|
|
Cite this paper: |
|
|
YUAN Shijin, LI Mi, WANG Qiang, ZHANG Kun, ZHANG Huazhen, MU Bin. Optimal precursors of double-gyre regime transitions with an adjoint-free method[J]. Journal of Oceanology and Limnology, 2019, 37(4): 1137-1153 |
|
|
|
|
|
|
|
Optimal precursors of double-gyre regime transitions with an adjoint-free method |
|
YUAN Shijin1, LI Mi1, WANG Qiang2, ZHANG Kun2, ZHANG Huazhen1, MU Bin1 |
|
1 School of Software Engineering, Tongji University, Shanghai 201804, China; 2 Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China |
|
Abstract: |
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. |
|
Key words:
optimal precursors|double-gyre regime transitions|conditional nonlinear optimal perturbation (CNOP)|Principal Component Analysis-based Simulated Annealing (PCASA)|multipleequilibria regimes
|
|
Received: 2017-12-17 Revised: 2018-02-12 |
|
|
|
|
References:
Budgell W P. 2005. Numerical simulation of ice-ocean variability in the Barents Sea region. Ocean Dynamics, 55(3-4):370-387. Chang Y L, Oey L Y. 2014. Analysis of STCC eddies using the Okubo-Weiss parameter on model and satellite data.Ocean Dynamics, 64(2):259-271. Di Lorenzo E. 2003. Seasonal dynamics of the surface circulation in the Southern California current system.Deep Sea Research Part Ⅱ:Topical Studies in Oceanography, 50(14-16):2 371-2 388. Dijkstra H A. 2005. Nonlinear Physical Oceanography:A Dynamical Systems Approach to the Large Scale Ocean Circulation and El Nino. Springer Science & Business Media, New York. Dinniman M S, Klinck J M, Jr W O S. 2003. Cross-shelf exchange in a model of the Ross Sea circulation and biogeochemistry. Deep-Sea Research Part Ⅱ, 50(22):3 103-3 120. Duan W S, Mu M, Wang B. 2004. Conditional nonlinear optimal perturbations as the optimal precursors for El Nino-Southern Oscillation events. Journal of Geophysical Research:Atmospheres, 109(D23):D23105. Feng M, Meyers G. 2003. Interannual variability in the tropical Indian Ocean:a two-year time-scale of Indian Ocean Dipole. Deep Sea Research Part Ⅱ:Topical Studies in Oceanography, 50(12-13):2 263-2 284. Haidvogel D B, Arango H G, Hedstrom K, Beckmann A, Malanotte-Rizzoli P, Shchepetkin A F. 2000. Model evaluation experiments in the North Atlantic Basin:simulations in nonlinear terrain-following coordinates.Dynamics of Atmospheres and Oceans, 32(3-4):239-281. Hill C, DeLuca C, Balaji, Suarez M, Da Silva A. 2004. The architecture of the Earth System modeling framework.Computing in Science & Engineering, 6(1):18-28. Jiang S, Jin F F, Ghil M. 1995. Multiple equilibria, periodic, and aperiodic solutions in a wind-driven, double-gyre, shallow-water model. Journal of Physical Oceanography, 25(5):764-786. Liu Y M. 2008. Maximum principle of conditional optimal nonlinear perturbation. Journal-East China Normal University (Natural Science), (2):131-134. (in Chinese with English abstract) Mahadevan A, Lu J, Meacham S, Malanotte-Rizzoli P. 2001.The predictability of large-scale wind-driven flows.Nonlinear Processes in Geophysics, 8(6):449-465. Marchesiello P, Mcwilliams J C, Shchepetkin A. 2003.Equilibrium structure and dynamics of the California current system. Journal of Physical Oceanography, 33(4):753-783. Moore A M, Arango H G, Di Lorenzo E, Di Cornuelle B, Miller A J, Neilson D J. 2004. A comprehensive ocean prediction and analysis system based on the tangent linear and adjoint of a regional ocean model. Ocean Modelling, 7(1-2):227-258. Moore A M. 1999. Wind-induced variability of ocean gyres.Dynamics of Atmospheres and Oceans, 29(2-4):335-364. Mu B, Wen S C, Yuan S J, Li H Y. 2015. PPSO:PCA based particle swarm optimization for solving conditional nonlinear optimal perturbation. Computers & Geosciences, 83:65-71. Mu M, Duan W S, Wang B. 2003. Conditional nonlinear optimal perturbation and its applications. Nonlinear Processes in Geophysics, 10(6):493-501. Mu M, Duan W S, Wang J C. 2002. The predictability problems in numerical weather and climate prediction. Advances in Atmospheric Sciences, 19(2):191-204. Mu M, Sun L, Dijkstra H A. 2004. The sensitivity and stability of the ocean's thermohaline circulation to finite-amplitude perturbations. Journal of Physical Oceanography, 34(10):2 305. Mu M, Yu Y S, Xu H, Gong T T. 2014. Similarities between optimal precursors for ENSO events and optimally growing initial errors in El Niño predictions. Theoretical and Applied Climatology, 115(3-4):461-469. Nauw J J, Dijkstra H A, Chassignet E P. 2004. Frictionally induced asymmetries in wind-driven flows. Journal of Physical Oceanography, 34(9):2 057-2 072. Nauw J J, Dijkstra H A. 2001. The origin of low-frequency variability of double-gyre wind-driven flows. Journal of Marine Research, 59(4):567-597. Oey L Y. 2008. Loop Current and deep eddies. Journal of Physical Oceanography, 38(7):1 426-1 449. Peliz A, Dubert J, Haidvogel D B et al. 2003. Generation and unstable evolution of a density-driven Eastern Poleward Current:The