GISS Hockey-Stick Adjustments

The earliest version of the US National Temperatures from the NCDC are in this report on Page 25 Climate Assessment. A Decadal Review 1981-1990. 1934, 1921, 1953, 1954 were warmer than the 1980s.
The actual data numbers have long been removed from the net by the NCDC. It is just a pdf and someone would have to digitize the data.
http://www1.ncdc.noaa.gov/pub/data/cmb/bams-sotc/climate-assessment-1981-1990.pdf
Also, a huge pdf 56 MB, Trends ’93 A compendium of data on Global Change
Contiguous United States Temperatures from 1900-1991 are on Page 689. Just annual and seasonal anomalies in C in a table. You can download the report on this page.
http://www.osti.gov/scitech/biblio/10106351
Lots and Lots of data in this report some of which can still be found here – US temps have been replaced of course by the NCDC in the database.
http://cdiac.ornl.gov/ftp/trends93/

“The bottom line is that there is no “temperature data,” prior to satellite measurements”
Thank you, Phil.

So this claim is cherry-picked, “In 7 years (December 2005 to December 2012), the rate of temperature rise for 1910-2005 has been adjusted up from +0.6 to +0.8 degree per century, an increase of approximately 30%.” with 1910 being picked, presumably because it’s at the bottom of the adjustment curve. Despite the cherry-picking, I still find the method troubling, and I agree with this claim, “Given how much the data has changed in the past 9 years, what might it be like 9 years from now? Can we trust it enough to make multi-billion dollar economic decisions based on it?”
Here’s the thing about adjustments. You need to label your units. Correctly.
If these are temperatures, then the temperature in Central Park should be the same, whether it’s 2005 or 2014.
That’s not happening in this data. The claim is because the data is “adjusted.” OK. There are some plausible reasons for that, but my simple mind needs an analog. Do we have any other adjusted data sources? Yes, the news always talks about “inflation adjusted dollars.” So maybe adjusted temperatures are like adjusted dollars? If so, there’s something missing. When the news quotes inflation adjusted dollars, they tell me the frame of reference, “buying power of a 2005 dollar,” for example. And the Minneapolis Fed gives me an inflation calculator so I can go to their web site and convert between 2014 dollars and 2005 dollars.
GISS data is labeled in degrees C. Not “2014 adjusted degrees C,” and they provide no way to compare between years. Giving the benefit of the doubt and assuming that all of the adjustment methods used are “above boards,” I’d still have to say that their implementation is incomplete. They need to label their units as adjusted degrees, and they owe the public a conversion tool to convert between years the same way the Minneapolis fed has done for inflation adjustments.
Also, if these adjustments are valid, once a temperature is measured and adjusted once for a set of stations at a specified time, the proportionate relationship between those stations should stay the same through all future adjustments until the end of time. It’s not apparent to me whether that is happening, but I suspect not.

This is a critical area as it underpins the whole of CAGW. Articles I have read over the past few years suggest every dataset has been altered in a similar fashion. The suspicion is that most of the alterations are fraudulent.
It suggests the organisations looking after this data are ‘not fit for purpose’ and the functions should be removed from these bodies.
At the end of the day it is just a bunch of data. Most governments have statistical departments which could take over this function. Using professional statisticians would also overcome some of the other criticisms on how the data is handled. Any adjustments should be requested via a committee and the reasons for any accepted published on the web in case someone wants to check or appeal against their inclusion.
There is another aspect of this. If any changes are fraudulent then in effect the data is being altered to provide more funding. Alternatively it may be regarded as using government funds to create propaganda. I would expect this to be illegal and making them open to a criminal prosecution.
These kind of articles come and go and are ignored by all apart from skeptics. What I would like to see is a coordinated effort to produce a document on how these datasets have been altered and a campaign to remove the functions from the current bodies. A campaign forever until things are changed. These datasets are so fundamental to CAGW that destroying their credibility would be a massive blow. I think that this is the one battle that if won would win the war.

the bottom line is always the same – unless they (i.e. any of the dataset holders) can provide the genuine real RAW data, along with each and every subsequent change/adjustment and the documented reason for each and every adjustment – we, the ‘consumers’ – have absolutely no idea what we are getting.
Now, if this was a food type consumer product 0 we could take it or leave it if we don’t like. However, this is, in effect, a WORLD consumed ‘product’ (although force fed product might be a better description) and we are not allowed to question it!
I don’t have a problem with that in itself – if mugs want to use such data with blind trust – so be it. However, when said data is used and upheld as scientifically valid – that changes the game – it must be reproducible and proven as valid. Frankly, to this day, I don’t think we have reliable data – and certainly not without questions as to its history or validity! Ergo, in almost any other scientific endeavour, the ‘results’ or ‘conclusions’ based on such data would be thrown out or at best held in very low esteem (think wagonload of salt here!)………

Kev-in-Uk says the GISS team should document each change and the effect on all the data from the previous change. The implication seems to be that he thinks they take the previous set of adjusted changes and make a further set of changes on that. Of course that isn’t what happens – each time they would go back and reprocess the raw data.
What happens is that some new anomaly comes to light in the temperature data set, leading to a requirement for a change to the adjustment process. A new version of the adjustment computer code would be written. Once this has been run against the raw data, the new set of temperature data set version would be subject to regression testing to check that the new code version has not re-introduced any of old identified problems and that it does indeed fix the newly identified anomaly. At that point the new version of data can be released with a note saying why the processing has changed.
All this should be standard IT practice – nothing special about temperature data sets here.
In fact it will be a two-step process – the raw station data is processed in the manner described above, then a separate step is run which aggregates the results with suitable weighted averaging to provide data for a region, set of grid points or globally.
Now what is the important thing? Is it important to know what errors were present in every single one of the past versions of data and to work out exactly how correction of these errors has changed every single station reading? This would be a total waste of time.
The important thing is to be able to show that the new set of data contains none of the original flaws or biases that the data analysts have spent so much time trying to identify. The real deliverable for openness is the new version of code used to make adjustments on the raw data. An expert reading this code could determine the method of adjustment, whether it resolved all the issues identified in the past, and whether it was likely to introduce any systematic biases of its own.
You would hope that over time the frequency and the effect of new adjustments would go down, and this seems to be what is happening to the GISTEMP data set.
There are also ways you can use to validate whether the adjusted data is as good as you can make it….
Someone else can write a completely different set of code using different techniques for resolving known anomalies in the same raw data, and this is what was done with the BEST project. BEST found that the adjustment techniques used by the NOAA were pretty good.
You can compare with the satellite data, available only since 1979. However, this has problems of its own. While weather stations take readings from one place at one point in time, satellite sensors in the various microwave bands read the average temperature over a particular (and usually fairly broad) range of heights in the atmosphere. To get the “lower troposphere” temperature you have to add and subtract temperatures for different bands, then make allowances for orbital changes. Further, somehow you have to back-calibrate all sensors from old satellites against the better sensors on the newer satellites, when not all old satellite sensors did not overlap in time with the new sensors. Some new satellite sensors have had to be disregarded because they were clearly faulty. All in all it makes ground station adjustments look easy which why the RSS and UAH processing often give very different answers from the same set of available sensor readings.
Some fairly significant problems were found with these satellite data sets in the early days, and it is still not clear how accurate they are. The resulting temperatures swing higher on the peaks and lower on the troughs of global temperature than the ground station readings. But it does provide a completely independent set of readings against which ground station readings can be compared.