Regional Arctic Sea-Ice Prediction: Potential Versus Operational Seasonal Forecast Skill

Recent Arctic sea-ice prediction efforts and forecast skill assessments have primarily focused on pan-Arctic sea-ice extent (SIE). In this work, we move towards stakeholder-relevant spatial scales, investigating the regional forecast skill of Arctic sea ice in a coupled dynamical prediction system. We perform two complementary analyses of regional sea-ice predictability based on: (1) a suite of retrospective initialized forecasts spanning 1981-2015 and (2) a set of “perfect model” predictability experiments, which consist of ensembles of simulations initialized with nearly identical initial conditions. The retrospective forecasts, made with an atmosphere-ocean-sea ice-land model initialized using a coupled data assimilation system, exhibit skill in predicting detrended regional SIE at lead times of up to 8 months. The regional SIE prediction skill scores are highly region and target month dependent, and generically exceed the skill of a damped persistence forecast. The prediction skill is notably high for winter and spring SIE in the Barents and Labrador Seas, which is tied to the forecast system’s skillful prediction of sea-surface temperature in these regions. We compare these quasi-operational skill scores with regional skill estimates derived from the perfect model experiments, which provide an upper bound of the prediction skill potentially achievable in this forecast system. Finally, we examine physical mechanisms underlying regional sea-ice predictability, and use these mechanisms to explain both the skill achieved in the initialized forecasts and the skill disparity between the initialized forecasts and perfect model experiments.