A new CAM6 + DART reanalysis with surface forcing from CAM6 to other CESM models

• Published in: Scientific reports Vol. 11; no. 1; pp. 16384 - 24
• Main Authors: Raeder, Kevin, Hoar, Timothy J., El Gharamti, Mohamad, Johnson, Benjamin K., Collins, Nancy, Anderson, Jeffrey L., Steward, Jeff, Coady, Mick
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.08.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/1046; 704/106; Atmospheric forcing; Atmospheric research; Data assimilation; Data collection; Humanities and Social Sciences; Learning algorithms; Machine learning; multidisciplinary; Plant growth; Science; Science (multidisciplinary); Statistical analysis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-92927-0

An ensemble Kalman filter reanalysis has been archived in the Research Data Archive at the National Center for Atmospheric Research. It used a CAM6 configuration of the Community Earth System Model (CESM), several million observations per day, and the Data Assimilation Research Testbed (DART). The data saved from this global, ∼ 1 ∘ resolution, 80 member ensemble span 2011–2019. They include ensembles of: sub-daily, real world, atmospheric forcing for use by all of the nonatmospheric models of CESM; weekly, CAM6, restart file sets; 6 hourly, prior hindcast estimates of the assimilated observations; 6 hourly, land model, plant growth variables, and 6 hourly, ensemble mean, gridded, atmospheric analyses. This data can be used for hindcast studies and data assimilation using component models of CESM; CAM6, CLM5, CICE5, POP2. MOM6, MOSART, and CISM; and non-CESM Earth system models. This large dataset (~ 120 Tb) has a unique combination of a large ensemble, high frequency, and multiyear time span, which provides opportunities for robust statistical analysis and use as a machine learning training dataset.