Never Mind The Area, Feel The Thickness
By Paul Homewood
For years, discussion of Arctic sea ice has revolved around extent. Indeed NSIDC’s regular monthly Arctic Sea Ice News talks about little else, particularly when there is an unusually low figure.
However, when extents are not so unusually low, the Arctic obsessives switch the goal posts to thickness/volume.
There is one problem however – there is no reliable measurement data around to track it.
As the Greenpeace activist, and part time NSIDC scientist, Julienne Stroeve admitted, “satellite measurements [of thickness] are not continuous in time, not continuous in space.” (See BBC interview at 4 mins in here).
To get around this slight problem, the obsessives turn to the PIOMAS Arctic Sea Ice Volume Reanalysis, shown above.
But this is not based on real data, though they seem to delude themselves that it does. Instead, it is a numerical model with components for sea ice and ocean and the capacity for assimilating some kinds of observation.
Or as someone put it, a model to estimate what area and thickness would have been elsewhere had we measured them.
Now PIOMAS might have its uses, and it may have some relation to reality. But we need to see what the PIOMAS team themselves say about its accuracy:
PIOMAS has been extensively validated through comparisons with observations from US-Navy submarines, oceanographic moorings, and satellites. In addition model runs were performed in which model parameters and assimilation procedures were altered. From these validation studies we arrive at conservative estimates of the uncertainty in the trend of ± 1.0 103 km3/decade. The uncertainty of the monthly averaged ice volume anomaly is estimated as ±0.75 103 km3. Total volume uncertainties are larger than those for the anomaly because model biases are removed when calculating the anomalies. The uncertainty for October total ice volume is estimated to be ±1.35 103 km3
Last October’s ice volume was 5500 cu km, so the uncertainty is a massive 25%.
It is hard to see how any significance can be read into recent trends, when error margins are so large, even assuming the model is reliable in the first place.