Subseasonal-to-seasonal prediction of arctic sea ice Using a Fully Coupled dynamical ensemble forecast system

Abstract

With the opening of Arctic passage for maritime transportation under global warming, accurate prediction of Arctic sea ice (ASI) on subseasonal-to-seasonal (S2S) timescales becomes increasingly crucial for the economy and society but challenging. This study examined the S2S hindcast skills of monthly ASI during 1992–2019 using Flexible Global Ocean-Atmosphere-Land System, Finite-Volume version 2 (FGOALS-f2), a global coupled model that includes an interactive dynamical sea ice component. Firstly, the prediction system can accurately capture the seasonal cycle of Arctic sea ice extent (SIE). Still, it slightly overestimates SIE in March–June and significantly underestimates SIE in August–December. Meanwhile, the declining trend of SIE is underestimated in predictions for all target seasons, particularly for spring. Secondly, relatively high skills with an average ACC of 0.44 are found in predicting monthly SIE anomalies (SIEA) after removing the long-term linear trend at a one-to-six-month lead. However, the one-to-six-month lead prediction skills in April drop significantly by 36% when the interdecadal variation is removed. We find that the variation of April SIEA after removing the linear trend is dominated by the Pacific Decadal Oscillation (PDO), which can be better predicted than interannual variability ahead of subseasonal timescales. Unlike the SIE error-based metrics considered in previous studies, the one-to-three-month lead prediction in FGOALS-f2 shows significantly better performance in predicting the spatial distribution of September ASI for extreme years compared to normal years. Finally, the skill for predicting minimum SIEA in FGOALS-f2 is considerably higher at a three-to-six-month lead than anomaly persistence, indicating the advantage of this dynamical prediction system at a longer lead time.