Multivariate Nonstationary Oscillation Simulation of Climate Indices With Empirical Mode Decomposition

The objective of the current study is to build a stochastic model to simulate climate indices that are teleconnected with the hydrologic regimes of large-scale water resources systems such as the Great Lakes system. Climate indices generally contain nonstationary oscillations (NSOs). We adopted a stochastic simulation model based on Empirical Mode Decomposition (EMD). The procedure for the model is to decompose the observed series and then to simulate the decomposed components with the NSO resampling (NSOR) technique. Because the model has only been previously applied to single variables, a multivariate version of NSOR (M-NSOR) is developed to consider the links between the climate indices and to reproduce the NSO process. The proposed M-NSOR model is tested in a simulation study on the Rossler system. The simulation results indicate that the M-NSOR model reproduces the significant oscillatory behaviors of the system and the marginal statistical characteristics. Subsequently, the M-NSOR model is applied to three climate indices (i.e., Arctic Oscillation, El Nino-Southern Oscillation, and Pacific Decadal Oscillation) for the annual and winter data sets. The results of the proposed model are compared to those of the Contemporaneous Shifting Mean and Contemporaneous Autoregressive Moving Average model. The results indicate that the proposed M-NSOR model is superior to the Contemporaneous Shifting Mean and Contemporaneous Autoregressive Moving Average model for reproducing the NSO process, while the other basic statistics are comparatively well preserved in both cases. The current study concludes that the proposed M-NSOR model can be a good alternative to simulate NSO processes and their teleconnections with climate indices.