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首页> 外文期刊>Hydrology and Earth System Sciences >Technical note: Changes in cross- and auto-dependence structures in climate projections of daily precipitation and their sensitivity to outliers
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Technical note: Changes in cross- and auto-dependence structures in climate projections of daily precipitation and their sensitivity to outliers

机译：技术说明：每日降水量的气候投影中交叉和自动依赖结构的变化及其对异常值的敏感性

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摘要

Simulations of regional or global climate models are often used for climate change impact assessment. To eliminate systematic errors, which are inherent to all climate model simulations, a number of post-processing (statistical downscaling) methods have been proposed recently. In addition to basic statistical properties of simulated variables, some of these methods also consider a dependence structure between or within variables. In the present paper we assess the changes in cross-and auto-correlation structures of daily precipitation in six regional climate model simulations. In addition the effect of outliers is explored making a distinction between ordinary outliers (i.e. values exceptionally small or large) and dependence outliers (values deviating from dependence structures). It is demonstrated that correlation estimates can be strongly influenced by a few outliers even in large datasets. In turn, any statistical downscaling method relying on sample correlation can therefore provide misleading results. An exploratory procedure is proposed to detect the dependence outliers in multivariate data and to quantify their impact on correlation structures.
机译：区域或全球气候模型的模拟通常用于气候变化影响评估。为了消除所有气候模型模拟所固有的系统错误，最近提出了许多后处理（统计缩减）方法。除了模拟变量的基本统计特性之外，这些方法中的一些还考虑变量之间或在变量之间的依赖性结构。本文中，我们评估了六个区域气候模型模拟中日降水的交叉和自相关结构的变化。此外，探讨了异常值的效果，以区分普通异常值（即，值小或大的值）和依赖异常值（偏离依赖结构的值）。结果表明，即使在大型数据集中，相关估计也可能受到几个异常值的强烈影响。反过来，依赖于样本相关性的任何统计缩小方法都可以提供误导性结果。提出了一种探索程序来检测多元数据中的依赖异常值，并量化它们对相关结构的影响。

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