Theoretical and Applied Climatology

, Volume 138, Issue 3–4, pp 1481–1492 | Cite as

Parameter sensitivity analysis of the short-range prediction of Kuroshio extension transition processes using an optimization approach

  • Qiang WangEmail author
  • Stefano Pierini
  • Youmin Tang
Original Paper


In this study, the optimization parameter sensitivity analysis (OPSA) method is applied to investigate the sensitivity of the short-range prediction of the Kuroshio extension (KE) transition process to parameter uncertainties as simulated with a reduced-gravity shallow water model. The results show that the uncertainties of the model parameters have important impacts on the short-range predictions of the KE transition processes: the predictions are found to be mainly affected by the uncertainties in the reduced-gravity and interfacial friction coefficients. On the contrary, the prediction is insensitive to the wind stress amplitude and the lateral eddy viscosity coefficient. Furthermore, if the sensitive parameters are more accurately estimated, the prediction will be significantly improved. It is therefore suggested that the OPSA method is a useful tool to assess parameter sensitivity for the prediction of the KE transition processes.



We would like to express our gratitude to the High Performance Computing Center (HPCC), Institute of Oceanology, Chinese Academy of Sciences, for providing excellent computing resources.

Funding information

This study was supported by the Qingdao National Laboratory for Marine Science and Technology (QNLM2016ORP0107), the National Natural Scientific Foundation of China (41576015, 41490644, and 41490640), the open fund of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography (QNHX1814), the NSFC Innovative Group (41421005), the National Programme on Global Change and Air-Sea interaction (GASI-IPOVAI-06), and the NSFC-Shandong Joint Fund for Marine Science Research Centers (U1606402). S. Pierini received support from the University of Naples Parthenope (contract n. DSTE315B).


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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.CAS Key Laboratory of Ocean Circulation and Waves, Institute of OceanologyChinese Academy of Sciences and Pilot National Laboratory for Marine Science and Technology (Qingdao)QingdaoChina
  2. 2.State Key Laboratory of Satellite Ocean Environment DynamicsSecond Institute of Oceanography, MNRHangzhouChina
  3. 3.College of OceanographyHohai UniversityNanjingChina
  4. 4.Dipartimento di Scienze e TecnologieUniversità di Napoli ParthenopeNaplesItaly
  5. 5.CoNISMaRomeItaly

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