Comparing the spatial structure of variability in two datasets against each other on the basis of EOF-modes
Comparing the spatial structure of variability in two datasets against each other on the basis of EOF-modes
复制标题
基于 EOF 模式比较两个数据集中变异性的空间结构
DOI：
10.1007/s00382-013-1708-x
复制
发表时间：
2014
期刊：
影响因子：
4.6
通讯作者：
D. Dommenget
中科院分区：
文献类型：
--
作者：
T. Bayr;D. Dommenget
在分析气候可变性或变化时，人们往往感兴趣的是，两个数据集中可变性模式的空间结构如何彼此不同，例如过去和未来气候之间或模型和观测之间。这种分析通常基于经验正交函数(EOF)分析或其他大型空间结构的简单指数。目前的分析提出了一个概念，即如何在EOF分析的基础上将两个多变量气候变率数据集相互比较，以及如何根据主要空间格局中的解释方差来量化两个数据集之间多变量空间结构的差异。文中还说明了如何定义和解释两个数据集之间的最大差异模式。我们以几个定义良好的人工例子为基础，并通过将我们的方法与文献中气候变化研究的例子进行比较来说明这种方法。这些文献的例子包括分析气候变化下北大西洋和欧洲的海平面气压、南半球的海平面气压、北半球的表面温度、北太平洋的海表面温度以及热带印度洋-太平洋降水的变率模式的变化。
In analysis of climate variability or change it is often of interest how the spatial structure in modes of variability in two datasets differ from each other, e.g. between past and future climate or between models and observations. Often such analysis is based on Empirical Orthogonal Function (EOF) analysis or other simple indices of large-scale spatial structures. The present analysis lays out a concept on how two datasets of multivariate climate variability can be compared against each other on basis of EOF analysis and how the differences in the multivariate spatial structure between the two datasets can be quantified in terms of explained variance in the leading spatial patterns. It is also illustrated how the patterns of largest differences between the two datasets can be defined and interpreted. We illustrate this method on the basis of several well-defined artificial examples and by comparing our approach with examples of climate change studies from the literature. These literature examples include analysis of changes in the modes of variability under climate change for the sea level pressure (SLP) of the North Atlantic and Europe, the SLP of the Southern Hemisphere, the surface temperature of the Northern Hemisphere, the sea surface temperature of the North Pacific and for precipitation in the tropical Indo-Pacific.