The Challenge of Arctic Sea Ice Thickness Prediction by ECMWF on Subseasonal Time Scales

A recent study has provided the first thorough assessment of subseasonal Arctic sea ice edge prediction in operational forecast systems. However, corresponding thickness (SIT) is still lacking. Here, SIT reforecasts from European Centre for Medium-Range Weather Forecasts (ECMWF) on time scales are evaluated using a well-developed reanalysis data set. The results show that ECMWF forecasts more skillful than persistence (PFs) during transition seasons lead times longer 30 days, indicating advantages dynamical prediction. suffers large initial errors and lower skill PF, especially March to June. Thus, predictions face many challenges, improving mean model assimilating satellite-estimated ocean analysis would significantly improve forecasts.