GHCN – Pacific Basin, Lies, Statistics, and Australia

Pacific Basin -  Fine Without Australia

Pacific Basin - Fine Without Australia

Orginal image.

The “red bits” are elevation, not temperature. The Pacific Ocean and even over into the Indonesian part are steady “islands of stablity” – once you take Australia out of the picture…

Another UPDATE: I’ve added a table of “Pacific without Australia and without New Zealand”. It’s dead flat. The Pacific Ocean an attendant islands are NOT participating in “Global” warming. Changes of thermometers in Australia and New Zealand are the source of any “change”.

UPDATE: I’ve added a bit on Indonesia down at the bottom. In making an extract of the Pacific Basin focused on islands (minus the cold New Zealand); I was wondering if Indonesia looked more like a Pacific Island or more like the Indian Ocean / Southeast Asia area. While it ends up looking very much like a Pacific Island group in temperature stability, there is a severe dropout of data in the early 1990s. I would surmise that some major event happened then (Tsunami? Political unrest? Both?) leading to a complete loss for a couple of years.

In http://chiefio.wordpress.com/2009/10/23/gistemp-aussy-fair-go-and-far-gone/

we saw that in 1992-93 there were 401 thermometers deleted. (More precisely “thermometer records” – a thermometer and modification history flag. It might be one instrument with two different “adjustement” histories to the data record.).

When I went looking at “continent code” 5, that includes Australia, Micronesia, Polynesia, Indonesia, all the “esias”… I found a curious thing. The whole group together had a very similar “bias” figure. In fact, the thermometer deletions looked suspiciously familar:

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
--------------------------------------------------------------------------
1992 23.5 23.8 23.0 20.4 17.4 14.8 14.6 14.9 16.4 19.1 22.3 23.6 19.5 531
1993 24.6 24.3 23.6 22.5 20.9 18.2 19.1 19.3 20.2 21.2 23.0 23.7 21.7 130

Exactly 401 thermometer records deleted.

So, since we know Australia has already been “cooked” by changes of thermometer locations, I decided to do “Region 5 minus Australia”. (Why bother with “Region 5 as a whole” when we already know that 4/5 of it is the Australian record.?)

Pacific Basin -  Fine Without Australia

Pacific Basin - Fine Without Australia

Lies, Damn Lies, and Australia

Here is the “thermometers by latitude” decades chart for the whole of “Region 5″ for comparison.

[chiefio@tubularbells analysis]$ cat Therm.by.lat5.Dec.LAT 
       Year SP -40   -30   -20   -10     0    10    20    30    40   -NP
DecPct: 1839   0.0   0.0   0.0   0.0   0.0 100.0   0.0   0.0   0.0   0.0 100.0
DecPct: 1849  81.8   0.0   0.0   0.0   0.0  18.2   0.0   0.0   0.0   0.0 100.0
DecPct: 1859  27.8  72.2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0 100.0
DecPct: 1869  18.3  75.0   0.0   0.0   6.7   0.0   0.0   0.0   0.0   0.0 100.0
DecPct: 1879  16.9  71.1   1.6   0.0   8.0   2.4   0.0   0.0   0.0   0.0 100.0
DecPct: 1889   8.4  69.7   9.7   2.1   4.2   3.6   2.3   0.0   0.0   0.0 100.0
DecPct: 1899   9.0  62.4  15.3   4.7   4.3   0.9   3.5   0.0   0.0   0.0 100.0
DecPct: 1909   7.7  62.4  17.9   6.7   2.7   0.4   2.3   0.0   0.0   0.0 100.0
DecPct: 1919   5.0  56.2  25.1   9.8   1.6   0.7   1.6   0.0   0.0   0.0 100.0
DecPct: 1929   4.8  56.0  24.0  10.6   1.4   1.4   1.8   0.0   0.0   0.0 100.0
DecPct: 1939   5.1  51.8  24.7  10.8   2.2   3.8   1.6   0.0   0.0   0.0 100.0
DecPct: 1949   5.9  46.9  29.4  11.4   2.6   2.3   1.3   0.0   0.0   0.0 100.0
DecPct: 1959   5.9  33.4  24.0  12.6   7.2   8.5   8.1   0.3   0.0   0.0 100.0
DecPct: 1969   5.7  29.2  19.9  12.1  15.0  10.7   7.3   0.2   0.0   0.0 100.0
DecPct: 1979   5.7  35.2  22.8  12.9   9.2   8.3   5.6   0.3   0.0   0.0 100.0
DecPct: 1989   5.9  36.7  24.3  14.4   8.2   6.6   3.6   0.3   0.0   0.0 100.0
DecPct: 1999   6.0  33.3  23.3  14.2   6.1  11.4   5.4   0.4   0.0   0.0 100.0
DecPct: 2009   6.0  20.2  18.2  14.2  11.1  21.5   8.5   0.4   0.0   0.0 100.0
 
For COUNTRY CODE: 5
[chiefio@tubularbells analysis]$ 

Were we can see that most of the bias for “1869 Decade Ending” on comes out of the “shrinkage” of the “below latitude 30″ band. Also notice that big jump in band 0 and 10 in the 1999 and 2009 decade endings. But we also know that “below latitude 30S” has mostly Australia and New Zealand. So unless New Zealand had one heck of a lot more thermometers than one would expect, these changes reflect what we’ve already seen as Australian deletions. But just to be sure, what does New Zealand look like?

Look at Therm.by.lat507.Dec.LAT (Y/N)? y
 
       Year SP -40   -30   -20   -10     0    10    20    30    40   -NP
DecPct: 1869  78.6  21.4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0 100.0
DecPct: 1879  80.8  19.2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0 100.0
DecPct: 1889  76.2  23.8   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0 100.0
DecPct: 1899  78.3  21.7   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0 100.0
DecPct: 1909  84.4  15.6   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0 100.0
DecPct: 1919  83.3  16.7   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0 100.0
DecPct: 1929  80.0  20.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0 100.0
DecPct: 1939  80.8  19.2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0 100.0
DecPct: 1949  72.2   9.3  18.5   0.0   0.0   0.0   0.0   0.0   0.0   0.0 100.0
DecPct: 1959  65.3  21.6  13.1   0.0   0.0   0.0   0.0   0.0   0.0   0.0 100.0
DecPct: 1969  61.5  24.8  13.6   0.0   0.0   0.0   0.0   0.0   0.0   0.0 100.0
DecPct: 1979  58.8  29.3  11.9   0.0   0.0   0.0   0.0   0.0   0.0   0.0 100.0
DecPct: 1989  60.8  25.7  13.5   0.0   0.0   0.0   0.0   0.0   0.0   0.0 100.0
DecPct: 1999  56.6  34.9   8.5   0.0   0.0   0.0   0.0   0.0   0.0   0.0 100.0
DecPct: 2009  51.8  36.1  12.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0 100.0
 
For COUNTRY CODE: 507
 

“Below 30S” is growing, but at the expense of “Below 40S”. Not much help to explaining the shrinkage in the Pacific Basin. (Though we note in passing that there is an “odd” discontinuity in the 20S band in the decade ending 1999. A “dig here” for later…)

I’ll give you the “Money Quote” here, rather than making you wade through all the charts of data to get to it: Without Australia, the Pacific Ocean Basin is not warming.

From about 1921 when the thermometer count reaches a “pretty good coverage” number forward into the 1980’s it is pretty much steady at 23C – 25C range. There is a “swoon” in the middle of WWII that I would expect was caused by the place being a battle ground and not too many tropical islands reporting, but plenty of Tasmania and New Zealand still showing up. And yes, the annual averages are running at 25x C now for a few years; but they did that in the mid 1960’s as well. (And 1938, even though there was not very good coverage and the Jet Age had not yet planted thermometers over tarmac at every tropical island in the pacific.)

So if you take out the Statistical Games played with Australia, the Pacific is a darned nice steady climate. And that is even with whatever that band drift was in New Zealand and the “Airport Heat Island Effect” left in!

“Ripper” was kind enough to make this graph which shows the relationships much more clearly. I really need to learn how to do that “graph thing” :-}

