Multivariate Bayesian analysis of atmosphere–ocean general circulation models

By Reinhard Furrer, Stephan R. Sain, D. Nychka, Gerald A. Meehl. Environmental and Ecological Statistics, 2007

135 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/d2b904026989abfeab2a7338398c57d3c3813b5f

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In Global and Planetary Change

In Management Science and Operations Research

Citations in the corpus

Statistical problems in the probabilistic prediction of climate change (2012, background): “…have assumed that the climate model outputs are independently normally distributed about the true climate, in other words, Xi|Yare independently normally distributed with mean Y for i = 1, 2, . . .,m (Furrer et al., 2007; Tebaldi and Knutti, 2007; Smith et al., 2009; and references therein).”
Challenges in Combining Projections from Multiple Climate Models (2010, methodology): “…suggest that the models are not independent and distributed around the truth, yet many Bayesian methods assume model independency (Giorgi and Mearns 2002, 2003; Tebaldi et al. 2004, 2005; Greene et al. 2006; Furrer et al. 2007b; Tebaldi and Knutti 2007; Smith et al. 2009; Tebaldi and Sansó 2009).”
Bayesian estimation of local signal and noise in multimodel simulations of climate change (2010, methodology): “[9] In studies of climate change, multimodel simulation ensembles have sometimes been treated in a formal probabilistic framework [e.g., Raisanen and Palmer, 2001; Reilly et al., 2001; Furrer et al., 2007a, 2007b ] ; but the ongoing challenge is how to assign probabilities to future events, where n”
Spatial Analysis to Quantify Numerical Model Bias and Dependence (2008, background): “Our results demonstrate that the statistical approaches of Tebaldi et al. (2005), Furrer et al. (2007), and Smith et al. (2007) may need to be extended due to the biasedness of the climate models and, more importantly, the dependence among biases from different AOGCMs.”
Weighting of model results for improving best estimates of climate change (2010, methodology): “Examples include the reliability ensemble averaging (REA) method of Giorgi and Mearns (2002), the climate prediction index of Murphy et al. (2004), the M statistics weighting used by Watterson (2008) and several approaches based on Bayesian statistics (e.g., Tebaldi et al. 2005; Greene et al. 2006; ”
Bayesian Modeling of Uncertainty in Ensembles of Climate Models (2009, background): “See also Levine and Berliner (1999), Berliner et al.”
The end of model democracy? (2010, background): “Ideas of down-weighting or eliminating models have been around for a while (Giorgi and Mearns 2002, 2003) but the widespread availability of perturbed physics and multi-model ensembles has sparked more interest in the community for methods to evaluate, combine and possibly weight models, and constra”
Bayesian Inference for the Spatial Random Effects Model (2011, methodology): “, Furrer et al. 2007; Lopes, Salazar, and Gamerman 2008). Then priors such as the inversegamma prior are put on the associated scale parameters. For more complicated Bayesian spatial models, such as the predictive process model discussed in Banerjee et al. (2008), the inverse-Wishart prior is assign”
Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling (2007, background): “Furrer et al. (2007) have gone one step further and produced pdfs at the grid point level, although this can be adapted to produce pdfs of regionally aggregated values.”
History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble (2013, background): “Bayesian methods have gained increasing attention in the study of climate models (for example Berliner and Kim 2008; Furrer et al. 2007), and a Bayesian calibration provided the UK Climate Projections (Murphy et al.”