BBQ in NYC - It all depends on the cuts you make...
Place ingredients in a blender, add Chili Pepper in the last step
This is a table of data. It was created by taking the GHCN Global Historic Climate Network data set, downloaded from NOAA, and selecting long lived locations. I’ve done this before, but included the “modification flag” in the long lived selection. This caused such things as England having a drop out of all thermometers, since they all had a change of “modification history” in the last couple of decades. So I decided to change the screen to find those sites that were long lived, then select ALL records for that LOCATION with no regard for the “modification flag”.
These thermometer records were also selected for the very best long lived thermometers. This set matches the “top 10%” from my
https://chiefio.wordpress.com/2009/08/13/gistemp-quartiles-of-age-bolus-of-heat/
posting.
From that posting we have:
Number: 1348 Size: 12.5 MB (27% of the records) Shortest life: 103 years Longest life: 286 years
Everything will stay the same for the base records in this set, but what changes is that we have 14.9 MB for 32.7% of the records. The added modification flags will extend the lives of some of these stations by the length of that new record. I did not bother to calculate new “site lifetimes”, I think you can see they are long.
OK, this is a “by year” table for the period from 1800 to 2008 (2009 is not over yet, so I just left it out of the analysis. I know, it’s been cold and would have been “way interesting”… but not yet… )
The chart is long, and please don’t “glaze” over it. I hope to figure out how to graph some of this “Real Soon Now” but it really is “down in the weeds” were we find the interesting bits in this data. So please, hang in there.
Conclusions
I’m going to break with my own tradition a bit here, and give the conclusions first, then you can go scan the table and confirm it for your self. Realize that this is the “raw” GHCN data before it goes into GIStemp. This is “all there is” that is real. Anything GIStemp (or any other data series) finds can only be found here or fabricated.
The final record in the table is the “average of the monthly data and yearly data above”. Basically, it is the “mean” of all time of all data in the set, by month and grand total in the yearly column. (This will be slightly different from the average of the averages in the column. See the comments for more discusstion of this point.)
OK, first off, we notice that in 1801 it was an annual mean of 10.3 and in 1802 it was 10.0 while the “average of all years” is 11.3. It was a little colder at the start of the series than for the average of the series. Though we start with all of 33 thermometer locations, so there is plenty of room for accidental historical selection bias in where these are sited. Inspection of the January column shows about -3C to +3C in the first decade. August is running about 18C – 20C. The average is a 0C January, middle of our range, and the average August is 21.5 (thought I note that the average July is 22.3C ) so we have about 2 C of August rise to get to the average. So we would expect about 1/100C per year, on average, with whatever cyclical ripple is in the data.
January
OK, lets take a scan down the data. Run your eye down a column, then come back here. First up, January. We see single digits positive and negative. -3 and -4 in 1811 – 1815 during “The Year Without A Summer” (where we note August was 17.x) and -5C in 1848. Then +4.8C in 1880. +2C in 1932. -2.3 in 1963 (IIRC it snowed in my home town that year, an unusual event). -3.5C in 1977 (when it snowed again). 2.6C in 2002. Then there is a dramatic fall off in locations (remember, I added back in records for any long life location that had a change of modification flag). What do we get? A solid series of 3.9C, 4.8C, 4.0C. While 1876 to 1880 has a warm series, it falls short of these temps and the rest of the series has mostly blips. This “whack up side the head” of 4C and more solid is just wrong. That it comes when locations are pruned is a smoking gun, IMHO, that no land temperature series can be trusted after that land location pruning.
But maybe it is just a quirk of that month?
December
Lets look at December. Mostly hanging out around 0C to 2C with a long term average of 1.6C and sporadic cold years at minus a degree or so. Some individual odd years like 1806 at 4.3C and 1877 at 5.9C (so it can get warm some times) but even 2005 was all of 0.9C. Nothing really odd here. Then the recent set, as the thermometers are strangled: 5.9C, 4.8C, 4.3C. That is just SO uncharacteristic of the rest of the data pattern as to be a flashing neon light for a fraud or error investigation.
Is it a winter thing?
Summer
Well, yes, I think so. August has an average of 21.5C and as we scan the August range, we find a fairly gentle rise from the 18-19C at the start to 20.something in the mid 1870s to a hot 23C in the 1936-37 years. Then sliding back to the 21C and even 20.x in some years of the 1960x and ’70s. Ending with 21.4C, 22.1C, 21.6C in these last few suspect years. So in August, we see no global warming. We do have cyclical ripple, but no big “jump” at the end.
Yet winter Jumps when thermometers are cut
November has the same thing. 6C gets turned into 8C. For February we have 1.4C average turned into 1.8, 3.5, and 5.2C. Not completely out of character, but statistically “wrong”. You can scan all the other monthly columns and see a similar thing. The data are “generally random with jiggles and a bit of cyclicality”, then in the last few years cold months get warm and warm months don’t.
IMHO, someone is cooking the books by cutting thermometers in places with cold winters. The thermometers in this list are all from very long lived locations. Short records from a change of modification flag are included in this screen. It isn’t an artifact of mod flag change.
WHY are long lived stations cut in the last few years? And why does that “warm winter”?
As time permits, I’ll try to figure out how to graph some of this and add a graph. I will also investigate exactly what station IDs got cut, and kept. I think that will be an enlightening list…
Data
The format is: Year, 12 “monthly averages” (of the Max-Min daily averages), the average of the data for those 12 months – that is the annual average, then the total number of thermometers in that year.
