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Seasonal forecast systems can skillfully predict summer Arctic sea ice up to 4 months in advance. For some regions, however, there is a springtime predictability barrier that causes forecasts initialized prior to May to be less skillful. Since this barrier has only been documented in a few general circulation models (GCMs), we evaluate GCMs participating in phase 5 of the Coupled Model Intercomparison Project. We first show sea ice volume skillfully predicts summer sea ice area (SIA) and has similar skill to a perfect model experiment. Given this result, we assess regional SIA predictability across each GCM and find a universal predictability barrier in late spring. For SIA at each summer target month in the marginal seas of the Arctic basin, a notable drop in prediction skill occurs from June to May in each GCM. This suggests summer sea ice forecasts initialized after 1 June will have substantially better prediction skill than forecasts initialized before.

Plain Language Summary A central goal of the sea ice community is to assess the ability of global climate models to accurately predict Arctic sea ice since regional forecasts are a pressing commodity for a broad range of stakeholders. Previous studies assessing sea ice prediction skill suggest that some regions in the Arctic have a "prediction skill barrier" in the spring season, where forecasts of summer sea ice made prior to May are substantially less accurate than forecasts made after May. However, this barrier has only been documented in a few global climate models. In this study, we employ a simple model that uses sea ice volume to predict summer sea ice area. After showing that this simple model reliably predicts regional Arctic sea ice area in the summertime, we test for this barrier across a range of global climate models and find that a spring predictability barrier exists across nearly all global climate models. This suggests that there may be a fundamental limit on skillful predictions of summer Arctic sea ice at regional scales, where forecasts made prior to 1 June will be substantially less accurate than forecasts made after 1 June.