The Worst of BEST

This post follows up my recent Paraguayan temperature puzzle post on homogenization and temperatures in central South America. In it I offer some insights into specifically how the Berkeley Earth Surface Temperature (BEST) adjustment procedures contrive to show warming across the whole of South America while the BEST raw records show a mixture of warming and cooling trends.

Figure 1 of the Paraguay post showed a map of warming and cooling gradients since ~1950 at selected South American stations based on linear trends measured from GHCN v2 raw records. Figure 1 below shows the BEST version of this map based on linear trends measured from BEST’s raw records (all the data used in this post are downloaded from BEST’s station data site). It’s not directly comparable to the earlier Paraguay map because BEST measures warming from the beginning of the record rather than after 1950, but we still see the same concentration of blue dots in Paraguay and in parts of Chile. Overall two-thirds of the BEST raw records show warming and one-third show cooling:

Figure 1:  Temperature gradients measured from BEST raw records at selected South American stations.

Figure 2 now shows what the map looks like after BEST adjusts the raw data. There is now no record in South America that shows cooling. All of them show variable amounts of warming.

Figure 2:  Temperature gradients at selected South America stations after BEST homogeneity adjustments

These two graphics alone are enough to suggest that BEST’s adjustments to the South American records are warming-biased, but before we can confirm this we need to tie up the loose ends. To do this we will look first at a specific example that demonstrates a warming bias in BEST’s adjustment procedures, and then we will follow up with a discussion of how BEST’s procedures might have generated this bias.

There are far too many records in South America for me to deal with all at once, so again I will use Paraguay as an empirical example of the way BEST’s adjustment procedures work. Figure 3 shows BEST’s raw monthly record for Mariscal Estigarribia (henceforth Mariscal), a record in northwest Paraguay:

Figure 3:  Mariscal raw record (reproduced from the BEST station data site)

The first steps in the adjustment process are to remove the obviously bogus readings (BEST does a reasonably good job of this) and, more importantly, to identify and quantify artificial shifts in the raw records. BEST identifies these shifts by comparing the raw records with a “temperature expectation” series, and comparing BEST’s Mariscal raw record with BEST’s Mariscal temperature expectation series identifies the four shifts shown by the black lines in Figure 4:

Figure 4:  Shifts in the Mariscal raw record identified by BEST (reproduced from the BEST station data site)

Between them these four shifts define a net cooling bias of around 0.5C in the Mariscal record. (Note that all four are classified as “empirical breaks”; there is no record of any coincident station moves or time-of-observation changes.) And when the shifts are adjusted out the Mariscal series becomes, unsurprisingly, a close match to the Mariscal temperature expectation series:

Figure 5: Comparison of adjusted BEST Mariscal record with BEST temperature expectation series (reproduced from the BEST station data site)

But where did BEST get the temperature expectation series from? According to Rohde et al.‘s 2013 description of BEST’s temperature averaging procedures it obtained it from weather stations in the “local region” surrounding Mariscal:

… we have provided a “regional expectation” time series, based on the Berkeley Earth expected temperatures in the neighborhood of the station. This incorporates information from as many weather stations as are available for the local region surrounding this location

So let us now look at the weather stations in the local region surrounding Mariscal, which I have arbitrarily defined to be within a circle with a radius of 500km centered on Mariscal. Inside this circle there are 13 reasonably well-distributed records – Nueva Asuncion, Puerto Casado, Las Lomitas, Yacuiba, Bahia Negra, Rivadavia, Concepcion, Camiri, Robore, Tarija, Puerto Suarez, Corumba and Asuncion. A simple average of these records yields the“local” series shown in Figure 6. The trend line shows about two-tenths of a degree of cooling since 1950:

Figure 6:  Mariscal “local” time series, constructed by averaging 13 records within 500km of Mariscal

Contrast this now with the BEST temperature expectation series for Mariscal. There’s a good peak-trough match, but the expectation series shows about a degree C more overall warming than the local series:

Figure 7:  Mariscal local time series compared with BEST’s temperature expectation series for Mariscal