1800 -0.1 0.0 1.3 12.6 15.5 15.7 18.6 19.3 15.3 10.0 6.1 1.8 9.6 33
1801 1.8 1.6 6.6 9.2 15.5 16.6 19.1 18.4 16.4 11.6 6.1 1.8 10.3 32
1802 -1.6 1.7 5.6 10.2 13.0 17.6 18.2 20.5 15.6 12.3 5.4 2.2 10.0 32
1803 -2.9 -0.8 4.2 11.3 12.6 16.8 20.3 19.7 13.4 9.6 5.0 1.4 9.2 33
1804 2.7 -0.4 2.0 8.2 15.0 17.6 18.9 18.3 16.0 10.2 3.5 -1.6 9.2 32
1805 -2.5 -0.3 3.4 7.2 12.1 15.6 18.1 17.6 15.6 6.7 2.1 1.1 8.0 33
1806 1.6 2.0 3.4 6.4 14.9 16.2 17.9 18.2 15.7 9.4 5.8 4.3 9.6 35
1807 -0.6 1.3 0.6 6.4 13.3 16.4 20.3 21.6 13.5 10.4 5.0 1.0 9.1 35
1808 -0.8 -1.4 -0.8 5.7 14.4 16.4 19.9 19.1 14.8 7.8 3.4 -3.4 7.9 34
1809 -3.7 1.3 1.8 4.6 13.8 16.1 18.0 18.1 14.1 8.3 2.3 1.9 8.0 35
1810 -2.6 -1.4 3.2 6.6 11.8 15.2 17.9 17.7 15.7 8.8 3.9 1.3 8.1 35
1811 -3.7 0.4 5.4 8.3 15.7 19.0 20.0 18.1 14.4 11.4 5.1 0.9 9.5 36
1812 -3.5 0.4 1.8 5.0 12.8 16.4 17.5 17.9 13.0 10.1 2.0 -4.7 7.3 38
1813 -3.4 2.0 3.4 9.5 13.8 16.1 18.3 17.5 14.4 8.6 4.2 -0.2 8.6 42
1814 -4.1 -3.8 1.5 9.4 10.7 15.7 19.4 17.8 13.1 8.5 4.8 1.8 7.9 40
1815 -3.9 2.0 5.3 8.9 13.9 16.5 17.1 17.3 14.1 9.9 3.1 -1.2 8.5 38
1816 -0.9 -1.8 2.5 8.1 12.0 15.9 17.2 16.4 13.8 9.3 3.6 0.1 8.0 42
1817 1.7 2.8 3.5 5.7 13.1 17.5 18.2 17.8 15.1 6.8 5.1 -1.4 8.8 43
1818 0.2 0.3 4.3 8.5 12.9 17.9 19.5 17.0 14.0 9.5 5.1 0.0 9.1 44
1819 0.5 0.9 3.9 8.9 13.4 17.7 19.3 19.1 15.5 9.2 3.3 -1.6 9.1 45
1820 -4.6 -0.1 2.6 9.9 13.9 16.4 18.7 19.1 14.1 8.9 2.4 -1.9 8.2 54
1821 -1.4 -0.9 2.7 9.1 13.1 15.5 17.6 18.3 15.3 9.9 5.4 1.7 8.8 56
1822 -0.5 2.1 6.8 9.9 15.1 19.1 20.1 18.7 15.2 10.9 6.0 -1.6 10.1 59
1823 -5.0 -1.5 3.8 8.2 14.0 17.4 19.0 19.3 15.0 9.6 3.3 0.5 8.6 59
1824 -0.8 0.0 3.0 7.7 12.6 16.6 19.1 18.4 15.6 9.4 4.5 2.0 9.0 64
1825 -0.4 -0.4 2.5 9.1 13.6 17.7 19.6 18.7 15.1 9.5 5.2 1.7 9.3 66
1826 -4.9 0.1 3.6 8.1 14.2 18.4 21.2 20.3 15.6 10.3 3.6 1.1 9.3 65
1827 -3.0 -2.7 4.2 10.0 14.6 18.2 20.3 18.5 15.3 10.2 2.0 0.6 9.0 69
1828 -2.4 -0.9 3.6 8.4 14.1 18.9 20.4 18.7 14.6 9.4 4.1 0.4 9.1 72
1829 -4.8 -4.2 0.9 7.7 13.8 17.4 19.6 18.3 14.3 8.1 0.8 -3.8 7.3 80
1830 -5.6 -3.0 3.4 9.5 13.5 17.7 20.5 19.3 14.2 9.3 5.9 0.0 8.7 83
1831 -4.7 -1.9 3.0 9.3 13.6 18.3 20.3 19.2 14.3 10.9 3.2 -2.5 8.5 87
1832 -2.5 -1.1 2.3 7.4 12.2 17.0 18.9 18.8 14.1 9.8 3.1 -1.1 8.2 90
1833 -2.5 0.3 1.9 8.1 15.2 17.9 19.4 17.4 14.5 9.2 4.1 1.0 8.8 91
1834 -1.8 0.6 3.7 8.1 14.3 18.1 21.4 20.1 15.9 9.4 3.9 -0.5 9.4 95
1835 -0.9 -0.6 2.7 7.4 13.2 17.6 19.8 18.1 14.3 9.3 1.8 -3.6 8.2 92
1836 -2.9 -2.2 3.5 7.7 11.8 16.8 18.8 17.5 13.8 8.4 2.0 -1.3 7.8 98
1837 -3.5 -1.6 0.2 5.9 11.9 17.1 18.8 19.1 14.1 8.9 4.0 -0.9 7.8 105
1838 -5.1 -5.1 2.5 6.3 12.7 17.8 20.0 18.4 15.3 8.4 2.4 -1.5 7.6 103
