Europe – Balkans and Former Yugoslavia

Former Yugoslavia and Balkans – 9

Surrounded by water on all sides. Many small countries in close proximity. If anything is going to give us an idea which way European temperatures are moving, this ought to be the place.

Albania – 601

Well. Dropping and then dropped on the floor in 1991. I’m sure it’s just an accidental thing and we’ll be seeing nothing at all like that again. Probably due to war or conflict in the area.

Albania Monthly Anomalies and Running Total

Albania Monthly Anomalies and Running Total

Bosnia / Herzegovina – 607

Dead flat and then dropped in 1992.

Bosnia Herzegovina Monthly Anomalies and Running Total

Bosnia Herzegovina Monthly Anomalies and Running Total

Undoubtedly because they don’t do electronic reporting or issue “CLIMAT” report.

Oh wait, they do!.

Then the only rational conclusion is that it’s due to war or conflict in the area.

Serbia – 639

WOW! Rising 3 C THEN pitching up faster to a rocket ride of heating. Good thing we have consistent reporting from such a fine location. Unlike those cooling or flat stations that were dropped due to war in the region. Good thing these folks were able to keep on reporting, being far removed from such issues… /sarcoff>

Serbia Monthly Anomalies and Running Total

Serbia Monthly Anomalies and Running Total

Moldova – 631

125 years of a consistent dropping trend. Who knew?

Moldova Monthly Anomalies and Running Total

Moldova Monthly Anomalies and Running Total

Montenegro – 632

Dropping for 40 years then dropped on the floor. Must be a ‘fluke’. But that’s OK, GIStemp can just fill in the temperatures from nearby Bosnia. Wait, it was dropped… Albania? Nope, dropped. I know, let’s use Serbia! Yeah, that’s the ticket!

Montenegro Monthly Anomalies and Running Total

Montenegro Monthly Anomalies and Running Total

Croatia – 609

A slightly rising “trend”, except it’s been dead flat since W.W.II and we’ve just gotten back to where we were in 1863 – 1874. Which is right? Who knows.

Croatia Monthly Anomalies and Running Total

Croatia Monthly Anomalies and Running Total

Macedonia – 648

Dropped 2 C from the start then dropped on the floor in 1992. Undoubtedly just a fluke. A one of a kind oddity. Not at all like any other pattern.

Macedonia Monthly Anomalies and Running Total

Macedonia Monthly Anomalies and Running Total

Bulgaria – 608

Flat. Just incredibly flat.

Bulgaria Monthly Anomalies and Running Total

Bulgaria Monthly Anomalies and Running Total

Romania – 637

150 years of steady and consistent dropping as CO2 accumulates through the whole industrial revolution. Then, in 1992, The Pivot and in 2 short decades were back up 2 C. Now THAT’S global warming! (Just ignore how that big yellow line bounces around as the thermometers are changed out… “nothing to see, move along, move along”…)

Romania Monthly Anomalies and Running Total

Romania Monthly Anomalies and Running Total

Greece

I already covered Greece under the “Mediterranean” listing, but felt bad about leaving it out of the Balkans… Besides, it looks so much like Romania with a long persistent drop suddenly recovered to zero in 2 short decades after The Pivot on massive thermometer change.

Greece Monthly Anomalies and Running Total

Greece Monthly Anomalies and Running Total

Conclusions

There is not a lot in common in these records other than a lot of dropping of temperatures over a long period of time as CO2 levels rose dramatically. Then the 1991 or so “bullseye” change of process and “presto!” instant Global Warming! Except in those places dropped from the record and in those that kept on dropping.

I’d bet that either not much is happening at all and it is just an artifact of bad measurement technique, or that there is a very shallow cooling trend and some incredibly bad change of process made in 1990. In no case would I bet on CO2 as having any relationship at all. A correlation plot of CO2 percent with temperature will have a negative correlation over the bulk of all time in the graphs, and then a non-correlation with the astounding hockey stick in the last 20 years as the rates are too divergent.

Substantially, we are “Dancing in the error bands” of our processes. Nothing more.

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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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4 Responses to Europe – Balkans and Former Yugoslavia

  1. oldtimer says:

    Re my earlier link to the CRU evidence, I understand and totally respect your wish to complete what you are presently doing on Europe et al.

    I have now read through it once – there are 78 pages of pdf including references. The bits about raw data sources, comparisons with the other sources will, I think, certainly interest you – in the fulness of time!

