Europe – Central

The Center of Europe

These countries make up the center of Europe. While a couple have some coastline, they are mostly inland, but not dominated by the cold Asian air nor moderated by the Gulf Stream or Mediterranean. Yet Asian winds can bring cold from time to time. The range of monthly anomalies can be quite large.

These countries often have very long temperature histories. As a result, some of these graphs are quite large. Please click on them to get a larger and more readable graph.

Germany

We saw Germany in an earlier posting as the annual average dT/yr while this version has the full monthly data. While there is some wobble to the data, Germany has little net trend. The start has both lower lows and higher highs. Once enough thermometers are in place, the trend is just dead flat.

Germany Monthly Anomalies and Running Total

Germany Monthly Anomalies and Running Total

Hungary

Not to be outdone by Germany, we have Hungary with an incredibly flat history.

Hungary Monthly Anomalies and Running Total

Hungary Monthly Anomalies and Running Total

Austria

And Austria with about 1/3 C rise in the trend line over 230 years. Hardly the stuff of worries.

Austria Monthly Anomalies and Running Total

Austria Monthly Anomalies and Running Total

Czech Republic

Finally something more interesting than dead flat, the Czech Republic has two flat segments with a “splice” at the “bullseye” where the monthly anomalies all run through zero at about 1950. I find it fascinating that the trend lines end up so close to parallel. Wonder fi there was a change of calibration at the start of the Cold War…

Czech Republic Monthly Anomalies and Running Total by Segment

Czech Republic Monthly Anomalies and Running Total by Segment

Slovakia

Slovakia too gets a “splice” at a “bullseye” point, but this one is in about 1942. The segment post splice is higher, but with a gently dropping trend line. Go figure… The record is shorter too. It starts at “only” 1852.

Slovakia Monthly Anomalies and Running Total by Segment

Slovakia Monthly Anomalies and Running Total by Segment

Switzerland

And as a complete surprise we have Switzerland with a fairly consistent rise over time. While it’s not at all what I would have expected (the Swiss are usually much better at things requiring care and calibration) it would appear that much of the “warming” available for “homogenizing” into the rest of central Europe is provided via the Swiss. Eyeballing that first Red segment, it looks like a “splice” step function higher about 1865. There is a bit more “lift”, but not much, from continued thermometer changes until “the usual” 1980 bump up and 1990 “bullseye” change of “Duplicate Numbers” (as NCDC calls them). That last segment getting a much steeper warming trend line; and notice how the ‘low going” peaks are much more clipped. Barely reaching -5 C where in prior history they reached -7 C and the occasional -8 C. Looking at the post 1980 part of the graph, the ‘density’ of lines above zero is higher than that below zero. It looks like the overall volatility is reduced but with the bottoms clipped more than the tops.

A comparison of the adjustment and QA processes applied to Swiss data as compared to Hungarian or German or even Austrian ought to yield some interesting insights…

Switzerland Monthly Anomalies and Running Total by Segments

Switzerland Monthly Anomalies and Running Total by Segments

Poland

And then we have Poland. I can just hear the “Polish Joke Books” being opened… I’d expected Poland to be either like Germany (no trend) or Ukraine (no trend) since it is bracketed between them. But no…

A wonderfully long thermometer history, starting in 1780. It begins with a very steep warming trend before the industrial revolution (as do several places with long histories, though when you get back to 1720 it is warm again in Sweden). Then Poland stabilizes from about 1836 to the end of World War Two. Next we get a similar step function higher to that seen in Czech (and again one wonders if calibration was different under Soviet guidance…) yet with a falling trend line. Then a “step function” higher still at the “bullseye” in 1990 during The Great Dying of Thermometers. Yet the trend line goes to nearly dead flat. Just amazing.

So I’m left to presume that “warming” in Germany and Ukraine in the GISS anomaly maps is “courtesy” of Poland and Switzerland, but with a little “lift” from Czech and Slovakia.

Poland Monthly Anomalies and Running Total

Poland Monthly Anomalies and Running Total

Conclusions

I think it’s pretty clear that each country has it’s own “issues”. The averaging and “homogenizing” of all these different countries together might well give a net “warming trend”, but it will not be because these places are all warming. It will be because some of them play with their instruments.

It is also pretty clear from the disjoint nature of the ‘step functions’ in nearby countries that it is not due to some general weather pattern shift in the region. Things happen “by country” and neither CO2 nor regional weather shifts (like the AO or AMO) can do that.