1839 -2.7 -1.4 0.2 6.7 13.5 17.5 20.5 18.5 14.9 9.9 2.6 -3.0 8.1 107
1840 -3.7 -1.3 1.3 8.8 13.5 17.7 19.6 19.0 14.5 7.9 3.3 -3.6 8.0 115
1841 -3.5 -3.8 2.6 7.6 14.4 18.1 19.5 19.1 15.4 9.2 3.1 0.2 8.4 119
1842 -3.4 -0.9 3.9 8.0 13.3 17.5 19.5 19.6 14.6 8.4 2.1 -0.3 8.5 117
1843 0.1 -0.9 0.8 8.1 12.7 17.4 19.6 19.6 15.8 9.0 3.5 1.0 8.8 122
1844 -3.1 -1.9 2.3 9.7 14.4 17.8 19.4 18.5 15.5 9.5 3.7 -2.0 8.6 125
1845 -0.6 -3.2 1.1 8.7 12.7 18.3 20.4 18.8 14.6 9.4 4.7 -1.3 8.6 126
1846 -1.4 -1.1 4.1 8.8 14.1 18.5 20.9 20.8 16.7 10.1 4.0 -1.6 9.4 117
1847 -3.4 -1.9 0.9 7.0 13.9 17.3 20.5 19.7 15.0 8.9 4.6 -1.0 8.4 122
1848 -5.0 -0.1 2.9 9.1 14.2 18.4 19.9 18.9 14.4 9.7 2.9 0.0 8.7 127
1849 -3.1 -0.5 3.1 7.2 13.7 18.1 20.0 19.2 15.0 10.3 5.6 -1.1 8.9 133
1850 -3.9 0.8 2.0 7.9 13.0 18.4 20.6 20.0 15.1 9.2 5.0 0.3 9.0 135
1851 -0.6 0.2 3.3 8.8 13.3 17.9 19.8 19.2 15.4 10.9 3.9 -0.1 9.3 145
1852 -1.4 -0.2 2.5 6.9 14.4 18.4 20.9 19.5 15.6 10.1 4.6 2.4 9.4 153
1853 0.3 -0.8 2.2 8.3 14.1 18.8 20.7 19.7 15.8 10.8 5.1 -0.7 9.5 148
1854 -1.6 0.0 4.4 9.0 15.0 18.3 21.6 20.3 16.5 11.7 4.6 1.2 10.0 163
1855 -1.4 -2.6 3.0 9.7 14.6 18.4 21.0 20.0 16.3 11.3 5.1 -1.3 9.5 168
1856 -1.7 -0.3 1.6 9.7 13.5 19.2 20.4 19.3 15.6 10.6 3.5 0.2 9.3 183
1857 -2.6 1.6 3.6 7.8 13.6 17.9 20.5 20.1 16.3 11.2 5.0 3.3 9.8 194
1858 0.8 -1.2 4.1 9.6 14.1 19.6 20.6 19.7 16.7 11.8 3.4 1.8 10.0 195
1859 0.8 2.6 6.3 9.3 15.1 18.3 21.0 20.1 15.6 10.7 5.9 -0.1 10.4 195
1860 0.9 0.0 3.9 9.1 14.6 18.3 19.7 19.4 15.7 10.9 4.7 0.0 9.7 175
1861 -1.9 2.7 5.4 8.7 12.8 18.2 19.7 19.4 15.4 11.1 5.5 1.9 9.9 189
1862 -0.9 -0.6 4.2 9.1 14.2 17.1 19.3 18.7 15.7 10.8 4.3 0.9 9.4 184
1863 1.8 1.6 4.0 9.1 14.2 17.2 19.1 19.2 15.0 10.3 5.7 1.5 9.8 182
1864 -1.5 0.9 4.4 8.3 13.4 17.8 19.8 18.6 15.1 9.0 4.0 -0.4 9.1 202
1865 0.0 0.0 3.5 10.1 14.8 17.8 19.8 18.6 17.2 10.6 6.6 1.7 10.0 211
1866 1.5 1.7 4.1 10.2 12.7 18.0 19.7 17.9 15.8 10.7 6.0 1.7 10.0 225
1867 -0.8 3.2 2.5 9.2 12.4 17.7 19.1 19.3 16.0 11.0 6.0 0.9 9.7 233
1868 -0.8 1.4 5.8 9.0 14.8 18.1 21.1 19.5 15.6 10.7 5.3 2.2 10.2 241
1869 2.2 4.1 4.2 10.3 14.1 17.3 19.9 19.3 16.3 9.9 5.6 2.4 10.4 251
1870 2.3 1.3 4.2 10.6 15.4 18.9 21.2 19.4 16.5 11.6 7.1 0.8 10.7 280
1871 0.8 2.1 7.5 11.0 14.8 18.5 20.6 20.5 15.9 11.8 5.2 0.9 10.8 318
1872 1.3 2.5 4.8 11.0 15.5 19.1 21.2 20.3 17.0 12.0 6.0 1.4 11.0 341
1873 1.1 1.7 5.6 9.6 14.3 19.4 21.2 20.5 16.4 11.4 5.7 3.3 10.8 375
1874 2.5 2.3 5.4 9.3 14.8 19.3 21.3 20.0 17.5 12.4 6.3 2.7 11.1 382
1875 -0.4 -0.1 4.3 9.6 15.6 19.3 20.9 20.2 16.6 11.5 5.9 3.5 10.5 396
1876 2.9 3.8 5.9 11.3 15.0 19.8 21.6 20.9 17.0 12.3 6.8 1.6 11.5 400
