Characterizing the uncertainty of climate change projections using hierarchical models

Abstract

This article focuses on the use of Bayesian hierarchical models for integration and comparison of predictions from multiple models and groups, and more specifically for characterizing the uncertainty of climate change projections. It begins with a discussion of the current state and future scenarios concerning climate change and human influences, as well as various models used in climate simulations and the goals and challenges of analysing ensembles of opportunity. It then introduces a suite of statistical models that incorporate output from an ensemble of climate models, referred to as general circulation models (GCMs), with the aim of reconciling different future projections of climate change while characterizing their uncertainty in a rigorous fashion. Posterior distributions of future temperature and/or precipitation changes at regional scales are obtained, accounting for many peculiar data characteristics. The article confirms the reasonableness of the Bayesian modelling assumptions for climate change projections' uncertainty analysis.