Pacific Basin - Ex- Australia Thermometer # and temps vs time

Pacific Basin - Ex- Australia; Thermometer Count and Temps

[chiefio@tubularbells analysis]$ cat Temps.5no501.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 20.2 21.0 20.1 19.1 17.7 15.6 15.5 15.8 17.3 17.7 19.2 19.3 18.2   9
1881 20.7 21.6 20.2 20.5 19.6 18.4 18.0 18.0 19.1 18.4 19.1 18.8 19.4   7
1882 21.5 20.9 21.3 20.5 19.0 18.3 17.6 17.5 18.9 18.8 19.7 21.2 19.6   7
1883 22.2 22.3 22.1 20.6 20.3 19.2 18.6 19.0 19.1 19.7 20.0 20.1 20.3   8
1884 19.8 20.9 21.0 20.4 19.4 18.8 18.2 18.9 19.3 19.8 20.2 20.5 19.8   8
1885 21.1 21.5 21.5 20.8 20.0 19.4 18.5 18.8 19.4 19.2 20.7 20.8 20.1   8
1886 21.7 22.0 21.9 21.9 21.2 19.6 18.3 19.5 20.2 20.8 21.2 21.0 20.8   9
1887 22.4 21.7 21.6 21.1 19.7 19.0 18.4 18.3 18.9 19.6 19.9 20.5 20.1   8
1888 20.9 20.6 21.2 20.7 20.2 19.3 18.4 19.2 19.4 20.3 20.0 20.4 20.0   8
1889 22.3 21.8 21.5 21.5 20.8 20.9 18.5 18.9 20.0 20.5 20.5 21.3 20.7   8
1890 21.6 22.0 21.7 21.7 20.3 19.7 19.0 19.4 20.2 20.7 20.4 21.7 20.7   9
1891 22.1 22.3 22.4 22.6 22.1 20.6 19.9 20.0 21.2 23.5 23.4 23.7 22.0  11
1892 23.3 23.8 24.1 23.5 22.2 21.9 20.7 21.0 20.8 21.8 22.2 22.1 22.3  11
1893 22.2 22.2 22.0 22.4 21.9 20.1 20.6 22.3 21.1 22.5 22.2 22.2 21.8  11
1894 22.3 22.0 22.0 21.4 20.7 20.2 19.2 19.2 19.5 20.8 20.8 22.0 20.8  12
1895 22.6 22.1 21.8 21.2 20.9 20.1 18.8 18.9 19.7 20.9 20.4 22.4 20.8  12
1896 20.8 21.7 21.5 20.8 18.9 18.8 17.8 17.5 19.6 19.9 20.5 21.9 20.0  10
1897 21.7 21.4 21.0 20.1 19.2 19.1 18.3 18.4 19.5 19.9 20.7 20.5 20.0  10
1898 21.7 21.0 20.2 20.2 19.1 17.7 18.2 18.4 19.4 19.9 20.0 21.3 19.8  10
1899 21.4 20.9 20.9 20.4 18.7 18.0 15.4 16.0 17.7 18.3 18.9 19.6 18.9   9
1900 20.1 20.1 20.5 19.9 18.1 16.5 16.2 17.2 18.7 18.9 20.0 20.7 18.9   9
1901 20.2 20.0 19.2 19.2 17.7 16.9 16.3 15.5 17.3 18.2 18.3 19.2 18.2   9
1902 20.4 20.1 20.3 18.8 17.3 16.6 15.4 16.0 16.1 17.2 17.9 18.6 17.9   9
1903 20.9 21.1 21.3 20.9 20.2 18.9 18.7 17.8 19.2 20.5 20.9 20.9 20.1  13
1904 20.7 20.7 21.4 20.5 19.6 18.9 18.2 18.3 18.9 20.0 20.1 20.8 19.8  13
1905 20.6 21.5 21.2 21.2 20.0 18.1 18.2 18.0 19.3 19.7 20.5 21.2 20.0  15
1906 21.3 21.5 20.9 20.3 19.4 19.1 18.7 18.7 19.7 20.3 20.5 21.6 20.2  16
1907 22.2 22.4 22.2 21.8 20.3 18.8 18.8 18.4 19.4 20.2 21.1 21.9 20.6  16
1908 22.1 22.0 22.0 21.5 20.4 20.9 18.4 19.0 19.8 20.2 20.5 21.2 20.7  16
1909 21.6 22.3 22.2 21.4 20.7 19.5 18.9 19.4 19.7 20.4 21.2 22.0 20.8  16
1910 22.1 22.7 22.1 21.2 20.5 21.2 18.8 19.2 19.9 20.6 20.8 22.0 20.9  16
1911 22.3 22.1 22.6 22.3 20.8 19.9 19.1 19.2 19.9 20.5 21.0 21.3 20.9  17
1912 22.1 21.9 21.9 21.7 20.6 19.8 19.2 19.2 20.5 20.7 21.1 21.8 20.9  17
1913 22.5 22.1 22.3 21.1 19.9 19.3 19.2 18.9 19.7 20.2 21.0 21.4 20.6  17
1914 22.4 22.5 22.3 22.1 20.7 19.6 18.4 18.7 19.5 20.6 21.2 21.6 20.8  17
1915 23.7 23.5 23.4 22.6 22.4 21.6 21.1 21.2 21.9 22.7 22.7 23.3 22.5  17
1916 23.1 23.8 24.0 23.2 22.0 21.9 21.1 21.3 21.4 21.9 22.8 23.3 22.5  17
1917 23.5 23.1 23.2 23.3 22.3 21.4 21.4 20.8 21.4 21.6 22.8 22.8 22.3  17
1918 22.7 22.8 22.8 22.7 21.9 20.9 20.1 20.8 21.3 22.1 22.3 22.4 21.9  17
1919 23.0 23.9 23.7 23.2 22.1 21.1 20.5 21.0 21.2 22.1 21.5 22.1 22.1  17
1920 22.2 23.1 22.8 22.5 22.0 20.8 20.7 20.3 21.0 21.7 22.0 22.9 21.8  16
1921 23.8 24.1 23.9 23.6 23.0 21.6 21.7 21.8 22.5 23.1 23.3 23.6 23.0  22
1922 24.2 24.4 24.1 24.0 23.2 22.4 21.8 22.2 22.6 23.4 23.4 24.0 23.3  23
1923 24.3 24.3 23.9 23.7 23.3 22.2 21.8 21.7 22.5 23.0 23.7 24.2 23.2  22
1924 24.5 24.8 24.7 24.6 23.8 22.9 22.4 22.8 23.3 23.4 23.9 23.8 23.7  23
1925 24.5 24.2 24.1 24.0 23.4 22.3 22.1 22.3 22.7 23.3 23.4 23.6 23.3  23
1926 23.8 24.1 24.0 24.3 23.8 22.6 22.2 22.3 22.9 23.4 23.5 23.8 23.4  22
1927 24.3 24.5 24.6 24.0 23.4 22.3 21.9 21.9 22.4 22.9 23.4 24.0 23.3  23
1928 24.6 24.6 24.7 24.6 23.7 22.3 21.9 22.5 22.9 23.2 23.6 24.2 23.6  22
1929 23.8 24.2 24.1 24.0 23.3 22.7 21.8 21.9 22.4 23.1 23.6 23.7 23.2  24
1930 24.6 24.5 24.8 24.3 23.9 23.0 22.6 23.0 23.1 23.4 24.0 24.4 23.8  29
1931 24.6 24.7 24.9 24.9 24.4 23.4 23.0 23.1 23.5 23.8 24.2 24.2 24.1  31
1932 24.3 24.8 24.8 24.8 24.3 23.5 23.3 23.3 23.6 24.3 24.5 24.7 24.2  34
1933 25.2 25.2 25.3 25.0 24.7 23.8 23.2 23.5 23.9 24.2 24.3 24.6 24.4  35
1934 24.6 24.8 24.7 25.0 24.5 24.1 23.5 23.6 24.0 24.4 24.8 24.9 24.4  36
1935 25.3 25.7 25.7 25.3 24.6 24.0 23.8 23.8 24.0 24.9 24.8 25.2 24.8  37
1936 25.1 25.5 25.3 25.5 24.7 24.0 23.5 23.7 24.0 24.5 24.7 25.0 24.6  37
1937 25.3 25.3 25.5 25.3 24.8 24.2 23.9 24.0 24.3 24.6 25.1 25.3 24.8  39
1938 25.7 25.9 25.8 25.8 25.1 24.5 24.2 24.2 24.3 24.7 24.9 25.0 25.0  41
1939 24.3 24.6 25.0 24.6 24.0 23.4 22.7 22.9 22.9 23.5 24.3 24.3 23.9  43
1940 24.6 24.5 24.6 24.3 23.8 22.8 22.5 22.5 22.9 23.4 23.8 24.2 23.7  47
1941 24.6 24.9 25.0 24.1 23.6 22.3 21.2 21.1 21.6 22.0 22.7 22.6 23.0  44
1942 22.7 23.1 22.7 22.2 21.1 20.2 19.2 19.3 20.3 21.1 21.5 22.3 21.3  30
1943 22.7 23.1 22.3 21.9 20.6 19.7 18.8 19.0 19.7 20.6 21.8 22.4 21.1  30
1944 22.8 23.0 22.7 22.0 20.6 19.2 18.8 18.8 19.3 20.1 21.0 22.0 20.9  28
1945 23.2 24.0 22.7 21.7 20.3 19.1 19.0 19.5 20.0 19.9 21.3 21.5 21.0  30
1946 22.9 23.0 22.6 22.2 21.0 20.4 20.1 20.1 20.2 21.1 21.4 22.5 21.5  39
1947 23.6 23.7 23.8 23.4 22.6 21.7 21.4 21.4 21.7 22.2 22.7 23.4 22.6  45
1948 24.0 23.6 23.9 23.4 22.8 22.0 21.5 21.3 21.9 22.5 22.8 23.3 22.8  53
1949 23.8 24.5 24.4 24.2 23.7 23.4 22.9 22.8 23.3 23.7 23.7 24.1 23.7  71
1950 24.6 24.6 24.7 23.7 23.4 23.6 22.3 22.5 22.6 23.0 23.6 23.8 23.5  80
1951 24.7 24.8 25.1 25.2 24.5 23.9 23.7 23.8 24.1 24.6 25.1 25.1 24.6 142
1952 25.0 25.3 25.2 25.3 25.0 24.5 23.9 23.9 24.1 24.5 24.8 24.8 24.7 146
1953 24.7 25.0 25.3 25.2 24.9 24.3 23.7 23.8 24.0 24.5 24.9 24.9 24.6 150
1954 25.0 25.2 25.2 25.1 24.8 24.2 23.7 23.7 23.9 24.2 24.3 24.5 24.5 152
1955 24.7 25.1 25.1 25.1 25.0 24.0 23.6 23.8 24.2 24.5 24.5 24.5 24.5 156
1956 24.8 25.0 25.1 25.3 24.8 24.4 23.8 23.9 24.2 24.7 24.8 24.8 24.6 164
1957 25.0 25.1 25.3 25.4 25.1 24.3 23.8 24.0 24.2 24.6 24.8 25.0 24.7 170
1958 25.2 25.4 25.5 25.3 25.1 24.5 24.0 24.0 24.3 24.8 24.7 24.9 24.8 172
1959 25.0 25.3 25.3 25.3 24.8 24.5 23.9 23.8 24.3 24.5 24.8 25.2 24.7 174
1960 25.2 25.2 25.4 25.5 25.3 24.6 24.3 24.4 24.7 25.0 25.2 25.1 25.0 206
1961 25.1 25.6 25.7 25.8 25.4 24.7 24.3 24.2 24.6 25.0 25.3 25.5 25.1 212
1962 25.4 25.3 25.6 25.7 25.7 25.0 24.6 24.4 24.8 25.4 25.3 25.2 25.2 221
1963 24.9 25.2 25.4 25.5 25.5 24.9 24.4 24.4 24.8 25.0 25.4 25.3 25.1 223
1964 25.7 25.7 25.7 25.8 25.4 24.9 24.5 24.6 24.9 25.0 25.0 25.0 25.2 225
1965 25.0 25.2 25.3 25.4 25.2 24.7 24.0 24.2 24.7 25.0 25.4 25.4 25.0 224
1966 25.3 25.6 25.7 25.8 25.3 24.7 24.4 24.5 24.9 25.1 25.3 25.2 25.2 229
1967 25.1 25.3 25.5 25.5 25.4 24.8 24.4 24.6 24.7 25.1 25.1 25.1 25.1 228
1968 25.1 25.2 25.7 25.5 25.4 25.0 24.5 24.5 24.8 25.0 25.1 25.2 25.1 221
1969 25.5 25.5 26.0 25.9 25.8 25.2 24.4 24.5 24.9 25.0 25.4 25.6 25.3 226
1970 25.7 25.7 26.0 25.9 25.6 25.1 24.6 24.6 24.9 25.1 25.3 25.4 25.3 222
1971 24.8 25.1 25.0 25.1 24.9 24.4 23.7 24.0 24.3 24.5 24.7 24.8 24.6 214
1972 24.7 25.1 25.1 25.2 24.8 24.1 23.9 23.9 24.2 24.6 25.1 25.2 24.7 218
1973 25.2 25.6 25.6 25.6 25.4 24.8 24.1 24.1 24.4 24.6 24.9 24.9 24.9 214
1974 24.7 25.1 25.0 25.2 24.9 24.2 24.0 24.0 24.5 24.6 24.9 25.0 24.7 214
1975 25.1 25.2 25.4 25.4 24.9 24.2 23.9 24.1 24.2 24.6 24.5 24.6 24.7 213
1976 24.5 24.5 24.9 24.6 24.1 23.4 22.9 23.4 23.4 24.1 24.5 24.7 24.1 158
1977 24.9 25.1 25.2 25.0 24.4 23.7 23.3 23.3 23.4 24.3 24.5 24.8 24.3 161
1978 25.1 25.1 25.4 25.2 24.8 23.7 23.6 23.1 23.7 24.1 24.5 24.9 24.4 164
1979 25.0 25.2 25.5 25.1 24.5 24.1 23.4 23.4 24.0 24.4 24.8 24.8 24.5 164
1980 25.2 25.4 25.3 25.1 24.6 24.0 23.2 23.5 23.8 24.4 24.4 24.7 24.5 163
1981 25.2 25.5 25.7 25.4 24.4 24.1 23.3 23.3 24.2 24.6 24.7 25.3 24.6 154
1982 24.9 25.1 25.1 24.5 24.0 23.8 23.1 23.3 23.5 24.0 24.7 24.8 24.2 122
1983 25.1 25.6 25.5 25.3 24.6 23.6 23.4 23.5 23.9 24.3 24.6 24.7 24.5 124
1984 24.9 25.4 25.4 25.2 24.7 24.2 23.7 23.7 24.3 24.3 24.7 25.0 24.6 125
1985 25.1 25.6 25.4 25.0 24.2 23.7 22.8 23.3 23.4 24.2 24.6 25.0 24.4 114
1986 25.3 25.3 25.1 25.0 24.4 23.6 23.4 23.2 23.6 24.4 24.8 25.4 24.5  89
1987 24.9 25.3 25.2 25.3 24.4 23.9 23.1 23.1 23.6 24.4 25.1 24.8 24.4 106
1988 25.5 25.7 25.7 25.3 24.7 24.1 23.6 23.7 23.9 24.5 24.5 24.6 24.7 104
1989 25.0 25.0 25.0 25.1 24.7 24.0 23.7 23.8 24.2 24.8 24.7 24.7 24.6 108
1990 25.2 25.7 25.8 25.5 24.9 24.2 24.0 23.4 23.6 24.5 25.2 25.6 24.8 104
1991 25.7 25.6 25.7 25.7 25.0 24.1 24.3 23.2 24.3 24.5 24.6 24.9 24.8 112
1992 25.0 25.4 25.4 25.3 25.2 24.9 24.1 24.1 24.1 24.0 24.4 24.6 24.7  85
1993 24.9 24.8 25.1 24.8 24.9 23.4 23.9 23.6 23.7 24.3 24.8 24.8 24.4  82
1994 25.1 25.3 25.2 25.0 24.4 22.6 22.9 24.2 23.9 24.3 25.6 24.9 24.4  81
1995 25.1 25.0 25.2 25.4 25.0 24.1 25.5 23.7 23.8 24.1 24.5 24.5 24.7  84
1996 25.0 25.0 25.7 25.4 24.5 23.5 24.8 24.0 24.6 24.6 24.3 24.6 24.7  91
1997 24.1 25.1 25.1 25.2 24.6 24.1 23.5 23.5 23.3 24.0 24.6 24.9 24.3  91
1998 25.0 26.0 25.2 24.6 24.9 24.2 23.6 23.8 23.7 24.4 24.4 24.8 24.5  81
1999 25.0 25.0 25.2 24.9 24.7 23.9 23.7 23.4 23.7 24.2 24.4 24.3 24.4  81
2000 24.9 25.3 25.2 25.1 24.2 24.3 22.4 23.0 24.3 24.7 24.3 25.0 24.4  84
2001 24.9 25.4 25.6 25.5 24.6 24.1 23.6 24.0 24.6 24.6 24.8 25.2 24.7  81
2002 25.5 25.5 25.9 25.4 25.0 24.8 24.4 23.8 24.1 24.2 24.2 24.7 24.8  80
2003 25.3 25.5 25.8 25.7 25.5 24.1 24.1 24.1 24.4 24.7 24.6 25.1 24.9  80
2004 25.8 25.7 25.6 25.9 25.4 24.7 23.9 24.2 24.0 24.7 25.0 25.2 25.0  86
2005 25.9 25.9 26.1 25.8 25.4 24.9 24.5 24.5 24.6 24.8 25.5 25.4 25.3  81
2006 25.6 25.7 26.0 26.0 25.1 24.7 24.2 24.3 24.4 24.5 25.2 25.3 25.1  83
2007 25.6 25.8 26.0 25.9 25.7 25.0 24.2 24.2 24.5 25.0 25.1 25.2 25.2  87
2008 25.4 25.9 25.4 25.5 24.9 24.7 24.4 24.5 24.9 25.1 25.5 25.4 25.1  95
     24.9 25.1 25.2 25.1 24.7 24.0 23.6 23.6 24.0 24.4 24.6 24.8 24.5
     23.8 24.0 24.0 23.7 23.1 22.3 21.8 21.9 22.4 22.9 23.2 23.5

For Oceana Country Codes "5xx" minus Australia CC 501

Though when I get time, I’m going to look into just what were those 6 stations added IN in 2004, what came out in 2005, and what are those 14 added in to 2009 as we get steadily more regular 25.xC readings. While I smell a rat in those changes, it’s a small rat.

A Brief Look At Indonesia

The “Thermometer Records By Lattitude” will not be very interesting, given that the whole place is “0” +/- a bit. So I looked at the temperature data and noticed a big gap:

1990 26.5 27.0 27.1 27.8 27.5 26.8 26.3  0.0  0.0  0.0 27.7 26.4 20.3  30
1991 26.6  0.0  0.0 27.2 26.9 26.8  0.0  0.0  0.0  0.0  0.0  0.0  9.0  27
1995  0.0  0.0  0.0  0.0 27.2  0.0 27.5  0.0 28.3 27.8  0.0  0.0  9.2  11
1996 26.3 25.8  0.0 26.8 27.7 26.6 26.7  0.0 27.1  0.0 27.0 27.6 20.1  14

We jump from 1991 to 1995. And the data on each side have a lot of holes in them, too. In this case “0.0” is missing data.

(I know, a bit sloppy since in theory it could be a valid temperature… a ‘future enhancement” request is in my work queue ;-) for this code. I’d originally made it to handle the entire data set where the average in any given year only has ‘missing data flag needed’ in the first couple of years back in 17xx and decided I could deal with those by hand or just “start time” in 1880 like GIStemp does.. It also works OK for regions and even major countries like Russia and Canada. Now I’ve “repurposed” the code for individual smaller countries and the “missing data” issue is much more of an issue. But for the tropics, it is valid to use 0.0 for “missing data” for now).

Inspection of the 1991 data showed a lot of holes in it (-9999 is the “missing data flag” in this file):

[chiefio@tubularbells analysis]$ grepmean ^503 | grep "1991"
5039603500011991-9999-9999-9999-9999-9999  278-9999-9999-9999-9999-9999-9999
5039607300001991-9999-9999-9999-9999  267-9999-9999-9999-9999-9999-9999-9999
5039610900001991-9999-9999-9999-9999-9999  284-9999-9999-9999-9999-9999-9999
5039616300121991  267-9999-9999-9999-9999  271-9999-9999-9999-9999-9999-9999
5039617100111991-9999-9999-9999-9999  277  274-9999-9999-9999-9999-9999-9999
5039622100001991  268-9999-9999  274-9999-9999-9999-9999-9999-9999-9999-9999
5039623700001991  255-9999-9999  270-9999-9999-9999-9999-9999-9999-9999-9999
5039629500001991  258-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
5039650900001991-9999-9999-9999  271-9999-9999-9999-9999-9999-9999-9999-9999
5039658100001991-9999-9999-9999  274-9999-9999-9999-9999-9999-9999-9999-9999
5039673900001991  259-9999-9999-9999-9999  264-9999-9999-9999-9999-9999-9999
5039674500001991-9999-9999-9999  285  283-9999-9999-9999-9999-9999-9999-9999
5039674700101991-9999-9999-9999-9999-9999  274-9999-9999-9999-9999-9999-9999
5039679700001991  265-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
5039680500001991  268-9999-9999-9999-9999  264-9999-9999-9999-9999-9999-9999
5039693300001991  272-9999-9999  284-9999-9999-9999-9999-9999-9999-9999-9999
5039701400001991  253-9999-9999  261  265-9999-9999-9999-9999-9999-9999-9999
5039704800001991-9999-9999-9999  268  272  267-9999-9999-9999-9999-9999-9999
5039718200001991-9999-9999-9999-9999  268-9999-9999-9999-9999-9999-9999-9999
5039723000001991  279-9999-9999-9999-9999  268-9999-9999-9999-9999-9999-9999
5039724000001991-9999-9999-9999  269  266  255-9999-9999-9999-9999-9999-9999
5039734000001991  268-9999-9999  269  266  261-9999-9999-9999-9999-9999-9999
5039737200001991-9999-9999-9999  272-9999-9999-9999-9999-9999-9999-9999-9999
5039753000001991  267-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
5039756000031991  265-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
5039772400001991  274-9999-9999-9999  265-9999-9999-9999-9999-9999-9999-9999
5039790000001991  272-9999-9999-9999-9999  266-9999-9999-9999-9999-9999-9999
[chiefio@tubularbells analysis]$ ls *7*

It seems you can see a lot things in the temperature history. Earthquakes, wars, revolutions, the growth of Jet Age airports and urban sprawl. But “Global Warming” is not one of them.

OK, what does Indonesia in isolation look like? (Being aware that the early 1990s might be a bit dodgy due the data drop outs sucking down the average)? It’s pretty much 26.x-27.x C straight down the years (then the 1990s “issue”) then the 27C picks up again on the other side. Oddly, 2008 shows 28 thermometers, but the dropouts have damaged the annual average for that year. In any case, it shows that there is no “Global Warming”, at least not in Indonesia. It also shows that ‘by country’ analysis for smaller countries has it’s own “issues”…