1877 2.0 5.2 6.2 11.0 14.8 19.6 21.5 20.9 17.4 12.7 8.4 5.9 12.1 413
1878 3.4 5.7 9.5 13.3 15.9 19.5 22.0 21.6 18.2 13.6 8.6 2.9 12.8 447
1879 1.9 3.7 7.9 11.6 16.1 19.3 21.5 20.9 17.5 14.3 7.6 3.1 12.1 462
1880 4.8 4.6 7.0 11.9 16.9 19.6 21.3 20.9 17.8 12.5 6.0 3.2 12.2 472
1881 0.0 2.3 5.8 10.6 16.6 18.8 21.6 21.1 18.0 12.3 7.1 4.6 11.5 531
1882 2.7 4.3 7.1 11.2 15.0 19.3 21.2 21.1 17.8 12.9 6.5 1.8 11.7 596
1883 -0.7 1.5 4.5 10.9 15.2 20.0 21.6 20.6 17.3 12.4 7.0 2.9 11.1 630
1884 0.0 2.1 5.2 10.2 15.6 19.2 21.3 20.6 18.1 13.2 6.3 1.7 11.1 669
1885 -0.8 0.8 4.8 10.8 15.3 19.3 21.9 20.4 17.4 12.0 6.8 3.1 10.9 695
1886 -0.6 1.2 4.9 11.8 16.3 19.3 21.8 21.4 18.1 12.8 6.2 1.9 11.2 740
1887 0.0 1.9 5.6 10.8 16.8 19.7 22.5 20.8 17.8 11.9 6.8 2.2 11.4 776
1888 -1.1 1.5 4.0 11.6 15.4 19.7 21.8 21.0 17.4 12.0 7.0 2.9 11.1 834
1889 0.9 1.0 6.3 11.7 16.3 19.7 21.8 21.0 17.1 11.9 6.4 4.4 11.5 901
1890 1.6 2.8 5.3 11.5 15.6 20.3 22.1 20.9 17.6 12.2 7.3 2.0 11.6 926
1891 -0.1 0.8 4.2 10.6 15.2 19.5 21.0 20.9 18.2 11.8 5.3 3.2 10.8 1026
1892 -1.1 2.1 4.2 10.1 15.0 19.9 21.8 21.4 17.9 12.2 5.6 -0.1 10.7 1086
1893 -3.2 -0.2 4.6 10.3 15.0 20.0 22.3 21.1 17.6 12.3 5.7 1.8 10.6 1134
1894 0.1 0.5 6.7 11.4 16.0 20.0 22.3 21.6 17.8 12.4 5.9 2.1 11.4 1173
1895 -1.6 -1.7 4.8 11.5 15.9 20.0 21.4 21.5 18.6 11.3 5.8 1.5 10.7 1226
1896 0.1 1.8 4.0 11.5 17.1 20.3 22.3 21.7 17.2 11.9 5.3 2.1 11.2 1257
1897 -0.9 1.4 4.9 11.0 16.0 19.8 22.5 21.2 18.8 13.1 5.8 0.6 11.1 1286
1898 1.1 1.6 5.3 10.2 15.7 20.2 22.3 21.9 18.5 11.6 5.4 0.8 11.2 1308
1899 0.2 -1.5 3.9 11.1 16.0 20.1 22.1 21.7 17.8 13.1 7.9 0.8 11.1 1317
1900 0.8 0.0 4.7 11.1 16.3 20.3 22.2 22.5 18.4 14.1 6.4 2.4 11.6 1344
1901 0.2 -0.4 5.5 10.6 15.9 20.4 23.6 22.0 17.5 12.9 5.8 0.6 11.2 1365
1902 0.0 0.1 6.2 10.5 16.3 19.5 21.9 21.1 17.0 12.4 7.2 0.4 11.0 1377
1903 0.2 0.7 6.8 10.8 15.8 18.6 21.7 20.9 17.3 12.4 5.5 0.1 10.9 1399
1904 -1.5 0.0 5.4 9.9 15.8 19.5 21.4 21.0 17.9 12.5 6.8 1.1 10.8 1420
1905 -1.6 -1.4 7.0 10.6 15.8 20.0 21.9 21.7 18.5 11.9 6.9 1.9 11.1 1415
1906 1.7 1.2 3.6 12.1 16.1 19.9 21.9 21.8 18.7 12.1 6.1 2.2 11.4 1421
1907 0.0 1.2 7.3 9.0 14.0 18.9 22.0 21.2 17.8 12.3 5.7 2.1 10.9 1438
1908 0.7 1.1 6.4 11.4 15.6 19.6 22.1 21.1 18.5 11.9 6.6 1.8 11.4 1440
1909 0.5 2.0 5.0 10.2 15.0 20.1 21.8 22.1 18.0 12.1 8.1 -0.6 11.1 1441
1910 0.1 0.2 8.8 12.0 15.2 19.7 22.4 21.1 18.2 13.1 5.5 0.8 11.4 1447
1911 0.6 1.4 6.1 10.4 16.8 20.9 22.3 21.4 18.6 12.1 5.1 2.2 11.4 1455
1912 -2.8 0.1 3.6 11.0 16.1 19.4 21.9 20.7 17.4 12.0 6.5 2.3 10.6 1454
1913 0.9 0.0 5.2 11.3 15.6 19.8 22.2 22.3 17.6 11.8 8.1 2.8 11.4 1455
1914 1.7 0.1 5.5 10.9 16.4 20.5 22.6 21.6 17.7 13.1 6.8 -0.3 11.3 1467
