Assimilating Summer Sea‐Ice Thickness Observations Improves Arctic Sea‐Ice Forecast
Abstract Accurate Arctic sea‐ice forecasting for the melt season is still a major challenge because of the lack of reliable pan‐Arctic summer sea‐ice thickness (SIT) data. A new summer CryoSat‐2 SIT observation data set based on an artificial intelligence algorithm may alleviate this situation. We a...
| Published in: | Geophysical Research Letters |
|---|---|
| Main Authors: | , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley
2024
|
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024GL110405 https://doaj.org/article/e29a913d014040869f79de7faf65c9e0 |
| Summary: | Abstract Accurate Arctic sea‐ice forecasting for the melt season is still a major challenge because of the lack of reliable pan‐Arctic summer sea‐ice thickness (SIT) data. A new summer CryoSat‐2 SIT observation data set based on an artificial intelligence algorithm may alleviate this situation. We assess the impact of this new data set on the initialization of sea‐ice forecasts in the melt seasons of 2015 and 2016 in a coupled sea ice‐ocean model with data assimilation. We find that the assimilation of the summer CryoSat‐2 SIT observations can reduce the summer ice‐edge forecast error. Further, adding SIT observations to an established forecast system with sea‐ice concentration assimilation leads to more realistic short‐term summer ice‐edge forecasts in the Arctic Pacific sector. The long‐term Arctic‐wide SIT prediction is also improved. In spite of remaining uncertainties, summer CryoSat‐2 SIT observations have the potential to improve Arctic sea‐ice forecast on multiple time scales. |
|---|