A Bayesian hierarchical model for climate change detection and attribution. Issue 11 (11th June 2017)

• Record Type: Journal Article
• Authors: Katzfuss, Matthias; Hammerling, Dorit; Smith, Richard L.
• Is Part Of: Geophysical research letters. Volume 44:Issue 11(2017)
• Page Start: 5720
• Page End: 5728
• Publication Date: 2017-06-11
• Subjects: optimal fingerprinting -- Bayesian model averaging -- regression -- empirical orthogonal functions -- global circulation models -- uncertainty; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1002/2017GL073688
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: Regression‐based detection and attribution methods continue to take a central role in the study of climate change and its causes. Here we propose a novel Bayesian hierarchical approach to this problem, which allows us to address several open methodological questions. Specifically, we take into account the uncertainties in the true temperature change due to imperfect measurements, the uncertainty in the true climate signal under different forcing scenarios due to the availability of only a small number of climate model simulations, and the uncertainty associated with estimating the climate variability covariance matrix, including the truncation of the number of empirical orthogonal functions (EOFs) in this covariance matrix. We apply Bayesian model averaging to assign optimal probabilistic weights to different possible truncations and incorporate all uncertainties into the inference on the regression coefficients. We provide an efficient implementation of our method in a software package and illustrate its use with a realistic application. Key Points: Novel computationally efficient Bayesian hierarchical approach for detection and attribution Uncertainty from covariance estimation incorporated in regression coefficient estimates A range of EOF truncations is considered and optimally weighted using Bayesian model averaging