Abstract. Ecosystem manipulative experiments are a powerful tool to
understand terrestrial ecosystem responses to global change because they
measure real responses in real ecosystems and yield insights into causal
relationships. However, their scope is limited in space and time due to
cost and labour intensity. This makes generalising results from such
experiments difficult, which creates a conceptual gap between local-scale
process understanding and global-scale future predictions. Recent efforts
have seen results from such experiments used in combination with dynamic
global vegetation models, most commonly to evaluate model predictions under
global change drivers. However, there is much more potential in combining
models and experiments. Here, we discuss the value and potential of a
workflow for using ecosystem experiments together with process-based models
to enhance the potential of both. We suggest that models can be used prior
to the start of an experiment to generate hypotheses, identify data needs,
and in general guide experimental design. Models, when adequately
constrained with observations, can also predict variables which are
difficult to measure frequently or at all, and together with the data they can
provide a more complete picture of ecosystem states. Finally, models can be
used to help generalise the experimental results in space and time, by
providing a framework in which process understanding derived from site-level
experiments can be incorporated. We also discuss the potential for using
manipulative experiments together with models in formalised model–data
integration frameworks for parameter estimation and model selection, a path
made possible by the increasing number of ecosystem experiments and diverse
observation streams. The ideas presented here can provide a roadmap to
future experiment–model studies.