Look at Temps.503.yrs.GAT (Y/N)? y
 
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 25.2 26.1 26.3 26.3 26.4 25.3 25.3 25.9 26.4 26.4 26.2 25.7 26.0   1
1881 25.7 25.8 25.8 26.9 27.1 26.4 26.3 26.5 26.5 27.0 26.6 26.7 26.4   1
1882 26.0 26.1 25.9 26.5 26.0 25.9 25.7 26.0 26.3 26.2 26.2 26.3 26.1   1
1883 26.3 25.4 26.6 26.2 26.3 26.7 26.2 26.1 26.6 26.5 26.3 25.7 26.2   1
1884 25.7 25.7 25.9 26.4 26.3 26.3 25.3 26.3 26.6 27.0 26.6 25.8 26.2   1
1885 25.1 25.3 25.9 26.7 26.5 26.2 26.0 26.3 26.5 27.2 26.7 26.2 26.2   1
1886 26.3 25.8 26.3 26.6 26.9 26.4 26.3 26.4 26.8 26.9 26.6 26.6 26.5   1
1887 26.0 25.5 26.2 26.5 26.2 25.8 25.3 26.1 25.9 26.4 26.3 26.0 26.0   1
1888 25.1 25.5 26.5 26.6 26.6 26.7 25.8 26.2 26.4 27.4 27.6 26.8 26.4   1
1889 26.9 26.7 26.7 27.5 27.4 26.7 26.3 26.3 26.9 26.8 26.7 26.7 26.8   1
1890 26.8 26.3 26.2 26.7 26.5 25.9 25.6 25.9 26.0 26.3 25.7 26.0 26.2   1
1891 25.9 25.7 26.3 26.6 27.0 26.4 25.9 25.6 26.9 27.9 27.4 26.6 26.5   1
1892 25.9 26.4 26.8 26.4 26.4 26.4 26.2 26.0 26.0 26.8 26.5 26.7 26.4   1
1893 25.7 25.5 26.1 26.5 26.7 25.7 25.6 26.0 26.5 26.4 26.6 25.7 26.1   1
1894 25.5 25.5 26.1 26.6 26.0 25.8 25.9 26.4 26.8 27.0 26.6 26.2 26.2   1
1895 25.9 25.6 26.2 26.6 26.7 26.3 25.4 26.0 27.2 27.5 26.8 26.5 26.4   1
1896 25.7 26.1 26.5 26.6 26.8 26.0 26.0 26.1 26.8 27.5 27.9 26.9 26.6   1
1897 26.5 27.0 27.0 26.7 27.4 26.9 26.4 26.7 27.2 27.2 26.9 26.2 26.8   1
1898 26.1 26.0 26.4 27.0 27.2 26.6 26.1 26.5 26.8 26.5 26.6 26.2 26.5   1
1899 26.0 25.7 26.1 26.4 26.8 26.3 26.3 26.1 26.6 27.2 27.1 26.1 26.4   1
1900 26.4 26.5 26.6 26.9 26.8 26.8 26.2 26.5 27.0 27.4 27.3 26.8 26.8   1
1901 26.3 25.7 25.9 27.0 27.3 26.5 26.2 26.1 26.9 27.2 27.1 26.4 26.6   1
1902 26.3 25.6 26.4 27.0 26.9 26.3 26.2 26.4 26.8 27.5 27.2 27.0 26.6   1
1903 26.8 26.1 27.0 27.0 26.6 26.5 26.3 26.8 27.3 26.6 26.9 25.8 26.6   1
1904 25.8 25.4 25.9 26.6 26.6 26.3 25.9 25.9 26.5 26.5 26.8 26.2 26.2   1
1905 26.4 26.2 27.1 26.8 26.6 27.3 26.4 26.5 26.7 27.8 27.1 27.1 26.8   1
1906 27.0 27.2 27.0 27.2 27.0 26.8 26.7 26.7 27.2 26.8 27.0 26.2 26.9   1
1907 26.3 26.0 26.4 26.5 26.7 26.1 26.4 26.1 26.6 27.2 26.9 26.4 26.5   1
1908 26.4 26.5 27.0 27.2 26.9 26.2 25.7 26.5 26.7 26.8 26.6 26.7 26.6   1
1909 26.8 26.5 26.7 27.0 27.0 26.3 26.3 26.9 27.0 27.1 26.3 25.7 26.6   1
1910 26.4 26.5 26.5 26.9 27.0 26.6 26.7 26.5 26.9 26.5 26.5 26.3 26.6   1
1911 26.1 25.7 26.8 27.3 27.0 26.9 26.2 25.8 26.9 27.3 26.9 27.0 26.7   1
1912 26.5 26.5 26.8 27.8 27.4 26.8 26.3 26.9 27.7 27.0 26.5 26.7 26.9   1
1913 26.7 26.6 26.3 27.0 27.0 26.5 26.7 26.3 27.0 27.4 27.1 26.9 26.8   1
1914 26.7 26.7 27.1 27.0 27.0 27.0 26.7 26.4 27.5 27.7 27.5 27.3 27.0   1
1915 26.8 26.9 27.3 26.9 27.2 26.7 26.5 26.2 26.6 27.5 27.0 26.4 26.8   1
1916 25.2 26.5 26.4 26.5 26.4 26.3 25.9 26.0 26.7 26.2 26.1 26.1 26.2   1
1917 25.6 25.8 26.4 26.6 26.8 26.7 26.8 26.9 26.6 26.3 26.9 25.5 26.4   1
1918 25.7 24.7 25.8 26.5 26.6 25.9 26.5 26.7 27.5 27.5 27.0 26.9 26.4   1
1919 26.6 26.6 26.9 27.2 26.7 26.5 26.2 26.6 27.1 27.6 26.5 26.2 26.7   1
1920 25.8 26.4 26.1 26.7 26.8 26.6 26.6 26.1 26.8 26.6 27.2 26.7 26.5   1
1921 26.5 26.1 26.6 26.7 27.2 26.3 26.6 26.8 27.0 27.4 26.6 26.5 26.7   1
1922 26.5 26.1 26.8 27.3 27.0 27.0 26.8 26.8 27.0 26.8 26.5 26.6 26.8   1
1923 25.9 26.2 27.0 27.2 27.4 26.9 26.2 26.0 26.7 27.5 27.3 26.9 26.8   1
1924 27.0 26.7 26.9 26.9 27.3 27.0 27.1 27.2 27.5 26.9 26.9 26.1 27.0   1
1925 26.3 26.1 26.4 26.6 26.9 26.8 26.7 26.9 27.9 27.7 27.3 27.0 26.9   1
1926 26.3 26.5 26.8 28.0 27.7 27.3 27.2 27.6 27.7 27.6 27.4 26.1 27.2   1
1927 26.5 26.5 26.6 27.1 26.8 26.7 26.8 27.2 27.5 27.5 27.1 26.9 26.9   1
1928 26.9 26.3 26.9 27.2 27.3 26.8 26.5 26.3 27.4 27.5 27.2 26.8 26.9   1
1929 26.0 26.4 26.2 27.1 27.3 26.9 26.4 26.9 27.5 27.5 27.1 26.3 26.8   1
1930 26.6 26.2 27.1 26.9 26.9 27.0 27.0 27.3 27.5 27.3 27.3 27.0 27.0   1
1931 26.7 27.2 27.2 27.2 27.3 27.1 26.4 27.1 27.2 27.4 26.6 26.7 27.0   3
1932 26.2 26.3 26.3 26.9 27.3 26.6 26.5 26.7 26.9 26.9 26.9 26.5 26.7   3
1933 26.6 26.2 26.6 26.6 27.1 26.7 26.5 27.1 26.7 27.1 26.5 26.3 26.7   3
1934 26.3 26.0 26.1 26.7 26.8 26.7 26.4 26.4 26.7 27.1 26.4 26.1 26.5   3
1935 26.4 26.6 27.0 26.6 27.2 26.5 26.1 26.3 26.6 27.1 27.0 26.9 26.7   3
1936 26.2 26.5 26.7 26.9 27.2 26.7 26.5 26.8 27.1 27.1 26.7 26.7 26.8   3
1937 26.1 26.8 26.9 26.9 26.9 26.7 26.1 26.6 26.9 27.1 27.3 26.2 26.7   3
1938 26.3 26.5 27.1 27.2 26.9 26.8 26.6 26.4 26.6 27.1 26.8 26.6 26.7   3
1939 26.3 26.4 26.7 27.0 26.9 26.4 26.7 26.8 26.6 26.7 27.0 26.7 26.7   3
1940 26.2 26.5 26.7 27.4 27.3 26.8 26.7 26.2 27.1 27.5 27.0 26.7 26.8   3
1941 26.8 27.0 27.3 27.6 27.5 27.4 26.5 26.6 26.6 26.9 27.3 26.9 27.0   3
1942 27.2 27.1 27.3 27.3 27.5 27.1 26.2 26.7 27.1 28.0 27.1 27.2 27.2   2
1943 26.3 26.3 26.6 27.0 27.2 26.7 26.5 27.1 27.5 27.1 26.7 26.7 26.8   1
1944 26.8 26.7 26.9 27.0 27.0 26.6 26.2 26.5 26.8 27.4 27.5 26.8 26.8   2
1945 27.2 26.8 26.9 27.2 27.0 26.3 26.1 26.7 27.2 27.5 26.8 27.3 26.9   2
1946 27.5 26.5 27.0 27.3 26.8 26.8 26.7 27.2 27.0 27.5 27.6 27.8 27.1   2
1947 27.7 27.5 27.1 26.8 27.4 27.2 25.7 26.2 26.1 26.9 26.4 27.4 26.9   3
1948 26.5 27.0 27.3 27.3 27.1 26.6 26.8 26.7 26.9 27.2 26.9 28.1 27.0   3
1949 26.8 26.5 26.6 26.7 26.6 26.1 25.6 25.7 26.8 27.1 26.6 27.0 26.5   7
1950 26.4 26.1 26.8 26.7 26.6 26.6 25.7 25.9 26.4 26.6 26.3 26.1 26.3   7
1951 25.7 25.7 26.4 26.4 26.4 26.1 25.5 25.9 26.6 27.2 27.8 27.0 26.4  10
1952 26.8 26.6 26.5 26.7 26.6 26.3 25.8 26.0 26.9 27.1 26.8 26.3 26.5  12
1953 26.1 26.2 26.6 26.7 26.5 26.2 25.8 26.1 26.6 27.2 27.2 26.8 26.5  13
1954 26.6 26.3 26.7 26.8 26.6 26.3 25.4 26.0 26.0 26.6 26.3 25.9 26.3  14
1955 26.1 26.0 26.3 26.2 26.5 26.0 25.5 25.8 26.3 26.3 25.8 26.0 26.1  13
1956 25.7 26.1 26.5 26.7 26.5 26.2 25.8 25.9 26.1 26.8 26.5 26.0 26.2  18
1957 26.1 26.0 26.4 27.0 26.8 26.6 26.1 26.0 26.3 26.8 27.1 26.5 26.5  18
1958 26.7 26.5 26.7 26.9 27.0 26.6 26.3 26.1 26.6 26.9 26.8 26.4 26.6  18
1959 25.9 26.3 26.4 26.4 26.5 26.0 25.5 25.4 26.1 26.6 26.8 26.5 26.2  19
1960 26.2 26.1 26.5 26.9 26.9 26.4 26.1 26.5 26.7 27.2 26.7 26.7 26.6  49
1961 26.3 26.5 26.9 27.1 27.1 26.2 25.9 25.9 26.5 27.1 27.2 26.8 26.6  51
1962 26.2 26.2 26.6 26.9 27.1 26.7 26.4 26.2 26.7 27.2 27.2 26.6 26.7  55
1963 25.8 26.0 26.4 27.2 27.3 26.9 26.3 26.2 26.8 27.2 27.6 27.0 26.7  56
1964 27.1 26.9 26.8 27.2 27.3 26.6 26.3 26.5 26.9 26.7 26.7 26.5 26.8  57
1965 25.9 26.4 26.5 26.8 26.9 26.6 26.1 26.2 26.8 27.3 27.6 27.2 26.7  54
1966 26.7 26.6 26.9 27.3 27.3 26.5 26.4 26.6 27.1 27.2 27.3 26.8 26.9  55
1967 26.4 26.5 26.7 27.0 27.1 26.5 26.1 26.4 26.8 27.4 27.3 26.8 26.8  56
1968 26.4 26.4 26.8 27.1 27.0 26.7 26.3 26.3 26.9 27.1 27.1 26.7 26.7  54
1969 26.8 26.8 27.3 27.3 27.3 26.7 26.2 26.3 26.7 27.2 27.3 26.9 26.9  55
1970 26.7 26.9 27.1 27.2 27.1 26.7 26.1 26.1 26.6 27.1 26.8 26.5 26.7  51
1971 26.2 26.3 26.4 26.8 26.8 26.2 25.9 26.2 26.8 26.8 26.5 26.6 26.5  50
1972 26.1 26.6 26.4 26.8 26.8 26.4 26.1 26.4 26.7 27.1 27.6 27.4 26.7  54
1973 27.0 27.2 27.0 27.3 27.0 26.9 26.5 26.7 26.7 27.2 27.0 26.4 26.9  51
1974 26.0 26.0 26.4 26.7 26.9 26.4 26.1 26.4 26.6 26.9 26.8 26.4 26.5  53
1975 26.4 26.3 26.5 27.0 26.8 26.3 26.2 26.4 26.6 26.7 26.6 26.3 26.5  52
1976 25.9 26.0 26.3 26.3 26.8 26.1 25.7 26.1 26.6 26.6 26.6 26.6 26.3  38
1977 26.2 25.9 26.2 26.8 26.8 26.2 26.1 25.8 26.5 27.1 27.3 26.6 26.5  40
1978 26.3 26.4 26.8 26.8 27.2 26.4 26.0 26.4 26.2 26.8 26.9 26.4 26.5  42
1979 26.6 26.7 26.7 27.0 27.2 26.6 26.0 26.4 26.8 27.1 27.0 26.5 26.7  42
1980 26.5 26.7 26.7 27.0 27.2 26.8 26.5 26.2 26.7 27.1 26.8 26.6 26.7  42
1981 26.2 26.3 26.8 26.9 27.3 26.9 26.6 26.8 26.6 27.3 27.2 26.5 26.8  41
1982 26.5 26.5 26.6 27.0 26.8 26.4 26.0 26.1 26.3 26.9 27.4 27.3 26.6  37
1983 26.9 27.2 27.6 27.6 27.1 26.9 26.3 26.6 26.8 27.2 27.0 26.9 27.0  39
1984 26.1 26.2 26.4 26.9 26.7 26.4 26.1 26.4 26.2 27.0 27.1 26.5 26.5  40
1985 26.5 26.7 27.0 27.0 27.3 26.7 26.0 26.2 26.5 27.0 27.0 27.2 26.8  33
1986 26.5 26.7 26.8 27.3 27.2 26.9 26.4 26.1 26.8 27.1 27.3 27.2 26.9   9
1987 26.9 26.9 27.2 27.4 27.5 27.1 26.5 26.5 26.9 27.3 27.5 27.2 27.1  30
1988 27.0 27.1 27.4 27.4 27.2 26.8 26.5 26.7 26.8 27.3 27.2 26.7 27.0  27
1989 27.0 26.7 26.8 27.1 27.0 26.7 26.4 26.4 26.8 27.2 27.2 27.1 26.9  31
1990 26.5 27.0 27.1 27.8 27.5 26.8 26.3  0.0  0.0  0.0 27.7 26.4 20.3  30
1991 26.6  0.0  0.0 27.2 26.9 26.8  0.0  0.0  0.0  0.0  0.0  0.0  9.0  27
1995  0.0  0.0  0.0  0.0 27.2  0.0 27.5  0.0 28.3 27.8  0.0  0.0  9.2  11
1996 26.3 25.8  0.0 26.8 27.7 26.6 26.7  0.0 27.1  0.0 27.0 27.6 20.1  14
1997  0.0 26.6 27.1 27.7 27.6 26.8 26.4 26.0 26.1  0.0 25.8 28.5 22.4  19
1998  0.0 27.6 27.5 27.2 28.3 28.3  0.0 27.7 27.1 26.8 27.2 27.0 22.9  12
1999  0.0  0.0 25.9  0.0 27.0 27.0 26.0 27.2  0.0 26.6 26.7 27.2 17.8  12
2000  0.0 26.9 26.7  0.0 27.0 26.0 26.9 26.0 26.8 26.9 27.2 26.5 22.2  16
2001 26.6 27.0 27.0 27.2 27.2 26.8 25.9 26.6 27.0 27.0 26.7 26.7 26.8  15
2002 26.9 27.0 27.2 27.6 27.5 26.8 26.6 26.8 26.6 27.1 27.2 27.2 27.0  13
2003 26.7 27.3 26.8 27.0 27.3 27.2 26.2 26.2 26.9 27.1 27.7 26.9 26.9  16
2004 27.6 27.0 27.1 28.1 27.5 27.1 26.7 26.7 26.5 27.2 27.4 27.1 27.2  18
2005 27.1 27.7 27.5 27.2 27.4 27.1 26.8 26.5 26.6 27.9 27.3 27.0 27.2  15
2006 26.6 26.9 27.3 27.2 27.3 26.7 26.3 26.2 26.3 27.0 27.8 27.8 26.9  18
2007 27.4 27.1 27.1 27.3 27.5 26.8 26.1 26.0 26.6 27.5 27.4 27.2 27.0  21
2008  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 27.0 27.3 27.6 27.0  9.1  28
     26.4 26.5 26.7 27.0 27.0 26.6 26.2 26.3 26.7 27.1 27.1 26.7 26.7
     26.4 26.4 26.7 27.0 27.0 26.6 26.2 26.4 26.8 27.1 26.9 26.7
 
For Country Code 503
[chiefio@tubularbells analysis]$

Pacific without Australia and New Zealand

It doesn’t get much more dead flat than this. ANY “anomaly map” or ANY claim that there is “Global Warming” in the Pacific is based on a fabrication of a fantasy. It just isn’t in the base data. Period.

