GISS Hockey-Stick Adjustments

Advertising the Truth and Truth in Advertising
There is a dichotomy here which needs exploring.
The problem stems from what does the USHCN data really mean and how is it managed and interpreted. Website states The United States Historical Climatology Network (USHCN) is a high quality data set of daily and monthly records of basic meteorological variables from 1218 observing stations across the 48 contiguous United States.
Steve Goddard commented it was the Coldest Year On Record In The US Through May 13 2014
Zeke Hausfather, a data scientist currently a Senior Researcher with Berkeley Earth chucked fuel on the fire when he wrote a series of articles
How not to calculate temperatures, parts 1,2 and 3 stating that Goddard was wrong
The U.S. Historical Climatological Network (USHCN) was put together in the late 1980s, with 1218 stations chosen from a larger population of 7000-odd cooperative network stations based on their long continuous records and geographical distribution. The group’s composition has been left largely unchanged, though since the late 1980s a number of stations have closed or stopped reporting.
And here is the crux. Mr Goddard reported real raw data, possibly with flaws in that missing temperature records were not counted. Zeke replied with an artificial model which was not designated an artificial model [see the blurb above from the website USHCN) is a high quality data set] yet treated this model data as the real data.
Steven Mosher to his credit has consistently said that it was estimations only, whereas other commentators like Nick Stokes have said that it is a virtually true data set. Steven unfortunately ignores the fact that the USHCN is put out as a historical data set when it is neither of those two things.
Further to this a deeper truth is hidden. The number of stations in the USHCN [a subset of the GHCN] IS 1218 , originally 1219 selected in 1987 with continuous records back to 1900. A large number of stations have closed over this time dropping the number of real stations to 833 reporting in March and April 2014.
Zeke has further suggested the number of real stations could be as low as 650. Some stations have been added to make it up to the 833 current stations. This implies that up to 40% of the data is artificial, made up by programmes that would be as adept at making a poker machine reel spin.
The data is adjusted in 2 ways according to Zeke and Steven and Nick. Infilling from surrounding stations if it appears erroneous for current temperatures with no comment on how low or high it is allowed to go before it is infilled. The past historical data is altered so the further back in time one goes the lower the so called historical data record is altered but it is not promoted or advertised or gazetted as a guess or estimate. It is put out as the truthful correct reading. Worse each day all these readings change as new readings are inputed on a daily, monthly [or mid next month computer input for the missing stations].
The second is a TOBS adjustment and a change of thermometers adjustment.
This results in a subtle underlying lowering of the whole historical record again presented as true historical data when it is anything but. Further it enables TOBS changes to be made to all missing data as in comparing it to surrounding stations gives an average reading but as the site itself was not working a TOBS is possibly made for that station as there is no proof that its Obs were done at the same time as the other stations.
Steven Goddard’s graphs may be flawed by missing real data, he says this is small. His temperature representations are at least real and accurate data.
Not an estimate dressed up as a drag queen of data, worse historical data when it is neither of those things.
USHCN addendum
it contained a contained a 138-station subset of the USHCN in 1992. This product was updated by Easterling et al. (1999) and expanded to include 1062 stations. In 2009 the daily USHCN dataset was expanded to include all 1218 stations in the USHCN.
This is quite a concern. If the 1992 version only had 138 stations used for its graphs could it be that these stations still exist and could still give a graph. Why were others discarded? How many of these best located stations have died the death and why? Did the addition of the new and massively infilled stations with TOBS adjustments cause the so called historical rise in temperatures
Final Note this question of truth, what is data and what is modelling, which is historically true and which has been written by the winners will persist until the agencies concerned label there models correctly and give raw data graphs, warts and all to the general public.

@ur momisugly miked1947 on July 3, 2014 at 9:44 pm:
Thanks for the link to GISS. However, you have to be careful because the answer given depends on what “the definition of ‘is’ is.”
From the link:

The Elusive Absolute Surface Air Temperature (SAT)
The GISTEMP analysis concerns only temperature anomalies, not absolute temperature. Temperature anomalies are computed relative to the base period 1951-1980. The reason to work with anomalies, rather than absolute temperature is that absolute temperature varies markedly in short distances, while monthly or annual temperature anomalies are representative of a much larger region. Indeed, we have shown (Hansen and Lebedeff, 1987) that temperature anomalies are strongly correlated out to distances of the order of 1000 km. (emphasis added)

So what is the definition of “strongly correlated?”
From Hansen and Lebedeff, 1987 (Abstract):

The temperature changes at mid- and high latitude stations separated by less than 1000 km are shown to be highly correlated; at low latitudes the correlation falls off more rapidly with distance for nearby stations. (emphasis added)

