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.
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