Iberian Poleward Current. Journal of Geophysical Research Oceans, 108(C8):3 268. Pierini S. 2006. A kuroshio extension system model study:decadal chaotic self-sustained oscillations. Journal of Physical Oceanography, 36(8):1 605-1 625. Pierini S. 2010. Coherence resonance in a double-gyre model of the Kuroshio Extension. Journal of Physical Oceanography, 40(1):238-248. Primeau F, Newman D. 2008. Elongation and contraction of the western boundary current extension in a shallowwater model:a bifurcation analysis. Journal of Physical Oceanography, 38(7):1 469-1 485. Primeau F. 2002. Multiple equilibria and low-frequency variability of the wind-driven ocean circulation. Journal of Physical Oceanography, 32(8):2 236-2 256. Qin X H, Duan W S, Mu M. 2013. Conditions under which CNOP sensitivity is valid for tropical cyclone adaptive observations. Quarterly Journal of the Royal Meteorological Society, 139(675):1 544-1 554. Qin X H, Mu M. 2011. A study on the reduction of forecast error variance by three adaptive observation approaches for tropical cyclone prediction. Monthly Weather Review, 139(7):2 218-2 232. Sapsis T P, Dijkstra H A. 2013. Interaction of additive noise and nonlinear dynamics in the double-gyre wind-driven ocean circulation. Journal of Physical Oceanography, 43(2):366-381. Shchepetkin A F, Mcwilliams J C. 2003. A method for computing horizontal pressure-gradient force in an oceanic model with a nonaligned vertical coordinate.Journal of Geophysical Research Oceans, 108(C3):3 090. Shchepetkin A F, Mcwilliams J C. 2005. The regional oceanic modeling system (ROMS):a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Modelling, 9(4):347-404. Shen J, Medjo T T, Wang S. 1999. On a wind-driven, doublegyre, quasi-geostrophic ocean model:numerical simulations and structural analysis. Journal of Computational Physics, 155(2):387-409. Simonnet E, Ghil M, Ide K, Temam R, Wang S H. 2003. Lowfrequency variability in shallow-water models of the wind-driven ocean circulation. Part Ⅱ:time-dependent solutions. Journal of Physical Oceanography, 33(4):729-752. Speich S, Dijkstra H, Ghil M. 1995. Successive bifurcations in a shallow-water model applied to the wind-driven ocean circulation. Nonlinear Processes in Geophysics, 2(3-4):241-268. Sura P, Penland C. 2002. Sensitivity of a double-gyre ocean model to details of stochastic forcing. Ocean Modelling, 4(3):327-345. Van Scheltinga A D T, Dijkstra H A. 2008. Conditional nonlinear optimal perturbations of the double-gyre ocean circulation. Nonlinear Processes in Geophysics, 15(5):727-734. Wang Q, Ma L B, Xu Q Q. 2013a. Optimal precursor of the transition from Kuroshio large meander to straight path.Chinese Journal of Oceanology and Limnology, 31(5):1 153-1 161. Wang Q, Mu M, Dijkstra H A. 2012. Application of the Conditional Nonlinear Optimal Perturbation Method to the Predictability Study of the Kuroshio Large Meander.Advances in Atmospheric Sciences, 29(1):118-134. Wang Q, Mu M, Dijkstra H A. 2013b. The similarity between optimal precursor and optimally growing initial error in prediction of Kuroshio large meander and its application to targeted observation. Journal of Geophysical Research:Oceans, 118(2):869-884. Wang Q, Tang Y M, Pierini S, Mu M. 2017. Effects of singularvector-type initial errors on the short-range prediction of kuroshio extension transition processes. Journal of Climate, 30(15):5 961-5 983. Warner J C, Geyer W R, Lerczak J A. 2005b. Numerical modeling of an estuary:A comprehensive skill assessment.Journal of Geophysical Research Oceans, 110(C5):C05001. Warner J C, Sherwood C R, Arango H G, et al. 2005a.Performance of four turbulence closure models implemented using a generic length scale method. Ocean Modelling, 8(1):81-113. Wen S C, Yuan S J, Mu B, Li H Y, Ren J H. 2015. PCGD:Principal components-based great deluge method for solving CNOP. In:2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, Sendai, Japan. Wilkin J L, Arango H G, Haidvogel D B, et al. 2005. A regional ocean modeling system for the Long-term Ecosystem Observatory. Journal of Geophysical Research Oceans, 110(C6):C06S91. Yu J S. 1998. Setting up and calibration analysis of three dimensional ocean current forecasting mode (2/4). http://www.doc88.com/p-704860833471.html. Accessed on 2012-12-30. (in Chinese) Zhang K, Wang Q, Mu M, Liang P. 2016. Effects of optimal initial errors on predicting the seasonal reduction of the upstream Kuroshio transport. Deep Sea Research Part I:Oceanographic Research Papers, 116:220-235. Zhang L L, Yuan S J, Mu B, Zhou F F. 2017b. CNOP-based sensitive areas identification for tropical cyclone adaptive observations with PCAGA method. Asia-Pacific Journal of Atmospheric Sciences, 53(1):63-73. Zhang X, Mu M U, Pierini S. 2017a. Optimal precursors triggering the kuroshio extension state transition obtained by the conditional nonlinear optimal perturbation approach. Advances in Atmospheric Sciences, 34(6):685-699.
|
|
|