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The fast decline of Arctic sea ice necessitates a stronger focus on understanding the Arctic sea ice predictability and developing advanced forecast methods for all seasons and for pan-Arctic and regional scales. In this study, the operational forecasting system combining an advanced eddy-permitting ocean–sea ice ensemble reanalysis ORAS5 and state-of-the-art seasonal model-based forecasting system SEAS5 is used to investigate effects of sea ice dynamics and thermodynamics on seasonal (growth-to-melt) Arctic sea ice predictability in 1993–2020. We demonstrate that thermodynamics (growth/melt) dominates the seasonal evolution of mean sea ice thickness at pan-Arctic and regional scales. The thermodynamics also dominates the seasonal predictability of sea ice thickness at pan-Arctic scale; however, at regional scales, the predictability is dominated by dynamics (advection), although the contribution from ice growth/melt remains perceptible. We show competing influences of sea ice dynamics and thermodynamics on the temporal change of ice thickness predictability from 1993–2006 to 2007–20. Over these decades, there was increasing predictability due to growth/melt, attributed to increased winter ocean heat flux in both Eurasian and Amerasian basins, and decreasing predictability due to advection. Our results demonstrate an increasing impact of advection on seasonal sea ice predictability as the region of interest becomes smaller, implying that correct modeling of sea ice drift is crucial for developing reliable regional sea ice predictions. This study delivers important information about sea ice predictability in the “new Arctic” conditions. It increases awareness regarding sea ice state and implementation of sea ice forecasts for various scientific and practical needs that depend on accurate seasonal sea ice forecasts.

Many recent studies have confirmed that variability in the stratosphere is a significant source of surface sub‐seasonal prediction skill during Northern Hemisphere winter. It may be beneficial, therefore, to think about times in which there might be windows‐of‐opportunity for skillful sub‐seasonal predictions based on the initial or predicted state of the stratosphere. In this study, we propose a simple, minimal model that can be used to understand the impact of the stratosphere on tropospheric predictability. Our model purposefully excludes state dependent predictability in either the stratosphere or troposphere or in the coupling between the two. Model parameters are set up to broadly represent current sub‐seasonal prediction systems by comparison with four dynamical models from the Sub‐Seasonal to Seasonal Prediction Project database. The model can reproduce the increases in correlation skill in sub‐sets of forecasts for weak and strong lower stratospheric polar vortex states over neutral states despite the lack of dependence of coupling or predictability on the stratospheric state. We demonstrate why different forecast skill diagnostics can give a very different impression of the relative skill in the three sub‐sets. Forecasts with large stratospheric signals and low amounts of noise are demonstrated to also be windows‐of‐opportunity for skillful tropospheric forecasts, but we show that these windows can be obscured by the presence of unrelated tropospheric signals.

Projected changes in the Northern Hemisphere stratospheric polar vortex are analyzed using Climate Model Intercomparison Project Phase 6 experiments. Previous studies showed that projections of the wintertime zonally averaged polar vortex strength diverge widely between climate models with no agreement on the sign of change, and that this uncertainty contributes to the regional climate change uncertainty. Here, we show that there remains large uncertainty in the projected strength of the polar vortex in experiments with global warming levels ranging from moderate (SSP245 runs) to large (Abrupt‐4xCO₂runs), and that the uncertainty maximizes in winter. Partitioning of the uncertainty in wintertime polar vortex strength projections reveals that, by the end of the 21st century, model uncertainty contributes half of the total uncertainty, with scenario uncertainty contributing only 10%. Regression analysis shows that up to 20% of the intermodel spread in projected precipitation over the Iberian Peninsula and northwestern US, and 20%–30% in near‐surface temperature over western US and northern Eurasian, can be associated with the spread in vortex strength projections after accounting for global warming. While changes in the magnitude and sign of the zonally averaged vortex strength are uncertain, most models (>95%) predict an eastward shift of the vortex by 8°–20° degrees in longitude relative to its historical location with the magnitude of the shift increasing for larger global warming levels. There is less agreement across models on a latitudinal shift, whose direction and magnitude correlate with changes in the zonally averaged vortex strength so that vortex weakening/strengthening corresponds to a southward/poleward shift.

The stratosphere can have a significant impact on winter surface weather on subseasonal to seasonal (S2S) timescales. This study evaluates the ability of current operational S2S prediction systems to capture two important links between the stratosphere and troposphere: (1) changes in probabilistic prediction skill in the extratropical stratosphere by precursors in the tropics and the extratropical troposphere and (2) changes in surface predictability in the extratropics after stratospheric weak and strong vortex events. Probabilistic skill exists for stratospheric events when including extratropical tropospheric precursors over the North Pacific and Eurasia, though only a limited set of models captures the Eurasian precursors. Tropical teleconnections such as the Madden‐Julian Oscillation, the Quasi‐Biennial Oscillation, and El Niño–Southern Oscillation increase the probabilistic skill of the polar vortex strength, though these are only captured by a limited set of models. At the surface, predictability is increased over the United States, Russia, and the Middle East for weak vortex events, but not for Europe, and the change in predictability is smaller for strong vortex events for all prediction systems. Prediction systems with poorly resolved stratospheric processes represent this skill to a lesser degree. Altogether, the analyses indicate that correctly simulating stratospheric variability and stratosphere‐troposphere dynamical coupling are critical elements for skillful S2S wintertime predictions.

The stratosphere has been identified as an important source of predictability for a range of processes on subseasonal to seasonal (S2S) time scales. Knowledge about S2S predictability within the stratosphere is however still limited. This study evaluates to what extent predictability in the extratropical stratosphere exists in hindcasts of operational prediction systems in the S2S database. The stratosphere is found to exhibit extended predictability as compared to the troposphere. Prediction systems with higher stratospheric skill tend to also exhibit higher skill in the troposphere. The analysis also includes an assessment of the predictability for stratospheric events, including early and midwinter sudden stratospheric warming events, strong vortex events, and extreme heat flux events for the Northern Hemisphere and final warming events for both hemispheres. Strong vortex events and final warming events exhibit higher levels of predictability as compared to sudden stratospheric warming events. In general, skill is limited to the deterministic range of 1 to 2 weeks. High‐top prediction systems overall exhibit higher stratospheric prediction skill as compared to their low‐top counterparts, pointing to the important role of stratospheric representation in S2S prediction models.