Statistical seasonal prediction of Arctic sea ice concentration based on spatiotemporal anomaly persistent method

Accurate prediction of Arctic sea ice is crucial for high-latitude and even mid-latitude climate prediction. It significantly affects atmospheric circulation, the environment, ecology, and maritime transport. This study developed a statistical prediction model to predict monthly Arctic sea ice conce...

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Bibliographic Details
Published in:Environmental research letters 2024-11, Vol.19 (11), p.114060
Main Authors: Lee, Gyu-Ri, Woo, Sung-Ho, Baek, Eun-Hyuk, Kim, Joo-Hong, Kim, Baek-Min, Jeong, Jee-Hoon
Format: Article
Language:English
Subjects:
Arctic sea ice
Atmospheric circulation
Climate prediction
Dynamic models
Ice environments
Latitude
Marine transportation
Ocean-atmosphere interaction
Orthogonal functions
Prediction models
prediction skill
Sea ice
season-reliant EOF
Seasonal variations
Statistical analysis
Statistical models
statistical prediction
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Summary:Accurate prediction of Arctic sea ice is crucial for high-latitude and even mid-latitude climate prediction. It significantly affects atmospheric circulation, the environment, ecology, and maritime transport. This study developed a statistical prediction model to predict monthly Arctic sea ice concentration (SIC) for up to one year based on the season-reliant empirical orthogonal functions (SEOFs) technique. Its prediction skill was compared with that of a dynamical prediction model. The spatiotemporal pattern of sea ice anomalies, which exhibit strong seasonality and are maintained for a significant period above the seasonal time scale by atmosphere-ocean interactions, was extracted using SEOFs. A prediction model was constructed by extrapolating from the recent anomalous state of sea ice to predict the future. Experimental retrospective predictions with monthly time resolution for 1982–2021 were performed to validate the prediction skill of Arctic SIC and areal extent. Statistically significant prediction skills were achieved over several months, even up to six months, exceeding the skill of the dynamical model.
ISSN:1748-9326
1748-9326
DOI:10.1088/1748-9326/ad7d1f