🤖 AI Summary
研究通过开发一种尖峰最优指纹框架，利用主要气候变率模式的尖峰结构并提供稳定的协方差估计，以解决气候变化检测和归因中的高维协方差估计问题。
📝 Abstract
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.