Lessons from a Multi-Model SIO Experiment

In the Sea Ice Outlook prediction system, we find that dynamical models not only show poor skill in forecasting observed September sea ice extent but are also equally unsuccessful at predicting each other, indicating a large divergence in model physics and/or initial conditions. Motivated by this, we have performed a modeling experiment with SIO models that include both fully coupled global models, and global/regional ice-ocean models. We run two sets of simulations — a control set initialized with a climatological May 1 sea ice thickness and an experiment set initialized with May 1 2015 sea ice thickness. This allows us to investigate both model uncertainty and error growth, and the importance of post-processing to forecast uncertainty. We find that without post-processing, model uncertainty is the dominant source of forecast uncertainty, while with post-processing, overall forecast uncertainty is greatly reduced and error growth dominates forecast uncertainty. This result suggests that uniform bias correction across SIO models’ forecasts will greatly reduce the forecast spread. We also investigate the spatial patterns of forecast uncertainty and find that forecast uncertainty grows at a faster pace along coastal areas compared to the central Arctic basin. Potential ways of offering forecast information based on probabilistic metrics are also explored.