The Great Dying of Thermometers
That next to the top green line is the Northern Cold band. The area we are talking about here. From 50N to 70N latitude. We see the thermometer count rise from the 1700s until a sudden Great Dying as the Thermometer Langoliers take their toll. The top blue line is the Northern Temperate band. 30N to 50N. We saw those thermometers slaughtered in the USA in:
https://chiefio.wordpress.com/2009/10/24/ghcn-california-on-the-beach-who-needs-snow/ where we found that all of four thermometers survive in California and they have gone to the beach in Southern California, except for one that is waiting at San Francisco Airport for a ride out of town…
Frozen North, Dominated by Canada and Russia
A great deal has been said about the “massive station dropouts in Siberia” with the end of the USSR. All sorts of blame for the “warming north” has been heaped on this vision of Siberia leaving the map. I’ve even used the notion in the metaphor of Siberian stations migrating to Italy. But what do the data say, when you ask them nicely and listen?
They say: Blame Canada!
UPDATE: The Russians have notice the selective use of thermometers and done a more detailed examination (having all the raw data to work with). They have found significant bias and claim that their country is being represented with a warm bias. You can read a nice description of it:
or get the original PDF (464 kB in Russian) of the report:
http://www.iea.ru/article/kioto_order/15.12.2009.pdf
Comparison of Decade Changes of Thermometers
Since this would be painful to read with annual charts in it for both Russia and Canada, I’m just going to put in the decade summaries. If anyone really wants the annual chart, let me know and I’ll put it in a comment. This is made even more strange by the fact that Russia, as near as I can tell, gets 2 Country Codes. It has 222 for everything roughly east of the Urals, and 638 for what I can best describe as “European Russia”. While this will be a PITA for this page, it will make the “by continent” analysis easier when I get around to it. I can actually put the “European part of Russia” in Europe (which is probably why it was done this way in the first place. Even if it did take me a while to figure it out.)
For Russia 638 (European):
[chiefio@tubularbells analysis]$ cat Therm.by.lat638.Dec.LAT
Year SP-45 50 55 60 65 70 75 80 85 -NP
DecLatPct: 1759 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1769 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1779 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1789 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1799 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1809 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1819 0.0 0.0 0.0 62.1 37.9 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1829 0.0 0.0 0.0 63.0 37.0 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1839 0.0 6.7 12.0 60.0 21.3 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1849 0.0 14.8 21.3 50.8 13.1 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1859 0.0 16.1 24.8 44.5 14.6 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1869 0.0 15.9 19.6 38.3 26.2 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1879 0.0 14.5 27.3 34.5 23.6 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1889 13.6 9.6 21.5 35.4 19.9 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1899 11.7 10.1 25.9 36.2 16.1 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1909 14.9 11.6 25.3 34.2 13.9 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1919 13.2 11.8 22.7 29.1 18.0 5.3 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1929 11.8 12.3 25.0 27.6 15.6 7.7 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1939 12.9 13.2 24.4 25.0 18.2 6.3 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1949 13.7 12.0 24.4 24.4 19.8 5.7 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1959 11.7 12.7 22.6 26.6 20.3 6.2 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1969 10.8 14.0 21.1 27.9 20.0 6.2 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1979 10.8 13.1 20.4 28.4 20.6 6.7 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1989 11.1 13.7 21.5 27.1 19.6 7.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1999 6.7 12.3 14.7 34.9 19.4 11.9 0.0 0.0 0.0 0.0 100.0
DecLatPct: 2009 2.7 11.5 19.1 30.1 23.0 13.7 0.0 0.0 0.0 0.0 100.0
For COUNTRY CODE: 638
[chiefio@tubularbells analysis]$
We lose a few of the more southernly stations and add a few way up north. The middle wobbles some, but ends with a lot up north. But what has NASA / NOAA done to the dataset lately?
Year SP-45 50 55 60 65 70 75 80 85 -NP
DecLatPct: 1989 11.1 13.7 21.5 27.1 19.6 7.0 0.0 0.0 0.0 0.0 100.0
LAT pct: 1990 9.9 13.2 16.5 31.9 17.6 11.0 0.0 0.0 0.0 0.0 100.0
LAT pct: 1991 6.2 12.5 12.5 37.5 18.8 12.5 0.0 0.0 0.0 0.0 100.0
LAT pct: 1992 4.8 9.5 14.3 38.1 23.8 9.5 0.0 0.0 0.0 0.0 100.0
LAT pct: 1993 4.8 9.5 19.0 38.1 19.0 9.5 0.0 0.0 0.0 0.0 100.0
LAT pct: 1994 6.2 12.5 12.5 37.5 18.8 12.5 0.0 0.0 0.0 0.0 100.0
LAT pct: 1995 7.1 14.3 7.1 35.7 21.4 14.3 0.0 0.0 0.0 0.0 100.0
LAT pct: 1996 6.7 13.3 13.3 33.3 20.0 13.3 0.0 0.0 0.0 0.0 100.0
LAT pct: 1997 6.7 13.3 13.3 33.3 20.0 13.3 0.0 0.0 0.0 0.0 100.0
LAT pct: 1998 0.0 14.3 14.3 35.7 21.4 14.3 0.0 0.0 0.0 0.0 100.0
LAT pct: 1999 0.0 7.7 15.4 38.5 23.1 15.4 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1999 6.7 12.3 14.7 34.9 19.4 11.9 0.0 0.0 0.0 0.0 100.0
LAT pct: 2000 0.0 7.7 15.4 38.5 23.1 15.4 0.0 0.0 0.0 0.0 100.0
LAT pct: 2001 0.0 7.7 15.4 38.5 23.1 15.4 0.0 0.0 0.0 0.0 100.0
LAT pct: 2002 0.0 14.3 14.3 35.7 21.4 14.3 0.0 0.0 0.0 0.0 100.0
LAT pct: 2003 0.0 7.7 15.4 38.5 23.1 15.4 0.0 0.0 0.0 0.0 100.0
LAT pct: 2004 0.0 7.1 14.3 35.7 28.6 14.3 0.0 0.0 0.0 0.0 100.0
LAT pct: 2005 4.3 13.0 21.7 26.1 21.7 13.0 0.0 0.0 0.0 0.0 100.0
LAT pct: 2006 4.3 13.0 21.7 26.1 21.7 13.0 0.0 0.0 0.0 0.0 100.0
LAT pct: 2007 4.3 13.0 21.7 26.1 21.7 13.0 0.0 0.0 0.0 0.0 100.0
LAT pct: 2008 4.3 13.0 21.7 26.1 21.7 13.0 0.0 0.0 0.0 0.0 100.0
LAT pct: 2009 4.2 12.5 20.8 25.0 25.0 12.5 0.0 0.0 0.0 0.0 100.0
DecLatPct:2009 2.7 11.5 19.1 30.1 23.0 13.7 0.0 0.0 0.0 0.0 100.0
[chiefio@tubularbells analysis]$
The 1980’s detail numbers are rock steady and almost exactly the same as the 1989 average shown. Then we have some “wobble” at both ends in the 1990’s. But it is the 2000s that are interesting. At the 2002 – 2005 band, we see a bit of “winnowing of the north” and “juicing of the south”. The 60 and 65 degree bands get thinned while the 50 and 55 bands grow. Oh, and a couple of percent get trimmed from the 70 band while the 45 band gains 4%. A little trim here, a little tuck there…
For Russia 222 (Siberian):
These are those infamous Siberian Thermometers, and their neighbors.
[chiefio@tubularbells analysis]$ cat Therm.by.lat222.Dec.LAT
Year SP-45 50 55 60 65 70 75 80 85 -NP
DecLatPct: 1819 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1829 0.0 0.0 47.6 0.0 52.4 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1839 0.0 0.0 26.2 43.1 30.8 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1849 0.0 0.0 26.1 57.1 16.8 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1859 0.0 0.0 36.8 50.4 12.8 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1869 0.0 0.0 50.9 36.6 12.5 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1879 8.4 0.0 47.4 40.0 4.2 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1889 5.4 0.0 36.6 30.9 16.9 10.3 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1899 3.9 1.7 34.6 31.1 19.2 9.1 0.3 0.0 0.0 0.0 100.0
DecLatPct: 1909 3.6 1.6 33.0 27.8 23.8 9.3 0.8 0.0 0.0 0.0 100.0
DecLatPct: 1919 3.7 5.1 35.9 27.2 18.4 8.3 1.4 0.0 0.0 0.0 100.0
DecLatPct: 1929 4.8 5.8 33.0 27.6 17.4 7.9 3.5 0.0 0.0 0.0 100.0
DecLatPct: 1939 3.0 4.6 28.6 28.1 21.9 8.8 4.4 0.5 0.0 0.0 100.0
DecLatPct: 1949 2.5 6.3 26.8 26.5 23.0 10.0 4.4 0.6 0.0 0.0 100.0
DecLatPct: 1959 2.8 6.6 23.7 24.8 23.5 11.4 5.0 2.0 0.2 0.0 100.0
DecLatPct: 1969 2.4 6.5 23.0 24.3 23.0 11.8 5.7 2.8 0.6 0.0 100.0
DecLatPct: 1979 2.6 6.4 23.3 24.5 23.1 11.9 5.4 2.2 0.6 0.0 100.0
DecLatPct: 1989 2.2 6.3 23.0 24.5 22.7 13.1 5.3 2.2 0.7 0.0 100.0
DecLatPct: 1999 1.5 4.5 18.7 23.9 21.6 18.5 5.6 4.4 1.4 0.0 100.0
DecLatPct: 2009 1.9 5.0 19.8 24.5 23.3 16.5 4.7 3.7 0.7 0.0 100.0
For COUNTRY CODE: 222
[chiefio@tubularbells analysis]$
As percentages, it’s looks pretty darned stable with an increase in the northern part over time and a bit of “middle aged spread”. How about as yearly temperatures?
[chiefio@tubularbells analysis]$ cat Temps.222.yrs.GAT
Thermometer Records, Average of Monthly Data and Yearly Average
by Year Across Month, with a count of thermometer records in that year
--------------------------------------------------------------------------
YEAR JAN FEB MAR APR MAY JUN JULY AUG SEPT OCT NOV DEC YR COUNT
--------------------------------------------------------------------------
1880-22.5-17.6 -8.2 -1.0 8.9 14.6 18.2 17.0 9.8 -0.2 -5.0-15.0 -0.1 7
1881-18.6-18.6 -9.7 0.2 7.0 12.3 17.1 17.1 9.2 0.9 -9.8-19.0 -1.0 11
1882-16.9-13.6 -6.9 -0.2 7.5 13.1 17.1 16.3 9.5 -2.2-13.1-24.4 -1.2 16
1883-22.8-18.4 -9.1 -2.4 6.2 12.9 17.5 15.0 7.6 0.5-12.4-16.1 -1.8 16
1884-20.1-18.2-13.9 -4.6 5.8 11.9 17.1 13.5 7.6 0.6-10.3-16.9 -2.3 18
1885-27.5-19.6-10.0 -3.8 4.4 13.1 16.7 13.9 8.0 -1.9-14.2-19.2 -3.3 19
1886-21.5-20.3-12.4 -0.6 6.2 12.3 18.3 15.1 10.0 -2.2-12.2-15.2 -1.9 22
1887-24.9-17.3-10.7 -0.7 7.7 14.8 18.8 14.6 9.3 -0.4-11.2-19.2 -1.6 24
1888-22.5-21.8-12.5 -1.1 8.2 14.8 18.7 15.3 8.7 -0.6-11.5-23.2 -2.3 26
1889-24.9-16.7-12.5 -0.1 7.0 14.1 17.9 15.6 8.9 -2.5-14.4-19.1 -2.2 28
1890-22.1-18.6-10.9 -1.5 3.9 14.4 18.7 15.6 9.2 1.7-15.6-18.3 -2.0 31
1891-22.9-17.9 -8.0 -3.0 7.0 13.4 17.3 15.0 8.0 -2.7-14.2-18.7 -2.2 37
1892-23.1-21.6-13.5 -2.5 8.3 15.4 18.0 15.4 9.0 0.6-12.6-20.3 -2.2 39
1893-26.7-19.6 -7.3 1.3 6.7 14.2 18.2 14.8 9.2 0.4-10.2-20.9 -1.7 38
1894-23.3-16.4-10.2 -2.4 8.3 13.7 17.8 15.7 9.4 -0.5-13.1-20.5 -1.8 42
1895-24.8-23.2-11.9 -2.6 6.2 13.3 17.7 14.5 9.4 0.8-11.0-19.0 -2.5 42
1896-22.0-18.8-12.3 -3.2 7.8 14.8 17.3 15.7 8.9 1.1-10.9-20.5 -1.8 44
1897-23.0-20.4-13.4 -1.1 8.2 14.6 17.6 15.3 9.5 -0.6-10.7-20.0 -2.0 44
1898-17.9-20.4-17.6 -1.5 6.1 14.0 17.9 15.3 9.0 -1.9-10.8-16.2 -2.0 45
1899-19.8-18.8-11.1 -0.7 6.7 13.1 16.4 15.1 9.5 2.1 -7.4-19.9 -1.2 45
1900-25.7-19.0 -9.5 -2.6 7.9 13.7 17.0 15.3 8.9 0.5-11.4-18.8 -2.0 47
1901-24.0-16.9 -9.2 0.1 7.4 14.4 17.5 14.7 7.9 -2.0-11.0-23.0 -2.0 47
1902-24.3-18.6-13.2 -3.7 5.1 13.4 17.9 14.7 8.5 -3.0-15.5-21.9 -3.4 49
1903-21.5-15.4-10.6 -1.3 6.2 13.2 17.5 14.8 8.2 -2.2-11.1-21.4 -2.0 50
1904-22.3-19.9-11.0 -1.8 7.5 14.2 16.8 15.2 7.9 -1.0 -9.1-19.2 -1.9 49
1905-19.9-19.4-12.6 -3.2 6.4 13.0 17.3 14.9 8.8 0.0-10.6-18.0 -1.9 49
1906-24.0-21.1 -8.7 1.3 7.6 15.2 17.2 16.1 8.3 -0.5-14.0-16.4 -1.6 49
1907-24.7-19.8-10.6 0.0 6.3 13.3 17.2 15.7 9.1 -0.6-14.5-23.6 -2.7 50
1908-25.2-18.7-14.2 -2.0 7.0 14.0 16.5 14.8 9.2 -0.6-12.9-20.5 -2.7 54
1909-23.6-18.6-14.8 -1.8 5.9 13.7 17.5 15.3 8.8 0.0 -9.8-18.8 -2.2 56
1910-22.9-17.5-13.3 -1.6 7.0 13.4 18.0 15.9 8.8 -0.4-13.3-21.7 -2.3 58
1911-23.3-19.9-13.6 -0.3 6.1 14.6 18.1 14.7 8.0 0.2 -9.0-19.8 -2.0 59
1912-20.7-20.8-14.3 -1.4 7.1 14.0 16.3 13.5 7.8 -4.5-13.6-22.8 -3.3 62
1913-23.1-21.0 -9.8 -1.7 6.2 13.1 16.3 14.9 8.3 -0.7-11.2-16.9 -2.1 62
1914-20.3-16.4-12.9 -2.0 7.4 13.4 16.0 15.9 9.0 -0.1-12.3-17.4 -1.6 66
1915-24.8-18.7-12.4 -1.8 7.4 14.1 17.5 14.3 8.3 -2.7-11.5-20.8 -2.6 65
1916-21.0-17.7-12.9 -2.7 5.8 13.4 17.2 14.8 8.0 0.2-10.9-22.3 -2.3 63
1917-21.6-19.8-12.8 -0.5 6.9 13.0 16.8 14.2 9.5 0.2-10.2-17.1 -1.8 64
1918-18.7-18.5-10.8 -1.1 5.2 13.4 16.6 14.6 9.1 0.5-10.8-22.0 -1.9 64