Where does this added warming come from? I can replicate it only by discarding all the local records that show cooling. The average of the remaining four records that show warming (Las Lomitas, Robore, Puerto Suarez and Corumba) is a respectably close match to the BEST expectation series:

Figure 8: Mariscal local time series with cooling records discarded compared with BEST’s temperature expectation series for Mariscal

And the match gets closer as we add more warming stations from outside the 500km “local” radius (Figure 9 adds the records from Corrientes, Resistencia, Formosa, Posadas, Ponta Pora and Pedro Juan Caballero):

Figure 9:  Mariscal local time series with cooling records discarded and more distant warming records added compared with BEST’s temperature expectation series for Mariscal

Clearly BEST has based its temperature expectation series for Mariscal largely if not entirely on surrounding records that show warming. Records that show cooling are ignored or at least heavily de-weighted. How does BEST do this?

Now I don’t suppose for a moment that BEST went through the records one by one and deliberately threw out those that showed cooling. The culprit has to be BEST’s adjustment procedures, which sausage-machine large numbers of records into a homogeneous whole. (And the whole is very homogeneous. The BEST adjusted series for Paraguay is hardly distinguishable from the BEST adjusted series for Brazil and Argentina.) The question therefore becomes, exactly how do BEST’s adjustment procedures do this? Here is what I believe to be the sequence, although a lot of back-and-forthing goes on:

BEST assesses record reliability (the quotes are again from Rohde et al.):

… we assess the overall “reliability” of the record by measuring each record’s average level of agreement with the expected field at the same location.

Here’s our first indicator. The records around Mariscal that show warming agree with the Mariscal “expected field” (i.e. the temperature expectation series) and those that show cooling don’t. Hence the warming records will receive a higher reliability ranking than the cooling records.

BEST then uses the reliability rankings to weight individual records:

Another problem is unreliability of stations …… To reduce the effects of such stations, we apply an iterative weighting procedure.

How much difference does this weighting make?

The (reliability metric) is used as an additional deweighting factor for each station …. this metric has a range between 2 and 1/13, effectively allowing a “perfect” station to receive up to 26 times the score of a “terrible” station.

De-weighting the records around Mariscal that show cooling by factors of up to 26 would certainly explain why they disappear during the adjustment process.

And as noted in the second quote final weights are assigned by an iterative weighting procedure:

The determination of the weighting factors is accomplished via an iterative process that seeks convergence. The iterative process generally requires between 10 and 60 iterations to reach the chosen convergence threshold of having no changes greater than 0.001°C in Tavg between consecutive iterations.

This iterative process is probably where the damage is done. Exactly how it’s done isn’t clear, but it may have to do with the fact that the iterations use every record within a 2,000 km radius (up to 300 are used to construct BEST’s final Paraguay series), and since about two-thirds of these records will show warming and only one-third cooling we might expect that the iteration process will progressively de-weight the cooling stations and converge on the warming stations.

Another possible contributor is BEST’s “scalpel” approach, which divides individual raw records into separate records whenever a “break” in the record is identified. The problem here is whether the breaks BEST identifies are artificial. The downturns in the Paraguayan records that define the main period of cooling in the 1960s and 1970s are all identified as artificial breaks even when no break in the raw record is visible, which is most of the time.

But regardless of its precise origin these results leave little doubt that BEST’s adjustment procedures have introduced artificial warming biases into the raw temperature records in and around Paraguay. (Yes, I know that in the Puzzle of Paraguay post I concluded that there really wasn’t enough information to say whether the Paraguayan records were cooling-biased or not, but the BEST adjustments would be the same in either case. They take no account of the potential impacts of international borders. The problem is structural and internal to BEST’s procedures.)

