Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison
The seasonal predictability of September Arctic sea ice extent (SIE) are investigated using 34 diverse models, including the ensemble forecasts from the fully-coupled Regional Arctic System Model (RASM). This research compares the skill of both dynamical and statistical models in predicting pan-Arctic, regional, and local sea ice. Dynamical models generally perform better, especially for regional predictions, while both model types can skillfully forecast pan-Arctic SIE anomalies. The models show the greatest accuracy during extreme ice years such as 1996, 2007, and 2012. The findings demonstrate the capacity of these systems to predict sea ice conditions months in advance.
By identifying the strengths and weaknesses of different prediction model types, this study provides insights that could improve operational sea ice predictions, which are critical for climate monitoring and resource management in the Arctic. The study highlights the potential to predict sea ice conditions with increasing accuracy, contributing to climate adaptation strategies.
Seasonal Arctic sea ice predictions are assessed using a combination of statistical and dynamical models. The study focuses on September Arctic sea ice extent (SIE), a key metric for understanding climate change in the region. By analyzing 34 models over the period from 2001 to 2020, the research finds that dynamical models tend to outperform statistical models, particularly at regional scales. However, both model types demonstrate skill in predicting pan-Arctic SIE anomalies months in advance. It also emphasizes the importance of accurate sea ice predictions during extreme sea ice years, where models show significant added value over basic reference forecasts. This research offers valuable insights for improving future operational sea ice forecasts.