.
Naysayers bang the drum about adjustments. But if none were done, then I bet we'd be hearing stories about how some station was moved, and ignored, so now the record is unreliable.
AppendixBeset by people who couldn't shed the idea that data should only be adjusted if fault is proved, I gave this analogy. Here is a table of BHP share prices. Note the final column - price adjusted for dividends and splits. It's not that the old prices were defective; it's just that dividends and splits do not alter the productivity of the company, or to what you get in total from your shareholding. If you want to make historic sense of the raw prices, you have to keep making allowances. But the adjusted prices give a continuous picture of what a holding is worth.
If you were compiling a stock index (eg Dow, cf GISS) that is what you would use.