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- Title
Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations.
- Authors
Zhang, Yong‐Fei; Bushuk, Mitchell; Winton, Michael; Hurlin, Bill; Gregory, William; Landy, Jack; Jia, Liwei
- Abstract
Because of a spring predictability barrier, the seasonal forecast skill of Arctic summer sea ice is limited by the availability of melt‐season sea ice thickness (SIT) observations. The first year‐round SIT observations, retrieved from CryoSat‐2 from 2011 to 2020, are assimilated into the GFDL ocean–sea ice model. The model's SIT anomaly field is brought into significantly better agreement with the observations, particularly in the Central Arctic. Although the short observational period makes forecast assessment challenging, we find that the addition of May–August SIT assimilation improves September local sea ice concentration (SIC) and extent forecasts similarly to SIC‐only assimilation. Although most regional forecasts are improved by SIT assimilation, the Chukchi Sea forecasts are degraded. This degradation is likely due to the introduction of negative correlations between September SIC and earlier SIT introduced by SIT assimilation, contrary to the increased correlations found in other regions. Plain Language Summary: The dramatic decline of Arctic sea ice, especially in summer, has received a lot of attention. The ability to better predict Arctic summer sea ice several months ahead of time will help decision making on protecting local communities and ecosystems and regulating economic activities in the Arctic. Climate dynamical models have shown reasonable skill in predicting Arctic summer sea ice on seasonal timescales, but also contain considerable errors. Integrating observed sea ice thickness conditions into the model in the summer melt season has a large potential to reduce such errors. The prediction skill of summer Arctic sea ice initialized before June 1st is found to be notably lower than that initialized afterward, which is known as the spring predictability barrier. Hence constraining initial conditions post‐June has great implications for summer Arctic sea ice predictions. This study combines a new year‐round satellite sea ice thickness observational product with the sea ice and ocean dynamical model at GFDL and examines its impact on the seasonal prediction of Arctic sea ice. We find that the prediction skill has been improved in general, although some uncertainties exist due to the limited temporal availability of the observations. Key Points: The representation of Arctic sea ice volume anomalies is significantly improved by assimilating year‐round sea ice thickness (SIT) observations from CryoSat‐2Arctic summer sea ice prediction skill is generally improved when initial conditions are constrained by satellite SIT observationsThe Arctic summer sea ice in the 2010s decade is particularly hard to predict due to anomalously low correlation between volume and extent
- Subjects
SEA ice; SPRING; SUMMER; ATMOSPHERIC models; DECISION making; FORECASTING
- Publication
Geophysical Research Letters, 2023, Vol 50, Issue 24, p1
- ISSN
0094-8276
- Publication type
Article
- DOI
10.1029/2023GL105672