Modelling Arctic Sea Ice (part 2)

Using a supplemented dataset incorporating NSIDC’s Sea Ice Index (SII) to explore the relationship with sea ice extent, sea surface and land surface temperate anomalies

This morning we’re going to move along the bus and go all standardised scores (Z Scores). I remember meeting these scores for the first time as a statistical newbie bod in training back in the ‘80s and it seemed like total magic. For those not familiar with this statistical technique I shall summarise it by stating that it very cleverly removes the awkwardness of counting something in raw units without losing any of the flavour.

Let us suppose you’ve got data for sea ice extent that ranges from 6,000,000 sq km to 14,000,000 sq km and back over a period of 30 years, and you’ve got sea surface temperature that ranges from 3°C to 5°C and back over the same period. If you try and plot these on the same graph you’ll end up with a horizontal line representing sea ice up at a mean of 10,000,000 sq km with sea surface temperature indistinguishable from the zero axis down at the bottom. If we re-scale both series using the Z transform we get to see both in all their wiggly glory.

When it comes to statistical modelling the raw units of measure are not important; what is important is how things wiggle about and whether those wiggles correlate in some manner. Let us now look at slides of the four key variables (sea ice extent, sea surface temperature, land surface temperature, atmospheric CO2) converted to their Z-score format.