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Optimal precursors of double-gyre regime transitions with an adjoint-free method

         

摘要

In this paper, we find the optimal precursors which can cause double-gyre regime transitions based on conditional nonlinear optimal perturbation (CNOP) method with Regional Ocean Modeling System (ROMS). Firstly, we simulate the multiple-equilibria regimes of double-gyre circulation under different viscosity coefficient and obtain the bifurcation diagram, then choose two equilibrium states (called jet-up state and jet-down state) as reference states respectively, propose Principal Component Analysis-based Simulated Annealing (PCASA) algorithm to solve CNOP-type initial perturbations which can induce double-gyre regime transitions between jet-up state and jet-down state. PCASA algorithm is an adjoint-free method which searches optimal solution randomly in the whole solution space. In addition, we investigate CNOP-type initial perturbations how to evolve with time. The results show: (1) the CNOP-type perturbations present a two-cell structure, and gradually evolves into a three-cell structure at predictive time; (2) by superimposing CNOP-type perturbations on the jet-up state and integrating ROMS, double-gyre circulation transfers from jet-up state to jet-down state, and vice versa, and random initial perturbations don't cause the transitions, which means CNOP-type perturbations are the optimal precursors of double-gyre regime transitions; (3) by analyzing the transition process of double-gyre regime transitions, we find that CNOP-type initial perturbations obtain energy from the background state through both barotropic and baroclinic instabilities, and barotropic instability contributes more significantly to the fast-growth of the perturbations. The optimal precursors and the dynamic mechanism of double-gyre regime transitions revealed in this paper have an important significance to enhance the predictability of double-gyre circulation.

著录项

  • 来源
    《中国海洋湖沼学报(英文版)》 |2019年第4期|1137-1153|共17页
  • 作者单位

    School of Software Engineering, Tongji University, Shanghai 201804, China;

    School of Software Engineering, Tongji University, Shanghai 201804, China;

    Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China;

    Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China;

    School of Software Engineering, Tongji University, Shanghai 201804, China;

    School of Software Engineering, Tongji University, Shanghai 201804, China;

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