1915 -0.2 2.8 3.6 12.5 15.1 19.2 21.6 20.6 18.1 12.9 7.1 1.5 11.2 1471
1916 0.2 0.9 5.0 10.6 15.7 18.8 22.9 21.7 17.3 11.9 6.1 0.0 10.9 1472
1917 -0.5 -0.9 4.4 10.1 13.6 19.4 22.6 21.2 17.6 10.7 6.7 -0.8 10.3 1471
1918 -3.1 1.3 7.2 10.2 16.2 20.3 21.7 22.1 16.6 13.7 6.3 2.6 11.2 1465
1919 0.9 1.2 5.7 11.0 15.4 20.5 22.6 21.5 18.6 12.8 5.4 -0.1 11.2 1457
1920 -0.4 1.0 6.0 9.8 15.7 19.8 22.0 21.3 18.5 13.0 5.5 1.7 11.1 1457
1921 2.2 2.9 8.4 11.9 16.3 21.1 23.3 21.7 19.1 12.9 6.3 2.4 12.3 1468
1922 -1.2 1.2 5.9 11.1 16.7 20.7 22.1 21.8 18.9 13.0 6.9 2.1 11.6 1466
1923 1.7 -0.2 4.7 10.3 15.5 20.0 22.4 21.3 18.3 12.0 7.0 3.7 11.3 1477
1924 -1.5 1.3 4.1 10.8 14.8 19.9 21.6 21.6 17.2 13.1 6.8 -0.2 10.7 1482
1925 0.0 3.4 6.6 12.4 15.4 20.6 22.4 21.7 19.1 10.3 5.9 1.3 11.5 1486
1926 0.1 2.8 4.7 10.0 16.0 19.5 22.2 21.8 17.9 12.5 5.9 0.8 11.1 1490
1927 0.0 3.1 6.0 11.1 15.5 19.3 22.2 20.6 18.5 13.5 7.3 -0.2 11.4 1490
1928 0.6 1.6 5.6 9.7 16.0 18.6 22.3 21.7 17.3 13.0 6.6 1.9 11.2 1501
1929 -2.0 -2.2 6.4 10.8 15.4 19.5 22.4 21.8 17.6 12.5 5.5 1.5 10.7 1501
1930 -1.8 3.7 5.6 12.0 15.9 20.1 23.1 22.3 18.5 11.8 6.6 0.8 11.5 1497
1931 0.8 2.4 4.6 10.9 15.6 21.0 23.5 21.8 19.6 13.7 7.7 3.1 12.0 1493
1932 2.0 2.4 3.6 11.2 16.1 20.4 22.7 22.2 18.2 12.3 5.6 1.0 11.4 1494
1933 1.7 0.1 5.3 10.7 16.1 21.3 23.1 21.6 19.3 12.7 6.2 2.0 11.6 1494
1934 1.7 0.9 5.5 11.6 17.7 21.2 23.7 22.2 17.8 13.4 7.9 1.4 12.0 1493
1935 -0.1 2.7 6.8 10.4 14.6 19.7 23.2 22.2 18.0 12.8 5.6 0.3 11.3 1495
1936 -1.2 -2.4 6.4 10.2 17.1 20.8 24.0 23.0 18.9 12.3 5.8 2.7 11.4 1505
1937 -1.0 0.9 4.3 10.5 16.4 20.3 22.9 23.1 18.4 12.3 5.8 0.7 11.2 1507
1938 0.4 2.6 7.7 11.6 15.9 20.0 22.8 22.8 18.8 13.8 6.6 1.4 12.0 1505
1939 1.4 0.9 5.8 10.8 16.8 20.6 23.0 22.1 19.0 12.6 6.5 3.3 11.9 1505
1940 -4.1 0.8 4.9 10.5 15.6 20.4 22.8 21.8 18.2 13.0 5.7 2.6 11.0 1510
1941 0.0 0.8 4.3 11.7 16.6 20.0 22.8 21.8 18.3 13.3 6.6 2.6 11.5 1512
1942 -0.9 -0.2 5.5 11.8 15.6 20.0 22.6 21.6 17.6 12.8 6.6 1.0 11.1 1504
1943 -1.0 2.3 4.5 11.3 15.9 20.6 22.8 22.3 17.6 12.4 5.7 1.5 11.3 1499
1944 1.5 2.1 4.7 10.1 17.0 20.4 22.2 21.9 18.4 12.9 6.3 0.0 11.4 1512
1945 -1.0 1.3 7.7 11.1 14.6 19.1 21.9 21.9 18.2 12.3 6.2 -0.8 11.0 1518
1946 0.5 2.0 7.9 12.2 15.2 20.0 22.5 21.3 18.0 12.3 6.7 2.2 11.7 1518
1947 0.4 -0.6 4.3 11.2 15.4 19.5 21.9 22.9 18.7 14.5 5.2 1.3 11.2 1528
1948 -0.9 0.5 4.8 12.0 16.1 20.3 22.3 21.7 18.5 12.1 6.9 1.5 11.3 1551
1949 0.3 1.5 5.4 11.4 16.7 20.5 22.7 21.8 17.4 13.1 7.3 2.0 11.6 1623
1950 0.1 2.0 5.0 10.4 16.2 20.0 21.5 20.9 17.7 13.9 5.7 1.3 11.2 1633
1951 0.3 1.5 4.9 11.0 16.1 19.4 22.1 21.8 17.8 12.7 5.2 2.2 11.2 1692
