Paraguayan Adjustments Not Supported By Regional Trends
By Paul Homewood
Raw Data
As we know, Puerto Casado is one of the Paraguayan stations where GHCN have turned a cooling trend into a warming one. To do this, they compare temperature trends against other stations in the region.
http://data.giss.nasa.gov/cgi-bin/gistemp/find_station.cgi?lat=-22.28&lon=-57.87&dt=1&ds=12
So let’s take a look at the other stations that GISS list as within 710 km. From the table above, we can identify the following sites, (excl the other Paraguayan ones, which, as we have already seen, have all had similar warming adjustments made to them).
| Location | Country | Airport Y/N |
Brightness Index |
Brightness Value |
| Ponta Pora | Brazil | Y | C | 32 |
| Las Lomitas | Argentina | C | 11 | |
| Formosa | Argentina | Y | C | 17 |
| Rivadavia | Argentina | A | 0 | |
| Yacuiba | Bolivia | A | 0 | |
| Posadas | Argentina | Y | C | 16 |
| Camiri | Bolivia | Y | C | 19 |
| Londrina | Brazil | ? | C | 23 |
http://data.giss.nasa.gov/gistemp/station_data/v3.temperature.inv.txt
A couple of notes:
1) GISS define the Brightness Index:
A = Dark
B = Dim
C = Bright
2) The Brightness Value ranges from 0 to 256, with the latter being the brightest. According to GISS, only sites with a value of 10 or less can be genuinely regarded as rural. All other sites should be adjusted for UHI, by comparing trends with the rural stations.
We can see then that only Rivadavia and Yacuiba qualify as being reliable. What have been the temperature trends there?
Rivadavia
http://data.giss.nasa.gov/cgi-bin/gistemp/show_station.cgi?id=301870650000&dt=1&ds=12
The first thing that stands out is that there is only one annual reading since 1990, and this is actually 2010. Consequently, Rivadavia plays no part in the calculation for 2014.
Nevertheless, there does appear to be a general warming between 1960 and 1990, so is this evidence that the cooling trend seen in Paraguay could have been wrong?
Apparently not, because again we see that GHCN have been busy adjusting temperatures at Rivadavia as well. In particular, all temperatures after 1980 have been adjusted up by nearly 1C.
I would also highlight the same, sharp drop in temperatures in the early 1970’s, which we saw at all of the Paraguayan sites, and which GHCN decided must be wrong.
ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v3/products/stnplots/3/30187065000.gif
Yacuiba
http://data.giss.nasa.gov/cgi-bin/gistemp/show_station.cgi?id=302853650000&dt=1&ds=12
We’ve finally found some global warming! Again there are large gaps in the record, which raise questions about the reliability of the long term trends.
Also, though the graph does not make it clear, there is no figure for 2014; the last number is for 2013.
However, things, as we know, are never quite what they seem.
This is the temperature record from the raw data, as GISS showed it in 2011. Note there is no data after 1988 – it would appear that they have discovered more recent data for 2002 and later, since their 2011 version.
http://data.giss.nasa.gov/cgi-bin/gistemp/show_station.cgi?id=302853650000&dt=1&ds=1
Suddenly, the upward trend from the 1950’s through to the 1980’s disappears. And, yet again, we see that drop in temperatures after 1970.
Once again, the magic GHCN algorithm has done its work!
ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v3/products/stnplots/3/30285365000.gif
Just to add the coup de grace, GHCN have increased the latest batch of temperatures, since measurements began again in 2002, by more than 1C, conveniently making this period warmer than the 1970’s and 80’s, rather than cooler as the thermometers suggest.
Where there is such a big gap, and no guarantees of quality assurance, this sort of data should simply be chucked out, at least as far as long term trends are concerned. To then make a large upwards adjustment in temperature for the last 13 years of data is frankly junk science.
Summary
It is claimed that historic temperature records at every current station in Paraguay have had to be adjusted, because trends at other nearby stations show them to be wrong.
When we check, however, we find that the only two genuinely rural stations within 700km both exhibit similar temperature trends to the Paraguayan ones. Both have been adjusted by GHCN to create an artificial warming trend.
As one station is Bolivian, and the other Argentine, it rather makes a nonsense of the idea that somehow it was Paraguay doing something wrong.
Meanwhile, we still wait for NCDC to comment.
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Comments are closed.
NOT A LOT OF PEOPLE KNOW THAT 
Don’t hold your breath.
