发表机构
Auburn University（奥本大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对气候变化检测与归因中的最优指纹法，提出尖峰最优指纹框架，利用尖峰协方差估计与变率膨胀调整，改善高维推断，产生更短且校准更好的置信区间，并在多区域得出不同归因结论。
AI 中文摘要
检测与归因（D&A）分析为量化外部强迫对观测到的气候变化的贡献提供了一个统计框架。最优指纹法作为D&A的主要方法，被构建为一个具有高维协方差结构的变量误差回归模型。由于协方差矩阵必须从有限数量的控制模拟中估计，且气候模型可能表现出与观测气候系统不同的变率模式，因此可靠的推断具有挑战性。我们开发了一个尖峰最优指纹框架，该框架利用主导气候变率模式的尖峰结构，同时在高维设置中提供稳定的协方差估计。所提出的方法开发了偏差校正的尖峰协方差估计，用于构建自适应权重矩阵，并纳入变率膨胀调整以考虑模型与观测在内部变率上的差异。我们进一步开发了一个有效的缩放因子估计器的不确定性量化程序，以及一个用于评估模型充分性的残差一致性检验。数值研究表明，与现有的实用方法相比，所提出的方法提高了估计精度和不确定性量化。应用于年平均近地表温度数据时，所提出的框架产生了更短且校准更好的置信区间，并在多个区域得出了不同的检测与归因结论，为气候归因结果的解释提供了新的见解。
英文摘要
Detection and attribution (D\&A) analyses provide a statistical framework for quantifying the contribution of external forcings to observed climate change. Optimal fingerprinting, the primary approach for D\&A, is formulated as an errors-in-variables regression with a high-dimensional covariance structure. Reliable inference is challenging because covariance matrices must be estimated from a limited number of control simulations, and climate models may exhibit variability patterns that differ from those of the observed climate system. We develop a spiked optimal fingerprinting framework that exploits the spiked structure of dominant climate variability modes while providing stable covariance estimation in high-dimensional settings. The proposed method develops bias-corrected spiked covariance estimation for constructing adaptive weight matrices and incorporates variability inflation adjustments to account for model--observation differences in internal variability. We further develop a valid uncertainty quantification procedure for the scaling factor estimators and a residual consistency test for assessing model adequacy. Numerical studies demonstrate improved estimation accuracy and uncertainty quantification compared with existing practical approaches. Applied to annual mean near-surface temperature data, the proposed framework produces shorter and better-calibrated confidence intervals and leads to different detection and attribution conclusions in several regions, providing new insights into the interpretation of climate attribution results.
