Mutual information based weighted variance approach for uncertainty quantification of climate projections

Future climate projections are a vital source of information that aid in deriving effective mitigation and adaptation measures. Due to the inherent uncertainty in these climate projections, quantifi-cation of uncertainty is essential for increasing its credibility in policymaking. While quantifying the uncertainty, often the possible dependency between the General Circulation Models (GCMs) due to their shared common model code, literature, ideas of representation processes, parame-terization schemes, evaluation datasets etc., are ignored. As this will lead to wrong conclusions, the inter-model dependency and the respective independence weights need to be considered, for a realistic quantification of uncertainty. Here, we present the detailed step-wise methodology of a "mutual information based independence weight " framework, that accounts for the linear and nonlinear dependence between GCMs and the equitability property.center dot A brief illustration of the utility of this method is provided by applying it to the multi-model ensemble of 20 GCMs.center dot The weighted variance approach seemingly reduces the uncertainty about one GCM given the knowledge of another.