The warming bias is also continent-wide (and probably hemisphere-wide too, although I have not verified this). My tabulation of BEST’s raw and adjusted results for 86 South American stations is a little too large to fit in the post, but here’s a summary of it. (Note that the warming trends are calculated over the total length of the record):

BEST’s adjustments have lowered the range of warming/cooling observed in the raw records from 6.50°C/century to 2.95°C/century, showing that they have indeed gone some way towards homogenizing the data, but in doing so they have also roughly doubled the amount of warming shown by the raw records. And after adjustment every single record shows some level of overall warming. Not one shows a cooling trend.

The warming bias introduced by the BEST adjustments is illustrated graphically in Figure 10, which plots the trend-line gradients of each of the 86 raw records against the adjustment applied to them. The red line shows what an unbiased homogenization operator passing through 0.0 would look like. The trend line through the BEST adjustments shows an average warming bias of about a degree C relative to this line. (The two outlier records that show near-zero warming and zero adjustment are Bariloche and Bahia Blanca in Argentina. How these records managed to escape unscathed is not known.)

Figure 10:  XY plot of raw record trend line gradients for 86 South American stations versus adjustments applied by BEST

Before closing I will briefly touch on two other problems with BEST’s adjustments. The first is that they homogenize the records so strongly that any variations in regional trends that might be present are frequently obliterated. As a result the BEST series are of limited use in studying regional trends.

The second is illustrated in Figure 11, which compares the raw record for the Amazonian city of Manaus with BEST’s adjusted series for the 1.6-degree grid block that Manaus is located in. There are some wild divergences between the raw record and BEST’s grid block series before 1940 but at least they both show similar amounts of warming after then.

Figure 11:  Manaus raw record versus adjusted BEST series for the Mariscal 1.6 degree grid block

The main feature of interest, however, is that Manaus record goes back only as far as 1910 while the Manaus grid block record goes back to 1824. Where does the extra 86 years of data come from? From far away. According to BEST all of the data between 1886 and 1910 were projected into the grid block from stations more than 1000 km from Manaus and almost all of the data before 1886 were projected in from three stations on islands in the Caribbean and Atlantic – St. Clair Trinidad, Codrington Barbados and Saint Vincent. Do we get meaningful results when we project temperatures from these three island records almost 2000 km south into the middle of the Amazon jungle? I suspect not.

Finally comes the question of the quality of the raw records BEST uses to extend temperatures in the Manaus grid block back before 1910. Figure 12 plots some of them up. Words are superfluous.

Figure 12:  Raw records used to extend the Manaus grid block record back before 1910.

This entry was posted in Climate change and tagged , , , . Bookmark the permalink.

45 Responses to The Worst of BEST

  1. Bernd Palmer says:

    An artificial world at BEST.

  2. A C Osborn says:

    Euan, I know I shouldn’t say it, but I will, “I told You so”.
    I picked this up last year when I looked at the UK, the UK “Overview” that they provide is in actual fact western Europe, look at London as a City on it’s own , or look at Swansea as a City and you will see exactly the same “shape” as western Europe, ie the Regional Expectation.
    I looked at Mumbles, Greenwich maritime and Valencia in Ireland, all flat as Raw Data, all the same shape as the Regional after “Quality Adjustments”.
    I was not kidding when I quoted Mosher as saying if you want to know what the actual historical temperature was look at the Raw data, if you want to know what the Model says it should be use BEST Final.
    They, along with NCDC & GISS really are re-writing history.
    Remember the NOAA mistake I pointed out when they quoted the “Actual Temperature” for 1997 instead of an anomaly, they forgot to remove it so when you compare it to later Anomalies + Baseline it shows that 1997 & 1998 were far warmer than today’s “hottest ever” record breaking weather.
    Warmists write it off as a simple “baseline problem”, yeah right.

  3. Sam Taylor says:

    So you couldn’t be bothered to look at their source code then?

    • Euan Mearns says:

      Sam, I despair! Sometimes you contribute in a positive and meaningful way and others like this comment, there is nothing for anyone to learn. If you have time and are able to look at the source code (good name for a movie) why don’t you do so and inform everyone about what is going on in Berkley.

