Winter Sea-Surface-Temperature Memory in the East/Japan Sea Under the Arctic Oscillation: Time-Integrated Forcing, Coupled Hot Spots, and Predictability Windows

We examine how the Arctic Oscillation (AO) shapes winter sea-surface-temperature (SST) variability in the East/Japan Sea, with a focus on sub-seasonal SST memory (how long anomalies persist) and air–sea coupling (where SST and atmospheric anomalies co-vary). Using daily OISST v2.1 and ERA5 reanalysis for 1993–2022, we first analyze winter persistence of SST and key atmospheric drivers and identify East Korea Bay and the Subpolar Front as hotspots of long-lived SST anomalies. A rank-reduced multivariate maximum covariance analysis then extracts the leading coupled mode between SST and a set of atmospheric fields under positive and negative AO phases; in both phases the coupled mode is front-anchored, but its amplitude and spatial focus differ. Finally, to quantify the mixed-layer memory, we construct Ornstein–Uhlenbeck-like time-integrated responses of the atmospheric principal components. The effective integration timescales, determined by maximizing zero-lag correlations with the SST mode, cluster at approximately 2–3 weeks for wind-stress curl and near-surface variables and 4–7 weeks for sea-level pressure and meridional wind, with longer timescales during negative AO. The time-integrated atmospheric responses exhibit SST-like persistence, confirming the mixed layer’s role as a stochastic integrator. These AO-conditioned memory windows define practical lead times over which integrated atmospheric indices can act as predictors of winter marine heatwaves and cold-surge-impacted SST anomalies.

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Publication Details

Journal
Remote Sensing
Published
2025-12-25
DOI
https://doi.org/10.3390/rs18010079
Primary Topic
Climate variability and models
Type
article
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article

Winter Sea-Surface-Temperature Memory in the East/Japan Sea Under the Arctic Oscillation: Time-Integrated Forcing, Coupled Hot Spots, and Predictability Windows

Gyuchang Lim, J. J. Park
Remote Sensing
Climate variability and models
article

Winter Sea-Surface-Temperature Memory in the East/Japan Sea Under the Arctic Oscillation: Time-Integrated Forcing, Coupled Hot Spots, and Predictability Windows

Gyuchang Lim, J. J. Park
article en

Abstract

We examine how the Arctic Oscillation (AO) shapes winter sea-surface-temperature (SST) variability in the East/Japan Sea, with a focus on sub-seasonal SST memory (how long anomalies persist) and air–sea coupling (where SST and atmospheric anomalies co-vary). Using daily OISST v2.1 and ERA5 reanalysis for 1993–2022, we first analyze winter persistence of SST and key atmospheric drivers and identify East Korea Bay and the Subpolar Front as hotspots of long-lived SST anomalies. A rank-reduced multivariate maximum covariance analysis then extracts the leading coupled mode between SST and a set of atmospheric fields under positive and negative AO phases; in both phases the coupled mode is front-anchored, but its amplitude and spatial focus differ. Finally, to quantify the mixed-layer memory, we construct Ornstein–Uhlenbeck-like time-integrated responses of the atmospheric principal components. The effective integration timescales, determined by maximizing zero-lag correlations with the SST mode, cluster at approximately 2–3 weeks for wind-stress curl and near-surface variables and 4–7 weeks for sea-level pressure and meridional wind, with longer timescales during negative AO. The time-integrated atmospheric responses exhibit SST-like persistence, confirming the mixed layer’s role as a stochastic integrator. These AO-conditioned memory windows define practical lead times over which integrated atmospheric indices can act as predictors of winter marine heatwaves and cold-surge-impacted SST anomalies.

Remote SensingVol. 18(1)
Kyungpook National University (KR)
Life below water
Openalex Percentile: Top 36%
Climate variability and models
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Winter Sea-Surface-Temperature Memory in the East/Japan Sea Under the Arctic Oscillation: Time-Integrated Forcing, Coupled Hot Spots, and Predictability Windows — Gyuchang Lim, J. J. Park · Remote Sensing (2025) | TGRS Research Map | TGRS