Analyses of ecosystem responses to global change must embrace the reality of multiple, interacting environmental factors. Ecosystem models demonstrate the importance of examining the combined effects of the gradually rising concentration of atmospheric CO2 and the climatic change that attends it. Models to forecast future changes need data support to be useful, and data–model fusion has become essential in global change research. There is a wealth of information on plant responses to CO2 and temperature, but there have been few ecosystem-scale experiments investigating the combined or interactive effects of CO2 enrichment and warming. Factorial experiments to investigate interactions can be difficult to design, conduct, and interpret, and their results may not support predictions at the ecosystem scale – in the context of global change they will always be case studies. An alternative approach is to gain a thorough understanding of the modes of action of single factors, and rely on our understanding (as represented in models) to inform us of the probable interactions. Multifactor (CO2 × temperature) experiments remain important, however, for testing concepts, demonstrating the reality of multiple-factor influences, and reminding us that surprises can be expected.