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About E.M.Smith

A technical managerial sort interested in things from Stonehenge to computer science. My present "hot buttons' are the mythology of Climate Change and ancient metrology; but things change...
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3 Responses to Europe – Central

  1. P.G. Sharrow says:

    At 5:30 this morning I awoke with your bulls-eye in my head and it dawned on me that it is not an artifact in the temperature record line. It is the point of origin! The record was created from that point forward. The 30 year baseline, 1960 to 1990, created at first from a few UHI soaked thermometers and then outliers added, corrected for lower then UHI readings. The 1800 to 1960s added later as time and resources permitted. These “Official” GISS, CRU etc. records grew up around a few collage professors AGW research papers databases and programs. This is why they are so poorly done. Always on the cheap. The 1990 to 2010 recordings have been added, corrected, as they occurred, from a few selected instruments mostly from air fields as they are automatically up dated and electronically forwarded, and all the outlying grid boxes can be computer generated from a few recordings. Always on the cheap and they are deathly afraid someone, like you, will figure out that they have been faking it all along.

    The present temperature “official” databases were all started around1990 -1993 to study and document Global Warming. That is why the “Bulls-eye”.

    You keep at this my friend, your scientific research of the creation of the “official” temperature records is awesome and will be a standard for a proper scientific research paper. How to follow the data to make the finding and not make a finding first, then gather the facts to prove it.

    Now, maybe I can take a nap.

    man happened to wordpress? the format is all messed up, or is it just me? non wordpress sites look ok.

    REPLY: [ Don't know about wordpress, looks fine to me both on a Mac / Safari and on a PC / Firefox. Haven't tried the Internet Exploder yet, need more coffee first ;-)

    Yes, the "bulllseye" detects "start of series", but it also detects "re-start" of series... There are times when the "Duplicate Numbers" (or "Modification History Flags" in non-NCDC speak) all reset for a location. Those also tend to get a bullseye - though often a more modest one. Oddly, there is sporadic evidence for such "restarts" about 1970, 1980, and 1990 plus or minus a year or two all through the data. I suspect it is an artifact of some difference in how a given BOM did their data collection or what process NCDC applied to each decade. Something like, perhaps, "We have daily data from 1980 on, let's compute our OWN version of monthly mean with daily 'QA' tossing outliers, but prior to 1980 all we have is archived monthly means outliers and all". Then you get different "Duplicate Numbers" at the splice (often with a bump of 'thermometer count' during an overlap period and reduced monthly volatility but not quite a bullseye). But when there is no overlap, you get a hard core bullseye. In the data you will often see a line of zeros right across the year. Sometimes it's split or scattered between a couple of years as different thermometers take a 'reset'. But yes, you are substantially correct in your "start" surmise. Just add "re-start" to the mix too...

    It was a design choice to leave it this way. When I first saw it, I made a variation that 'smooths the splice' by joining "Duplicate Number" records for the same location. I would get substantially the same overall graph (with a smoother splice point with less jumps), but no 'bullseye' in the monthly lines. A smoother splice... All the "Climate Scientists" claimed you needed to do it that way. GIStemp does it that way (with the "combine series at one location" code in the STEP1 "splice and homogenize" process). But I looked at it and saw a great forensics tool if you didn't try to "hide the splice"... I wanted to see what the data said, not what an ironed over flattened smoothing / splicing program would say... And I'm glad you like the results ;-)
    -E.M.Smith ]

  2. P.G. Sharrow says:

    Yes wordpress is fine now, it went all strange last night while I was on WUWT.
    I prefer your warts and all presentation, it makes the changes stand out. leave the fakey stuff to the climate science pros. ;-q

  3. Alexej Buergin says:

    Switzerland:
    Meteoswiss publishes a comparison between raw and homogenized data:
    http://www.meteoschweiz.admin.ch/web/de/klima/klima_heute/homogene_reihen.Par.0054.DownloadFile.tmp/vergleichoriginalhomogen.pdf
    These are only graphs, starting from around 1870, and show that a big part of the temperature rise is from “homogenization”. The data is hidden behind a pay wall. UHI is NOT corrected, even though most of the stations are urban. The example of Sion, in a narrow valley, is almost breathtaking.
    Yes, the raw data are probably recorded correctly, but I would not buy a used car from somebody at Meteoswiss.
    (As one would expect, winter was cold at high altitudes with not much UHI, and average down low in the cities.)

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