Guidelines for Working with Multi-Model Ensembles in CMIP

Earth System Models (ESMs) are the key tool for studying the climate under changing conditions. Over recent decades, it has been established to not only rely on projections of single models, but to combine various ESMs in multi-model ensembles (MMEs) to improve robustness and quantify the uncertainty of the projections. The data access for MME studies has been fundamentally facilitated by the World Climate Research Programme’s Coupled Model Intercomparison Project (CMIP) - a collaborative effort bringing together ESMs from modelling communities all over the world. Despite the CMIP standardisation processes, addressing specific research questions using MMEs requires unique ensemble design, analysis, and interpretation choices. Based on our collective expertise of the Fresh Eyes on CMIP initiative, we have identified common issues and questions encountered while working with climate MMEs. In this project we aim to provide a comprehensive literature review giving an overview over the considerations that have to be taken into account for these decisions. In detail, we provide statistics tracing the development of the field throughout the last decades, we outline guidelines synthesising existing studies regarding model evaluation, model dependence, weighting methods and uncertainties. We summarize a collection of tools and other useful resources for MME studies, we furthermore review common questions and strategies, and finally, we outline emerging trends, such as the integration of machine learning techniques, single model initial-condition large ensembles (SMILES), and computational resource considerations.