    One taster:
    “Figure 4.1.1 compares CRU station locations with 7280 stations used in GHCN. This is not an easy
    comparison to make as station identifiers are not unique for stations without World Meteorological
    Organization (WMO) identifiers (i.e. the standard international station numbering system used by
    WMO). For these non-WMO identifiers we have additionally assessed locations and station names.
    Of the total number of 5121 stations in the current CRU set, 4466 are in common with GHCN. This
    leaves 655 unique stations in CRU and an additional 2814 unique stations in GHCN. Of the 655
    stations unique to CRU, only 430 have sufficient data for the 1961–1990 base period (the other 225
    are not used, therefore, to construct the gridded land temperature dataset, CRUTEM3).”

    So they are saying there are 7280 stations in GHCN. They also say later that all this data is still available. They also produce charts which purport to show that there is good correlation between the various data sets, including GHCN raw data. They also say that their baseline period is 1961-1990 (the standard WMO period).

    I can email you the pdf (3667KB) if you can provide an email address.

    REPLY: [ I’d rather not have more email. The link is enough and it’s in the posting. Per 7280 stations: Yes, there are that many in GHCN over the entire lifetime of the data. So, for example, you have a bunch of stations with about 10 years of data in the 1970-1980 period. They are there, and counted, but kind of useless. In THE PRESENT there are about 1200 stations. So we compare 1200 in the present to about 6000 in the past (after the junky and short ones are dropped in GIStemp). Not real sure what that comparison is going to mean, but that’s what they do…

    In the end, it looks like it’s just CRU trying to justify that they did not completely lose their data as most of it is still in GHCN. Small comfort as GHCN “has issues”… and “short stations” is one of them. As an example of a “in in the past, gone now” problem:

    Here is the “anomaly report” for “Nullagine” in Australia:

    Look at ./DTemps/Temps.rM50194312001.yrs.dT (Y/N)? y
                                                                                                   
    Thermometer Records, Average of Monthly dT/dt, Yearly running total
    by Year Across Month, with a count of thermometer records in that year
    -----------------------------------------------------------------------------------
    YEAR     dT dT/yr  Count JAN  FEB  MAR  APR  MAY  JUN JULY  AUG SEPT  OCT  NOV  DEC
    -----------------------------------------------------------------------------------
    1992   0.18 -0.18    1  -1.3 -0.1 -0.6 -0.2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
    1991   0.07  0.12    1   1.8  0.6  0.2 -0.3  0.2  0.6  1.2  0.5 -1.1  2.5 -4.4 -0.4
    1990  -0.87  0.93    1  -0.7 -0.3  1.9  1.4  0.9  1.8  0.1  1.3 -0.7  0.2  2.8  2.5
    1989  -0.09 -0.78    1  -0.8 -0.9 -1.7 -0.6  0.8 -2.9 -1.8 -0.5  0.7 -2.5  0.9  0.0
    1988  -0.63  0.54    1   1.5  2.3  0.8 -1.1 -0.2  0.3  0.2 -0.1  0.1  3.6  0.7 -1.6
    1987  -0.38 -0.25    1  -0.9 -1.5 -1.8  0.0 -1.0  0.3  3.6 -0.9 -0.1  2.0 -2.0 -0.7
    1986  -0.16 -0.23    1  -0.7  0.0  0.7  0.8 -0.2  0.7 -3.2  0.0  0.0 -1.1 -0.2  0.5
    1985  -0.62  0.46    1   1.5 -0.5  2.5  0.7 -1.0 -0.2  1.6 -0.2  2.3 -3.3  0.7  1.4
    1984  -0.57 -0.04    1   0.1  0.1 -1.4  2.8  1.7 -0.4 -0.1  0.0 -3.5  1.3  0.3 -1.4
    1983  -0.68  0.11    1   0.4  2.4  1.4 -2.5  0.4  1.4 -1.2  0.2  1.7 -0.5 -1.3 -1.1
    1982  -0.38 -0.30    1  -1.6  0.6 -0.6 -2.7 -0.6 -0.6  0.6 -0.2 -2.3 -0.6  3.3  1.1
    1981   0.30 -0.68    1   0.3 -2.4 -2.8  2.9 -1.4 -1.9 -0.3 -0.5 -0.8  1.8 -2.2 -0.9
    1980  -0.52  0.82    1   0.0  0.0  1.9 -0.7  4.4  1.6  0.3  1.5  3.7 -1.4 -1.3 -0.1
    1979  -1.66  1.13    1   0.1  2.0  0.6  0.0 -0.9  1.5  1.8  1.4  2.2  1.1  1.4  2.4
    1978  -0.67 -0.98    1  -0.2 -3.4  0.3  0.0 -1.5 -0.8 -2.1 -2.0 -1.5 -0.7 -0.2  0.3
    1977  -1.13  0.46    1   1.3  1.5  0.6 -0.3 -0.1 -0.2 -0.2  0.0 -0.6  2.7  2.6 -1.8
    1976  -0.78 -0.35    1  -0.9  1.3 -1.6  0.0 -1.2 -0.3 -0.2  1.1 -1.7 -0.2 -1.9  1.4
    1975  -1.15  0.37    1   2.8 -0.6  0.3  3.0  2.3  0.9  0.1 -1.1  1.2 -1.3 -1.5 -1.7
    1974  -0.08 -1.07    1  -2.8 -2.6 -1.3 -3.6 -2.4 -1.1  1.8  0.4  0.1 -1.7  0.0  0.3
    1973  -0.90  0.83    1   0.0  2.6  1.1  1.9  1.7 -1.7  0.0  0.0 -0.4  1.5  3.1  0.1
    1972  -0.91  0.01    1   0.1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
    1971  -0.02 -0.89    1  -1.9 -1.6 -1.0 -1.2  0.0  0.0 -2.1  0.0  1.6 -2.2 -1.7 -0.6
    1970  -0.55  0.53    1   0.2  1.9  0.9  0.7 -1.6  3.1  0.2  0.5 -0.3  1.0 -1.0  0.8
    1969  -1.12  0.57    1   1.6 -1.4  1.1 -0.2  3.7 -1.4  2.2  2.2 -0.8  0.5 -0.1 -0.6
    1968  -0.68 -0.43    1   1.8  2.4  0.0 -1.1 -4.4  0.8 -0.7 -2.0 -0.4 -1.4 -0.9  0.7
    1967  -1.18  0.50    1   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  3.0  3.1 -0.1
    1966  -1.18  0.00    1   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
     
