| description abstract | limate change simulations based on climate models are inevitably uncertain. This uncertainty typically stems from parametric and structural uncertainties in climate models as well as climate forcings. However, combining model simulations with instrumental observations using appropriate statistical methods is an effective approach for describing this uncertainty. In this study, the authors applied Bayesian model averaging (BMA), a statistical postprocessing method, to an ensemble of climate model simulations from the Paleoclimate Modelling Intercomparison Project phase 3 (PMIP3) and phase 5 of the Coupled Model Intercomparison Project (CMIP5). Uncertainties, weights, and variances of individual model simulations were estimated from a training period using the National Centers for Environmental Prediction?National Center for Atmospheric Research (NCEP?NCAR) reanalysis dataset. The results presented here demonstrate that the BMA method is successful and attains a positive performance in this study. These results show that the selected proxy-based reconstructions and simulations are consistent with BMA estimates regarding climate variability in the past 1000 years, though differences can be found for some periods. The authors conclude that BMA is an effective tool for describing uncertainties associated with individual model simulations, as it accounts for the diverse capabilities of different models and generates a more credible range of past climate change over a relatively long-term period based on multimodel ensemble simulations and training data. | |
