The time series

Motivation

Students studying the effects of climate on ecosystems (and human systems) often use yearly or monthly averages. The longer term context for these measures can be overlooked, particularly when local records only cover a limited period. The south of England does have reasonably good temperature records going back to the beginning of the 19the century. However no single meteorological station will cover the whole period. There is a need for a tool to contextualise the daily measurements and produce rolling averages in order to identify periods with ecological significance (e.g. cold snaps in winter, heat waves, late springs etc)

Methodology

  1. All stations in the NOAA global historical climate data set (ghcnd) were found within a radius of 1 degree centred on 51.5 degrees North and 1.3 degrees west.
  2. Daily temperature records for tmin and tmax were downloaded for each station.
  3. The overall means of tmin and tmax were calculated for each station. Note that different stations records cover different time periods, but this calculation is then used in step 4.
  4. The daily anomaly for each stations tmin and tmax was calculated by subtracting the record from the mean.
  5. The daily anomalies for all the stations active on any given day was averaged to find a mean anomaly.
  6. As the nomalies would be centred around zero, the overall mean for whole region was calculated and added to the anomalies.
  7. The daily average temperature was then taken as the mean of tmin and tmax.

Justification

On any given day the temperatures over the region as shown by a typical weather map (e.g Windy) are quite consistent. Therefore the anomalies from the mean value will tend to be shared between stations.

Caveats

During some periods only a few stations were recording. In other periods most stations produced records. As the anomalies are averaged this may produce more extreme results when there are fewer stations.

Using the dygraph

  • The figure by default shows a rolling mean over 365 days (i.e. mean temperatures over the last year). This can be changed to daily records by replacing the number in the box shown in the bottom left to 1. Rolling monthly means can also be shown by typing in 31. Longer term averages use multiples of 365.

  • The mouse can be used to select parts of the graph both horizontally and vertically. So the mean values alone can be shown by selecting only the central area using a vertical window. Single years can be selected using a horizontal window.

  • The dygraph may take some time to adjust to the new selection.

Dygraph

Stations included in the analysis

Discussion

Students expecting to see clear evidence of consistent climate change may be rather surprised by the empirical data. The reason is that the variability within each day between the maximum temperature and minimum temperatures, and between days are much larger than the changes in the mean.

The variation due to climate change can be isolated by focussing on the average temperature and zooming to the period between 1990 and 2010. A rolling ten year mean shows the tendency.

However this change can be placed in a more historical context.

Unusual events such as heat waves and cold snaps are distributed fairly randomly along any comparatively minor trends of changes to mean temperatures.