Impact of super resolution SIT data for seasonal sea ice predictions
Yiguo Wang
Accurate seasonal prediction of Sea Ice Extent (SIE) is critical for understanding Arctic climate dynamics and holds significant societal and environmental implications. The Norwegian Climate Prediction Model (NorCPM) has previously demonstrated skill in predicting pan-Arctic SIE up to 12 months ahead, primarily using ocean data and coarse-resolution Sea Ice Concentration (SIC) and Sea Ice Thickness (SIT) data. However, its capacity for regional SIE prediction remains limited to a few months, depending on season and region.
This study aims to improve seasonal SIE prediction by incorporating high-resolution SIC and SIT data into NorCPM. The high-resolution sea ice data are classed to SIC data in each category. NorCPM assimilates the category SIC data. We assess the performance of this approach by conducting retrospective ensemble prediction for 2023. The experiment is initialized in March and run for up to 13 months. The prediction will be validated against observations and compared to the standard version of prediction using coarse-resolution sea ice data.