  4. Euan Mearns says:

    Roger, if your speculation is true then these are my comments:

    1) Not mentioned here is BEST’s treatment of UHI. I gather part of their argument is that cities occupy such a small area that UHI gets weighted out of the equation. But what if UHI gets projected into a regional trend used for homogenisation?

    2) I think we are agreed upon that sets of congruous records can be used to define the temperature history of a region. Records spatially mixed with the congruous set that differ to it may be suspect and should not be used. So comparing a record to a regional expectation as a means of assessing quality is a good approach. But I gotta say that adjusting a record to that expectation sounds bonkers. I don’t know what they are thinking about.

    3) Projecting temperatures into areas with no data is equally dubious. There are some large areas with little cover. Sahara dessert for one. You just have to accept that those areas have no cover. If you were going to project a temperature trend from a point into an area you would have to take into account the following:

    a) Latitude
    b) Altitude (mean altitude of the area projected into)
    c) Does it belong to the same congruous area as the source station? i.e. does it have the same climatic pattern.

    Do you know if anyone is taking station altitude into account in any of these compilations. It shouldn’t affect trend but will have major effect on actual temperature. For example, coastal stations in Chile can hardly be used to define the average temperature of Chile. Normalising to a fixed altitude would be one way of reducing the spread in absolute temperatures. Not sure if this would reduce “record end” effects or not.

    • Not mentioned here is BEST’s treatment of UHI.

      I haven’t looked into this in any detail, but two records within striking distance of Paraguay that show strong urban warming gradients (Sao Paulo and Buenos Aires) seem to be appropriately handled by the BEST algorithm, which lowers the raw trend from 2.45 to 1.08 degrees/century at Sao Paulo and from 1.74 to 0.81 degrees/century at BA. The BEST algorithm in fact does a reasonable job of homogenizing records that show warming – even when they show too much of it. It’s the records that show cooling (or not much warming) that it can’t handle.

      Records spatially mixed with the congruous set that differ to it may be suspect and should not be used.

      That’s why I didn’t use Sao Paulo or Buenos Aires when I put my global and hemispheric series together. 🙂

      If you were going to project a temperature trend from a point into an area you would have to take into account the following:

      a) Latitude
      b) Altitude (mean altitude of the area projected into)
      c) Does it belong to the same congruous area as the source station? i.e. does it have the same climatic pattern.

      If you’re looking at anomalies you only need take c) into account.

      • A C Osborn says:

        How can data out to 1000Km be in the “same congruous area”, unless the target is in the middle of a very large flat continent with only 1 prevailing wind.
        Areas 50 miles apart seperated by high hills quite often have different temperatures and weather characteristics, or coastal areas on one side of the UK versus coastal areas on the other side.

        • A C Osborn says:

          Have a look at Australia and New Zealand temperatures on NuSchool and look at what the winds and Oceans are doing around them to see what I mean.

        • There are many places, such as the Arctic, Central Asia. Chile and Sub-Saharan Africa, where temperature trends are congruous over distances of thousands of kilometers. There are also places, such as on either side of the Andes, on either side of the Himalayas and across the Davis Strait between Canada and Greenland, where they aren’t. But I’ve never found anywhere where they are significantly different over distances as short as 50 miles.

          • A C Osborn says:

            Roger, David below here has the same concerns as me about Local Climates.
            Last month in the UK when the wind switched from the prevailing Westerly to a high pressure driven slow North Easterly wind the temperature on the east of the Pennines was 10 degrees C lower at night than the westerly side of the Pennines (ie west coast) about 50 miles away. This situation only lasted a couple of days, but if a blocking high had been involved it could have lasted weeks, where the west side of the Pennines would have eventually cooled to the same as the east side.
            So if you have the topography, prevailing winds/Ocean Currents and the right weather conditions this can happen anywhere.
            BEST would ignore this “disparity” and homogenise any Stations in the Eastern Pennine condition to the rest of the UK and Europe.
            That weather “short term climate) was real and should not be ignored.
            That is how Valencia statuon in Ireland, which is totally flat ends up with the same warming and shaped trend as the rest of the UK and Europe.
            Science it is not.

