将极端事件概率归因于源变量
Attributing extreme-event probability to a source variable
- Ghent University（根特大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出基于伴随响应的极端事件概率归因方法，精确分解源变量对超阈值概率的贡献，并应用于欧洲热浪，发现土壤湿度贡献强烈依赖阈值。
AI中文摘要：
信息流理论通过目标变量的香农熵的变化率来量化方向耦合，而香农熵是一个对分布尾部不敏感的总体泛函。我们转而研究源变量如何对目标变量超过阈值的概率做出贡献。对于源可加漂移，源变量在边际概率流中所占的份额是精确的，并且该份额可分解为平均强迫部分和条件超额部分，其中条件超额部分在独立条件下消失。一个恒等式将这两种描述联系起来：Liang信息流是特定源电流导数的密度加权平均值，而超额电流则是该电流在阈值处的取值。这解释了为什么基于熵的耦合在饱和状态下会失效——在饱和状态下，源变量对极端事件贡献最大；Rényi信息流、高阶展开和Fisher归一化响应也因相同原因而失效。通量分解虽然是精确的，但并不能进行归因：其各项是几乎相互抵消的总传输量。真正能进行归因的量是基于后向生成器构建的伴随响应，将其解读为相对变化时，在两个数量级的事件概率范围内具有均匀的准确性。我们刻画了在遗漏驱动因素、隐藏慢记忆、状态依赖耦合和乘性噪声条件下该估计器的运行包络。在正确设定下，归因结果具有可重复的百分之十至百分之二十五的不足；而我们测试的错误设定会使其偏差向上最多达百分之九十。将所提方法应用于再分析数据中的2003年欧洲和2010年俄罗斯热浪，土壤湿度的归因贡献强烈依赖于阈值，从中等阈值处的百分之几水平上升到最罕见事件时的两倍，因此，未注明阈值而引用的贡献是不完整的。
英文摘要：
Information-flow theory quantifies directional coupling through the rate of change of a target's Shannon entropy, a bulk functional insensitive to the tail of the distribution. We ask instead how a source variable contributes to the probability that the target exceeds a threshold. For source-additive drift the source's share of the marginal probability current is exact, and it separates into a mean-forcing part and a conditional-excess part that vanishes under independence. An identity links the two descriptions: the Liang information flow is the density-weighted mean of the derivative of the specific source current, whereas the exceedance current is its level at the threshold. This explains why entropy-based coupling collapses in saturated regimes where the source contributes most to the extreme; the Rényi information flow, a higher-order expansion and a Fisher-normalised response fail for the same reason. The flux decomposition, although exact, does not attribute: its terms are gross transports that nearly cancel. The quantity that does attribute is an adjoint response built from the backward generator which, read as a relative change, is uniformly accurate across two decades of event probability. We map the operating envelope of the estimator under omitted drivers, hidden slow memory, state-dependent coupling and multiplicative noise. Under correct specification the attribution carries a reproducible shortfall of ten to twenty-five per cent; the misspecifications we test bias it upward by up to ninety per cent. Applied to the 2003 European and 2010 Russian heatwaves in reanalysis, the attributed contribution of soil moisture is strongly threshold dependent, rising from the per cent level at moderate thresholds to a factor of two at the rarest, so a contribution quoted without its threshold is underspecified.
