This work provides a high-level review of sea ice models used for climate studies and of the recent advances made with these models to understand sea ice predictability. Seasonal sea ice can be predicted based on mechanisms associated with long-lived ice thickness or ocean heat anomalies. On longer timescales, although internal climate variability is an important source of uncertainty, anthropogenic signals have already emerged from internal climate noise. While models differ in the magnitude and timing of predictable signals, many ice predictability characteristics are robust across multiple models.
Earth system model studies have provided new insights on sea ice predictability across timescales, which in turn provide useful information for building more skillful forecast systems.
Results from the Multi-Model Large Ensemble (MMLE), consisting of 270 ensemble members from 6 climate models, are used to explore factors impacting sea-ice predictability, including climate drivers arising from anthropogenic factors and from internal dynamics. Arctic sea ice is projected to decline due to rising greenhouse gasses in coming decades, but internal variability remains an important source of uncertainty in the rate of future ice loss and the predictability of the timing of climate signals. Ice anomalies in all MMLE models exhibit a persistence of several months and reemerge at other times of year, associated with ice-thickness and ocean heat content anomalies. These sources of memory should enable predictive skill on seasonal to annual timescales and can guide system requirements to realize that skill. The most significant climate impacts of new sea-ice modeling advances occur through their influence on feedbacks among the ice, atmosphere, and ocean. Because sea-ice predictions are strongly influenced by the simulated atmosphere and ocean, developments are also needed within these models to better simulate winds, radiation fields, and ocean heat transport, among other properties.