Germany – Not Warming

Germany – Give Them A Free Pass On Carbon Offsets?

This is an interesting little graph. It shows the “change of temperatures in a year”, that is the dT/yr, and the cumulative change over the life of all the thermometers, dT. What surprised me most when I made it was that it was so flat that the two lines were on top of each other. I had to make the dT/yr line semitransparent to make it readable. ( You ought to be able to click on the graph for a really big version).

Germany - Change of Temperature Over Time is Flat

Germany - Change of Temperature Over Time is Flat

I was so surprised at the 1850 to date flatness that I extended it back to 1750 just to see if they were still stable in the far past. Yup. 260 years, flat.

First off, this is interesting because the Germans are generally known for being real sticklers about issues of precision and quality. Not the sort of folks to cut corners on where or how temperatures are to be measured.

Secondly, because other countries in the same general area do have warming trends in their data. And then there is poor Turkey, burning up in comparison. All of which argues for instrument error as much more likely than CO2. At least, I can’t think of any way for CO2 to “Give Germany a pass”.

And yet Germany has a strong “Green” movement that is highly vocal about “Global Warming” and has policies promoting the various forms of carbon reduction. One can only guess that the politicians there don’t look at their own temperatures much.

But this did give me an idea on how to fix “Global Warming”. For any country who’s change of temperature over time is within a small fraction of flat, give them a complete “free ride” on all the Carbon Cap & Trade, CO2 Sequestration, Light Bulb Police, you name it. Declare them a “Guilt Free Zone”. Somehow I think we would find Global Warming evaporate in a tide of precision and calibration…

In that spirit, I hereby declare Germany a “Carbon Guilt-Free Zone”. Enjoy!

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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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21 Responses to Germany – Not Warming

  1. Heading Out says:

    It’s an interesting suggestion given that Missouri, which gets 85% of its electrical energy from coal, also has an almost flat temperature over the past 114 years , sorry that the derivation is a bit simplistic for this site.

  2. Viv Evans says:

    Now that is a brilliant idea!
    The moaning greenies could then try to make the politicians give away all the Carbon Tax money to their favourite Third World countries – and all citizens would know at once that it never was about CAGW and saving the planet, but about distributingtheir well-earned wealth, without them having a say.

    Would be interesting to see how that will work …

  3. P.G. Sharrow says:

    Chiefio this needs to be posted on WUWT . The world needs to see this ASAP. At last a base line to work against.

  4. j ferguson says:

    One might ask whether the Co2 stays home or goes awandering? Maybe the concentrations (if any) don’t accumulate in the vicinity of the source.

  5. j ferguson says:

    Hi EM,
    In re-reading my comment, it seems pretty moderate. I assume that your recent noteriety has resulted in an assault on your site by folks who should be seen and not heard. It’s too bad.

    John

    REPLY: [ I was just on the road today until about 10 minutes ago… Sorry for the delay. Yeah, I had a load of trolls try to graffiti the place. It’s kind of interesting. When I first started, it was wide open. Any comment went up immediately. Over time, some spam filtering. Then some content. Now a troll filter or three. Oh Well. It looked kind of like some folks were then assigned to be “Nice and Helpful” by constantly saying “Maybe you didn’t see this, but you ought to take a look at this guy who thinks you are an idiot” with links to attacks. I’m sure some of them were well intentioned friends, but the 100% “hit” on each new posting was, er, a red flag. So they now get filtered as well. Why it’s so hard to get the point across (as in the About tab) that this is to be a yard party with friends and you are expected to be polite – and attacking and insulting the host is usually not considered polite at a yard party ;-) is one of the aspects of the blog-o-sphere that I just don’t “get”. But it will continue to be a place where folks can discuss things, politely, and where I’ll be taking the stage from time to time to say “Here’s an interesting bit” and “Here’s the code that made it” with a “geek friendly” tone. And when someone pees in the pool or throws their wine on the host they will be politely escorted off the premises…. “Life is too short to drink bad wine.” And trolls and graffiti artists are just bad wine… -E.M.Smith ]

  6. Pingback: NASA data worse than UEA « TWAWKI

  7. DirkH says:

    Guten Tag, Herr Chiefio,
    yeah it does appear pretty flat to me… we still have snow here in Hamburg on March 11th, pretty unusual. Usually Hamburg barely goes below 0 degrees C in winter. Coldest temperatures since 1981. Cool phase of the PDO, i would say.

    Nice to see that you analysed Germany. In fact, i would have expected to see at least a little bit of the “end of the LIA”… surprising.

    REPLY:[
    Guten Abend Dirk,

    Willkommen

    Ja, das überrascht mich auch. But if you look about 1815 you see a very nice drop after the volcano and the low excursions are fairly low then. There are some stories about the LIA mostly being sporadic cold periods and then some relief. It may be that the ‘average anomaly’ does not capture the impact of a very cold December… If I get time I’ll look at the graph ‘by month’ and see if it has something interesting to say..

    BTW, I think you are right about the PDO impact…

    At any rate, the dT/dt “tool” does catch warming trends in other countries and other continents. I’m pretty sure it is not biased. FWIW, one surprise for me was that 1934 (and the 1930’s in general) were very hot in the USA. I expect to see that in other places. Instead I found a cold excursion in the Indian Ocean and the Pacific near Australia… while Germany and Europe are singularly uneventful then. I think there are Global scale oscillations that we just can’t capture well with sparse thermometers and gaping holes over the souther oceans… -E.M.Smith ]

  8. DirkH says:

    Oh, and this one should be of interest to you:
    http://pajamasmedia.com/blog/climategate-stunner-nasa-heads-knew-nasa-data-was-poor-then-used-data-from-cru/

    REPLY: [ Nice one! Yeah, calling GISS and CRU independent when CRU have said there data were over 90%+ identical with GHCN and GHCN is the input to GISS that just sort of mangles parts of it (like the UHI ‘correction’ that often goes the wrong way…) is just laughable… if it didn’t hurt so much … -E.M.Smith ]

  9. Thomas B Gray says:

    If your dT is (T sub i) minus (T sub 0) and (T sub 0) = 0

    and

    dT/dt is (T sub i) minus (T sub (i-1)) then are you not just

    plotting (T sub(i-1)) and (T sub i) ??

    What am I missing??

    Tom

    REPLY: [ The first pass through the data is substantially the same as First Differences, with the exception that I do not reset on a data gap. The first data, for a given thermometer record, is the initial BASE or zero record. The next time that month for that thermometer has data (typically the following year record, but on a data gap it may go further) you compute the anomaly. T(sub1) – T(sub0) so if the start was 10 C and next year that month was 11 C you would have 11 – 10 = 1 C of “increase” or dT/yr would be 1 and that is how the basic data file is made. I then do a report from that file which starts in the present and reads backwards through the file. For each year it makes the average dT (so if you had 3 thermometers with +1.5 +1.1 +1.9 you would sum those three anomalies and divide by 3 to get 4.5 / 3 = +1.5 as the average dT in that year) then a running total of those averages for the geography in question is made (that is, if 222 is the country code, all of Asian Russia records would be in the file so you get the running total average increase for the whole of Asian Russia. You could also have specified a single thermometer, or any substring of the StationId. That is the dT column.

    This “backward time” step has the advantage of setting your present value as the norm based on the present thermometers (supposedly better and more accurate than those at the ‘start of time’ which for some places is in the 1700’s ) and ought to give a more accurate profile of change over time (that first 1700 era record, if 10 C high, could give a -10 C anomaly the next year that you could spend decades trying to work off ;-) For the present, with more than the single thermometer most places start with at the ‘start of time’, we ought to be getting very good average anomalies up until that 1700’s single thermometer odd duck hits. So the really early end is where the strange bits show up, if any, not the last 100 years.

    On my “to do’ list is to try doing the first ‘anomaly file creation step’ with backward time as well. I don’t expect anything of significance to happen, but it would be good to measure. (That ‘odd duck’ +10 that gives the -10 the next year would instead show a more or less neutral value in 1701 and a +10 in the last year. Basically, it would put the ‘oddity’ in the year that caused it instead of in the following year. Since it all happens in the first year or so of a place when it only has one old dodgy thermometer, I don’t see much urgency … but I’m going to test it any way.

    That’s the overview. Real Code, of course, has more details in it. But those don’t matter to the method. I’m going to post the code once I settle on a ‘forward time’ vs ‘reverse time’ for that first step of anomaly creation and make a more automated ‘wrapper script’ to run it.

    So in essence, I just look at monthly anomalies year to year, make an average of them for a given place, then “sum them up” starting now and looking back in time so we have a more appealing view.

    Basically, I wanted to make the ‘anomaly step’ happen as early as possible and be as pure as possible. Making an annual average THEN doing the anomaly has “issues” if a few months are missing and you are averaging data full of holes. Doing First Differences has issues with holes in the data. ( And “digging in the clay” have shown much of the data, near 50%, can be holes, so handling holey data is very important). So what have you got? Monthly Means. Make the anomaly with them, and do it as your very first step. And on a data gap, wait for that month to have a valid datum and then do the anomaly. (So a 5 year gap in July with a 0.1 C / year trend, would put a +0.5 C jump in year 5, but would preserve that trend rather than just drop it as F.D. does or fill it in with imaginings as The Reference Stations Method / Climatology does).

    These absolutely pure anomalies are then averaged for a year, for a place, and a running total is made. That is dT.

    So, can I say 3 more ways and make it even more confusing? :-) Probably 8-0

    Maybe I’ll just post the crude code and let folks pick it over. ;-)

    At any rate, the goal was to make as absolutely pure an anomaly process as possible with no in-fill, no imagined data, no dropping, no resetting on gaps, no “Thermometer A” to “Thermometer B”, no “Basket” to “Basket” (and, horrors, certainly no “Basket A” to “Basket B”…), and no “averaging temperatures prior to anomaly creation”, and no UHI or other adjustments, modifications, etc. etc. etc.

    So, IMHO, this is the absolutely cleanest “The Data, and Nothing But The Data” anomaly based process possible (and certainly as close to that as I could get). My goal being to have a “benchmark” against which to measure GIStemp and to compare both to the actual temperature averages ( that show the actual bias in the data… and not just the tend bias, all the biases.) It just turned out to be a pretty dandy tool for actually looking at the data as well. It was the USA graph in the Spherical Cows posting that got me thinking about it as more general purpose. That the 1934, and 1815-1818 periods were as recorded in history was pretty strong confirmation for me that it was working well. And that a 1/2 century of my life was reported as I remember it. That helped ;-)

    -E.M.Smith ]

  10. The NOAA GHCN database contains 11 stations in Germany that have long term data. A plot of these shows a distinct warming trend after the early 1980s.

    The CRU – CRUTEM3 database (5×5 degree grids) takes 4 grids to cover Germany – it shows a similar trend to the GHCN data.

    See the plots here: http://www.appinsys.com/GlobalWarming/Germany.htm

    Why the discrepancy in the data?
    What is your Germany data source?

    REPLY: [ I can’t explain what other people do. I can speculate. i would speculate that, for example, GISS cuts off the data in 1880. Look at that graph with an 1880 cut off, you get a warming trend. Further, if you look at 1980 on the graph above, it is a cool point (more below the zero line than above) so a trend started then would also show warming. You can pick many warming start points and many cooling start points. So I’d speculate that the starting point might matter.

    What CRU and GISS do with ‘in-fill’, adjusting whatever can cause most any trend to happen for a given place. (There are many examples on WUWT of A/B blinkers showing cooling trends in the input data turned into warming trends in the result). So I’d suggest looking at their “start of time” being in a cold point and there process doing more than “The Data, and Nothing But The Data” for a given place.

    The only other thing I could add is that the graphs to which you link show a baseline several decades long and some are using grids. So they are not pure anomaly of “self to self” incrementally. They are anomalies relative to a computed average baseline. So a thermometer that did not exist in that baseline will be compared to some other thermometer. Anomaly of “Thermometer A” to “Basket B” or some such. Not very “pure”… So like I said, I can’t explain why other folks do what they do. I can explain what I do and why.

    My source for the Germany data is the NOAA / NCDC GHCN “unadjusted” data set from February 2010 download. And there is nothing added to it in any way by my processing (a little is taken away in that I average things together and averaging is used to hide detail). I create anomalies as the first step (though this uses ALL the available data, so may diverge from the ‘long lived’ set) and average them per year. That’s pretty much it. (With some details about when to start time). It was intended to be a “Dirt Simple” anomaly process, so there really aren’t any “frills” in it to complicate things.

    This is “young code” in that I’m still polishing it. There is the possibility that I’ve got an undetected bug in it, but I don’t think so (what programmer ever does ;-). It reliably finds warming trends in places, like Turkey, that have a rosy patch on the GISS map and it reproduced what I know of the USA history well.

    If anyone wants to duplicate this, it isn’t all that hard (description above) and I’d be willing to help someone code up the same process in another language if they wanted to do so. (It’s a pretty trivial process.) -E.M.Smith ]

  11. P.G. Sharrow says:

    Now UHI in Germany can be accurately gauged to get a real correction factor in stead of a wildass guesstament based on light pollution :-p

  12. E.M.Smith says:

    For those who get grumpy when I do tables, this will make you grumpy, so don’t look. I just don’t feel like dealing with all the graph making stuff right now just to answer a question.

    OK, for those who don’t mind looking at blocks of numbers:

    I took the input data to the above chart and selected only those records with a “0” modification flag. This may not be the same as the “11 long lived” but will have significant overlap. (The ‘count’ rises to 20-30 in some parts) The first thermometer somewhere is typically the “0” mod flag, so this ought to be the “old musty ones” but will also include some “young but new” and some “old with big gaps” so not ‘long lived’ by some measures.

    The report does show a warming trend from 1980 to date. So the answer to the above question about why those graphs of those 11 stations shows a warming trend is the same “Selection Bias” in this report. That particular sample shows that trend.

    (Which group of stations is “correct”? Good luck with that… We’ve been regularly assured that selection bias doesn’t matter by “climate scientists”, but I don’t buy it.)

    But I would be remiss in my cherry harvesting duties if I did not point out that 1780 and 1762 are both positive while 1847 is almost neutral (as are several other years in the 1800’s) :-)

    For any given year, you ought to be able to add up the monthly anomalies and divide by 12 to get the annual average anomaly (dT/yr) and then add that to the dT to get the next year value for dT. You may need to shrink the font or look at ‘page source’ to see the full width of the report – for folks wishing to check it.

    (One of the ‘open issues’ I’m still thinking about in the code is the whole ‘missing anomaly handling’. At present I just let every value participate in the annual average – and in a somewhat ugly way via ignoring a missing data flag handler in the code – so years with a bunch of zeros can get a very small dT/yr. But other years when that data show up will get a large monthly dT and so a proportionately larger dT/yr. So ought I to in-fill the missing anomalies with an average over that gap instead of putting it all in that one month? Is it ‘right’ to count the anomaly for Jan 2008 as 0 and for the year as 0.08 due to the missing data? But then 2009 gets a January of -5.4 and stronger annual number because of it… The only year where this ‘bites me’ is the last year where I divide by 12 even though there is only one valid month so far. But isn’t it right to assume the rest of the year is normal until proven otherwise? But what if the next to last year had zeros and I’m carrying forward an ‘unclosed thermometer series’, should I reference to the average for closure, or just let it lapse? Isn’t lapsing best, as there are no data yet? It’s that kind of little tiny edge case stuff that takes all the polish and makes the difference between ‘rough but workable’ and ‘polished’ code and why I’m not posting the code yet…)

    OK, here’s the report. It’s the dT column you want to look at and it’s going to show a negative number in the 1980’s. I cut this one off at 1750 (just like I cut the above graph off at 1750) as that’s the place where Germany drops to 1 thermometer and things get more volatile (and probably less reliable).

