This project has the following goals: (i)to adaptively and locally separate the varying global warming trend and the multi-decadal climate variability, both spatially and temporally; (ii)to reveal the characteristics of temporal evolution from 1850 to present of some observed low-frequency climate modes, e.g., Pacific multidecadal variability, Atlantic multidecadal variability, and the North Atlantic Oscillation; (iii) to compare these characteristics to their counterparts of low-frequency climate modes in climate model simulations; and (iv)to search for physical mechanisms of the evolution of low frequency climate modes.
The main tool that will be used is the Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) (MEEMD), developed less than one year ago by the PI. The MEEMD is a novel extension of a sparse time series decomposition method without a priori determined bases, the Empirical Mode Decomposition, for spatial-temporal multi-dimensional data analysis. By applying the method to historical climate fields and Coupled Model Intercomparison Project (CMIP3/CMIP5) model simulations, the researcher will examine spatial-temporal characteristics of the evolutions of multidecadal variability and trend of the climate of the past one and a half centuries, as well as their counterparts in model simulations. This will provide insights and clues for further improvement of climate models.
The research has the following broader impact: (i) improvement of the adaptive and spatial-temporally local analysis method may prove useful in other scientific, engineering, or biomedical field that involves data analysis; (ii) there is training of future scientists and technology professionals in both data analysis theory and methods; and (iii) the research will help to disseminate the knowledge, techniques and tools developed in this project through publishing in the open literature, and making new software tools open source for use by a wider community.