[chiefio@tubularbells analysis]$ more Temps/Temps.LIST.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 25.7 26.6 26.7 26.8 27.2 26.3 26.3 26.8 26.9 27.0 26.5 26.0 26.6   3
1881 26.0 26.2 26.4 26.9 27.4 26.9 26.9 26.9 26.9 27.4 27.1 26.7 26.8   3
1882 26.9 26.7 26.7 27.2 26.9 26.7 26.6 26.6 26.9 26.7 26.5 26.5 26.7   3
1883 26.2 26.0 27.1 27.2 27.4 27.1 26.9 27.0 26.8 26.6 26.4 26.0 26.7   4
1884 25.5 25.8 26.4 27.0 27.1 26.6 26.1 26.7 26.6 26.8 26.6 25.6 26.4   4
1885 25.8 25.7 26.4 27.4 27.5 27.0 26.7 26.9 27.1 27.3 26.8 26.2 26.7   4
1886 25.5 25.2 26.2 27.2 27.6 27.2 26.9 27.2 27.3 27.1 26.3 26.2 26.7   5
1887 25.3 25.2 25.9 26.7 26.9 26.8 26.6 26.7 26.4 26.4 26.1 25.3 26.2   4
1888 24.7 25.2 26.6 27.7 28.0 27.5 26.7 27.1 27.2 27.2 27.0 26.2 26.8   4
1889 26.2 26.2 26.9 28.4 28.6 27.8 27.1 27.3 27.3 27.2 26.4 25.7 27.1   4
1890 25.7 25.7 26.3 26.8 26.9 26.6 26.2 26.5 26.4 26.1 25.5 25.7 26.2   5
1891 25.9 26.0 26.7 27.4 28.1 27.3 27.0 26.6 27.0 27.3 26.8 26.2 26.9   7
1892 25.8 26.4 27.0 27.0 27.7 27.0 26.8 26.6 26.9 26.8 26.3 25.8 26.7   7
1893 25.5 25.7 26.3 27.4 26.8 26.6 26.5 26.7 26.9 26.7 26.3 25.8 26.4   7
1894 25.4 25.5 26.3 27.0 26.9 26.9 27.0 26.6 26.6 26.8 25.9 25.6 26.4   7
1895 25.8 25.8 26.4 27.2 27.3 27.1 26.6 26.8 27.3 27.5 26.8 26.1 26.7   7
1896 26.4 26.6 27.0 27.2 27.3 26.8 27.0 26.7 27.1 27.6 27.5 27.0 27.0   5
1897 26.2 26.6 27.0 27.4 27.6 27.8 26.8 27.0 27.2 27.4 27.3 26.8 27.1   5
1898 26.5 26.6 26.8 27.4 27.6 26.6 26.8 26.9 27.1 27.0 26.8 26.6 26.9   5
1899 26.3 26.2 26.7 27.1 27.3 26.9 26.2 26.1 26.6 26.8 26.5 26.0 26.6   4
1900 26.1 26.4 26.8 27.2 27.2 26.7 26.2 26.5 26.9 27.1 27.4 27.0 26.8   4
1901 26.9 26.5 27.0 27.9 27.9 27.2 27.0 26.7 27.4 27.4 27.4 26.7 27.2   4
1902 26.7 26.2 27.0 27.5 27.7 27.2 27.1 27.0 27.2 27.6 27.4 27.5 27.2   4
1903 26.3 26.0 27.1 27.6 27.7 27.7 27.0 26.9 27.0 26.9 26.4 25.6 26.8   8
1904 25.3 25.1 26.4 26.8 27.1 26.7 26.4 26.4 26.6 26.8 26.5 26.0 26.3   8
1905 26.1 26.1 26.8 27.9 27.6 27.3 26.8 26.6 26.9 27.4 26.7 26.9 26.9   9
1906 26.8 27.1 27.2 28.0 27.7 27.4 27.2 27.1 26.5 26.2 26.3 26.1 27.0  10
1907 25.8 25.9 26.2 26.6 26.5 26.0 25.7 25.3 25.7 26.2 26.0 25.7 26.0  10
1908 25.9 26.1 26.3 27.0 26.3 25.8 25.5 26.2 25.8 26.1 25.8 26.3 26.1  10
1909 26.2 26.4 26.6 26.8 26.5 26.2 25.5 26.1 25.8 26.2 26.1 25.6 26.2  10
1910 25.9 26.1 26.3 26.6 26.2 25.8 26.0 25.7 26.0 25.9 25.6 26.2 26.0  10
1911 26.1 26.0 26.5 26.6 26.5 26.2 25.6 25.7 25.7 26.0 26.2 26.6 26.1  11
1912 26.3 26.5 26.9 27.0 27.0 26.5 25.7 25.8 26.2 26.1 26.1 26.0 26.3  11
1913 26.1 26.2 26.8 26.7 26.4 26.1 25.7 25.3 25.5 25.5 26.0 26.1 26.0  11
1914 25.7 26.3 26.8 27.2 27.0 26.2 26.4 25.4 25.7 26.0 26.5 26.7 26.3  11
1915 26.2 26.7 26.7 26.4 26.3 26.1 25.2 25.3 25.5 25.8 25.8 25.9 26.0  12
1916 25.5 26.2 26.1 26.3 25.8 25.6 25.3 25.6 25.2 25.4 25.5 25.5 25.7  12
1917 25.4 25.4 25.7 26.2 25.8 25.5 25.4 25.1 25.0 25.1 25.6 25.3 25.5  12
1918 24.7 24.7 25.5 25.8 25.7 25.6 24.8 25.0 25.1 25.4 25.6 25.6 25.3  12
1919 25.8 26.3 26.6 26.9 26.2 25.3 24.7 25.0 26.2 26.4 25.2 25.2 25.8  12
1920 25.5 25.8 25.9 26.3 26.1 25.3 25.1 24.9 25.1 25.1 25.4 25.7 25.5  11
1921 25.9 26.2 26.5 26.0 25.6 25.2 25.4 25.2 25.5 25.8 25.8 25.8 25.7  17
1922 26.1 26.2 26.5 26.7 26.2 25.9 25.2 25.4 25.6 25.9 25.9 26.1 26.0  18
1923 26.2 26.6 26.4 26.7 26.4 25.9 25.5 25.2 25.4 25.9 25.9 26.0 26.0  17
1924 26.2 26.5 26.7 26.7 26.9 26.2 25.9 26.1 26.2 26.1 26.2 26.0 26.3  18
1925 26.4 26.1 26.6 26.9 26.6 25.9 25.5 25.9 26.1 25.9 26.1 25.9 26.2  18
1926 25.8 26.4 26.5 26.8 27.0 26.2 25.8 25.8 26.1 26.3 26.4 26.2 26.3  17
1927 26.1 26.3 26.8 26.9 26.6 26.0 25.4 25.4 25.6 25.7 26.1 26.2 26.1  18
1928 26.5 26.4 26.9 27.0 26.8 25.8 25.4 26.0 26.1 26.1 26.3 26.5 26.3  17
1929 25.6 26.2 26.2 26.6 26.4 25.8 25.1 25.1 25.4 25.7 25.9 25.9 25.8  19
1930 26.3 26.2 26.8 26.8 26.6 25.9 25.7 25.6 25.7 26.1 26.3 26.2 26.2  24
1931 26.4 26.6 26.9 27.1 27.0 26.4 25.8 25.9 26.4 25.9 26.2 25.9 26.4  26
1932 25.9 26.3 26.5 26.8 26.7 26.1 26.0 25.9 25.9 26.2 26.2 26.3 26.2  29
1933 26.4 26.5 26.7 26.9 27.0 26.3 25.7 25.9 26.0 26.1 26.1 26.0 26.3  30
1934 26.1 26.1 26.4 26.9 26.8 26.7 26.0 25.8 25.9 26.2 26.3 25.9 26.3  31
1935 26.2 26.6 26.9 26.8 26.7 26.4 26.0 25.8 26.1 26.5 26.5 26.2 26.4  32
1936 26.3 26.8 26.9 27.0 26.7 26.1 25.8 25.6 25.9 26.0 26.2 26.5 26.3  32
1937 26.6 26.8 27.0 27.0 26.6 26.4 26.0 26.0 26.1 26.3 26.6 26.5 26.5  34
1938 26.6 26.7 26.9 27.1 26.7 26.4 26.3 26.1 26.0 26.2 26.1 26.2 26.4  36
1939 26.2 26.4 26.9 26.7 26.5 25.9 25.7 25.7 25.4 25.7 26.4 26.0 26.1  37
1940 26.0 26.3 26.7 26.9 26.7 25.9 25.7 25.2 25.5 25.9 26.2 26.1 26.1  40
1941 26.4 26.8 26.9 26.7 26.4 25.6 25.2 25.2 25.4 25.7 26.3 26.4 26.1  36
1942 26.8 26.9 27.0 26.4 25.7 25.1 24.2 24.4 24.7 25.5 25.7 26.1 25.7  22
1943 26.4 26.6 26.4 26.0 25.3 24.4 24.0 24.5 24.6 25.4 26.0 26.1 25.5  22
1944 26.3 26.3 26.5 26.0 25.4 24.5 23.7 23.7 24.4 24.7 25.3 26.0 25.2  20
1945 26.5 26.6 26.6 26.1 25.0 24.4 24.3 24.4 24.6 25.0 25.6 26.2 25.4  22
1946 26.8 26.7 26.8 26.6 25.6 25.2 24.9 25.1 24.8 25.5 26.0 26.5 25.9  31
1947 27.0 26.8 27.0 27.1 26.6 26.2 25.6 25.6 25.7 25.9 25.9 26.3 26.3  37
1948 26.3 26.4 26.8 26.8 26.4 25.9 25.5 25.3 25.5 25.9 25.9 26.1 26.1  44
1949 26.0 26.4 26.6 26.7 26.3 26.1 25.7 25.4 25.8 26.1 25.9 26.1 26.1  62
1950 26.2 26.3 26.5 26.6 26.3 26.2 25.5 25.6 25.6 26.0 26.2 26.1 26.1  70
1951 26.0 26.2 26.4 26.9 26.4 26.2 25.8 26.0 26.1 26.4 26.6 26.4 26.3 127
1952 26.4 26.5 26.7 26.9 26.9 26.5 25.9 25.7 25.9 26.3 26.4 26.1 26.3 131
1953 26.0 26.3 26.7 26.9 26.6 26.1 25.7 25.7 25.9 26.4 26.5 26.2 26.2 134
1954 26.3 26.3 26.5 26.8 26.6 26.2 25.8 25.7 25.8 25.9 25.8 25.8 26.1 136
1955 25.8 26.1 26.3 26.5 26.6 25.9 25.5 25.6 25.9 26.0 25.8 25.7 26.0 141
1956 25.7 26.1 26.4 26.5 26.4 26.0 25.6 25.6 25.7 26.1 26.1 25.9 26.0 149
1957 26.0 26.1 26.4 26.8 26.7 26.1 25.7 25.8 25.9 26.1 26.2 26.3 26.2 155
1958 26.3 26.4 26.7 26.9 26.8 26.4 25.8 25.8 26.0 26.2 26.0 26.0 26.3 157
1959 26.0 26.3 26.5 26.6 26.5 26.4 25.8 25.5 25.8 26.1 26.2 26.3 26.2 159
1960 26.1 26.1 26.5 26.8 26.7 26.2 25.8 26.0 26.1 26.2 26.3 26.2 26.2 191
1961 25.9 26.5 26.7 26.9 26.7 26.0 25.7 25.6 25.9 26.1 26.4 26.3 26.2 195
1962 26.1 26.0 26.5 26.7 26.8 26.3 25.9 25.7 26.0 26.5 26.4 26.1 26.2 202
1963 25.6 25.9 26.3 26.7 26.8 26.3 25.8 25.8 26.1 26.2 26.6 26.4 26.2 204
1964 26.7 26.5 26.6 26.9 26.7 26.3 25.9 25.9 26.1 26.2 26.1 25.9 26.3 206
1965 25.7 26.1 26.2 26.5 26.5 26.0 25.5 25.6 26.0 26.3 26.5 26.4 26.1 205
1966 26.2 26.4 26.7 27.0 26.6 26.1 25.9 26.0 26.2 26.3 26.4 26.2 26.3 210
1967 26.0 26.1 26.4 26.6 26.7 26.2 25.8 25.8 26.1 26.3 26.3 26.0 26.2 209
1968 26.0 26.0 26.6 26.6 26.7 26.4 26.0 25.9 26.2 26.3 26.3 26.3 26.3 202
1969 26.4 26.4 27.0 27.1 27.1 26.6 25.9 25.9 26.1 26.4 26.5 26.5 26.5 207
1970 26.5 26.6 27.0 27.1 27.0 26.5 25.9 25.9 26.2 26.4 26.4 26.3 26.5 203
1971 25.8 26.0 26.1 26.5 26.4 25.9 25.6 25.7 26.0 26.1 26.0 26.0 26.0 197
1972 25.8 26.3 26.3 26.6 26.6 26.1 25.8 25.7 25.9 26.3 26.5 26.5 26.2 201
1973 26.5 26.7 26.9 27.1 26.9 26.6 26.1 26.0 26.1 26.2 26.4 26.1 26.5 197
1974 25.8 26.0 26.3 26.6 26.5 26.1 25.8 25.8 26.1 26.2 26.3 26.0 26.1 197
1975 26.1 26.2 26.5 26.8 26.6 26.1 25.7 25.8 26.0 26.1 26.1 25.9 26.2 196
1976 25.9 26.0 26.4 26.3 26.2 25.6 25.2 25.2 25.3 26.1 26.3 26.2 25.9 141
1977 26.3 26.4 26.6 26.7 26.4 25.9 25.5 25.3 25.5 26.0 26.3 26.3 26.1 144
1978 26.3 26.4 26.8 26.7 26.7 25.9 25.6 25.4 25.6 25.9 26.1 26.2 26.1 147
1979 26.3 26.6 26.8 26.7 26.5 26.2 25.6 25.5 26.0 26.2 26.3 26.2 26.2 147
1980 26.5 26.7 26.8 26.8 26.7 26.1 25.7 25.6 25.8 26.2 26.3 26.3 26.3 146
1981 26.3 26.5 26.8 26.8 26.4 26.0 25.6 25.4 25.9 26.2 26.3 26.4 26.2 141
1982 26.5 26.5 26.7 26.6 26.3 26.0 25.3 25.3 25.4 25.9 26.3 26.4 26.1 110
1983 26.5 26.8 27.1 27.1 26.7 26.1 25.7 25.7 26.0 26.3 26.2 26.3 26.4 112
1984 26.2 26.5 26.7 26.9 26.6 26.0 25.6 25.6 25.8 26.1 26.4 26.3 26.2 113
1985 26.3 26.9 27.0 26.9 26.6 25.9 25.3 25.6 25.8 26.2 26.4 26.6 26.3 102
1986 26.5 26.5 26.7 26.8 26.6 26.0 25.7 25.6 25.8 26.2 26.5 26.3 26.3  78
1987 26.1 26.6 26.8 27.0 26.6 26.1 25.3 25.3 25.8 26.3 26.6 26.3 26.2  94
1988 26.8 26.9 27.2 27.1 26.9 26.5 25.7 25.8 26.0 26.2 26.1 25.9 26.4  92
1989 26.3 26.2 26.4 26.8 26.5 25.9 25.7 25.8 25.9 26.2 26.1 26.0 26.1  96
1990 26.4 26.8 26.9 27.0 26.7 26.1 25.8 25.6 25.5 25.9 26.3 26.4 26.3  95
1991 26.5 26.6 26.7 26.9 26.5 25.7 25.8 25.3 25.5 25.9 26.0 26.1 26.1 102
1992 26.2 26.4 26.8 26.8 26.7 26.2 25.8 25.9 26.0 25.9 26.0 26.2 26.2  74
1993 26.3 26.1 26.6 26.8 26.7 25.9 26.0 25.6 25.7 25.9 26.4 26.5 26.2  71
1994 26.5 26.7 27.1 26.9 26.6 25.9 25.5 25.6 25.5 25.7 25.9 26.1 26.2  71
1995 26.3 26.3 26.7 26.9 26.8 26.6 26.2 26.2 25.8 26.1 26.2 26.0 26.3  76
1996 26.1 26.2 27.0 26.8 26.3 25.8 26.0 25.7 25.8 26.0 26.3 26.2 26.2  81
1997 25.8 26.4 26.7 27.1 26.9 26.3 25.8 25.8 26.0 26.2 26.5 26.8 26.4  81
1998 26.9 27.2 27.5 27.6 27.6 26.8 26.2 26.3 26.1 26.9 26.6 26.6 26.9  71
1999 26.5 26.5 26.9 26.9 26.8 26.2 26.0 25.6 25.7 26.0 26.1 26.1 26.3  71
2000 26.3 26.8 26.8 27.0 26.6 26.2 25.3 25.5 26.3 26.6 26.4 26.3 26.3  75
2001 26.7 26.6 27.1 27.2 26.7 26.3 26.0 26.1 26.4 26.3 26.2 26.2 26.5  72
2002 26.5 26.6 27.2 27.1 26.9 26.6 26.5 26.2 26.1 26.4 26.5 26.7 26.6  71
2003 26.4 26.9 27.0 27.4 27.2 26.1 26.1 25.9 26.0 26.2 26.4 26.6 26.5  72
2004 27.0 26.9 27.0 27.4 27.0 26.5 25.9 26.1 25.9 26.2 26.5 26.6 26.6  78
2005 27.0 27.0 27.3 27.1 26.9 26.8 26.1 26.0 26.2 26.5 26.8 26.4 26.7  73
2006 26.5 26.7 27.2 27.2 26.8 26.5 26.2 25.9 25.9 26.1 26.7 26.8 26.5  75
2007 26.6 26.8 27.0 27.1 27.2 26.7 26.3 26.1 26.2 26.7 26.4 26.4 26.6  78
2008 26.3 26.2 26.5 26.8 26.5 26.4 25.7 25.8 26.3 26.5 26.8 26.6 26.4  86
     26.2 26.4 26.6 26.8 26.7 26.2 25.7 25.7 25.9 26.2 26.3 26.2 26.2
     26.2 26.3 26.7 26.9 26.7 26.2 25.9 25.9 26.0 26.2 26.3 26.2
 
For Country Codes 502 503 504 505 506 508 509 51 52 53 54
[chiefio@tubularbells analysis]$ 

Australia, 501, and New Zealand, 507, are excluded. The rest are:

502  New Hebrides / Tuvalu / Samoa area
503  Indonesia
504  Gilbert Ilands? ( 155 E to 171 W at the equator)
505  Malasia
506  Nauru (Kiribati? I wish folks who stop changing names...)
508  Papua New Guinea
509  Philippines
51x - 54x are various chunks of islands scattered across the Pacific basin.

There is nothing numbered abover 54x, so this list is
exhaustive for the Pacific Region.

In Conclusion

I see no way to reconcile the steady temperatures of the Pacific / Indonesian basins during periods of decent coverage and steady thermometer counts with a runaway greenhouse scenario.

About these ads

About E.M.Smith

A technical managerial sort interested in things from Stonehenge to computer science. My present "hot buttons' are the mythology of Climate Change and ancient metrology; but things change...
This entry was posted in NCDC - GHCN Issues and tagged . Bookmark the permalink.