    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
    -----------------------------------------------------------------------------------
    2010   0.03 -0.03    6  -0.4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
    2009   0.45 -0.42    9  -5.4 -2.7 -1.2 -0.2 -0.1 -1.8  1.0  2.1  2.2 -0.2  1.3  0.0
    2008   0.38  0.08    9   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 -0.1  0.4  0.8 -0.2
    2007   0.15  0.22    9   6.8  4.2  4.4  3.9  0.8  0.6 -4.8  1.1 -4.8 -3.7 -3.1 -2.7
    2006  -0.47  0.62    9  -2.8 -1.5 -3.1 -0.3 -0.3  0.2  3.9 -2.2  3.8  2.9  3.6  3.2
    2005  -0.38 -0.08    9   0.0 -0.4  0.0  0.0  0.1  0.2  0.0 -0.3  0.0  0.0  0.0 -0.6
    2004   0.28 -0.66    1   0.8  4.2 -2.4  0.8 -2.6 -5.0 -1.6 -4.0  0.4  3.9 -1.4 -1.0
    2003   0.18  0.10    1   0.4 -7.0  0.5  0.2  1.4  2.6  2.0  4.8  1.7 -2.7 -1.3 -1.4
    2002  -0.13  0.31    1  -2.3  2.2 -0.4  1.5 -2.2  3.8 -1.1 -1.7  1.1 -3.6  3.8  2.6
    2001   0.58 -0.71    1   1.2 -1.0  0.9 -3.2  0.5 -3.5  2.7  0.9 -2.8  2.6 -2.9 -3.9
    2000  -0.05  0.63    1  -2.1  4.3 -0.1  1.2 -0.1  2.7 -3.0  0.8 -2.3  1.2  2.9  2.0
    1999  -0.22  0.17    1   0.7 -3.4  0.1  0.1  0.3 -1.7  1.3  0.0  2.9 -0.5  1.4  0.8
    1998  -0.45  0.23    1   4.9 -1.1 -1.2  1.8  1.2  1.5  0.8 -1.9 -0.8  1.6 -2.4 -1.6
    1997  -1.80  1.35    1  -1.2  5.5  4.7 -1.9  1.5 -1.0  0.0  2.6  3.4 -1.3 -0.7  4.6
    1996  -1.23 -0.57    1   0.2  0.0 -5.6  0.6 -0.4  1.5  0.4  0.0 -1.0 -2.8  1.8 -1.5
    1995  -1.08 -0.15    1   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 -3.1  3.2 -2.0  0.1
    1991  -0.12 -0.97   26  -0.8 -6.8 -0.5 -0.1 -3.7 -1.5  2.3 -0.3  1.4 -1.6 -0.3  0.3
    1990  -0.22  0.11   28   0.0  2.2  0.0  0.3  0.2 -0.1 -0.9  1.3 -1.4 -0.2  1.5 -1.6
    1989  -0.77  0.54   28  -0.7  1.4  4.4 -0.6 -0.3  0.3  0.5  0.0  1.2  0.9  0.0 -0.6
    1988  -2.38  1.61   28   9.1  2.3  3.0 -0.9  4.3  1.2  0.3  1.6 -1.0  0.2 -1.8  1.0
    1987  -1.82 -0.56   28  -5.5  5.7 -3.4  2.6 -4.2 -1.9 -0.2 -0.8  2.8 -0.3 -1.0 -0.5
    1986  -2.36  0.54   28   5.1 -3.0  0.2 -1.2  0.8  2.2 -0.3 -0.1 -1.9  0.6  5.0 -0.9
    1985  -1.84 -0.52   28  -6.3 -2.7  0.7  0.5  2.2 -0.1  1.6 -0.6  1.2 -1.2 -3.9  2.4
    1984  -0.73 -1.12   28  -2.8  0.6 -2.5 -1.6 -0.5 -2.4 -4.4 -0.9 -1.4  1.0  1.2  0.3
    1983  -0.93  0.21   28   6.1 -0.7  0.3  1.9 -0.9  0.0  1.3  0.6 -2.0 -0.4 -2.3 -1.4
    1982  -1.70  0.77   28  -0.7  0.1 -2.2 -1.2 -0.6  1.0  2.5  1.1  1.7  1.5  1.5  4.5
    1981  -2.12  0.42   36   0.7 -1.5  3.1  1.4  2.0  0.2  0.6 -0.1  0.1  0.0  1.0 -2.5
    1980  -2.01 -0.11   29   1.6  3.5 -0.4 -0.2 -1.7 -1.7  0.0  0.9  0.6 -0.5 -0.6 -2.8
    1979  -1.92 -0.09   29  -4.9 -0.5 -1.1 -0.1  0.8  1.5 -0.2  0.3  1.1 -0.6 -0.7  3.3
    1978  -1.16 -0.76   28   0.2 -3.5 -1.1  0.6 -0.2 -0.6 -0.9 -0.6  0.2 -0.9 -0.5 -1.8
    1977  -1.33  0.17   28  -0.4  1.9  4.1 -1.0 -0.9 -1.4 -2.4 -0.3 -0.7  0.6  0.1  2.5
    1976  -0.77 -0.57   29  -3.5 -0.9 -2.4  0.1  0.9  2.4  0.9 -2.5 -2.7  1.6  1.6 -2.3
    1975  -0.97  0.20   28   1.2 -1.7 -1.7 -1.1  0.8  0.6  3.0  1.7  2.3  2.1 -1.8 -3.0
    1974  -1.44  0.47   28   2.8  1.8  1.1  2.7 -1.4 -1.9 -1.9 -0.4 -0.9 -1.7  1.4  4.1
    1973  -1.83  0.39   27   2.8 -0.7 -1.0 -2.1  1.0  1.4 -0.5  1.8  3.4  0.3 -0.9 -0.8
    1972  -1.21 -0.62   27  -1.6  0.5  4.1 -0.8 -2.5  0.6 -0.2 -2.2 -1.3 -2.1  0.6 -2.6
    1971  -2.01  0.80   27   1.8  2.8 -0.1  2.4  2.6 -3.0  1.7  1.2 -1.0  0.2 -1.9  2.9
    1970  -1.98 -0.03   31  -2.7  0.7  0.4 -1.5 -1.5  2.2 -2.1  0.3 -0.6 -1.2  0.6  5.1
    1969  -1.50 -0.48   30   1.4 -2.8 -3.8 -2.2  2.0 -1.2  2.2  0.1  0.4  0.0  1.0 -2.9
    1968  -0.77 -0.72   30  -1.7 -2.0 -0.9  2.6 -1.8  1.3 -2.5 -0.1 -0.4 -1.0 -0.1 -2.1
    1967  -1.19  0.42   37   3.0 -0.8  2.1 -2.0 -0.3 -2.1  3.2  0.9  0.5  0.1  1.9 -1.5
    1966  -2.17  0.97   37  -3.7  5.5  0.7  1.8  1.8  1.4  0.5  0.5  0.5  2.6  0.7 -0.6
    1965  -1.44 -0.73   37   3.3 -2.6  1.8 -2.0 -2.4 -1.5 -3.2 -1.0 -0.9  1.4 -3.5  1.9
    1964  -2.47  1.03   37   4.9  6.2 -1.9  0.2  1.6  1.1  0.3  0.0  0.0 -1.1 -2.4  3.4
    1963  -2.42 -0.05   37  -8.8 -5.6  2.3  0.3  2.2  1.5  2.7  0.0  1.3 -0.9  4.5 -0.1
    1962  -0.66 -1.76   36   2.3 -4.2 -5.9 -2.3 -0.6 -1.7 -0.1  0.1 -4.0 -1.4 -1.0 -2.3
    1961  -1.14  0.48   37  -0.5  3.7  1.6  2.9 -2.5 -0.3 -0.3 -0.1  3.6  1.4 -2.1 -1.6
    1960  -0.41 -0.73   38   0.0  0.2 -1.9 -2.0  0.1  0.0 -4.1 -1.5 -1.4  0.2  2.5 -0.9
    1959  -1.35  0.94   38   0.2 -1.7  6.5  4.2 -0.4  1.7  2.4  0.5 -0.5 -0.5 -0.8 -0.3
    1958  -0.97 -0.38   38  -0.4 -1.9 -6.5 -2.4  3.4 -2.2 -0.7  1.8  2.7  0.1 -0.3  1.9
    1957  -2.74  1.77   38   0.0 13.4  3.3  2.2 -2.7  3.5  0.9  1.0 -1.9  0.8  2.2 -1.5
    1956  -2.00 -0.74   38   1.8 -7.6  2.4 -1.7  2.1 -1.3 -0.6 -2.8  0.2  0.3 -1.4 -0.3
    1955  -1.87 -0.13   39   1.7  1.5 -3.7  1.0 -1.8 -1.5  2.8  1.0  0.0 -1.8 -0.2 -0.6
    1954  -0.69 -1.18   38  -2.3 -3.2 -0.5 -2.8 -0.8  0.0 -2.8 -0.7 -0.1 -0.6 -0.8  0.5
    1953  -1.63  0.94   38  -0.6  0.0  2.0 -1.0  0.9  0.7 -0.5 -1.0  2.6  2.9  2.7  2.6
    1952  -0.87 -0.77   32  -0.9 -1.5  0.1  2.6  0.6 -0.2  0.9  0.3 -3.6 -0.3 -4.3 -2.9
    1951  -0.89  0.02   29   0.8 -0.1 -1.0  0.1 -0.9 -1.1 -0.2  0.0  0.6 -0.2  0.9  1.4
    1950  -0.41 -0.48    9  -2.4  1.6  2.5 -3.7  1.9  3.9  0.4  0.4 -3.7 -2.7  0.7 -4.7
    1949  -0.47  0.06    9  -1.7  1.2 -3.4  0.3 -1.4 -1.4  1.6  0.6  2.2  1.7 -0.7  1.7
    1948  -0.93  0.47    8   7.9  6.3  3.0  0.2 -0.9 -2.3 -3.0 -2.3 -3.3  0.8 -0.7 -0.1
    1947  -1.13  0.20    8  -3.0 -8.2 -1.0 -0.6  0.6  3.2  0.3  2.4  2.9  1.0  1.3  3.5
    1946  -0.63 -0.50    8   2.6 -1.6 -1.8  1.7  0.0 -2.0  0.6  0.0  0.4 -2.7  0.3 -3.5
    1945  -1.22  0.58    8  -7.3  4.9  5.2  0.1  2.3  2.5  0.5 -3.8  0.8  0.8 -0.7  1.7
    1944  -0.52 -0.69    8   3.1 -4.1 -5.2 -0.6 -1.1  0.0 -0.5  1.7 -0.9 -1.5  1.5 -0.7
    1943  -2.04  1.52    8   7.1  7.9  4.0  1.8  0.5 -0.3  1.6  0.6 -1.5 -1.1 -0.4 -2.0
    1942  -2.38  0.34    8  -2.1 -5.1 -1.6  1.9  3.3 -1.8 -2.1  2.7  3.3  3.5  1.0  1.1
    1941  -2.80  0.42    8   3.2  4.0  0.9 -2.6 -3.0 -0.5  2.1  0.4 -0.2  0.0 -3.6  4.3
    1940  -1.22 -1.58    8 -11.1 -5.7  1.1 -0.8  1.8  0.4 -0.8 -2.9 -0.7  1.3  0.0 -1.6
    1939  -0.83 -0.38    8   1.3  0.8 -5.8  4.2 -0.6  0.0  0.0 -0.1 -0.5 -2.3 -1.9  0.3
    1938  -0.92  0.09    8   2.7 -1.1  3.8 -2.9 -3.8 -0.5 -0.2  0.5  0.3 -0.6  4.2 -1.3
    1937  -1.13  0.21    7  -4.2  2.0 -2.0  0.9  2.5  0.6  0.4  0.9  0.6  3.9 -0.6 -2.5
    1936  -1.26  0.12    7   4.4 -1.4  2.7 -0.3  2.3 -1.4 -1.0 -0.4 -1.0 -2.7 -1.2  1.5
    1935  -0.27 -0.99    9  -1.3  0.6 -2.1 -3.0 -2.9  1.2 -0.3  0.1 -1.3 -0.7  1.3 -3.5
    1934  -2.08  1.82    9   2.5  1.4 -0.1  3.4  1.8  2.4  0.1 -0.6  1.7  0.6  1.2  7.4
    1933  -1.21 -0.88    8  -4.8  2.5  4.6 -0.4 -1.3 -0.5  0.2 -1.8 -1.9  0.1 -1.8 -5.4
    1932  -2.07  0.87    9   1.9 -1.3  0.6  1.8 -2.8 -1.8  0.9  3.4  5.3  1.4 -0.1  1.1
    1931  -0.72 -1.36    9  -2.6 -0.8 -4.6 -3.0  3.5 -2.4  0.6 -0.4 -3.1 -1.5 -1.7 -0.3
    1930  -2.07  1.36   19   7.0  9.4  1.8  3.9 -1.4  3.7 -1.3 -1.4 -2.5 -1.2  1.4 -3.1
    1929  -1.14 -0.93   19  -6.5-12.0 -0.3 -3.0  3.2  0.5 -0.9  1.0  3.0  1.2 -1.4  4.0
    1928  -1.48  0.33   19   0.0  1.6 -3.0  0.4 -1.0  0.0  1.5  0.0 -0.9  0.2  3.4  1.8
    1927  -0.78 -0.69   19   1.8 -3.7  2.0 -2.7  0.2  0.5  0.0  0.3 -1.1  0.4 -3.5 -2.5
    1926  -1.21  0.42   20  -2.6  0.6  2.7  1.6 -3.1 -1.2 -0.5 -0.4  3.7 -0.4  4.1  0.6
    1925  -2.02  0.81   20   5.4  6.5 -0.9  1.8  0.0 -0.2  0.9  2.0 -2.6 -0.8 -0.8 -1.6
    1924  -1.53 -0.48   20  -4.3 -3.7 -2.7 -0.4  2.1  4.0 -1.8 -1.3  0.4 -1.0 -0.1  3.0
    1923  -2.36  0.83   20   4.0  2.2  1.4  1.2 -1.8 -4.5  2.9  0.5  2.0  5.3  0.5 -3.8
    1922  -0.52 -1.84   20  -6.8 -2.4 -2.2 -2.1 -0.4  1.2 -3.1 -2.0 -2.5 -6.0  2.5  1.7
    1921  -0.92  0.41   20   1.8 -1.7 -0.3 -1.7  0.0 -0.4  1.2  2.3  0.6  4.2 -1.1  0.0
    1920  -2.18  1.26   21   1.6  3.1  3.1  4.0  2.5 -0.2  3.1 -1.2 -2.0  0.8  0.5 -0.2
    1919  -0.99 -1.19   21  -0.1 -1.9 -1.0 -3.7 -2.5  2.1 -2.1  0.3  1.9 -1.9 -2.3 -3.1
    1918  -2.00  1.01   21   3.5  5.4  4.1  4.6 -1.1 -5.9 -0.8 -0.7 -1.6  0.5 -2.0  6.1
    1917  -1.09 -0.91   21  -6.4 -4.6 -4.1 -3.8  1.9  6.1  1.4  0.7  2.7 -1.1  0.4 -4.1
    1916  -1.57  0.48   21   3.5 -0.1  2.3  1.4  0.0 -4.9 -0.4  0.6  0.1  2.1  3.0 -1.8
    1915  -1.07 -0.51   21   3.4 -2.0 -3.0 -3.0  2.4  3.2 -1.0 -2.0 -0.5 -1.8 -1.8  0.0
    1914  -1.02 -0.05   21  -2.8  1.7 -1.4  1.9 -1.6 -0.3  2.9  2.3 -0.2 -1.3 -3.4  1.6
    1913  -1.67  0.65   21   1.2 -1.2  0.0  0.9  0.4 -0.8 -3.4  0.9  3.6  3.0  4.2 -1.0
    1912  -0.46 -1.21   21  -0.3  1.2  2.1 -0.4 -1.1  0.5 -1.4 -5.6 -5.4 -2.0 -2.2  0.1
    1911  -1.16  0.70   21  -2.6 -1.1  0.3  0.0  0.7 -1.4  3.9  3.7  2.6 -0.5  2.5  0.3
    1910  -2.20  1.04   21   3.2  5.0  1.9 -0.5  1.4  2.3  0.3 -0.3 -0.8 -1.0  0.4  0.6
    1909  -2.06 -0.14   21   0.4 -3.7 -0.8  2.5 -2.4 -3.0 -2.1  1.7  0.6  2.2  0.5  2.4
    1908  -1.62 -0.43   21  -1.8  2.6 -0.2 -0.7  0.1  2.2  2.6 -1.4 -1.1 -3.3 -2.1 -2.1
    1907  -1.18 -0.44   21  -1.4 -1.9  0.4 -2.1  0.0  0.1 -2.7 -0.6  0.7  1.4 -2.8  3.6
    1906  -1.54  0.36   21   2.9 -0.8 -2.4  2.2  1.1 -2.4 -1.5  0.0 -0.2  5.4  3.0 -3.0
    1905  -1.06 -0.48   21  -0.6  0.1  1.4 -3.1 -0.4  1.7 -0.2 -0.1  0.8 -3.9 -0.1 -1.4
    1904  -1.11  0.05   21  -1.7 -2.5 -2.6  4.4 -0.1  0.5  2.6  1.0 -1.7 -1.3 -1.1  3.1
    1903  -2.32  1.21   21  -2.3  4.8  2.2 -3.2  4.0 -0.4  0.4  0.8  1.4  2.7  2.9  1.2
    1902  -1.93 -0.38   21   6.6  2.9  1.3 -0.1 -4.2 -0.4 -2.3 -1.5 -1.0 -1.9 -1.1 -2.9
    1901  -1.12 -0.82   21  -4.7 -5.2  1.5  1.3  1.9 -0.3 -0.1  0.0 -0.6  0.2 -2.1 -1.7
    1900  -1.40  0.28   21  -1.4 -0.8 -2.2 -0.6 -0.2  1.4  1.2 -0.5  1.3  0.9 -1.6  5.9
    1899  -0.98 -0.42   21   0.0  0.9 -0.2  0.0 -0.3 -0.2  2.8 -1.3 -1.1 -1.1  1.7 -6.2
    1898  -1.60  0.62   21   4.9  0.0 -2.0  0.0  0.8 -2.0 -2.0  1.0  1.4  1.6  1.9  1.8
    1897  -1.98  0.38   22  -1.8  1.6 -0.3  1.3 -0.1  0.3 -0.5  2.7 -0.5 -1.2  1.4  1.6
    1896  -2.25  0.27   22   3.1  6.4  4.0 -2.5 -1.3  0.9 -0.3 -2.1 -2.5  1.5 -3.6 -0.3
    1895  -1.52 -0.73   22  -2.1 -8.3 -3.2 -1.4  0.9  1.4 -0.4  1.0  4.4 -0.8  0.0 -0.3
    1894  -1.72  0.20   22   5.3 -0.2  0.3  1.0 -1.0 -1.5  0.3 -1.4 -1.4 -1.8  2.7  0.1
    1893  -2.07  0.35   23  -5.1  1.3  3.7  1.8  0.0  0.5  1.2 -1.1 -1.3  2.3 -1.0  1.9
    1892  -2.22  0.15   22   2.8  1.3 -2.1  2.0 -0.4  0.4  0.0  3.2  0.0 -2.5  0.5 -3.4
    1891  -2.36  0.14   22  -6.2  1.7 -1.2 -1.6 -0.8  0.8  0.7 -1.3  0.6  2.5 -0.2  6.7
    1890  -2.29 -0.07   21   4.4 -0.2  3.5  0.0 -2.0 -4.4 -0.9  1.0  1.9 -0.7  0.3 -3.7
    1889  -2.73  0.44   21  -0.5  0.0 -0.5  1.1  3.4  2.5  1.6  0.3 -1.4  1.5 -0.8 -1.9
    1888  -2.71 -0.02   21   1.8 -1.5  0.0 -1.2  2.3  0.3 -4.0 -0.6  0.4  0.9  0.3  1.0
    1887  -1.67 -1.03   21  -2.5  1.9  0.4 -1.6 -2.6  1.4  1.9 -1.1 -2.7 -4.0 -2.0 -1.5
    1886  -1.93  0.26   21   1.7 -6.0 -1.9 -0.5  2.6 -2.5 -0.3  2.3  2.2  2.0  2.3  1.2
    1885  -1.12 -0.82   21  -5.8  0.5 -2.2  3.2 -2.7  3.6 -1.0 -2.4 -1.4 -0.4  0.8 -2.0
    1884  -1.78  0.66   21   3.1  0.2  6.3 -0.1  0.2 -3.1  1.7  1.0  0.8 -0.6 -2.5  0.9
    1883  -1.33 -0.45   21  -0.4  0.9 -7.6 -1.6  0.5  1.9 -0.1  1.1  0.3 -0.5  0.2 -0.1
    1882  -2.26  0.93   21   5.5  1.2  3.1  2.1  0.1 -0.8 -2.1 -1.1  0.9  4.0 -1.7  0.0
    1881  -1.26 -1.00   21  -2.1 -0.4 -1.2 -3.1  0.5  0.1  1.1 -0.6 -2.2 -2.9  1.9 -3.1