1952 1.6 2.8 4.4 11.5 15.9 21.1 22.8 21.8 18.3 11.8 6.0 2.0 11.6 1692
1953 2.1 2.5 6.8 10.7 16.1 21.0 22.5 21.8 18.4 13.6 6.8 2.7 12.0 1694
1954 -0.2 3.4 5.1 12.1 15.1 20.5 22.9 21.8 18.8 13.1 7.3 2.4 11.8 1705
1955 0.6 1.2 5.2 12.0 16.5 19.2 23.0 22.5 18.5 13.1 5.2 0.7 11.4 1708
1956 0.1 0.3 5.1 10.2 16.3 20.5 21.7 21.4 17.5 13.5 5.8 3.3 11.3 1707
1957 -0.7 3.6 5.4 11.4 16.0 20.3 22.5 21.5 17.8 11.8 6.4 3.6 11.6 1724
1958 0.8 0.8 4.3 10.9 16.5 19.5 21.9 21.8 17.9 12.9 7.4 1.0 11.3 1722
1959 0.0 1.6 6.3 11.6 16.6 20.5 22.4 22.3 18.1 12.1 5.2 2.8 11.6 1726
1960 0.2 1.0 2.7 11.8 15.5 20.1 22.1 21.6 18.4 12.7 6.9 1.2 11.1 1730
1961 0.0 3.6 7.3 10.4 15.2 20.2 22.0 21.6 18.0 12.9 6.5 1.2 11.5 1841
1962 -0.2 2.2 4.6 11.4 17.0 19.5 21.5 21.3 17.3 13.1 6.8 1.4 11.3 1840
1963 -2.5 0.8 6.2 11.5 16.2 20.0 22.2 21.3 18.2 14.6 7.9 -0.5 11.3 1846
1964 0.9 1.0 4.8 11.3 16.6 20.0 22.5 20.8 17.6 12.0 7.0 1.6 11.3 1844
1965 0.3 0.7 3.8 10.9 16.3 19.5 21.5 20.9 17.2 12.6 6.8 3.2 11.1 1843
1966 -1.5 0.9 6.5 10.7 15.5 19.8 22.7 21.0 17.5 12.2 6.8 1.1 11.1 1845
1967 0.7 0.9 6.7 11.5 15.1 19.6 21.7 21.1 17.4 12.9 6.4 1.8 11.3 1849
1968 -0.8 0.9 7.2 11.5 15.1 19.9 21.6 21.1 17.6 12.5 6.2 0.5 11.1 1850
1969 -1.3 0.3 3.5 11.5 16.0 19.1 22.2 21.6 17.9 11.7 6.7 1.2 10.8 1849
1970 -1.4 1.9 4.7 11.1 16.3 20.0 22.3 21.6 18.2 12.2 6.3 1.8 11.2 1845
1971 -0.1 1.3 4.7 10.8 15.2 20.2 21.6 21.3 18.1 13.3 6.6 2.9 11.3 1844
1972 -0.8 1.0 6.2 11.0 16.0 19.6 21.8 21.5 17.5 11.7 5.6 1.4 11.0 1842
1973 0.2 2.1 7.4 10.9 15.5 20.3 22.1 21.7 17.6 13.1 6.8 1.9 11.6 1842
1974 0.3 2.0 7.0 11.5 15.6 19.4 22.3 21.0 17.0 12.2 6.5 2.4 11.4 1838
1975 1.7 1.5 5.2 10.1 16.5 19.7 22.3 21.5 17.4 12.6 6.9 2.1 11.4 1833
1976 -0.2 3.2 6.0 11.6 15.2 19.7 21.8 20.8 17.2 10.3 4.7 0.4 10.8 1830
1977 -3.5 2.1 7.3 12.4 16.9 20.3 22.5 21.1 17.9 12.0 7.3 1.4 11.4 1828
1978 -1.5 -1.1 5.4 11.1 15.6 19.8 21.9 21.2 18.2 12.3 6.8 0.5 10.8 1821
1979 -2.9 -1.0 6.3 10.3 15.8 19.8 21.7 21.1 18.2 12.6 6.3 3.5 10.9 1815
1980 0.0 0.7 4.6 11.1 15.9 19.8 22.7 21.7 18.3 11.8 6.5 2.3 11.2 1810
1981 0.2 2.5 6.1 12.4 15.3 20.6 22.5 21.5 17.7 11.9 7.0 1.6 11.6 1657
1982 -2.4 0.7 5.6 10.2 16.6 18.9 22.3 21.3 17.8 12.3 6.5 3.6 11.1 1564
1983 1.5 2.7 6.3 10.2 15.2 19.5 22.9 22.9 18.3 12.8 6.9 -0.9 11.5 1562
1984 -0.5 2.6 4.6 10.5 15.8 20.3 22.1 21.9 17.3 12.9 5.9 2.0 11.2 1622
1985 -2.6 -0.3 6.4 12.4 16.7 19.5 22.2 21.3 17.5 12.7 5.6 -0.5 10.9 1620
1986 1.1 1.1 7.1 12.1 16.5 20.8 22.5 21.1 17.8 12.5 6.0 1.7 11.6 1618
1987 -0.5 3.0 5.8 11.5 17.1 20.7 22.5 21.4 18.0 11.4 6.9 2.6 11.7 1767
1988 -0.5 0.9 6.2 11.3 16.5 20.8 23.1 22.5 17.9 11.7 6.4 2.1 11.5 1762