1919-25.1-20.0-12.9 -1.0 5.4 12.7 17.4 15.6 9.5 1.8-12.0-18.6 -2.3 62
1920-17.7-19.2 -8.0 1.3 8.9 14.7 17.1 15.8 8.6 -1.7-11.6-20.0 -1.0 55
1921-18.0-16.5 -9.3 0.3 7.7 16.4 18.4 15.5 9.2 0.7 -9.6-18.7 -0.3 54
1922-23.3-19.7 -9.7 -0.8 7.3 14.4 17.9 15.0 8.7 0.1 -9.9-15.5 -1.3 56
1923-19.5-18.4-10.0 -2.5 6.5 14.7 17.4 15.9 10.1 2.4 -9.3-14.7 -0.6 57
1924-21.1-19.1-10.8 -0.5 8.0 13.0 18.1 15.2 9.6 0.3 -7.0-15.4 -0.8 58
1925-16.3-16.9-11.6 -0.5 6.7 14.0 16.9 16.0 9.8 1.5 -9.1-17.1 -0.5 67
1926-19.4-18.5 -8.5 -1.9 5.4 13.4 16.2 15.1 9.4 -0.3-10.2-20.1 -1.6 72
1927-23.7-19.1-13.9 -1.6 7.2 13.8 17.1 14.6 9.6 0.3 -9.9-20.7 -2.2 73
1928-21.8-19.4-13.0 -2.1 6.1 13.3 16.8 15.3 8.7 0.1-13.1-19.9 -2.4 77
1929-24.8-22.7-12.3 -2.6 5.9 13.1 16.3 14.3 7.7 0.0-10.7-23.6 -3.3 78
1930-20.6-21.6-11.1 -3.7 5.5 12.7 16.5 15.2 7.9 -0.4-10.7-21.7 -2.7 81
1931-25.5-24.4-12.0 -3.5 5.4 13.8 17.0 15.4 9.4 1.1-10.9-18.8 -2.8 78
1932-18.1-19.5-12.1 -1.0 5.2 12.6 16.9 14.6 9.6 1.3-12.1-18.1 -1.7 84
1933-24.6-23.2-15.0 -2.6 4.9 12.3 16.7 14.4 7.7 0.1-12.1-19.6 -3.4 91
1934-19.7-15.4-13.2 -4.2 7.0 13.1 15.5 14.3 6.8 -0.2-10.2-18.6 -2.1 97
1935-21.7-15.3-11.9 -3.4 5.4 12.7 16.6 14.6 8.5 -0.6-14.1-21.2 -2.5 108
1936-23.6-20.4-14.6 -2.5 5.2 12.7 16.6 14.3 8.5 -0.3-12.2-17.5 -2.8 119
1937-21.4-19.4-16.3 -4.0 5.0 13.3 16.0 13.3 7.9 -0.3-12.6-22.2 -3.4 122
1938-23.2-19.3-11.6 -0.2 6.1 12.9 16.3 14.7 8.1 -1.8-11.2-24.5 -2.8 124
1939-25.0-18.1-11.8 -1.3 5.5 13.7 16.2 13.8 7.7 -2.1-12.4-17.4 -2.6 126
1940-26.3-20.4-11.9 -1.5 4.8 12.8 16.2 14.7 8.0 -3.4-12.6-21.5 -3.4 128
1941-25.0-23.1-15.3 -4.9 5.1 13.1 15.6 14.2 8.0 -0.9-14.2-23.8 -4.3 127
1942-22.9-22.3-15.9 -4.0 4.8 12.8 16.2 13.6 8.0 -1.7-12.4-18.4 -3.5 130
1943-23.4-18.9-11.0 -0.3 8.2 12.8 16.5 14.4 8.7 -0.2-13.9-19.9 -2.2 132
1944-21.4-18.9-10.8 -2.4 6.1 12.9 16.0 14.0 9.3 -0.7-14.7-20.9 -2.6 133
1945-24.5-22.6-14.9 -1.0 5.9 12.9 15.4 14.4 8.3 -0.4-12.9-24.0 -3.6 131
1946-22.9-20.6-15.7 -2.5 4.6 12.1 16.4 14.3 6.1 -1.9-13.0-23.3 -3.9 132
1947-26.2-22.0-13.4 -1.2 5.6 12.3 15.2 13.3 8.1 0.9-14.1-23.4 -3.7 134
1948-21.3-20.0-12.1 -1.4 6.4 12.9 16.4 14.1 8.0 -0.3-11.2-20.9 -2.5 135
1949-18.8-19.7-13.9 -2.1 5.7 12.3 15.5 14.7 7.4 0.0-14.7-23.3 -3.1 136
1950-24.4-20.0-12.8 -3.1 5.1 12.7 15.9 14.1 7.7 -1.3-15.2-20.3 -3.5 137
1951-25.2-24.8-13.9 -2.7 5.0 11.7 15.5 13.6 8.2 -0.7-12.9-16.9 -3.6 142
1952-23.3-22.9-15.4 -5.0 4.5 12.1 16.4 14.0 8.3 -3.1-18.5-24.1 -4.8 142
1953-24.1-22.3-13.0 -1.6 5.4 12.8 16.2 13.9 7.7 -1.5-16.3-19.9 -3.6 142
1954-24.9-23.1-14.9 -2.9 3.8 12.8 16.1 13.9 7.5 -1.0-13.9-22.2 -4.1 142
1955-21.1-23.8-17.4 -4.7 5.3 12.6 15.2 13.7 6.6 -1.6-13.2-22.7 -4.3 142
1956-24.1-22.5-15.0 -5.7 4.6 12.3 15.5 13.0 6.5 -2.5-14.4-21.4 -4.5 142
1957-22.3-22.7-17.0 -4.4 5.6 12.6 15.1 13.4 7.3 -2.4-15.1-20.7 -4.2 143
1958-24.2-20.9-17.0 -6.1 3.3 11.3 15.0 13.5 6.0 -2.6-13.5-22.1 -4.8 144
1959-23.1-20.0-11.5 -4.7 4.7 12.7 15.0 13.9 8.3 -2.5-13.8-22.7 -3.6 144
1960-26.1-21.3-16.5 -4.9 3.5 12.0 15.0 12.8 7.2 -3.8-16.0-20.7 -4.9 144
1961-24.0-19.9-12.6 -4.2 4.4 11.2 15.3 13.1 7.5 -3.5-13.9-21.0 -4.0 144
1962-20.2-18.0-12.9 -2.7 5.6 11.8 16.0 13.0 6.9 -3.1-15.0-20.6 -3.3 144
1963-21.4-18.4-14.8 -5.4 3.8 11.2 15.4 13.2 6.9 -1.5-12.6-20.6 -3.7 144
1964-23.6-22.9-15.9 -7.3 4.2 11.6 15.2 13.2 6.7 -3.6-14.6-20.7 -4.8 144
1965-24.1-23.4-13.2 -4.8 4.1 11.7 15.3 12.8 7.0 -2.1-17.1-23.6 -4.8 144
1966-25.4-26.0-15.9 -6.4 4.5 11.6 14.8 13.3 7.7 -3.0-14.7-23.6 -5.3 144
1967-24.2-22.3-11.9 -2.3 5.7 11.9 15.9 12.7 6.4 0.4-13.4-19.4 -3.4 144
1968-23.6-19.7 -9.5 -3.0 5.2 11.0 15.1 13.0 5.8 -3.1-17.8-25.6 -4.3 144
1969-27.9-28.0-16.6 -5.6 2.8 10.9 16.0 12.7 6.4 -3.4-13.3-22.4 -5.7 143
1970-24.4-21.7-14.8 -4.4 3.9 11.4 15.6 12.3 7.8 -3.7-14.0-21.3 -4.4 143
1971-21.9-24.4-15.4 -4.9 4.5 11.9 15.1 13.1 7.9 -1.4-10.7-20.3 -3.9 139
1972-26.9-20.6-13.7 -2.7 3.8 11.1 14.6 12.7 6.0 -2.3-16.9-20.6 -4.6 139
1973-24.5-20.6-13.7 -4.1 4.8 12.6 15.0 13.5 7.3 -2.8-12.5-19.5 -3.7 139
1974-25.0-24.0-13.3 -3.6 4.4 11.5 16.0 13.9 8.1 -3.3-17.0-23.0 -4.6 139
1975-21.8-21.0-11.3 -2.4 5.2 12.5 15.3 13.1 7.7 -2.0-12.9-19.9 -3.1 139
1976-21.6-22.8-14.9 -3.4 4.3 12.1 15.2 13.0 7.4 -5.5-15.5-22.9 -4.6 139
1977-26.3-25.1-14.8 -2.9 5.9 13.0 15.4 12.9 7.1 -4.4-12.6-21.6 -4.5 139
1978-22.7-21.9-12.6 -4.0 3.9 11.8 15.1 12.7 7.0 -1.7-10.3-23.6 -3.9 139
1979-26.6-21.9-14.2 -6.3 5.2 12.0 15.3 12.7 7.2 -3.4-14.9-19.4 -4.5 139
1980-24.5-21.2-15.3 -5.6 4.3 12.3 15.4 13.5 7.3 -2.3-14.2-19.4 -4.1 139
1981-20.9-20.3-14.0 -2.6 4.9 12.6 15.2 13.8 6.9 -1.9-13.8-18.6 -3.2 143
1982-25.0-19.2-15.5 -1.8 4.9 12.1 15.5 13.2 7.1 -3.9-14.1-18.4 -3.8 139
1983-19.8-20.0-11.6 -4.5 4.0 11.7 15.2 13.7 7.7 -1.7-11.0-17.5 -2.8 139
1984-21.0-21.9-13.1 -6.3 5.8 12.7 15.8 13.0 7.1 -2.8-16.3-23.4 -4.2 139
1985-25.3-23.2-14.5 -3.7 4.0 12.1 14.8 13.2 7.2 -2.1-12.6-21.7 -4.3 139
1986-24.3-20.5-12.8 -3.2 4.2 12.4 15.4 13.1 6.9 -2.0-12.5-23.0 -3.9 136
1987-26.1-21.7-14.9 -5.8 4.5 11.1 15.0 13.3 6.4 -2.8-18.1-22.5 -5.1 138
1988-22.7-22.2-13.9 -4.1 4.8 12.3 15.9 13.5 8.0 -1.1-12.9-18.3 -3.4 138
1989-23.5-17.0-10.0 -4.6 5.3 12.2 15.6 13.1 6.9 -2.0-14.2-19.7 -3.2 137
1990-26.3-21.7 -9.6 -2.6 5.3 11.6 15.0 12.6 5.3 -2.4-15.6-21.7 -4.2 123
1991-23.0-22.7-17.1 -4.2 5.4 12.0 16.3 13.0 6.9 -2.2-13.9-23.0 -4.4 70
1992-22.7-20.0-14.8 -6.7 4.6 10.4 13.3 12.0 4.1 -3.8-17.6-21.3 -5.2 74
1993-22.0-18.9-11.3 -4.2 3.7 11.4 15.6 12.8 6.1 -3.7-15.5-22.5 -4.0 72
1994-24.8-22.0-15.1 -5.2 3.5 11.7 14.5 12.3 7.0 -1.1-15.3-23.4 -4.8 70
1995-21.5-17.3-13.4 -3.9 4.4 11.2 14.7 13.6 7.3 -1.4-12.2-21.3 -3.3 65
1996-23.8-21.2-12.3 -6.0 4.2 11.0 15.7 11.6 4.9 -3.0-13.0-21.6 -4.5 66
1997-23.7-20.2-12.2 -1.2 5.2 11.3 14.5 12.9 6.7 -1.2-15.6-24.3 -4.0 68
1998-26.0-21.7-14.1 -6.1 3.0 11.9 17.6 13.8 5.5 -4.5-19.2-21.1 -5.1 65
1999-25.0-20.4-19.6 -5.9 4.7 11.3 16.0 12.4 6.3 -2.8-15.2-19.2 -4.8 65
2000-26.2-20.1-13.2 -2.9 5.0 13.1 15.3 13.5 6.8 -4.1-17.5-24.8 -4.6 67
2001-27.6-23.9-14.9 -4.9 5.4 13.2 15.5 13.9 6.1 -3.3-11.6-22.8 -4.6 68
2002-22.0-19.4-10.6 -4.6 5.4 13.0 15.6 13.4 7.0 -3.5-14.6-25.1 -3.8 65
2003-23.4-21.3-11.9 -3.3 5.0 12.8 15.5 14.0 7.7 -1.3-16.0-18.5 -3.4 65
2004-23.0-21.5-14.7 -5.4 4.7 12.4 15.4 11.9 6.7 -4.0-13.3-25.8 -4.7 67
2005-23.2-24.0-15.0 -5.0 4.8 12.4 15.7 12.9 7.7 -1.5-13.0-21.1 -4.1 94
2006-27.8-22.0-13.4 -6.9 4.9 13.4 15.5 13.0 7.9 -2.8-14.0-18.8 -4.2 95
2007-18.6-21.9-12.6 -0.6 5.1 12.2 16.5 14.5 8.4 -0.4-12.1-16.2 -2.1 95
2008-25.3-20.7-10.1 -6.2 4.0 12.4 14.9 12.9 7.0 -0.1-12.0-20.4 -3.6 96
-23.4-21.1-13.5 -3.4 5.2 12.4 15.9 13.7 7.6 -1.8-13.5-20.9 -3.6
-23.1-20.3-12.8 -2.9 5.6 12.8 16.3 14.1 7.9 -1.4-12.9-20.5
For Country Code 222
[chiefio@tubularbells analysis]$
Despite a significant dropout of about 50% in 1991, a darned steady temperature series. I’m not seeing a lot of warming in that Siberian history. If anything, both the January and the annual averages show a cooling trend. But then a bunch of thermometers get added IN about 2005. (Haven’t I seen that year before somewhere?)
Now when did all that ‘warmer in Siberia’ news happen… wasn’t it just about 2005 to 2006? Surely just a coincidence…
The changes from 2003 to make it into the 2009 set “does not speak to me”. I guess I just don’t know enough Russian Geography. But something about it just looks odd. A “+” at the front is an added station, a “-” is a deleted station. It just makes me wonder “why?”.
[chiefio@tubularbells analysis]$ cat Russian.diff + 22220046000 GMO IM.E.T. 80.62 58.05 20 0R -9HIxxCO 1x-9WATER A 0 + 22220744000 MALYE KARMAKU 72.37 52.70 15 8R -9HIxxCO 1x-9TUNDRA A 0 + 22221982000 OSTROV VRANGE 70.98 -178.48 5 270R -9HIxxCO 1x-9WATER A 0 + 22223074000 DUDINKA 69.40 86.17 19 50S 20FLxxno-9x-9WOODED TUNDRA C 65 + 22223405000 UST'-CIL'MA 65.43 52.27 68 60R -9FLxxno-9A-9MAIN TAIGA A 0 + 22223631000 BEREZOVO 63.93 65.05 27 95R -9FLxxno-9A-9NORTH. TAIGA C 8 + 22223711000 TROICKO-PECER 62.70 56.20 139 106R -9FLxxno-9A-9MAIN TAIGA C 9 + 22223891000 BAJKIT 61.67 96.37 262 441R -9MVxxno-9A-9MAIN TAIGA B 0 + 22223914000 CERDYN' 60.40 56.52 207 109R -9FLMAno-9x-9COOL MIXED A 6 + 22223921000 IVDEL' 60.68 60.45 95 190S 15HIxxno-9x-9BOGS, BOG WOODS C 12 + 22223955000 ALEKSANDROVSK 60.43 77.87 48 90R -9FLxxno-9A-9MAIN TAIGA C 14 + 22224329000 SELAGONCY 66.25 114.28 236 337R -9HIxxno-9x-9NORTH. TAIGA A 0 + 22224343000 ZHIGANSK 66.77 123.40 92 90R -9FLxxno-9x-9NORTH. TAIGA A 7 + 22224908000 VANAVARA 60.33 102.27 260 248R -9HIxxno-9A-9MAIN TAIGA A 0 + 22224966000 UST'-MAJA 60.38 134.45 170 152R -9HIxxno-9A-9MAIN TAIGA B 0 + 22225551000 MARKOVO 64.68 170.42 26 60R -9FLMAno-9A-9NORTH. TAIGA A 0 + 22225594000 BUHTA PROVIDE 64.42 -173.23 17 166R -9HIxxCO 1x-9TUNDRA B 10 + 22227995000 SAMARA (BEZEN 52.98 49.43 46 117U 1216HIxxno-9x-9COOL CROPS C 7 + 22228064000 LEUSI 59.62 65.72 72 60R -9FLFOno-9x-9BOGS, BOG WOODS A 0 + 22228138000 BISER 58.52 58.85 463 388R -9MTxxno-9x-9COOL MIXED B 15 + 22228434000 KRASNOUFIMSK 56.65 57.78 206 240S 40HIxxno-9x-9COOL GRASS/SHRUBC 16 + 22228493000 TARA 56.90 74.38 73 78S 22FLxxno-9x-9MAIN TAIGA C 18 + 22228552000 SADRINSK 56.07 63.65 89 121U 82FLxxno-9x-9COOL CROPS C 17 + 22228722000 UFA 54.72 55.83 104 133U 969FLxxno-9x-9COOL GRASS/SHRUBC 43 + 22229612000 BARABINSK 55.33 78.37 120 100S 37FLxxno-9x-9BOGS, BOG WOODS C 11 + 22230372000 CARA 56.90 118.27 711 845R -9MVxxno-9A-9MAIN TAIGA A 0 + 22230433000 NIZNEANGARSK 55.78 109.55 487 873R -9MVxxLA-9x-9WATER A 6 + 22230521000 ZIGALOVO 54.80 105.22 426 607S 10HIxxno-9x-9COOL CONIFER A 0 + 22230636000 BARGUZIN 53.62 109.63 489 1136R -9MVxxno-9x-9E. SOUTH. TAIGA C 7 + 22230879000 NERCINSKIJ ZA 51.32 119.62 619 756R -9HIxxno-9x-9COOL FIELD/WOODSC 0 + 22230925000 KJAHTA 50.37 106.45 797 846S 15HIxxno-9x-9COOL GRASS/SHRUBB 10 + 22230949000 KYRA 49.57 111.97 908 1500R -9HIxxno-9x-9COOL FIELD/WOODSC 0 + 22230965000 BORZJA 50.40 116.52 676 566S 28FLxxno-9x-9COOL CROPS C 11 + 22231137000 TOKO 56.28 131.13 850 890R -9HIxxno-9A-9MAIN TAIGA A 0 + 22231329000 EKIMCAN 53.07 132.98 542 588R -9MVxxno-9x-9MAIN TAIGA A 0 + 22231707000 EKATERINO-NIK 47.73 130.97 73 202R -9FLxxno-9x-9COOL CROPS C 0 + 22231829000 MYS ZOLOTOJ 47.32 138.98 26 75R -9MVxxCO 1x-9COASTAL EDGES A 0 + 22231873000 DAL'NERECENSK 45.87 133.73 101 62S 28FLxxno-9A 3COOL MIXED B 0 + 22232061000 ALEKSANDROVSK 50.90 142.17 31 79S 20HIxxCO 1x-9MAIN TAIGA C 6 + 22232098000 PORONAJSK 49.22 143.10 8 34S 24FLxxCO 1x-9WATER C 10 + 22232165000 JUZNO-KURIL'S 44.02 145.87 49 0R -9HIxxCO 2x-9WATER A 0 + 22232389000 KLJUCI 56.32 160.83 29 372R -9MVxxno-9A-9TUNDRA B 14 - 22223804000 SYKTYVKAR 61.72 50.83 119 80U 171FLxxno-9x-9MAIN TAIGA C 38 - 22225703000 SEJMCHAN 62.92 152.42 205 265R -9HIxxno-9A-9NORTH. TAIGA B 8 - 22228440000 SVERDLOVSK USSR 56.80 60.60 237 275U 1211FLxxno-9x-9COOL MIXED C 50 - 22229838000 BARNAUL 53.43 83.52 184 199U 533FLxxno-9x-9COOL GRASS/SHRUBB 6 - 22230469000 KALAKAN 55.12 116.77 613 899R -9HIxxno-9x-9TUNDRA A 0 - 22230635000 UST'-BARGUZIN 53.42 109.02 461 492R -9MVxxLA-9x-9E. SOUTH. TAIGA C 7 - 22231510000 BLAGOVESCENSK 50.25 127.57 132 183U 172FLxxno-9x-9COOL CROPS C 13 - 22231735000 HABAROVSK 48.53 135.23 72 82U 528FLxxno-9x-9COOL FOR./FIELD C 13 - 22232411000 ICA 55.58 155.58 6 3R -9FLxxCO 1x-9SIBERIAN PARKS A 0 - 22232583000 PETROPAVLOVSK 52.98 158.65 24 281U 215MVxxCO 1x-9WATER C 12 - 22235121000 ORENBURG 51.68 55.10 117 145U 459FLxxno-9x-9COOL GRASS/SHRUBB 9 [chiefio@tubularbells analysis]$
So if you can make heads or tails out of these station changes, please let us all know.
UPDATE: And Ellie did. I swear, that woman is a wizard. How, in less time than I can make a cup of tea, did she do this:
Changes in Siberian Russia
You can now clearly see more are added south, out of the snow, than are deleted.
Oh, and the rest of the Siberian stations in 2009?