    1880  -3.04  1.78   21  -0.7  0.1  2.3  2.5  1.3 -0.8  2.7 -0.7  0.3  0.1  2.9 11.4
    1879  -1.29 -1.75   21  -2.0 -1.7 -1.0 -2.4 -3.1  0.2 -1.0  0.4 -0.1 -1.7 -2.2 -6.4
    1878  -1.37  0.07   21  -3.1 -0.5  1.0  2.4  3.3 -2.3 -0.7 -0.3  3.5  2.2 -2.9 -1.7
    1877  -1.55  0.18   21   5.9  1.5 -1.9 -2.1  0.9  1.9 -1.0 -0.2 -1.8 -3.3  4.1 -1.8
    1876  -2.02  0.47   21  -4.6  5.1  3.1  1.1 -4.4 -0.8  0.6 -1.0 -1.3  3.9 -0.5  4.4
    1875  -1.59 -0.42   21   0.0 -4.0 -2.8 -1.9  4.1  1.3 -2.2  3.4 -1.3 -2.7  1.2 -0.2
    1874  -1.20 -0.39   21  -1.1  0.7 -1.1  2.6 -0.5 -0.4  0.7 -2.4  2.6 -0.2 -2.8 -2.8
    1873  -0.53 -0.67   21   2.1 -2.3 -0.1 -2.4 -2.7  0.6  0.4  1.9 -1.9  0.1 -2.0 -1.7
    1872  -3.09  2.56   21   5.8  2.0  0.0  2.1  2.8  2.7  0.8 -1.4  0.2  3.3  5.4  7.0
    1871  -2.68 -0.41   21  -4.9  3.9  3.7 -0.8 -3.0 -2.5 -1.0  1.9  2.3 -1.8 -3.2  0.5
    1870  -1.54 -1.14   21   0.7 -9.3  0.1 -2.4  0.0  2.3  0.0  0.1 -2.8  1.0  0.6 -4.0
    1869  -0.31 -1.23   21   0.3  1.6 -2.5  2.9 -3.9 -4.1  0.0 -3.2 -0.3 -1.5  1.1 -5.2
    1868  -1.83  1.53   33  -0.5 -0.3  2.2 -0.5  5.1  1.5  3.1  1.6  0.7  0.2 -0.7  5.9
    1867  -0.92 -0.92   34  -4.3  0.5 -0.8 -1.3  1.4 -2.3 -0.8  1.6 -0.9  0.7 -1.2 -3.6
    1866  -1.34  0.42   35   3.5  7.5  2.9 -0.7 -6.0  3.6 -3.3 -0.6 -0.2 -1.8 -1.3  1.5
    1865  -3.13  1.79   34   4.8 -3.6 -4.9  3.8  6.0 -1.1  3.8  1.9  2.1  1.4  3.7  3.6
    1864  -0.67 -2.46   34  -7.5 -3.2 -0.2 -2.2 -2.1 -0.3  0.2 -3.6  0.3 -3.0 -2.1 -5.8
    1863  -1.08  0.40   35   4.1  2.4 -1.0 -1.1 -2.3  0.6 -0.5  1.5 -1.1  0.2  0.6  1.4
    1862  -1.37  0.29   34   3.7 -3.1  0.4  3.3  4.3 -2.6 -1.7 -1.6  0.6  0.6 -0.9  0.5
    1861  -2.33  0.96   34  -7.1  4.9  3.4 -0.7 -2.5  1.9  2.3  2.3  0.2  1.8  2.9  2.1
    1860  -0.56 -1.77   34   0.6 -4.5 -4.5 -0.5  0.2 -1.2 -4.5 -3.3 -0.5 -1.8 -1.4  0.2
    1859  -2.13  1.57   34   3.1  6.0  4.5  0.0  1.8 -1.9  3.2  1.8 -1.7  0.7  4.0 -2.6
    1858  -0.86 -1.28   35  -0.2 -3.3 -1.3  0.0 -1.6  2.3 -1.4 -2.0  0.2 -1.7 -4.4 -1.9
    1857  -1.68  0.82   35  -2.0 -1.7  1.3 -1.4  1.7  0.6  2.5  1.9  2.4  1.0  2.4  1.1
    1856  -2.73  1.06   34   2.9  6.4  0.0  2.2  0.0  0.2 -1.2  0.1 -0.1 -0.7 -1.2  4.1
    1855  -1.32 -1.42   28  -2.7 -5.6 -2.3 -1.4 -2.0  0.6 -1.0  0.9 -0.5  1.6  0.3 -4.9
    1854  -2.37  1.05   29  -3.1  2.0  5.7  2.1  1.3 -1.1  0.0 -0.1  0.1  0.0 -0.6  6.3
    1853  -0.47 -1.90   29   0.0 -3.9 -3.1  0.2 -1.6  0.4 -1.0 -0.9 -0.4  1.3 -4.3 -9.5
    1852  -1.61  1.14   29   1.6  0.8 -2.1 -3.5  3.8  0.5  3.0  0.8  1.8 -2.6  5.7  3.9
    1851  -1.82  0.21   31   7.1 -2.3  2.0  0.3 -2.5 -0.9 -0.6  0.3 -0.1  3.2 -4.0  0.0
    1850  -1.66 -0.16   27  -4.9  0.5 -1.6  1.2 -1.7  0.5  0.6  0.8 -1.3 -1.7  2.8  2.9
    1849  -1.25 -0.41   27   6.0  0.2 -2.0 -2.4  0.2 -0.9 -1.0 -0.4  0.3 -1.2 -0.9 -2.8
    1848  -1.76  0.51   25  -4.0  3.5  2.1  3.7 -1.3  1.9 -0.8 -1.9  1.0  1.4 -0.9  1.4
    1847  -0.10 -1.66   23  -3.7 -4.6 -3.6 -3.1  2.6 -3.7 -0.7 -1.4 -3.3 -2.5  1.1  3.0
    1846  -2.15  2.05   23   0.8  8.8  9.8 -0.1  1.9  1.5  1.0  4.7  2.4  1.6 -1.7 -6.1
    1845  -2.04 -0.11   23   1.1 -4.1 -5.6 -0.9 -1.9  1.3  3.3  0.3 -1.3  0.2  0.4  5.9
    1844  -0.98 -1.07   22  -1.7 -3.0 -0.6  0.7  1.2  1.2 -1.7 -3.0  0.4  0.3 -0.3 -6.3
    1843  -1.46  0.48   21   4.6  1.7 -1.6  2.2 -2.8 -1.9  0.0 -2.5 -0.2  1.7  4.0  0.6
    1842  -0.98 -0.48   21  -2.4  4.1 -0.8 -2.4 -2.3  1.8  1.2  3.8 -1.2 -3.2 -3.6 -0.8
    1841  -2.27  1.29   21  -0.9 -4.0  4.9 -0.2  4.5 -0.9 -0.4  0.0  1.6  3.3 -0.6  8.2
    1840  -1.43 -0.83   22   0.3 -0.5 -0.6  4.4 -0.3 -2.1 -2.3  0.9 -1.3 -2.7  0.1 -5.9
    1839  -2.93  1.49   22   7.7  4.9 -2.2 -0.6  0.0  2.0  1.2  0.4  0.0  1.0  2.4  1.1
    1838  -2.11 -0.82   23  -8.5 -4.7  2.7  0.0  2.0 -0.1  0.8 -2.8  2.4 -0.6 -0.7 -0.3
    1837  -1.39 -0.72   23   0.5  0.1 -6.0 -2.1  0.0 -0.5 -0.8  1.8 -0.2 -0.7  0.5 -1.2
    1836  -1.46  0.07   23  -1.4 -2.0  2.8  0.3 -1.5  0.0 -1.6 -0.6 -1.7  1.9  2.0  2.6
    1835  -0.07 -1.39   22  -2.7  1.5 -0.1  0.4 -3.0 -0.8 -2.3 -2.5 -0.7 -0.9 -3.1 -2.5
    1834  -1.37  1.30   24   7.7 -2.7  1.5  0.0 -1.0 -0.2  4.6  5.1  2.3  0.6  0.3 -2.6
    1833  -1.71  0.34   24  -2.2  3.1 -1.0 -1.8  4.9  1.7  0.1 -3.8  0.0 -0.9  1.1  2.9
    1832  -1.12 -0.59   24   1.9 -0.4 -0.7 -1.5 -1.0  0.3 -1.9  0.3  0.2 -2.7 -0.9 -0.7
    1831  -2.23  1.12   24   4.0  4.5 -0.5  0.7 -0.9 -0.1  0.0  1.0  0.0  3.9 -1.6  2.4
    1830  -3.51  1.27   22  -2.3  0.1  2.5  1.5  1.1  0.0  0.5  0.8  0.0  0.6  4.5  6.0
    1829  -1.25 -2.26   21  -5.1 -3.4 -2.0 -0.7 -0.9 -1.3 -0.5 -0.4 -0.9 -1.0 -2.9 -8.0
    1828  -1.52  0.27   19   3.2  5.5 -0.3 -0.7 -1.2  0.1 -0.9 -1.0 -1.0 -1.4  2.3 -1.4
    1827  -1.24 -0.28   18   3.2 -8.2  0.6  1.7  2.9 -0.1 -0.4 -3.1  0.0 -0.2 -1.7  2.0
    1826  -1.04 -0.20   19  -6.9  1.4  2.9 -1.4 -1.2  1.3  2.3  2.8 -0.1  1.3 -2.4 -2.4
    1825  -1.10  0.06   19   0.4 -1.3 -1.6  2.6  1.3  0.4  0.2  0.5 -0.2 -0.5 -0.6 -0.5
    1824  -1.98  0.88   18   7.5  0.5 -1.2 -0.1 -2.4  0.3  0.9 -1.3  1.2  0.8  2.4  2.0
    1823  -0.46 -1.52   17  -7.2 -1.7 -2.9 -2.4 -0.7 -3.6 -1.7  0.7  0.4 -2.0 -2.4  5.2
    1822  -1.46  1.00   16   0.9  5.0  3.5 -1.0  3.2  5.0  2.6  0.0 -1.4  1.8 -0.7 -6.9
    1821  -2.53  1.07   16   4.1 -2.1  1.4  0.7 -2.3 -0.3 -1.2 -1.3  2.1  0.5  6.1  5.1
    1820  -1.30 -1.23   14  -4.2 -0.6 -1.8  0.1 -0.1 -2.8 -1.7  0.4 -1.7 -0.1 -1.7 -0.5
    1819  -1.52  0.22   12  -0.7  0.8  0.2 -0.2  1.4 -0.3  0.5  2.4  0.4 -0.3 -2.6  1.0
    1818  -1.99  0.47   12  -0.5 -1.5  1.2  5.2  0.3 -0.1  1.6 -0.4 -1.2  2.7 -0.5 -1.1
    1817  -3.13  1.14   10   2.3  4.5  0.1 -4.1  1.3  3.4  0.9  1.5  2.5 -2.7  3.9  0.1
    1816  -2.19 -0.94   10   4.0 -4.6 -3.5 -0.7 -3.0 -2.2  0.0 -1.0 -0.5 -0.5 -0.3  1.0
    1815  -2.82  0.63   10  -1.1  8.0  5.4 -1.4  2.9  1.8 -3.1 -0.5  1.3  1.3 -3.2 -3.9
    1814  -2.32 -0.50   10   0.3 -7.2 -2.3  0.4 -2.4 -0.5  2.3  1.1 -0.8 -0.6  1.5  2.2
    1813  -2.90  0.58    9  -0.2  0.8  0.0  4.5 -0.4 -1.0  0.2 -1.4 -0.3 -1.6  1.3  5.1
    1812  -0.31 -2.59    8   1.4  0.6 -3.1 -5.3 -2.8 -3.5 -3.7 -0.7 -1.3 -2.3 -3.8 -6.6
    1811  -1.73  1.43    9  -1.2  3.2  1.3  2.3  4.1  4.4  1.8  0.0 -2.0  3.6  0.7 -1.1
    1810  -1.60 -0.13    9  -2.1 -6.1  1.7  2.7 -1.9 -0.5  0.0 -0.2  2.2  0.8  1.9 -0.1
    1809  -2.05  0.45    9  -1.7  4.5  3.2 -1.1 -0.7 -0.6 -2.0 -1.3 -0.3  0.2 -1.1  6.3
    1808  -0.69 -1.36    8   0.3 -2.9 -1.3 -1.0  1.5 -0.1 -0.5 -3.1  1.6 -3.5 -1.8 -5.5
    1807  -0.34 -0.35    8  -2.4 -0.9 -3.4  1.2 -1.3  0.3  3.3  5.1 -2.1  1.7 -0.7 -5.0
    1806  -2.49  2.15    8   5.0  3.4  1.0 -1.3  3.6  0.6  0.4  1.0 -0.2  2.5  4.8  5.0
    1805  -1.08 -1.42    7  -7.1  0.6  1.2 -1.0 -3.1 -2.0 -1.3 -1.2  0.0 -4.0 -2.0  2.9
    1804  -1.38  0.30    7   7.7  0.3 -1.4 -3.5  3.7  1.4 -1.7 -1.9  2.8  1.6 -1.6 -3.8
    1803  -0.47 -0.91    7  -0.1 -2.2 -1.6  1.7 -1.4 -1.9  2.6 -1.4 -2.5 -3.9  0.0 -0.2
    1802   0.02 -0.49    7  -6.0  0.0 -1.1  0.5 -3.3  2.4 -0.7  3.0 -1.0  1.1 -0.9  0.1
    1801  -0.53  0.56    8   1.5  1.9  5.0 -4.9 -0.2  1.1  0.1 -1.5  0.6  2.5  0.0  0.6
    1800  -2.33  1.80    9   5.4 -1.4 -2.2  7.5  4.3 -1.2  0.6  1.5  1.1 -0.1  1.3  4.8
    1799  -0.56 -1.77    9  -5.1 -2.4 -0.6 -3.5 -3.0 -2.3 -1.3 -1.1 -1.6 -0.5  0.7 -0.6
    1798   0.03 -0.59    9  -0.2  0.4  0.4 -0.3 -0.9  2.4 -1.8 -0.3  0.2  0.1 -1.0 -6.1
    1797  -0.78  0.81    8  -5.4  0.0  2.0  2.6  2.4 -1.2  2.1  0.7 -0.9  0.5  2.0  4.9
    1796  -0.79  0.02    8  13.9  1.8 -2.1 -3.7  0.2 -1.6  2.0  0.2  0.5 -4.5 -0.1 -6.4
    1795   0.21 -1.00    8  -8.1 -4.2 -4.3 -1.1 -1.1 -0.2 -5.3  1.0  3.2  3.9 -2.0  6.2
    1794  -0.58  0.79    8   2.6  1.4  3.3  5.6  1.7  2.9  1.0 -1.9 -0.5 -2.1  0.0 -4.5
    1793  -0.84  0.26    8  -1.7  3.5 -0.7 -3.3  0.0 -1.5  1.4  0.3  0.2  2.0  1.4  1.5
    1792  -0.54 -0.30    8  -2.7 -1.7  0.1 -0.5 -0.1  0.3  0.7 -0.4 -0.4  0.0  1.3 -0.2
    1791  -0.91  0.37    7   1.5 -1.8  0.0  4.4 -2.4 -0.6  2.0  1.7  0.4  0.3 -1.2  0.1
    1790  -1.38  0.47    7   4.0  0.9  6.0 -2.3 -0.7  2.2 -1.6 -0.1 -1.1 -0.1 -0.1 -1.4
    1789  -1.82  0.43    8  -3.4  1.9 -3.0  0.4  2.0 -2.7 -1.6  1.2 -1.4  0.7  1.7  9.4
    1788  -1.23 -0.58    8   2.3 -1.4 -2.0  1.2  2.0  0.4  2.6 -1.0  1.1 -2.1 -1.9 -8.2
    1787  -2.74  1.51    6  -2.7  3.0  4.3 -3.4 -0.7  0.0  1.3  2.0  2.0  4.7  4.5  3.1
    1786  -3.33  0.58    8   1.3  2.6  4.1  5.0  0.1  1.7 -0.9  0.0 -2.7 -1.1 -3.7  0.6
    1785  -2.99 -0.33    8   5.4 -0.2 -5.3 -0.7 -3.2 -1.5 -1.1 -0.8  0.1  2.3 -0.1  1.1
    1784  -0.97 -2.02    8  -9.0 -6.4 -0.3 -3.8  0.9 -0.8 -2.1 -1.1  0.6 -4.3  0.9  1.1
    1783  -1.98  1.01    8   0.8  6.5 -1.3  1.2  0.8 -0.6  0.0  0.7  0.5  3.0  3.0 -2.5
    1782  -0.35 -1.62    8   2.7 -4.7 -2.0 -3.3 -1.7 -0.1  0.7 -3.1 -1.8 -1.1 -3.7 -1.4
    1781  -1.09  0.74    8   1.4  2.3 -1.4  2.9  0.6  1.5  0.5  0.4  1.3 -1.8  0.2  1.0
    1780   0.34 -1.43    6   0.2 -5.1  1.3 -4.1 -0.8  0.6 -0.2  0.0 -1.9 -1.3 -1.6 -4.3
    1779  -0.33  0.67    6  -1.6  3.7  1.7  0.4  0.0 -0.7 -1.8  0.2  3.2  3.7 -0.1 -0.6
    1778  -1.53  1.20    5   1.3  0.9 -0.2  4.4  1.6  0.6  3.6  0.9 -0.6 -2.2 -0.5  4.6
    1777  -1.82  0.28    5   6.0 -4.7 -0.7 -1.3  2.7 -0.8 -1.4  0.3 -0.7  1.5  2.4  0.1
    1776  -0.34 -1.47    5  -7.9 -1.2 -0.2  0.6 -1.2 -2.0 -0.3 -0.9 -1.9 -1.3  0.4 -1.8
    1775  -1.39  1.05    5   0.3  1.9 -0.5 -3.2 -1.6  1.7  1.8  1.5  3.3  0.4  4.0  3.0
    1774  -0.26 -1.13    5  -3.0  2.6  2.4  1.3 -1.2  1.1 -0.3 -0.5 -2.6 -2.3 -5.9 -5.2
    1773  -0.44  0.18    6   2.6 -2.5 -1.1  1.2  2.9 -1.1  0.4  0.4 -0.1  0.1 -1.4  0.8
    1772  -1.99  1.55    5   1.4  5.0  5.1  2.4 -5.5  1.2 -0.4  1.8  1.5  1.7  4.5 -0.1
    1771  -1.08 -0.91    5  -1.0 -2.7 -1.0 -2.5  2.4  0.6 -0.2 -2.4 -1.6  0.3 -2.2 -0.6
    1770  -1.13  0.04    5  -2.0 -0.2 -2.6 -1.6  0.9  0.0 -0.1  1.1  0.8  3.0  0.0  1.2
    1769  -1.32  0.19    4   4.5  0.2  1.4  0.4 -0.5 -0.8 -0.4 -0.7  0.8 -2.2 -0.3 -0.1
    1768  -1.27 -0.05    3   3.1 -4.0 -1.8  1.4  1.3  1.3  0.6 -0.7 -1.5 -1.3 -2.0  3.0
    1767  -0.76 -0.51    3  -5.6  4.5 -0.7 -3.9 -2.5 -1.6  0.0  1.0 -0.1  1.1  2.7 -1.0
    1766  -0.73 -0.02    3  -3.9  2.8 -1.9  1.0  2.9  0.0  1.5 -1.7  1.3 -1.8  0.0 -0.5
    1765  -0.33 -0.40    3  -1.8 -7.4  3.5  1.2 -4.1  1.7 -3.7  2.2  1.0  1.9  0.7  0.0
    1764  -0.67  0.33    3   8.7 -0.2 -0.2  0.6  2.9 -1.6  1.0 -2.4 -1.1  0.6 -0.6 -3.7
    1763  -0.61 -0.06    3  -7.1  3.7  1.7 -3.9 -2.5 -0.5  0.0  2.9 -1.6  0.5  0.3  5.8
    1762   0.19 -0.80    3   2.3 -2.3 -5.4  2.8 -0.4 -1.7  0.6 -3.5 -1.7 -0.2 -0.7  0.6
    1761  -0.06  0.25    3   1.0  3.5  3.8 -0.5  1.5  1.1 -0.2  1.8  1.2 -2.8 -0.5 -6.9
    1760  -0.52  0.46    3  -2.7 -2.2 -1.4  0.4  1.2  0.1 -1.2 -0.4  0.8 -0.1  3.7  7.3
    1759  -1.00  0.48    3   4.4  1.6  0.7  0.6 -1.8  0.0  2.4  0.0  1.0  1.8 -1.9 -3.0
    1758  -0.27 -0.73    2  -2.3 -1.5 -2.4 -2.1  1.8 -0.6 -5.8 -1.1  0.4  1.2 -0.6  4.2
    1757   0.03 -0.30    2  -1.8 -0.2 -0.1  0.7 -0.5  0.7  1.1  0.3 -2.1 -2.8  1.0  0.1
    1756  -2.23  2.27    1  10.8 11.4  0.3  3.0 -3.0  0.0  1.9 -0.1  2.6  3.2  1.6 -4.5
    1755  -1.57 -0.67    1  -5.0 -4.8  0.0  0.0  0.0  0.0  0.0 -1.7  0.0  0.0  0.0  3.5
    1751  -1.23 -0.33    1  -2.0 -6.2 -1.4 -2.6  4.1  1.6 -0.2  1.9  0.2  0.6  0.0  0.0
    1750  -2.03  0.80    1  -1.4  3.5  6.8  1.7 -1.6  1.1  2.0 -0.1  0.0 -0.7  0.0 -1.7
    