From Hansen and Lebedeff, 1987 (pg 3 of the pdf):

For example, in these regions [the United States and Europe] the average correlation coefficient for 1000-km separation was found to be within the range 0.5-0.6 for each of the directions defined by 45° intervals. We did not investigate whether the correlations are more dependent on direction at low latitudes.
…..
The 1200-km limit is the distance at which the average correlation coefficient of temperature variations falls to 0.5 at middle and high latitudes and 0.33 at low latitudes. (emphasis added)

So the definitions map as follows:
“[S]trongly correlated” in the GISS link maps to “highly correlated” … “at mid- and high latitudes” in the abstract. NO mention is made in the webpage of the low latitude caveat in the abstract.
Further,
“[H]ighly correlated” … “at mid- and high latitudes” in the abstract maps to “0.5 at middle and high latitudes and 0.33 at low latitudes” at 1200 km in the body of the text.
I would not define correlations of 0.5 and 0.33 as “high” or “strong,” yet all of the adjustments and temperature reconstructions (including BEST, I believe) depend on this definition of “high” or “strong” correlation, IMHO. It would seem that if a common sense definition of a “high” or “strong” correlation as 0.9 or above were used, then all of these reconstructions and/or adjustments (including, probably, BEST) would probably disintegrate.
Furthermore, the statement that “absolute temperature varies markedly in short distances, while monthly or annual temperature anomalies are representative of a much larger region” also needs to be examined for logic and commons sense.
Mathematically, the temperature anomaly is defined as the difference between the absolute temperature and the average temperature over a base period (in this case 1951-1980). If we let T_anom be the temperature anomaly, T_abs the absolute temperature and T_base the average temperature over the base period, the formula can be written as follows:
T_anom = T_abs – T_base
Since, T_base is a constant for the purpose of calculating anomalies, it would follow that if “absolute temperature varies markedly in short distances,” so would temperature anomalies. However, they added the modifier “monthly or annual,” so, presumably they are comparing daily absolute temperatures to “monthly or annual” anomalies. If you add the modifier “monthly or annual” to absolute temperatures, wouldn’t “monthly or annual” absolute temperatures also be “representative of a much larger region,” if anomalies are supposed to be? Maybe, to be kind, their wording is very poor. I think what they may be trying to say is that the derivative (or the first difference) of temperature is “representative of a much larger region,” but given that the correlation coefficients of temperature are so poor, why would the derivative (or first difference) of temperature be any better? I have never seen any calculations of correlation coefficients of temperature derivatives (or first differences), so why should we take these statements as having any validity?

for E M Smith
This is a guess only to explain the continuing, increasing and divergent lowering of records in the past.
The algorithm for changing TOBS in the past is still incorporated for changing stations in the present.
This results in a subtle underlying lowering of the whole historical record again presented as true historical data. TOBS changes are made to all missing data at stations when comparing them to surrounding stations which gives average readings but as the sites were not working a TOBS is possibly made for those stations as there is no proof that their observations were done at the same required time as the other stations. Hence all current TOBS readings, and there are quite a few, have an inbuilt rise in temperature applied to the average temperatures when calculated. Even worse this then forces backwards changes on all the past recorded TOBS stations dropping them lower.
Otherwise the past records would have stayed the same [Zeke says they are always adjusted each day] and only the current data would be modified. Take out the link changing backwards and the system becomes much fairer though still historically wrong and should require all USHCN graphs to be labeled as estimates not true data itself.

Nick Stokes says at July 3, 2014 at 5:22 pm

I don’t see a hockey stick here. But the fact is that GISS doesn’t do much adjusting at all now. Since GHCN V3 came out, they have used the GHCN adjusted data, with Menne’s pairwise homogenization. That’s the main reason for the change.

“But the fact is that GISS doesn’t do much adjusting at all now.“?
That may be true but seeing as they don’t state:
1 How much adjusting they still do.
2 When they adjust.
3 What they adjust.
4 Why they adjust.
5 How they adjust.
6 What the impact of the adjustment is.
7 What the justification for that assessment of the impact of the adjustment is…
Well, the data is junk.
No legitimate organisation or individual scientist can use this contaminated gibberish anymore.

GISS doesn’t have to do any adjusting if it uses GCHN as this already has USHCN in it and its own similar adjustments due to massive station dropout.

John Harmon,
I’m not so sure the Congressional record can’t be changed. After all, congress-folk are allowed to record a speech at a later date and insert it into the record when they were not even present the day the debate took place. I hope that the actual date they made the recording is prominently displayed but I have not checked.