Is your ensemble of CMIP6 models consistent with IPCC AR6?

The Intergovernmental Panel on Climate Change (IPCC) assesses the sensitivity of the climate system to increases in greenhouse gas concentrations using multiple lines of evidence, covering paleoclimate data, historical observations, and numerical Earth system model (ESM) simulations. Within IPCC’s latest Assessment Report (AR6), there is, for the first time, a non-negligible difference between the most likely rate of warming estimated in the report and the average warming rate simulated by the ESMs that participated in the Coupled Model Intercomparison Project (CMIP6). This discrepancy occurs because a large number of CMIP6 models have projected future warming rates that are higher than previously reported but quite unlikely according to historical observations. The consequence is that using a random selection of CMIP6 simulations is likely to overestimate historical and future warming (compared to what is assessed in the IPCC report), potentially leading to avoidable inconsistencies when compared to observations or greater projected changes compared to what could be inferred from CMIP5.

As this constitutes a wide-spread obstacle and limitation to using CMIP6 simulations ‘out of the box’, we propose here a simple model weighting method with the objective to address this problem. Our approach can be used to 1) evaluate the extent to which any given set of CMIP6 simulations is consistent with IPCC-assessed warming rates and 2) calculate the appropriate model weights so that potential inconsistencies are reduced as much as possible. The calculation of the weights is solely based on the user’s selection of a CMIP6 subset and does not require any data manipulation. The weights can then be easily implemented in existing analyses to calculate weighted (i.e. instead of just arithmetic) multi-model means, weighted quantiles, etc. We demonstrate the interest and flexibility of the method with some examples, including global to regional assessments of historical and projected changes in temperature and precipitation. We illustrate the extent to which applying model weights can reconcile otherwise divergent scientific results and provide assessments that are more robust across CMIP generations.