We employ a moist energy balance model (MEBM), representing atmospheric heat transport as the diffusion of near‐surface moist static energy, to evaluate sources of uncertainty in the meridional pattern of surface warming. Given zonal mean patterns of radiative forcing, radiative feedbacks, and ocean heat uptake, the MEBM accurately predicts zonal mean warming as simulated by general circulation models under increased CO2. Over a wide range of latitudes, the MEBM captures approximately 90% of the variance in zonal mean warming across the general circulation models, with approximately 70% of the variance attributable to differences in radiative feedbacks alone. Partitioning the radiative feedbacks into individual components shows that the majority of the uncertainty in the meridional pattern of warming arises from uncertainty in cloud feedbacks. Isolating feedback uncertainty within specific regions demonstrates that tropical feedback uncertainty leads to surface warming uncertainty that is global and nearly uniform with latitude, whereas polar feedback uncertainty leads to surface warming uncertainty that is largely confined to the poles.

Improved knowledge of the contributing sources of uncertainty in projections of Arctic sea ice over the 21st century is essential for evaluating impacts of a changing Arctic environment. Here, we consider the role of internal variability, model structure and emissions scenario in projections of Arctic sea-ice area (SIA) by using six single model initial-condition large ensembles and a suite of models participating in Phase 5 of the Coupled Model Intercomparison Project. For projections of September Arctic SIA change, internal variability accounts for as much as 40%–60% of the total uncertainty in the next decade, while emissions scenario dominates uncertainty toward the end of the century. Model structure accounts for 60%–70% of the total uncertainty by mid-century and declines to 30% at the end of the 21st century in the summer months. For projections of wintertime Arctic SIA change, internal variability contributes as much as 50%–60% of the total uncertainty in the next decade and impacts total uncertainty at longer lead times when compared to the summertime. In winter, there exists a considerable scenario dependence of model uncertainty with relatively larger model uncertainty under strong forcing compared to weak forcing. At regional scales, the contribution of internal variability can vary widely and strongly depends on the calendar month and region. For wintertime SIA change in the Greenland-Iceland-Norwegian and Barents Seas, internal variability contributes 60%–70% to the total uncertainty over the coming decades and remains important much longer than in other regions. We further find that the relative contribution of internal variability to total uncertainty is state-dependent and increases as sea ice volume declines. These results demonstrate that internal variability is a significant source of uncertainty in projections of Arctic sea ice.

A model relating future SIA to present SIA and local sea-ice sensitivity is used to explain the intermodel spread in Arctic SIA projections• Biases in simulating present-day SIA contribute most to the intermodel spread with model differences in Arctic warming contributing the rest • Under high emissions, constraints suggest the Arctic will likely be ice-free in Septem-

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.

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.