          • AC: You are talking days, weeks and months. I am talking years, decades and centuries.

            Alaska is an example of the impacts of a long-term wind shift. During the 1976 PDO phase shift winds in Alaska abruptly shifted to warm, moist southwesterlies, and temperatures over all of Alaska (and much of far Eastern Siberia too) abruptly rose and stayed there. The side of the hill the station happened to be on made no difference

          • A C Osborn says:

            Roger, yes but both situations are treated the same by BEST.

  5. Javier says:


    In this figure you have a possible explanation on why the Paraguay area stations are showing cooling while the surrounding stations show warming. If this type of thing happens often enough, it suggests that the raw data is the real deal.

  6. manicbeancounter says:

    Thanks Roger for another excellent post. You seem to have gone beyond others in trying to understand the methods that BEST is employing, and the consequences of them. The part missing is to understand why BEST should

    (a) Chose an expectation based on the records with warming
    (b) Reliability of data is based on conformance to these records

    Core to this is the belief that “the world is warming and humans are the cause of it”. Everything else is due to data errors and noise. So the expectation is not based on some regional mean, median or modal pattern, but on the records that best conform to the perceived global pattern. What is not comprehended is that are potentially other explanations of temperature change, or that real, cleansed, temperature data is mostly noise, with exogenous influences having vague and chaotic impacts.

    Regards Kevin

    • Kevin:

      I don’t think BEST “chose” their expectation series. I think they were more likely a product of BEST’s homogenization algorithm. But the fact that the algorithm delivered the expected results (i.e more warming) probably went some way towards convincing BEST that it was working correctly.

      Anyway, in my continued efforts to try to understand the methods that BEST is employing I hsve finally found the global variogram that “forms the foundation of BEST’s kriging process”. I post it below for reference:

      It isn’t actually a variogram because it plots correlation rather than covariance on the Y axis, which I think makes it a correlogram. The most important thing it shows is what we in the kriging trade call the “range”, which is the separation distance at which temperature trends cease to be correlated. This occurs at a distance of 3,000km, where the black line goes asymptotic close to zero. These results make it acceptable to allow all records within 3,000km of the calculation point to contribute to the value at the calculation point provided they are properly de-weighted with increasing distance, with the weights being determined from the range and the intercept of the black line on the Y-axis (known as the “nugget”). BEST, however, cuts the “search radius” to 2,000km, which is conservative, but what isn’t conservative is that BEST imposes no limit on the number of stations within the search radius that can contribute. As a result we find locations in the Australian ourback where over 500 records contribute to the temperature estimates, locations in Europe where over 2,000 contribute and locations in the US where up to 9,000 contribute. This will strongly homogenize the records (in the trade it’s called “smearing”) but in many cases local records will get swamped by distant records even though distant records are de-weighted in the kriging process. There are also questions as to whether it’s appropriate to apply a global variogram in all parts of the world, but I haven’t looked at that yet.

  7. A C Osborn says:

    Euan & Roger, I have taken the liberty of contacting William Briggs to ask if he has time to look at these posts. He says he will if he has time.
    But he sent to me this link to his work on Time Series.
    Which I think will be well worth a read, especially about BEST.

  8. concernclub says:

    Euan and others,

    if you want to do some real climate science, why don’t you collect and analyse the
    glaciers in the Andes for example? Deforestation makes huge effects and the simple minded
    temperature data interpretations are nothing more than speculations as long as you have
    all the facts together.

    for the glaciers however you can do a great job ..
    (or perhaps you prefer just spread misinformations?)

    • A C Osborn says:

      Why don’t you try doing some research in to Glacier loss yourself, starting with the end of the Last Ice Age to see what Glacier loss really looks like. You do know that most of North America and Northern Europe were under massive Glaciers.
      Starting here might just put it in to perspective for you.

      • concernclub says:

        just asking/proposing something to you to become famous
        and might understand better than others what matters and what not.

        For me, I have enough data and some physics laws telling me
        that the precautionary principle should be applied
        if one does not want to take the risk in getting out of control
        co2 levels.