In 2009, the Siberian set is:
[chiefio@tubularbells analysis]$ cat 222.stns 22220046000 GMO IM.E.T. 80.62 58.05 20 0R -9HIxxCO 1x-9WATER A 0 22220069000 OSTROV VIZE 79.50 76.98 11 0R -9FLxxCO 2x-9WATER A 0 22220292000 GMO IM.E.K. F 77.72 104.30 15 0R -9FLxxCO 1x-9WATER A 0 22220674000 OSTROV DIKSON 73.50 80.40 47 0R -9HIxxCO 1A-9WATER A 0 22220744000 MALYE KARMAKU 72.37 52.70 15 8R -9HIxxCO 1x-9TUNDRA A 0 22220891000 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A 12 22221432000 OSTROV KOTEL' 76.00 137.87 8 30R -9FLxxCO 1x-9POLAR DESERT A 0 22221946000 COKURDAH 70.62 147.88 61 60R -9FLxxno-9A-9TUNDRA B 0 22221982000 OSTROV VRANGE 70.98 -178.48 5 270R -9HIxxCO 1x-9WATER A 0 22223074000 DUDINKA 69.40 86.17 19 50S 20FLxxno-9x-9WOODED TUNDRA C 65 22223205000 NAR'JAN-MAR 67.63 53.03 12 60S 17FLxxno-9x-9TUNDRA A 14 22223330000 SALEHARD 66.53 66.67 16 30S 22FLxxno-9x-9BOGS, BOG WOODS B 13 22223405000 UST'-CIL'MA 65.43 52.27 68 60R -9FLxxno-9A-9MAIN TAIGA A 0 22223472000 TURUHANSK 65.78 87.93 38 64R -9FLxxno-9A-9NORTH. TAIGA A 0 22223552000 TARKO-SALE 64.92 77.82 27 82R -9FLxxno-9A-9NORTH. TAIGA B 15 22223631000 BEREZOVO 63.93 65.05 27 95R -9FLxxno-9A-9NORTH. TAIGA C 8 22223711000 TROICKO-PECER 62.70 56.20 139 106R -9FLxxno-9A-9MAIN TAIGA C 9 22223724000 NJAKSIMVOL' 62.43 60.87 51 150R -9FLxxno-9A-9MAIN TAIGA A 0 22223884000 BOR 61.60 90.02 58 113R -9FLxxno-9A-9MAIN TAIGA B 8 22223891000 BAJKIT 61.67 96.37 262 441R -9MVxxno-9A-9MAIN TAIGA B 0 22223914000 CERDYN' 60.40 56.52 207 109R -9FLMAno-9x-9COOL MIXED A 6 22223921000 IVDEL' 60.68 60.45 95 190S 15HIxxno-9x-9BOGS, BOG WOODS C 12 22223933000 HANTY-MANSIJS 61.02 69.03 46 90S 25FLxxno-9x-9MAIN TAIGA C 28 22223955000 ALEKSANDROVSK 60.43 77.87 48 90R -9FLxxno-9A-9MAIN TAIGA C 14 22224125000 OLENEK 68.50 112.43 220 262R -9MVxxno-9x-9NORTH. TAIGA A 0 22224143000 DZARDZAN 68.73 124.00 39 90R -9FLxxno-9x-9NORTH. TAIGA A 0 22224266000 VERHOJANSK 67.55 133.38 137 270R -9MVMAno-9x-9NORTH. TAIGA A 0 22224329000 SELAGONCY 66.25 114.28 236 337R -9HIxxno-9x-9NORTH. TAIGA A 0 22224343000 ZHIGANSK 66.77 123.40 92 90R -9FLxxno-9x-9NORTH. TAIGA A 7 22224507000 TURA 64.27 100.23 168 356R -9MVxxno-9x-9MAIN TAIGA A 0 22224641000 VILJUJSK 63.77 121.62 111 109R -9FLMAno-9x-9MAIN TAIGA B 12 22224688000 OJMJAKON 63.25 143.15 741 845R -9HIxxno-9A-9TUNDRA A 0 22224738000 SUNTAR 62.15 117.65 133 152R -9FLxxno-9A-9MAIN TAIGA C 0 22224817000 ERBOGACEN 61.27 108.02 291 240R -9HIxxno-9A-9MAIN TAIGA A 0 22224908000 VANAVARA 60.33 102.27 260 248R -9HIxxno-9A-9MAIN TAIGA A 0 22224959000 JAKUTSK 62.02 129.72 101 142U 152HIxxno-9x-9COLD IRRIGATED C 35 22224966000 UST'-MAJA 60.38 134.45 170 152R -9HIxxno-9A-9MAIN TAIGA B 0 22225173000 MYS SMIDTA 68.90 -179.37 4 0R -9FLxxCO 1x-9TUNDRA B 10 22225248000 ILIRNEJ 67.25 167.97 353 542R -9MVxxLA-9x-9NORTH. TAIGA A 0 22225325000 UST'-OLOJ 66.55 159.42 127 223R -9MVxxno-9x-9NORTH. TAIGA A 0 22225399000 MYS UELEN 66.17 -169.83 3 0R -9HIxxCO 1x-9WATER A 0 22225400000 ZYRJANKA 65.73 150.90 43 90R -9FLMAno-9A-9NORTH. TAIGA B 16 22225551000 MARKOVO 64.68 170.42 26 60R -9FLMAno-9A-9NORTH. TAIGA A 0 22225563000 ANADYR' 64.78 177.57 61 77R -9FLxxCO 5A-9TUNDRA A 0 22225594000 BUHTA PROVIDE 64.42 -173.23 17 166R -9HIxxCO 1x-9TUNDRA B 10 22225744000 KAMENSKOE 62.43 166.08 10 256R -9MVxxno-9x-9WOODED TUNDRA A 0 22225954000 KORF 60.35 166.00 4 21R -9MVxxCO 1A-9WOODED TUNDRA A 0 22227995000 SAMARA (BEZEN 52.98 49.43 46 117U 1216HIxxno-9x-9COOL CROPS C 7 22228064000 LEUSI 59.62 65.72 72 60R -9FLFOno-9x-9BOGS, BOG WOODS A 0 22228138000 BISER 58.52 58.85 463 388R -9MTxxno-9x-9COOL MIXED B 15 22228275000 TOBOL'SK 58.15 68.25 50 30U 62FLxxno-9x-9BOGS, BOG WOODS C 11 22228434000 KRASNOUFIMSK 56.65 57.78 206 240S 40HIxxno-9x-9COOL GRASS/SHRUBC 16 22228493000 TARA 56.90 74.38 73 78S 22FLxxno-9x-9MAIN TAIGA C 18 22228552000 SADRINSK 56.07 63.65 89 121U 82FLxxno-9x-9COOL CROPS C 17 22228698000 OMSK 55.02 73.38 122 95U 1014FLxxno-9x-9COOL CROPS C 60 22228722000 UFA 54.72 55.83 104 133U 969FLxxno-9x-9COOL GRASS/SHRUBC 43 22229231000 KOLPASEVO 58.32 82.95 75 60S 25FLxxno-9x-9COOL MIXED C 23 22229263000 ENISEJSK 58.45 92.15 79 188S 20FLxxno-9x-9SOUTH. TAIGA C 12 22229282000 BOGUCANY 58.38 97.45 133 248R -9HIxxno-9x-9SOUTH. TAIGA B 12 22229570000 KRASNOJARSK 56.03 92.75 276 261U 796HIxxno-9x-9SOUTH. TAIGA C 31 22229612000 BARABINSK 55.33 78.37 120 100S 37FLxxno-9x-9BOGS, BOG WOODS C 11 22229807000 IRTYSSK 53.35 75.45 94 120R -9FLxxno-9x-9COOL IRRIGATED C 9 22229866000 MINUSINSK 53.70 91.70 254 369U 56HIxxno-9x-9COOL CROPS C 17 22230054000 VITIM 59.45 112.58 190 307R -9HIxxno-9A-9TUNDRA B 7 22230230000 KIRENSK 57.77 108.07 259 340R -9HIxxno-9A-9TUNDRA B 8 22230309000 BRATSK 57.28 101.75 416 446U 214HIxxLA-9x-9SOUTH. TAIGA A 0 22230372000 CARA 56.90 118.27 711 845R -9MVxxno-9A-9MAIN TAIGA A 0 22230433000 NIZNEANGARSK 55.78 109.55 487 873R -9MVxxLA-9x-9WATER A 6 22230521000 ZIGALOVO 54.80 105.22 426 607S 10HIxxno-9x-9COOL CONIFER A 0 22230554000 BAGDARIN 54.47 113.58 903 1206R -9HIFOno-9A-9E. SOUTH. TAIGA A 7 22230636000 BARGUZIN 53.62 109.63 489 1136R -9MVxxno-9x-9E. SOUTH. TAIGA C 7 22230673000 MOGOCA 53.75 119.73 625 875S 18HIxxno-9x-9TUNDRA C 13 22230710000 IRKUTSK 52.27 104.32 469 490U 550FLxxno-9x-9COOL FOR./FIELD C 51 22230758000 CITA 52.08 113.48 671 881U 303HIxxno-9x-9E. SOUTH. TAIGA C 28 22230879000 NERCINSKIJ ZA 51.32 119.62 619 756R -9HIxxno-9x-9COOL FIELD/WOODSC 0 22230925000 KJAHTA 50.37 106.45 797 846S 15HIxxno-9x-9COOL GRASS/SHRUBB 10 22230949000 KYRA 49.57 111.97 908 1500R -9HIxxno-9x-9COOL FIELD/WOODSC 0 22230965000 BORZJA 50.40 116.52 676 566S 28FLxxno-9x-9COOL CROPS C 11 22231004000 ALDAN 58.62 125.37 682 720S 18HIxxno-9x-9MAIN TAIGA C 16 22231088000 OHOTSK 59.37 143.20 6 34R -9HIMACO 1x-9WATER B 8 22231137000 TOKO 56.28 131.13 850 890R -9HIxxno-9A-9MAIN TAIGA A 0 22231168000 AJAN 56.45 138.15 8 0R -9HIxxCO 1A-9WATER A 0 22231253000 BOMNAK 54.72 128.93 357 452R -9FLFOLA-9x-9MAIN TAIGA A 0 22231329000 EKIMCAN 53.07 132.98 542 588R -9MVxxno-9x-9MAIN TAIGA A 0 22231369000 NIKOLAEVSK-NA 53.15 140.70 68 220S 30HIxxCO 1x-9COOL CONIFER C 19 22231416000 IM POLINY OSI 52.42 136.50 73 62R -9HIMAno-9x-9COOL CONIFER C 0 22231707000 EKATERINO-NIK 47.73 130.97 73 202R -9FLxxno-9x-9COOL CROPS C 0 22231829000 MYS ZOLOTOJ 47.32 138.98 26 75R -9MVxxCO 1x-9COASTAL EDGES A 0 22231873000 DAL'NERECENSK 45.87 133.73 101 62S 28FLxxno-9A 3COOL MIXED B 0 22231960000 VLADIVOSTOK 43.12 131.93 184 13U 550HIxxCO 2x-9COASTAL EDGES C 27 22232061000 ALEKSANDROVSK 50.90 142.17 31 79S 20HIxxCO 1x-9MAIN TAIGA C 6 22232098000 PORONAJSK 49.22 143.10 8 34S 24FLxxCO 1x-9WATER C 10 22232150000 JUZNO-SAHALIN 46.92 142.72 24 208U 140MVxxCO15A 4COOL CONIFER C 16 22232165000 JUZNO-KURIL'S 44.02 145.87 49 0R -9HIxxCO 2x-9WATER A 0 22232389000 KLJUCI 56.32 160.83 29 372R -9MVxxno-9A-9TUNDRA B 14 22232618000 NIKOL'SKOE 55.20 165.98 18 81R -9HIxxCO 1x-9WATER A 0 [chiefio@tubularbells analysis]$
So I’m seeing a slight warming of winter temperatures in Siberia from this station “Merry Go Round”, but nothing speaks to me. I think these would need to be put on a map or have their altitudes checked to make sense of it. Or if someone is familiar with Russia, advice and insight welcomed!
Canada Has an Odd Twist
The thermometer counts drop precipitously, but the percentage up “way north” rises… until it gets caught.
[chiefio@tubularbells analysis]$ cat Therm.by.lat403.Dec.LAT
Year SP-45 50 55 60 65 70 75 80 85 -NP
DecLatPct: 1779 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1829 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1839 0.0 46.2 0.0 53.8 0.0 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1849 46.3 12.2 0.0 41.5 0.0 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1859 62.7 23.5 0.0 13.7 0.0 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1869 78.9 21.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1879 41.0 53.7 3.2 2.1 0.0 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1889 29.6 45.1 19.0 6.2 0.0 0.0 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1899 19.3 43.0 31.7 3.4 1.9 0.7 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1909 17.7 37.8 35.8 4.7 3.4 0.6 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1919 12.6 38.9 38.5 5.1 3.7 1.1 0.0 0.0 0.0 0.0 100.0
DecLatPct: 1929 11.0 39.4 38.0 5.8 4.5 1.2 0.1 0.0 0.0 0.0 100.0
DecLatPct: 1939 9.0 39.2 36.1 7.4 5.8 2.2 0.3 0.0 0.0 0.0 100.0
DecLatPct: 1949 8.3 38.6 34.4 7.8 6.8 2.5 1.1 0.4 0.0 0.0 100.0
DecLatPct: 1959 7.7 37.7 32.1 7.2 7.4 3.9 1.8 1.5 0.6 0.0 100.0
DecLatPct: 1969 7.2 34.5 32.3 7.9 7.6 6.1 2.1 1.7 0.7 0.0 100.0
DecLatPct: 1979 6.0 33.7 33.3 9.4 7.5 5.8 1.9 1.7 0.6 0.0 100.0
DecLatPct: 1989 6.1 34.0 33.3 9.3 7.2 6.4 2.0 1.2 0.5 0.0 100.0
DecLatPct: 1999 7.1 30.7 27.6 9.9 9.2 8.3 3.6 3.1 0.5 0.0 100.0
DecLatPct: 2009 5.3 14.6 34.2 16.0 12.4 10.8 4.3 2.4 0.0 0.0 100.0
For COUNTRY CODE: 403
[chiefio@tubularbells analysis]$
Canadian Thermometers Over Time, By Latitude
Here we see the typical thermometers spreading out with modernity, until 2009. Something strange happens then. When numbers in averages change by a lot, we must look at detail. What does the detail of the last two decades look like?
Year SP-45 50 55 60 65 70 75 80 85 -NP
LAT pct: 1990 6.3 36.3 32.0 9.6 6.3 4.3 2.3 2.0 0.8 0.0 100.0
LAT pct: 1991 7.1 27.1 22.4 9.4 11.8 11.8 4.7 4.7 1.2 0.0 100.0
LAT pct: 1992 7.3 26.8 22.0 9.8 12.2 12.2 4.9 4.9 0.0 0.0 100.0
LAT pct: 1993 7.3 26.8 22.0 9.8 12.2 12.2 4.9 4.9 0.0 0.0 100.0
LAT pct: 1994 8.1 29.7 21.6 5.4 13.5 13.5 5.4 2.7 0.0 0.0 100.0
LAT pct: 1995 8.1 27.0 21.6 8.1 10.8 13.5 5.4 5.4 0.0 0.0 100.0
LAT pct: 1996 7.7 23.1 23.1 10.3 12.8 12.8 5.1 5.1 0.0 0.0 100.0
LAT pct: 1997 7.7 23.1 23.1 10.3 12.8 12.8 5.1 5.1 0.0 0.0 100.0
LAT pct: 1998 9.1 22.7 25.0 13.6 11.4 11.4 4.5 2.3 0.0 0.0 100.0
LAT pct: 1999 8.3 20.8 29.2 14.6 10.4 10.4 4.2 2.1 0.0 0.0 100.0
DecLatPct: 1999 7.1 30.7 27.6 9.9 9.2 8.3 3.6 3.1 0.5 0.0 100.0
LAT pct: 2000 8.3 20.8 29.2 14.6 10.4 10.4 4.2 2.1 0.0 0.0 100.0
LAT pct: 2001 8.3 20.8 29.2 14.6 10.4 10.4 4.2 2.1 0.0 0.0 100.0
LAT pct: 2002 5.1 12.8 30.8 17.9 12.8 12.8 5.1 2.6 0.0 0.0 100.0
LAT pct: 2003 5.6 8.3 30.6 19.4 13.9 13.9 5.6 2.8 0.0 0.0 100.0
LAT pct: 2004 5.6 8.3 30.6 19.4 13.9 13.9 5.6 2.8 0.0 0.0 100.0
LAT pct: 2005 4.5 11.4 36.4 15.9 13.6 11.4 4.5 2.3 0.0 0.0 100.0
LAT pct: 2006 2.5 10.0 37.5 15.0 15.0 12.5 5.0 2.5 0.0 0.0 100.0
LAT pct: 2007 2.3 13.6 36.4 15.9 13.6 11.4 4.5 2.3 0.0 0.0 100.0
LAT pct: 2008 4.2 16.7 37.5 12.5 12.5 10.4 4.2 2.1 0.0 0.0 100.0
LAT pct: 2009 5.7 20.0 45.7 17.1 8.6 0.0 0.0 2.9 0.0 0.0 100.0
DecLatPct:2009 5.3 14.6 34.2 16.0 12.4 10.8 4.3 2.4 0.0 0.0 100.0
Close up of latest deletions
Here we see that three northern bands have been gutted entirely. There are now NO thermometers (as of 2009) in the 65-70, 70-75, and 80-85 bands. 1992 saw the 80-85 band die. 2009, the others. Due to the general slaughter of thermometers, that 75-80 band is ONE thermometer.
That’s right. ONE thermometer for everything north of LAT 65. Who needs Northwest Territories, The Yukon Territories, or Baffin Island anyway… GIStemp can just estimate it from the satellite ice map projection synthesis interpolation estimates. (Yes, it does that…). Oh, wait, you say there have been sensor issues with the polar ice satellites?
EUREKA,N.W.T. 79.98 -85.93 10 222R -9MVxxCO 1A-9WATER A 0
Best put a stakeout across the street from it. There’s been a lot of murder of innocent thermometers in his neighborhood and he’s the only one still alive. Must have been starving polar bears eating them!
The “N.W.T.” in the name is in error, the LAT and LONG put it near Greenland. Here is what it looks like from “the airport”:
Eureka, Canada, Weather Station
The full size image is here, with an even larger 4,596 × 2,760 pixels fuller sized image in a link. I think the equipment in near the dome; I’ve seen similar domes at weather stations in areas with rough weather. But how rough?
From the wiki page:
The complex is powered by diesel generators. The station is supplied on a tri-weekly basis with fresh food and mail by air, and annually in the late summer, a supply ship from Montreal brings heavy supplies.
Eureka has been described as “The Garden Spot of the Arctic” due to the flora and fauna abundant around the Eureka area, more so than anywhere else in the High Arctic. …
Further down, under “Climate” it says:
Winters are frigid, but summers are slightly warmer than at other places in the Canadian Arctic.
I guess now we know why it was kept…
How Much Dropout? In Numbers? For European Russia and Canada:
For this, we do need to go to “belly of the snake” land down in the weeds of detail listings by years.
