https://repository.kopri.re.kr/handle/201206/15771 2026-04-06T10:05:46Z https://repository.kopri.re.kr/handle/201206/16575 Title: Optimal localization radius of data assimilation for Arctic sea ice initialization using CICE5/DART Authors: Kim, Ji-Soo; Chung, Inchae; Noh, Young-Chan; Choi, Yonghan; Kim, Joo-Hong; Lee, Jeong-Gil; Lee, Sang-Moo Abstract: In data assimilation (DA), localization, which adjusts the influence of observations on model state vectors, is an essential process for improving initial conditions. Given that the localization radius varies depending on the model and observation, sensitivity tests were conducted to identify the optimal localization radius for assimilating satellite-derived sea ice concentration and sea ice thickness into Los Alamos Sea Ice Model version 5 (CICE5) using Data Assimilation Research Testbed (DART). In all experiments, the updated Arctic sea ice initial conditions were generally improved across Pan-Arctic regions and time periods. Based on the sensitivity tests, the optimal localization radius for univariate DA was approximately 0.05 radians which is the default value in CICE5/DART. However, for multivariate DA, it was around 0.02 radians. This implies that the optimal localization condition for assimilation may vary depending on whether univariate or multivariate observations are being assimilated. 2025-11-01T00:00:00Z https://repository.kopri.re.kr/handle/201206/16578 Title: The role of sea ice in present and future Arctic amplification Authors: Chung, Eui-Seok; Kim, Seong-Joong; Ha, Kyung-Ja; Malte Stuecker; Lee, Sun-Seon; Kim, Joo-Hong; Jun, Sang-Yoon; Tamas Bodai Abstract: The importance of sea-ice loss on the Arctic amplification of near-surface warming remains contentious, as Arctic amplification emerges even in model experiments with disabled surface-albedo feedback. Here we show that the characteristics and underlying dynamics of Arctic amplification may change greatly in a future ice-free climate using a series of climate model experiments. Our analysis indicates that although Arctic amplification continues over the 22nd century, it weakens markedly with a less distinct seasonality in a future ice-free climate. These changes are found to occur because the strength and seasonality of Arctic amplification in the current climate are attributed mainly to a tight coupling between cold-season lapse-rate feedback and sunlit-season surface-albedo feedback. The substantial differences in the characteristics of simulated Arctic amplification between the current and future ice-free climate therefore suggest that the presence of Arctic sea ice is an essential component of the current Arctic amplification regime. 2025-11-01T00:00:00Z https://repository.kopri.re.kr/handle/201206/16597 Title: Unraveling the Warm Arctic？Cold Eurasia Pattern: Interplay of Arctic Amplification and Internal Variability in Shaping Midlatitude Weather Authors: KU, HO-YOUNG; Muyin Wang; james Overland; Kim, Seong-Joong; YANG, GUN-HWAN; KIM, BAEK-MIN Abstract: The warm Arctic？cold Eurasia (WACE) pattern, identified as Arctic warming and midlatitude cooling over recent decades, has been a subject of intense scientific debate regarding its causal relationship and implications for midlatitude weather extremes. This study investigates the primary drivers of WACE, examining the complex interplay between external forcing and internal variability. Using the empirical orthogonal function analysis on 84 years (1941？2024) of winter (DJF) temperature over the Northern Hemisphere, we identify three dominant modes of variability: Arctic amplification (AA), the Arctic Oscillation (AO), and the Barents Oscillation (BO). AA, accounting for 27% of the total variance, captures the dominant warming pattern across the Arctic region and reflects a pronounced long-term trend. In contrast, the AO and BO modes (explaining 13.8% and 9.7%, respectively) exhibit considerable internal variability with negligible long-term trends. From 1990 to 2014, the interaction between these modes largely explains the observed WACE pattern, with AA driving Arctic warming and negative AO phase contributing to Eurasian cooling. Meanwhile, the change point detection reveals a shift in Arctic climate regimes, marking a transition from a cold Arctic regime (1947？80) to a warm Arctic regime (2004？24). During the warm regime, weakened meridional potential vorticity gradients and increased East Siberian blocking frequency are observed under negative AO and BO phases. Idealized model experiments corroborate showing that Arctic warming amplifies potential vorticity gradient reductions under negative AO and BO phases. These findings highlight the WACE pattern as driven by the intricate interaction between AA and internal variability, emphasizing the balance between external forcing and internal processes in shaping Arctic climate and midlatitude impacts. 2025-09-01T00:00:00Z https://repository.kopri.re.kr/handle/201206/16102 Title: Sea Ice Initialization and Its Impact on Winter Seasonal Prediction Skill over the Northern Hemisphere in Coupled Forecast System Authors: Sim Ji-Han; Kim Baek-Min; Lee Jeong-Gil; Lim Young-Kwon; Kim, Joo-Hong; Kim Ju Heon 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. 2025-08-01T00:00:00Z