Sunday, February 19, 2012

Combined GMST trend viewer.

In a few posts I have been showing plots of all possible trends that you could calculate from global temperature time series, with some emphasis on statistical significance. The original post just showed trends; the next showed trends that lacked statistical significance in faded colors, and then I tried to show more detail with plots of confidenced limits and t-statistics.

The plots are available for various land, ocean and combined datasets. I added SST at some stage, and the various gadget capabilities improved. There are some similar posts with special datasets.

I did some more programming to allow a fuller set of summary statistics when you click on the main plot. So I thought it was a good time to gather the various facilities in a single plot, since they use the same dataset.

Here is a brief summary of the things you can do:
  • You can choose from 11 datasets (right buttons)
  • You can choose time periods - this shows the same data on an enlarged scale for shorter recent periods
  • You can select the variable to display - just trend, trend with significance marked, or the upper or lower confidence limit (CI), or the t-statistic, which is the ratio of the calculated trend to its standard error.
  • With each dataset selection, a graph of the time series appears top right. There are two balls, blue and red, indicating the ends of the selected trend period (showing the trend). You can click on the red and blue bars to change this selection; there are also nudgers at each corner of the plot.
  • The triangle plot has the start of the trend period on the y-axis, and the end on the left. You can click on any point to show details. There appear next to the plot on the right, and the time series plot will update to show the trend.
  • At the bottom right is a url which incorporates the current state. You can copy it to use as a link; it will bring up the post with the selections as you had them when you copied.

I'll briefly discuss some of the scientific/technical issues below the plot, but the main discussion is in the posts linked above. Here is the plot:

Plot Data
Display Years
Upper CI trend
Lower CI trend
t-statistic trend
Land and Ocean
Land Only
Sea Surface

You'll notice that the colors are mainly rainbow, with some added gray/browns for significant levels. These include trends 0 and 1.7 °C/century - the latter represents a kind of recent average useful as a visual marker. On t-statistics I've colored 1.96 and -1.96, as the 95% significance levels.

There are 255 rainbow color levels - the legend shows a selection of representative values. The scales are non-linear; using an inverse tan mapping so that all possible values are within the color range.

The CI plots are useful for looking up whether a chosen level has been significantly exceeded (or under-run). People are often looking to see if some forecast level has been significantly deviated from, usually on the low side. To check, say, where the trand has been significantly less than 2°C/century, look at the lower CI plot and the corresponding color level. You'll notice that this plot has the zero level in a gray color; that corresponds to the border of significance in the "trend with significance" style plot.

The nudgers in the time series plot move the balls my a small amount, varying depending on how far from the center you click. Smallest jump is 1 pixel, and then by 2x steps to 16. The movements are:
  • Top left (blue) blue ball only
  • Top rightt (purple) both in parallel
  • Bottom left (gold) both in opposite direction
  • Bottom right (red) red ball only
As they move they show the new trend. The gold is useful for showing trends centred on a point in time; the pruple for trends of fixed length.

A note on performance - there are 264 images here, typically 50 Kb They are downloaded when first requested, so the page loads quite quickly, but if you request a lot of pictures, you may notice a slowdown, since they are held in memory.

I'll try to keep this version updated as new data comes in, and add new datasets as appropriate.


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