    For comparison, the set of all German thermometers shows:

    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
    -----------------------------------------------------------------------------------
    2010   0.15 -0.15   26  -1.8  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
    2009   0.50 -0.35   33  -5.1 -3.0 -0.1  2.8 -0.6 -2.2  0.2  1.6  2.2 -0.8  1.5 -0.7
    2008   0.75 -0.25   33  -0.7  0.2 -1.6 -2.8  0.1 -0.2  0.0  0.5 -0.1  0.7  1.2 -0.3
    2007   0.50  0.25   33   6.9  4.0  4.8  3.6  1.1  0.5 -4.3  1.1 -4.6 -3.8 -3.4 -2.9
    2006  -0.12  0.62   33  -4.0  0.1 -2.3 -0.9  0.0  0.5  4.1 -0.7  2.4  1.4  3.1  3.8
    2005  -0.14  0.02   33   1.9 -2.6 -0.5  0.0  0.9  0.8  0.9 -2.0  0.8  0.8  0.0 -0.8
    2004   0.31 -0.45   26  -0.4  3.6 -0.9  0.9 -2.5 -3.9 -2.1 -1.8 -0.1  3.9 -1.7 -0.4
    2003   0.41 -0.10   25  -1.6 -6.3 -0.2  0.4  0.3  2.0  1.2  1.3  1.0 -2.1  0.9  1.9
    2002  -0.08  0.49   26   0.5  2.6  1.2  0.7 -0.3  2.3 -0.7  0.3  1.4 -3.7  1.2  0.4
    2001   0.85 -0.93   24   0.0 -1.6 -0.9 -3.2 -0.5 -2.5  3.2  0.8 -2.2  1.7 -2.3 -3.7
    2000   0.44  0.41   25  -1.6  3.4 -0.3  1.4  1.0  1.8 -3.6  0.4 -2.9  1.6  2.6  1.1
    1999   0.01  0.43   25   0.6 -4.3  0.5  0.0 -0.3 -1.3  2.5  0.4  3.5  0.5  2.1  1.0
    1998  -0.12  0.12   25   4.0  0.7 -0.7  2.7  1.4  0.7 -0.6 -3.1 -0.8  0.8 -2.6 -1.0
    1997  -1.37  1.26   28  -1.0  4.7  3.8 -2.2  1.2  0.2  1.1  2.5  2.9 -1.5 -0.4  3.8
    1996   0.14 -1.52   27  -2.1 -4.3 -2.3  0.4 -0.9 -0.4 -4.5 -0.8 -2.2 -1.3  1.4 -1.2
    1995   0.59 -0.45   19  -1.6  2.9 -1.8  0.0  0.0 -0.7 -0.7  0.3 -0.3  4.2 -3.3 -4.4
    1994  -0.12  0.71   23   0.2  0.0  1.6 -1.4 -1.4  0.0  4.4  0.8  0.5  0.3  3.2  0.3
    1993   0.33 -0.44   28   0.8 -1.2 -0.9  0.9  0.0 -0.8 -1.1 -1.5 -0.6  0.6 -1.9  0.4
    1992   0.33  0.00   25   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
    1991   1.25 -0.92   27  -0.8 -6.5 -0.5 -0.1 -3.6 -1.4  2.2 -0.2  1.3 -1.5 -0.3  0.3
    1990   1.16  0.09   30   0.0  2.3  0.0  0.3  0.1 -0.2 -0.9  1.3 -1.5 -0.2  1.5 -1.6
    1989   0.63  0.53   30  -0.7  1.4  4.3 -0.7 -0.2  0.3  0.5  0.0  1.2  0.9  0.0 -0.7
    1988  -0.98  1.62   30   9.1  2.3  3.0 -0.9  4.4  1.2  0.2  1.6 -1.0  0.2 -1.8  1.1
    1987  -0.43 -0.56   30  -5.5  5.7 -3.3  2.7 -4.3 -2.0 -0.2 -0.8  2.8 -0.3 -1.0 -0.5
    1986  -0.97  0.54   30   5.1 -3.0  0.2 -1.3  0.8  2.2 -0.3  0.0 -1.9  0.6  5.0 -0.9
    1985  -0.46 -0.51   30  -6.3 -2.7  0.7  0.5  2.3 -0.1  1.6 -0.7  1.2 -1.1 -3.9  2.4
    1984   0.66 -1.12   30  -2.8  0.6 -2.5 -1.5 -0.5 -2.5 -4.4 -0.9 -1.4  1.0  1.2  0.3
    1983   0.47  0.19   30   6.1 -0.8  0.3  1.9 -1.0  0.0  1.3  0.6 -2.0 -0.4 -2.3 -1.4
    1982  -0.23  0.70   30  -0.7  0.0 -2.1 -1.1 -0.6  0.9  2.4  1.0  1.6  1.4  1.4  4.2
    1981  -0.65  0.42   36   0.7 -1.5  3.1  1.4  2.0  0.2  0.6 -0.1  0.1  0.0  1.0 -2.5
    1980  -0.53 -0.12   35   1.8  3.4 -0.3 -0.2 -1.6 -1.7 -0.2  1.0  0.7 -0.7 -0.6 -3.0
    1979  -0.53 -0.01   35  -4.8 -0.3 -1.0 -0.2  0.9  1.6 -0.1  0.4  1.1 -0.3 -0.6  3.2
    1978   0.27 -0.80   34   0.1 -3.6 -1.2  0.7 -0.2 -0.6 -1.0 -0.6  0.2 -1.0 -0.7 -1.7
    1977   0.12  0.16   34  -0.3  1.9  4.1 -1.1 -0.9 -1.6 -2.4 -0.3 -0.7  0.5  0.2  2.5
    1976   0.59 -0.48   34  -3.4 -0.8 -2.2  0.1  0.9  2.5  1.0 -2.4 -2.7  1.8  1.6 -2.1
    1975   0.44  0.15   33   1.1 -1.6 -1.9 -1.2  0.8  0.6  2.9  1.6  2.3  2.1 -1.8 -3.1
    1974  -0.06  0.50   33   3.0  1.9  1.3  2.7 -1.4 -1.9 -1.8 -0.5 -1.0 -1.9  1.4  4.2
    1973  -0.41  0.35   33   2.5 -1.0 -1.2 -2.1  1.1  1.5 -0.5  1.9  3.5  0.3 -1.0 -0.8
    1972   0.19 -0.60   33  -1.5  0.8  4.3 -1.0 -2.6  0.6 -0.3 -2.2 -1.4 -2.1  0.7 -2.5
    1971  -0.48  0.67   33   1.5  2.3  0.0  2.0  2.2 -2.6  1.4  1.0 -0.8  0.1 -1.6  2.5
    1970  -0.44 -0.03   33  -2.7  0.7  0.4 -1.5 -1.5  2.2 -2.2  0.3 -0.6 -1.2  0.6  5.1
    1969   0.03 -0.47   32   1.4 -2.8 -3.8 -2.2  2.0 -1.2  2.2  0.1  0.4  0.0  1.1 -2.9
    1968   0.76 -0.72   32  -1.6 -2.0 -0.9  2.6 -1.8  1.3 -2.5 -0.1 -0.4 -1.0 -0.2 -2.1
    1967   0.35  0.41   39   3.1 -0.9  2.1 -2.0 -0.3 -2.1  3.2  0.9  0.5  0.1  1.8 -1.5
    1966  -0.63  0.97   39  -3.7  5.5  0.7  1.9  1.8  1.3  0.5  0.5  0.5  2.6  0.7 -0.6
    1965   0.07 -0.70   39   3.3 -2.5  1.8 -1.9 -2.4 -1.5 -3.2 -1.0 -0.9  1.4 -3.5  2.0
    1964  -0.94  1.02   39   4.9  6.1 -1.9  0.2  1.5  1.2  0.3  0.0  0.0 -1.1 -2.4  3.4
    1963  -0.90 -0.04   39  -8.9 -5.6  2.3  0.3  2.2  1.5  2.8  0.0  1.3 -0.9  4.6 -0.1
    1962   0.87 -1.77   38   2.3 -4.2 -5.9 -2.3 -0.6 -1.7  0.0  0.1 -4.0 -1.5 -1.0 -2.4
    1961   0.38  0.48   39  -0.5  3.7  1.6  2.8 -2.4 -0.2 -0.3 -0.1  3.5  1.4 -2.1 -1.6
    1960   1.12 -0.74   40  -0.1  0.2 -2.0 -2.0  0.1  0.0 -4.0 -1.5 -1.4  0.2  2.5 -0.9
    1959   0.21  0.92   40   0.2 -1.7  6.5  4.2 -0.5  1.7  2.4  0.4 -0.6 -0.5 -0.8 -0.3
    1958   0.57 -0.37   40  -0.4 -1.8 -6.5 -2.4  3.4 -2.2 -0.7  1.8  2.7  0.1 -0.3  1.9
    1957  -1.17  1.75   40  -0.1 13.4  3.3  2.2 -2.7  3.5  0.9  0.9 -1.9  0.8  2.2 -1.5
    1956  -0.43 -0.75   40   1.8 -7.7  2.3 -1.6  2.1 -1.3 -0.6 -2.7  0.3  0.3 -1.5 -0.4
    1955  -0.30 -0.12   41   1.7  1.6 -3.7  1.0 -1.8 -1.5  2.8  1.0  0.0 -1.8 -0.2 -0.6
    1954   0.86 -1.16   40  -2.3 -3.1 -0.5 -2.8 -0.8  0.0 -2.8 -0.6 -0.1 -0.6 -0.8  0.5
    1953  -0.08  0.94   40  -0.6  0.0  2.0 -1.0  0.9  0.7 -0.4 -1.0  2.5  2.9  2.7  2.6
    1952   0.68 -0.77   33  -0.9 -1.5  0.1  2.6  0.6 -0.2  0.9  0.3 -3.6 -0.3 -4.3 -2.9
    1951   0.66  0.02   30   0.8 -0.1 -1.0  0.1 -0.9 -1.0 -0.2  0.0  0.5 -0.2  0.9  1.4
    1950   1.14 -0.48    9  -2.4  1.6  2.5 -3.7  1.9  3.9  0.4  0.4 -3.7 -2.7  0.7 -4.7
    1949   1.08  0.06    9  -1.7  1.2 -3.4  0.3 -1.4 -1.4  1.6  0.6  2.2  1.7 -0.7  1.7
    1948   0.62  0.47    8   7.9  6.3  3.0  0.2 -0.9 -2.3 -3.0 -2.3 -3.3  0.8 -0.7 -0.1
    1947   0.42  0.20    8  -3.0 -8.2 -1.0 -0.6  0.6  3.2  0.3  2.4  2.9  1.0  1.3  3.5
    1946   0.92 -0.50    8   2.6 -1.6 -1.8  1.7  0.0 -2.0  0.6  0.0  0.4 -2.7  0.3 -3.5
    1945   0.33  0.58    8  -7.3  4.9  5.2  0.1  2.3  2.5  0.5 -3.8  0.8  0.8 -0.7  1.7
    1944   1.02 -0.69    8   3.1 -4.1 -5.2 -0.6 -1.1  0.0 -0.5  1.7 -0.9 -1.5  1.5 -0.7
    1943  -0.49  1.52    8   7.1  7.9  4.0  1.8  0.5 -0.3  1.6  0.6 -1.5 -1.1 -0.4 -2.0
    1942  -0.83  0.34    8  -2.1 -5.1 -1.6  1.9  3.3 -1.8 -2.1  2.7  3.3  3.5  1.0  1.1
    1941  -1.25  0.42    8   3.2  4.0  0.9 -2.6 -3.0 -0.5  2.1  0.4 -0.2  0.0 -3.6  4.3
    1940   0.33 -1.58    8 -11.1 -5.7  1.1 -0.8  1.8  0.4 -0.8 -2.9 -0.7  1.3  0.0 -1.6
    1939   0.72 -0.38    8   1.3  0.8 -5.8  4.2 -0.6  0.0  0.0 -0.1 -0.5 -2.3 -1.9  0.3
    1938   0.62  0.09    8   2.7 -1.1  3.8 -2.9 -3.8 -0.5 -0.2  0.5  0.3 -0.6  4.2 -1.3
    1937   0.42  0.21    7  -4.2  2.0 -2.0  0.9  2.5  0.6  0.4  0.9  0.6  3.9 -0.6 -2.5
    1936   0.29  0.12    7   4.4 -1.4  2.7 -0.3  2.3 -1.4 -1.0 -0.4 -1.0 -2.7 -1.2  1.5
    1935   1.28 -0.99    9  -1.3  0.6 -2.1 -3.0 -2.9  1.2 -0.3  0.1 -1.3 -0.7  1.3 -3.5
    1934  -0.53  1.82    9   2.5  1.4 -0.1  3.4  1.8  2.4  0.1 -0.6  1.7  0.6  1.2  7.4
    1933   0.34 -0.88    8  -4.8  2.5  4.6 -0.4 -1.3 -0.5  0.2 -1.8 -1.9  0.1 -1.8 -5.4
    1932  -0.53  0.87    9   1.9 -1.3  0.6  1.8 -2.8 -1.8  0.9  3.4  5.3  1.4 -0.1  1.1
    1931   0.83 -1.36    9  -2.6 -0.8 -4.6 -3.0  3.5 -2.4  0.6 -0.4 -3.1 -1.5 -1.7 -0.3
    1930  -0.53  1.36   19   7.0  9.4  1.8  3.9 -1.4  3.7 -1.3 -1.4 -2.5 -1.2  1.4 -3.1
    1929   0.41 -0.93   19  -6.5-12.0 -0.3 -3.0  3.2  0.5 -0.9  1.0  3.0  1.2 -1.4  4.0
    1928   0.07  0.33   19   0.0  1.6 -3.0  0.4 -1.0  0.0  1.5  0.0 -0.9  0.2  3.4  1.8
    1927   0.77 -0.69   19   1.8 -3.7  2.0 -2.7  0.2  0.5  0.0  0.3 -1.1  0.4 -3.5 -2.5
