Abstract
Abstract. We present a novel Bayesian statistical approach to computing model weights in climate change projection ensembles in order to create probabilistic projections. The weight of each climate model is obtained by weighting the current day observed data under the posterior distribution admitted under competing climate models. We use a linear model to describe the model output and observations. The approach accounts for uncertainty in model bias, trend and internal variability, including error in the observations used. Our framework is general, requires very little problem-specific input, and works well with default priors. We carry out cross-validation checks that confirm that the method produces the correct coverage.
Highlights
Regional climate models (RCMs) are powerful tools to produce regional climate projections (Giorgi and Bates, 1989; Christensen et al, 2007; van der Linden and Mitchell, 2009; Evans et al, 2013, 2014; Mearns et al, 2013; Solman et al, 2013; Olson et al, 2016b). These models take climate states produced by global climate models (GCMs) as boundary conditions, and solve equations of motion for the atmosphere on a regional grid to produce regional climate projections
Current computing power is allowing for ensembles of regional climate models to be performed, allowing for sampling of model structural uncertainty (Christensen et al, 2007; Giorgi and Bates, 1989; van der Linden and Mitchell, 2009; Mearns et al, 2013; Solman et al, 2013)
The technique is applied to a regional climate model ensemble and compared with results found in previous work (Olson et al, 2016a)
Summary
Regional climate models (RCMs) are powerful tools to produce regional climate projections (Giorgi and Bates, 1989; Christensen et al, 2007; van der Linden and Mitchell, 2009; Evans et al, 2013, 2014; Mearns et al, 2013; Solman et al, 2013; Olson et al, 2016b). Current computing power is allowing for ensembles of regional climate models to be performed, allowing for sampling of model structural uncertainty (Christensen et al, 2007; Giorgi and Bates, 1989; van der Linden and Mitchell, 2009; Mearns et al, 2013; Solman et al, 2013). Along with these ensemble modelling studies, methods for extracting probabilistic projections have followed (Buser et al, 2010; Fischer et al, 2012; Kerkhoff et al, 2015; Olson et al, 2016a; Wang et al, 2016). The technique is applied to a regional climate model ensemble and compared with results found in previous work (Olson et al, 2016a)
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