Probabilistic multimodel regional temperature change projections

By A. Greene, L. Goddard, Upmanu Lall. 2006

146 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/414c7e3240e6bc10b85bab1fe45752e5225ac05b

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Citations in the corpus

Bayesian Modeling of Uncertainty in Ensembles of Climate Models (2009, background): “…regional mean projections, this one included discussion of two formal statistical assessments of uncertainty derived from multimodel ensembles for regional projections, one due to Tebaldi et al. (2004, 2005), which was a precursor to the present approach, and the other due to Greene et al. (2006).”
Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling (2007, methodology): “…has been some interest in the production of pdfs for regional-scale change (e.g. Stott, 2003; Tebaldi et al., 2004b, 2005; Dessai et al., 2005; Greene et al., 2006; Räisänen and Ruokolainen, 2006; Stott et al., 2006), with some studies aimed directly at the production of regional change…”
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”
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;”
Climate model dependence and the replicate Earth paradigm (2013, background): “If, for example, one subscribed to the ‘truth-plus-error’ paradigm (e.g. Tebaldi et al. 2005; Greene et al. 2006; Furrer et al. 2007; Smith et al. 2009), and believed that a ‘perfect’ model should match observations plus a noise”
How reliable are climate models? (2007, methodology): “More recently, Tebaldi et al. (2005) and Greene et al. (2006) used Bayesian methods to derive continuous probability distributions of climate change.”
The use of the multi-model ensemble in probabilistic climate projections (2007, methodology): “(2004), Greene et al. (2006) and Furrer et al. (in press) are compared in figure 1. The PDFs represent projections of temperature change in winter (DJF) and summer (JJA) for the end of the twenty-first century under the SRES A1B scenario. Also shown is the empirical distribution of the GCM projectio”
Challenges in Combining Projections from Multiple Climate Models (2010, methodology): “…et al. 2005; Knutti et al. 2006; Murphy et al. 2007) and in Bayesian methods using MME (Giorgi and Mearns 2002, 2003; Tebaldi et al. 2004, 2005; Greene et al. 2006; Furrer et al. 2007b; D ow nloaded from http://journals.am etsoc.org/jcli/article-pdf/23/10/2739/3968315/2009jcli3361_1.pdf by…”