Is there a Homogenisation Bias in Paraguay’s Temperature Data?

Last month Paul Homewood at Notalotofpeopleknowthat looked at the temperature data for Paraguay. His original aim was to explain the GISS claims of 2014 being the hottest year.

One of the regions that has contributed to GISS’ “hottest ever year” is South America, particularly Brazil, Paraguay and the northern part of Argentina. In reality, much of this is fabricated, as they have no stations anywhere near much of this area…

….there does appear to be a warm patch covering Paraguay and its close environs. However, when we look more closely, we find things are not quite as they seem.

In “Massive Tampering With Temperatures In South America“, Homewood looked at the “three genuinely rural stations in Paraguay that are currently operating – Puerto Casado, Mariscal and San Juan.” A few days later in “All Of Paraguay’s Temperature Record Has Been Tampered With“, he looked at remaining six stations.

After identifying that all of the three rural stations currently operational in Paraguay had had huge warming adjustments made to their data since the 1950’s, I tended to assume that they had been homogenised against some of the nearby urban stations. Ones like Asuncion Airport, which shows steady warming since the mid 20thC. When I went back to check the raw data, it turns out all of the urban sites had been tampered with in just the same way as the rural ones.

What Homewood does not do is to check the data behind the graphs, to quantify the extent of the adjustment. This is the aim of the current post.

Warning – This post includes a lot of graphs to explain how I obtained my results.

Homewood uses comparisons of two graphs, which he helpful provides the links to. The raw GHCN data + UHSHCN corrections is available here up until 2011 only. The current after GISS homogeneity adjustment data is available here.

For all nine data sets that I downloaded both the raw and homogenised data. By simple subtraction I found the differences. In any one year, they are mostly the same for each month. But for clarity I selected a single month – October – the month of my wife’s birthday.

For the Encarnacion (27.3 S,55.8 W) data sets the adjustments are as follows.

In 1967 the adjustment was -1.3C, in 1968 +0.1C. There is cooling of the past.

The average adjustments for all nine data sets is as follows.

This pattern is broadly consistent across all data sets. These are the maximum and minimum adjustments.

However, this issue is clouded by the special adjustments required for the Pedro Juan CA data set. The raw data set has been patched from four separate files,

Removing does not affect the average picture.

But does affect the maximum and minimum adjustments. This is shows the consistency in the adjustment pattern.

The data sets are incomplete. Before 1941 there is only one data set – Ascuncion Aero. The count for October each year is as follows.

In recent years there are huge gaps in the data, but for the late 1960s when the massive switch in adjustments took place, there are six or seven pairs of raw and adjusted data.

Paul Homewood’s allegation that the past has been cooled is confirmed. However, it does not give a full understanding of the impact on the reported data. To assist, for the full year mean data, I have created temperature anomalies based on the average anomaly in that year.

The raw data shows a significant cooling of up to 1oC in the late 1960s. If anything there has been over-compensation in the adjustments. Since 1970, any warming in the adjusted data has been through further adjustments.

Is this evidence of a conspiracy to “hide a decline” in Paraguayan temperatures? I think not. My alternative hypothesis is that this decline, consistent over a number of thermometers is unexpected. Anybody looking at just one of these data sets recently, would assume that the step change in 40-year-old data from a distant third world country is bound to be incorrect. (Shub has a valid point) That change goes against the known warming trend for over a century from the global temperature data sets and the near stationary temperatures from 1950-1975. More importantly cooling goes against the “known” major driver of temperature recent change – rises in greenhouse gas levels. Do you trust some likely ropey instrument data, or trust your accumulated knowledge of the world? The clear answer is that the instruments are wrong. Homogenisation is then not to local instruments in the surrounding areas, but to the established expert wisdom of the world. The consequent adjustment cools past temperatures by one degree. The twentieth century warming is enhanced as a consequence of not believing what the instruments are telling you. The problem is that this step change is replicated over a number of stations. Paul Homewood had shown that it probably extends into Bolivia as well.

