This paper presents the development of a new entropy-based feature selection method for identifying and quantifying impacts. Here, impacts are defined as statistically significant differences in spatio-temporal fields when comparing datasets with and without an external forcing in Earth system models. Temporal feature selection is performed by first computing the cross-fuzzy entropy to quantify similarity of patterns between two datasets and then applying changepoint detection to identify regions of statistically constant entropy. The method is used to capture temperate north surface cooling from a 9-member simulation ensemble of the Mt. Pinatubo volcanic eruption, which injected 10 Tg of SO2 into the stratosphere. The results estimate a mean difference decrease in near surface air temperature of -0.560 K with a 99% confidence interval between -0.864 K and -0.257 K between April and November of 1992, one year following the eruption. A sensitivity analysis with decreasing SO2 injection revealed that the impact is statistically significant at 5 Tg but not at 3 Tg. Using identified features, a dependency graph model composed of 68 nodes and 229 edges directly connecting initial aerosol optical depth changes in the tropics to solar flux and temperature changes before the temperate north surface cooling is presented.

翻译：本文提出了一种新的基于熵的特征选择方法，用于识别和量化影响。此处，影响被定义为在比较地球系统模型中存在与不存在外部强迫的数据集时，时空场中出现的统计学显著差异。时间特征选择首先通过计算交叉模糊熵来量化两个数据集之间模式的相似性，然后应用变点检测来识别熵值在统计上恒定的区域。该方法用于捕捉皮纳图博火山喷发（向平流层注入10 Tg SO2）的9成员模拟集合中温带北部地表降温现象。结果表明，在喷发后一年的1992年4月至11月期间，近地表气温的平均差异下降为-0.560 K，其99%置信区间介于-0.864 K与-0.257 K之间。一项关于SO2注入量递减的敏感性分析表明，当注入量为5 Tg时影响具有统计学显著性，而在3 Tg时则不显著。利用识别出的特征，本文构建了一个包含68个节点和229条边的依赖图模型，该模型直接将热带地区初始气溶胶光学厚度的变化与温带北部地表降温发生前的太阳通量和温度变化联系起来。