This study explores the prediction skill of the northern hemisphere (NH) sea ice thickness (SIT) modes of variability in a state-of-the-art coupled forecast system with respect to two statistical forecast benchmarks. Application of the K-means clustering method on a historical reconstruction of SIT from 1958 to 2013, produced by an ocean-sea-ice general circulation model, identifies three Arctic SIT clusters or modes of climate variability. These SIT modes have consistent patterns in different calendar months and their discrete time series of occurrences show persistence on intraseasonal to interannual time scales. 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. Furthermore, ROC diagrams stratified in groups of 3 sequential forecast months show that Arctic SIT mode forecasts initialized on 1 November typically lose resolution with forecast time more slowly than forecasts initialized on 1 May.

The authors acknowledge funding support for this study from the PICA-ICE (CGL2012-31987) Project funded by the Ministry of Economy and Competitiveness of Spain, the SPECS (GA 308378) Project funded by the Seventh Framework Programme (FP7) and the PRIMAVERA (GA 641727) project funded by the Horizon 2020 framework of the European Commission. NSF was a recipient of the Juan de la Cierva-incorporación postdoctoral fellowship from the Ministry of Economy and Competitiveness of Spain. NCJ was supported by NOAA’s Climate Program Office. The authors acknowledge the computer resources, technical expertise and assistance provided by the Red Española de Supercomputación through the Barcelona Supercomputing Center in Barcelona, Spain, and by the European Centre for Medium–Range Weather Forecasts in Reading, UK. The authors thank Stefan Siegert and an anonymous reviewer for their constructive inputs, and Francois Massonnet, Javier Garcia-Serrano, Omar Bellprat, Louis-Philippe Caron, Matthieu Chevallier, Torben Koening, Mitch Bushuk and Jonathan Day for valuable discussions. Analyzed global sea ice historical reconstruction with ORCA1 NEMO-LIM2 is available upon request.

Peer Reviewed