    1926   0.34  0.42   20  -2.6  0.6  2.7  1.6 -3.1 -1.2 -0.5 -0.4  3.7 -0.4  4.1  0.6
    1925  -0.47  0.81   20   5.4  6.5 -0.9  1.8  0.0 -0.2  0.9  2.0 -2.6 -0.8 -0.8 -1.6
    1924   0.02 -0.48   20  -4.3 -3.7 -2.7 -0.4  2.1  4.0 -1.8 -1.3  0.4 -1.0 -0.1  3.0
    1923  -0.81  0.83   20   4.0  2.2  1.4  1.2 -1.8 -4.5  2.9  0.5  2.0  5.3  0.5 -3.8
    1922   1.03 -1.84   20  -6.8 -2.4 -2.2 -2.1 -0.4  1.2 -3.1 -2.0 -2.5 -6.0  2.5  1.7
    1921   0.62  0.41   20   1.8 -1.7 -0.3 -1.7  0.0 -0.4  1.2  2.3  0.6  4.2 -1.1  0.0
    1920  -0.63  1.26   21   1.6  3.1  3.1  4.0  2.5 -0.2  3.1 -1.2 -2.0  0.8  0.5 -0.2
    1919   0.56 -1.19   21  -0.1 -1.9 -1.0 -3.7 -2.5  2.1 -2.1  0.3  1.9 -1.9 -2.3 -3.1
    1918  -0.45  1.01   21   3.5  5.4  4.1  4.6 -1.1 -5.9 -0.8 -0.7 -1.6  0.5 -2.0  6.1
    1917   0.46 -0.91   21  -6.4 -4.6 -4.1 -3.8  1.9  6.1  1.4  0.7  2.7 -1.1  0.4 -4.1
    1916  -0.03  0.48   21   3.5 -0.1  2.3  1.4  0.0 -4.9 -0.4  0.6  0.1  2.1  3.0 -1.8
    1915   0.48 -0.51   21   3.4 -2.0 -3.0 -3.0  2.4  3.2 -1.0 -2.0 -0.5 -1.8 -1.8  0.0
    1914   0.53 -0.05   21  -2.8  1.7 -1.4  1.9 -1.6 -0.3  2.9  2.3 -0.2 -1.3 -3.4  1.6
    1913  -0.12  0.65   21   1.2 -1.2  0.0  0.9  0.4 -0.8 -3.4  0.9  3.6  3.0  4.2 -1.0
    1912   1.09 -1.21   21  -0.3  1.2  2.1 -0.4 -1.1  0.5 -1.4 -5.6 -5.4 -2.0 -2.2  0.1
    1911   0.39  0.70   21  -2.6 -1.1  0.3  0.0  0.7 -1.4  3.9  3.7  2.6 -0.5  2.5  0.3
    1910  -0.65  1.04   21   3.2  5.0  1.9 -0.5  1.4  2.3  0.3 -0.3 -0.8 -1.0  0.4  0.6
    1909  -0.51 -0.14   21   0.4 -3.7 -0.8  2.5 -2.4 -3.0 -2.1  1.7  0.6  2.2  0.5  2.4
    1908  -0.08 -0.43   21  -1.8  2.6 -0.2 -0.7  0.1  2.2  2.6 -1.4 -1.1 -3.3 -2.1 -2.1
    1907   0.37 -0.44   21  -1.4 -1.9  0.4 -2.1  0.0  0.1 -2.7 -0.6  0.7  1.4 -2.8  3.6
    1906   0.01  0.36   21   2.9 -0.8 -2.4  2.2  1.1 -2.4 -1.5  0.0 -0.2  5.4  3.0 -3.0
    1905   0.49 -0.48   21  -0.6  0.1  1.4 -3.1 -0.4  1.7 -0.2 -0.1  0.8 -3.9 -0.1 -1.4
    1904   0.44  0.05   21  -1.7 -2.5 -2.6  4.4 -0.1  0.5  2.6  1.0 -1.7 -1.3 -1.1  3.1
    1903  -0.77  1.21   21  -2.3  4.8  2.2 -3.2  4.0 -0.4  0.4  0.8  1.4  2.7  2.9  1.2
    1902  -0.38 -0.38   21   6.6  2.9  1.3 -0.1 -4.2 -0.4 -2.3 -1.5 -1.0 -1.9 -1.1 -2.9
    1901   0.43 -0.82   21  -4.7 -5.2  1.5  1.3  1.9 -0.3 -0.1  0.0 -0.6  0.2 -2.1 -1.7
    1900   0.15  0.28   21  -1.4 -0.8 -2.2 -0.6 -0.2  1.4  1.2 -0.5  1.3  0.9 -1.6  5.9
    1899   0.57 -0.42   21   0.0  0.9 -0.2  0.0 -0.3 -0.2  2.8 -1.3 -1.1 -1.1  1.7 -6.2
    1898  -0.05  0.62   21   4.9  0.0 -2.0  0.0  0.8 -2.0 -2.0  1.0  1.4  1.6  1.9  1.8
    1897  -0.43  0.38   22  -1.8  1.6 -0.3  1.3 -0.1  0.3 -0.5  2.7 -0.5 -1.2  1.4  1.6
    1896  -0.70  0.27   22   3.1  6.4  4.0 -2.5 -1.3  0.9 -0.3 -2.1 -2.5  1.5 -3.6 -0.3
    1895   0.03 -0.73   22  -2.1 -8.3 -3.2 -1.4  0.9  1.4 -0.4  1.0  4.4 -0.8  0.0 -0.3
    1894  -0.17  0.20   22   5.3 -0.2  0.3  1.0 -1.0 -1.5  0.3 -1.4 -1.4 -1.8  2.7  0.1
    1893  -0.52  0.35   23  -5.1  1.3  3.7  1.8  0.0  0.5  1.2 -1.1 -1.3  2.3 -1.0  1.9
    1892  -0.67  0.15   22   2.8  1.3 -2.1  2.0 -0.4  0.4  0.0  3.2  0.0 -2.5  0.5 -3.4
    1891  -0.81  0.14   22  -6.2  1.7 -1.2 -1.6 -0.8  0.8  0.7 -1.3  0.6  2.5 -0.2  6.7
    1890  -0.74 -0.07   21   4.4 -0.2  3.5  0.0 -2.0 -4.4 -0.9  1.0  1.9 -0.7  0.3 -3.7
    1889  -1.18  0.44   21  -0.5  0.0 -0.5  1.1  3.4  2.5  1.6  0.3 -1.4  1.5 -0.8 -1.9
    1888  -1.16 -0.02   21   1.8 -1.5  0.0 -1.2  2.3  0.3 -4.0 -0.6  0.4  0.9  0.3  1.0
    1887  -0.13 -1.03   21  -2.5  1.9  0.4 -1.6 -2.6  1.4  1.9 -1.1 -2.7 -4.0 -2.0 -1.5
    1886  -0.38  0.26   21   1.7 -6.0 -1.9 -0.5  2.6 -2.5 -0.3  2.3  2.2  2.0  2.3  1.2
    1885   0.43 -0.82   21  -5.8  0.5 -2.2  3.2 -2.7  3.6 -1.0 -2.4 -1.4 -0.4  0.8 -2.0
    1884  -0.23  0.66   21   3.1  0.2  6.3 -0.1  0.2 -3.1  1.7  1.0  0.8 -0.6 -2.5  0.9
    1883   0.22 -0.45   21  -0.4  0.9 -7.6 -1.6  0.5  1.9 -0.1  1.1  0.3 -0.5  0.2 -0.1
    1882  -0.71  0.93   21   5.5  1.2  3.1  2.1  0.1 -0.8 -2.1 -1.1  0.9  4.0 -1.7  0.0
    1881   0.29 -1.00   21  -2.1 -0.4 -1.2 -3.1  0.5  0.1  1.1 -0.6 -2.2 -2.9  1.9 -3.1
    1880  -1.49  1.78   21  -0.7  0.1  2.3  2.5  1.3 -0.8  2.7 -0.7  0.3  0.1  2.9 11.4
    1879   0.26 -1.75   21  -2.0 -1.7 -1.0 -2.4 -3.1  0.2 -1.0  0.4 -0.1 -1.7 -2.2 -6.4
    1878   0.18  0.07   21  -3.1 -0.5  1.0  2.4  3.3 -2.3 -0.7 -0.3  3.5  2.2 -2.9 -1.7
    1877  -0.00  0.18   21   5.9  1.5 -1.9 -2.1  0.9  1.9 -1.0 -0.2 -1.8 -3.3  4.1 -1.8
    1876  -0.47  0.47   21  -4.6  5.1  3.1  1.1 -4.4 -0.8  0.6 -1.0 -1.3  3.9 -0.5  4.4
    1875  -0.04 -0.42   21   0.0 -4.0 -2.8 -1.9  4.1  1.3 -2.2  3.4 -1.3 -2.7  1.2 -0.2
    1874   0.35 -0.39   21  -1.1  0.7 -1.1  2.6 -0.5 -0.4  0.7 -2.4  2.6 -0.2 -2.8 -2.8
    1873   1.02 -0.67   21   2.1 -2.3 -0.1 -2.4 -2.7  0.6  0.4  1.9 -1.9  0.1 -2.0 -1.7
    1872  -1.54  2.56   21   5.8  2.0  0.0  2.1  2.8  2.7  0.8 -1.4  0.2  3.3  5.4  7.0
    1871  -1.13 -0.41   21  -4.9  3.9  3.7 -0.8 -3.0 -2.5 -1.0  1.9  2.3 -1.8 -3.2  0.5
    1870   0.01 -1.14   21   0.7 -9.3  0.1 -2.4  0.0  2.3  0.0  0.1 -2.8  1.0  0.6 -4.0
    1869   1.24 -1.23   21   0.3  1.6 -2.5  2.9 -3.9 -4.1  0.0 -3.2 -0.3 -1.5  1.1 -5.2
    1868  -0.28  1.53   33  -0.5 -0.3  2.2 -0.5  5.1  1.5  3.1  1.6  0.7  0.2 -0.7  5.9
    1867   0.63 -0.92   34  -4.3  0.5 -0.8 -1.3  1.4 -2.3 -0.8  1.6 -0.9  0.7 -1.2 -3.6
    1866   0.21  0.42   35   3.5  7.5  2.9 -0.7 -6.0  3.6 -3.3 -0.6 -0.2 -1.8 -1.3  1.5
    1865  -1.58  1.79   34   4.8 -3.6 -4.9  3.8  6.0 -1.1  3.8  1.9  2.1  1.4  3.7  3.6
    1864   0.87 -2.46   34  -7.5 -3.2 -0.2 -2.2 -2.1 -0.3  0.2 -3.6  0.3 -3.0 -2.1 -5.8
    1863   0.47  0.40   35   4.1  2.4 -1.0 -1.1 -2.3  0.6 -0.5  1.5 -1.1  0.2  0.6  1.4
    1862   0.18  0.29   34   3.7 -3.1  0.4  3.3  4.3 -2.6 -1.7 -1.6  0.6  0.6 -0.9  0.5
    1861  -0.78  0.96   34  -7.1  4.9  3.4 -0.7 -2.5  1.9  2.3  2.3  0.2  1.8  2.9  2.1
    1860   0.99 -1.77   34   0.6 -4.5 -4.5 -0.5  0.2 -1.2 -4.5 -3.3 -0.5 -1.8 -1.4  0.2
    1859  -0.58  1.57   34   3.1  6.0  4.5  0.0  1.8 -1.9  3.2  1.8 -1.7  0.7  4.0 -2.6
    1858   0.69 -1.28   35  -0.2 -3.3 -1.3  0.0 -1.6  2.3 -1.4 -2.0  0.2 -1.7 -4.4 -1.9
    1857  -0.13  0.82   35  -2.0 -1.7  1.3 -1.4  1.7  0.6  2.5  1.9  2.4  1.0  2.4  1.1
    1856  -1.18  1.06   34   2.9  6.4  0.0  2.2  0.0  0.2 -1.2  0.1 -0.1 -0.7 -1.2  4.1
    1855   0.23 -1.42   28  -2.7 -5.6 -2.3 -1.4 -2.0  0.6 -1.0  0.9 -0.5  1.6  0.3 -4.9
    1854  -0.82  1.05   29  -3.1  2.0  5.7  2.1  1.3 -1.1  0.0 -0.1  0.1  0.0 -0.6  6.3
    1853   1.08 -1.90   29   0.0 -3.9 -3.1  0.2 -1.6  0.4 -1.0 -0.9 -0.4  1.3 -4.3 -9.5
    1852  -0.06  1.14   29   1.6  0.8 -2.1 -3.5  3.8  0.5  3.0  0.8  1.8 -2.6  5.7  3.9
    1851  -0.27  0.21   31   7.1 -2.3  2.0  0.3 -2.5 -0.9 -0.6  0.3 -0.1  3.2 -4.0  0.0
    1850  -0.11 -0.16   27  -4.9  0.5 -1.6  1.2 -1.7  0.5  0.6  0.8 -1.3 -1.7  2.8  2.9
    1849   0.30 -0.41   27   6.0  0.2 -2.0 -2.4  0.2 -0.9 -1.0 -0.4  0.3 -1.2 -0.9 -2.8
    1848  -0.21  0.51   25  -4.0  3.5  2.1  3.7 -1.3  1.9 -0.8 -1.9  1.0  1.4 -0.9  1.4
    1847   1.45 -1.66   23  -3.7 -4.6 -3.6 -3.1  2.6 -3.7 -0.7 -1.4 -3.3 -2.5  1.1  3.0
    1846  -0.60  2.05   23   0.8  8.8  9.8 -0.1  1.9  1.5  1.0  4.7  2.4  1.6 -1.7 -6.1
    1845  -0.49 -0.11   23   1.1 -4.1 -5.6 -0.9 -1.9  1.3  3.3  0.3 -1.3  0.2  0.4  5.9
    1844   0.57 -1.07   22  -1.7 -3.0 -0.6  0.7  1.2  1.2 -1.7 -3.0  0.4  0.3 -0.3 -6.3
    1843   0.09  0.48   21   4.6  1.7 -1.6  2.2 -2.8 -1.9  0.0 -2.5 -0.2  1.7  4.0  0.6
    1842   0.57 -0.48   21  -2.4  4.1 -0.8 -2.4 -2.3  1.8  1.2  3.8 -1.2 -3.2 -3.6 -0.8
    1841  -0.72  1.29   21  -0.9 -4.0  4.9 -0.2  4.5 -0.9 -0.4  0.0  1.6  3.3 -0.6  8.2
    1840   0.12 -0.83   22   0.3 -0.5 -0.6  4.4 -0.3 -2.1 -2.3  0.9 -1.3 -2.7  0.1 -5.9
