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Recent Progress in Applications of the Conditional Nonlinear Optimal Perturbation Approach to Atmosphere-Ocean Sciences

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Abstract

The conditional nonlinear optimal perturbation (CNOP for short) approach is a powerful tool for predictability and targeted observation studies in atmosphere-ocean sciences. By fully considering nonlinearity under appropriate physical constraints, the CNOP approach can reveal the optimal perturbations of initial conditions, boundary conditions, model parameters, and model tendencies that cause the largest simulation or prediction uncertainties. This paper reviews the progress of applying the CNOP approach to atmosphere-ocean sciences during the past five years. Following an introduction of the CNOP approach, the algorithm developments for solving the CNOP are discussed. Then, recent CNOP applications, including predictability studies of some high-impact ocean-atmospheric environmental events, ensemble forecast, parameter sensitivity analysis, uncertainty estimation caused by errors of model tendency or boundary condition, are reviewed. Finally, a summary and discussion on future applications and challenges of the CNOP approach are presented.

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Authors and Affiliations

  1. Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai, 200438, China

    Mu Mu

  2. CMA-FDU Joint Laboratory of Marine Meteorology, Shanghai, 200438, China

    Mu Mu

  3. Shanghai Frontiers Science Center of Atmosphere-Ocean Interaction, Shanghai, 200438, China

    Mu Mu

  4. CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences and Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266071, China

    Kun Zhang

  5. Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing, 210098, China

    Qiang Wang

  6. College of Oceanography, Hohai University, Nanjing, 210098, China

    Qiang Wang

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  1. Mu Mu
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  2. Kun Zhang
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  3. Qiang Wang
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Correspondence to Mu Mu, Kun Zhang or Qiang Wang.

Additional information

This work was supported by the National Natural Science Foundation of China (Nos. 41790475, 92158202, 42076017, 41576015) and Guangdong Major Project of Basic and Applied Basic Research (No. 2020B0301030004).

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Mu, M., Zhang, K. & Wang, Q. Recent Progress in Applications of the Conditional Nonlinear Optimal Perturbation Approach to Atmosphere-Ocean Sciences. Chin. Ann. Math. Ser. B 43, 1033–1048 (2022). https://doi.org/10.1007/s11401-022-0376-8

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  • DOI: https://doi.org/10.1007/s11401-022-0376-8

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