For Russia (European 638) , not very bad. The thermometer count is that far right column after all the monthly and annual temperature averages:
[chiefio@tubularbells analysis]$ cat Temps.638.yrs.GAT
Thermometer Records, Average of Monthly Data and Yearly Average
by Year Across Month, with a count of thermometer records in that year
--------------------------------------------------------------------------
YEAR JAN FEB MAR APR MAY JUN JULY AUG SEPT OCT NOV DEC YR COUNT
--------------------------------------------------------------------------
1880 -9.7 -8.0 -5.7 2.0 10.6 15.4 18.6 18.1 12.8 2.7 -0.7 -5.2 4.2 9
1881-10.1 -7.7 -3.4 3.5 11.3 15.7 19.2 17.6 10.8 4.7 -0.7 -5.9 4.6 16
1882 -2.4 -5.3 0.0 3.8 12.5 17.0 20.9 20.3 13.7 4.0 -0.7 -7.2 6.4 17
1883-11.2 -6.9 -4.1 4.8 13.6 18.5 19.9 17.2 13.9 7.1 2.5 -2.5 6.1 18
1884 -7.0 -5.4 -3.8 2.3 9.3 17.3 18.8 15.6 10.6 7.8 -0.4 -3.6 5.1 18
1885 -9.3 -4.7 -1.6 3.9 12.0 16.3 21.9 16.5 11.4 6.3 -3.0 -4.2 5.5 19
1886 -7.9-10.1 -3.9 5.2 12.1 16.8 18.9 17.5 11.3 4.4 0.7 -0.6 5.4 20
1887 -6.7 -5.1 -3.5 4.6 13.7 14.9 19.6 18.0 15.3 5.7 0.3 -3.8 6.1 21
1888 -9.6 -8.4 -5.3 6.6 12.0 15.1 19.1 17.9 12.7 7.2 -1.6 -9.9 4.7 22
1889-10.2 -6.8 -5.2 5.7 14.7 16.7 19.7 18.0 11.9 8.5 0.2 -5.8 5.6 23
1890 -6.8 -5.7 0.9 7.3 13.0 17.7 21.2 19.7 13.9 5.6 -2.5 -8.4 6.3 21
1891-12.3 -5.7 -0.5 4.3 12.7 16.1 20.4 16.8 11.1 5.6 -4.8 -3.1 5.1 27
1892-11.0 -6.5 -3.6 2.6 12.0 16.4 19.0 17.1 13.1 5.0 -0.6 -9.5 4.5 29
1893-15.5-12.1 -3.1 1.6 10.4 16.1 19.2 17.5 11.8 7.5 -0.6 -4.8 4.0 29
1894 -7.7 -4.6 -3.1 5.1 13.1 15.7 18.1 18.7 9.2 3.7 -0.5 -6.5 5.1 28
1895 -7.5-10.6 -2.5 3.2 11.8 17.3 19.4 17.2 11.4 8.2 -0.6 -7.0 5.0 28
1896-10.8 -9.5 -3.3 2.1 11.5 17.7 19.2 18.7 12.7 9.3 -2.7 -7.5 4.8 30
1897-10.0 -8.8 -3.6 5.7 16.6 17.7 20.3 18.6 13.6 6.1 -2.4 -8.2 5.5 29
1898 -6.0 -8.2 -6.9 2.8 14.1 16.7 20.7 18.8 12.3 3.3 1.4 -2.9 5.5 28
1899 -4.2 -7.8 -4.4 6.1 11.7 15.1 20.5 16.3 14.0 7.1 1.7 -8.9 5.6 30
1900-10.3 -8.4 -3.0 3.7 11.5 15.4 19.0 18.9 11.2 8.1 -1.3 -4.1 5.1 30
1901 -6.3 -6.0 -1.3 6.9 12.4 20.0 19.9 19.4 11.9 6.6 -1.4 -6.3 6.3 31
1902 -6.6 -6.0 -3.1 2.6 11.4 17.0 18.7 17.5 10.8 3.4 -4.2 -8.5 4.4 31
1903 -6.8 -3.1 -0.8 8.3 12.7 18.6 19.9 18.0 12.5 4.6 1.0 -4.6 6.7 32
1904 -7.0 -4.2 -3.1 4.6 10.7 15.0 17.7 17.5 12.0 7.5 0.0 -5.4 5.4 31
1905 -9.0 -5.0 -2.5 5.0 14.0 18.3 19.0 17.9 13.1 8.4 1.9 -3.5 6.5 31
1906 -6.3 -6.5 -0.5 7.4 16.8 18.5 20.8 16.7 10.4 5.7 0.0 -4.2 6.6 30
1907-11.6 -7.3 -2.2 4.8 11.0 17.6 20.4 16.7 11.6 7.5 -3.9 -9.6 4.6 32
1908 -9.3 -6.2 -4.9 4.5 10.2 17.1 19.1 17.7 12.8 4.7 -3.2 -6.7 4.6 31
1909 -7.9 -8.3 -2.9 3.4 10.7 16.3 19.1 18.1 16.1 9.3 1.1 -2.9 6.0 30
1910 -5.5 -4.5 -0.7 7.5 13.4 17.4 20.3 16.9 12.7 4.7 0.4 -2.6 6.7 31
1911 -8.8-12.0 -3.5 4.8 12.9 16.6 18.8 18.4 11.6 5.2 2.6 -3.9 5.2 31
1912-10.7-10.9 -0.2 3.4 10.3 19.1 17.3 18.0 12.4 2.0 -0.2 -3.7 4.7 33
1913 -7.7 -9.5 -0.7 7.5 9.8 14.7 19.4 19.4 12.9 3.6 1.6 -3.4 5.6 34
1914 -8.0 -2.5 -0.2 3.9 12.5 17.4 20.3 16.1 11.0 4.6 -2.6 -3.4 5.8 37
1915 -4.6 -5.1 -5.3 5.3 10.9 15.2 20.1 16.0 11.5 4.3 -1.2 -8.6 4.9 39
1916 -6.1 -4.5 -3.8 5.1 9.8 16.0 19.1 15.7 10.0 4.7 0.9 -5.1 5.2 35
1917 -8.8-13.3 -6.9 5.6 8.6 17.9 18.5 18.6 12.7 7.4 1.7 -4.9 4.8 36
1918 -7.9 -6.9 -3.7 5.7 7.2 15.2 17.6 15.0 12.9 8.9 0.2 -5.5 4.9 35
1919 -8.0 -9.1 -5.7 5.0 9.0 16.9 19.2 16.3 13.8 7.2 -3.6 -6.6 4.5 33
1920 -8.3 -8.1 0.0 7.7 14.2 16.2 19.8 18.9 13.2 1.9 -2.5 -6.9 5.5 33
1921 -7.4 -8.8 -0.6 8.4 15.4 18.3 17.6 17.5 11.0 4.4 -2.8 -7.0 5.5 33
1922 -7.8 -8.3 -2.4 4.9 12.6 16.5 19.9 17.0 11.8 4.3 0.6 -4.4 5.4 34
1923 -5.0 -9.7 -2.6 1.5 11.6 16.1 18.3 15.3 14.1 8.0 2.9 -4.2 5.5 34
1924 -9.2 -9.5 -4.0 3.7 12.6 17.2 17.8 18.0 14.3 6.4 0.0 -5.8 5.1 34
1925 -4.7 -2.8 -1.9 5.3 12.2 14.9 19.7 17.7 12.0 4.2 -1.2 -5.2 5.9 36
1926 -9.1 -8.8 -3.2 2.6 11.4 16.5 17.1 15.1 11.3 4.0 2.2 -6.4 4.4 37
1927-11.7 -7.8 -3.4 4.3 10.3 16.9 19.6 18.9 12.6 5.2 -1.6 -9.2 4.5 40
1928 -7.1 -9.3 -5.9 3.3 11.4 14.2 18.2 16.2 12.3 5.0 1.1 -4.8 4.5 42
1929-10.1-16.4 -6.7 -0.5 13.1 15.1 18.8 19.5 11.1 8.5 1.8 -5.3 4.1 43
1930 -4.3 -8.7 -1.2 5.7 12.4 14.3 18.8 20.2 10.6 6.2 1.0 -8.0 5.6 43
1931 -9.7-11.3 -3.7 3.9 13.2 16.1 21.5 18.5 11.7 6.0 -1.5 -6.4 4.9 42
1932 -4.2-11.8 -5.5 5.4 12.8 16.9 19.3 19.5 13.3 7.2 -0.2 -2.2 5.9 45
1933-10.6 -9.6 -4.3 4.7 10.0 15.7 20.4 16.2 12.5 6.1 -1.2-10.7 4.1 45
1934 -6.4 -6.2 -2.2 5.0 14.3 15.1 20.2 17.8 13.0 7.6 2.1 -6.7 6.1 45
1935 -9.1 -4.2 -2.6 5.6 10.4 16.8 17.5 18.1 12.9 8.8 -1.1 -3.6 5.8 44
1936 -5.1-11.0 -2.7 4.7 11.9 19.1 21.7 19.1 11.6 4.3 1.8 -1.8 6.1 49
1937 -9.0 -7.0 -1.3 6.7 11.8 17.0 19.9 19.2 15.2 7.2 1.4 -5.8 6.3 49
1938 -7.0 -5.3 -1.3 5.0 12.0 16.6 22.7 20.6 15.5 7.7 2.6 -8.1 6.8 49
1939 -7.9 -5.1 -2.8 3.9 11.5 17.7 20.0 18.6 10.1 3.6 0.9 -5.1 5.5 49
1940-15.4-11.1 -5.4 4.3 11.0 16.3 20.1 19.6 13.7 4.1 1.5 -6.2 4.4 48
1941-12.4 -7.5 -5.6 2.9 8.7 14.2 20.6 17.6 11.1 3.8 -4.1-10.8 3.2 47
1942-14.9-10.1 -8.5 2.9 11.5 16.1 17.9 16.2 10.9 5.3 -3.7 -6.9 3.1 40
1943-13.3 -6.8 -2.9 6.1 12.7 16.8 19.5 17.7 11.6 6.1 0.2 -3.8 5.3 38
1944 -3.8 -4.5 -0.9 3.7 11.9 15.7 18.6 16.6 13.7 6.9 0.0 -6.3 6.0 47
1945 -9.0 -9.3 -4.9 3.1 8.8 15.6 18.6 18.5 11.5 3.6 -2.0-10.3 3.7 49
1946 -7.6 -7.7 -3.4 4.2 11.5 18.3 19.1 19.1 13.1 2.2 -1.2 -6.0 5.1 50
1947-10.1-11.8 -3.2 5.3 10.2 17.5 18.8 17.3 12.8 4.8 0.4 -3.4 4.9 50
1948 -6.3 -7.6 -4.7 4.7 14.8 19.8 17.7 17.7 12.0 5.7 -0.2 -5.5 5.7 49
1949 -4.2 -7.4 -3.5 4.4 13.7 16.7 18.2 17.2 12.6 5.0 0.4 -3.8 5.8 50
1950-15.7 -7.2 -2.1 8.8 12.2 15.6 16.7 16.0 13.3 6.3 0.0 -3.8 5.0 50
1951 -9.6-10.4 -3.0 8.3 9.8 16.6 18.3 19.5 12.7 4.1 -2.1 -2.5 5.1 58
1952 -4.4 -5.6 -7.6 4.0 9.8 16.3 18.9 17.4 12.4 5.5 -0.4 -5.2 5.1 59
1953 -8.0-11.5 -3.1 6.1 11.2 18.5 19.0 18.6 10.7 5.5 -2.8 -4.8 4.9 59
1954-11.7-14.7 -3.5 3.5 12.6 18.0 21.2 18.3 13.3 6.6 0.1 -2.7 5.1 59
1955 -5.7 -6.1 -4.9 2.4 10.7 15.4 18.6 17.8 13.7 8.3 -2.4-11.9 4.7 59
1956 -8.9-15.5 -4.8 3.0 10.8 18.6 16.1 16.4 9.5 5.4 -4.9 -4.3 3.4 59
1957 -7.1 -2.9 -6.1 5.5 13.5 16.1 19.6 18.5 13.7 5.6 -0.6 -3.5 6.0 59
1958 -7.1 -6.6 -5.3 3.3 11.4 15.3 17.7 16.9 9.8 6.0 0.0 -7.6 4.5 59
1959 -5.0 -6.3 -1.8 5.1 11.7 17.0 20.3 18.1 9.6 2.8 -2.6 -8.5 5.0 59
1960 -8.3 -8.5 -4.9 4.9 11.4 17.4 21.0 17.1 11.0 4.3 -1.6 -0.9 5.2 59
1961 -5.8 -3.3 -0.2 4.0 11.3 18.6 19.4 17.5 10.8 7.3 -0.7 -5.4 6.1 59
1962 -5.1 -5.7 -3.5 7.0 12.6 14.8 18.3 16.2 12.1 6.4 1.9 -6.2 5.7 59
1963-12.7 -9.0 -8.6 4.2 14.8 14.7 19.2 17.8 14.2 6.4 0.3 -7.7 4.5 59
1964 -8.0 -9.2 -5.6 3.8 11.0 17.4 19.2 16.4 12.0 6.6 -1.6 -3.6 4.9 59
1965 -8.6-10.2 -3.0 2.8 9.5 16.5 17.7 16.6 13.3 4.2 -3.3 -2.2 4.4 59
1966 -9.8-10.0 -2.1 5.8 12.8 16.2 20.0 17.9 10.7 6.0 1.2 -8.0 5.1 59
1967-11.3 -9.3 -0.4 6.6 13.9 15.7 18.3 19.0 12.4 8.3 2.0 -8.0 5.6 59
1968-13.6 -7.4 -1.5 4.8 12.0 17.1 16.5 17.5 12.0 3.7 -1.4 -5.5 4.5 59
1969-14.5-12.4 -5.9 4.9 9.8 15.3 17.6 17.2 11.6 4.9 1.3 -7.2 3.6 59
1970 -9.2 -7.9 -1.3 5.9 11.7 16.2 20.1 16.9 12.0 5.5 -0.9 -6.3 5.2 59
1971 -4.2 -9.8 -4.2 3.3 11.1 15.5 18.9 17.4 12.7 4.1 0.0 -5.2 5.0 49
1972-14.3 -8.0 -3.3 6.0 11.7 18.5 21.8 21.0 11.7 5.9 -0.4 -1.6 5.8 50
1973 -9.6 -4.3 -1.7 7.0 12.5 17.3 18.6 16.3 8.5 4.0 -2.0 -6.1 5.0 50
1974-10.0 -4.1 -0.6 3.4 9.8 16.8 19.4 16.9 13.8 8.3 0.7 -1.9 6.0 50
1975 -4.4 -6.9 0.2 8.4 14.5 17.6 19.3 16.4 14.0 4.4 -1.4 -4.1 6.5 50
1976-10.6-11.8 -4.5 5.6 10.9 14.3 16.9 16.1 10.6 0.5 -1.0 -4.3 3.6 50
1977-11.2 -6.9 -1.3 6.4 12.9 16.9 18.9 16.6 10.6 3.1 1.9 -7.6 5.0 50
1978 -7.7 -9.8 -0.4 3.7 10.2 14.2 17.1 15.7 11.0 4.5 0.6-12.4 3.9 50
1979-10.3 -8.8 -0.9 2.7 14.8 16.1 17.9 18.0 12.5 3.9 0.1 -4.0 5.2 50
1980-11.0 -8.3 -5.9 5.1 9.9 17.6 18.2 15.6 11.4 5.4 -1.6 -3.6 4.4 50
1981 -4.8 -5.4 -3.8 3.0 12.0 18.1 20.9 18.2 12.6 8.6 0.4 -3.5 6.4 58
1982-10.2 -8.2 -1.9 5.6 11.8 13.5 18.6 16.8 12.8 4.7 1.5 -1.6 5.3 50
1983 -4.5 -5.8 -2.2 8.6 13.4 15.3 19.4 16.5 12.8 6.4 -1.4 -4.0 6.2 50
1984 -4.5 -8.4 -2.4 5.7 15.1 16.4 18.9 15.8 12.8 6.3 -1.7 -8.1 5.5 50
1985-12.1-13.6 -4.0 4.2 12.2 15.6 17.3 19.4 11.7 5.7 -1.1 -7.3 4.0 50
1986 -6.9-12.1 -0.9 7.2 11.6 17.9 18.5 17.3 10.7 5.6 0.0 -7.8 5.1 50
1987-15.2 -7.5 -5.8 2.1 12.2 17.3 17.6 15.5 10.7 5.2 -3.1 -7.3 3.5 49
1988 -8.0 -7.1 -1.2 4.4 12.1 18.3 21.2 17.6 12.1 5.9 -4.1 -6.6 5.4 49
1989 -4.5 -2.4 1.4 7.1 12.3 19.1 19.2 17.6 13.0 5.8 -0.8 -5.2 6.9 49
1990 -7.5 -0.5 0.8 7.0 9.6 14.2 17.7 15.8 9.8 4.9 -1.3 -4.0 5.5 44
1991 -8.0 -7.5 -3.8 5.4 11.6 17.0 18.2 16.1 10.3 7.4 0.3 -5.7 5.1 16
1992 -6.7 -5.9 0.0 2.6 10.5 15.2 17.4 16.5 12.9 1.3 -2.6 -4.0 4.8 21
1993 -5.2 -5.8 -2.5 2.3 11.5 13.2 16.5 15.5 8.1 3.6 -7.9 -6.0 3.6 21
1994 -6.5-12.8 -3.6 6.1 9.0 14.3 17.1 15.7 12.3 5.5 -2.5 -6.3 4.0 16
1995 -6.2 -1.3 -0.5 5.8 10.4 17.6 17.7 15.6 11.8 6.4 -2.5 -8.2 5.6 14
1996 -9.0-10.3 -4.3 3.5 9.6 14.4 18.0 16.4 10.0 5.4 2.7 -6.6 4.2 15
1997 -9.1 -6.5 -2.3 2.8 9.9 16.3 17.5 16.9 9.7 4.0 -2.9 -7.8 4.0 15
1998 -7.3-12.3 -4.4 1.0 10.0 16.4 18.3 14.7 10.7 4.6 -7.6 -7.1 3.1 14
1999 -9.5 -8.8 -3.0 5.8 6.3 17.6 19.1 15.7 11.0 6.4 -4.3 -3.6 4.4 13
2000 -7.5 -4.7 -2.4 6.0 8.8 15.0 18.7 15.8 9.7 6.4 -0.8 -4.6 5.0 13
2001 -4.5 -9.2 -4.7 7.0 9.5 15.0 20.2 15.7 11.4 4.0 -1.7-10.5 4.3 13
2002 -7.3 -3.1 -0.4 4.8 9.8 14.6 20.0 15.3 10.3 2.8 -2.7-12.1 4.3 14
2003-10.9 -8.4 -3.7 2.5 12.0 11.6 19.4 16.5 10.8 5.0 0.2 -3.4 4.3 13
2004 -8.0 -7.8 -1.5 2.9 9.9 13.8 18.4 16.3 11.7 4.9 -1.5 -4.3 4.6 14
2005 -4.3 -8.4 -7.1 3.7 11.5 14.7 18.0 16.9 12.2 6.4 2.1 -4.4 5.1 23
2006-10.8-12.0 -4.9 5.0 11.2 17.9 17.3 18.1 12.8 4.9 -0.6 -0.3 4.9 23
2007 -3.2-11.9 1.7 4.6 12.5 15.4 18.5 19.7 12.2 7.2 -2.0 -3.0 6.0 23
2008 -6.2 -4.1 -1.2 5.0 8.8 13.7 17.4 15.5 10.5 7.8 1.6 -2.5 5.5 23
-8.6 -8.0 -3.1 4.9 11.8 16.6 19.0 17.5 12.1 5.5 -0.5 -5.6 5.1
-8.3 -7.8 -3.0 4.7 11.6 16.4 19.0 17.4 12.0 5.6 -0.7 -5.6
“Only” down by about 1/2 from their peak; and I don’t see a lot of pattern to their temperatures changing. Well, 2005 January was a lot warmer than 2003 or 2004 and the 2005 December (at -4.4) was warmer than 2001-2002 but still in the normal ranges. Just near the warm end of them.