27 Responses to GHCN – Pacific Basin, Lies, Statistics, and Australia

  1. rob r says:

    Hi

    If you have have left New Zealand in the Pacific mix right the way through then the early part of the record, especially pre 1930 (or so), will be heavily biased towards relatively cool NZ temperatures. There should be numerous long records from NZ in the database. Its been a generally well-civilized British colony since the mid 1840’s. The whole country was settled by Europeans at about the same time.

    In NZ mean annual temps range from about 9.8 deg C in the south (Invercargill) to about 16 deg C in (Northland).

    If you were to look at the Pacific without both NZ and Australia the Pacific is likely to look even more stable.

    REPLY: “NZ is south of -30 for the most part, so you can see the composite of it and Tasmania / NSW in those bands (as opposed to the rest of the Pacific). By the time GIStemp “begins time” in 1880, the impact is modest. But yes, I think you see that impact in WWII particularly when the hot mid tropics drop out under Japanese occupation. I’m presently looking at the N.Z. impact in the mix, and as a stand alone entity. Real Soon Now there will be more. But I’m doing these postings in “real time”. When I know something, you know something, within about an hour… IMHO, Real Science doesn’t get any better, or more real, than this. 8-) -ems”

  2. rob r says:

    By the way it would be interesting for us Kiwis to see the NZ subset in isolation from all the rest. Yet another job so no hurry.

    REPLY: ” I’m actually working on it right now ;-) So far, I’ve found out that Kiwi’s really like to play with there instruments. A lot of the “more then less” of thermometer records is “modification flags” coming and going. Up to 4 for one station in one year! As soon as I’ve figured out what this all means to me, you’ll see it. I’d guess about 3 hours. (I need dinner “soon”…) -ems”

  3. Ripper says:

    Here is a graph

    REPLY: “WOW! You can really see the spread from New Zealand out into the Pacific, the dropout in WWII, then the rise of the Jet Age Airports on every Island, followed by the very flat stability; then the “blip” at the end with the late 1990’s early 2000’s “something changing” in the thermometer records. This is what I’m digging into right now. What is that “something at the end”? I intend to link to this graph in the body of the article, unless you ask me not to… -ems”

  4. Hi ‘EM’ I see the saga continues ;-) You have made it very clear that ‘averaging stations and then looking for trends’ is a silly practice. An alternative is to ‘find the trend for each station and then average all of these trends’. This can be done in a variety of ways.
    The one that would carry most weight scientifically would be a full dataset analysis with a general linear statistical model (subscripts are not possible in this web site) e.g.
    temp= b * Time + Station effect
    The overall regression (b) would tell you what Temp (temp) trend has been but it will be FREE of the station effects. the assumption would be that temperature change has been steady (linear) over the 100 year period (up is + down is -) This seems reasonable to me.
    Stations can come in and out wherever & whenever but they will NOT bias the trend (b).
    If you need help with this I can do it but I would appreciate help formatting the data if you had time. What do you think? PS I can dredge up some old FORTRAN code that will calculate the trends separately for each station if you wanted it.

  5. Ripper says:

    Go for it E.M.

    When I get time i will do up some more for the others.

  6. E.M.Smith says:

    Alexander E McClintock: Hi ‘EM’ I see the saga continues ;-) You have made it very clear that ‘averaging stations and then looking for trends’ is a silly practice.

    Yes, it is. But someone else owns the “rules” for this game.

    An alternative is to ‘find the trend for each station and then average all of these trends’. This can be done in a variety of ways.If you need help with this I can do it but I would appreciate help formatting the data if you had time. What do you think? PS I can dredge up some old FORTRAN code that will calculate the trends separately for each station if you wanted it.

    Well, to paraphrase a movie: ‘Silly is as Silly Does.’ :-) I didn’t set the ground rules; someone else decided that ‘an average of averages of averages of averages that have been interpolated and adjusted, then averaged’ means something.

    All I can do is to use those ‘rules’ to the best of my ability to show that they are a really dumb idea, I think I’m making some progress ;-) And I agree completely that I would approach the problem by taking a trend for each station, then averaging the trends. It is a much more sane way to approach the issue. I am willing to do whatever it takes to do whatever I can to show the truth (whatever it turns out to be.) And all help is appreciated.

    If you would like to do a bit, tell me what I can do to help you and I will do it. If you would rather I do it, I will, but there is only ‘so much of me’ so the results become ‘rate limited’. Tell me what format you want, for which data, and I’ll provide it.

    BTW: I’d love to have the “old FORTRAN”. It took me a while to get the C and Pascal pushed out of my brain enough to do decent FORTRAN again; but now that I have FORTRAN loaded, swapping back to C is a bit of a pain… To the extent possible, I’d rather not keep swapping languages and I suspect that a web search would show up a C++ package … :-{

    I expect that ‘SmithTemp’ will be ready for first use about 1 month from now (with fixes for the crap in GIStemp including a proper load of USHCN.v2 data). Sooner IFF I can swap 2 more cups of coffee per day for 2 hours of sleep 8-0

    Shortly after that, I intend to do the “real and valid trend analysis” that you suggest. If you think this is an error of priorities, let me know. I’m very open to guidance about priorities. I didn’t expect to head off into ‘GHCN is Broken!’ land. Just thought I needed to ‘characterize the data’ so I knew what I had; and expected it to be good data. About a week from now I expect to be done with that, but, you never know…

    And now that I know the source data is broken, and the nature of the breakage, I think I can do a much better job of trend analysis. Frankly, I think skipping that step (characterize your inputs) has resulted in 90%+ of the published papers to be built on fantasies…

    So, in summary, send code to the email address in the ‘ABOUT’ tab up top and let me know if, and how, I can be of service. The armour may be a bit tarnished due to no squire (see the actual reason for “black knights”… ) but the devotion to honor and truth above all else is is clear. And I think I’ve shown a decent bit of skill in swinging my broad sword against the AGW dragon ;-)

  7. E.M.Smith says:

    Here is an updated version of the Indonesia temperature data:

    Do the extract / process for Temps.503 (Y/N)? y
     
     
    -rw-rw-r--    1 chiefio  chiefio      9487 Oct 30 11:26 Temps.503.yrs.GAT
     
    Look at Temps.503.yrs.GAT (Y/N)? y
     
    1880 25.2 26.1 26.3 26.3 26.4 25.3 25.3 25.9 26.4 26.4 26.2 25.7 26.0   1
    1881 25.7 25.8 25.8 26.9 27.1 26.4 26.3 26.5 26.5 27.0 26.6 26.7 26.4   1
    1882 26.0 26.1 25.9 26.5 26.0 25.9 25.7 26.0 26.3 26.2 26.2 26.3 26.1   1
    1883 26.3 25.4 26.6 26.2 26.3 26.7 26.2 26.1 26.6 26.5 26.3 25.7 26.2   1
    1884 25.7 25.7 25.9 26.4 26.3 26.3 25.3 26.3 26.6 27.0 26.6 25.8 26.2   1
    1885 25.1 25.3 25.9 26.7 26.5 26.2 26.0 26.3 26.5 27.2 26.7 26.2 26.2   1
    1886 26.3 25.8 26.3 26.6 26.9 26.4 26.3 26.4 26.8 26.9 26.6 26.6 26.5   1
    1887 26.0 25.5 26.2 26.5 26.2 25.8 25.3 26.1 25.9 26.4 26.3 26.0 26.0   1
    1888 25.1 25.5 26.5 26.6 26.6 26.7 25.8 26.2 26.4 27.4 27.6 26.8 26.4   1
    1889 26.9 26.7 26.7 27.5 27.4 26.7 26.3 26.3 26.9 26.8 26.7 26.7 26.8   1
    1890 26.8 26.3 26.2 26.7 26.5 25.9 25.6 25.9 26.0 26.3 25.7 26.0 26.2   1
    1891 25.9 25.7 26.3 26.6 27.0 26.4 25.9 25.6 26.9 27.9 27.4 26.6 26.5   1
    1892 25.9 26.4 26.8 26.4 26.4 26.4 26.2 26.0 26.0 26.8 26.5 26.7 26.4   1
    1893 25.7 25.5 26.1 26.5 26.7 25.7 25.6 26.0 26.5 26.4 26.6 25.7 26.1   1
    1894 25.5 25.5 26.1 26.6 26.0 25.8 25.9 26.4 26.8 27.0 26.6 26.2 26.2   1
    1895 25.9 25.6 26.2 26.6 26.7 26.3 25.4 26.0 27.2 27.5 26.8 26.5 26.4   1
    1896 25.7 26.1 26.5 26.6 26.8 26.0 26.0 26.1 26.8 27.5 27.9 26.9 26.6   1
    1897 26.5 27.0 27.0 26.7 27.4 26.9 26.4 26.7 27.2 27.2 26.9 26.2 26.8   1
    1898 26.1 26.0 26.4 27.0 27.2 26.6 26.1 26.5 26.8 26.5 26.6 26.2 26.5   1
    1899 26.0 25.7 26.1 26.4 26.8 26.3 26.3 26.1 26.6 27.2 27.1 26.1 26.4   1
    1900 26.4 26.5 26.6 26.9 26.8 26.8 26.2 26.5 27.0 27.4 27.3 26.8 26.8   1
    1901 26.3 25.7 25.9 27.0 27.3 26.5 26.2 26.1 26.9 27.2 27.1 26.4 26.6   1
    1902 26.3 25.6 26.4 27.0 26.9 26.3 26.2 26.4 26.8 27.5 27.2 27.0 26.6   1
    1903 26.8 26.1 27.0 27.0 26.6 26.5 26.3 26.8 27.3 26.6 26.9 25.8 26.6   1
    1904 25.8 25.4 25.9 26.6 26.6 26.3 25.9 25.9 26.5 26.5 26.8 26.2 26.2   1
    1905 26.4 26.2 27.1 26.8 26.6 27.3 26.4 26.5 26.7 27.8 27.1 27.1 26.8   1
    1906 27.0 27.2 27.0 27.2 27.0 26.8 26.7 26.7 27.2 26.8 27.0 26.2 26.9   1
    1907 26.3 26.0 26.4 26.5 26.7 26.1 26.4 26.1 26.6 27.2 26.9 26.4 26.5   1
    1908 26.4 26.5 27.0 27.2 26.9 26.2 25.7 26.5 26.7 26.8 26.6 26.7 26.6   1
    1909 26.8 26.5 26.7 27.0 27.0 26.3 26.3 26.9 27.0 27.1 26.3 25.7 26.6   1
    1910 26.4 26.5 26.5 26.9 27.0 26.6 26.7 26.5 26.9 26.5 26.5 26.3 26.6   1
    1911 26.1 25.7 26.8 27.3 27.0 26.9 26.2 25.8 26.9 27.3 26.9 27.0 26.7   1
    1912 26.5 26.5 26.8 27.8 27.4 26.8 26.3 26.9 27.7 27.0 26.5 26.7 26.9   1
    1913 26.7 26.6 26.3 27.0 27.0 26.5 26.7 26.3 27.0 27.4 27.1 26.9 26.8   1
    1914 26.7 26.7 27.1 27.0 27.0 27.0 26.7 26.4 27.5 27.7 27.5 27.3 27.0   1
    1915 26.8 26.9 27.3 26.9 27.2 26.7 26.5 26.2 26.6 27.5 27.0 26.4 26.8   1
    1916 25.2 26.5 26.4 26.5 26.4 26.3 25.9 26.0 26.7 26.2 26.1 26.1 26.2   1
    1917 25.6 25.8 26.4 26.6 26.8 26.7 26.8 26.9 26.6 26.3 26.9 25.5 26.4   1
    1918 25.7 24.7 25.8 26.5 26.6 25.9 26.5 26.7 27.5 27.5 27.0 26.9 26.4   1
    1919 26.6 26.6 26.9 27.2 26.7 26.5 26.2 26.6 27.1 27.6 26.5 26.2 26.7   1
    1920 25.8 26.4 26.1 26.7 26.8 26.6 26.6 26.1 26.8 26.6 27.2 26.7 26.5   1
    1921 26.5 26.1 26.6 26.7 27.2 26.3 26.6 26.8 27.0 27.4 26.6 26.5 26.7   1
    1922 26.5 26.1 26.8 27.3 27.0 27.0 26.8 26.8 27.0 26.8 26.5 26.6 26.8   1
    1923 25.9 26.2 27.0 27.2 27.4 26.9 26.2 26.0 26.7 27.5 27.3 26.9 26.8   1
    1924 27.0 26.7 26.9 26.9 27.3 27.0 27.1 27.2 27.5 26.9 26.9 26.1 27.0   1
    1925 26.3 26.1 26.4 26.6 26.9 26.8 26.7 26.9 27.9 27.7 27.3 27.0 26.9   1
    1926 26.3 26.5 26.8 28.0 27.7 27.3 27.2 27.6 27.7 27.6 27.4 26.1 27.2   1
    1927 26.5 26.5 26.6 27.1 26.8 26.7 26.8 27.2 27.5 27.5 27.1 26.9 26.9   1
    1928 26.9 26.3 26.9 27.2 27.3 26.8 26.5 26.3 27.4 27.5 27.2 26.8 26.9   1
    1929 26.0 26.4 26.2 27.1 27.3 26.9 26.4 26.9 27.5 27.5 27.1 26.3 26.8   1
    1930 26.6 26.2 27.1 26.9 26.9 27.0 27.0 27.3 27.5 27.3 27.3 27.0 27.0   1
    1931 26.7 27.2 27.2 27.2 27.3 27.1 26.4 27.1 27.2 27.4 26.6 26.7 27.0   3
    1932 26.2 26.3 26.3 26.9 27.3 26.6 26.5 26.7 26.9 26.9 26.9 26.5 26.7   3
    1933 26.6 26.2 26.6 26.6 27.1 26.7 26.5 27.1 26.7 27.1 26.5 26.3 26.7   3
    1934 26.3 26.0 26.1 26.7 26.8 26.7 26.4 26.4 26.7 27.1 26.4 26.1 26.5   3
    1935 26.4 26.6 27.0 26.6 27.2 26.5 26.1 26.3 26.6 27.1 27.0 26.9 26.7   3
    1936 26.2 26.5 26.7 26.9 27.2 26.7 26.5 26.8 27.1 27.1 26.7 26.7 26.8   3
    1937 26.1 26.8 26.9 26.9 26.9 26.7 26.1 26.6 26.9 27.1 27.3 26.2 26.7   3
    1938 26.3 26.5 27.1 27.2 26.9 26.8 26.6 26.4 26.6 27.1 26.8 26.6 26.7   3
    1939 26.3 26.4 26.7 27.0 26.9 26.4 26.7 26.8 26.6 26.7 27.0 26.7 26.7   3
    1940 26.2 26.5 26.7 27.4 27.3 26.8 26.7 26.2 27.1 27.5 27.0 26.7 26.8   3
    1941 26.8 27.0 27.3 27.6 27.5 27.4 26.5 26.6 26.6 26.9 27.3 26.9 27.0   3
    1942 27.2 27.1 27.3 27.3 27.5 27.1 26.2 26.7 27.1 28.0 27.1 27.2 27.2   2
    1943 26.3 26.3 26.6 27.0 27.2 26.7 26.5 27.1 27.5 27.1 26.7 26.7 26.8   1
    1944 26.8 26.7 26.9 27.0 27.0 26.6 26.2 26.5 26.8 27.4 27.5 26.8 26.8   2
    1945 27.2 26.8 26.9 27.2 27.0 26.3 26.1 26.7 27.2 27.5 26.8 27.3 26.9   2
    1946 27.5 26.5 27.0 27.3 26.8 26.8 26.7 27.2 27.0 27.5 27.6 27.8 27.1   2
    1947 27.7 27.5 27.1 26.8 27.4 27.2 25.7 26.2 26.1 26.9 26.4 27.4 26.9   3
    1948 26.5 27.0 27.3 27.3 27.1 26.6 26.8 26.7 26.9 27.2 26.9 28.1 27.0   3
    1949 26.8 26.5 26.6 26.7 26.6 26.1 25.6 25.7 26.8 27.1 26.6 27.0 26.5   7
    1950 26.4 26.1 26.8 26.7 26.6 26.6 25.7 25.9 26.4 26.6 26.3 26.1 26.3   7
    1951 25.7 25.7 26.4 26.4 26.4 26.1 25.5 25.9 26.6 27.2 27.8 27.0 26.4  10
    1952 26.8 26.6 26.5 26.7 26.6 26.3 25.8 26.0 26.9 27.1 26.8 26.3 26.5  12
    1953 26.1 26.2 26.6 26.7 26.5 26.2 25.8 26.1 26.6 27.2 27.2 26.8 26.5  13
    1954 26.6 26.3 26.7 26.8 26.6 26.3 25.4 26.0 26.0 26.6 26.3 25.9 26.3  14
    1955 26.1 26.0 26.3 26.2 26.5 26.0 25.5 25.8 26.3 26.3 25.8 26.0 26.1  13
    1956 25.7 26.1 26.5 26.7 26.5 26.2 25.8 25.9 26.1 26.8 26.5 26.0 26.2  18
    1957 26.1 26.0 26.4 27.0 26.8 26.6 26.1 26.0 26.3 26.8 27.1 26.5 26.5  18
    1958 26.7 26.5 26.7 26.9 27.0 26.6 26.3 26.1 26.6 26.9 26.8 26.4 26.6  18
    1959 25.9 26.3 26.4 26.4 26.5 26.0 25.5 25.4 26.1 26.6 26.8 26.5 26.2  19
    1960 26.2 26.1 26.5 26.9 26.9 26.4 26.1 26.5 26.7 27.2 26.7 26.7 26.6  49
    1961 26.3 26.5 26.9 27.1 27.1 26.2 25.9 25.9 26.5 27.1 27.2 26.8 26.6  51
    1962 26.2 26.2 26.6 26.9 27.1 26.7 26.4 26.2 26.7 27.2 27.2 26.6 26.7  55
    1963 25.8 26.0 26.4 27.2 27.3 26.9 26.3 26.2 26.8 27.2 27.6 27.0 26.7  56
    1964 27.1 26.9 26.8 27.2 27.3 26.6 26.3 26.5 26.9 26.7 26.7 26.5 26.8  57
    1965 25.9 26.4 26.5 26.8 26.9 26.6 26.1 26.2 26.8 27.3 27.6 27.2 26.7  54
    1966 26.7 26.6 26.9 27.3 27.3 26.5 26.4 26.6 27.1 27.2 27.3 26.8 26.9  55
    1967 26.4 26.5 26.7 27.0 27.1 26.5 26.1 26.4 26.8 27.4 27.3 26.8 26.8  56
    1968 26.4 26.4 26.8 27.1 27.0 26.7 26.3 26.3 26.9 27.1 27.1 26.7 26.7  54
    1969 26.8 26.8 27.3 27.3 27.3 26.7 26.2 26.3 26.7 27.2 27.3 26.9 26.9  55
    1970 26.7 26.9 27.1 27.2 27.1 26.7 26.1 26.1 26.6 27.1 26.8 26.5 26.7  51
    1971 26.2 26.3 26.4 26.8 26.8 26.2 25.9 26.2 26.8 26.8 26.5 26.6 26.5  50
    1972 26.1 26.6 26.4 26.8 26.8 26.4 26.1 26.4 26.7 27.1 27.6 27.4 26.7  54
    1973 27.0 27.2 27.0 27.3 27.0 26.9 26.5 26.7 26.7 27.2 27.0 26.4 26.9  51
    1974 26.0 26.0 26.4 26.7 26.9 26.4 26.1 26.4 26.6 26.9 26.8 26.4 26.5  53
    1975 26.4 26.3 26.5 27.0 26.8 26.3 26.2 26.4 26.6 26.7 26.6 26.3 26.5  52
    1976 25.9 26.0 26.3 26.3 26.8 26.1 25.7 26.1 26.6 26.6 26.6 26.6 26.3  38
    1977 26.2 25.9 26.2 26.8 26.8 26.2 26.1 25.8 26.5 27.1 27.3 26.6 26.5  40
    1978 26.3 26.4 26.8 26.8 27.2 26.4 26.0 26.4 26.2 26.8 26.9 26.4 26.5  42
    1979 26.6 26.7 26.7 27.0 27.2 26.6 26.0 26.4 26.8 27.1 27.0 26.5 26.7  42
    1980 26.5 26.7 26.7 27.0 27.2 26.8 26.5 26.2 26.7 27.1 26.8 26.6 26.7  42
    1981 26.2 26.3 26.8 26.9 27.3 26.9 26.6 26.8 26.6 27.3 27.2 26.5 26.8  41
    1982 26.5 26.5 26.6 27.0 26.8 26.4 26.0 26.1 26.3 26.9 27.4 27.3 26.6  37
    1983 26.9 27.2 27.6 27.6 27.1 26.9 26.3 26.6 26.8 27.2 27.0 26.9 27.0  39
    1984 26.1 26.2 26.4 26.9 26.7 26.4 26.1 26.4 26.2 27.0 27.1 26.5 26.5  40
    1985 26.5 26.7 27.0 27.0 27.3 26.7 26.0 26.2 26.5 27.0 27.0 27.2 26.8  33
    1986 26.5 26.7 26.8 27.3 27.2 26.9 26.4 26.1 26.8 27.1 27.3 27.2 26.9   9
    1987 26.9 26.9 27.2 27.4 27.5 27.1 26.5 26.5 26.9 27.3 27.5 27.2 27.1  30
    1988 27.0 27.1 27.4 27.4 27.2 26.8 26.5 26.7 26.8 27.3 27.2 26.7 27.0  27
    1989 27.0 26.7 26.8 27.1 27.0 26.7 26.4 26.4 26.8 27.2 27.2 27.1 26.9  31
    1990 26.5 27.0 27.1 27.8 27.5 26.8 26.3-99.0-99.0-99.0 27.7 26.4 27.0  30
    1991 26.6-99.0-99.0 27.2 26.9 26.8-99.0-99.0-99.0-99.0-99.0-99.0 26.9  27
    1995-99.0-99.0-99.0-99.0 27.2-99.0 27.5-99.0 28.3 27.8-99.0-99.0 27.7  11
    1996 26.3 25.8-99.0 26.8 27.7 26.6 26.7-99.0 27.1-99.0 27.0 27.6 26.8  14
    1997-99.0 26.6 27.1 27.7 27.6 26.8 26.4 26.0 26.1-99.0 25.8 28.5 26.9  19
    1998-99.0 27.6 27.5 27.2 28.3 28.3-99.0 27.7 27.1 26.8 27.2 27.0 27.5  12
    1999-99.0-99.0 25.9-99.0 27.0 27.0 26.0 27.2-99.0 26.6 26.7 27.2 26.7  12
    2000-99.0 26.9 26.7-99.0 27.0 26.0 26.9 26.0 26.8 26.9 27.2 26.5 26.7  16
    2001 26.6 27.0 27.0 27.2 27.2 26.8 25.9 26.6 27.0 27.0 26.7 26.7 26.8  15
    2002 26.9 27.0 27.2 27.6 27.5 26.8 26.6 26.8 26.6 27.1 27.2 27.2 27.0  13
    2003 26.7 27.3 26.8 27.0 27.3 27.2 26.2 26.2 26.9 27.1 27.7 26.9 26.9  16
    2004 27.6 27.0 27.1 28.1 27.5 27.1 26.7 26.7 26.5 27.2 27.4 27.1 27.2  18
    2005 27.1 27.7 27.5 27.2 27.4 27.1 26.8 26.5 26.6 27.9 27.3 27.0 27.2  15
    2006 26.6 26.9 27.3 27.2 27.3 26.7 26.3 26.2 26.3 27.0 27.8 27.8 26.9  18
    2007 27.4 27.1 27.1 27.3 27.5 26.8 26.1 26.0 26.6 27.5 27.4 27.2 27.0  21
    2008-99.0-99.0-99.0-99.0-99.0-99.0-99.0-99.0 27.0 27.3 27.6 27.0 27.2  28
         26.4 26.5 26.7 27.0 27.0 26.6 26.2 26.3 26.7 27.1 27.1 26.7 26.7
         26.4 26.4 26.7 27.0 27.0 26.6 26.2 26.4 26.8 27.1 26.9 26.7 26.7
     