1989 2.2 0.8 6.7 11.5 16.1 20.0 22.5 21.4 17.7 12.8 6.6 -0.5 11.4 1759
1990 3.7 4.6 8.4 12.0 16.0 20.9 22.6 22.3 19.4 13.6 9.0 2.9 12.9 1634
1991 0.8 4.9 7.9 13.0 17.8 21.1 23.2 22.6 18.7 13.6 6.2 3.5 12.7 1391
1992 2.4 4.9 7.7 12.0 16.6 19.9 22.1 21.1 18.5 12.9 6.3 1.8 12.1 1198
1993 0.8 0.5 5.7 10.9 16.9 20.5 23.2 22.8 17.9 12.5 5.6 2.4 11.6 1179
1994 -1.1 0.6 7.4 12.5 16.4 21.9 23.1 22.2 19.0 13.4 7.9 3.6 12.2 1091
1995 1.4 2.9 7.3 11.0 16.0 20.6 23.7 23.9 18.5 13.6 6.0 1.5 12.2 1068
1996 -0.4 2.0 4.4 11.0 16.7 21.2 22.8 22.4 18.1 12.9 5.1 2.1 11.5 1068
1997 -0.2 3.2 7.7 10.0 15.5 20.7 23.1 22.1 19.2 13.0 5.9 2.3 11.8 1058
1998 2.4 4.7 6.1 11.7 18.1 20.9 23.9 23.3 20.7 13.7 7.9 3.3 13.0 1053
1999 1.2 4.5 6.2 12.2 16.6 20.8 24.0 22.8 18.6 13.0 9.2 3.1 12.6 1059
2000 1.0 4.6 8.5 11.9 17.8 20.9 23.0 23.2 18.8 13.6 5.1 -1.4 12.2 1057
2001 0.3 1.8 5.6 12.7 17.6 20.9 23.5 23.4 18.6 13.0 9.6 3.6 12.5 1047
2002 2.6 3.5 5.6 12.7 15.9 21.8 24.3 22.9 19.9 12.1 6.5 2.3 12.5 1040
2003 0.1 0.9 7.0 11.9 16.7 20.4 23.7 23.7 18.6 13.6 7.5 2.7 12.2 1039
2004 -0.4 1.8 8.6 12.3 17.5 20.5 22.7 21.5 19.5 13.8 8.1 2.6 12.3 1039
2005 1.3 3.6 6.1 12.3 16.0 21.5 24.0 23.2 20.3 13.8 8.1 0.9 12.5 1022
2006 3.9 1.8 6.5 12.9 16.6 20.3 22.4 21.4 18.1 13.5 8.9 5.9 12.6 999
2007 4.8 3.5 9.2 12.7 17.2 20.2 21.7 22.1 18.4 14.2 7.9 4.8 13.0 223
2008 4.0 5.2 8.8 12.8 16.7 20.1 22.2 21.6 17.7 13.5 8.6 4.3 12.9 225
0.0 1.4 5.6 11.0 15.9 20.0 22.3 21.5 17.9 12.5 6.4 1.6 11.3
FWIW, this topic is also covered (and with some nice graphics) at WUWT:
http://wattsupwiththat.com/2009/10/13/how-bad-is-the-global-temperature-data/
Worth a visit.
Musings from the Chiefio 
The more I think about it, a great little project for someone, even without a lot of computer skills (or maybe even better done by someone no so encumbered by a focus on minutia ;-) would be a FOIA investigation into the Thermometer Langoliers.
A Freedom Of Information Request of NOAA and NASA and anyone else who is involved in the care and feeding of the GHCN asking for all documents, emails, and history surrounding the decisions to delete thermometers in the 1990 to 2009 period ought to be a gold mine. Especially those meeting announcements and schedules of exactly whom was in the meetings and being a decision maker.
Someone decided to cook the data by deleting thermometers. I’d like to know who.
I’m too much in the technical weeds to take on another project right now and don’t know enough about the FOIA stuff to do it. But for someone skilled in such things…
So even though these are really long-lived locations, there is still a huge bulge in the middle years and massive drop-off recently.