The 2009 European Russian Stations:
[chiefio@tubularbells analysis]$ cat 638.stns 63822113000 MURMANSK 68.97 33.05 51 41U 381FLxxCO20x-9NORTH. TAIGA C 107 63822165000 KANIN NOS 68.65 43.30 49 5R -9FLxxCO 1x-9WATER A 0 63822217000 KANDALAKSA 67.15 32.35 25 48S 43HIxxCO 1x-9NORTH. TAIGA C 15 63822550000 ARHANGEL'SK 64.50 40.73 8 27U 385FLxxCO20x-9MAIN TAIGA C 23 63822602000 REBOLY 63.83 30.82 182 188R -9HIxxLA-9x-9MAIN TAIGA B 8 63822641000 ONEGA 63.90 38.12 13 34S 25FLxxCO 5x-9MAIN TAIGA C 7 63822802000 SORTAVALA 61.72 30.72 19 47S 22HIxxLA-9x-9COOL MIXED B 22 63822820000 PETROZAVODSK 61.82 34.27 110 99U 234HIxxLA-9x-9MAIN TAIGA C 39 63822837000 VYTEGRA 61.02 36.45 56 76R -9HIFOno-9x-9WATER C 17 63826063000 ST.PETERBURG 59.97 30.30 6 15U 4588FLxxCO 3x-9WARM FOR./FIELD C 92 63826781000 SMOLENSK 54.75 32.07 239 207U 276FLxxno-9x-9COOL CROPS C 17 63827037000 VOLOGDA 59.32 39.92 130 120U 237FLxxno-9A 3COOL GRASS/SHRUBB 9 63827051000 TOT'MA 59.88 42.75 134 167S 20FLxxno-9x-9COOL MIXED A 0 63827333000 KOSTROMA 57.73 40.78 126 91U 255HIxxno-9x-9COOL GRASS/SHRUBB 6 63827595000 KAZAN' 55.60 49.28 116 76U 993FLxxno-9A 1COOL FOR./FIELD A 9 63827612000 MOSKVA 55.83 37.62 156 150U 8011FLxxno-9x-9COOL FOR./FIELD C 83 63827648000 ELAT'MA 54.95 41.77 136 122R -9FLxxno-9x-9COOL FOR./FIELD C 0 63827823000 PAVELEC 53.78 39.25 209 150R -9FLxxno-9x-9COOL FIELD/WOODSC 7 63834123000 VORONEZ 51.70 39.22 149 150U 783HIxxLA-9x-9COOL CROPS C 53 63834163000 OKTJABR'SKIJ 51.63 45.45 201 225R -9HIxxno-9x-9COOL CROPS A 0 63834866000 JASKUL' 46.18 45.35 -7 0R -9FLDEno-9x-9WARM GRASS/SHRUBC 9 63834880000 ASTRAHAN' 46.28 48.05 -23 0U 461FLxxno-9A 2WARM IRRIGATED C 11 63834949000 STAVROPOL' 45.12 42.08 452 300U 258HIxxno-9x-9WARM CROPS C 10 63837472000 MAHACKALA 42.83 47.55 32 45U 251HIxxCO 1x-9WARM GRASS/SHRUBB 0 [chiefio@tubularbells analysis]$
Well, at least they have 3 stations above 65N Latitude. Those 3 will give them stellar coverage of The Barents Sea!
(The two numbers after the name are LAT and LONG.)
For Canada:
Again, what you want to watch is the steady build of thermometer count on the far right, then the crash as they are taken out back and shot.
[chiefio@tubularbells analysis]$ cat Temps.403.yrs.GAT
Thermometer Records, Average of Monthly Data and Yearly Average
by Year Across Month, with a count of thermometer records in that year
--------------------------------------------------------------------------
YEAR JAN FEB MAR APR MAY JUN JULY AUG SEPT OCT NOV DEC YR COUNT
--------------------------------------------------------------------------
1880 -6.7 -8.2 -6.9 2.3 11.6 16.3 18.6 17.1 13.5 6.5 -3.1 -9.4 4.3 57
1881-13.0 -9.3 -2.0 2.2 11.7 14.0 18.7 18.7 15.4 6.0 -1.3 -4.0 4.8 63
1882-10.5 -7.0 -4.9 1.1 8.0 14.8 17.9 18.1 13.6 8.2 -0.6 -7.6 4.3 67
1883-14.4-12.1 -8.6 2.0 8.4 15.8 17.1 16.6 11.9 5.4 -1.2 -9.0 2.7 68
1884-14.4-11.5 -6.3 2.8 9.3 16.1 15.8 16.9 12.9 5.7 -2.1-10.1 2.9 71
1885-14.2-14.7 -9.5 2.0 9.5 14.3 17.8 15.4 11.6 5.6 0.1 -7.3 2.5 79
1886-14.6-11.2 -5.9 4.3 9.8 15.2 18.0 16.7 11.6 6.7 -1.8-11.2 3.1 79
1887-15.6-13.4 -7.3 1.7 11.7 15.6 19.1 15.9 11.5 4.3 -2.3 -9.0 2.7 78
1888-16.1-11.2 -8.2 0.5 7.9 15.0 16.8 15.7 11.9 4.6 -1.5 -6.0 2.4 79
1889 -8.4-12.3 -1.6 4.6 10.1 14.5 17.5 16.7 12.7 4.9 -0.3 -7.1 4.3 82
1890-13.4-11.8 -6.4 2.1 7.7 15.1 17.7 15.9 11.8 6.3 0.0 -9.0 3.0 90
1891 -8.5-12.5 -5.8 4.8 9.2 14.6 16.3 16.8 13.9 6.3 -1.7 -4.4 4.1 89
1892-10.9 -9.5 -4.5 2.5 8.1 14.8 17.8 17.0 13.2 6.7 -2.4 -9.5 3.6 93
1893-13.8-13.7 -6.7 0.0 9.4 15.6 17.4 17.1 11.6 6.1 -2.6-10.5 2.5 101
1894-12.3-11.6 -3.3 3.6 9.8 15.9 18.5 16.9 12.5 6.4 -2.8 -6.7 3.9 104
1895-13.1-11.0 -6.2 5.2 10.6 15.0 16.9 15.9 11.5 4.8 -2.3 -7.2 3.3 114
1896-12.5 -8.8 -6.8 3.2 10.6 15.1 17.9 16.5 11.3 5.5 -5.6 -6.9 3.3 115
1897-11.0-10.2 -7.2 3.9 10.3 13.7 18.1 16.4 12.9 6.4 -4.4 -8.2 3.4 126
1898-10.0 -8.8 -3.5 3.8 10.7 14.9 17.8 17.3 12.9 5.2 -2.7 -7.5 4.2 130
1899-11.6-13.4 -8.4 3.0 8.9 14.3 17.3 16.2 11.7 6.1 1.6 -6.5 3.3 134
1900 -8.6-11.5 -6.4 5.3 10.4 15.4 17.1 17.1 12.5 7.8 -2.8 -6.7 4.1 136
1901-11.3-10.8 -4.1 4.5 11.2 14.3 18.0 17.3 11.6 7.2 -2.3 -7.0 4.0 132
1902 -9.4 -8.0 -2.5 3.7 9.7 12.2 17.0 16.1 11.8 6.1 -1.3-10.1 3.8 138
1903-10.5 -9.7 -4.2 3.1 9.3 14.3 16.4 15.0 10.8 6.5 -2.3 -8.3 3.4 141
1904-12.7-15.4 -7.3 2.9 10.0 14.0 16.8 15.5 11.1 6.2 0.5 -9.4 2.7 139
1905-13.2-11.6 -2.8 3.7 9.2 13.8 17.5 16.6 12.4 4.4 -0.7 -6.1 3.6 142
1906 -8.2 -9.2 -5.6 5.3 9.2 14.9 18.5 17.3 13.1 6.8 -1.3-10.1 4.2 140
1907-16.1-11.6 -5.4 0.0 6.3 14.1 16.7 14.9 11.4 5.6 -0.6 -6.1 2.4 150
1908 -9.3-10.0 -6.9 2.4 10.0 14.4 18.0 15.6 13.0 5.9 -0.4 -8.7 3.7 161
1909-14.2-11.0 -3.9 0.4 8.9 14.7 17.0 16.5 12.6 5.4 -2.4 -9.3 2.9 164
1910 -9.5-12.7 -0.4 5.2 9.0 14.7 17.7 15.4 11.2 6.3 -3.0 -9.3 3.7 168
1911-15.8-11.1 -4.1 2.4 10.6 15.1 17.0 15.7 10.8 5.7 -5.1 -6.9 2.9 175
1912-15.5 -9.5 -7.2 3.1 10.2 14.7 16.3 15.0 10.9 5.8 -0.4 -6.8 3.0 189
1913-13.1-12.1 -6.2 4.8 8.8 14.6 16.4 16.2 11.6 5.1 0.0 -4.8 3.4 208
1914-10.7-13.6 -3.7 2.7 10.4 14.1 17.9 16.0 11.8 7.3 -1.9-10.7 3.3 228
1915-10.6 -7.3 -2.9 7.0 9.5 13.2 16.2 16.9 11.3 6.3 -1.7 -7.4 4.2 251
1916-16.2-11.6 -7.2 3.8 8.8 13.7 18.3 16.6 11.6 4.8 -2.2-11.3 2.4 256
1917-14.6-14.5 -5.6 1.6 8.2 13.1 17.8 16.2 11.6 3.5 0.2-15.4 1.8 258
1918-14.1-12.4 -4.4 3.6 8.8 13.7 16.6 16.1 10.9 6.1 -0.5 -7.1 3.1 274
1919 -8.0-10.6 -6.6 3.8 10.0 16.0 17.8 16.7 12.1 2.6 -5.0-11.7 3.1 265
1920-15.7 -9.0 -5.0 0.3 9.2 14.2 17.4 17.3 12.3 7.0 -2.1 -7.7 3.2 271
1921-10.7 -9.0 -5.0 3.7 10.3 15.9 18.7 15.6 11.7 6.5 -4.8 -8.9 3.7 276
1922-12.6-14.3 -4.9 3.2 10.8 15.0 16.9 16.7 12.3 5.3 -1.4-12.5 2.9 285
1923-13.4-13.5 -9.0 1.5 9.0 15.3 17.4 15.1 12.0 6.3 0.3 -6.2 2.9 295
1924-13.8 -9.3 -3.5 1.7 8.4 13.3 17.0 15.5 11.4 6.9 -3.0-13.6 2.6 300
1925-14.5-10.2 -4.6 4.1 9.2 14.5 16.9 16.5 11.1 1.8 -2.4 -7.0 2.9 300
1926 -9.1 -8.7 -4.8 2.1 9.4 13.2 17.3 15.5 9.2 5.0 -3.8-10.7 2.9 312
1927-12.5-11.0 -3.1 2.2 7.9 13.7 17.1 15.9 11.6 6.3 -5.7-13.3 2.4 303
1928-10.5 -9.1 -4.8 0.7 10.5 13.8 17.4 15.8 10.8 4.8 -0.8 -6.0 3.5 299
1929-15.6-13.0 -3.6 1.8 8.1 14.0 16.9 16.2 11.1 6.3 -2.4-11.4 2.4 310
1930-16.0 -9.1 -4.7 3.6 9.1 15.0 17.5 17.2 11.6 4.1 -1.5 -6.1 3.4 318
1931 -9.1 -6.4 -4.8 3.7 9.3 14.6 17.8 16.4 12.0 6.6 -1.7 -7.5 4.2 321
1932-10.8-12.1 -8.2 2.8 10.0 14.9 16.3 17.0 11.8 4.2 -4.8-10.7 2.5 321
1933-12.0-12.5 -6.2 1.9 9.2 14.7 16.9 16.8 11.1 3.5 -4.9-16.7 1.8 323
1934-11.4-11.9 -6.5 3.4 10.4 13.8 16.9 15.0 10.3 5.3 -1.1-11.4 2.7 334
1935-16.9 -8.8 -7.7 0.7 7.9 13.4 18.0 15.6 10.7 4.4 -5.2 -9.2 1.9 338
1936-15.4-19.4 -4.5 0.6 10.2 14.2 18.1 16.1 10.9 4.3 -3.1-10.1 1.8 338
1937-15.9-11.4 -5.5 3.0 10.1 15.0 17.9 16.7 11.9 5.4 -3.1-10.9 2.8 344
1938-11.8-13.0 -4.1 2.3 9.0 14.8 17.7 16.3 13.3 7.0 -3.2 -8.4 3.3 351
1939-11.0-15.2 -8.3 2.0 9.3 12.7 17.4 16.9 10.9 3.5 -1.1 -5.5 2.6 353
1940-12.9-10.4 -5.5 1.6 9.6 13.1 17.1 16.3 13.1 5.7 -5.0 -8.9 2.8 352
1941-12.8 -9.8 -5.6 4.1 9.5 14.7 18.2 15.3 10.2 4.9 -2.1 -8.7 3.2 357
1942-10.5-10.5 -3.0 3.0 9.1 13.7 16.5 15.9 11.2 6.1 -4.5-12.8 2.8 371
1943-16.7-10.2 -9.4 1.5 7.2 12.5 16.9 15.4 11.3 6.2 -1.4 -8.9 2.0 383
1944 -9.0-11.8 -8.0 2.4 10.0 13.6 16.6 16.1 11.8 5.9 -2.3 -9.7 3.0 393
1945-12.9-10.7 -3.1 0.2 6.9 12.3 16.4 16.0 10.2 4.7 -5.6-11.8 1.9 394
1946-12.8-13.4 -2.5 2.4 8.0 13.0 16.4 15.4 11.4 4.6 -4.7-11.9 2.2 392
1947-13.1-12.0 -5.6 0.0 7.0 12.7 17.4 15.9 10.5 6.9 -3.2 -9.6 2.2 395
1948-12.6-15.3 -9.5 -0.8 8.4 13.5 16.5 15.7 11.8 5.0 -1.9-11.7 1.6 409
1949-14.2-15.6 -6.7 2.5 7.8 13.5 16.4 16.0 10.5 4.2 -2.1-11.9 1.7 407
1950-19.9-14.4 -9.1 -1.1 7.4 13.0 15.7 14.2 10.3 3.6 -5.3-10.2 0.4 422
1951-15.0-12.7 -8.7 1.4 8.6 12.4 16.1 14.7 10.1 2.7 -5.3-13.4 0.9 441
1952-15.9-11.1 -7.3 2.7 8.3 13.0 16.6 15.2 10.9 3.8 -2.2 -7.7 2.2 441
1953-13.8 -9.6 -6.1 1.2 7.8 12.8 16.1 15.6 10.5 5.5 -1.0 -8.8 2.5 449
1954-18.5 -8.6 -7.8 -2.6 6.7 12.8 15.6 14.6 9.9 4.2 -1.6 -8.8 1.3 450
1955-12.5-13.1-10.3 1.4 7.6 13.8 16.8 15.8 9.6 4.7 -7.6-14.3 1.0 455
1956-13.6-13.5 -9.6 -0.6 5.9 12.7 15.3 14.4 8.7 3.6 -3.3-12.2 0.7 458
1957-18.0-13.0 -6.3 0.1 7.2 12.4 15.7 14.2 10.6 3.3 -3.8 -9.8 1.0 476
1958-10.2-13.0 -5.5 0.8 7.8 11.8 15.8 15.1 9.9 3.6 -5.1-13.6 1.4 478
1959-16.1-14.9 -7.9 -0.3 6.2 12.1 16.4 14.3 9.6 1.4 -6.1 -8.6 0.5 482
1960-14.1-10.8-10.5 -0.4 7.9 12.5 15.9 14.9 10.1 3.7 -4.6-10.9 1.1 488
1961-15.1-12.7 -8.5 -1.1 6.2 13.2 16.0 15.5 9.6 2.9 -4.7-12.7 0.7 491
1962-15.7-16.4 -7.8 -0.7 6.2 12.6 14.6 14.5 9.3 4.2 -3.6-11.0 0.5 500
1963-15.8-13.4 -8.9 0.0 6.0 12.4 15.9 14.5 9.7 5.6 -3.9-12.9 0.8 503
1964-13.2-10.9-11.1 -1.1 6.9 11.7 15.6 13.3 8.5 3.2 -5.4-14.8 0.2 508
1965-16.4-15.5 -9.7 -0.8 6.6 11.9 14.8 14.2 7.6 3.1 -6.2-10.9-0.1 521
1966-17.9-12.7 -6.9 -1.6 6.1 12.3 15.7 14.3 10.2 2.7 -6.6-11.1 0.4 536
1967-14.5-15.4-10.8 -2.6 5.4 12.7 15.6 15.2 10.9 3.4 -4.7-10.6 0.4 542
1968-16.3-13.2 -5.7 0.1 6.3 11.6 15.0 13.0 10.6 4.3 -4.6-13.5 0.6 547
1969-17.9-11.9 -8.6 0.1 6.3 11.8 14.8 14.9 9.2 2.3 -3.4 -8.8 0.7 564
1970-16.8-12.7 -8.2 -0.9 6.0 13.1 16.0 15.1 9.0 3.5 -5.2-15.1 0.3 576
1971-17.7-12.5 -8.4 -0.1 7.4 12.6 15.1 15.0 9.9 4.0 -5.2-14.6 0.5 571
1972-18.1-17.0 -9.3 -2.2 7.0 12.1 14.4 14.5 7.6 1.1 -5.5-15.4-0.9 572
1973-13.7-13.8 -5.4 -0.4 7.2 12.8 16.0 15.3 9.8 4.0 -7.3-11.2 1.1 590
1974-17.8-13.9-10.8 -1.0 4.9 12.5 15.2 14.1 8.5 2.2 -3.9 -9.0 0.1 596
1975-15.3-14.6 -9.6 -1.6 7.6 12.8 16.9 13.9 9.5 3.2 -4.7-12.9 0.4 593
1976-14.8-12.4 -9.2 1.4 7.2 12.6 15.2 14.6 10.2 2.1 -4.8-13.4 0.7 584
1977-14.3 -9.6 -5.5 1.2 8.2 12.6 15.1 13.8 9.2 3.8 -4.8-14.3 1.3 579
1978-15.9-12.4 -8.8 -0.8 7.1 12.1 15.3 14.0 8.9 2.7 -6.9-12.4 0.2 575
1979-15.5-18.8 -6.9 -0.9 6.7 12.4 16.2 14.3 10.0 3.7 -3.2 -9.7 0.7 571
1980-15.0-12.0 -8.2 1.7 8.1 12.0 15.2 14.3 8.5 3.5 -3.6-14.7 0.8 561
1981-10.6 -8.7 -3.8 0.1 7.8 11.9 15.9 15.8 10.1 2.9 -2.0-10.1 2.4 553
1982-19.2-14.3 -8.8 -1.8 7.0 12.2 15.7 13.3 10.0 3.6 -6.0-10.3 0.1 544
1983-12.1-10.7 -6.7 0.5 5.8 13.0 15.9 15.6 9.8 3.5 -3.1-15.3 1.3 535
1984-13.6 -7.4 -7.2 1.7 6.6 12.6 16.0 15.7 8.2 2.6 -5.8-13.6 1.3 524
1985-13.1-13.5 -6.4 -0.3 7.6 11.7 15.7 13.9 9.2 2.7 -9.2-11.3 0.6 511
1986-11.2-12.7 -6.2 0.4 7.9 11.9 14.9 14.5 8.5 3.1 -7.7 -8.9 1.2 506
1987-11.2-10.2 -6.6 2.0 7.6 13.3 15.9 13.7 10.9 3.4 -3.5 -8.4 2.2 499
1988-14.5-12.8 -6.1 1.2 8.3 13.2 16.1 15.1 9.6 3.2 -4.4-11.6 1.4 500
1989-13.9-14.8-10.6 0.3 7.5 13.2 16.5 15.2 10.3 3.2 -6.5-13.7 0.6 496
1990-11.1-13.3 -4.9 1.2 7.1 13.2 15.4 14.6 10.0 1.8 -5.9-13.0 1.3 277
1991-20.2-15.1-12.2 -3.3 4.7 11.3 14.2 13.7 7.6 -0.2 -7.7-14.5-1.8 44
1992-16.1-16.7-11.3 -5.6 3.4 9.6 12.6 12.5 6.7 0.2 -7.9-15.3-2.3 41
1993-17.8-18.4-11.2 -3.9 4.8 10.3 13.9 13.2 7.5 1.0 -8.0-19.2-2.3 41
1994-18.7-18.6-10.2 -2.7 5.6 11.8 15.8 13.5 9.0 6.1-19.8 0.0-0.7 37
1995-15.0-16.6-10.2 -0.6 -1.7 13.7 15.3 14.9 8.9 2.5 -7.7-16.2-1.1 37
1996-19.8-15.1-12.9 -4.1 4.0 11.7 14.5 13.8 8.6 0.6 -8.9-14.2-1.8 39
1997-18.9-17.4-14.3 -5.1 3.0 10.8 14.2 12.8 8.6 0.5 -6.1-11.2-1.9 39
1998-18.6-13.2-10.6 -1.0 7.0 12.3 15.5 14.6 9.8 2.4 -4.6-11.8 0.1 44
1999-16.8-13.0 -8.0 -1.4 5.7 11.4 14.5 14.0 9.5 0.9 -5.1-11.0 0.1 48
2000-16.8-13.5 -7.7 -2.8 4.3 10.4 15.0 13.3 8.2 1.8 -5.6-15.2-0.7 48
2001-14.1-16.3-10.2 -2.5 6.0 11.1 15.0 14.7 9.7 0.3 -7.3-13.6-0.6 48
2002-18.7-18.5-15.8 -7.2 1.4 9.7 13.5 12.5 7.8 -0.3 -8.5-12.9-3.1 39
2003-18.0-21.2-15.9 -5.9 3.9 9.7 14.0 13.2 8.4 1.9 -8.0-13.4-2.6 36
2004-21.5-15.7-13.1 -3.2 3.0 10.6 14.9 13.3 8.4 2.2 -5.5-15.0-1.8 36
2005-18.3-13.8 -9.2 -0.2 6.4 11.9 14.9 13.9 9.6 3.8 -3.7-13.3 0.2 44
2006-14.6-15.8-10.6 -2.1 5.4 11.5 14.9 12.8 8.5 1.6 -8.6-11.9-0.7 40
2007-16.3-18.0-13.4 -3.7 2.9 10.6 15.0 12.5 7.1 1.4 -8.3-16.5-2.2 44
2008-20.6-21.6-17.7 -4.4 2.6 8.9 13.3 11.3 7.9 1.8 -6.0-15.2-3.3 48
-14.2-12.4 -7.1 0.8 7.8 13.1 16.3 15.2 10.3 4.1 -4.0-11.0 1.6
-14.0-12.5 -7.2 1.0 7.9 13.3 16.4 15.3 10.6 4.3 -3.8-10.6
For Country Code 403
[chiefio@tubularbells analysis]$
Here we see that 1989-91 was “The Great Dying” in the frozen north of Canada. Though, oddly, the only effect seems to be that winters show up a bit colder
Unfortunately, this trail goes cold in 2008, since 2009 is not done yet, I can not calculate an annual result for that year.