    For Country Code 50194312001
     
    From input file ./data/v2.mean.inv11.M.dt
    

    That I just happen to have available as I’ve been made curious about Marble Bar and I’m doing a set on all the stations near it and how they get merged…. (Posting with data “soon”)…

    So it starts in 1966 and ends in 1992. There are many more that are even shorter, but this one, being longer than 20 years, will make it through GIStemp and form part of the baseline against which the current station in the area will be compared:

    [chiefio@Hummer 2010]$ grepmean 5019431 | grep ^…………2000
    5019431200002000 285 299 284 273 208 187 200 196 241 260 268 302
    [chiefio@Hummer 2010]$ inin 50194312000
    50194312000 PORT HEDLAND -20.10 119.57 10 8S 13FLxxCO 5A 5WATER A 0

    Notice that Port Hedland is classed as “Water”. While Nullagine is not:

    [chiefio@Hummer 2010]$ inin ^5019431
    50194312000 PORT HEDLAND -20.10 119.57 10 8S 13FLxxCO 5A 5WATER A 0
    50194312001 GOLDSWORTHY (GOLDSWORTHY) -20.35 119.52 45 38R -9FLDECO30x-9SAND DESERT A 0
    50194313000 WITTENOOM -22.23 118.33 464 537R -9HIDEno-9x-9WARM GRASS/SHRUBA 0
    50194315000 MARBLE BAR -21.17 119.75 189 239R -9HIDEno-9x-9SAND DESERT A 0
    50194315001 NULLAGINE (NULLAGINE POST OFFI -21.88 120.12 380 434R -9HIDEno-9x-9SAND DESERT A 0
    50194318000 NYANG -23.03 115.03 112 110R -9HIDEno-9x-9WARM GRASS/SHRUBA 0
    50194318001 NYANG (WINNING) -23.17 114.53 75 67R -9HIDEno-9x-9WARM GRASS/SHRUBA 0
    50194319000 TELFER -21.70 122.22 294 321R -9HIDEno-9x-9SAND DESERT C 18

    Sand and desert all about, yet Port Hedland survives for “comparison”…

    So it’s all well and good to say that Nullagine Post Office is in the record, but then it gets compared to Port Heland (the only surviving station in “the area”). And my question becomes “why not just compare Port Hedland to Port Hedland and Nullagine to Nullagine”?

    And what good is it to say “Port Hedland” as captured in GHCN prior to 1990 is like the same data from the same instrument handed over by the same BOM as captured in some other place like CRU? It’s looking where the problem isn’t.