Paul, out of interest what sort of graphs do the other (non rural) stations produce?
As their raw data should show some UHI which should have been removed by the Quality Adjustments.
I only ask as I am sure any warmists looking at your analysis will say you “cherry picked” those 2.
Yeah I tend to agree with Philip Bratby, I am not holding mine either, but the time must come when they are called to account. The calls are becoming a little more frequent –
http://www.thegwpf.com/u-s-weather-agencies-on-rack-over-hottest-year-claim/
I see you get an honourable mention Paul.
Up to now what answers (excuses) there have been have held no water.
It is also beginning to dawn on some, even those who are not necessarily of a sceptic leaning that the whole thing is increasingly circular – as per many comments over at the Bish’s establishment on yesterday’s thread –
http://bishophill.squarespace.com/blog/2015/1/30/on-tuning-climate-models.html
Some (many) modellers are tuning their models to agree with a surface temp dataset that has itself been “adjusted” to agree with AGW theory and the whole thing goes around and around.
As people have noticed the 30’s have usually (no, sorry always) been cooled in these adjustments not only to fit the models, but because the 30’s were embarrassingly warm for the AGW story of thermageddon just around the corner. I first became aware of the size of the problem through WUWT covering changes made to Icelandic data for the 30’s about 3 years ago.
I can’t spot where I saw now, but I gather that one of the IPCC Japanese climate scientists has said that people will be angry when they find out what has been done.
Once again Paul, well done and thank you for all your hard work.
One of the things that they had to remove from the raw Data are the “Step Changes” in the temperature trends.
They are quite large, anything up to 2 degrees C.
This of course in no way fits in with the nice gradual warming you would expect from a nice gradual increase in CO2.
So there have been some historical data where they have actually increased the past temperatures for some years while cooling large swathes of other years.
The result of this is to achieve M Mann’s Hockey Stick Shaft and make it easier for Models to fit the data.
One of the BEST scientists made the mistake of showing graphs of what the Raw data would look like for both the USA and Global Land Temperatures and it is absolutely nothing like the current NCDC/GISS/BOM/HADCRUT or BEST data trends.
“One of the BEST scientists made the mistake of showing graphs of what the Raw data would look like for both the USA and Global Land Temperatures and it is absolutely nothing like the current NCDC/GISS/BOM/HADCRUT or BEST data trends.”
Now that is something I would love to see !
and
Paul, please keep a wary eye on the GISS data.
This is only the start.
Gavin et al are going to have to do even more “adjustments” this year to maintain the “hottest ever” for 2015
Presumably, if all the GHCN data used by GISS etc is available to everyone, then it should be possible for someone to come up with an algorithm that produces a reliable temperature record. Does one already exist?
In theory, that’s what BEST tried, using everything rather than just GHCN. In practice, it is not possible. The surfacestations.org project showed why. You have to know the quality of each stations siting, including in the past. The metadata is often missing, for example in GHCN for Puerto Casada, or contradictory in WMO for Puerto Casada. There are too few remaining special cases like Rutherglen in Australia. The regional expectations thing is an attempted workaround that doesn’t work. Rutherglen and BEST 166900 (Amundsen Scott at the south pole) are sufficent proofs.
But it doesn’t really matter except for exposing how deviously suspect the whole thing has become. And that is not hard for CAGW generally, with many angles in addition to temperature per se. Been writing about that now in parts of two books. Selection bias, false certainty, inherent model inadequacies, improper methods (hockey stick), provably faulty consequences papers, failed predictions, absurd findings/predictions, even academic misconduct. But it takes a lot of braking to stop a bandwagon with this much momentum. It does look like the wheels are starting to fall off, thanks to efforts of many including Paul here.
Does Paraguay have a weather agency like NOAA or BOM that compiles raw data in an unmanipulated manner? If they do, it would be interesting to hear what their opinion is of the “new and improved” GHCN data.
Getting kind of bored with this “Bad Station” stuff. Listening to Mosh, Zeke and others, I find it plausible that out of hundreds of records, some just ain’t going to work out the way we might like to see them. I see quite often the “cooling of the past” in numerous examples, but what is it they are doing that cools the past..Rather than this hap hazard weekly posting of examples scattered about, can’t someone do maybe a continent…any continent, an show a thorough analysis of a given “substantial” geographic area. Don’t know how to get my head around this whole thing because it’s starting to appear to be more of a rock throwing campaign than anything else. Annoying as hell when mosh and company say “write your own code, do you own analysis, and you can be the hero”,,,, Really annoying……..but I’m beginning to think he my have a point.
