Massive Tampering With South African Temperatures
By Paul Homewood
There are only ten GHCN stations currently operating in South Africa, and only one of these, Calvinia, is classified by GISS as rural. It has a population of 9000, and is situated inland in the Northern Cape province.
| Airport Y/N? | Pop K | |
| Calvinia | N | 9 |
| Capetown | Y | |
| George | Y | |
| Pietersberg | Y | |
| Upington | Y | |
| De Aar | N | 18 |
| Port Elizabeth | Y | |
| East London | Y | |
| Kimberley | Y | |
| Bloemfontein | N | 256 |
This is the actual temperature trend at Calvinia, based on GHCN V2 raw data in 2011.
http://data.giss.nasa.gov/cgi-bin/gistemp/show_station.cgi?id=141686180000&dt=1&ds=1
There has been no warming since the start of the record. Yet the current version of GISS, which is based on adjusted GHCN data, has miraculously morphed into a sharply rising trend.
http://data.giss.nasa.gov/cgi-bin/gistemp/show_station.cgi?id=141686180000&dt=1&ds=12
Temperatures prior to 1989 have been marked down by around 0.7C, and those 1940’s ones by even more.
So, what about the other nine sites? We have three with long, and pretty much continuous, records back to the 19thC.
First raw, and then adjusted.
There was a large drop in temperatures at Port Elizabeth between 1950 and 1951, but there also large drops at that time at Capetown, Kimberley and other sites, including East London, which is nearby. There is no evidence that the change was due to anything but natural factors.
All the graphs have one thing in common – that tell tale peaking of temperatures around 1940, which we see so often. There is even a glimpse of this at Calvinia, where the warmest year on record was 1945.
All of these stations will, of course, have been heavily affected by UHI effects since the 1940’s.
For instance, much of the warming at Port Elizabeth in recent decades is likely due to the siting of sensors in the middle of the runways at the airport there.
http://www.ncdc.noaa.gov/homr/#ncdcstnid=30095751&tab=LOCATIONS
Out of the ten stations mentioned above, there has been marked warming trends introduced by adjustments at eight. One, De Aar shows little change, but Upington, oddly enough, bucks the trend with a cooling trend added. However, when the adjustments at all ten are averaged together, the overall effect is obvious.
One oddity is the 1990’s period, when seemingly temperatures were over adjusted up, only to be adjusted down since. It is one thing questioning whether temperature measurements taken in the 1880’s were accurate, but the 1990’s? Are we seriously saying they were understated by half a degree? This is clearly a nonsense, and it goes to the heart of how adjustments have corrupted the temperature dataset.
No reputable scientists would go near this garbage with a barge pole.
Sources
1) GHCN V2 raw data:
http://data.giss.nasa.gov/gistemp/station_data_v2/
2) GHCN V3 adjusted data:
http://data.giss.nasa.gov/gistemp/station_data/ (Select “GHCN” under dataset)
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NOT A LOT OF PEOPLE KNOW THAT 
Busted, yet again.
Reblogged this on WeatherAction News and commented:
“The most effective way to destroy people is to deny and obliterate their own understanding of their history.” George Orwell
its more like the 1984 re-writing of history!
Reblogged this on Climatism and commented:
The nefarious intent of NASA/GISS temperature adjustments is no more evident than when dealing with UHI (Urban Heat Island effect).
In every case I’ve seen, there is a consistent pattern of adjusting temperatures ‘up’, in the latter half of 20th century, as cities artificially warm with infrastructure growth, rather than adjusting them ‘down’ to compensate for this well known UHI effect.
This inverse UHI logic, leading to an increase of late 20th century temperatures, is further compounded by massively adjusting mid-century (1940’s-1960’s) temperatures ‘down’!
Cooling the past and warming the present, to fit the ‘global warming’ narrative.
This is scientific fraud and it-is-a-crime.
I just googled the land area of South Africa and it comes back as 1.221 million km². So thats one station per 120,000 km² (roughly) or just over 346km x 346km.