    Now, the three major red flags I see waving from your “taster” are these:

    1) The talk of good correlation in the baseline of 1961-1990 conveniently “cuts off” right at the “bulls eye” Pivot Points. So great, glad they are all in agreement that things were flat or falling in most places right up to 1990… Now, about that supertanker of Fudge that shows up in 1991-1992 ??? The ASSUMPTION is that if they agree before the doctoring they will agree afterwards. That is, IMHO, a boatload of bull pucky… yet it keeps coming around in all these Hypothetical Cow arguments of the form: An vs Bn means An+m must match Bn+m even though Bn+m is not available for questioning…

    So right off the bat, I’m not real thrilled in the approach. It smells too much like the wrong end of a Hypothetical Cow… But I’ve finished Europe now, so I’ll read it in the next day or two and give it a fair chance. I’m just responding to a synopsis and they might have more there to clarify.

    2) Charts and “Photographs with circles and arrows on the back showing the details of the…” and showing it matches “raw GHCN”… OK, don’t know if they said “raw” or you did, but:

    THERE IS NO RAW GHCN Period.

    There simply can not be.

    The data are presented as MONTHLY AVERAGES. By definition a monthly average or mean is not RAW, it is a mathematical product. Further, you will find that the GHCN data set includes flags for at least 2 kinds of “estimates”. This “raw” data may in fact be estimated or filled in from other locations. Each country hands over their monthly averages to NCDC but only AFTER they have done their local “QA” and “estimating” and averaging processes. By the time it reaches NCDC it isn’t even “raw”. Frankly, I’m not sure what it is. (There are multiple ways possible to calculate a “monthly mean” and they may vary by country… Is it “(Monthly Max + Monthly MIN) / 2” or is it “(Ave Daily Max + Ave Daily Min) / 2 ” or does one do a fancier curve fit? NCDC admits this when they describe the MIN and MAX as using the “Duplicate Number” of the MEAN but that there is only one way to calculate an average MIN or MAX so the “Duplicate Number” is not meaningful for them, only for the MEAN…)

    So I’m immediately concerned that the Good Correlation is just saying they keep all the same “oddities” of provenance.

    3) Selection Bias. The “survivor” stations in GHCN are HIGHLY biased towards airports. IFF they are matching so well, I’d be immediately “on alert” that they have simply captured the same “Airport Selection Bias” in their set.

    So, hopefully this will be sufficient for folks to see why I don’t get all excited about Yet another Hypothetical Spherical Cow argument purporting to show just how great all the old data is so the new data must be Just Dandy too. It is always the same form (and a broken form at that) to the argument. (Thus the “Hypothetical Cow” posting a while back…)

    I’d really rather not waste a lot of time constantly revisiting the same broken counter arguments (and that’s why I’ve discouraged a lot of the postings pointing at yet another Hypothetical Cow). But it’s possible I’ve not given this one a “fair shake” as I’m just responding to the “taster”… However, please “give it a rest” until I HAVE read it. Tossing rocks at what you SUSPECT something may be is not “good form” and not something I like to indulge in… so I’d rather just let it happen in the fullness of time… -E.M.Smith ]

  2. A C Osborn says:

    Amazing how CO2 warming affects 1 or 2 countries in a group of 9.
    It is obviously “selective” on where it warms us up. ROFL.

  3. oldtimer says:

    I will give it a rest – as you ask.

    On clarification the use of the word “raw” was sloppy language on my part; their chart refers to “unadjusted station data”. Re station numbers most people would miss your central, and key, point that the station count used by GCHN has dropped so much since 1990.

    REPLY: [ But it’s not just you. GISS on their website refer to the “GHCN raw” data. NCDC calls it “unadjusted” but that just sounds like a fancy name for “raw” and masks that it does have changes in the data. The whole lot of them just can’t bring themselves to say “The Data After We Mucked With It, But Trust Us, We’re Climate Scientists”… and the usage slides onto everyones tongue. I’ve seen veteran meteorologists “caught” using the “raw” data from the GISS site and had to explain to them that it isn’t “raw” no matter what the web widget says. I think it’s a key point folks need to understand: NOBODY has raw data but the individual country BOMs and then only if they kept it in an archive…

    But yes, The Great Dying of Thermometers in 1990 is a watershed moment. And probably more important than the fact that in “climate science” raw means half baked. ;-)
    -E.M.Smith ]

  4. oldtimer says:

    On reading the House of Commons Select Committee report into Climategate, I see that, in his evidence, Prof Jones refers to the GCHN data as “raw” as does the Pro Vice Chancellor of the University of East Anglia, a Prof Trevor Davies. Glad to read it was not just me imagining things.

    REPLY: [ It’s endemic. Heck, I even find myself talking about the “raw” GHCN input data some times… “Bailiff, Whack His… !!” and if you don’t do it, you end up constantly explaining to the folks who do: why it isn’t…. So it’s a “No Foul” to do it, but you need to do the try over if called! ;-)
    -E.M.Smith ]

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