![]() As in that paper, potential temperature outcomes produced the probabilistic simple climate model but not represented within the downscaled CMIP5 dataset were represented by ‘model surrogates’, produced using linear pattern scaling, with residuals added to represent high-frequency variability and non-linearities. The target global mean temperature distributions for 2080-2099 used were identical to those of Rasmussen et al. This method weights projections by comparing their global mean surface temperature projections to those of a probabilistic simple climate model, in this case (as in Rasmussen et al., 2016) the MAGICC6 model ( Meinshausen et al., 2011). To produce a probabilistic ensemble, we used the Surrogate Model/Mixed Ensemble (SMME) method of Rasmussen et al. This dataset is bias-corrected and downscaled using the Bias-Correction Spatial Disaggregation (BCSD) method ( Thrasher et al., 2012).ĬMIP5 projections do not inherently constitute a probability distribution rather, they are an ensemble of opportunity, composed of runs conducted by climate modeling teams participating on a voluntary basis and running models that roughly represent ‘best-estimate’ projections of climate behavior. In particular, we used downscaled CMIP5 climate projections prepared by the US Bureau of Reclamation ( Brekke et al., 2013). The climate projections show on this map are based on Representative Concentration Pathway 2.6, 4.5, and 8.5 ( van Vuuren et al., 2012) experiments run by global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) exercise ( Taylor et al., 2012). All daily projections from this analysis are freely available online here. The climate projection methodology is described in full in Rasmussen et al. ![]()
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