    1839  -1.38  1.49   22   7.7  4.9 -2.2 -0.6  0.0  2.0  1.2  0.4  0.0  1.0  2.4  1.1
    1838  -0.56 -0.82   23  -8.5 -4.7  2.7  0.0  2.0 -0.1  0.8 -2.8  2.4 -0.6 -0.7 -0.3
    1837   0.16 -0.72   23   0.5  0.1 -6.0 -2.1  0.0 -0.5 -0.8  1.8 -0.2 -0.7  0.5 -1.2
    1836   0.09  0.07   23  -1.4 -2.0  2.8  0.3 -1.5  0.0 -1.6 -0.6 -1.7  1.9  2.0  2.6
    1835   1.48 -1.39   22  -2.7  1.5 -0.1  0.4 -3.0 -0.8 -2.3 -2.5 -0.7 -0.9 -3.1 -2.5
    1834   0.18  1.30   24   7.7 -2.7  1.5  0.0 -1.0 -0.2  4.6  5.1  2.3  0.6  0.3 -2.6
    1833  -0.16  0.34   24  -2.2  3.1 -1.0 -1.8  4.9  1.7  0.1 -3.8  0.0 -0.9  1.1  2.9
    1832   0.43 -0.59   24   1.9 -0.4 -0.7 -1.5 -1.0  0.3 -1.9  0.3  0.2 -2.7 -0.9 -0.7
    1831  -0.68  1.12   24   4.0  4.5 -0.5  0.7 -0.9 -0.1  0.0  1.0  0.0  3.9 -1.6  2.4
    1830  -1.96  1.27   22  -2.3  0.1  2.5  1.5  1.1  0.0  0.5  0.8  0.0  0.6  4.5  6.0
    1829   0.30 -2.26   21  -5.1 -3.4 -2.0 -0.7 -0.9 -1.3 -0.5 -0.4 -0.9 -1.0 -2.9 -8.0
    1828   0.03  0.27   19   3.2  5.5 -0.3 -0.7 -1.2  0.1 -0.9 -1.0 -1.0 -1.4  2.3 -1.4
    1827   0.31 -0.28   18   3.2 -8.2  0.6  1.7  2.9 -0.1 -0.4 -3.1  0.0 -0.2 -1.7  2.0
    1826   0.51 -0.20   19  -6.9  1.4  2.9 -1.4 -1.2  1.3  2.3  2.8 -0.1  1.3 -2.4 -2.4
    1825   0.45  0.06   19   0.4 -1.3 -1.6  2.6  1.3  0.4  0.2  0.5 -0.2 -0.5 -0.6 -0.5
    1824  -0.43  0.88   18   7.5  0.5 -1.2 -0.1 -2.4  0.3  0.9 -1.3  1.2  0.8  2.4  2.0
    1823   1.09 -1.52   17  -7.2 -1.7 -2.9 -2.4 -0.7 -3.6 -1.7  0.7  0.4 -2.0 -2.4  5.2
    1822   0.09  1.00   16   0.9  5.0  3.5 -1.0  3.2  5.0  2.6  0.0 -1.4  1.8 -0.7 -6.9
    1821  -0.98  1.07   16   4.1 -2.1  1.4  0.7 -2.3 -0.3 -1.2 -1.3  2.1  0.5  6.1  5.1
    1820   0.25 -1.23   14  -4.2 -0.6 -1.8  0.1 -0.1 -2.8 -1.7  0.4 -1.7 -0.1 -1.7 -0.5
    1819   0.03  0.22   12  -0.7  0.8  0.2 -0.2  1.4 -0.3  0.5  2.4  0.4 -0.3 -2.6  1.0
    1818  -0.44  0.47   12  -0.5 -1.5  1.2  5.2  0.3 -0.1  1.6 -0.4 -1.2  2.7 -0.5 -1.1
    1817  -1.58  1.14   10   2.3  4.5  0.1 -4.1  1.3  3.4  0.9  1.5  2.5 -2.7  3.9  0.1
    1816  -0.64 -0.94   10   4.0 -4.6 -3.5 -0.7 -3.0 -2.2  0.0 -1.0 -0.5 -0.5 -0.3  1.0
    1815  -1.27  0.63   10  -1.1  8.0  5.4 -1.4  2.9  1.8 -3.1 -0.5  1.3  1.3 -3.2 -3.9
    1814  -0.77 -0.50   10   0.3 -7.2 -2.3  0.4 -2.4 -0.5  2.3  1.1 -0.8 -0.6  1.5  2.2
    1813  -1.35  0.58    9  -0.2  0.8  0.0  4.5 -0.4 -1.0  0.2 -1.4 -0.3 -1.6  1.3  5.1
    1812   1.24 -2.59    8   1.4  0.6 -3.1 -5.3 -2.8 -3.5 -3.7 -0.7 -1.3 -2.3 -3.8 -6.6
    1811  -0.18  1.43    9  -1.2  3.2  1.3  2.3  4.1  4.4  1.8  0.0 -2.0  3.6  0.7 -1.1
    1810  -0.05 -0.13    9  -2.1 -6.1  1.7  2.7 -1.9 -0.5  0.0 -0.2  2.2  0.8  1.9 -0.1
    1809  -0.50  0.45    9  -1.7  4.5  3.2 -1.1 -0.7 -0.6 -2.0 -1.3 -0.3  0.2 -1.1  6.3
    1808   0.86 -1.36    8   0.3 -2.9 -1.3 -1.0  1.5 -0.1 -0.5 -3.1  1.6 -3.5 -1.8 -5.5
    1807   1.21 -0.35    8  -2.4 -0.9 -3.4  1.2 -1.3  0.3  3.3  5.1 -2.1  1.7 -0.7 -5.0
    1806  -0.94  2.15    8   5.0  3.4  1.0 -1.3  3.6  0.6  0.4  1.0 -0.2  2.5  4.8  5.0
    1805   0.47 -1.42    7  -7.1  0.6  1.2 -1.0 -3.1 -2.0 -1.3 -1.2  0.0 -4.0 -2.0  2.9
    1804   0.17  0.30    7   7.7  0.3 -1.4 -3.5  3.7  1.4 -1.7 -1.9  2.8  1.6 -1.6 -3.8
    1803   1.08 -0.91    7  -0.1 -2.2 -1.6  1.7 -1.4 -1.9  2.6 -1.4 -2.5 -3.9  0.0 -0.2
    1802   1.57 -0.49    7  -6.0  0.0 -1.1  0.5 -3.3  2.4 -0.7  3.0 -1.0  1.1 -0.9  0.1
    1801   1.02  0.56    8   1.5  1.9  5.0 -4.9 -0.2  1.1  0.1 -1.5  0.6  2.5  0.0  0.6
    1800  -0.78  1.80    9   5.4 -1.4 -2.2  7.5  4.3 -1.2  0.6  1.5  1.1 -0.1  1.3  4.8
    1799   0.99 -1.77    9  -5.1 -2.4 -0.6 -3.5 -3.0 -2.3 -1.3 -1.1 -1.6 -0.5  0.7 -0.6
    1798   1.58 -0.59    9  -0.2  0.4  0.4 -0.3 -0.9  2.4 -1.8 -0.3  0.2  0.1 -1.0 -6.1
    1797   0.77  0.81    8  -5.4  0.0  2.0  2.6  2.4 -1.2  2.1  0.7 -0.9  0.5  2.0  4.9
    1796   0.76  0.02    8  13.9  1.8 -2.1 -3.7  0.2 -1.6  2.0  0.2  0.5 -4.5 -0.1 -6.4
    1795   1.76 -1.00    8  -8.1 -4.2 -4.3 -1.1 -1.1 -0.2 -5.3  1.0  3.2  3.9 -2.0  6.2
    1794   0.97  0.79    8   2.6  1.4  3.3  5.6  1.7  2.9  1.0 -1.9 -0.5 -2.1  0.0 -4.5
    1793   0.71  0.26    8  -1.7  3.5 -0.7 -3.3  0.0 -1.5  1.4  0.3  0.2  2.0  1.4  1.5
    1792   1.01 -0.30    8  -2.7 -1.7  0.1 -0.5 -0.1  0.3  0.7 -0.4 -0.4  0.0  1.3 -0.2
    1791   0.64  0.37    7   1.5 -1.8  0.0  4.4 -2.4 -0.6  2.0  1.7  0.4  0.3 -1.2  0.1
    1790   0.17  0.47    7   4.0  0.9  6.0 -2.3 -0.7  2.2 -1.6 -0.1 -1.1 -0.1 -0.1 -1.4
    1789  -0.27  0.43    8  -3.4  1.9 -3.0  0.4  2.0 -2.7 -1.6  1.2 -1.4  0.7  1.7  9.4
    1788   0.32 -0.58    8   2.3 -1.4 -2.0  1.2  2.0  0.4  2.6 -1.0  1.1 -2.1 -1.9 -8.2
    1787  -1.19  1.51    6  -2.7  3.0  4.3 -3.4 -0.7  0.0  1.3  2.0  2.0  4.7  4.5  3.1
    1786  -1.78  0.58    8   1.3  2.6  4.1  5.0  0.1  1.7 -0.9  0.0 -2.7 -1.1 -3.7  0.6
    1785  -1.44 -0.33    8   5.4 -0.2 -5.3 -0.7 -3.2 -1.5 -1.1 -0.8  0.1  2.3 -0.1  1.1
    1784   0.58 -2.02    8  -9.0 -6.4 -0.3 -3.8  0.9 -0.8 -2.1 -1.1  0.6 -4.3  0.9  1.1
    1783  -0.43  1.01    8   0.8  6.5 -1.3  1.2  0.8 -0.6  0.0  0.7  0.5  3.0  3.0 -2.5
    1782   1.20 -1.62    8   2.7 -4.7 -2.0 -3.3 -1.7 -0.1  0.7 -3.1 -1.8 -1.1 -3.7 -1.4
    1781   0.46  0.74    8   1.4  2.3 -1.4  2.9  0.6  1.5  0.5  0.4  1.3 -1.8  0.2  1.0
    1780   1.89 -1.43    6   0.2 -5.1  1.3 -4.1 -0.8  0.6 -0.2  0.0 -1.9 -1.3 -1.6 -4.3
    1779   1.22  0.67    6  -1.6  3.7  1.7  0.4  0.0 -0.7 -1.8  0.2  3.2  3.7 -0.1 -0.6
    1778   0.02  1.20    5   1.3  0.9 -0.2  4.4  1.6  0.6  3.6  0.9 -0.6 -2.2 -0.5  4.6
    1777  -0.27  0.28    5   6.0 -4.7 -0.7 -1.3  2.7 -0.8 -1.4  0.3 -0.7  1.5  2.4  0.1
    1776   1.21 -1.47    5  -7.9 -1.2 -0.2  0.6 -1.2 -2.0 -0.3 -0.9 -1.9 -1.3  0.4 -1.8
    1775   0.16  1.05    5   0.3  1.9 -0.5 -3.2 -1.6  1.7  1.8  1.5  3.3  0.4  4.0  3.0
    1774   1.29 -1.13    5  -3.0  2.6  2.4  1.3 -1.2  1.1 -0.3 -0.5 -2.6 -2.3 -5.9 -5.2
    1773   1.11  0.18    6   2.6 -2.5 -1.1  1.2  2.9 -1.1  0.4  0.4 -0.1  0.1 -1.4  0.8
    1772  -0.44  1.55    5   1.4  5.0  5.1  2.4 -5.5  1.2 -0.4  1.8  1.5  1.7  4.5 -0.1
    1771   0.47 -0.91    5  -1.0 -2.7 -1.0 -2.5  2.4  0.6 -0.2 -2.4 -1.6  0.3 -2.2 -0.6
    1770   0.42  0.04    5  -2.0 -0.2 -2.6 -1.6  0.9  0.0 -0.1  1.1  0.8  3.0  0.0  1.2
    1769   0.23  0.19    4   4.5  0.2  1.4  0.4 -0.5 -0.8 -0.4 -0.7  0.8 -2.2 -0.3 -0.1
    1768   0.28 -0.05    3   3.1 -4.0 -1.8  1.4  1.3  1.3  0.6 -0.7 -1.5 -1.3 -2.0  3.0
    1767   0.79 -0.51    3  -5.6  4.5 -0.7 -3.9 -2.5 -1.6  0.0  1.0 -0.1  1.1  2.7 -1.0
    1766   0.82 -0.02    3  -3.9  2.8 -1.9  1.0  2.9  0.0  1.5 -1.7  1.3 -1.8  0.0 -0.5
    1765   1.22 -0.40    3  -1.8 -7.4  3.5  1.2 -4.1  1.7 -3.7  2.2  1.0  1.9  0.7  0.0
    1764   0.88  0.33    3   8.7 -0.2 -0.2  0.6  2.9 -1.6  1.0 -2.4 -1.1  0.6 -0.6 -3.7
    1763   0.94 -0.06    3  -7.1  3.7  1.7 -3.9 -2.5 -0.5  0.0  2.9 -1.6  0.5  0.3  5.8
    1762   1.74 -0.80    3   2.3 -2.3 -5.4  2.8 -0.4 -1.7  0.6 -3.5 -1.7 -0.2 -0.7  0.6
    1761   1.49  0.25    3   1.0  3.5  3.8 -0.5  1.5  1.1 -0.2  1.8  1.2 -2.8 -0.5 -6.9
    1760   1.03  0.46    3  -2.7 -2.2 -1.4  0.4  1.2  0.1 -1.2 -0.4  0.8 -0.1  3.7  7.3
    1759   0.55  0.48    3   4.4  1.6  0.7  0.6 -1.8  0.0  2.4  0.0  1.0  1.8 -1.9 -3.0
    1758   1.28 -0.73    2  -2.3 -1.5 -2.4 -2.1  1.8 -0.6 -5.8 -1.1  0.4  1.2 -0.6  4.2
    1757   1.58 -0.30    2  -1.8 -0.2 -0.1  0.7 -0.5  0.7  1.1  0.3 -2.1 -2.8  1.0  0.1
    1756  -0.68  2.27    1  10.8 11.4  0.3  3.0 -3.0  0.0  1.9 -0.1  2.6  3.2  1.6 -4.5
    1755  -0.02 -0.67    1  -5.0 -4.8  0.0  0.0  0.0  0.0  0.0 -1.7  0.0  0.0  0.0  3.5
    1751   0.32 -0.33    1  -2.0 -6.2 -1.4 -2.6  4.1  1.6 -0.2  1.9  0.2  0.6  0.0  0.0
    1750  -0.48  0.80    1  -1.4  3.5  6.8  1.7 -1.6  1.1  2.0 -0.1  0.0 -0.7  0.0 -1.7
    

