CCHDO: serving easily accessible, global, high-quality hydrographic data for research, climate monitoring, and education

This grant will allow continued operation of an oceanographic data server (the CLIVAR and Carbon Hydrographic Data Office, or CCHDO) at Scripps Institution of Oceanography. This facility provides to the community different types of data obtained by the Global Ocean Ship-Based Hydrographic Investigations Program (GO-SHIP), which is a component of the Climate Observing System and the Global Ocean Observing System. The CCHDO makes data available for diverse purposes related to research and education. Data are used to assess the state and health of the ocean and for verification of ocean models. The project will develop learning modules for undergraduate and graduate students and will improve ocean informatics. This activity will continue to operate the CLIVAR and Carbon Hydrographic Data Office (CCHDO) at Scripps Institution of Oceanography. This facility aggregates, standardizes and disseminates hydrographic, carbon, and tracer data obtained by the Global Ocean Ship-Based Hydrographic Investigations Program (GO-SHIP), which is a component of the Climate Observing System and the Global Ocean Observing System. The CCHDO carries out 5 main activities related to data availability for: a) research and education; b) diverse audiences; c) global climatologies; d) climate assessments; and e) archiving with NOAA for long-term preservation. Data are used to assess the ocean storage and transport of heat, freshwater, carbon and nutrients; to determine the ocean’s uptake of anthropogenic carbon and heat; to monitor changes in ocean oxygen and acidification; and to evaluate ocean circulation and ventilation. Also, CCHDO data are used for model initiation, calibration and validation, as well as for Argo float quality control. The project will also develop learning modules for undergraduate and graduate students using the data archives. Finally, CCHDO will effect improvements to ocean informatics through open source software and data clusters for co-learning. This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences. 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.