Regional probabilities of precipitation change: A Bayesian analysis of multimodel simulations

By C. Tebaldi, L. Mearns, D. Nychka, Richard L. Smith. 2004

248 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/36d887dfef58df9b6e89707541caf918f14a3179

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In Global and Planetary Change

In Management Science and Operations Research

Citations in the corpus

Changes in Spatiotemporal Precipitation Patterns in Changing Climate Conditions (2016, background): “Studies generally analyze precipitation at individual model grid cells (Knutson and Manabe 1995; Semenov and Bengtsson 2002; Stephens and Ellis 2008) or over broad regions (Tebaldi et al. 2004; Giorgi and Bi 2005).”
Model and parameter uncertainty in IDF relationships under climate change (2015, background): “The uncertainties due to GCM models are studied in developing IDF relationships for future without considering the uncertainties in the parameters of distribution fitted to the extreme data [32,38,46,52].”
A climate model projection weighting scheme accounting for performance and interdependence (2017, methodology): “…predictions using emergent constraints [Hall and Qu, 2006; Boé et al., 2009a; Huber et al., 2011; Mahlstein and Knutti, 2012; Cox et al., 2013; Fasullo et al., 2015], interpolation in a model space [Sanderson et al., 2015b], Bayesian methods [e.g., Tebaldi et al., 2004], or combinations thereof.”
A Bayesian approach to climate model evaluation and multi‐model averaging with an application to global mean surface temperatures from IPCC AR4 coupled climate models (2006, methodology): “…climate change detection and attribution [Min et al., 2004, 2005; The International Ad Hoc Detection and Attribution Group (IDAG), 2005, and references therein; Min and Hense, 2006, hereinafter referred to as MH06] as well as for future projections of climate changes [e.g., Tebaldi et al., 2004].”
Nonparametric methods for modeling GCM and scenario uncertainty in drought assessment (2007, methodology): “Bayesian methods have been applied to multimodel ensembles to characterize uncertainty and probability distribution functions (PDFs) for future climate changes at regional scales [Tebaldi et al., 2004, 2005].”
The use of the multi-model ensemble in probabilistic climate projections (2007, methodology): “Tebaldi et al. (2004) and Furrer et al. (in press) produce similar curves, particularly with regard to the location of their central mass, but also for most cases similar in width.”
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).”
The end of model democracy? (2010, methodology): “... weight models, and constrain projections using observations (Annan et al. 2005; Eyring et al. 2007; Forest et al. 2002; Furrer et al. 2007a, b; Greene et al. 2006; Hall and Qu 2006; Hargreaves et al. 2004; Jun et al. 2008a, b; Knutti 2008a; Knutti et al. 2009; Lopez et al. 2006; Murphy et al. 20”
Bayesian Modeling of Uncertainty in Ensembles of Climate Models (2009, methodology): “We also applied this procedure to the original version of the univariate analysis as proposed by Tebaldi et al. (2005). The results, in Figure 9, show that the Tebaldi et al. (2005) model displays a high degree of sensitivity of the posterior density to the perturbations.”