Bayesian multilevel analysis of variance for relative comparison across sources of global climate model variability

The projections are additionally subjected to distinct anthropogenic forcings, specified by future greenhouse gas emissions scenarios. These two factors, together with their temporal effects and interaction, create several potential sources of variation in final climate projection output.

Multilevel statistical models, and specifically multilevel ANOVA, have come to be widely used for many reasons, not least of which is their ability to comprehensively assess many different sources of variation. In this article, a Bayesian multilevel ANOVA approach is applied to climate projections to assess each of these sources of variation, estimate the uncertainty regarding the assessment, and to allow comparison across all sources.