But what happens if the converse happens? What if there is a step rise in some ropey data set from the 1970s and 1980s? This might be large, but not inconsitent with what is known about the world. It is unlikely to be adjusted downwards. So if there have been local or regional step changes in average temperature over time both up and down, the impact will be to increase the rate of warming if the data analysts believe that the world is warming and human beings are the cause of it.

Further analysis is required to determine the extent of the problem – but not from this unpaid blogger giving up my weekends and evenings.

Kevin Marshall

All first time comments are moderated. Please also use the comments as a point of contact, stating clearly that this is the case and I will not click the publish button, subject to it not being abusive. I welcome other points of view, though may give a robust answer.


  1. Craig gustafson

     /  10/02/2015

    Your comment about further analysis being needed but not by this unpaid blogger giving up weekends and evenings literally made me blow my coffe out my nose laughing….that was funny. I tend to have an unusual sense of humor, Cuz I really get that.
    Maybe I’m reading this wrong but as a joe blow who tries to look at everything with objectivity which I admit is difficult. I found your paragraph starting with “is this a conspiracy ” to be troubling. You seem so easily brush the data aside because it doesn’t coinside with your theology. A conspiracy is exactly what everybody in from the center to the the right thinks is going on and here is a little evidence proving that and you have desided to go with “the instruments are wrong”…think about that, think about what what you just said.
    How about maybe they are right and as far as going against the main drivers, greenhouse gas, no proof out there that is a main driver. Which is why the data is so important. I would think as a scientist data that goes against my hypothesis would get my full attention not just fluffed off as “the instruments must be wrong”

    • manicbeancounter

       /  10/02/2015

      I am sorry. I utterly reject the idea of a conspiracy in the narrow sense of a group of people getting together to lie and deceive. The pattern of adjustments globally is too small and not sophisticated enough. If I was going to fiddle figures, it would be much better hidden than this.
      What I do find in a number of areas is lack of quality standards and sense-checking of figures. Academics are the wrong people to churn out temperature anomalies. They come from a culture where success is in producing something novel and new, so are always wanting to tinker. But then they can lose the audit trail on the previous adjustments, and do some more. There is also a strong belief in the “science”.
      What is actually needed is some sort of computer program with set rules for detecting anomalies and making the appropriate adjustments. Also, for having a proper audit trail from raw data to final result. It can be adapted from data mining programs, such as for stock market, or currency market data.


       /  25/11/2015

      Science used to be a “truth machine”. But in the age of “global warming”, it is now a “propaganda machine”. These adjustments are made all over the world. The raw data has been lost by CRU. It is all an attempt to discredit the surprisingly large number of physicists, geologists, geographers, mathematicians and meteorologists who do not believe that man made Co2 causes much, if any at all, global warming. And altering observations to comply with groupthink is a time honored road to catastrophe. In this case, economic catastrophe.

      • manicbeancounter

         /  26/11/2015

        Investigating the Paraguay data suggests that it is not deliberate manipulation of the data to get a desired result. Rather it is not objectively questioning the results of complex data processing. Instead it is having a fixed view of the world, that is held to be be the standard by which to assess the data. I investigate in a later post. A blogger who states very eloquently this world view then shows how intolerant people can be when their beliefs, which they view as the real world, are challenged.

  2. Hi Kevin, you are probably aware of this, but it’s not very clear from your post.
    The current version of Gisstemp, if you go to their site (link in your post) and look at station data, starts from GHCN v3 (adj).
    In other words, they start from data that has already been adjusted by GHCN.
    And in fact the big adjustment that cools the past before 1970 is done by GHCN, not GISS.
    You can see this clearly in the plot for Encarnacion at the GHCN ftp site,
    And similarly for the other sites in Paraguay
    30886086000.gif Puerto Casado
    30886068000.gif Mariscal

    What is confusing, and rather bonkers, is that GISS then apply their own “homogeneity adjustment” to the already adjusted data!

    I would like to write all this up in detail properly, but like you I don’t have the time!

  3. Ron C.

     /  20/02/2015

    I take your point about the adjustments cooling the past and warming the present deriving from a confirmation bias rather than a conspiracy, a la Lewandowsky.

