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Sea Ice Initialization and Its Impact on Winter Seasonal Prediction Skill over the Northern Hemisphere in Coupled Forecast System

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Title
Sea Ice Initialization and Its Impact on Winter Seasonal Prediction Skill over the Northern Hemisphere in Coupled Forecast System
Other Titles
결합 예측 시스템에서 해빙 초기화의 북반구 겨울 계절 예측 기술에의 영향
Authors
Sim Ji-Han
Kim Baek-Min
Lee Jeong-Gil
Lim Young-Kwon
Kim, Joo-Hong
Kim Ju Heon
Keywords
Arctic OscillationBarents OscillationCoupled forecast systemSea ice initializationWarm Arctic-Cold EurasiaWinter seasonal prediction
Issue Date
2025-08
Citation
Sim Ji-Han, et al. 2025. "Sea Ice Initialization and Its Impact on Winter Seasonal Prediction Skill over the Northern Hemisphere in Coupled Forecast System". JOURNAL OF CLIMATE, 38(16): 3989-4001.
Abstract
Recent advancements in coupled models and Arctic sea ice satellite observations have prompted research on sea ice initialization. To assess its impact on winter surface air temperature (SAT) seasonal prediction skill, three initialization methods based on nudging are evaluated using the Community Earth System Model, version 2 (CESM2). The methods include 1) generating ocean/sea ice initial conditions (ICs) solely from atmospheric forcing (Exctrl), 2) building upon Exctrl by directly nudging sea ice concentration to observation and thickness to reanalysis data to produce improved ICs (Exicenudge), and 3) further enhancing Exicenudge by applying additional atmospheric forcing to adjust model balance (Exbalance). The retrospective predictions are initialized on 21 October for 24 years from 1993 to 2016. The anomaly correlation coefficients from the retrospective predictions are 0.27, 0.15, and 0.45 for northern Eurasia and 0.23, 0.27, and 0.39 for southern Eurasia in Exctrl, Exicenudge, and Exbalance, respectively. The Exbalance demonstrates the highest prediction skill, with notable improvements in areas associated with the warm Arctic-cold Eurasia pattern. The Exbalance accurately simulates the SAT distribution, which is characterized by the Barents Oscillation, and effectively captures the polar vortex, a crucial factor in determining Arctic temperatures. The enhanced prediction skill in Exbalance can be attributed to improved SST bias of ICs and better-balanced sea ice ICs with the atmosphere, significantly reducing the strong warm bias within the Arctic Ocean compared to Exicenudge. Altogether, this study highlights that when model bias is substantial, maintaining model balance is more critical than assimilating sea ice conditions that closely match observations for improving seasonal prediction skill.
URI
https://repository.kopri.re.kr/handle/201206/16102
DOI
http://dx.doi.org/10.1175/JCLI-D-24-0524.1
Type
Article
Station
해당사항없음
Indexed
SCIE
Appears in Collections  
2025-2025, 지구시스템모델 기반 북극-한반도 통합 재해기상 예측 시스템(KPOPS-Earth)의 개발 및 활용 (25-25) / 김주홍 (PE25010)
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