        But, much more serious problems exist in front of the door
        and governments have decided already to not do anything
        about the CO2 problem.
        The co2 annual increase will start to go down once
        we have the peak oil and peak etc behind us.

        that is more fascinating because it is happening at some
        point during the next years.

        • A C Osborn says:

          Dream on.

          • concernclub says:

            same happy dreams for you!

            lets do the bet with the arctic minimum ice in September
            4.5 million km2 or less is my bet much less than
            the considered by you wrong IPCC models predict!

            if you believe what you are saying you can be confident to win!

          • Euan Mearns says:

            Considering September Arctic Sea ice area has been below 4.5 million sq kms for 16 of the last 17 years …… (censored commentary)

        • louis says:

          during the next years …. ?
          Coal produces the highest CO2 emissions of the common fuel types and with reserve estimates stretching beyond a 100 year estimate it doesn’t look like peak/depletion of coal will have much effect on the policies being decided on today.
          Also … Euan ……. famous ….. ?
          Have you looked at …….

    • Euan Mearns says:

      Michael, you are rumoured to be a physicist working at CERN or ETH. I don’t know. I can only assume that there is a game of pass the physicist going on. The Earth is in an interglacial period. If you are incapable of working out that glaciers have a tendency to melt in interglacials then I’m afraid your level of intellect and scientific understanding falls way way below that I am seeking from commenters on this blog. Especially when you talk about tropical glaciers.

      Consider this a final warning. Any more ungrounded comments and you go on moderation. At some point you guys are going to have to confront the fact that your arguments are built on quick sand.

      Please detail what misinformation you consider I am spreading.

      • concernclub says:

        your misinformation you just wrote:
        “below 4.5 million sq kms for 16 of the last 17″

        just look at the facts and reconsider. Just 3 years! (2007/2011/2012)

        for the rest
        “The Earth is in an interglacial period. If you are incapable of working out that glaciers have a tendency to melt in interglacials then I’m afraid your level of intellect and scientific understanding falls way way below that I am seeking from commenters on this blog.”

        well, you understand I presume as a basis to participate
        on your own blog(including the owners?).
        During the cooling which began some 6000 years ago
        the glaciers are supposed to grow or decline?

        “An often-cited 1980 study by Imbrie and Imbrie determined that, “Ignoring anthropogenic and other possible sources of variation acting at frequencies higher than one cycle per 19,000 years, this model predicts that the long-term cooling trend that began some 6,000 years ago will continue for the next 23,000 years.”[23]”

        anyway yes, CERN and ETH right, thats me and its not a rumor as you know my email!

        just tell me and all others that you can not stand facts opposing your worldview and I stop writing comments.


        • louis says:

          Today’s comparatively warm climate has been the exception more than the rule during the last 500,000 years or more. If recent warm periods (or interglacials) are a guide, then we may soon slip into another glacial period. But Berger and Loutre argue in their Perspective that with or without human perturbations, the current warm climate may last another 50,000 years. The reason is a minimum in the eccentricity of Earth’s orbit around the Sun.

        • Euan Mearns says:

          Concernclub says:

          same happy dreams for you!

          lets do the bet with the arctic minimum ice in September
          4.5 million km2 or less is my bet much less than
          the considered by you wrong IPCC models predict!

          if you believe what you are saying you can be confident to win!

          Euan says:

          Considering September Arctic Sea ice area has been below 4.5 million sq kms for 16 of the last 17 years …… (censored commentary)

          Note my emphasis on AREA. And Concernclub claims this to be mis-information. Links to a source that uses sea ice EXTENT and notably in his original bet does not specify what he is talking about. I just don’t have time to engage in these petty exchanges!

          • concernclub says:

            don’t have the time, me neither!
            But to make things clear:
            the official satellite data are from the link I always
            added here.
            as far as one see your link
            claims to get the data from this site as well.

            Apparently the data are not the same.

            (and yes you forgot to mention that
            since 6000 years roughly we are “cooling”
            because of the Milankovitch cycles ..)

            so what do you expect for the glaciers?