    What I don’t get is the disrespect of the adjusters for the reality of micro climates. BEST acknowledges that some 30% of US records show a cooling trend over the last 100 years. Why can’t reported cooling be true?

    I did a study of the CRN top rated US surface stations. Most remarkable about them is the extensive local climate diversity that appears when station sites are relatively free of urban heat sources. 35% (8 of 23) of the stations reported cooling over the century. Indeed, if we remove the 8 warmest records, the rate flips from +0.16°C to -0.14°C. –

    In order to respect the intrinsic quality of temperatures, I calculated monthly slopes for each station, and combined them for station trends.

    See more at:

    In the Quest for the mythical GMST, these records have to homogenized, and also weighted for grid coverage, resulting in cooling being removed as counter to the overall trend.

    Once a researcher believes that rising CO2 causes rising temperatures, and since CO2 keeps rising, then temperatures must continue to rise, cooling is not an option. In fact 2015 dare not be cooler than 2014.

    • manicbeancounter

       /  22/02/2015

      Thank you Ron for the comment.
      Your comment about micro climates is important. I would use the term “clusters of anomalies” as the identifier. That is, if one weather station goes out of kilter with neighboring weather stations, or if the weather stations of one country go out of kilter with that of a neighboring country, then an adjustment is required. In the latter case, it would be first wise to identify reasons, such a as a change in methodology or a complete overhaul of the weather stations in a country or region. However, in the absence of a reason to the contrary, then a cluster of anomalies should be taken as an identifier of some local anomaly.
      Part of the problem, I believe, is failure to look at the data in different ways and for others to cross-check the results. In the case of Paraguay, if someone looked at a weather station in isolation they would conclude that the fall in temperatures in the 1960s, with a steep decline at the end, was due to faulty instruments or methodology. But cross-checking with other surface temperature stations in the case of Paraguay would have largely validated the raw data.

  4. Ron C.

     /  22/02/2015

    Manic, thanks for your response.

    I`m not sure about your clusters of anomalies. It sounds similar to the pairwise homogenization technique, which assumes that two local climates move in tandem. Thus, if one of them diverges, it must be adjusted back in line.

    But I question that premise. It’s obvious that a mountaintop site will typically show lower temps than a nearby sea level site. But it is wrong to assume that changes at one should be consistent with changes in the other. Not only are the absolute readings different, the patterns of changes are also different. Infilling does violence the the local climate realities. It is perfectly normal than one place can have a cooling trend at the same time another place is warming.

    Weather stations measure the temperature of air in thermal contact with the underlying terrain. Each site has a different terrain, and for a host of landscape features documented by Pielke Sr., the temperature patterns will differ, even in nearby locations. However, if we have station histories (and we do), then trends from different stations can be compared to see similarities and differences..

    In summary, temperatures from different stations should not be interchanged or averaged, since they come from different physical realities. The trends can be compiled to tell us about the direction, extent and scope of temperature changes.

    • manicbeancounter

       /  24/02/2015

      Thanks for coming back. You seem to be more knowledgeable on the issues of micro climates than I am.
      Could I suggest that there are possibly a number of over-laid possible patterns in the data? There can be the very local patterns, due to terrain. There can be the regional patterns – like the fall in temperatures in Paraguay and maybe Bolivia at the end of the 1960s. There can also be global patterns where the average is warmer (MWP) is much colder (the ice ages).
      The problem is a conflict between known issues with the reported data (which create biases in the data that should be offset) and imposing a set view of how the data “ought” to appear, which eliminates the richness and complexities in the data.
      The approach should be to adjust, but to be highly conservative in the adjustments, carefully documenting the impact, and follow simple adjustment standards.
      My latest post looks at Reykjavik data. The raw data appears to show much wider swings in average temperature than the global average. The GHCN and GISS adjustments change the story, emasculating the early twentieth century fluctuation.
      A reason for choosing the handle “manicbeancounter” is I believe that to understand data you need to look at it from different perspectives. It often means going up blind alleys, but these can be as important as reaching plausible answers.

  5. Geckko

     /  10/08/2015

    So you thesis is that the data was what was expected so it is appropriate to adjust the data.

    Do you even know what the word “science” means?

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     /  07/11/2015

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