But we know what’s coming. With one thermometer in the frozen north, GIStemp must fill in from the south. Look for record warmth in the “Arctic North” of Canada in 2009.
Then again, we’ve been having temperatures from the Real Canada that would freeze brass monkeys, so maybe, just maybe, nature will have cooked this Canadian Goose with frost before it could hatch…
The surviving Canadian stations in 2009:
[chiefio@tubularbells analysis]$ cat 403.stns 40371066000 HIGH LEVEL, A 58.62 -117.17 338 337R -9HIxxno-9x-9BOGS, BOG WOODS A 12 40371069000 SLAVE LAKE, A 55.30 -114.78 581 603R -9HIxxLA-9A-9SOUTH. TAIGA C 36 40371079000 THOMPSON WEAT 55.80 -97.85 204 217S 14FLxxno-9A 5MAIN TAIGA C 10 40371101000 SANDSPIT,B.C. 53.25 -131.82 6 0R -9HIxxCO 1A-9WATER A 0 40371109000 PORT HARDY,B. 50.68 -127.37 22 55R -9HIxxCO 1A-9WATER A2 0 40371120000 COLD LAKE,ALT 54.42 -110.28 541 537R -9FLxxno-9A-9BOGS, BOG WOODS C 49 40371122000 BANFF,ALTA. 51.18 -115.57 1384 1774R -9MVxxno-9x-9COOL CONIFER C 30 40371185000 DANIEL'S HARB 50.23 -57.58 19 11R -9HIxxCO 1x-9WATER A 0 40371197000 PORT-AUX-BASQ 47.57 -59.17 40 29R -9HIxxCO 1x-9COOL CONIFER C 18 40371600000 SABLE ISLAND, 43.93 -60.02 4 0R -9FLxxCO 1x-9WATER A 0 40371603000 YARMOUTH,N.S. 43.83 -66.08 43 25R -9FLxxCO 5A-9WATER C2 10 40371726000 PARENT,QUE. 47.92 -74.62 441 475R -9HIxxLA-9x-9COOL MIXED B2 12 40371727000 BAGOTVILLE,QU 48.33 -71.00 159 177U 135HIxxno-9A10COOL CROPS C3 24 40371733000 GORE BAY,ONT. 45.88 -82.57 193 183R -9HIxxLA-9x-9WATER A1 0 40371813000 NATASHQUAN,QU 50.18 -61.82 11 12R -9FLxxCO 1A-9MAIN TAIGA B 0 40371816000 GOOSE,NFLD. 53.32 -60.42 49 23R -9HIxxLA-9A-9MAIN TAIGA C 30 40371818000 CARTWRIGHT,NF 53.70 -57.03 14 15R -9HIxxCO 1A-9NORTH. TAIGA A 0 40371826000 Nitchequon 53.33 -70.90 -999 541R -9FLxxLA-9A-9NORTH. TAIGA A 0 40371827000 LA GRANDE RIV 53.63 -77.70 195 184R -9FLxxLA-9A-9NORTH. TAIGA A 9 40371828000 SCHEFFERVILLE 54.80 -66.80 518 545R -9HIxxLA-9A-9NORTH. TAIGA A 0 40371834000 GERALDTON,ONT 49.78 -86.93 349 337R -9FLxxLA-9x-9MAIN TAIGA A2 0 40371842000 SIOUX LOOKOUT 50.12 -91.90 390 379R -9FLxxLA-9A-9E. SOUTH. TAIGA C2 27 40371844000 BIG TROUT LAK 53.80 -89.88 224 210R -9FLxxLA-9A-9NORTH. TAIGA A 0 40371862000 ESTEVAN,SASK. 49.22 -102.97 581 572R -9FLxxno-9x-9COOL CROPS B2 7 40371869000 PRINCE ALBERT 53.22 -105.68 428 448S 31FLxxno-9A 2COOL FIELD/WOODSC 33 40371894000 ESTEVAN POINT 49.38 -126.55 7 8R -9FLxxCO 1x-9WATER A1 0 40371898000 PRINCE RUPERT 54.30 -130.43 34 15S 16HIxxCO 1A 5COOL CONIFER B 0 40371906000 FORT CHIMO 58.10 -68.40 36 44R -9HIxxno-9A-9WOODED TUNDRA A 13 40371907000 INUKJUAK, QUE 58.45 -78.12 6 19R -9HIxxCO 1x-9WATER A 0 40371915000 CORAL HARBOUR 64.20 -83.37 64 115R -9HIxxCO 6x-9TUNDRA A 0 40371917000 EUREKA,N.W.T. 79.98 -85.93 10 222R -9MVxxCO 1A-9WATER A 0 40371945000 FORT NELSON,B 58.83 -122.58 382 328R -9HIxxno-9x-9MAIN TAIGA A 0 40371950000 SMITHERS,B.C. 54.82 -127.18 523 642R -9MVxxno-9A-9COOL CONIFER C 25 40371964000 WITHEHORSE, Y 60.72 -135.07 703 947S 15MVxxno-9x-9TUNDRA C 60 40371966000 DAWSON,Y.T. 64.05 -139.13 370 926R -9MVxxno-9A-9WOODED TUNDRA A 0 [chiefio@tubularbells analysis]$
I’m sure that is more than enough to cover one of the largest and coldest continental areas on the planet. It’s not like this is one of the coldest places on the planet outside of Antarctica or anything.
Conclusion
For me, this looks like a small and in some ways subtile issue with Russia, while Canada has been reamed on the back side. (Best button up yer Long Johns, eh? Don’t be wanten any more bits ta be freezen and fallen off back there. Gettin a might thin already!) But I don’t see a large impact on the average temperatures. This would need “real graphs” to pick up detail I can’t get by inspection of the charts.
The major issue this surfaces for me is the general “Great Dying” of thermometers.
Why?
Who?
With what effect?
Can you really trust the “composite instrument” measuring the world temperature when it has 90% of the parts removed in North America? And with 90% of North America removed, the impact of the warmer parts of the world becomes just that much more. And as we saw in the “California” posting, thermometer deletion is warming the place…
More such “by country” and “by region” analysis in:
https://chiefio.wordpress.com/2009/11/03/ghcn-the-global-analysis/
Musings from the Chiefio 
This would need “real graphs” to pick up detail I can’t get by inspection of the charts.
Canada:
http://sites.google.com/site/elliesgraphs/canada-and-russia
Russia needs different treatment
Nice graphs, Ellie.
E.M., am I correct in surmising that the coverage for the U.S. appears to have shifted to the major urban centers? This implies that whatever changes are happening there (land use & population) are going to be reflected in the record going forward. Lots of urban signals.
@Ellie
Absolutely beautiful graphs. I especially like the way you can see the blue northern thermometers evaporate to brown in 2009!
By the way, you might want to look at the “subtracted” line, it’s not clear to me ;-)
@Harold Vance:
The US Coverage seems to be concentrated in major urban areas, on flat ground, at airports. (Though I’ve not done a real analysis of that, just an scan down the remaining station chart).
Yes, those changes (UHI, Airport Jet Engine Exhaust and Tarmac) will be “warming the planet” going forward.
EM – “subtracted” line? Huh? Which graph?
REPLY: Um, in the text… -ems
Ah. Fixed. Thats what I get for doing three things at once.
Russia – thermometer approx locations has taken a bit of work: http://sites.google.com/site/elliesgraphs/russia
Done at the expense of graphs, but worth it (IMHO ;-)
The current Houston station (the airport), is classified as WARM CROPS. I doubt if this classification means anything as far as adjustments go. A better description (category) would be HOT URBAN or HOT EXHAUSTS/WAKE VORTICES.
The square footage of concrete is truly impressive out there. Massively long runways with huge jets coming and going.
I think that we should survey the 130-odd remaining stations in greater detail. I will begin tonight with Texas and the neighboring states.
Help me to see if I’ve understood this.
1. Reduce number of thermometer reading reports collected and archived – fewer actual temperatures.
2. Use remaining actual reports and extrapolate (share?) the readings with grid squares that don’t now (or maybe never did) have thermometers.
3. while you’re at it, adjust the numbers to reflect best understanding of how actual temperatures in adjacent grid squares would scale relative to the ones you actually have.
E.M. is this substantially it?
John
REPLY: I would only suggest that the phrase “best understanding” be replaced with “best flights of fancy” … -ems
I can continue to confirm that 2007 has only 134 records for station ID prefix “425”. For this subset of stations, there are no duplicate station IDs, so there is only one “thermometer version” for each. Two of the stations, McGrath and Galveston, have reached version #6.
REPLY: Thanks again! -ems
Here are the results of my casual survey of GHCN stations for year 2007 in Louisiana and Texas. I used google maps and entered the latitude and longitude for each station as found in the GHCN stations table. Here is an example of a record that I used:
Station ID 42572240000
Location: LAKE CHARLES/
Latitude: +30.1
Longitude: -93.22
Anyway, here is what I found:
TEXAS
Total Stations: 12
Definitely Airports: 7
Probable Airports: 4
In City: 1
LOUISIANA
Total Stations: 2
Definitely Airports: 1
In or Near City: 1
Of the fourteen stations that I checked, it is my opinion that twelve are airports (urban) and that the other two are in urban settings (Galveston is in the latter). All were urban, in my opinion.
The latitude coordinates from GHCN weren’t all that precise. For some reason, Google put the marker due south of four airports. The markers were close enough to raise suspicion that the station was probably at the airport.
Watts has probably already cataloged the 134 stations in his surface stations database. I may check there tomorrow night.
Based on my own casual survey, my guess is that 80% or more of the 134 stations are in fact in highly urban settings and that more than 50% (the majority) are at airports. These are just guesses, of course.
Here is the data along with my comments. Hopefully, it won’t get butchered:
“STATION ID”,”LOCATION”,”DESCRIPTION”,”FACILITY”,”TYPE OF AREA”,”LATITUDE”,”LONGITUDE”
42572240000,”LAKE CHARLES/”,”LAKE CHARLES REGIONAL AIRPORT”,”AIRPORT”,”URBAN”,30.1,-93.22
42572242000,”GALVESTON, TX”,”IN MIDDLE OF GALVESTON”,,”URBAN”,29.3,-94.80
42572243000,”HOUSTON UNITED”,”BUSH INTERCONTINENTAL AIRPORT”,”AIRPORT”,”URBAN”,29.9,-95.35
42572248000,”SHREVEPORT/RE”,”SOUTH OF SHREVEPORT”,”?”,”URBAN”,32.4,-93.78
42572250000,”BROWNSVILLE/I”,”BROWNSVILLE/SOUTH PADRE ISLAND INT’L AIRPORT”,”AIRPORT”,”URBAN”,25.9,-97.42
42572254000,”AUSTIN/ROBERT”,”ROBERT MUELLER MUNICIPAL AIRPORT”,”AIRPORT”,”URBAN”,30.3,-97.70
42572255000,”VICTORIA/VICT”,”OUTSKIRTS OF VICTORIA JUST SOUTH OF AIRPORT”,”AIRPORT(?)”,”URBAN”,28.8,-96.92
42572256000,”WACO,MADISON-“,”WACO REGIONAL AIRPORT”,”AIRPORT”,”URBAN”,31.6,-97.22
42572259000,”DALLAS-FORT W”,”DALLAS FORT WORTH INTERNATIONAL AIRPORT”,”AIRPORT”,”URBAN”,32.9,-97.03
42572263000,”SAN ANGELO/MA”,”SOUTH OF SAN ANGELO REGIONAL AIRPORT”,”AIRPORT(?)”,”?”,31.3,-100.50
42572265000,”MIDLAND/MIDLA”,”SOUTHEAST OF MIDLAND INTERNATIONAL AIRPORT”,”AIRPORT(?)”,”?”,31.9,-102.18
42572266000,”ABILENE/MUN.,”,”ABILENE REGIONAL AIRPORT”,”AIRPORT”,”URBAN”,32.4,-99.68
42572267000,”LUBBOCK/LUBBO”,”SOUTH OF LUBBOCK PRESTON SMITH INT’L”,”AIRPORT(?)”,”URBAN”,33.6,-101.82
42572363000,”AMARILLO/INTL”,”RICK HUSBAND AMARILLO INTERNATIONAL AIRPORT”,”AIRPORT”,”URBAN”,35.2,-101.72
j ferguson, don’t forget that the majority of those lucky 134 remaining thermometers are likely in middle of urban heat islands, some of which are rather large in scale. The airports DFW & IAH come to mind.
I just don’t see how they can eliminate the UHI contamination from the readings. Are these adjustments done on a site by site basis, and do the adjustments reflect the actual amount of concrete, jet traffic, car traffic, etc., in the area, all of which are changing gradually (increasing) over time? I seriously doubt it.
REPLY: It is much more pernicious than that. GIStemp does a “lookaside” at nearby (up to 1000 km away) “rural” stations to get the adjustment that it applies. So what happens when the nearest ‘rural’ station is yet another urban airport?… -ems
I just went to the NOAA MMS station locator. I found the link at the surface stations project:
http://mi3.ncdc.noaa.gov/mi3qry/login.cfm
The GHCN station ID contains the WMO ID and you can search the online database using the WMO ID. This database has much better lat and long data.
At any rate, I looked up all fourteen of the stations that I casually surveyed earlier. Sure enough, they are all at airports. The fourteen stations for TX and LA are all at airports.
Maybe they should apply for frequent flier miles.
Make that a 1000-mile bonus for every new high or higher low.
lmao.
Harold Vance: Make that a 1000-mile bonus for every new high or higher low.
Hadn’t thought of that ;-) Maybe they get a kickback for showing “airport clear and warm, no snow” 8-0
Hi Harold Vance and E.M. thank you for your notes.
In this probably even more idle speculation, suppose that the record manipulators decided that UHI sited thermometers produced reliable long term temperature change signals even though the base temperature might be several C above rural temperatures.
Suppose also that our manipulators had found that with a minus several C correction, the rural readings were linear with the UHI readings.
VOILA, You don’t need anything but the UHI data anymore, you can fudge the rest of the grids because the non-UHI sites can be assumed to be either linear or otherwise mathematically related in a consistent way to the UHI sites.
I’ve got my Ubuntu (Not SunOS 4.3.1, unfortunately) up and running, I’ve downloaded the datasets you’ve referred to, limbered up Grep, Diff, WC and my almost forgotten scripting skills and am looking forward to doing the things I enjoyed so many years ago, trying to understand what I was looking at.
Cheers,
John
@J Ferguson:
I could sort of see that line of reasoning. It only requires extreme arrogance… It is all based on the notion that “Anomalies, grids, and boxes” can fix it all up real nice.
Except it doesn’t (I do need to actually finish what started me on this GHCN investigation in the first place: benchmark creation that can measure the degree of “Q” GIStemp has as a filter. Basically, can it filter out a 10% noise, or a 90% ?) Some preliminary observations on GIStemp showed up to STEP2 it is a mild amplifier, not a filter. And STEP3 is shown to be an amplifier over the oceans where coverage is sparse and the grid box “sources” are largely hot tarmac at island airports while the “destinations” are pristine cooler water. From that, I would expect the same behaviour on land. Delete the cooler rural thermometers and the hot tarmac rural ones “bleed in” to the cool ares. Basically, GIStemp does not have perfect “Q” or “Quality factor” as a filter. Not really a surprise as no filter is perfect. The “hard bit” is measuring just exactly how far it falls short of perfection.
So it is possible that The Thermometer Langoliers simply believed in the perfection of their creation and set about cutting out the “superfluous” thermometers. But it is also possible they new darned well that the “Q” was not 100% and were exploiting this artifact. The exact pattern of changes would speak to that motivation. So far the only place where change in thermometers makes the record colder is Canada. So the question becomes: Did they gut Canada in isolation, or as part of a N. America pruning? If done in isolation (comparison of exact dates of cuts) it would speak to “misguided belief in perfect filters”. If done with a larger set of deletions leading to net warming, it could just be to “provide cover” by looking more neutral.
So, stupidity/arrogance or malice? Nice choice, eh?
The other “minor issue” is that the way GIStemp does it’s UHI “correction” requires those small rural stations (and the more of them the better) to perform “correctly”. The more stations are deleted from the record, the worse GIStemp UHI processing will be. When all that is left is long lived records in hot airports, it has no “Rural Reference Stations” for its “Reference Station Method” to use; so can do no UHI correction… This effect will be slowly manifest as one station at a time has a failure to find enough reference stations to use. A secondary effect is that to the extent a station is dropped, its temperatures freeze in time. The kept history can not cool (adjusting the UHI downward) in a cooling trend by adding more cooling years onto the newer end. Were new data accumulating, that “reference record” would be cooling too, and show the need for more UHI correction. To the best of my knowledge no peer reviewed validation of the Reference Station Method has been done in the context of station change / deletion. Basically, this is unexplored territory and not a behaviour supported by the literature that justifies the use of The Reference Station Method in the first place.
It all comes down to this: Did the people doing the thermometer cuts know that? Yes? Cuts malice. No? Cuts plausibly stupidity. (Though it is possible they were “modeling the data” and simply tried some cuts, ran GIStemp, got warming? Kept cuts. Got cooling? Try different cuts… So a further question would be: Did they work with or communicate regularly with the folks running GIStemp in between releases of data to the public?)
