«Conditional Nonlinear Optimal Perturbation and its application in the investigations of weather and climate predictability«
In atmospheric and oceanic studies, it is important to investigate the uncertainty of model solutions. By fully considering nonlinearity under appropriate physical constraints, the conditional nonlinear optimal perturbation (CNOP) approach was proposed to reveal the optimal perturbations of initial conditions, boundary conditions, model parameters, and model tendencies that cause the largest simulation or prediction uncertainties. This talk reviews the progress of applying the CNOP approach to atmosphere-ocean sciences during the past 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.
