Fingerprint Analysis for Climate Change Detection and Attribution under a Latent Factor Model
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.
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BibTeX
@misc{li2026fingerprint,
title = {{Fingerprint Analysis for Climate Change Detection and Attribution under a Latent Factor Model}},
author = {Haoran Li and Yan Li},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.13488},
eprint = {2609.13488},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.13488},
}
RIS
TY - UNPB
TI - Fingerprint Analysis for Climate Change Detection and Attribution under a Latent Factor Model
AU - Haoran Li
AU - Yan Li
PY - 2026
DA - 2026/09/11
PB - arXiv
DO - 10.48550/arxiv.2609.13488
UR - https://arxiv.org/abs/2609.13488
AB - 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.
ER -