I've dug out my spreadsheet in which I decided volume wasn't as good as the later area method I outlined for CT Area, I've updated it. The linear best fit to a scatter plot of Volume at daily maximum, and CT Area at daily minimum gives an equation:

MinArea = 0.2387*MaxVolume -2.3484.

R2 = 0.8003.

In reality a power function is more likely, but a linear fit probably works OK for this year and as Andrew is working with linear (ax +b) form equations I'll follow suit.

I've used the above equation to calculate a projected area for each year based on the actual volume at maximum. I've then worked out the difference between the two figures for each year - the error. The largest errors are +0.653M km^2 and -0.989M km^2. Which is why I concluded that this method wasn't as good as using CT Area on June 20th.

Andrew's method is different from this, so the stats and performance will be different.

Andrew,

2006 is an outlier, at -2.3 sigma, but any practical prediction system will always encounter outliers. In any case the method I use is adapted for the post 2007 period, which exhibits greater summer losses, as I outlined in my recent blog post I suspect this is due to ice state causing greater melt.

The idea of substantial predictability based on winter volume excites me because I have been saying for some time that extent and area are sideshows with volume being the key issue. Such predictability would fit into that framework very neatly.

Have you considered that in using the total volume for your regression you're introducing noise due to ice volume outside the Arctic Ocean which plays no part in the state at minimum? You might want to try using the volume within the Arctic Ocean at maximum, I've got regional volume, using Cryosphere Today's regions, worked out here:

http://dosbat.blogspot.co.uk/2013/12/regional-piomas-volume-data.htmlSee under 'The Data', that data is derived from PIOMAS gridded data, which is monthly, however it was pointed out to me last year that using daily values might be a further source of noise, so using monthly averages might be of use.

I've just redone the method I outlined above using Arctic Ocean, Greenland Sea and CAA total April Volume, the std dev drops from 0.371 to 0.358, but there are still large outliers, the range is from -0.92 to 0.565M km^2. Not a great gain but your method may benefit.