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◆ Journal of Advances in Modeling Earth Systems2026-07-31· Eddy

Latitude‐Dependent Sensitive Areas for Targeted Observations of Mesoscale Eddies Associated With Sea Surface Height Anomaly Forecasts

Lin Jiang, Wansuo Duan, Hui Wang

原始摘要(英文原文)· Original abstract
Abstract By employing the conditional nonlinear optimal perturbation approach within a two‐layer quasigeostrophic (QG) model, we investigate the sensitive areas for targeted observations of mesoscale eddies relevant to sea surface height anomaly (SSHA) forecasting and reveal their pronounced latitude dependence. Our results show that the spatial characteristics of sensitive areas may differ markedly among eddies located in different latitudinal bands, although these areas are generally situated in regions characterized by clear high‐to low‐velocity gradients, with velocity decreasing spatially along the direction of eddy rotation. For eddies at high latitudes, sensitive areas are primarily located in regions exhibiting the most pronounced high‐to low‐velocity gradients within the vortex. However, at low latitudes, the sensitivity is not determined solely by the magnitude of the velocity gradient; instead, the zonal velocity direction within the gradient region also plays a crucial role. Specifically, sensitive areas preferentially emerge where high‐to low‐velocity gradients coincide with an eastward velocity component, even when the gradients are relatively weaker. Observing system simulation experiments further confirm that prioritizing data assimilation in the identified sensitive areas within specific latitudinal regimes significantly improves SSHA forecast skill. From a dynamical perspective, the latitude‐dependent sensitivity patterns can be physically interpreted in terms of barotropic instability modulated by the planetary ‐effect. These findings provide practical guidance for optimizing targeted observation strategies in realistic oceanic settings, highlighting the critical role of latitude in the selection of observation sites for mesoscale eddies and ultimately enhancing SSHA forecasting capability.
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