Effects of Wind Stress Uncertainty on Short-Term Prediction of the Kuroshio Extension State Transition Process

Author:

Zhang Hui12,Wang Qiang34ORCID,Mu Mu561,Zhang Kun1,Geng Yu78

Affiliation:

1. a CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China

2. b University of Chinese Academy of Sciences, Beijing, China

3. c Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing, China

4. d College of Oceanography, Hohai University, Nanjing, China

5. e Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai, China

6. f Institute of Atmospheric Sciences, Fudan University, Shanghai, China

7. g State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

8. h Institute of Tibetan Plateau Meteorology, Chinese Academy of Meteorological Sciences, Beijing, China

Abstract

Abstract Based on the conditional nonlinear optimal perturbation for boundary condition method and Regional Ocean Modeling System (ROMS), this study investigates the influence of wind stress uncertainty on predicting the short-term state transitions of the Kuroshio Extension (KE). The optimal time-dependent wind stress errors that lead to maximum prediction errors are obtained for two KE stable-to-unstable and two reverse transitions, which exhibit local multieddies structures with decreasing magnitude as the end time of prediction approaches. The optimal boundary errors initially induce small oceanic errors through Ekman pumping. Subsequently, these errors grow in magnitude as oceanic internal processes take effect, which exerts significant influences on the short-term prediction of the KE state transition process. Specifically, during stable-to-unstable (unstable-to-stable) transitions, the growing error induces an overestimation (underestimation) of the meridional sea surface height gradient across the KE axis, leading to the predicted KE state being more (less) stable. Furthermore, the dynamics mechanism analysis indicates that barotropic instability is crucial for the error growth in the prediction of both the stable-to-unstable and the reverse transition processes due to the horizontal shear of flow field. But work generated by wind stress error plays a more important role in the prediction of the unstable-to-stable transitions because of the synergistic effect of strong wind stress error and strong oceanic error. Eventually, the sensitive areas have been identified based on the optimal boundary errors. Reducing wind stress errors in sensitive areas can significantly improve prediction skills, offering theoretical guidance for devising observational strategies.

Funder

National Natural Science Foundation of China

Publisher

American Meteorological Society

Subject

Oceanography

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