Abstract. This work evaluates the statistical predictability of the Arctic sea ice volume (SIV) anomaly –
here defined as the detrended and deseasonalized SIV – on the interannual timescale.
To do so, we made use of six datasets, from three different atmosphere–ocean general circulation models,
with two different horizontal grid resolutions each. Based on these datasets, we have developed a statistical
empirical model which in turn was used to test the performance of different predictor variables, as well as to
identify optimal locations from where the SIV anomaly could be better reconstructed and/or predicted. We tested
the hypothesis that an ideal sampling strategy characterized by only a few optimal sampling locations can provide
in situ data for statistically reproducing and/or predicting the SIV interannual variability. The results
showed that, apart from the SIV itself, the sea ice thickness is the best predictor variable, although total sea
ice area, sea ice concentration, sea surface temperature, and sea ice drift can also contribute to improving the
prediction skill. The prediction skill can be enhanced further by combining several predictors into the statistical
model. Applying the statistical model with predictor data from four well-placed locations is sufficient for
reconstructing about 70 % of the SIV anomaly variance.
As suggested by the results, the four first best locations are placed at the transition Chukchi Sea–central Arctic–Beaufort Sea (79.5∘ N, 158.0∘ W), near the North Pole (88.5∘ N, 40.0∘ E), at the transition central Arctic–Laptev Sea (81.5∘ N, 107.0∘ E), and offshore the Canadian Archipelago (82.5∘ N, 109.0∘ W), in this respective order. Adding further to six well-placed locations, which explain about 80 % of the SIV anomaly variance, the statistical
predictability does not substantially improve taking into account that 10 locations explain about 84 % of that variance.
An improved model horizontal resolution allows a better trained statistical model so that the reconstructed
values better approach the original SIV anomaly. On the other hand, if we inspect the interannual variability, the
predictors provided by numerical models with lower horizontal resolution perform better when reconstructing the original
SIV variability.
We believe that this study provides recommendations for the ongoing and upcoming observational initiatives, in terms of an Arctic optimal
observing design, for studying and predicting not only the SIV values but also its interannual variability.
Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations