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Abstract Antarctic sea ice exhibits considerable regional variability that is influenced by ocean and atmospheric conditions. Previous studies have suggested that this variability may be predictable on seasonal-to-interannual time scales. Here, we use initial-value predictability experiments of the Community Earth System Model, version 2 (CESM2), paired with analysis of the CESM2 large ensemble, to further assess the inherent predictability in regional Antarctic sea ice conditions. As in previous studies, we find that Antarctic sea ice area predictability is high for several months after initialization. It is then lost when ice retreats, and predictability is regained in the following ice advance period. In our simulations, this process acts on multiyear time scales with little sensitivity to the seasonal initialization timing but has a strong regional dependence. Long-lived ocean temperature anomalies in the vicinity of the winter ice edge are the primary source of sea ice predictability. Different predictability characteristics occur across regions, depending on how these ocean temperature anomalies are advected relative to regional sea zones. Our results show that sea ice predictability can impart predictability to primary productivity in the Southern Ocean due to its impact on light availability. This has implications for the understanding and management of Southern Ocean marine ecosystems. 

Abstract Climate impacts are not always easily discerned in wild populations as detecting climate change signals in populations is challenged by stochastic noise associated with natural climate variability, variability in biotic and abiotic processes, and observation error in demographic rates. Detection of the impact of climate change on populations requires making a formal distinction between signals in the population associated with long‐term climate trends from those generated by stochastic noise. The time of emergence (ToE) identifies when the signal of anthropogenic climate change can be quantitatively distinguished from natural climate variability. This concept has been applied extensively in the climate sciences, but has not been explored in the context of population dynamics. Here, we outline an approach to detecting climate‐driven signals in populations based on an assessment of when climate change drives population dynamics beyond the envelope characteristic of stochastic variations in an unperturbed state. Specifically, we present a theoretical assessment of the time of emergence of climate‐driven signals in population dynamics (). We identify the dependence ofon the magnitude of both trends and variability in climate and also explore the effect of intrinsic demographic controls on. We demonstrate that different life histories (fast species vs. slow species), demographic processes (survival, reproduction), and the relationships between climate and demographic rates yield population dynamics that filter climate trends and variability differently. We illustrate empirically how to detect the point in time when anthropogenic signals in populations emerge from stochastic noise for a species threatened by climate change: the emperor penguin. Finally, we propose six testable hypotheses and a road map for future research. 

Abstract. The potential for multiyear prediction of impactful Earthsystem change remains relatively underexplored compared to shorter(subseasonal to seasonal) and longer (decadal) timescales. In this study, weintroduce a new initialized prediction system using the Community EarthSystem Model version 2 (CESM2) that is specifically designed to probepotential and actual prediction skill at lead times ranging from 1 month outto 2 years. The Seasonal-to-Multiyear Large Ensemble (SMYLE) consists of acollection of 2-year-long hindcast simulations, with four initializations peryear from 1970 to 2019 and an ensemble size of 20. A full suite of output isavailable for exploring near-term predictability of all Earth systemcomponents represented in CESM2. We show that SMYLE skill for ElNiño–Southern Oscillation is competitive with other prominent seasonalprediction systems, with correlations exceeding 0.5 beyond a lead time of 12months. A broad overview of prediction skill reveals varying degrees ofpotential for useful multiyear predictions of seasonal anomalies in theatmosphere, ocean, land, and sea ice. The SMYLE dataset, experimentaldesign, model, initial conditions, and associated analysis tools are allpublicly available, providing a foundation for research on multiyearprediction of environmental change by the wider community.