    For Country Code 503
    [chiefio@tubularbells analysis]$ 
    

    And now that I’ve got the proper “missing data flag” processing in it, I’ll give a code listing too. But be advised, I just put this stuff in today. I usually like to “kick around” a piece of code for a week or two before I’m comfortable that I didn’t mess something up. But in this case, I’ll make an exception. Just be nice if you find that I’ve introduced an “unexpected feature” ;-)

    [chiefio@tubularbells analysis]$ cat lmyears.f 
    C2345*7891         2         3         4         5         6         712sssssss8
    C     Program:  lmyears.f
    C     Written:  October 30, 2009
    C     Author:   E. M. Smith
    C     Function: To produce a list of Global Average Temperatures for 
    C     each year of data in a GHCN format file, with one GAT for each 
    C     month and a total GAT for that year.  Summary GAT records are 
    C     produced for the whole data set as a "crossfoot" cross check of 
    C     sorts.  While you might think it silly to make a "global average 
    C     temperature" for a 130 year (1880 to date) or 308 year (1701 the 
    C     first data in GHCN, to date) interval, once you accept the idea 
    C     of adding together 30 days, or 365 days of records, or 
    C     thermometers from all over the planet "means something":  
    C     Where does it end?
    
    C     Personally, I think the whole idea of a GAT is bogus, 
    C     but if you accept it as a concept (and GIStemp and the AGW 
    C     movement do) then you must ask:
    C     "in for a penny, in for a pound":  
    C     When does the GAT cease to have some value, and exactly why?...  
    C
    C     So I produce GAT in several ranges and you can inspect it 
    C     and ponder.
    
    C     Copyright (c) 2009
    C
    C     This program is free software; you can redistribute it and/or 
    C     modify it under the terms of the GNU General Public License as 
    C     published by the Free Software Foundation; either version 2, 
    C     or (at your option) any later version.
    C
    C     This program is distributed in the hope that it will be useful,
    C     but WITHOUT ANY WARRANTY; without even the implied warranty of
    C     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    C     GNU General Public License for more details.
    C
    C     You will notice this "ruler" periodically in the code.
    C     FORTRAN is position sensitive, so I use this to help me keep 
    C     track of where the first 5 "lable numbers" can go, the 6th 
    C     position "continuation card" character, the code positions up
    C     to number 72 on your "punched card" and the "card serial number"
    C     positions that let you sort your punched cards back into a proper
    C     running program if you dropped the deck.  (And believe it or 
    C     not, I used that "feature" more than once in "The Days Before
    C     Time And The Internet Began"... 
    
    C2345*7891         2         3         4         5         6         712sssssss8
    
    C     Oddly, within the 7-72 positions, FORTRAN is not position 
    C     sensitive.  This was so if you put in an accidental space 
    C     character, you didn't need to repunch a whole new card...
    C     Oh, and if you type a line past the 72 marker, you can cut
    C     a variable name short, creating a new variable, that FORTAN
    C     will use as an implied valid variable.  So having "yearc"
    C     run past the end can turn it into "year" "yea" "ye" "y"
    C     which will then be the actual variable you are using in that
    C     line, not the one that prints out on your card.  The 
    C     source of endless bugs and mirth 8-}
    
    C     General Housekeeping.  Declare and initialize variables.
    C
    C     itmp    - an array of 12 monthly average temperatures for any
    C               given record.  The GHCN average temp for that 
    C               station/year.
    C     incount - the count of valid data items falling in a given month
    C               for a year.  An array of months-counts with valid data.
    C     nncount - the count of valid data items falling in a given month
    C               for all years.  An array of months-counts w/ valid data.
    C     itmptot - Array of the running total of temperatures, by month.
    C     ymc     - count of months for the total of a year  with some 
    C               valid data.
    C     ntmptot - running total of all temperatures, by month column.
    C     icc id iyr nyr iyrmax m iyc - countrycode, Stn ID, on year 
    C               of data as monthly averages of MIN/MAX temps, max year 
    C               so far, month, iyc In Year Counter: # recs in year.
    C     tmpavg  - Array of average temperatures, by month. The data 
    C               arrive as an INTEGER with an implied decimal point 
    C               in itmp.  This is carried through to the point where 
    C               we divide by 10 and make it a "REAL" or floating point 
    C               number in this variable.
    C     ttmpavg - Total of temperature data by month for all years.
    C     tymc    - count of months for the total of all data with some 
    C               valid data.
    C     eqwt    - Total of monthly averages of temperature data, by month.
    C     eqwtc   - Counter of months with valid data.
    C     eqwtg   - Grand Total of calculated monthly averages of MIN/MAX 
    C               averages.  Divided by eqwtc for Grand Avg.
    C     gavg    - Global Average Temperature.  GAT is calculated by 
    C               summing tmpavg monthly averages that have valid data, 
    C               then dividing by the count of them with vaid data.
    C    ggavg    - The Grand Grand Average Temperature, 
    C               whatever it means... 
    C     line    - A text buffer to hold the file name of the input file
    C               to be processed, passed in as an aguement at run time.
    C     oline   - A text buffer for the output file name, 
    C               set to the input_file_name.GAT
    
    C2345*7891         2         3         4         5         6         712sssssss8
    
          integer incount(12), nncount(12), itmptot(12), ntmptot(12)
          integer itmp(12) 
          integer icc, id, iyr, nyr, iyrmax, m, iyc
    
          real tmpavg(12), ttmpavg(12), eqwt(12), eqwtc(12)
          real gavg, ggavg, ymc, tymc, eqwtg
    
          character*128 line, oline
    
          data incount /0,0,0,0,0,0,0,0,0,0,0,0/
          data nncount /0,0,0,0,0,0,0,0,0,0,0,0/
          data itmptot /0,0,0,0,0,0,0,0,0,0,0,0/
          data ntmptot /0,0,0,0,0,0,0,0,0,0,0,0/
          data itmp    /0,0,0,0,0,0,0,0,0,0,0,0/
    
    C      data tmpavg  /0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0./
    C2345*7891         2         3         4         5         6         712sssssss8
    
          data tmpavg  /-99.,-99.,-99.,-99.,-99.,-99.,-99.,-99.
         *,-99.,-99.,-99.,-99./
    
          data ttmpavg /0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0./
          data eqwt    /0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0./
          data eqwtc   /0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0./
    
          icc   =0 
          id    =0 
          iyr   =0 
          nyr   =0 
          iyrmax=0
          m     =0
          iyc   =0
    
          gavg  =0.
          ggavg =0.
          eqwtg =0.
          ymc   =0.
          tymc  =0.
    
          line =" "
          oline=" "
    
    C     Get the name of the input file, in GHCN format.  The file 
    C     must be sorted by year (since we sum all data by month 
    C     within a year.) The name of the output file will be that of 
    C     the input_file.yrs.GAT where GAT stands for Global Average 
    C     Temperature.
    
    C2345*7891         2         3         4         5         6         712sssssss8
    
          call getarg(1,line)
          oline=trim(line)//".yrs.GAT"
          open(1,file=line,form='formatted')
          open(10,file=oline,form='formatted')              ! output
    
    C     Read in a line of data (Country Code, ID, year, temperatures)
    C     Set the max year so far to this first year, set the "LASTID" 
    C     to zero so it will fail the equality test later.
    
          read(1,'(i3,i8,1x,i4,12i5)',end=200) icc,id,iyr,itmp
          iyrmax = iyr
          LASTID = 0
          rewind 1
    
       20 CONTINUE
          
          read(1,'(i3,i8,1x,i4,12i5)',end=200) icc,id,iyr,itmp
    
          if(iyr .gt. iyrmax) then
    
    C      if you have a new year value, you come into this loop,
    C      calculate the Monthly Global Average Temperatures, the 
    C      Yearly GAT for iyrmax.
    C      Print it all out, and move on.
    
            do m=1,12
    
              if (incount(m) .ne. 0) then
    
    C      We keep a running total of tenths of degree C in itmptot,
    C      by month. Then we divide this by the integer count of 
    C      valid records that went into each month.  This truncates 
    C      the result (I think this is valid, since we want to know 
    C      conservatively how much GIStemp warmed the data
    C      not how much my math in this diagnostic warms the data ;-)  
    
    C      So we have a "loss" of any precision beyond the "INTEGER" 
    C      values being divided, but since they are in 1/10C, we are 
    C      tossing 1/100C of False Precision, and nothing more.  
    C      THEN we divide by 10. (REAL) and yield a temperature 
    C      average for that month for that year (REAL).
    C      I could do a 'nint' instead:  nint(itmptot(m)/incount(m)) 
    C      and get a rounded result rather than truncated, but I 
    C      doubt if it's really worth if for a "hand tool" that I'd 
    C      like to be a conservative one.  If I truncate, then any 
    C      "warming" of the data is from GIStemp, not this tool. 
    C      (Or GHCN, now that I'm using to analyse the input data
    C      as well as the code itself.)
    
    C2345*7891         2         3         4         5         6         712sssssss8
    
    C       Diagnostic write to check missing data flag handling.
    C       write(*,*) "tmpavg: ", tmpavg
    
                tmpavg(m) = (itmptot(m)/incount(m))/10.
    
    C       Diagnostic write to check missing data flag handling.
    C       write(*,*) "TMPavg: ", tmpavg
    
                gavg      = gavg+tmpavg(m)
                ymc       = ymc+1.
    
    C       We put a running total of yearly averages together, 
    C       along with a count for tmpavg, it is the total of 
    C       monthly temperature averages divided by the count of 
    C       months with data in them (converted to C from 1/10 C).
    C       For eqwt it is a running total of those averages that are 
    C       used at the end to calculate a "monthly average of 
    C       monthly averages ".
    C       Basically, the first form, gavg, weights each recored 
    C       equally, while the second form gives equal weight to 
    C       each month, regardless of number of records in that month.  
    C       Which one is right?  You get to choose...  (And THAT
    C       is just one of the issues with an "average of averages of
    C       averages" means something...
    
