• Corpus ID: 251371405

Bayesian Quantification of Covariance Matrix Estimation Uncertainty in Optimal Fingerprinting

  title={Bayesian Quantification of Covariance Matrix Estimation Uncertainty in Optimal Fingerprinting},
  author={Samuel Baugh and Karen A. McKinnon},
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… 



Uncertainty in optimal fingerprinting is underestimated

Detection and attribution analyses of climate change are crucial in determining whether the observed changes in a climate variable are attributable to human influence. A commonly used method for

Optimal fingerprinting under multiple sources of uncertainty

This work describes an inference procedure based on likelihood maximization, inspired by a recent article dealing with a similar situation in geodesy, and finds the procedure to outperform existing procedures when the latter wrongly neglect some sources of uncertainty.

Adaptation of the optimal fingerprint method for climate change detection using a well-conditioned covariance matrix estimate

This new approach allows the confirmation and extension of previous results regarding the detection of an anthropogenic climate change signal over France, and is shown to be more powerful than the basic “guess pattern fingerprint”, and than the classical use of a pseudo-inverted truncation of the empirical covariance matrix.

Checking for model consistency in optimal fingerprinting

A simple consistency check based on standard linear regression is proposed which can be applied to both the space-time and frequency domain approaches to optimal detection and demonstrated to the problem of detection and attribution of anthropogenic signals in the radiosonde-based record of recent trends in atmospheric vertical temperature structure.

Estimating signal amplitudes in optimal fingerprinting, part I: theory

Abstract There is increasingly clear evidence that human influence has contributed substantially to the large-scale climatic changes that have occurred over the past few decades. Attention is now

Confidence intervals in optimal fingerprinting

A bootstrap method is shown to give correct confidence intervals in both strong- and weak-signal regimes, and always produces finite confidence intervals, in contrast to the likelihood ratio method which can give unbounded intervals that do not match the actual uncertainty.

A Bayesian hierarchical model for climate change detection and attribution

This work applies Bayesian model averaging to assign optimal probabilistic weights to different possible truncations and incorporates all uncertainties into the inference on the regression coefficients, taking into account the uncertainties in the true temperature change due to imperfect measurements and the uncertainty associated with estimating the climate variability covariance matrix.

A new statistical approach to climate change detection and attribution

We propose here a new statistical approach to climate change detection and attribution that is based on additive decomposition and simple hypothesis testing. Most current statistical methods for

Integrated Optimal Fingerprinting: Method Description and Illustration

AbstractThe present paper introduces and illustrates methodological developments intended for so-called optimal fingerprinting methods, which are of frequent use in detection and attribution studies.

Identifying human influences on atmospheric temperature

A multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change, finding no evidence that signal-to-noise ratios are spuriously inflated by model variability errors.