A useful approach to sensitivity and predictability studies in geophysical fluid dynamics: conditional non-linear optimal perturbation  

作　　者：Qiang Wang Mu Mu Guodong Sun 

机构地区：[1]CAS Key Laboratory of Ocean Circulation and Waves.Institute of Oceanology,Chinese Academy of Sciences,Qingdao 266071,China [2]Pilot National Laboratory for Marine Science and Technology(Qingdao),Qingdao 266237,China [3]Center for Ocean Mega-Science,Chinese Academy of Sciences,Qingdao 266071,China [4]Department of Atmospheric and Oceanic Sciences,Institute of Atmospheric Sciences,Fudan University,Shanghai 200438,China [5]LASG,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China [6]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《National Science Review》2020年第1期214-223,共10页国家科学评论（英文版）

基　　金：supported by the National Natural Science Foundation of China(41576015);the Qingdao National Laboratory for Marine Science and Technology(QNLM2016ORP0107);the Strategic Priority Research Program of Chinese Academy of Sciences(XDA20060502);the NSFC Innovative Group(41421005);the National Programme on Global Change and AirSea Interaction(GASI-IPOVAI-06);the NSFC-Shandong Joint Fund for Marine Science Research Centers(U1606402);the Youth Innovation Promotion Association,Chinese Academy of Sciences(2015060)

摘　　要：In atmospheric and oceanic studies,it is important to investigate the uncertainty of model solutions.The conditional non-linear optimal perturbation(CNOP)method is useful for addressing the uncertainty.This paper reviews the development of the CNOP method and its computational aspects in recent years.Specifically,the CNOP method was first proposed to investigate the effects of the optimal initial perturbation on atmosphere and ocean model results.Then,it was extended to explore the influences of the optimal parameter perturbation,model tendency perturbation and boundary condition perturbation.To obtain solutions to these optimal perturbations,four kinds of optimization approaches were developed:the adjoint-based method,the adjoint-free method,the intelligent optimization method and the unconstrained optimization method.We illustrate the calculation process of each method and its advantages and disadvantages.Then,taking the Zebiak–Cane model as an example,we compare the CNOPs related to initial conditions(CNOP-Is)calculated by the above four methods.It was found that the dominant structures of the CNOP-Is for different methods are similar,although some differences in details exist.Finally,we discuss the necessity and possible direction for designing a more effective optimization approach related to the CNOP in the future.In atmospheric and oceanic studies, it is important to investigate the uncertainty of model solutions. The conditional non-linear optimal perturbation(CNOP) method is useful for addressing the uncertainty. This paper reviews the development of the CNOP method and its computational aspects in recent years.Specifically, the CNOP method was first proposed to investigate the effects of the optimal initial perturbation on atmosphere and ocean model results. Then, it was extended to explore the influences of the optimal parameter perturbation, model tendency perturbation and boundary condition perturbation. To obtain solutions to these optimal perturbations, four kinds of optimization approaches were developed: the adjoint-based method, the adjoint-free method, the intelligent optimization method and the unconstrained optimization method. We illustrate the calculation process of each method and its advantages and disadvantages. Then, taking the Zebiak–Cane model as an example, we compare the CNOPs related to initial conditions(CNOP-Is) calculated by the above four methods. It was found that the dominant structures of the CNOP-Is for different methods are similar, although some differences in details exist.Finally, we discuss the necessity and possible direction for designing a more effective optimization approach related to the CNOP in the future.

关 键 词：CNOP NON-LINEAR optimization ATMOSPHERE OCEAN 

分 类 号：P73[天文地球—海洋科学] P40