          • Euan Mearns says:

            If you do not know the difference between sea ice AREA (Cryosphere Today) and sea EXTENT (nsidc) then you should not be lecturing us on what is and what is not good climate science and you certainly should not be placing bets on sea ice.

            Glaciers can either expand, contract or stay constant. Given their very dynamic nature the latter is quite unlikely. So they will normally expand or contract.

          • A C Osborn says:

            What I find most interesting and telling about concerclub’s posts is that here we have important work clarifying that the Adjustments to the Temperature datasets is roughly in accordance with NCDC/GISS declarations of increasing the warming trend by around 0.5 degrees C.
            Plus you have identified substantial UHI which does not appear to be corrected for (because as BEST says it doesn’t do anything to the Global Trend).
            So of the 0.8 degrees C warming of the last 100 years only 0.3 degrees C “may” be real.

            concernclub doesn’ say “ho look isn’t that good we don’t have to worry about Catastrophic warming anymore”.

            No, instead we get complete changes in Subject, “look at the Arctic Sea Ice” (note not the record breaking Antarctic Sea Ice) and “look the Glaciers are disappearing”.

            When anyone with any real interest would know that Glaciers disappeared at far faster rates in the past and that Arctic Sea Ice can hardly be melting when the Arctic temperature is from -9 to -36 degrees C.

            Any bets that it will be Sea Level or Ocean Acidification next?

          • William says:

            Glaciers disappeared at far faster rates in the past

            Do you have references to published papers for that?

            Arctic Sea Ice can hardly be melting when the Arctic temperature is from -9 to -36 degrees C.

            What does that mean exactly? Everyone knows that Arctic sea ice melts every year to some extent and has dropped in volume hugely over the last few decades, so this assertion is very strange.

    • Euan Mearns says:

      if you want to do some real climate science

      Which is a derogatory comment, common among bottom feeding green trolls. You guys just don’t know how to behave, do you?

      These mountain top glaciers in the high tropical Andes are a relic from a different climatic era – I don’t know when. But they probably owe their existence as much to precipitation patterns as temperature history.

  9. David A says:

    Looking at the first two maps, it appears that something in the BEST methodology data mines for warm stations.

    A silly question, but are you certain BEST adjusts for surrounding areas based 100 percent on the raw readings. In other words, having adjusted one station warmer based on surrounding regions, is that station, now warmer, then possibly used to further adjust nearby stations?

    Evan, at WUWT indicated a propensity to adjust rural to urban. The same was found in Australia. Have the stations in the raw SA map been checked to see if the cool one’s tend to be rural? BTW, true rural is hard to find, as even small population increases have been shown to have significant UHI affects. Also, the great station die off, gave more weight to fewer stations, and increasing the influence of those stations, gives them even greater weight.

    Were the surrounding stations impact on each other determined before or after UHI adjustments?

    I know these are simple obvious concerns, but the questions are at least cogent.

    I question the entire idea of adjusting to surroundings when there were no TOBS factors or station moves to consider. When you factor in the very large change in the number of stations, the location of stations, the UHI factor, and then the blending of whatever is left of the entire record, well a term like FUBAR may not be entirely unreasonable.

    BTW, near coastlines anomalies can very over distances at little as a few miles. I have seen summers in San Diego where the coast only, (and I mean no more then one or two miles inland), is cooler then average, but 10 miles inland is consistently hotter then average. John Coleman called it the WOF; “Wall of Fog” ,

    Further, persistent jet stream patterns such as in the US warm West / cold East produce very different trends relatively close to each other. These should never be blended, as ocean and land boundaries can have sharp divides as the NE coast did this year in areas where warmer then average SST interacted with cold Arctic flows.

    Has anyone done a comparison of RSS to see how well it correlates geographically to surface readings. In areas it is very good. For instance the RRR off of California it well represented here.

    The cool in central Africa RSS, had virtually 0 surface readings, and was filled in from surrounding warm areas by GISS.

