If you are new to climate science, you might be wondering what, exactly, this ‘temperature anomaly’ thing is that you keep hearing about. I know I was a bit confused at first! This post explains the concept, using a real-world example.

Absolute temperatures (yearly averaged) from two sites in the UK: one urban (St. James Park, green) and one rural (Rothamsted, red). Although the urban site is consistently warmer, the two sites show the same warming trend. But is there a way to compare them directly? Data from Jones et al. 2008, kindly provided by Dr. Jones.

Cities tend to be warmer than their surrounding countrysides, a fact known as the urban heat island effect (UHI). This occasionally is offered as an alternative explanation for greenhouse warming, but it fails on closer inspection. We can use data from Jones et al. (2008) [PDF] to see one reason UHI can’t explain observed warming. One time series is from St. James Park, in the city of London; the other is from nearby Rothamsted, a rural site some tens of miles away. As you can see, the urban location is consistently about 2 C warmer; however, the warming is nearly identical at both sites (a strongly significant 0.03 deg C/year). Jones et al. note:

“… the evolution of the time series is almost identical. As for trends since 1961 all sites give similar values …  in terms of anomalies from a common base period, all sites would give similar values.”

This gives us a hint about what a temperature anomaly is: it’s a measure of how much warmer or colder a temperature is compared to some reference point. The anomaly usually gets calculated by selecting a convenient period of time, calculating its average, and subtracting the average from each point in the time series:

Anomaly = Data – Mean (over a common base period)

I’ve taken the absolute temperature data graphed above and calculated temperature anomalies relative to the 20 year period between 1970 and 1990, marked out by the horizontal bars. I calculate the average value of each time series during this period and then subtract that value from each point in the time series. Graphically, this takes the two zigzag lines in the above image and slides them and the horizontal bars vertically, so that both bars are aligned with the horizontal axis. This sets the average of both base periods to zero. When we do this, we do indeed see that the temperature anomalies not only tell the same 0.03C/year story as before, they’re virtually identical:

The same data in the first figure, except that each time series has been re-aligned so that their average value over 1970-1990 is zero. This realignment shows that, beside the systematic warming caused by UHI, the average temperatures in the two locations unfold almost identically.

In this example, the difference was only a few degrees between the two sites. But to talk about the change in global temperatures, you need to deal with even bigger temperature differences – think the Arctic vs the Sahara. Temperature anomalies allow us to measure the trend in global temperature, in the face of these large regional temperature differences.

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Jones, P., Lister, D., & Li, Q. (2008). Urbanization effects in large-scale temperature records, with an emphasis on China Journal of Geophysical Research, 113 (D16) DOI: 10.1029/2008JD009916