    C       I just put them here so you can see that they are, in fact,
    C       different...
    
                eqwt(m)   = eqwt(m)+tmpavg(m)
                eqwtc(m)  = eqwtc(m)+1
    
              end if
            end do
    
            gavg=gavg/ymc
    
    C2345*7891         2         3         4         5         6         712sssssss8
    
    C Write out the Year, the averages, the grand avg, and the 
    C number of thermometers in the year
    
            write(10,'(i4,12f5.1,f5.1,i4)') iyrmax,tmpavg,gavg,iyc
    
    C     Diagnostic "writes", should you wish to use them.
    C       write(*,*) "iyc: ", iyc
    C2345*7891         2         3         4         5         6         712sssssss8
    C       write(*,'("GAT/year: "i4,12f7.2,f7.2,i6,f7.2)') iyrmax,
    C    *tmpavg,gavg,iyc,ymc
    
    C      probably paranoia, but we re-zero the monthly arrays of data.
    C      ande pack tmpavg with missing data flags of -99
    C      
            do m=1,12
              incount(m) =0      
              itmptot(m) =0
              tmpavg(m)  =-99.
              ymc        =0.
            end do
    
            gavg   =0.
            iyc    =0
            LASTID =0
            iyrmax =iyr
    
    C     hang on to the present year value and ...
    
          end if
    C     End of "new year" record handling.
    
    C     So we have a new record (for either a new year or for the 
    C     same year.) If it is valid data (not a missing data flag) 
    C     add it to the running totals and increase the valid 
    C     data count by one.
    
    C2345*7891         2         3         4         5         6         712sssssss8
    
    C     Increment the running total for stations in this year. 
    C     In Year Counter
    
          if (id .NE. LASTID) then
              iyc  = iyc+1
              LASTID = id
          end if
    
    C     For each month, skipping missing data flags, increment 
    C     the valid data counter for that month incount, add that 
    C     temperature data (in 1/10 C as an integer) into the 
    C     yearly running total itmptot.
    C     Also do the same for the total records count nncount 
    C     and running total of all temperatures (by month) ntmptot.
    
          do m = 1,12
    
            if (itmp(m) .gt. -9000) then
              incount(m) = incount(m)+1
              itmptot(m) = itmptot(m)+itmp(m)
              nncount(m) = nncount(m)+1
              ntmptot(m) = ntmptot(m)+itmp(m)
            end if
          end do
    
    C     and go get another record
          goto 20
    
    C     UNTIL we are at the end of the file.
      200 continue
    
    C2345*7891         2         3         4         5         6         712sssssss8
    
    C     Here we use the method vetted in the earlier program 
    C     totghcn.f where we hold the temps as integers in 1/10 C
    C     until the very end, then we do a convert to real 
    C     (via divide by 10.) and cast into a real (ttmpavg) 
    C     that is the total average temperature for that month for 
    C     the total data.  
    
    C     ggavg is the grand total GAT, but after it it stuffed with 
    C     valid data we must divide it by the number of months with 
    C     valid data.  It is the "Average of yearly averages of 
    C     monthly averages of daily MIN/MAX averages".
    C     Why?  Heck, GIStemp is a "serial averager", thought it might 
    C     be fun to see what you get.
    
    C     We also show the average of all the individual monthly 
    C     data.  That gives a different value.  
    C     Will the real GAT please stand up? ... 
    
    C     I would chose to use the average of all data in a month
    C     since it is less sensitive to the variation of number 
    C     of thermometers in any given year, but you might chose a 
    C     different GAT.  Averaging the data directly gives weight 
    C     to the years with more data.  Averaging the monthly 
    C     averages gives each month equal weight.  Choose one...
    C     Rational? No. 
    C     But it is the reality on the ground..
    C     Basically, if you do "serial averaging", the order of the
    C     averaging will change your results.  As near as I can tell,
    C     GIStemp (and the whole AGW movment) pay no attention to this
    C     Inconvenient Fact.
    
          do m=1,12
              if (nncount(m).ne.0) then
                ttmpavg(m)=(ntmptot(m)/nncount(m))/10.
                ggavg=ggavg+ttmpavg(m)
                tymc=tymc+1
              end if
              eqwt(m)=eqwt(m)/eqwtc(m)
              eqwtg=eqwtg+eqwt(m)
          end do
    
    C     ggavg is the grand average of monthly averages for a month.
    C     eqwt is the sum of all months averages.  eqwtc is the 
    C     count of all months with valid data.  So this is the 
    C     place where the total gets divided by the count to give the
    C     average of all averages in a month.
    
          ggavg=ggavg/tymc
          eqwtg=eqwtg/tymc
    
          write(10,'(4x,12f5.1,f5.1)') ttmpavg,ggavg
          write(10,'(4x,12f5.1,f5.1)') eqwt,eqwtg
    
          stop
          end
    [chiefio@tubularbells analysis]$ 
    
  8. E.M.Smith says:

    Oh, and this is the “wrapper script” I put around the FORTRAN program to manage the context:

    
    [chiefio@tubularbells analysis]$ cat dotest
    #       First off, sort v2.mean into a version for reporting by year.
    
    echo " "
    echo -n "Do the extract / process for v2.mean for ${1-501} (Y/N)? "
    read ANS
    echo " "
    
    if [ "$ANS" = "Y" -o "$ANS" = "y" ] 
    then
         PAT=^${1-501}
         echo $PAT
         grep $PAT ./v2.mean > v2.meanC.${1-501}
    
         ls -l v2.meanC.${1-501}
    
         echo Now Sort
         echo
    
         sort -n -k1.13,1.16 -k1.1,1.12  v2.meanC.${1-501} > Temps.${1-501}
    
         echo 
         echo Post Sort
         echo 
    fi
    
    ls -l Temps.${1-501}
    
    echo " "
    echo "Doing GAT Yearlies w/ Missing Flag: lmyears"
    echo " "
    
    echo " "
    echo -n "Do the extract / process for Temps.${1-501} (Y/N)? "
    read ANS
    echo " "
    
    if [ "$ANS" = "Y" -o "$ANS" = "y" ] 
    then
         bin/lmyears Temps.${1-501}
    
         echo " " >> Temps.${1-501}.yrs.GAT
         echo For Country Code ${1-501} >> Temps.${1-501}.yrs.GAT
         echo " "
         echo "Produced:"
         echo " "
         ls -l Temps.${1-501}.yrs.GAT
    fi
    
    echo " "
    echo -n "Look at Temps.${1-501}.yrs.GAT (Y/N)? "
    read ANS
    echo " "
    
    if [ "$ANS" = "Y" -o "$ANS" = "y" ] 
    then
         cat Temps.${1-501}.yrs.GAT
    fi
    
    echo " "
    ls -l v2.meanC.${1-501} Temps.${1-501}
    echo " "
    
    echo -n "Clean up / Delete intermediate files (Y/N)? "
    read ANS
    echo " "
    
    if [ "$ANS" = "Y" -o "$ANS" = "y" ] 
    then
         rm v2.meanC.${1-501} Temps.${1-501}
    fi
    [chiefio@tubularbells analysis]$ 
    
  9. E.M.Smith says:

    Oh, and this is the code that produces the “Thermometer Records by Latitude Band” numbers. Please note that it depends on the existence of a “brute force” concatenation file. I simply took the v2.mean file and did a lookup / match of the v2.inv station data to each line of the v2.mean file. I’m not proud of that. It is terribly inefficient. Really, the whole thing ought to be in a database and the two tables joined on statationID as a key field. But when you are in a hurry and have excess compute power, and especially when something will be done exactly one time and uses not very much very cheap disk, well, sometimes a bigger hammer wielded fast is better than a small scalpel in a year or two… So, with that “mea culpa” to the Gods of Code Bloat, here is the code. Wrapper script first, thenFORTRAN. I’ve not put all the comments in this one, but it’s the same “public use GNU license as the last one”:

    [chiefio@tubularbells analysis]$ cat docust
    echo " "
    echo "Remember to update BANDS with 9 LAT bands prior to use"
    echo " "
    echo "Need to make a joined GHCN with v2.inv data for the ${1-403} records "
    echo " "
    echo -n "Make the Extract of v2.inv.id.withlat (Y/N)?  "
    
    read ANS
    if [ "$ANS" = "Y" -o "$ANS" = "y" ]
    then
         ls -l ${2-./vetted/v2.inv.id.withlat}
         echo " "
         grep "^${1-403}" ${2-./vetted/v2.inv.id.withlat} > v2.${1-403}.withlat
         echo " "
         ls -l v2.${1-403}.withlat
    fi
    
    echo " "
    echo "Then sort the Special GHCN with v2.inv by year"
    echo "into a version for reporting."
    echo " "
    echo -n "Re-sort the selected records back into year order (Y/N)? "
    
    read ANS
    echo " "
    
    if [ "$ANS" = "Y" -o "$ANS" = "y" ] 
    then
         sort -n -k1.13,1.16 v2.${1-403}.withlat > Therm.by.lat${1-403}
         ls -l Therm.by.lat${1-403}
    fi
    
    
    echo " "
    echo -n "Do the Count of therm/yrs by latatitude (Y/N)? "
    
    read ANS
    echo " "
    
    if [ "$ANS" = "Y" -o "$ANS" = "y" ] 
    then
         echo bin/latcust Therm.by.lat${1-403}
         bin/latcust Therm.by.lat${1-403}
         echo " " >> Therm.by.lat${1-403}.Dec.LAT
         echo " " >> Therm.by.lat${1-403}.per.LAT
         echo For COUNTRY CODE:  ${1-403} >> Therm.by.lat${1-403}.Dec.LAT
         echo For COUNTRY CODE:  ${1-403} >> Therm.by.lat${1-403}.per.LAT
    fi
    
    ls -l Therm.by.lat${1-403}.Dec.LAT Therm.by.lat${1-403}.per.LAT
    
    echo " "
    echo -n "Look at Therm.by.lat${1-403}.Dec.LAT (Y/N)? "
    
    read ANS
    echo " "
    
    if [ "$ANS" = "Y" -o "$ANS" = "y" ] 
    then
         cat Therm.by.lat${1-403}.Dec.LAT
    fi
    
    echo " "
    echo -n "Look at Therm.by.lat${1-403}.per.LAT (Y/N)? "
    
    read ANS
    echo " "
    
    if [ "$ANS" = "Y" -o "$ANS" = "y" ] 
    then
         cat Therm.by.lat${1-403}.per.LAT
    fi
    
    echo " "
    ls -l v2.${1-403}.withlat Therm.by.lat${1-403}
    echo " "
    
    echo -n "Clean Up / Remove intermediate files   (Y/N)? "
    
    read ANS
    echo " "
    
    if [ "$ANS" = "Y" -o "$ANS" = "y" ] 
    then
         echo rm  v2.${1-403}.withlat Therm.by.lat${1-403}
    fi
    
    exit 
    
    

    Notice that ./vetted/v2.inv.id.withlat is that concatenated v2.mean with v2.inv data per line. The BANDS file lets you change the latitude bands without re-compiling the program, but it is “position sensitive” so you need keep things spaced at the specified spacing. It looks like:

    [chiefio@tubularbells analysis]$ cat Bands.nh
       0  10  20  30  40  50  60  70  80
    [chiefio@tubularbells analysis]$ 
    

    This an example for looking at the Northern Hemisphere by 10 degree increments. I have a set of these and just link one to BANDS as I decided what latitude sets I’ll be looking at. Not very elegant, but it works. Just remember that if you have a value moved over one space, 10 can become 1 when the program reads it, off by one …

    OK, here’s the FORTRAN. If anyone wants to translate it to C, or anything else, and post the equivalent, feel free!

    [chiefio@tubularbells analysis]$ cat latcust.f
    C2345*fff1         2         3         4         5         6         712sssssss8
    C
    C    this program sorts records into latitude bands
    C    Input must already be sorted by year and filtered to 
    C    selected latitude A spcial v2.mean+v2.inv concatinated 
    C    file is the source
    C
    C    There is an input file named "BANDS" that holds 9 latitude 
    C    integers. S to N
    C
    
          integer itmp(12), icc, id, iyr, iyrmax, m, iyc, kyr, ky, band(9)
          real latcnt(11), lattot(11)
        
          real  latitude, kount
          character*128 line, oline, pline
    
          data latcnt  /0,0,0,0,0,0,0,0,0,0,0/
          data lattot  /0,0,0,0,0,0,0,0,0,0,0/
    
          icc=0 
          id=0 
          iyr=0 
          iyc=0
          iyrmax=0
          kyr=0
          latitude=0
          kount=1.
    
    C     Believe it or not, the program may make bogus values,
    C     unless you do this initialization.
    
          do m=1,11
             latcnt(m)=0
             lattot(m)=0
          end do
    
    C     Get the name of the input file, in modified GHCN format.  
    C     The file must be sorted by year (since we sum all data by 
    C     month within a year.) The name of the output file will be 
    C     that of the inputfile.yrs.LAT
    C     The input file ought to be a GHCN format file with V2.inv data
    C     concatenated per line.  
    
          call getarg(1,line)
          oline=trim(line)//".per.LAT"
          pline=trim(line)//".Dec.LAT"
          open(1,file=line,form='formatted')
          open(10,file=oline,form='formatted')              ! output
          open(12,file=pline,form='formatted')              ! output
    
          open(2,file="BANDS",form='formatted')              ! output
          read(2,'(9i4)',end=300) band
    
    C2345*fff1         2         3         4         5         6         712sssssss8
    
    C     Read in a line of data (Country Code, ID, year, temperatures, 
    C     latitude)
    
    C     For each year, total "thermometer counts" increment decade 
    C     counts, increment thermometer counts by latitude.
    
          write(10,'(" ")')
          write(10,'("       Year SP",i4,2x,i4,2x,i4,2x,i4,2x,i4,2x,i4
         *,2x,i4,2x,i4,2x,i4,"   -NP")') band
          write(12,'("       Year SP",i4,2x,i4,2x,i4,2x,i4,2x,i4,2x,i4
         *,2x,i4,2x,i4,2x,i4,"   -NP")') band
    C      write(10,'("         Year SP-45    50    55    60    65    70    
    C     * 75    80    85    -NP ")')
    C      write(12,'("           Year SP-45    50    55    60    65    70    
    C     * 75    80    85    -NP ")')
    
          read(1,'(12x,i4,12i5,33x,f6.2)',end=200) iyr,itmp,latitude
          iyrmax=iyr
          rewind 1
    
       20 read(1,'(12x,i4,12i5,33x,f6.2)',end=200) iyr,itmp,latitude
    
          if(iyr.gt.iyrmax) then
    
    C      if you have a new year value, you come into this loop, print 
    C      the  total thermomether count per latitude for that year
    C      calculate the decade total thermometer count, and every decade
    C      print it all out, and move on.
    
    C2345*fff1         2         3         4         5         6         712sssssss8
    
    C       increment the latitude totals for the decade
    
            do m=1,11
               lattot(m)=lattot(m)+latcnt(m)
            end do
    
            do m=1,11
               latcnt(m)=(latcnt(m)/latcnt(11))*100.
            end do
    
    C        write(10,'("LAT year: "i4,9i6,1x,i5)') iyrmax,latcnt, iyc
            write(10,'("LATpct: "i4,11f6.1,1x)') iyrmax,latcnt
    
            if (mod(iyr,10).eq.0) then
    
    C ok, at this point we want to print out the decade average of thermometer
    C counts by latitude band. In 5 degree increments.
    C we would do that by printing out 
    
                 kyr=iyrmax
    
                 do m=1,11
                    lattot(m)=((lattot(m))/lattot(11))*100.
                 end do
    
                 kyc=nint(kyc/kount)
    
           write(10,'(" ")')
    C      write(10,'(" ",f6.1)') kount
    C      write(10,'("DecadeLat: "i4,9i5,1x,i5)') kyr,lattot, kyc
    
          write(10,'("DecPct: "i4,11f6.1)') kyr,lattot
          write(10,'(" ")')
          write(12,'("DecPct: "i4,11f6.1)') kyr,lattot
    
    C  Then we set the decade counter to zero and reset the decade array.
                 do m=1,11
                    lattot(m)=0
                 end do
                 kount=0.
                 kyc=0
            end if
    
    C      we re-zero the array of latitude counts for the year.
    C      
            do m=1,11
              latcnt(m)=0
            end do
           
            kount=kount+1.
            iyrmax=iyr
            iyc=0
    
          end if
    
    C2345*fff1         2         3         4         5         6         712sssssss8
    
    C     So we have a new record for a new year or for the same year.
    C     We count the thermometer regardless of the data flag 
    C     (not many are all zero, and the thermometer did exist) and
    C     we add a count to that thermometers latitude for that year.
    
          iyc=iyc+1
          kyc=kyc+1
    
          if     (latitude .lt. band(1) ) then
             latcnt(1)=latcnt(1)+1
          else if(latitude .lt. band(2) .and. latitude .ge. band(1) ) then
             latcnt(2)=latcnt(2)+1
          else if(latitude .lt. band(3) .and. latitude .ge. band(2) ) then
             latcnt(3)=latcnt(3)+1
          else if(latitude .lt. band(4) .and. latitude .ge. band(3) ) then
             latcnt(4)=latcnt(4)+1
          else if(latitude .lt. band(5) .and. latitude .ge. band(4) ) then
             latcnt(5)=latcnt(5)+1
          else if(latitude .lt. band(6) .and. latitude .ge. band(5) ) then
             latcnt(6)=latcnt(6)+1
          else if(latitude .lt. band(7) .and. latitude .ge. band(6) ) then
             latcnt(7)=latcnt(7)+1
          else if(latitude .lt. band(8) .and. latitude .ge. band(7) ) then
             latcnt(8)=latcnt(8)+1
          else if(latitude .lt. band(9) .and. latitude .ge. band(8) ) then
             latcnt(9)=latcnt(9)+1
          else if(latitude                             .ge. band(9) ) then
             latcnt(10)=latcnt(10)+1
          else
           write(*,*) "You can't get here, compiler error Or dirty Data! " 
          end if
        
          latcnt(11)=latcnt(11)+1
    
    C     and go get another record
          goto 20
    
    C     UNTIL we are at the end of the file where we print the last average
      200 continue
    
          do m=1,11
             lattot(m)=lattot(m)+latcnt(m)
          end do
    
            do m=1,11
               latcnt(m)=(latcnt(m)/latcnt(11))*100.
            end do
    
    C      write(10,'("LAT year: "i4,9i6,1x,i5)') iyrmax,latcnt, iyc
          write(10,'("LATpct: "i4,11f6.1)') iyrmax,latcnt
    
          do m=1,11
             lattot(m)=((lattot(m))/lattot(11))*100.
          end do
    
          kyc=nint(kyc/kount)
    
          kyr=iyrmax
    
    C      write(10,'(" ",f6.1)') kount
    C      write(10,'("DecadeLat: "i4,9i5,1x,1i4)') kyr,lattot, kyc
    
          write(10,'(" ")')
          write(10,'("DecPct: "i4,11f6.1)') kyr,lattot
          write(12,'("DecPct: "i4,11f6.1)') kyr,lattot
    
    C2345*fff1         2         3         4         5         6         712sssssss8
      300 continue
    
          stop
          end
    [chiefio@tubularbells analysis]$ 
    

    Yeah, I’ll post the “how to merge v2.mean and v2.inv files “sometime”; but I did it a while ago and need to find where I saved the code…

  10. Ian Beale says:

    E.M.

