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Regression-based optimal fingerprinting techniques for climate changedetection and attribution require the estimation of the forced signal as wellas the internal variability covariance matrix in order to distinguish betweentheir influences in the observational record. While previously developedapproaches have taken into account the uncertainty linked to the estimation ofthe forced signal, there has been less focus on uncertainty in the covariancematrix describing natural variability, despite the fact that the specificationof this covariance matrix is known to meaningfully impact the results. Here wepropose a Bayesian optimal fingerprinting framework using a Laplacian basisfunction parameterization of the covariance matrix. This parameterization,unlike traditional methods based on principal components, does not require thebasis vectors themselves to be estimated from climate model data, which allowsfor the uncertainty in estimating the covariance structure to be propagated tothe optimal fingerprinting regression parameter. We show through a CMIP6validation study that this proposed approach achieves better-calibratedcoverage rates of the true regression parameter than principal component-basedapproaches. When applied to HadCRUT observational data, the proposed approachdetects anthropogenic warming with higher confidence levels, and with lowervariability over the choice of climate models, than principal-component-basedapproaches.

Bayesian Quantification of Covariance Matrix Estimation Uncertainty in Optimal Fingerprinting