The United Kingdom's Met Office recently released temperature data for about 1700 weather stations across the globe from 1701 to 2009.
Even if the projection is not very friendly, it is possible to recognise the main mass bodies on Earth. The density of stations is non uniform with some areas over represented and some areas under represented. This might affect the stability and validity of global averages over time.
It can be seen how the number of measurements increased dramatically in the middle 20th century. But what caused that sharp increase in the amount of data?
It can be seen how the number of stations has increased over time for each region.
- Artic represents the data recorded by stations north of the Artic Circle.
- North represents the data recorded by stations between the Arctic Circle and the Tropic of Cancer.
- Tropic represents the data recorded by stations between the Tropic of Cancer and the Tropic of Capricorn.
- South represents the data recorded by stations between the Tropic of Capricorn and the Antarctic Circle.
- Antartic represents the data recorded by stations south of the Antarctic Circle.
After understanding a bit better the evolution of the number of stations, I was interested in trying to see if I could find any meaningful pattern in the temperature data. So first I did an exploratory plot with the average monthly temperature for each region.
The first thing that caught my attention is the seasonal variation of the temperatures, and that displaying them in a scatter plot makes it unintuitive to understand that the right end of a plot is connected to the left end of the same plot (December - January). Then I decided to give it a try using polar coordinates.
Temperatures are represented radially, the angular magnitude corresponds to the months in a calendar year, while colors represent the years. The fact that the ellipse is not centered shows the seasonality of the data.
Then I decided to try to get one step further and try to show in an animation the temporal evolution of this data, and with my first Processing script ever, I created the following animation.
This graph shows a clear increase in the yearly temperature averages in the last 50 year! In a similar way that the visualization done by EagerEyes does. But is it the real story?
This is the same plot as before, but we have added in a color coded scale the mean latitude value for each measurement. The fact that the number of stations in the dataset changes over time, brings the mean latitude of the stations south (almost 10 degrees). Therefore, not all the temperatures have the same reference level. All in all, the chart is a case of apples to oranges comparison and it is telling a misleading story. If we plot explicitly the mean latitude variation, and the mean temperature variation, we can see that the variations follow each other.
This relationship between both magnitudes can be measured by the correlation factor, and in this case it is -0.7115842. Even if correlation does not imply causation, it should be a clear indicator for anyone to pay extra attention to the way in which the manipulate and present the information, as it is very easy to produce visualisations that will support a given idea even if the data says something different.
For the rest of the regions the correlation between temperature and latitude mean values are:
The difference in sign for the North and South are due to the fact that latitudes have opposite sign. While in the north lower latitudes bring the position closer to the tropic (higher temperatures), in the south this effect is achieved with higher latitudes.
In the next days I will post the R scripts I used to analyse the data, as well as the Processing program so that you can reproduce this analysis.
In these graphs, climate change cannot be seen, and it is meant as an exercise to illustrate how easy it is to produce plots that are misleading. Unfortunately, as of today, I lack the skills to reproduce the analysis that have been published in peer-reviewed papers with this dataset, but if you know how to do it, please go ahead and show us! I am eager to learn :)
As usual, I will be very grateful for any comments you have about how to improve the visualisations and the analysis.