This is the landing page for the Twitter account IsItHotOrNot. I’ll probably move it to a proper home at some point.
IsItHotOrNot displays the global temperature anomaly, month by month. Currently the data is sourced from GISTEMP, specifically the “Global-mean monthly land-ocean temperature index”. The file,
GLB.Ts+dSST.txt, also has annual and seasonal values, but I don’t currently use those.
What does the number mean in “2009 Nov +0.680, hottest November on record”? It’s November’s temperature anomaly (in degrees C). The anomaly for November is the difference between this November and the average November temperature between 1951 and 1980, the base or reference period. This year’s November was 0.68 degrees C warmer than the average between 1951 and 1980. Obviously the anomalies for other months are calculated in a similar way. Note that this method takes account of the fact that Novembers are, on the whole, colder than Junes, by using the average November temperature between 1951 and 1980, not the average temperature. In other words it does seasonal correction.
It would be nice to say “about 1.0 degrees C” warmer than pre-industrial levels, like the EEA do, and I can do that by simply adding 0.3 to all the values, but since the errors for the 1850 to 1899 (which is what is usually used when you see the phrase “pre-industrial”) average are larger (on account of having fewer records), I decided not to do that. You can wing the error and add 0.3 to all the values if you like.
There are other temperature indexes available: NCDC do one; there’s the HadCRUT3 diagnostics; the Japanese Metereological Agency have their global average surface temperature anomalies. The different indexes use different reference periods (NCDC use 1900 to 1999, HadCRUT3 use 1961 to 1990, JMA use 1971 to 2000), the only effect of which is to move the graphs up and down the page (it does mean that if you want to compare the time series from two different providers, you need to rebase them to give them a common reference period).
Where does the data come from? Well, from GISTEMP, but the land and ocean index is a data product synthesized from other sources:GHCN (land), USHCN (USA land), HadSST2 (ocean), Reynolds SST (ocean), SCAR READER (antarctic land). The other indexes use more or less the same data, the differences are mostly in how it is processed.
Why GISTEMP? All the indexes are basically the same, so it doesn’t really matter which one I pick. Because of my work on Clear Climate Code I happened to be familiar with their datasets, so that was simpler from a programming perspective. The differences between the indexes are typically very small, but it does mean that the statements like: “second hottest September on record” are very sensitive to the exact dataset used. The JMA had September 2009 tying for first place, whereas GISTEMP have it second. I regard the “hottest November on record” stuff as nothing more than a popular discussion point, not an absolute truth.