• Corpus ID: 251371405

Bayesian Quantification of Covariance Matrix Estimation Uncertainty in Optimal Fingerprinting

@inproceedings{Baugh2022BayesianQO,
  title={Bayesian Quantification of Covariance Matrix Estimation Uncertainty in Optimal Fingerprinting},
  author={Samuel Baugh and Karen A. McKinnon},
  year={2022}
}
Regression-based optimal fingerprinting techniques for climate change detection and attribution require the estimation of the forced signal as well as the internal variability covariance matrix in order to distinguish between their influences in the observational record. While previously developed approaches have taken into account the uncertainty linked to the estimation of the forced signal, there has been less focus on uncertainty in the covariance matrix describing natural variability… 

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