Data CI Pilot: NCAR and NEON Cyberinfrastructure Collaborations to Enable Convergence Research Linking the Atmospheric and Biological Sciences

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Numerical models of weather and climate such as those developed at the National Center for Atmospheric Research (NCAR) are essential research tools to advance atmospheric science. At the same time, the National Ecological Observatory Network (NEON) provides long-term, comprehensive data on the state of the terrestrial ecosystem at sites distributed across the US. Ecologists use these data to develop a predictive understanding of ecosystems at large spatial scales. NCAR models are highly complementary to NEON data products and can inform data quality, the types of data to collect, and the frequency of data collection. As these models have matured, the role of terrestrial ecosystems in climate processes has come to the forefront. To maximize the benefits to atmospheric science and ecology, this project will deliver new cyberinfrastructure tools to bring together NEON observational data products and NCAR modeling capabilities. As a result, scientific discovery and innovation will be advanced at the confluence of the geosciences and biological sciences, through more seamless and rapid integration of NCAR models and NEON observations, and the ability to provide model outputs to NEON for use by the ecological community. This project builds upon NCAR’s modeling of the land surface and its terrestrial ecosystems and NEON's observations and monitoring of terrestrial ecosystems to develop synergies between two heretofore independent research communities. We will link NCAR’s modeling capabilities in terms of the Community Earth System Model (CESM) and its Community Land Model (CLM) with NEON's measurement network through two cyberinfrastructure activities: First, we will create a portable containerized computing environment that allows university researchers to use and scientifically develop the model in conjunction with NEON data, with seamless transition across a variety of computational platforms from high performance supercomputers to laptops to cloud computing. The container image will be further enhanced by the addition of a built-in JupyterLab environment for visualization and analysis, and a collection of common notebooks that provide in-line examples and documentation of analysis methods, model configurations, and additional data transformations. The modeling system will be enhanced with automated data acquisition and preprocessing for standard configurations, enabling scientists to focus on the research rather than the technical details. Second, we will develop an automated operational analysis mode to allow for near-real time modeling of terrestrial ecosystems across the NEON network, both to verify model simulations and to supplement NEON measurements with model output, as well as validate NEON input data. We will use a cloud-based version of CESM/CLM in conjunction with cyberinfrastructure that monitors NEON outputs to spin up a configurable set of analysis runs whenever new data are available. The operational analysis will include site-specific runs focused on NEON measurement locations, giving regular feedback to modeling and observational teams, improving the quality of both. The ability to ingest near real time data is a novel capability, in contrast with standard datasets for model evaluation that are typically several years old.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.