It just shows how clever these guys are that they can calculate the average surface temperature so accuratly from such little data and how dumb the average Joe is that this generally goes unquestioned……..
Link posted to Jo Nova, Andrew Bolt, Steven Goddard.
Its good that you are using the same graph formatting that SG uses. 🙂
Thanks for continuing to try to bring this ANTI-SCIENCE to the light of day.!
Adjustment of temperature measurements to account for extraneous effects, such as radiation from an unshielded probe are commonplace, but the theory and value of these adjustment is normally shown.
It can never be accepted that adjusted figures are not fully justified so NOAA have a moral duty, far beyond the congress politics, to justify their apparent fiddles. It is hard to believe that the adjustments can have a scientific basis so long after the measurements were taken: what have NOAA learned about the old measurements to cause them to be adjusted, they have to come clean?
I lived and worked for three years on a hill near Addo, slightly north of Port Elizabeth, and remember temperature readings exceeding 40C during summers. That was back in the late 1980s.
‘All of these stations will, of course, have been heavily affected by UHI effects since the 1940’s’
But they still have to ‘adjust’ them upwards to get the graph to show the (fake) warming they want? That gives a clue as to how far ahead of the true figures they must now be.
Should get this to Booker
Booker is omniscient, he doesn’t need a big bat being shone on a cloud in order to turn up.
Paul, just wondered if you could put both data sets onto the same graph, (different colours of course) as the vertical axes are slightly different in each. It would be easier to see the changes. Thanks.
The graphs are the actual GISS ones. I’ll download a couple and run my own, but they never look “official”!
Here is Calvinia, with old and new on the same graph:
Note that the adjustment is all GHCN, not GISS. The key shows change in degrees C per decade. The “new” is Gistemp 2015_09, the “old” is Gistemp 2009_03, both with corresponding GHCN-M data. The two green lines, solid and dotted, are GHCN-M unadjusted. They almost overlap, the slight differences arising as GISS combined the two raw observation records in 2009, whereas this is now done by GHCN in 2015. The Gistemp adjusted record in 2009 (black dotted) also overlaps these two, as Gistemp does not adjust on the basis that this is a rural station.
The red and black solid lines are GHCN-M adjusted (red) used as input by Gistemp in 2015, and the “adjusted” Gistemp output, which has not been further adjusted by Gistemp, again on the basis of a rural classification. These again overlap, any slight difference arising from rounding errors. Gistemp provides temperatures to 0.1 degrees, GHCN to 0.01 degrees.
It is the GHCN-M Pairwise Homogenization which needs to be examined here, not GISS adjustments which are not carried out for Calvinia.
Pathetic,Katrina was a category 5 and it was more that 10 years ago,there has not been a storm in the gulf like it since.
All I can say is it is obvious that there is a huge well funded push to promote global warming, anyone with half a brain can see it. However there is money and power, its best to not think about it too much or you will go insane as this doctor did for proposing washing hands before delivering babies.
https://en.wikipedia.org/wiki/Ignaz_Semmelweis
A major take-home point here is that, if the station is deemed rural by GISS, any adjustment made is by GHCN, not by GISS, and for stations deemed urban by GISS any adjustment made can be made by either GHCN or GISS, or by both cumulatively. My problem with blog post titles such as “German Professor Uncovers Massive GISS Tampering” (sorry Paul!) is that these wrongly concentrate attention on GISS and draw attention away from GHCN. “Between the years 2010 and 2012 the data measured since 1881 were altered so that they showed a significant warming, especially after 1950” suggests that the raw data has been changed – but the proper comparison, of the unadjusted GHCN-M v3 data now as downloaded from NOAA with the earlier unadjusted GHCN-M v2 data as downloaded from GISS shows that this is not the case.