As time permits, I’ll try to figure out how to graph some of this and add a graph.
The individual month graphs show the expected rise in temperature up to 1880 as records in warmer places are added and a flat plateau from 1880-1980 before they take off again. It is clear from December and January especially that warm/SH places are added in 1880 and cold/NH ones are dropped in 1990. I can send them if you wish.
@Ellie
It’s a bit easier for me to link them into a page directly from the site you used for the last one. But I’ve also been able to convert them from Excel on my end (though there is some kind of loss of image quality). If the image site you have is not a long term thing, I can take them in email or download from the site, and upload them here.
Whatever works for you tickles me plum to death ;-)
Ah, didn’t notice you’d linked to the other one. Cheers.
If you can use .png formats, check your email. If not I’ll upload them tomorrow.
E. M. Smith,
Thank you for posting the monthly data. I have loaded them in a spreadsheet for further study. I tried to replicate the monthly averages and the annual averages. In both cases I have found deviations. Have you used weighted averages, ie., by number of days in month or number of stations?
@Ellie: PNG is fine. Yeah, I need to check email. Some little things came up (see the Brazil posting) and i’ve been up most of the night…
@Mike Rankin:
There is a difference between an average of averages and an average of data. What my program does is average the base data (so the annual average is the average of all monthly data for all thermometer records that year, not the average of all monthly averages of the thermometer records for each month). It makes a small, but as you saw, real difference.
This is one of my complaints about the whole AGW “Confessions Of A Serial Averager” approach to Global Average Temperature. (And yes, this is a proposed title for a future posting on the subject). Exactly how you average, and in what order, can have a significant impact on the result. Yet no body is bothering to look into that since it is sort of a “math geek” thing. We just get handed “The Global Average Temperature” and the fact that it is the result of literally dozens of averaging steps, none vetted for impact nor proper result, is simply ignored.
So we have the Min and Max for each day averaged. Then these daily averages are averaged for a month for one location and that is what NOAA puts in the GHCN data. Ought it to be the average of the MINS averaged with the average of the MAXS instead? What is the impact on the GAT from that change? We then take the monthly “Average Means” and average them. Ought they be averaged over months first and then geography, or geography first and then months, to give the Annual Global Average Temperature? And what does that due to the number?
How much merit can be assigned to a change in the 1/10 or 1/100 C place when the order of your arithmetic can change it?
And then these numbers of averages of averages of averages of averages inside a cell (grid, box, zone, whatever) are averaged against each other to get anomalies and grid averages and…
At the end of all this, is there really anything that you can assign as meaning to a change in the 1/10 C place? Or maybe even the 1 C place? Nobody has evaluated the impact of this Sin of Serial Averaging as near as I can tell. Yet $Trillion dollar decisions are being made based on the results.
And as you have seen, even ONE change of order of averaging has visible results…
But yes, when I wrote the code that produced this table I had to decide if I would “average the averages” so that my results would be a “cross foot” accounting check on the individual month values, or if I would “average the base data” that is often the more accurate representation of reality (since you are one step closer to the data in your result). I chose the “average the data by month and print that AND average the data by year and print that” rather than “AND average the averages so it looks better on the print out and crossfoot checks”. Besides, this way you can do the ‘average of averages’ and see how much difference it makes.
Computer programmers are faced with this kind of choice all the time and usually the person asking for the ‘result’ does not care or even understand why you ask which one they want. It is often just left for the programmer to pick one (or they are told to pick the one that makes it “look right”, which may be part of why I choose the “is better” over “looks right” this time ;-) Which one is “right”? Flip a coin…
Just don’t tell anyone that your results are based in part on a coin toss; they might start to doubt the validity of averages of averages of averages of averages of…
E. M. Smith,
I have looked at plots of the monthly and annual data. It is clear to me that the first seventy years are very different from the next 110 years and the final 29 years. What is not clear is how to extract more information. One possible means is to develop a centroid of the stations. By this I mean that the numerical average of lattitude, longitude and altitude be calculated for each year for the component stations. This would make a metric for the qualitative movement of stations you have previously expressed. Is this feasible with the information you have at hand?
By the way, I applaud your undertaking this thankless task of delving into the black box of GISS.
I suppose such a centroid could be done. It would involve matching the station numbers for each year to the v2.inv file station data and pulling out the LAT LON and altitude (as described in the “How long is a long…” posting. The only real hard bit I see is that this centroid will change from year to year as the specific thermometer locations change, so you get to do it 200 times…
Well, that, and I’m not sure that a hypothetical location is the same as a disbursed set of real locations; but the idea is an interesting one. It would be fun to watch it wander around the world…
Temps look like they were going down in the few years before Tambora (April 1815) and Krakatoa (August 1883).
Does it look like that to you?
Regards
It does look like a bit of a ripple down. It would be easier to detect on a plot of the data with volcanos marked. There is a theory that changes in the earth rotation rate causes both cooling and increased volcanic activity.
Lies, damned lies, and statistics.
The falsification of data and the conspiracy to commit same etc, constitutes serious criminal activity. Further, the granting of public funds for research warrants a federal investigation. I’m hoping the perpetrators, including possibly Professor Michael Mann, director of Pennsylvania State University’s Earth System Science Centre and a regular contributor to the popular climate science blog Real Climate, and their facilitators will be tracked down and prosecuted to the fullest extent the law allows. — Michael Santomauro