Addressing Model Data Archiving Needs for the Department of Energy’s Environmental Systems Science Community

Researchers in the Department of Energy’s ESS program use a variety of models to advance robust, scale-aware predictions of terrestrial and subsurface ecosystems. ESS projects typically conduct field observations and experiments coupled with modeling exercises using a model-experimental (ModEx) approach that enables iterative co-development of experiments and models, and ensures that experimental data needed to parameterize and test models are collected. Thus preserving “model data” comprising the outputs from simulations, as well as driving, parameterization and validation data with associated codes is becoming increasingly important. The ES...  more

Researchers in the Department of Energy’s ESS program use a variety of models to advance robust, scale-aware predictions of terrestrial and subsurface ecosystems. ESS projects typically conduct field observations and experiments coupled with modeling exercises using a model-experimental (ModEx) approach that enables iterative co-development of experiments and models, and ensures that experimental data needed to parameterize and test models are collected. Thus preserving “model data” comprising the outputs from simulations, as well as driving, parameterization and validation data with associated codes is becoming increasingly important. The ESS-DIVE repository stores data associated with the ESS programs and conducted a months long survey of the ESS community to identify needs for archiving, sharing, and utilizing model data. Here, we present the results of the community survey, and the proposed ESS-DIVE approach over the short-term (next 3 years) and long-term (4-10 years) to support the needs of the ESS modeling community. In the short-term ESS-DIVE proposes to work on functionality that supports archiving of model data associated with publications, with an emphasis on developing community guidelines and standards that make the data more discoverable, accessible and usable. The long-term vision is to broadly enable data-model integration, and knowledge generation from model and observational data. This vision will be achieved through close partnerships with the ESS community.