It’s a bit like complaining to your garage that your car keeps breaking down.
They turn around and say “buy your own tools and fix it yourtself!”
My algorithm would be to take the GHCN raw data, multiply by 1 , then subtract the square of 0. If anyone disputes the result, I’d do a Gavin and say that it’s working as intended. Given current state of climate record, could anyone prove that my scheme is less valid than anyone else’s? I doubt that would make me an hero but it might say something about green’s motivation if they think that making stuff up gives them hero status.
In today’s Philip Eden column he refers to the eleven year sunspot cycle and states ‘over the next few years we should move towards a peak of activity’. I wonder what he bases that on?
I read a comment on another thread that said Paraguay is so small, it doesn’t matter. Good Gravy. Fraud is Fraud.
When, just three years ago, there was a similar flurry about Iceland, I posted a survey of GHCN adjustments, with histograms. There was a spread with a mean that was modestly positive. I did another post showing that some of that was due to TOBS in the US, for known reasons.
With that earlier post I included a Google Maps app. This shows GHCN stations with data about the trend change on adjustment. You can colour by ranges of trend change, click to show details etc. You can also, if so minded, drill down to see if GHCN thinks they are in the middle of a river etc.
That map was limited to stations with more than sixty years data, which excludes Puerto Casades etc. I’ve put up a new post which has the same app, but showing stations with more than thirty years data. Below is a pic of the Paraguay region. Pink warming on adjustment, cyan cooling, yellow either no change or less than 30 years data.
Have you got a list of the Paraguayan ones by category, Nick?
I’ve found no current ones that have gone the other way, but I have not checked ones that are not current
Paul,
By Category, do you mean urban/rural etc?
There is a button on the app which lets you filter these – also airport etc. To see rural, say, go to the line that says Urban. Then click the button that says Urban – it changes to Rural. Click the left radio button to make itblack. Then click, say, pink at the top, and all the rural stations turn pink.
Sorry, Nick.
I meant cooling and warming
Paul,
Not a list. But you can color them (as in the pic – details in a comment), and then just click on each one – the metadata pops up.
Thanks Nick
Nick, remind us all again why GHCN adjustments to Iceland’s data set are acceptable when the Iceland Met Office say’s they are not?
Nick
When I click on Concepcion, for instance, it comes up cyan, as cooling. Yet GHCN say it is a warming adj.
ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v3/products/stnplots/3/30886134000.gif
Same with a couple of others. Am I doing something wrong?
Paul,
On mine, it shows yellow, which means either no adjustment or less than 30 years eligible data. Clicking the station marker says the adjustment made 1,69 C/Cen difference – ie warming, and data from 1959 to 2011. So why yellow? The balloon gives start and end years, but there could be a lot missing.
I should add, BTW, that this is from the 2012 post, with data of that age.
I’ve added at the bottom of the post a bit more on usage.
Interesting Nick
Does this mean that the figures have changed since 2012?
PS Have you seen how the Reykjavik adjustments have changed three times in the past year?
Thanks Nick
As far as I can make out, there is just one negative adj in Paraguay, Pedro Juan, although the adj actually go up and down through the record, more or less cancelling out.
How are you trying to display your charts and images? Almost all of them show as bolded code (HTML?) and nothing else.
E.g.:
image
http://data.giss.nasa.gov/cgi-bin/gistemp/find_station.cgi?lat=-22.28&lon=-57.87&dt=1&ds=12
That link brings me to the station list, Brian. Then you just click on the station you want.
But you should get the grpahs from this link, for instance
http://data.giss.nasa.gov/cgi-bin/gistemp/show_station.cgi?id=308860860000&dt=1&ds=12
Paul
A lot of influential bloggers in different scientic areas and who do a nice little sideline in alarmism, are linking to articles like this one as ‘proof’ that you got it all wrong:
http://www.factcheck.org/2015/02/nothing-false-about-temperature-data/
It’s riddled with holes and circular arguments (eg. The temperature dropped long before the station resiting and not after and the adjustments were done simply because the raw values weren’t “expected”).
Have they really come up with no excuse whatsoever for the actual nuts and bolts reason for adjusting all three stations other than making vague references to regional adjustments and no data to prove it?
If NCDC are saying nothing, that speaks volumes.