BTW, on a semi-regular basis folks start to explore that “UHI fixed so who needs so many dodgy rural?” line of reasoning. The problem is the mental model: The assumption is that a single station is looked at, a reasonable UHI for that station is applied (by hand?) and the record blessed for use. This misses the point that GIStemp as a core behaviour takes un-adjusted records and trys to UHI adjust them based on the rest of the thermometers in the composite data set. It does a “look aside” at “nearby – up to 1000km away” rural stations and calculates an average offset from them, then uses that to make its own UHI correction. If the “rural” station isn’t (and many are mis-marked) or if the “rural” station has a perverse inverse relationship (like SFO is inverse with the Central Valley of California due to the way the coastal fog gets sucked in when the valley gets hot) this UHI method will make errors. (As shown in the “Slice of Pisa” thread).
Also, it tries first to look closest, then “spirals out” until it gets enough stations (20 IIRC) for the UHI “correction”. So to the extent that you prune thermometers from the record, the GIStemp UHI “correction” will need to reach further away for the “reference”. And the further away you get, the worse the correlation between stations.
Basically, simple inspection of the GIStemp code and method shows that it has a UHI “correction” method that is sensitive to thermometer count and location. “Thinning out the herd” of thermometers will change the UHI adjustment and not for the better. It can do no else. The only unknown bit is “how badly”… (And did the Thermometer Langoliers know this…)
BTW, if you want to run your own copy of the orginal GIStemp, let me know. I have a “tarball” that ought to run on your Ubuntu box and a place that I can put it up for ftp (that I’ve been meaning to do for a couple of months now… sigh.)
Here is a look at missing values for the entire v2.mean table (October 2009):
“MONTH”,”MISSING”,”% OF TTL MISSING”,”% OF ALL VALUES”
1,31808,8.4%,0.44%
2,30300,8.0%,0.42%
3,29570,7.8%,0.41%
4,29493,7.8%,0.41%
5,29739,7.9%,0.42%
6,30085,8.0%,0.42%
7,31564,8.4%,0.44%
8,31545,8.4%,0.44%
9,30833,8.2%,0.43%
10,32364,8.6%,0.45%
11,33559,8.9%,0.47%
12,35948,9.5%,0.50%
The graph forms a shallow parabola.
Am I correct in assuming that month 12 is December?
It looks like months 11 and 12 could well be a proxy for the traditional Western holiday calendar. lol.
REPLY: “Yes, month 12 is December. I think we need a new term to describe that ‘Turkey Day through New Years’ data drop out… The Holiday Heat Island Effect HHI? or maybe New-Turkey Heat Island Effect in honor of the end points ;-) NTHI. Gee, just gather more data in summer than in winter and … -ems”
E.M.
This process is so perverse that it triggers all my “can’t believe it” responses. But the data manufacturing from less than the whole cloth is so obvious. I grasped the gist of what you’ve said here earlier. It reminds me so much of the sort of stock picking schemes we used to fool with when we first got dial-up modems, super-calc and Mbasic in 1981or 82(I think it was.) Only we knew we didn’t really have any routines that actually understood what they were doing. And we didn’t, and we didn’t trade with this stuff either.
But the guys that have done this are using it. And they have to know it’s inventing information not reporting it.
Depending on available time, it’s going to take me a few weeks to come up to speed on any worthwhile pattern discovery. I wrote my last script was in 1993. I haven’t needed to think this way since then.
Thanks again for your continuing efforts in this area.
John
REPLY: “You are most welcome. I’m in it for the long haul. The first 6 months or so were the hardest because the ‘pay dirt’ is so far under the surface. But you get there in time… Frankly, a large part of my purpose has been to break through this overburden so other folks can get a jump start. (Thus the source code and software available for anyone who wants it. Posting the technical guts will cost me some populist audience, but will make life easier for anyone else heading down this road. That’s the more important part.) And yes, part of my ‘they are doing WHAT?’ came out of my experience with stock prediction methods. I presently make my lunch money by knowing what works, and what is BS… -ems”
John, it is my opinion that the thermometers in urban areas are decent proxies for economic development but not much else.
The problem with picking urban locations to measure climate change is that most of the earth’s surface is not in fact urban. This implies that the best spots for picking up a signal are not in the cities and definitely not at airports. Rather, the best spots are going to be either in the rural areas or out on the oceans.
Ironically, most of the earth’s surface is covered with water, but this portion has scant coverage by real thermometers.
The trillion dollar question is why urban? Why would scientists willfully pick some of the worst possible spots for detecting changes in climate? Why make your job anymore complex than it has to be? The focus should be on the collection of data of the highest possible quality. Pick the best spots and take your readings. If the fate of the earth really is in the balance, why on earth would you handicap the collection of decent data? It just makes no sense whatsoever.
REPLY: “Unless your goal is other than accurate measurement. Then it makes sense… Though in fairness, no body wanted to fund a global climate system until recently, but you MUST have temperature over the runway to do the ‘density altitude’ calculation that tells you if you can get your airplane off the ground. It may partly just be a matter of ‘use what you can get’. -ems”
One might wonder how many, and which, of the stations deleted from the v2.inv are still “publishing elsewhere” and if so, is there any pattern in the relationship among those reporting “elsewhere”, those retained, and those deleted.
One might suppose that temp data sets easily discovered elsewhere even if deleted from v2.inv might be more consistent with those “saved.” Hanlon doesn’t include evil in his paradigm, or does he?
If there is “purpose” behind the deletions, it doesn’t look as though very many people would have to be privy to the decisions, maybe fewer than a dozen – this assuming that those making use of this inventory “don’t care.”
This business of “consensus of thousands of climate scientists” seems nonsense in the likelihood that not even hundreds have sensed the biases of this data inventory.
Harold, I agree with your points. I was not trying to be obtuse (maybe I can do it without tryiing). My assumption was that “someone” may have decided that airport thermometer reporting was more reliable and consistent.
Then maybe that someone, or one of his cohorts, had discovered a function which could remove the UHI effects for application of these readings outside of UHI locations. So reliable reporting is more important than relationship of the measurements to the broader, dare I say global, environment. Again, idle speculation. I think that this bit is what E.M’s deconstruction of GISTEMP will tell us.
j ferguson
One might wonder how many, and which, of the stations deleted from the v2.inv are still “publishing elsewhere” and if so, is there any pattern in the relationship among those reporting “elsewhere”, those retained, and those deleted.
I would love to have the time to track that down. For the USA, we have the answer. Almost all of them are still reporting and the data is directly available from the same web site where GIStemp picks up the USHCN file (that cuts off in 2007). They simply need to also pick up the USHCN.v2 file and merge it. The ‘pattern’ is that those kept are much hotter than those deleted.
One might suppose that temp data sets easily discovered elsewhere even if deleted from v2.inv might be more consistent with those “saved.”
We actually have an answer for this, as well. Given the “California” example that shows a very broken representation of what is left in GHCN and a very good coverage vis USHCN.v2 it is clear that what is kept is far worse than what is readily available but not used.
Hanlon doesn’t include evil in his paradigm, or does he?
Hanlon’s Razor does include malice (evil), but simply puts a threshold test in front of an appeal to malice. When “stupidity” is not sufficient to explain something, then you may attribute to malice. (As a restatement in the positive).
If there is “purpose” behind the deletions, it doesn’t look as though very many people would have to be privy to the decisions, maybe fewer than a dozen
I would expect the one “data set manager” (identified under the “California” thread: https://chiefio.wordpress.com/2009/10/24/ghcn-california-on-the-beach-who-needs-snow/ ) and a small committee of key “users” which would be Hanson’s GIStemp and ??? (Many folks use the GIStemp output, not the GHCN input, so there is this large filter between them and the meeting table…)
– this assuming that those making use of this inventory “don’t care.”
Or trust their product provider and don’t look up stream. Heck, I’ve been using the GHCN data for almost a year now doing a full investigation (with a forensic mindset) and only just figured out that the data set was broken.
This business of “consensus of thousands of climate scientists” seems nonsense in the likelihood that not even hundreds have sensed the biases of this data inventory.
True, but I would add to that the question: “If a million people have reached consensus based on bad data; what is the validity of their analyses, conclusions, and this consensus?” Every one of them as the implicit or explicit assumption “I conclude {FOO} IFF the input data are correct and unbuggered.” (Politicians love to bugger the input data and let everyone reach “consensus”. Look at the WMD driven push into Iraq and the present folks “without healthcare” where the numbers are fudged up by including illegal immigrants. “Gin up the numbers” is a known and widely practiced art in political circles.)
Then maybe that someone, or one of his cohorts, had discovered a function which could remove the UHI effects for application of these readings outside of UHI locations. So reliable reporting is more important than relationship of the measurements to the broader, dare I say global, environment. Again, idle speculation.
This is a nice description of what we would LIKE the world to be (and it might be that way from some other countries weather services) with a clean function adjusting for UHI. But it is not GIStemp.
I think that this bit is what E.M’s deconstruction of GISTEMP will tell us.
It is what I’m attempting to do. But given what I know of the way UHI adjustment works in GIStemp, thermometer deletions will make it worse, not better. And the UHI adjustment is an automated thing via looking aside at “nearby rural” thermometers and fudging the numbers to maintain a consistent relationship. Often the “rural” is not rural, the distance is so great as to render a supposed relationship moot, and the premise that a UHI correction is an offset when it is in fact a changing moving target is on the face of it broken. (If you have a dragster accelerating off the line, is the “offset” of the first second of travel really how far it will go in the second second?… and the tenth?)
So, IMHO, the UHI correction is broken and thermometer deletions will make it more so.
The most “plausible” explanation for Airport thermometers is just that they are available.
Every single pilot learns about “Density Altitude”. As it gets hotter, the air “thins out”. You can land a plane at Denver on a cool morning, then be unable to take off that afternoon if it gets hot. In the morning in cold air, you were flying at the equivalent of 6000 ft (for example) at STP (standard temperature and pressure) but when it got hot and the air density dropped, you are now at the equivalent of 9000 ft. If you have a service ceiling of 8000 on your aircraft with the load you have: You can not take off and you crash at the end of the runway trying. (Guess how they learned about Density Altitude…)
So every airport MUST have a thermometer and it MUST report temperatures near the runway. Pilots (and passengers like me!) demand to not crash because they did their Density Altitude calculation with a nice pristine climate thermometer in the forest near the airport while the tarmac was 3 C hotter. (I remember a discussion of this in ground school – it was that important – where the instructor cautioned us to allow a degree or two extra for the thermometer being badly located away from the actual runway surface.)
Now supose you are a climatologist. The FAA is getting boatloads of money for airports and putting in great new temperature gear. The military is getting boat loads of money and building military bases (which also need weather data… remember D-Day was weather driven…) and military airports with great temperature gear. When you ask for your money, you are given a hearty hand shake and told “They have thermometers, use theirs.”. Yes, I think that is largely what happened.
NOW, climatology has become a ‘hot topic’ (snicker ;-) but the historical data are based on “what they could get” and a lot of that was at the shiny new airports built during the growth of The Jet Age… NOW they are building CRN. NOW it’s too late.
Most of my flying (3500 hrs) was east of the Mississippi, with a few trips to Montana, Nebraska, New Mexico, and West Texas. I’ve been away from it for more than 20 years, but my memory is that I got the OAT from the gauge in the cockpit, and did the density calculation with the prayer-wheel. The only plane I flew where this could be critical was a C-421b flown at gross out of a 3300 foot runway (less than balanced field length) in East Texas – often and always scary although there was a road and no bumps around if we had to put it down. We had a Robertson kit on it, but it could still be dicey on the kind of days they have down there, even at msl of 50 if I remember right.
I have a friend who trained in C-140s and Luscombes in Denver area. My C-120 wouldn’t climb all that well above 6000 msl and I wondered what it was like with these things in Denver.
I take it you don’t mind these OT excursions. If you do, say so and I’ll try to control myself.
Again, thanks much for your detailed observations addressed to my many questions.
best regards, John
@j ferguson:
Don’t mind OT at all most of the time. Adds some human interest to the dull blocks of charts… I’ll only “step on them” if it becomes a “thread highjack” or an AGW Troll trying to use FUD to distract.
FWIW, some of us did our training time in smaller machines without the benefit of a built in Outside Air Temp gage. Heck, my glider time was in a machine with nothing electronic or electrical at all… Just wings, tail fins, and sky… and my balloon time, well, balloons don’t have generators and don’t like lead-acid batteries all that well. Our “electronics” were a hand held radio and a remote pyrometer in the top of the balloon to tell you how much of the “burn you just put in” had reached the top of the canopy. (It can take a minute or three… a “newby” mistake is: “Burn. nothing. BURN. nothing. BURN BURN BURN! .. Shoot up about 2000 ft above goal altitude waiting for balloon to cool down ;-)
For a hot air balloon, density altitude on a hot day can be a real beast… Full basket at cool dawn; going nowhere at 110 F in the afternoon… The still cool air in the early dawn is why so many balloon meets happen at “O-dark 30 in the morning!” (and why I haven’t been to one in years… but I digress ;-)
And, IIRC, the little Cessna we used had no gage either – though we were encouraged to put a thermometer in our flight bag … ( Map? check. Prayer wheel? check. Flashlight? check. Cooking Thermometer? what?… hey, they were more durable than glass… Though my favorite is a nice metal one for photography chemistry. Very precise near normal room temperatures and very durable. The things you do when you are young and have dreams but no money…)
SideBar: For those non-pilots: The “prayer wheel” is a metal circular slide rule customized for flight calculations. I still have mine somewhere. They were / are commonly called a “computer” which, while technically true, always bugged me. At any rate, you do your pre-flight calculations and gas up with what ought to get you there, and take off. Part way in, you are not where you ought to be and “out comes the prayer wheel” while you start figuring your actual winds and drift, your real “made good to destination” and figure out how far the gas you have left will take you given what the “facts are in the air”… and you pray that the “gas remaining” and the “V made good” get to the airport before the “aw-shit happens”…
Yes, today most of that is done with electronic hand held things. But you are still encouraged to have a non-electric “prayer wheel” just in case you run out of batteries or get hit by lightning and the electronics fry (which does happen some times…)
It is partly that training to “Calculate, then plan, then in the air re-calculate, and re-calculate, and check and re-check, then re-calculate” that caused me to have a “they do what?” moment with some of what the climate folks do. If they were pilots, they would be in a charred pile of wreckage right now. And probably a few thousand miles off their flight plan…
UPDATE: A foggy memory works it’s way to the surface… Not only did we use the “airport temps” for the preflight calculations, then check the temperature again at the plane to see if we need to re-do them, but: On Approach, you were to radio ahead and get temperature data (those of us without radios were encouraged to phone before departure and “use conservative calculations”) and then figure out:
a) Can I land? You might be flyable at 150 knots, but fall like a rock at landing speed…
b) If I land, can I get off again? You can often land at a higher density altitude than you can take off again.
I remember the instructor telling stories that at some high mountain airports they regularly get sight seers land, only to find out they can not depart. So the family and luggage take the taxi “down slope” somewhere and the pilot drains enough fuel (or does not fuel up) until the bare airplane can take off to meet said family at the lower airport… Either that, or wait until it is really cold in the dead of night…
And the in-cabin OAT gage doesn’t help with that “on approach” calculation, so airports have thermometers.
Odd, E.M.
I don’t think I ever flew anything without an oat except the Schwiezer I-34 and I’m not sure about that. I have to assume that your training was all adjacent to mountains and so would have been focussed on altitude density issues more than my midwest air school.
I don’t think I ever once thought about temperature enroute other than worrying about whether I needed to lighten up the departure load at the next stop for runway length and add a leg to the trip due to reduced fuel. Oh, and carb icing, of course.
Somehow, I had always thought that the air-speed indicator worked same as wings. It counted molecules, so if you flew indicated speeds, they would work wherever the plane could fly. But ground-speed on landing would go up a bunch where the air density was reduced.
In 1974 I was out in San Jose and rented a 150. I was told not to fly it to an airport in the Sierras that I think was named “Meadowland” or something similar. Of course first thing I did was check the chart, I think field elevation was 6500 msl, but I could be way off. So I didn’t go there.
john
j ferguson
Odd, E.M.
I don’t think I ever flew anything without an oat except the Schwiezer I-34 and I’m not sure about that. I have to assume that your training was all adjacent to mountains and so would have been focussed on altitude density issues more than my midwest air school.
BINGO! In the foot hills of the Sierra Nevada, and everyone wanted to “Fly to Tahoe”. Look up “Truckee Tahoe airport”… and then realize there are several higher than that. BTW, the first plane I went up in was cloth and hand propped… Don’t remember any radios or lights … think it was “daylight magnetos only” but I was about 15 then, and it was “the real old job”…
http://www.truckeetahoeairport.com/
I don’t think I ever once thought about temperature enroute
Here, it can be fogged over and 50 F at 0 MSL at departure, and 100F at 6000 to 7000 ft MSL at destination. It’s “an issue”…
Somehow, I had always thought that the air-speed indicator worked same as wings. It counted molecules, so if you flew indicated speeds, they would work wherever the plane could fly.
I believe that is correct.
But ground-speed on landing would go up a bunch where the air density was reduced.
And that’s the problem. You are used to “reduce to 80 knts for landing” and find out that it ought to have been “reduce? what do you mean reduce?” at 100 ft over outer marker… And your prop doesn’t work as well in the low density altitude either… or your engine…
In 1974 I was out in San Jose and rented a 150. I was told not to fly it to an airport in the Sierras that I think was named “Meadowland” or something similar. Of course first thing I did was check the chart, I think field elevation was 6500 msl, but I could be way off. So I didn’t go there.
That sounds like the one (and the plane!) that we used for our training… though I thought the altitude was higher. We were required to plan the route and arrival / departure. Substantially everyone would get stuck on the density altitude problem… You could land, though you came in real hot, but then you were stuck. BTW, where I was located at the time was about 200 miles closer to the mountains, so it was a really big attractor… IIRC, the darned thing would barely get off the ground at sea level with 2 guys of 200 lbs each and a load of fuel… at several thousand MSL, it was hard to lift off with a heavy pilot alone…
Yeah, here it is:
http://en.wikipedia.org/wiki/Cessna_150#Specifications_.281977_Cessna_150M.29
Useful load of 492 pounds.
So two big guys, a flight bag, luggage, and… Sorry, no ability to take off above a couple of thousand msl on a hot day.
And yes, it was a fairly frequent thing “in real life” too. Which is why you got the ‘warning’. It’s about 20 msl in Sacramento to about 7000+ msl up slope in about 100 miles. So you were about 1 flight hour from “aw shit” and everyone wanted to go ‘look at the mountains’… The number of “weekend warriors who would do a 1 hour “hop and pop” was large, and then one day they would decide to go East…
On the other hand, our ‘western’ training for thunderstorms consisted entirely of: “Don’t fly near a thunderstorm, its a bad idea and the downdraft can crash you.”
They did mention that if we ever went to the midwest we would want to know more about them ;-)
But anyway, back at the thermometer issue:
Airports need thermometers, so airports have thermometers, so the climate guy gets stuck with “Money? Why don’t you go use the thermometer at the airport? They already have one they use for weather reporting.”
It all comes down to weather is not climate, and we have a ‘weather temperature reporting system’.
john (j ferguson), I know that you weren’t trying to be obtuse. I was just wondering out loud why on earth scientists would select the worst locations possible for trying to pick up a signal for AGW. It makes no sense unless the scientists (and their bosses?) have ulterior motives.
I soloed in a Piper Tomahawk a long time ago but don’t remember doing much, if any, in the way of air density calcs even though it was hotter than hell at the airport. Maybe it was something to do with the altitude (50 feet above sea leve). lol.