    Oh, and anyone wanting to check that I calculated the actual base anomalies correctly can pick a year, like that 1750, and look at the one thermometer in that year vs the next year and see if the difference is what shows up in the chart. You could even do the three in 1760 to see if I got the averaging right. The input is in 1/10 C while the above has been converted to C already:

    6171038400001750 12 49 82 97 137 174 200 184 142 75-9999 15
    6171038400001751 -8 -13 68 71 178 190 198 203 144 81-9999-9999
    6171038400001755 -58 -61-9999-9999-9999-9999-9999 186-9999-9999-9999 50
    6171038400001756 50 53 71 101 148-9999 217 185 170 113 48 5
    6171038400001757 13 49 69 116 138 205 239 192 128 57 68 7
    6171038400001758 -34 18 50 92 157 189 188 196 136 81 55 28
    6171038400001759 39 42 63 94 125 198 213 197 144 106 26 -25

    So 1.2 dropping to -0.8 is total change of -2.0 in January 1751. Then we have 4 missing years, but when January shows up again, we have a -5 C change. Etc.

    The -9999 is a missing data flag and ought to show up as zero in the anomaly field.

    November of 1756 is not zero because there is older data than 1750 that set an initial value to which it is compared (not in the sample above). But you get the idea. It really is pretty easy to check that this code is doing what it claims to do. It’s the deciding that you really want to do that that’s the hard bit ;-)

  13. Ryan says:

    They get rather upset at the glacier on top of the Zugsptize melting. They cover it up in summer to try and halt its demise. Strangely enough it isn’t actually ever warmer Summers that cause the worst of the melting – it is rain. Warm rain falling on the ice causes rapid melting of the glacier surface. Got a feeling that this has been going on for quite some time?

    Nevertheless there remains much hand-wringing amongst German environmentalists about global warming, and the response has actually been to apply yet more technology to counter the alleged impact of… all the technology. Typically German – not in their nature to give up on technology just yet. Of course the German’s see AGW as a money spinning opportunity and are working hard on wind turbine, carbon sequestration, low-energy lighting, solar energy and other low-CO2 technologies. So the German response to AGW is actually to consume more of the world’s resources to combat it – I’m not sure that was quite what the environmentalists had envisaged.

  14. Ausie Dan says:

    Hi Chiefio
    I have done a pilot study of six Australian sites, scattered right over the country. I have sent it to WUWT but so far no response.

    Half of the sites have rapid increases in temperature, the rest moderate or none. The difference between these two groups is in the monthly mean maximum temperatures – the minimum means clump quite closely.

    The other unexpected factor is that it is the maximums that are accelerating in the growth sites, not the minimums, which the UHI theory would predict.
    I have no answer for this.

    My main point however is that the temperature gradient for each site is very constant (some going back to the 1850’s).
    It seems as though individual places have their own micro climates with persisting degrees of warming or cooling.
    Your all of Germany bears that out.
    I seem to remember that Anthony had a similar study of rural stations in the USA.

    You may care to examine individual German sites and also look at seperate monthly averages for maximums and minimums.

    REPLY: [ Yeah, the whole MAX vs MIN thing keeps nagging at me as something I ought to do. But I was driven to get the dT code done first. At this point, though, it’s darned near trivial to do the same thing on the v2.min and v2.max files and be able to put up 3 lines on a graph and say: Hmmm now that’s interesting….

    If you want to send me a pointer to “your stuff” I’d be happy to look at it. It sounds interesting and I’d be willing to put it up as a ‘guest posting’ if it pans out (and assuming you wouldn’t mind being ‘up’ at a second fiddle joint like mine ;-)

    So, yeah, at the end of the day it is looking more and more like a bunch of micro-climates that wander back and forth on several decade to hundred year cycles (like that 1934 hot America / cool Indian ocean thing) and depending on what bag of thermometers you grab, you get a different “global warming” answer… And perhaps with some equipment change bias thrown in for seasoning and ‘low lows’ clipping as Stevenson Screens leave the “grassy knoll” and electronic gizmos get put on a short electric cord near the building… ( I do wonder how you can feed power into a tin can and NOT have a tiny bit of it hanging around inside the can making it just a tinsy bit slower to cool off than an old wooden ventilated box with no power feed… but that’s just a nagging suspicion at this point).

    But I regularly have rocks tossed at me for having the audacity to think that paying attention to exactly which thermometers you are using might actually matter… and that individual station biases are an important thing … and that maybe you ought not to run around tossing out 4/5 of your thermometers more or less randomly from the data set…

    To me the whole “Global Warming” measurement process is just a giant calorimetry experiment. You have a calorimeter (the earth) and you have heat in (the sun) and you have heat out (IR radiation) and you are trying to measure the net accumulated in the calorimeter. As any lab guy can tell you, calorimetry is very easy to screw up and one of the easiest ways to do it is by changing the thermometers or their locations in the rig or by recalibrating them mid-measurement. Yet that’s exactly what we’re doing on a massive scale globally…

    Then when you point this out, you get berated for not accepting that “the anomaly will fix it”. Well, the anomaly won’t fix it (as shown with the Mod Flag 0 vs All comparison above); it just lets you admire it’s more subtile “nuances” ;-)
    -E.M.Smith ]

  15. A C Osborn says:

    Chiefio, I can’t see somewhere to comment on your Roasting Turkey Article.
    I was wondering if you would like to look at Warwick Hughes website, he has the old (pre 2000) CRU datasets over there including Turkey up to 1999. It might be worth comparing them to the current values that you used in your analysis to see if they have been adjusted since 2000.

    REPLY: [ There isn’t any restriction on comments by article. There ought to be a comment block at the bottom of that page too (and I see one when I look at it now). But in any case: I hope to try this process on lots of different data sets (and get lots of other folks to try it out too ;-) but the easier way to see if the data have changed is to just compare the two sets of data…. if they are in the same formats, it’s as easy (under Unix, Linux, or the MacOS) as doing “diff foo bar”. So, you got a little linky poo to connect that website? -E.M.Smith ]

  16. E.M.Smith says:

    Here is the code that does the actual creation of the anomaly part. It is pretty trivial. It is not fully commented (missing my usual identification section for one thing… and the copyleft… and..) and I’ve not given it a final ‘preening’, but it’s what I’m running.

    Also note that it says it expects a special form of the v2.mean file with the inventory data concatenated on the end of each line. That is for future experiments involving selecting records via the A Airstation flag or the population or RSU Rural/Sub/Urban flags. It does not strictly require that format at present. Ok, enough mealy mouthed “it’s not done yet”… I said it’s a “Work In Progress”, so here it is.

    dTdt.f at it’s wort stage:

    [chiefio@Hummer 2010]$ cat dTdt.f

    C2345*fff1         2         3         4         5         6         712sssssss8
    C
    C    this program converts temperatures into delta Temp / delta time
    C    Input must already be sorted by Station ID and by year 
    C    A spcial v2.mean+v2.inv concatinated file is the source (so that
    C    station meta-data are attached to temperatures
    C
    
          integer temps(12), otemps(12), dtemps(12)
          integer yr,  m, bad
    
          character*12 stnID, ostnID 
          character*128 meta, oline, line
    
          data temps   /0,0,0,0,0,0,0,0,0,0,0,0/
          data otemps  /0,0,0,0,0,0,0,0,0,0,0,0/
          data dtemps  /0,0,0,0,0,0,0,0,0,0,0,0/
    
          yr     = 0 
          m      = 0
          bad    = -9999
    
          stnID  = " "
          ostnID = " "
          meta   = " "
          oline  = " "
          line   = " " 
    
    
    C     Get the name of the input file, in modified GHCN format (w/ metadata).
    C     The file must be sorted by year (since we do a delta year to year.)
    C     The name of the output file will be that of the inputfile.dt
    C     The input file ought to be a GHCN format file with V2.inv data
    C     concatenated at the end of each temperature line, as will the 
    C     output file have metat data after each dT/dt line.
    
          call getarg(1,line)
          oline=trim(line)//".dt"
          open(1,file=line,form='formatted')
          open(10,file=oline,form='formatted')              ! output
    
    
    C2345*fff1         2         3         4         5         6         712sssssss8
    
    C     Read in a line of data 
    C     (Country Code/StnID, year, temperatures, meta)
    
       20 read(1,'(a12,i4,12i5,a96)',end=999) stnID,yr,temps,meta
    C     write (*,*) "Input Data"
    C     write (*,*) stnID, yr, temps, meta
    
    C      if you have a new record for the same station, find dT/dt.
    
    C2345*fff1         2         3         4         5         6         712sssssss8
    
          if(stnID .eq. ostnID) then
            do m=1,12
               if ( (otemps(m).ne.bad) .and. (temps(m).ne.bad) ) then
    C              write (*,*) "non-bad temp and otemp"
                   dtemps(m) = temps(m) - otemps(m)
                   otemps(m) = temps(m)
               else
    C              write (*,*) "Somebody Bad "
                   dtemps(m)=0
                   if ( temps(m) .ne. bad ) then
    C                   write (*,*) "temps non-bad +++++"
                        otemps(m) = temps(m)
                   end if
               end if
    
             end do
    
          else
               ostnID=stnID
               do m=1,12
                  dtemps(m)=0
                  otemps(m)=temps(m)
               end do
          end if 
    C     write (*,*) "ORDINARY Output"
    C     write (*,*) stnID, yr, dtemps, meta
          write (10,'(a12,i4,12i5,a96)') stnID, yr, dtemps, meta
    
          go to 20
    
      999 continue
    C     write (*,*) "Final RECORD"
    C     write (*,*) stnID, yr, dtemps, meta
    
          close (1)
          close (10)
    
    C2345*fff1         2         3         4         5         6         712sssssss8
     9999 continue
    
          stop
          end
    
  17. E.M.Smith says:

    Just for the sake of ‘completing the set’ I made an extract of the non-zero Mod Flags for Germany and ran the report for them, too. They show a pronounced cooling since 1950’s (though there is only one thermometer at that point) and 1960’s. Don’t know exactly what to make of it at this point other than that I think it matters what thermometers you keep, and when you add / drop them:

    [chiefio@Hummer DTemps]$ cat EMS.n0.617.yrs.dT 
    Produced from input file: DTemps/EMS.n0.617                                                                                                               
     
