We determine robust modes of the northern hemisphere (NH) sea ice variability on seasonal to interannual time scales disentangled from the long-term climate change. This study focuses on sea ice thickness (SIT), reconstructed with an ocean–sea-ice general circulation model, because SIT has a potential to contain most of the interannual memory and predictability of the NH sea ice system. We use the Kmeans cluster analysis to determine three NH SIT clusters/modes in a historical reconstruction of SIT from 1958 to 2013. Compositing analysis of the NH surface climate conditions associated with each cluster indicates that wind forcing seem to be the key factor driving the formation of interannual SIT cluster patterns during the winter. Furthermore, we explore the prediction skill of these NH SIT modes of variability in a state-of-the-art coupled forecast system with respect to two statistical forecast benchmarks. More specifically, we use the EC-Earth2.3 coupled climate model to produce five-member 12-month-long monthly forecasts of the NH SIT modes initialized on 1 May and 1 November every year from 1979 to 2010. We use a three-state first-order Markov chain and climatological probability forecasts determined from the historical SIT mode reconstruction as two statistical reference forecasts. The analysis of ranked probability skill scores (RPSSs) relating these three forecast systems shows that the dynamical SIT mode forecasts typically have a higher skill than the Markov chain forecasts, which are overall better than climatological forecasts. The evolution of RPSS in forecast time indicates that the transition from the sea-ice melting season to growing season in the EC-Earth2.3 forecasts, with respect to the Markov chain model, typically leads to the improvement of prediction skill. The reliability diagrams overall show better reliability of the dynamical forecasts than that of the Markov chain model, especially for 1 May start dates, while dynamical forecasts with 1 November start dates are overconfident. The relative operating characteristics (ROC) diagrams confirm this hierarchy of forecast skill among these three forecast systems.
Fučkar, N.S., Guemas, V., Johnson, N.C. et al. Clim Dyn (2016) 47: 1527.
https://doi.org/10.1007/s00382-015-2917-2
Fučkar, N.S., Guemas, V., Johnson, N.C. et al. Clim Dyn (2018).
https://doi.org/10.1007/s00382-018-4318-9