Applications of Conditional Nonlinear Optimal
Perturbation to the Predictability Studies

Professor Mu Mu

Fudan University, Shanghai

23 May, 10:30 am, in 2155

23 May, 10:30 am, in 2155

In this presentation, I will introduce a nonlinear optimization approach to the predictability studies in atmosphere and oceans, which is conditional nonlinear optimal perturbation (CNOP). For initial perturbation problem, CNOP satisfies a given constraint and has the largest nonlinear evolution at the prediction time, which is a natural generalization of the linear singular vector to the nonlinear regime. When considering perturbations of model parameters, CNOP causes the largest departure from a given reference state at prediction time.

The physical meaning of CNOP depends on the problems, which could represent the optimal precursors for a weather or climate event onset, for example, the precursors of blockings, north Atlantic oscillation (NAO), and ENSO events, etc. In predictability studies, CNOP stands for the initial error, or parameter errors, that has the largest negative effect on prediction, and in sensitivity analysis, CNOP is the most unstable (sensitive) mode.

I will briefly present some applications of CNOP to the ENSO spring predictability barrier, Indian Ocean dipole, North Atlantic oscillation (NAO) onset, and ocean circulation of Kuroshio path variations. An interesting phenomenon that the similarities between optimal precursors and optimally growing initial errors will be shown, and related targeted observations issues will be discussed.

The challenges, related to the calculations of CNOP, to the targeted observations of tropical cyclones, and to ensemble forecasts, will be discussed too.