    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
    -----------------------------------------------------------------------------------
    2010   0.18 -0.18   20  -2.2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
    2009   0.52 -0.34   24  -5.0 -3.2  0.2  3.9 -0.8 -2.3  0.0  1.4  2.2 -1.1  1.6 -1.0
    2008   0.90 -0.38   24  -0.9  0.3 -2.2 -3.9  0.2 -0.3  0.0  0.6 -0.1  0.8  1.4 -0.4
    2007   0.64  0.26   24   7.0  4.0  4.9  3.4  1.2  0.5 -4.1  1.1 -4.5 -3.9 -3.5 -3.0
    2006  -0.02  0.66   24  -4.4  0.7 -2.0 -1.1  0.2  0.7  4.2 -0.1  1.9  0.9  2.9  4.0
    2005  -0.07  0.06   24   2.7 -3.4 -0.8  0.0  1.2  1.1  1.2 -2.7  1.1  1.2  0.0 -0.9
    2004   0.35 -0.42   25  -0.4  3.6 -0.9  0.9 -2.5 -3.8 -2.1 -1.7 -0.1  4.0 -1.7 -0.4
    2003   0.44 -0.09   24  -1.7 -6.3 -0.2  0.4  0.3  1.9  1.2  1.2  1.0 -2.0  1.0  2.1
    2002  -0.07  0.51   25   0.6  2.6  1.3  0.7 -0.2  2.3 -0.7  0.4  1.4 -3.7  1.1  0.3
    2001   0.87 -0.94   23   0.0 -1.6 -1.0 -3.3 -0.5 -2.5  3.2  0.8 -2.1  1.6 -2.3 -3.6
    2000   0.49  0.38   24  -1.6  3.4 -0.3  1.4  1.0  1.7 -3.6  0.4 -3.0  1.6  2.5  1.1
    1999   0.03  0.46   24   0.6 -4.3  0.5  0.0 -0.3 -1.2  2.6  0.5  3.5  0.5  2.1  1.0
    1998  -0.08  0.11   24   3.9  0.8 -0.7  2.7  1.4  0.6 -0.7 -3.1 -0.8  0.8 -2.6 -1.0
    1997  -1.35  1.27   27  -1.0  4.7  3.8 -2.2  1.2  0.3  1.2  2.5  2.9 -1.5 -0.4  3.8
    1996   0.20 -1.55   26  -2.2 -4.4 -2.2  0.4 -0.9 -0.5 -4.7 -0.8 -2.2 -1.3  1.4 -1.2
    1995   0.67 -0.47   18  -1.7  3.1 -1.9  0.0  0.0 -0.7 -0.8  0.3 -0.2  4.3 -3.4 -4.6
    1994  -0.04  0.71   23   0.2  0.0  1.6 -1.4 -1.4  0.0  4.4  0.8  0.5  0.3  3.2  0.3
    1993   0.40 -0.44   28   0.8 -1.2 -0.9  0.9  0.0 -0.8 -1.1 -1.5 -0.6  0.6 -1.9  0.4
    1992   0.40  0.00   25   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
    1991   0.40  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
    1990   0.38  0.02    2   0.5  2.9  0.0  0.5 -0.4 -0.7 -1.0  1.5 -3.1 -0.2  1.9 -1.7
    1989  -0.12  0.50    2  -1.2  0.8  4.2 -1.4  0.0  0.6  1.5  0.0  1.8  0.8  0.0 -1.1
    1988  -1.77  1.65    2   9.7  2.5  2.5 -0.8  4.9  1.2 -0.3  1.5 -1.0  0.2 -2.1  1.5
    1987  -1.18 -0.58    2  -6.6  5.5 -2.8  3.4 -4.3 -2.7 -0.3 -0.8  2.8 -0.2 -1.0  0.0
    1986  -1.74  0.56    2   5.4 -3.4  0.2 -1.8  0.5  3.1 -0.2  0.5 -2.0  0.8  5.0 -1.4
    1985  -1.15 -0.59    2  -6.3 -2.8  0.4  0.3  2.7 -0.1  1.4 -1.6  1.0 -1.1 -3.8  2.8
    1984  -0.11 -1.04    2  -2.6  1.1 -2.1 -0.8 -0.1 -2.7 -5.0 -1.2 -1.3  1.0  1.2  0.0
    1983  -0.18  0.07    2   6.3 -1.2  0.3  0.5 -1.6  0.2  1.6  1.3 -2.2 -0.7 -2.4 -1.3
    1982  -0.18  0.00    2   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
    1980   0.05 -0.22    6   2.3  3.0 -0.2  0.0 -1.2 -1.9 -1.0  1.5  0.9 -1.5 -0.7 -3.9
    1979  -0.26  0.31    6  -4.5  0.7 -0.5 -0.8  1.0  2.0  0.4  0.5  1.0  0.9  0.2  2.8
    1978   0.75 -1.01    6  -0.3 -4.4 -1.8  1.3 -0.4 -0.9 -1.1 -0.8  0.4 -1.5 -1.5 -1.1
    1977   0.73  0.02    6  -0.2  1.9  3.7 -1.6 -1.2 -2.5 -2.3 -0.3 -0.6  0.3  0.3  2.7
    1976   0.79 -0.06    5  -3.1 -0.7 -1.1  0.4  1.2  3.6  1.5 -2.1 -2.8  2.6  1.2 -1.4
    1975   0.83 -0.04    5   0.5 -1.2 -2.8 -1.3  0.8  0.6  2.4  1.0  2.4  2.5 -1.5 -3.9
    1974   0.21  0.62    5   4.1  2.7  2.1  3.1 -1.8 -2.1 -1.3 -0.9 -1.4 -2.6  1.3  4.3
    1973   0.07  0.13    6   1.2 -2.3 -2.1 -2.0  1.6  1.6 -0.7  2.3  3.7  0.2 -1.0 -0.9
    1972   0.63 -0.56    6  -1.2  2.0  5.2 -2.1 -3.0  0.7 -1.1 -2.2 -1.8 -2.2  1.1 -2.1
    1971   0.49  0.14    6   0.4  0.4  0.0  0.4  0.4 -0.5  0.2  0.3 -0.2  0.1 -0.3  0.5
    1970   0.44  0.05    2  -2.6  1.2  0.4 -1.1 -1.8  2.3 -2.4  0.4 -0.5 -0.6  0.1  5.2
    1969   0.83 -0.39    2   1.1 -3.0 -3.4 -2.1  2.3 -1.2  2.3  0.3  0.4 -0.4  2.2 -3.2
    1968   1.63 -0.80    2  -1.6 -1.8 -1.0  2.3 -1.7  1.1 -2.6 -0.3 -0.4 -1.3 -0.5 -1.8
    1967   1.37  0.27    2   3.6 -1.5  2.1 -2.6 -0.6 -1.9  3.4  0.6  0.5  0.1  1.4 -1.9
    1966   0.28  1.08    2  -4.5  5.9  1.0  2.5  2.0  1.2  0.2  0.6  0.6  3.3  1.0 -0.8
    1965   0.92 -0.63    2   4.3 -1.9  2.0 -1.7 -2.5 -2.1 -3.5 -1.0 -1.0  0.8 -3.6  2.6
    1964  -0.09  1.01    2   4.7  5.6 -2.0  0.0  1.5  1.9  0.3 -0.1 -0.3 -0.8 -2.6  3.9
    1963  -0.05 -0.04    2  -9.5 -5.4  2.0 -0.3  2.3  1.2  3.3  0.0  1.4 -0.7  4.9  0.3
    1962   1.56 -1.61    2   2.7 -4.1 -5.6 -1.9 -0.3 -1.2  0.2  0.3 -3.8 -2.0 -0.8 -2.8
    1961   1.25  0.31    2  -0.3  2.3  1.4  2.2 -1.1  0.0 -0.3 -0.3  1.9  0.9 -1.2 -1.8
    1960   1.91 -0.66    2  -0.6  0.3 -2.1 -2.2  0.0  0.2 -3.7 -0.9 -1.5  0.3  2.9 -0.6
    1959   1.12  0.78    2   0.5 -2.2  6.9  4.7 -1.3  1.4  1.7  0.0 -0.6 -0.2 -1.0 -0.5
    1958   1.31 -0.18    2   0.2 -1.7 -7.1 -2.7  4.3 -2.2 -0.5  2.1  2.7  0.0  0.0  2.7
    1957  -0.52  1.82    2  -1.1 14.9  3.9  2.1 -2.8  3.9  0.9  0.5 -2.1  0.7  2.7 -1.7
    1956   0.34 -0.86    2   2.3 -9.2  1.9 -1.3  1.7 -1.7  0.1 -2.0  0.7  0.3 -1.9 -1.2
    1955   0.47 -0.12    2   2.1  2.2 -3.9  1.4 -1.1 -1.6  2.2  0.5 -0.5 -2.0 -0.4 -0.4
    1954   1.27 -0.81    2  -1.9 -2.6 -0.4 -2.2 -0.3  0.7 -3.2 -0.1 -0.1 -0.5 -0.2  1.1
    1953   0.66  0.62    2  -0.7  0.0  2.1 -0.7  0.7  0.7  0.0 -1.0  1.6  1.9  1.4  1.4
    1952   1.74 -1.08    1  -1.0 -2.2 -0.6  2.8  0.4 -0.5  1.0  0.1 -3.9 -0.8 -4.8 -3.5
    1951   1.74  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
    

    Probably ought to double check that I’m averaging all the monthly values into a dT/yr correctly and not accidentally over weighting any one thermometer (i.e. things are sorted correctly so that the ‘new station’ only shows up after all the old station records are averaged, etc.) but at a first glance it looks more or less reasonable. Have to go into the details to be sure, though. (What? You thought THIS was the details?)

    So I’ll pick some year, like 1956 or 1958 and ‘run it by hand’ and see if I get the same values as each set of data gave via the program. Just not right now… right now it’s time for “tea and toast’ ;-)

  18. E.M.Smith says:

    OH Bother. Can’t have tea and cookies until the mind is properly unburdened…. So I looked in more detail. To me, it looks like the key events happen in the late 1980’s to early 2000’s where the old “0” flags are being retired (gee, where have we seen things centered on the 1990 point and instrument change before ;-)

    In 2004 they are both about 1/3 C then they diverge ( the Zero Only report also “takes a big gap” from 1991 to 1995 and we find 1995 divergent between to two by rather a lot at 1.67 C of divergence (absolute range). Then there is another discontinuity in about 1988. That divergence, with some minor wandering back and forth, then persists onward as the ‘changes’ in the two series are not big enough to erase it. So it looks to me like the “difference” is whatever was done to the instruments during that “Gap and Trade” where we have thermometer dropping and swapping going one.

    But “The Anomaly Will Fix It”…. oh, wait, it doesn’t ;-)

    From the “zero only” report we have:

    2004   0.28 -0.66    1   0.8  4.2 -2.4  0.8 -2.6 -5.0 -1.6 -4.0  0.4  3.9 -1.4 -1.0
    2003   0.18  0.10    1   0.4 -7.0  0.5  0.2  1.4  2.6  2.0  4.8  1.7 -2.7 -1.3 -1.4
    2002  -0.13  0.31    1  -2.3  2.2 -0.4  1.5 -2.2  3.8 -1.1 -1.7  1.1 -3.6  3.8  2.6
    2001   0.58 -0.71    1   1.2 -1.0  0.9 -3.2  0.5 -3.5  2.7  0.9 -2.8  2.6 -2.9 -3.9
    2000  -0.05  0.63    1  -2.1  4.3 -0.1  1.2 -0.1  2.7 -3.0  0.8 -2.3  1.2  2.9  2.0
    1999  -0.22  0.17    1   0.7 -3.4  0.1  0.1  0.3 -1.7  1.3  0.0  2.9 -0.5  1.4  0.8
    1998  -0.45  0.23    1   4.9 -1.1 -1.2  1.8  1.2  1.5  0.8 -1.9 -0.8  1.6 -2.4 -1.6
    1997  -1.80  1.35    1  -1.2  5.5  4.7 -1.9  1.5 -1.0  0.0  2.6  3.4 -1.3 -0.7  4.6
    1996  -1.23 -0.57    1   0.2  0.0 -5.6  0.6 -0.4  1.5  0.4  0.0 -1.0 -2.8  1.8 -1.5
    1995  -1.08 -0.15    1   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 -3.1  3.2 -2.0  0.1
    1991  -0.12 -0.97   26  -0.8 -6.8 -0.5 -0.1 -3.7 -1.5  2.3 -0.3  1.4 -1.6 -0.3  0.3
    1990  -0.22  0.11   28   0.0  2.2  0.0  0.3  0.2 -0.1 -0.9  1.3 -1.4 -0.2  1.5 -1.6
    1989  -0.77  0.54   28  -0.7  1.4  4.4 -0.6 -0.3  0.3  0.5  0.0  1.2  0.9  0.0 -0.6
    1988  -2.38  1.61   28   9.1  2.3  3.0 -0.9  4.3  1.2  0.3  1.6 -1.0  0.2 -1.8  1.0
    1987  -1.82 -0.56   28  -5.5  5.7 -3.4  2.6 -4.2 -1.9 -0.2 -0.8  2.8 -0.3 -1.0 -0.5
    1986  -2.36  0.54   28   5.1 -3.0  0.2 -1.2  0.8  2.2 -0.3 -0.1 -1.9  0.6  5.0 -0.9
    1985  -1.84 -0.52   28  -6.3 -2.7  0.7  0.5  2.2 -0.1  1.6 -0.6  1.2 -1.2 -3.9  2.4
    

    While the “all data” report has:

    2004   0.31 -0.45   26  -0.4  3.6 -0.9  0.9 -2.5 -3.9 -2.1 -1.8 -0.1  3.9 -1.7 -0.4
    2003   0.41 -0.10   25  -1.6 -6.3 -0.2  0.4  0.3  2.0  1.2  1.3  1.0 -2.1  0.9  1.9
    2002  -0.08  0.49   26   0.5  2.6  1.2  0.7 -0.3  2.3 -0.7  0.3  1.4 -3.7  1.2  0.4
    2001   0.85 -0.93   24   0.0 -1.6 -0.9 -3.2 -0.5 -2.5  3.2  0.8 -2.2  1.7 -2.3 -3.7
    2000   0.44  0.41   25  -1.6  3.4 -0.3  1.4  1.0  1.8 -3.6  0.4 -2.9  1.6  2.6  1.1
    1999   0.01  0.43   25   0.6 -4.3  0.5  0.0 -0.3 -1.3  2.5  0.4  3.5  0.5  2.1  1.0
    1998  -0.12  0.12   25   4.0  0.7 -0.7  2.7  1.4  0.7 -0.6 -3.1 -0.8  0.8 -2.6 -1.0
    1997  -1.37  1.26   28  -1.0  4.7  3.8 -2.2  1.2  0.2  1.1  2.5  2.9 -1.5 -0.4  3.8
    1996   0.14 -1.52   27  -2.1 -4.3 -2.3  0.4 -0.9 -0.4 -4.5 -0.8 -2.2 -1.3  1.4 -1.2
    1995   0.59 -0.45   19  -1.6  2.9 -1.8  0.0  0.0 -0.7 -0.7  0.3 -0.3  4.2 -3.3 -4.4
    1994  -0.12  0.71   23   0.2  0.0  1.6 -1.4 -1.4  0.0  4.4  0.8  0.5  0.3  3.2  0.3
    1993   0.33 -0.44   28   0.8 -1.2 -0.9  0.9  0.0 -0.8 -1.1 -1.5 -0.6  0.6 -1.9  0.4
    1992   0.33  0.00   25   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
    1991   1.25 -0.92   27  -0.8 -6.5 -0.5 -0.1 -3.6 -1.4  2.2 -0.2  1.3 -1.5 -0.3  0.3
    1990   1.16  0.09   30   0.0  2.3  0.0  0.3  0.1 -0.2 -0.9  1.3 -1.5 -0.2  1.5 -1.6
    1989   0.63  0.53   30  -0.7  1.4  4.3 -0.7 -0.2  0.3  0.5  0.0  1.2  0.9  0.0 -0.7
    1988  -0.98  1.62   30   9.1  2.3  3.0 -0.9  4.4  1.2  0.2  1.6 -1.0  0.2 -1.8  1.1
    1987  -0.43 -0.56   30  -5.5  5.7 -3.3  2.7 -4.3 -2.0 -0.2 -0.8  2.8 -0.3 -1.0 -0.5
    1986  -0.97  0.54   30   5.1 -3.0  0.2 -1.3  0.8  2.2 -0.3  0.0 -1.9  0.6  5.0 -0.9
    1985  -0.46 -0.51   30  -6.3 -2.7  0.7  0.5  2.3 -0.1  1.6 -0.7  1.2 -1.1 -3.9  2.4
    

    So we take a bunch of instrument changes, and depending on how you account for them you get warming, or not… “The Other Guys” do a lot of “in-filling” and “interpolating” and “correcting” and splicing Mod Flags together and find warming. I look at each instrument series as a “self to self only” anomaly and find no warming.

    YMMV, especially if you sit around playing with your instruments…

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  21. A C Osborn says:

    Chiefio, that is wierd, I can see the Comments for all them now.
    the link is this one
    http://www.warwickhughes.com/agri/crudata.htm

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