    1. From earlier posts people were urging you to publish. Seems to me there are two avenues to get this saga out much wider.

    a. Peer reviewed

    b. As a story as in the examples of “The Cuckoo’s Egg” or “The Ultra Secret” about Enigma. You already have the chapters for this more or less

    c. Hell – why restrict – Both!

    Seems to me that any will take more time than the end of the year

    2. This you get first go at critiquing –

    Can we refer to GISTemp as “The Piltdown Man of Climate Science”?

    Or, more bravely, can we refer to agw as “The Piltdown Man of Climate Science”?

    Use if useful

    REPLY: “Thanks, I may well use it, it’s catchy! BTW, there is also a 3rd way to publish. ‘Internet Viral’ … Given the time to Copenhagen, that is the best choice for rapid impact. And that’s what I’m doing. I’ve deliberately put up code, methods, etc. so that others can join in too, should they wish… On a longer cycle, I’m open to the peer reviewed idea. It will take more time than I can put in pre-Copenhagen and the time cycle is too long for ‘in the real world’ impact in time; so it’s a ‘stake in the heart after he’s chased back into his crypt with a cross, holy water, and sunshine’ for after Copenhagen.
    ;-)

    Public lay press is an interesting idea I’d not thought about. Has potential. Anyone who wants to use the information here and do so is free to do it. If no one else does, I’ll give it a go eventually. I know I’m potentially giving up an interesting place in the AGW history to someone else who ‘publishes first’. Sometimes a bit of self sacrifice for the greater good is needed. I would hope that I’d get at least a footnote… but the code is published ‘open source’ deliberately so that anyone with better ‘field position to publish has the tools needed to do the deed. This is that Celtic ‘anyone can call for a war, and whoever shows up does what they can’ approach. There will be time enough for glory in the sagas later… -ems “

  11. PeterA says:

    According to NASA’s latest global temperature anomaly data ( http://data.giss.nasa.gov/gistemp/graphs/ ) the world is still warming and we haven’t had any cooling over the past few years. This is despite the satellite data showing cooling. I get the impression here, perhaps mistakenly, that land based temperature measuring stations are being dropped off the list relatively more in the colder regions and new ones are being added relatively more in the warmer ones. Is this what’s actually happening? If true then this is amazing to say the least. Why aren’t scientists ringing alarm bells? Is this another hockey stick in the making?

  12. curious says:

    “From earlier posts people were urging you to publish. Seems to me there are two avenues to get this saga out much wider.”…

    or ask if you can do a guest post at CA?

    REPLY: “Any method, to any audience, by any participants who wish to move the ball down field. I’m doing ‘knock ons’ mostly. I wouldn’t mind doing a kick at the goal, but I’m just as happy to ‘head it’ on to someone better placed. CA knows who and where I am. If it has merit, they know how to reach me. After I knock out my next 2 weeks worth of workload (and let the ‘viral internet’ thing cook just a while longer), I’d be up for proactively approaching someone like CA. If they want something sooner, email is in the ‘about tab’ up top. -ems”

  13. E.M.Smith says:

    PeterA
    According to NASA’s latest global temperature anomaly data ( http://data.giss.nasa.gov/gistemp/graphs/ ) the world is still warming and we haven’t had any cooling over the past few years. This is despite the satellite data showing cooling. I get the impression here, perhaps mistakenly, that land based temperature measuring stations are being dropped off the list relatively more in the colder regions and new ones are being added relatively more in the warmer ones. Is this what’s actually happening?

    That is EXACTLY what is happening. See the UPDATE for the pacific islands minus N.Z. and Australia above. You can’t have “Global” warming if the whole darned Pacific is out of the game… so you juice the two large land masses in that “region” then GIStemp spreads the juice a few thousand km in all directions. It is, as near as I can tell, as subtile fraud.

    There is the remote chance of a self deception (a la Clever Hans) where the folks think they are building a “more representative system” by deletions, find more warming, and self confirmation bias lets them all “high five” over their great wisdom. But that’s a high hurdle of stupidity to cross at this point.

    The facts of the warming migration of the thermometers are clear. It is only the motivation that is unclear.

    If true then this is amazing to say the least. Why aren’t scientists ringing alarm bells? Is this another hockey stick in the making?

    IMHO, it is another Hockey Stick, but on steroids. I also suspect that most folks just didn’t know until recently. Folks trusted NOAA to provide clean data. Instead they cooked the record (knowingly or not). How long does it take to catch a Madoff or a Ponzi? It’s all about the appearance of trust.

    Forensic accounting is a dreadfully dull field. Lots of flat charts of numbers. Make them temperatures and it’s even worse. I’d guess about 8 in 10 see the blocks of numbers, glaze, and leave the site. Even those who care. It’s hard to expect your average “A type glory hound” with a “publish or perish” over his head to want to go investigating tables of numbers from NOAA. It’s easier just to trust…

  14. E.M.Smith says:

    rob r
    By the way it would be interesting for us Kiwis to see the NZ subset in isolation from all the rest. Yet another job so no hurry.

    REPLY: ” I’m actually working on it right now ;-)

    As soon as I’ve figured out what this all means to me, you’ll see it. I’d guess about 3 hours. (I need dinner “soon”…) -ems”

    Well, I got pulled into the rest of the pacific instead, then hit the sleep wall ;-) BUT, take a look at the “update” on the pacific with N.Z. and Australia both pulled out.

    Next up ought to be “Polynesia, without N.Z.” where I’ll show more detail on that batch of ocean AND show the N.Z. records and why taking them out increases stability. Basically, it’s a more complicated story that involves a LOT of changes of instruments / modification flags.

  15. Geoff Sherrington says:

    The original source of virtually all the Australian land data is the Bureau of Meteorology. I have an email from them stating that what other people do with these numbers is beyond their control.

    Elsewhere, in several places, I have shown how different processing organisations, like GISS and KNMI, get quite results quite different from the BOM. Indeed, the BOM get quite different results from themselves as they update data and change station densities in different climates for calculation.

    For general use, the BOM dropped out most temp data prior to 1910 or so, because of uncertainties of the introduction date of Stevenson screens. Some other data users tended to ignore this drop out and persist back to the 1850s.

    Also, there was a widespread change from Hg thermomenters to thermocouples, with daily readings replaced by half hour, about 1988-1996, depending on station, then another set of instrument changes about 2000-2005 or so.

    In the period after 1990 Australia agreed to move to a Reference Climate Station system of about 107 stations from a menu of nearly 1700 stations that had some type of records, many of not much value. But the RCS concept does not seem enthusiastic and political attention here has focussed on ocean heat content this year.

    Please go ahead and use GISS data at your peril, but be kind to your workload and don’t invest a lot of time in it. Why not use the BOM data instead? It’s closer to the source.

    There are portents of evil here. When you report that “we saw that in 1992-93 there were 401 thermometers deleted”, I recalled that Error 401 in Windows is

    “If you have just logged on and received the 401 Unauthorized error, it means that the credentials you entered were invalid for some reason.”

    Lucky. They might have deleted 404 thermometers.

  16. E.M.Smith says:

    Geoff Sherrington:
    In the period after 1990 Australia agreed to move to a Reference Climate Station system of about 107 stations from a menu of nearly 1700 stations that had some type of records, many of not much value.

    Well, at least we have a “handle” for all the circa 1990 station dropouts. “RCS”. Unfortunately, this shift does not seem to have been studied as to impact on GIStemp nor vetted as to what it does to the GIStemp products…

    Please go ahead and use GISS data at your peril, but be kind to your workload and don’t invest a lot of time in it. Why not use the BOM data instead? It’s closer to the source.

    Well, because I’m not using the data. I’m assessing it’s impact on GIStemp, and GIStemp does use the GHCN data set (and so these deletions do have an impact). So when it hits the news that we’ve had 115 year record heat in California due to GIStemp saying so, and I find out it is because we have 4 remaining thermometers in California (3 on or near the beach in Los Angeles area and one at the airport in San Francisco; who needs a thermometer in the snowy mountains, anyway…) I don’t really get a choice as to what data to use…


    There are portents of evil here. When you report that “we saw that in 1992-93 there were 401 thermometers deleted”, I recalled that Error 401 in Windows is

    “If you have just logged on and received the 401 Unauthorized error, it means that the credentials you entered were invalid for some reason.”

    Lucky for me I live on non-Windows platforms! Can’t remember the last time I saw a 40x error or The Blue Screen Of Death ;-)

    In any case, thanks for the pointer to RCS as the mystery coordinating process for global thermometer deletions and general screwing up of GIStemp products due to tampering with it’s inputs. At least now I have a name for the source of the disease…

    UPDATE: Many heads of the hydra… A bit of digging has shown Reference Climate System to be the Australian system, Reference Climate Network to be the US system, … There does not seem to be “one system” with “one name”. It will take more time than I can put into it right now to sort this out, so it goes on the “Dig Here, Alot, Someday” list. But it looks as though about 1989 – 1990 a bunch of climatology / weather departments decided to push for standardized upgraded systems; but did not appreciate that deletions of the old systems would wreck havoc on the GIStemp process and render the station data too volatile for other uses from the transition of thermometer counts. But it does look to me like that process, those meetings, and those folks are the likely genesis of The Thermometer Langoliers.

  17. Henry says:

    Kiribati = Gilberts (in Gilbertese) with “ti” pronounced as in “station”

    similarly nearby Kiritimati = Christmas

  18. ellie says:

    E.M.

    Your Indonesia data shows much more than you give it credit for. There is significant warming going on, but broken down into two chunks with an offset between that moderates the overall trend. (I’ve ignored the messing around in the 1990s – some may be Pinatubo related).

    http://sites.google.com/site/elliesgraphs/indonesia

    The trends of the lines give an idea of the amount of warming that might be translated through GIStemp into anomalies for the grids in that region, which the second one (if you’ll excuse me using one of those awful anomaly maps from GISS) shows in the 0.5-1.0 Celsius band.

    REPLY: “Interesting graph. I think my “by eye” visualization has a built in “clip one or two data points as outliers” and tries to only fit one line unless I push it ;-) At any rate, yeah, something odd in Indonesia; but not CO2 driven warming.. Though GIStemp WILL try to ‘spread it around’… -ems”

  19. Geoff Sherrington says:

    The BOM website explains “The establishment of the network followed a request by the World Meteorological Organization to all of its member nations in 1990.”
    See

    http://www.bom.gov.au/climate/change/reference.shtml

  20. j ferguson says:

    Hi E.M.
    Could you post “meansortidyr” which appears to be a script called from mkinvmean?

    btw chroot lives, alas for setuid.

    REPLY: “Probably ought to be ‘in-lined’ for the posting ( I broke it out as a stand alone hand tool ).:

    [chiefio@tubularbells vetted]$ cat bin/meansortidyr
    sort -n -k1.1,1.16 ${1-“../../STEP0/input_files/v2.mean”} > ${2-“v2.sort.ccidyr”}
    [chiefio@tubularbells vetted]$

    basically, sort the file by “id” (including the leading country code and trailing modification flag) then by “year”.

  21. j ferguson says:

    setuid lives. turns out it isn’t in the standard ubuntu 9.10 distribution but an apt-get gets it.

    hah!

  22. Geoff Sherrington says:

    E.M. Smith,

    Having carefully read the serious parts above, I came to “Thermometer Langoliers”. In Stephen King’s book “Four Past Midnight” which also mentions them, there is a review stating “Brian suggests that they make an emergency landing in Bangor, Maine while they figure out what has happened.”

    This feeds back to a Steve Mac C.A. title “The Rain in Spain falls mainly onto Maine” or similar, relating to a geographic error in weather station coordinates. Most amusing.

    Back to serious mode. I am a part-time hobbyist on these global warming matters, but already I feel I could write a book about the discrepancies, strange adjustments, failures to compute errors properly, the difficulty facing an innocent researcher who wants to select a temperature series but does not know the possible pitfalls …

    In a lot of other science, matters like these tend to reach a stage of maturity before research funds, time and intelligence are invested in them. There are now quite a few papers that are dated because today’s favourite temperature series is not relevant to the one they used. I’ve not seen many – any -corrections or retractions, just “moving on”. Steve has used the term “cleaning up the campsite before leaving” and I think it’s an excellent analogy.

    REPLY: “Bizarre isn’t it? We are basically doing a calorimeter test on the planet, yet all the thermometers keep changing, and the furnace in the room wanders up and down, and the calorimeter has IR semitransparent windows that change opacity with the operating level of the near by nuclear plant, and… It makes the Pons et. a. calorimetry of “Nuclear Cold Fusion” look positively stellar by comparison. Yet, when “caught out” the answer is “Look! Over here! A New Shiny Thing!!!”… -ems”

  23. Ken McMurtrie says:

    Hi again,
    Having much enjoyment from your findings, as have no belief in IPCC credibility. Nice to see some facts being published.
    Have just read the following reader comment in this posting of yours:
    The BOM (Australian) website explains “The establishment of the network followed a request by the World Meteorological Organization to all of its member nations in 1990.”
    See

    http://www.bom.gov.au/climate/change/reference.shtml

    I tried to access it. No such item, according to the search.
    Presumably this means a cover up process.
    Keep up the good work.

  24. Geoff Sherrington says:

    Ken McMurtrie

    The site worked when I listed it. So the change has been recent.

    The BoM supply products like daily station records to third parties under agreements. If you talk to some of these parties, you get the impression that the record consists of a number of versions, up to a dozen for some stations, which versions might or might not be numbered or otherwise identified. Therefore, a researcher who does not know this is at risk of selcting a time series that has been made obselete, then use it tp publish a paper. Such a paper runs a risk of being invalid in correlation strength.

    There is an ever-increasing need for a clean white sheet of paper and a reworking of all the global stations that maths can derive as necessary and sufficient to create a plausible global data set. We have the assistance of some satellite derived factors, but there are obvious reasons to go back in time as far as possible.

    Unfortunately, it now seems post-Climategate that a number of earlier authors should be publishing corrections to papers with correlations made on superceded data. I suspect some intellectual laziness is acting – or a fear that no sooner will the revision be finished than a new data string will be issued.

    I don’t seek to stop the process of improvement. I do want to stop the process of uncontrolled and undocumented “improvement”.

  25. E.M.Smith says:

    The link just worked for me, though it isn’t all that informative (mostly just a map of Australia with dots on it and some praise for a “high quality network”

    Australia’s Reference Climate Station Network

    The Australian Reference Climate Station (RCS) network has been established for high quality, long-term climate monitoring, particularly with regard to climate change analysis. The establishment of the network followed a request by the World Meteorological Organization to all of its member nations in 1990.

    Around 100 RCSs have been selected from the existing Australian observation network. Preference was given to stations with

    * high quality and long climate records,
    * a location in an area away from large urban centres, and
    * a reasonable likelihood of continued, long-term operation.

    The Reference Climate Station network is shown on the map below (current to October 2007). </b?

  26. boballab says:

    WUWT just ran a piece from Andrew Bolt that showed the work of an Australain version of Surfacestations.org on that so called “high quality” network. Turns out that they have the same problems as the US has: Stations sitting next to buildings, next to AC units on unsuitable surfaces and inside junk piles.

    http://wattsupwiththat.com/2010/03/21/find-the-weather-station-in-this-photo/

    Maybe they are reworking the presentation :)

Comments are closed.