I can vouch for the “code” integrity of the Gistemp output. I have worked through the publicly available code line by line, translated the code to a more modern language to ease extensions to the code to allow further analysis, and compared the published GISS output with my own. They match. The code carries out the process described in the related papers. That however is not to say that carrying out that process will necessarily achieve the aim of correctly adjusting temperature records. The broken line used in adjusting an urban station to match the trend of regional rural stations may or may not be adequate for this purpose. Rather obviously, if previously Gistemp adjusted GHCN data which was unadjusted, and only for urban effects, other than for USHCN stations, but now Gistemp starts with adjusted GHCN data for all stations instead and incorporates those prior adjustments, GISS is conceding that it regards its earlier approach as inferior (although this might not be the case since the sequential application of automated adjustment procedures by GHCN and then by GISS might, or might not, improve adjustment).
In my opinion however the major problem with Gistemp is the failure to verify that the GHCN data used as input is fit for purpose. The location meta data in the GHCN inventory used to examine night time luminance for urban/rural classification contains many errors which lead to misclassification of numerous stations. Since the adjustment carried out by Gistemp is specifically for urban effects, it is simply inadequate to claim that “I’m not surprised at all that there are serious mistakes in this inventory file. It has been traditionally treated with less than the proper care […] Unfortunately, we don’t have the manpower to check out all entries of that file”. Had I been presented with such an excuse by an undergraduate student doing project work before I retired he or she would have received guidance that such data should be treated with caution, and sampled to assess the frequency of such errors and their likely impact if it was still proposed to use the data.
It is not only the GHCN inventory metadata which may be problematic. While the unadjusted GHCN-M station data is stable over time, the same cannot be said for the adjusted GHCN-M data. Here is an example (which happens to be the first station for which I examined the changing GHCN adjustments over time – but there are very many other similarly volatile station adjustments). As well as the stable unadjusted data you can see two adjusted versions from January 2015, as well as the two largest adjustments in either direction which I have seen for this station using GHCN-M version 3 (these may not however be the very largest adjustments, as I have only archived data from time to time rather than near daily before April 2014, and so may have missed days with even larger adjustments). Trends are fitted to the complete data series and from 1966 on, and the range of adjusted values for 1880 and 1978 marked:
and focusing on the period from 1960 on to show better detail:
Marseille/Marignane has almost complete data since 1880 (data missing from July 1943 to August 1945 for obvious reasons, and from May 1970 to March 1971 for less obvious reasons, as well as three single months. It seems beyond belief that a well behaved adjustment procedure could produce such drastic changes in adjusted values a few days apart in January 2015 in a record of this length, based on the arrival of December 2014 data for some stations (and I have examined nearby stations as well as Marseille/Marignane and not seen any exceptional values for December 2014). As Gistemp is published only for a single date each month it will be interesting to see whether or not Gistemp compensates for such volatile GHCN adjustments by running Gistemp with GHCN data for varying dates over a month, but for the moment this is a lower priority task. You might be forgiven for thinking that this would be a study which GISS should already have carried out.
Based on looking at a small number of urban stations my impression is that most of the adjustment comes from GHCN, with a smaller contribution coming from GISS. This initial impression may of course be revised as I examine more stations. I have spent much more time examining the stability of GHCN adjustments, and only now started to examine the relative adjustment contributions of GHCN and GISS. GISS may or may not have been wise to change from use of unadjusted GHCN-M v2 to use of adjusted GHCN-M v3. They may even possibly have made themselves redundant if adding their automated urban adjustments to automated GHCN adjustments turns out to add insufficient value. But there is a degree of logic to their decision. Rural stations as well as urban stations may have been relocated in the past, or have had instrumentation changes, and these rural records may genuinely require adjustment.
I’ve posted some material on urban corrections at http://oneillp.wordpress.com/2015/11/24/urban-corrections-in-gistemp-and-ghcn-m-then-and-now/ with more material to follow.
As a footnote, anyone objecting that the proper term is homogenisation/homogenization not adjustment is invited to show grounds why such unstable adjustments merit the term.
Hansen and Schmidt are Goreites
Political appointments
GISS is junk science and Schmidt is no scientist, he is a modeler, mathematics is not science, they are a tool scientists use (If Einstein only did equations without them being related to an underlying science (physics), it would not be science. Someone forgot to tell him that.
GISS produces political junk