    Yet 1998 RSS is clearly far warmer then 2014.

    • Euan Mearns says:

      David, first comment has to be approved. Now you can comment freely.

    • are you certain BEST adjusts for surrounding areas based 100 percent on the raw readings. In other words, having adjusted one station warmer based on surrounding regions, is that station, now warmer, then possibly used to further adjust nearby stations?

      As I understand it BEST uses an iterative procedure which progressively adjusts the station reliability weights until convergence is achieved. So I guess only the first iteration uses the raw data. After that they adjust adjusted data.

      Have the stations in the raw SA map been checked to see if the cool one’s tend to be rural?

      There are a few city stations in South America with obvious urban warming gradients, such as Rio, Sao Paulo, Buenos Aires and Rosario, but they make up only a small fraction of the total. Elsewhere I’ve found no correlation between population and warming. But without knowing where the station is (and was) relative to the population center, which in most cases we don’t, we can’t say with any certainty which stations are rural and which aren’t.

      Were the surrounding stations impact on each other determined before or after UHI adjustments?

      As discussed above the process was iterative, and as I noted in a reply to Euan above it seems to have mostly gotten rid of spurious urban warming gradients.The impacts of urban warming, like the reports of Mark Twain’s death, are exaggerated anyway.

  10. Leo Smith says:

    IF (temperature does not accord with global warming)
    announce(global warming supported by adjusted data);

  11. A C Osborn says:

    Eaun, you might like to take a look at this article on trend lines by W Briggs.

    It reflects your experience with trying different Baselines.

  12. clivebest says:

    These are my C.BEST results from 2012 onwards

    See also how CRU rejected cooling S.American stations in favour of warming Arctic stations when moving to CRUTEM4

    and in particular

    • Euan Mearns says:

      It immediately becomes obvious that the bulk of observed warming is concentrated in the Northern Hemisphere : Eastern Europe, Russia, central Asia, India, China, Japan, Middle East, North Africa. These are all areas of rapid population increase, development and industrialisation. There is essentially no warming at all in the Southern Hemisphere. Bolivia, Peru, Paraguay and Argentina all appear to be cooling. Even Australia and Zealand are static or cooling. The US is evenly divided and the UK shows essentially no signal at all.

      I’ve not seen these articles before, but this is pretty well where I’m heading. Radiative physics that many warmists want to ram down our throats, to a certain degree determined by the fabric of the Earth’s surface that is radiating.

      I’ve been hunting for global warming in the N hemisphere where there are no people, struggling to find it. But there are some interesting anomalies with data treatment. Most long records begin around 1880 – end of LIA in N hemisphere when it was cold. And we are now post a warm climax. It is inevitable that any linear regression run through data that begin cold and end warm (coincidentally) are going to show warming.

      • William says:

        “And we are now post a warm climax.”

        Does that mean what “post” implies – that temps are dropping? Is there evidence of that? Or even that temps are not rising?

        • Euan Mearns says:

          William, I am sick and tired of this continual and relentless scrutiny. There is 1 of me and 10,,000 of you. Go figure. You have a right to post comments on this blog and with that right comes a responsibility to use it with a huge degree of consideration and discretion for those who provide the platform for your opinion. Go figure what is important to scrutinise and what is not. Where on the planet is warmer now than it was in the 1930s / 40s. You piss me off once again and I’ll simply divert your comments to trash where many of them belong. Right now you are nothing more than a green troll time waster.

          • William says:

            Where on the planet is warmer now than it was in the 1930s / 40s.

            From your Oz data, the GHCN V2 2001-11 average for Alice Springs is about a degree warmer than its average in the thirties and forties. For Woomera, 2 degrees, Halls Creek is about one degree warmer. No other Oz stations have data now and in 30s/40s.

            Even Aberdeen is half a degree warmer.

  13. A C Osborn says:

    Euan, interestingly this post by Bob Tisdale on the Sea Surface Temperatures shows that yet again warmists are picking the Anomaly Baseline that shows the most warming.

Comments are closed.