One thing that I do remember at the airport is that the highway just before the runway was a lot like an elevator. Every time you flew over it on approach in the summer, the plane would catch a decent and very noticeable updraft. To me, it was and still is anecdotal evidence that large areas of tarmac make lousy sites for anything to do with temperature.
REPLY: “Hot air balloonists are extraordinarily aware of this (and I was ground crew for a lot of years. My only ‘flight as balloon pilot’ was inside the Hangar One at Mountain View NAS! It’s one BIG hanger! Oh, and I think my FBO just was really tired of retrieving HIS airplane from that 7000 ish msl ‘honey pot’ upslope about an hour and wanted to make Darned Sure he didn’t have to go do it, AGAIN… IIRC, his wife was the ‘120 lb pilot’ who could fly it out of where it ought not to have been. Me being about 200 lbs may have also gotten me a bit more attention. Lets see, 480 useful load, minus 200, so 280 lbs, minus passenger, adjust for altitude… Yup, HE’S going to be The One 8-| -ems”
Harold, the only conceivable reason for using airport locations might be reliability of reporting. I have such confidence in our bureaucrats that it is not at all hard for me to imagine someone thinking, “We get the airport reports every day and in our format without fail. Why should we fool with the Ag Schools and country librarians?”
I’m trying to think of an analogous confusion of means and goals, but can’t. Maybe throwing the baby out with the bathwater.
Has anyone asked the keeper of the V2 inventory why they dropped all these reporting stations even though they are still reporting?
j ferguson
Has anyone asked the keeper of the V2 inventory why they dropped all these reporting stations even though they are still reporting?
And that, IMHO, is the literally $Trillion Dollar Question …
(Their name and contact information is up under the “California” thread..)
I’ll take a shot at it. I can be very humble. I wouldn’t be at all surprised that this site is being monitored, maybe as much as anything, to see what you come up with.
REPLY: “At this point, I’m fairly certain it is. The ‘4 in California at the Beach’ posting caused a lot of traffic. I’m still waiting for it to hit the ‘real news’, but that will take someone else ‘taking ku’ with the information. Many of the folks were ‘looking but not talking’. That is a signature behaviour of what happens when the AGW folks have been ‘caught out’ on a whopper. Yes, some percentage is the skeptics just reading. But the ratio changes… Frankly, that’s part of why I make code and methods public. So that it isn’t about me, it’s all about the data and what they say. Monitor me all you want, there are 1000 folks with the data and methods. And the truth just is. -ems”
E.M., did you ever buy any CRAY (the stock) when it was young?
Harold Vance
E.M., did you ever buy any CRAY (the stock) when it was young?
Nope. By then I’d figured out that the problem space was more or less a bell curve. Low compute load stuff on one end. Near infinite compute load stuff on the other end.
Over time, compute capacity of any given “marketing size” or “market segment” of machine increases (i.e. desktops, minis, mainframes, supers: Each get higher performance and start to consume some of the problem domain serviced by the larger size in prior years).
That means that over time, the part of the bell curve of the problem space that can be addressed by any given “marketing size” or “market segment” of the computer industry increases on the small end and the part it can not address shrinks on the big end. But the cost of using a Super or Mainframe for a trivial compute problem is very high; so the mini or the PC takes the low end of the problem space market due to lower costs.
Basically, you can imagine a vertical line starting at the left, slowly moving to the right, dividing that bell curve. On the very far right is “not computable with machines today”, on the left is “supercomputer needed” As that line moves to the right over time, the “not computable” space gets smaller and smaller and the “supercomputer space” gets larger. (At the start of time, everything needed a “supercomputer” of the day, the Univac et. al.).
When the line gets to about 1/2 way to the peak of the bell, add a new line at the left. The “Mainframe”. When it reaches 1/2 of the way up to the peak, add a line labeled “Mini Computer”. Then one for personal computer…
Now the bell curve is asymptotic to zero on the right side. There is always some class of problem that is an “infinite compute” problem. (Primes. Golomb rulers.) but the part of the problem space that needs a ‘supercomputer’ just keeps shrinking as you move past the peak. A bit later, the same thing happens to mainframes. (Remember “Amdahl” and the other Mainframe Clones? Where are they now?…).
Eventually the same thing happens to the Mini makers (thus the troubles Sun has had and why it survives now mostly due to the software part of the company, and added a Sun Workstation based on an Intel chip). Anyone remember the dozen or so OTHER mini-makers that are now gone? Tandem was eaten by HP (who will, long term, survive, but by making more things with an Intel chip in it (and printers…), and less “mini computers”. HP is remarkably facile at shifting with market segment changes. They are long term a great company.)
So I saw that the “end game” is a desktop machine that could do 99% of everything folks were buying a Cray to do and, well, it’s a bad idea to buy into a shrinking market… So I held a chunk of Apple instead. Not a bad choice at the time.
Then Cray got merged with SGI (speaking of hot minicomputer makers that fell to the sliding of the problem scope dividers… and then ran the wrong way to the Big Iron end via buying Cray when they could have made an SGI PC instead and done better…), then they decided to join the herd of folks using a gaggle of PC chips to make a high performance compute cluster in a box…
And the “supercomputer makers” will survive doing that, but it is in that small tail: off to the far right of the total compute problem market space.
Interestingly enough, this trend continues to today!
But now we need to add a line for “hand held compute gizmo”. So we have RIMM with the Blackberry, and Apple with the various iPod, iPhone and iWhatzit things doing ever more web surfing, email, et. al. And what are the hot stocks? The hand held gizmo folks. And what are the ones not moving? Microsoft and desk top clone makers… Yes, the desktop folks are on the top of the bell curve now, but …
You must move to the small end of the curve as new technologies start to consume your market segment, or you will be pushed to that crowded “Big Iron” end of the curve and be fighting for your life against all the other folks who end up there, in an ever shrinking pool…
I know, far too long an answer. But it gives you a tool to always know what company will grow and which will shrink or die…
SIDEBAR: Linux vs Microsoft, and why I like Linux:
I do not want this to turn into a thread highjack for religious wars of MS vs Linux. I’m only putting this here as an explanation for folks do not understand why Linux geeks do not like MS very much.
Microsoft is well aware of the astounding growth of compute power over time (a double, roughly every 18 months, per Moore’s Law). They have a policy (as I see it from their actions) of assuming that growth of capacity exists for THEIR use, not yours. You can just go buy a bigger machine and they get to use up that capacity growth. So they write code that is very “fat” (large disk space) and terribly inefficient; but it can be easily written with not much labor that does not have to be very attentive. Now they are not the only people doing this, but they do it more than most. The term of art in the industry for software that gets bigger and fatter and less efficient over time is “Code Bloat”. So because they want to use more automated tools and have poorer programming technique, year over year, (which costs them less…) you get to spend more on hardware. Even though a new desktop machine has more compute power than a SuperComputer of just a few years ago, it will be “too slow” with the next release of Microsoft “Product”. Fundamental Truth: “Bad Software can consume all the growth of capacity of Good Hardware, and then some. -ems”
I resent that arrogance and I am offended by that wasteful treatment of hardware.
Linux, too, has code bloat in some parts. Not nearly as bad as MS does, and mostly focused in the newer graphics oriented bits. More importantly, what there is gets constantly polished to make it less of an issue. And, any time you wish, you can “drop down” to the older non-graphics parts. You can use the hardware directly with the very small, very fast, very efficient “unix like” tools of the command line interface. The stuff you see me post here. What this means is that I can have the flashy glossy GUI (Graphical User Interface) AND whenever I want it, drop down to the level where my machine lets me do what took a Cray just a decade or two ago. I have my “supercomputer” whenever I want it.
Linux is respectful of the money I spent on hardware and lets me have use of all of it whenever I want. Linux does not demand that I go throw more money down the toilet because there is a new release and someone else wanted to suck up those computes to lower their software development costs via bad technique. And that is why I can run GIStemp on a box that started life as a x486 box with all of 128 Meg of memory (and have lots of capacity to spare).
So I use MS products when I’m forced to do so (and especially if I’ve been given a copy one or two release levels back that “is no good anymore” to the prior owner due to a new MS release… “Free is good. -ems”). You just will not ever see me go buy a new computer to run some new MS “stuff”, nor will you ever see me go buy a copy of MS “stuff”… it will be free to me in a year or two anyway…
Now, Linux can not run an MS Excel spread sheet (yes, some emulators exist, I’m dodging that discussion). To some of us, this is a feature…
(Obligatory mention of Apple: You can drop down to the “Mach” level of the Mac machine via the terminal window and get almost all the same Unix / Linux tools and benefits. The present class of Apple hardware IS supercomputer class is so many ways… but with that delicious Apple GUI and with commercial software available. And that is why my laptop that I use as a front end to my Linux compute engine has a couple of orders of magnitude more capacity… The only reason I’m not running GIStemp on it is that the spectacular security on the Mac makes things like becoming “super user” and installing compilers a pain. I can do it, but it’s easier to just set up the old Linux box for that stuff…)
Amazing. I didn’t have much faith in temp measurements after reading WUWT for long time, but would never imagine the extent of the current corruption. I think your posting needs way, way more exposure. Best regards.
REPLY: [ Thanks! I can only make the posting. It is up to others to choose exposure. But I do what I can. -ems ]
In 1990 IPCC was set up, Berlin Wall came down, the D.E.W line was shutting down, possible reasons for the Canadian lose of “thermometers” especially in the far north.
RPLY: [ Possible, but I have a hard time believing that there was not one thermometer in The Yukon and The Northwest Territories that was not part of the DEW line… If anyone knows of a Canadian BOM web site with all their thermometers in it, t’would be an interesting contrast to GHCN right now. -E.M.Smith ]
Worked at Eureka, 1979-80! very lovely spot in summer, but yes, winters were not fun! In Feb. 79 we set the North American record cold month of -47.9C which i believe still holds true today(i arrived in march thankfully!) Of course back then, the term was called Global Cooling! Had a ride over to Alel Heilbert Island to bring some mail to a couple of Glaciologists (helicopter ride over), and they told us the glaciers were receding…yes, even back then during the global cooling scare…yes, and the other weather station up there Isacchen ? was recently closed back then and it was a much harser location. Interesting that they have dropped Alert from the records, as they still record temps at the top of Ellesmere Is. as does Grise Fiord at the bottom of the island.
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According to Environment Canada there are at least 168 operating weather stations in Northern Canada as of 2009. These were all north of 60 degrees – located in the Yukon, Nunavut, and Northwest Territories.
http://www.climate.weatheroffice.ec.gc.ca/advanceSearch/searchHistoricDataStations_e.html?searchType=stnProv&timeframe=1&lstProvince=NWT+&optLimit=yearRange&StartYear=2008&EndYear=2010&Month=1&Day=7&Year=2010&selRowPerPage=25&cmdProvSubmit=Search
So, there appears to be adequate coverage – and as another poster pointed out there are a lot of dots representing northern Canada on:
http://www.ncdc.noaa.gov/oa/climate/ghcn-monthly/caption.php?fig=fig1
I suspect if you count them they will total around 168…
It is difficult to get the data online from Environment Canada – at least I haven’t figured out how to get the xls files they claim are there – to do a check on the data, however the Canadian meteorological readings are generally considered to be amongst the best on the planet.
Whether the data has been correctly interpreted is another question, but there are many weather stations in northern Canada.
REPLY: [ As I’ve pointed out many times, but I guess one more is needed: Yes, folks gather the data. Then it goes to NOAA / NCDC who promptly drop it on the floor. The “GHCN : Global Historic Climate Network” data set is what is used by the English folks at the University of East Anglia to produce HadCRUT (their temperature series), by NCDC to produce thier adjust temperature series, and by NASA / GISS via GIStemp to produce their data series. The whole point is that the deletions from GHCN cause ALL the major data series (and thus all the research based on them) to be problematic at best and flat out wrong at worst. -E.M.Smith ]
This is what Phil Jones from the CRU calls “value added data’?
This is fraud, there is no value in data that has been subject to such blatant manipulation to fit a predetermined outcome.
There are about 50 communities in the NWT and Nunavut which have airports and have had continuous temperature readings for decades.
There are 23 or so airports in Nunavut with scheduled flights.
http://en.wikipedia.org/wiki/List_of_airports_in_Nunavut
There are about 10 in NWT with sched flights
http://en.wikipedia.org/wiki/List_of_airports_in_the_Northwest_Territories
There are 3 or 4 in Yukon with sched flights
http://en.wikipedia.org/wiki/List_of_airports_in_Yukon
There is no reason to drop to one thermometer other than to achieve things with the data. ;)
I have been to Eureka many times and would like to make a couple comments.
First the coordinates:
Your source is EUREKA,N.W.T. 79.98 -85.93
If you multiply .98 by 60 you get 58.8 which = 79.58.8 for lat
Also multiply .93 by 60 you get 55.8 which = 85.55.8W
Environment Canada lists the coordinates as 79° 58.800′ N and 85° 55.800′ W, which is the same as your source.
The N.W.T. should be NUNAVUT, but that has only been in effect for 10 years so they likely haven’t changed it yet.
Second: The dome you refer to is where they launch the Radiosonde balloons. Twice a day they send a balloon with a radio transmitter into the wild blue yonder. It has nothing to do with the ocean. And yes the weather recording instruments are near there.
http://en.wikipedia.org/wiki/Radiosonde
Finally they only call it the garden spot because it has growth that resembles plants. A lot of the high arctic is covered in gravel so to see some moss is a big deal. The high 1971-2000 was 8.8 degrees C – not good tomato growing weather!
However, close to Eureka there is some petrified wood and geologists say that there were trees there at on time.
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At high school in New Zealand in the mid 1950s, I belonged to the Air Training Corps (ATC) and was mad keen on aviation, so did all the flying I could in anything I was allowed into – DH Tiger Moths, DH Rapides, etc. Everybody I mixed with knew that Weather Stations were sited at airports, but in those days of smallish regional airports with one tarsealed runway and the remainder a very large area of mown grass and scheduled commercial air traffic limited to the occasional liveried National Airways Corporation DC3 wandering in and out, airports were an essentially rural environment. In high school on hot days, we knew the temp reported on our local radio station at noon was taken at the airport and was always way lower than the school’s science department weather station readings as the school was in the middle of an urban heat island, although that term had not been coined yet. Common sense and our own perceptions told us it was always warmer in town.
I suspect that, as the world advanced, as airports grew, jet propulsion became the norm, airfields were concreted over but officialdom kept on using those handy airport weather stations and saw no need to shift them or build new ones in the rural hinterland – after all, flight engineers on commercial aircraft need a reasonably accurate temp over the runways to calculate fuel/passenger loads on takeoff and who cares if the temps are not accurate for the surrounding area. In those days, weather was just weather and we took what we got; everybody in our town in an agricultural area grumbled about forecasts never being correct but we all realised how difficult the science was as we were very aware of the small size of our island nation compared with the vastness of the surrounding Pacific Ocean. Most of us knew that a major problem the Axis forces had during WWII was lack of good forecasting as they were isolated in Europe from good weather information from upwind, so to speak.
In those simpler times we all knew the impracticabilities of siting a meaningful number of weather stations in the ocean – it was just too unimaginably vast to even consider. Our government made do with outlying weather stations in Antarctica, Campbell and Roull Islands down in the deep South and from our even smaller Pacific neighbours . We also knew that various bird species such as Blue Herons got blown over from Australia from time to time, and the smoke from seasonal Aussie bushfires would turn our sun orange and stink up the atmosphere and there was nothing we could do about it.
Until computers became common equipment in the world; suddenly us peasants were bombarded with claims that all of the uncertainties in life would be unravelled and made plain with the right programme and a sufficient application of grant monies. It was mostly snake oil, of course, and some of us realised this, but Bill Gates beame incredibly rich and convinced governments that everybody needed a computer otherwise they would never have a share of the world’s goodies.
Scientists, good and bad, have been basing careers on ‘computer modelling’ ever since; okay for something with limited and observable parameters but the earth and its multitudinous systems is way too big and way too complex and way too difficult to observe accurately to be knowable, despite the awsome computational power of Cray supercomputers and the like. The fraudsters who have promoted CAGW for their own nefarious ends overlooked a few factors in their scheming, however; the insatiable human needs for truth, freedom and to communicate. Add the communicative and information-disseminating power of the internet to these factors and truth and freedom will win. There will always be Bad People. And there will always be Good People. The Good Guys appear to be winning. Science can never be trusted if it is based on secrets and lies.
I firmly believe that we are seeing the birth of a new world order based on openness, honesty and truth and Chiefio, and all the other seekers and communicators of knowledge, truth and freedom are the vanguard of that movement.
Thanks from me,at least!
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You are dead on in our claim about canada being a “warm year up north” george Monbiot made the claim that Canada was 5 to 10 degrees above normal in the north. I looked at the temperature data for Alert which is a decent proxy for the North and from environment Canada’s website it showed a normal winter, Monbiots claim was 5 to 10 degrees off.
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This is some interesting data that I will need more time to mull over but…
I work in the Northern part of Canada and have been wondering why we keep hearing about how much warmer it is. We don’t feel warmer, nor are the winters milder or the summers hotter, but those with tall foreheads in the south keep telling us we are warmer. Your site may just explain this phenomena…we are not warmer we are just being fed a large helping of male bovine escheatment.
BTW if I see the guy who’s killing our thermometers I’ll turn him into Polar Bear bait. I gotta go up to Eureka to do some work on the generator soon so I’ll keep an eye out.
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“Eureka has been described as “The Garden Spot of the Arctic” due to the flora and fauna abundant around the Eureka area, more so than anywhere else in the High Arctic. …”
Thanks for the laugh. My brother-in-law was the senior government official at Eureka in the 70s (population about 8). He built a small greenhouse on the side of the building using some plastic sheets and grew a few tomato plants in it. He then designed a postmark for Eureka (he was also effectively the postmaster) which had ‘Eureka, Garden Spot of the Arctic’ around the circumference in reference to his half dozen tomato plants. I think we still have an envelope around the house with this on it. The design was also put on some tee-shirts. If you were to met him, you’d quickly realise that this is how his sense of humour works.
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Gentlemen:
There ARE stations in BOLIVIA that record temperature precipitation &c.:
http://www.senamhi.gov.bo/meteorologia/recorddeinformacion.php
They are, of course, located at the airports but many airports have very little or no traffic, POTOSÍ e. g.
In BOLIVIA there are twelve mountains > 6’000 mts. AMSL, there is the vast ALTIPLANO > 3’600 mts. AMSL and there are the tropical lowlands, i. e. many locations where interesting and uncorrupted (100 % “rural”) weather data could be collected.
Given these conditions, the sparse population and BOLIVIA’S area of 1’100’000 sq. kms. I don’t think that any serious investigation/study can neglect the existence of such considerable and varied area.
(I apologize for assailing the irrefutable prophesy of the climate catastrophe.)
Saludos, don Matías.
S 17.35775°, W 066.14577°
2’740 m AMSL.
@ Don Matias
We here that have been discussing this at this site have found multiple sources of Bolivian Data, the problem has been getting NOAA to open their eyes to it. They seem to have a strange aversion to looking at all the available data.
I found a case up in Canada when looking at comparing the gridded GIStemp trend maps. NOAA cuts off the data from Alert Canada at 1991 (which is the source of GISS data) however when you access the Candian Governments website you find data upto 2005. Strange that I can find 14 years worth of data from there but the people soaking up my tax dollars at NOAA can’t.
You can see the fallacy of GISS Infilling at this link to my blog where I show the ramifications of that 14 years of not used data.