Ecosystems ecologists are being challenged to address the increasingly complex problems that comprise Big Science. These problems include multiple levels of biological organization that cross multiple interacting temporal and spatial scales, from individual plants, animals and microbes to landscapes, continents, and the globe. As technology improves, the availability of data, derived data products, and information to address these complex problems are increasing at finer and coarser scales of resolution, and legacy “dark data” are being brought to light.  Data analytics are improving as “big” data increase in importance in other fields that are improving access to these data.  New data sources (crowd sourcing, social media), and ease of communication and collaboration among ecosystems ecologists and other disciplines are increasingly possible via the internet. It is increasingly important that ecosystems ecologists be able to communicate their findings, and to translate their concepts and findings into concrete “bits” of information that a general public can understand. Traditional approaches that portray ecosystem sciences as a dichotomy between empirical research and theoretical research will keep the field from fully contributing to the complexity of global change questions, and will keep ecosystems ecologists from taking full advantage of the data and technology available. Building on previous research, we describe a more forward-looking, integrated empirical-theoretical-modeling approach that is iterative with learning to take advantage of the elements of Big Science. We suggest that training ecosystems ecologists in this integrated approach will be critical to addressing complex Earth system science questions, now and in the future.