2020
DOI: 10.1007/s00382-019-05097-1
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The fastest growing initial error in prediction of the Kuroshio Extension state transition processes and its growth

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Cited by 5 publications
(3 citation statements)
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“…This is consistent with Wang et al. (2020) who suggested that the US is more difficult to predict than the SU because of the stronger nonlinearity of the US processes.…”
Section: Energetics Mechanisms Of Transitionssupporting
confidence: 92%
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“…This is consistent with Wang et al. (2020) who suggested that the US is more difficult to predict than the SU because of the stronger nonlinearity of the US processes.…”
Section: Energetics Mechanisms Of Transitionssupporting
confidence: 92%
“…Considering that 𝐴𝐴 −𝑈𝑈 ′ ∇ ⋅ EKE is the nonlinear component, its stronger effect indicates that nonlinearity is crucial and unneglectable for the US transitions. This is consistent with Wang et al (2020) who suggested that the US is more difficult to predict than the SU because of the stronger nonlinearity of the US processes. Although ADV migrates EKE into KE downstream, no EKE enhancement occurs there (Figures 14b and 14d), indicating that downstream is the key region of EKE dissipation.…”
Section: The Ussupporting
confidence: 91%
“…The LSV method searches along the linear evolution of perturbations, while the CNOP method considers the nonlinear dynamic features. Due to the nonlinearity of the atmosphere and ocean, the CNOP method has been widely applied to study the predictability of various high-impact events, such as the El Niño-Southern Oscillation (Yang et al, 2020), the Madden-Julian Oscillation (Wei et al, 2019), and the Kuroshio Extension bimodality (Geng et al, 2020;Wang, Mu, & Pierini, 2020). In the present study, taking into account the high nonlinearity of the ACC (Rintoul, 2018), we employ the CNOP approach to explore the OPR that triggers the sudden shift in the ACC transport based on Regional Ocean Modeling System (ROMS).…”
mentioning
confidence: 99%