Bayesian Modeling of Uncertainty in Ensembles of Climate Models

By Richard L. Smith, C. Tebaldi, D. Nychka, L. Mearns. 2009

252 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/d47ed9b3e7442023475cfbafcaa1e63680dea185

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Citations in the corpus

Reducing the error in biomass estimates strongly depends on model selection (2015, methodology): “BMA is a useful technique that is commonly used in climate forecast (Raftery et al. 2005; Smith et al. 2009) but probably remains underused in forestry (Li et al. 2008; Picard et al. 2012).”
Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging (2011, background): “The need for postprocessing of ensemble output is also discussed in the climate literature (Kharin and Zwiers 2002; Tebaldi and Knutti 2007; Smith et al. 2009).”
The role of uncertainty in climate change adaptation strategies—A Danish water management example (2013, unknown): “…(GCM) and regional climate models (RCM) are typically assessed by ensemble modelling, where multiple climate models with different process equations are used for making probabilistic projections (e.g. Tebaldi et al. 2005; Smith et al. 2009; Christensen et al. 2007a, 2010; Déqué and Somot 2010).”
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).”
The end of model democracy? (2010, background): “Some studies have recently proposed to down-weight or eliminate some ‘bad’ climate models, recalibrate projections or estimate uncertainties based on metrics of skill (e.g. Boe et al. 2009; Eyring et al. 2005; Giorgi and Mearns 2003; Perkins and Pitman 2009; Perkins et al. 2009; Schmittner et al. 20”
Reliability of the CMIP3 ensemble (2010, methodology): “…truth-centred paradigm appears to have arisen as a post-hoc interpretation of the ad-hoc weighting procedure known as ‘‘Reliability Ensemble Averaging’’ or REA [Giorgi and Mearns, 2002; Nychka and Tebaldi, 2003], and it has since been widely adopted [e.g., Tebaldi et al., 2005; Smith et al., 2009].”
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).”
Climate model dependence and the replicate Earth paradigm (2013, background): “201 202 If, for example, one subscribed to the ‘truth-plus-error’ paradigm (e.g. Tebaldi et al., 2005; Greene 203 et al., 2006; Furrer et al., 2007; Smith et al., 2009), and believed that a ‘perfect’ model should 204 match observations plus a noise term, then model independence is naturally…”
Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6 (2020, background): “More generally, continued efforts are needed to include physical constraints when characterizing projection uncertainty, with the goal of striking the right balance between rewarding model skill, honoring model consensus and guarding against model interdependence (Giorgi and Mearns, 2002; O’Gorman a”
Manual for building tree volume and biomass allometric equations: from field measurement to prediction (2012, unknown): “This is called “Bayesian model averaging” and although it has been very widely used for weather forecasting models (Raftery et al., 2005; Furrer et al., 2007; Berliner & Kim, 2008; Smith et al., 2009) it is little used for forestry models (Li et al.”