How to use the interactive graph / analyse tool
The interactive graph on this site uses the analyse tool to create the data. I'll describe here how to use the interactive graph - it should be fairly clear from this and the usage output of analyse how this works if you're using the tool directly
The first thing you have to do is to choose a raw data source. This site automatically checks for new data at the public websites of the various organisations listed in the credits page. So you should always have the latest and best data to hand.
The main sources to use when you're starting are:
After choosing your raw data source, you can then choose a number of processing steps to apply to the data to create the graph. If you don't choose any, you just get the raw data itself.
The steps can be applied in any order, and can even be repeated if you're feeling really adventurous. I've limited them to 10 for the interactive graph to stop the table getting too big, but in the analyse tool you can have as many as you like (if you really need more than 10 steps I might suggest you should be running it locally!)
Some steps take a numeric value which defines what they do - what the value does is defined below. Others don't take a value and you can just leave it blank.
1: Data selection
These are the easiest, and just select the time window you want to graph
If you want to select exact months, you have to use the decimal year, so June 1944 is 1944.42 (June = month 6, counting from 1; month 5 counting from zero; 5/12 = 0.42).
- HADCRUT3 from 1920 to June 1944
- HADCRUT3 for the last decade (whenever you do it)
- HADCRUT3 January values
2: Data transformation
These are simple modifications of individual data values:
- SIDC SSN scaled and offset (to make comparable with temperature anomaly)
- SIDC SSN normalised (an easier way to get the same effect)
- HADCRUT3 decadal average rates of change
- ESRL CO2 detrended annual mean
3: Simple averaging
The simplest way to remove short-term 'noise' from a graph is by averaging samples. Alternatively, you might be more interested in the 'noise' than the long term trend...
- HADCRUT3 with an 11-year (132 month) running mean
- HADCRUT3 with the 11-year (132 month) running mean removed
- HADCRUT3 compressed to annual averages
4: Trend lines
To see the overall trend in the data, you can use a best-fit trend line:
- Trend line of HADCRUT3 over the full range
- Trend line of HADCRUT3 over the last 30 years
- HADCRUT3 data (annually smoothed) with long-term and 30-year trend lines
Note that since the graphs are auto-scaling, trend lines by themselves will always be the same shape! It's the vertical scale that matters.
5: Frequency-domain (Fourier) analysis
These are the most complex and most powerful of the processing steps. Fourier analysis works on the principle that any waveform can be decomposed into a combination of simple sine waves (here is Wikipedia's page on Fourier analysis).
Note: When using these processes keep in mind whether you are in the frequency domain or time (normal) domain, otherwise you can get some very strange results indeed!