Ecosystem responses to rising CO 2 concentrations are a major source of uncertainty in climate change projections. Data from ecosystem-scale Free-Air CO 2 Enrichment (FACE) experiments provide a unique opportunity to reduce this uncertainty. The recent FACE Model-Data Synthesis project aimed to use the information gathered in two forest FACE experiments to assess and improve land ecosystem models. A new 'assumption-centred' model intercomparison approach was used, in which participating models were evaluated against experimental data based on the ways in which they represent key ecological processes. By identifying and evaluating the main assumptions causing differences among models, the assumption-centred approach produced a clear roadmap for reducing model uncertainty. Here, we explain this approach and summarize the resulting research agenda. We encourage the application of this approach in other model intercomparison projects to fundamentally improve predictive understanding of the Earth system.