Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations

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 GF...

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Bibliographic Details
Published in:Geophysical Research Letters
Main Authors: Yong‐Fei Zhang, Mitchell Bushuk, Michael Winton, Bill Hurlin, William Gregory, Jack Landy, Liwei Jia
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2023
Subjects:
Online Access:https://doi.org/10.1029/2023GL105672
https://doaj.org/article/d339bbe64182499fbd312ebd78929ab7
Description
Summary: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.