Temporal-Spatial Evolutions of Low-Frequency Climate Variability and Warming Trend

本项目的目标是：（1）从时空上适应性地、局部地分离全球变暖趋势和气候年代际变率的变化;（2）揭示1850年至今观测到的一些低频气候模态的时间演变特征，如：太平洋的年代际变率，大西洋的年代际变率，北大西洋涛动;（iii）比较这些特征的对应物的低频气候模式在气候模式模拟;和（iv）寻找低频气候模式演变的物理机制。将使用的主要工具是多维包络经验模式分解（MEEMD）（MEEMD），由PI在不到一年前开发。MEEMD是一种新的扩展稀疏时间序列分解方法，没有先验确定的基础，经验模式分解，时空多维数据分析。通过将该方法应用于历史气候场和CMIP 3/CMIP 5模式模拟，研究了过去一个半世纪气候的年代际变率和趋势演变的时空特征，以及它们在模式模拟中的对应物。该研究具有以下广泛的影响：（i）改进自适应和时空局部分析方法可能在其他涉及数据分析的科学、工程或生物医学领域中被证明是有用的;（ii）在数据分析理论和方法方面对未来的科学家和技术专业人员进行培训;（iii）在气候模式的基础上改进气候模式。及（iii）有关研究将有助透过在公开文献中发表，以及将新的软件工具公开予更广泛的社群使用，从而传播本计划所发展的知识、技术及工具。

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.