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Abstract This study investigates the occurrence of the Weddell Sea polynya under an idealized climate change scenario by evaluating simulations from climate models of different ocean resolutions. The GFDL-CM2.6 climate model, with roughly 3.8-km horizontal ocean grid spacing in the high latitudes, forms a Weddell Sea polynya at similar time and duration under idealized climate change forcing as under preindustrial forcing. In contrast, all convective models forming phase 5 of the Coupled Model Intercomparison Project (CMIP5) show either a cessation or a slowdown of Weddell Sea polynya events under climate warming. The representation of the Antarctic Slope Current and related Antarctic Slope Front is found to be key in explaining the differences between the two categories of models, with these features being more realistic in CM2.6 than in CMIP5. In CM2.6, the freshwater input driven by sea ice melt and enhanced runoff found under climate warming largely remains on the shelf region since the slope front restricts the lateral spread of the freshwater. In contrast, for most CMIP5 models, open-ocean stratification is enhanced by freshening since the absence of a slope front allows coastal freshwater anomalies to spread into the open ocean. This enhanced freshening contributes to the slowdown the occurrence of Weddell Sea polynyas. Hence, an improved representation of Weddell Sea shelf processes in current climate models is desirable to increase our ability to predict the fate of the Weddell Sea polynyas under climate change. 

Abstract Compared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen and Bellingshausen, Indian, and West Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently-developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal timescales. 

Abstract The decline of Arctic sea ice extent has created a pressing need for accurate seasonal predictions of regional summer sea ice. Recent work has shown evidence for an Arctic sea ice spring predictability barrier, which may impose a sharp limit on regional forecasts initialized prior to spring. However, the physical mechanism for this barrier has remained elusive. In this work, we perform a daily sea ice mass (SIM) budget analysis in large ensemble experiments from two global climate models to investigate the mechanisms that underpin the spring predictability barrier. We find that predictability is limited in winter months by synoptically driven SIM export and negative feedbacks from sea ice growth. The spring barrier results from a sharp increase in predictability at melt onset, when ice‐albedo feedbacks act to enhance and persist the preexisting export‐generated mass anomaly. These results imply that ice thickness observations collected after melt onset are particularly critical for summer Arctic sea ice predictions.