On the Statistical Estimation of Asymmetrical Relationship Between Two Climate Variables
On the Statistical Estimation of Asymmetrical Relationship Between Two Climate Variables
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DOI：
10.1029/2022gl100777
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发表时间：
2022-10-28
影响因子：
5.2
通讯作者：
Kwon, Young-Oh
中科院分区：
文献类型：
--
作者：
Frankignoul, Claude;Kwon, Young-Oh
本文讨论并比较了气候研究中常用的两种检测不对称性的简单方法，即复合分析和非对称线性回归。非对称回归显示只有当从自变量和因变量中去除正事件和负事件的各自平均值（即非零y截距）时才能提供无偏估计。复合分析总是提供有偏差的结果，并且严重低估了不对称性，尽管对于非常大的阈值不太如此，这不能用于有限的观测数据。因此，应该使用无偏非对称回归，即使小样本的不确定性可能很大。对与厄尔尼诺和拉尼娜有关的海面温度和冬季海平面压力信号的估计不对称差异进行了说明。
Two simple methods commonly used to detect asymmetry in climate research, composite analysis, and asymmetric linear regression, are discussed and compared using mathematical derivation and synthetic data. Asymmetric regression is shown to provide unbiased estimates only when the respective mean of positive and negative events is removed from both independent and dependent variables (i.e., non-zero y-intercepts). Composite analysis always provides biased results and strongly underestimates the asymmetry, albeit less so for very larger thresholds, which cannot be used with limited observational data. Hence, the unbiased asymmetric regression should be used, even though uncertainties can be large for small samples. Differences in estimated asymmetry are illustrated for the sea surface temperature and winter sea level pressure signals associated with El Nino and La Nina.