With all of the hoopla over recent temperatures, I decided to see how far back in time I could extend my U.S. surface temperature analysis based upon the NOAA archive of Integrated Surface Hourly (ISH) data.
The main difference between this dataset and the others you hear about is that trends are usually based upon daily maximum and minimum temperatures (Tmax and Tmin), which have the longest record of observation. Unfortunately, one major issue with those datasets is that the time of day at which the maximum or minimum temperature is recorded makes a difference, due to a double-counting effect. Since the time of observation of Tmax and Tmin has varied over the years, this potentially large effect must be adjusted for, however imperfectly.
Here I will show U.S. temperature trends since 1943 based upon 4x per day observations, always made at the same synoptic times 00, 06, 12, and 18 UTC. This ends up including only about 50 stations, roughly evenly distributed throughout the U.S., but I thought it would be a worthwhile exercise nonetheless. Years before 1943 simply did not have enough stations reporting, and it wasn’t until World War II when routine weather observations started being made on a more regular and widespread basis.
The following plot shows monthly temperature departures from the 70-year (1943-2012) average, along with a 4th order polynomial fit to the data, and it supports the view that the 1960s and 1970s were unusually cool, with warmer conditions existing in the 1940s and 1950s (click for large version):
It’s too bad that only a handful of the stations extend back into the 1930’s, which nearly everyone agrees were warmer in the U.S. than the 40’s and 50’s.




Once again, let’s leave out the 1930′s. I am so tired of these dog and pony shows.
Indeed Chuck but Roy W. does explain his choices. Just not enough data to suit pre-WWII.
I always love his ’4th order polynomial fit for entertainment purposes only’. He’s got a dry sense of humour does Dr Spencer.
If you detrend HadCRUT by the quadratic curve of best fit you get this.