Community modelling is a promising paradigm to develop complex evolving and adaptable modelling systems that can share methods, data and models more easily within specialized communities. Why then are cooperative modelling communities still quite rare and do not propagate easily? Why has open source been so successful for software development, yet open models are still quite exotic? One difference between software and models is that software shares some common language. Models often use very different principles, theories, and semantics. For example hydrodynamic models, ecological models, and decision support models may have limited commonalities, In these cases, the disciplinary problem being solved may be the impediment to communication and to development of effective community tools these principles to another; it becomes difficult for one model to talk to another one. Similar problems prevail in data operations, when data sets (which are also models of sort) are hard to integrate with other data. An issue of contemporary interest is how will community data and models be implemented within environmental observatories. The environmental observatory may are become the ultimate driver for advancing research with a clear need for interoperability standards and functionality. There are at least three facets to the problem: • Lack of common modelling and software tools to enable modularity and connectivity; • Insufficient community understanding or access to basic tools; • Lack of social motivation and communication skills to enable communal work and sharing environments. The goals of this paper are to explore these areas with respect to the following points: • Understand the interoperability needs of the community for data and models within a participatory and collaborative framework; • Discuss research scenarios that would benefit from interoperability and explore interoperability architecture and standards supporting these scenarios; • Explore environmental system observatory ontologies, with particular attention to mapping variables to concepts; • Discuss common access protocols, enabling models to automatically search for data needed and link to data servers. Design data interoperability for model input/output to help link models.