The Seasonal-to-Multiyear Large Ensemble (SMYLE) Prediction System using the Community Earth System Model Version 2

• Main Authors: Yeager, Stephen Gerald, Rosenbloom, Nan, Glanville, Anne A., Wu, Xian, Simpson, Isla, Li, Hui, Molina, Maria J., Krumhardt, Kristen, Mogen, Samuel, Lindsay, Keith, Lombardozzi, Danica, Wieder, Will, Kim, Who Myung, Richter, Jadwiga H., Long, Matthew, Danabasoglu, Gokhan, Bailey, David, Holland, Marika, Lovenduski, Nicole, Strand, Warren G.
• Format: Text
• Language: English
• Published: 2022
• Subjects: Sea ice
• Online Access: https://doi.org/10.5194/gmd-2022-60; https://gmd.copernicus.org/preprints/gmd-2022-60/

The potential for multiyear prediction of impactful Earth system change remains relatively underexplored compared to shorter (subseasonal to seasonal) and longer (decadal) timescales. In this study, we introduce a new initialized prediction system using the Community Earth System Model Version 2 (CESM2) that is specifically designed to probe potential and actual prediction skill at lead times ranging from 1 month out to 2 years. The Seasonal-to-Multiyear Large Ensemble (SMYLE) consists of 2-year long hindcast simulations that cover the period from 1970 to 2019, with 4 initializations per year and an ensemble size of 20. A full suite of output is available for exploring near-term predictability of all Earth system components represented in CESM2. We show that SMYLE skill for El Niño-Southern Oscillation is competitive with other prominent seasonal prediction systems, with correlations exceeding 0.5 beyond a lead time of 12 months. A broad overview of prediction skill reveals varying degrees of potential for useful multiyear predictions of seasonal anomalies in the atmosphere, ocean, land, and sea ice. The SMYLE dataset, experimental design, model, initial conditions, and associated analysis tools are all publicly available, providing a foundation for research on multiyear prediction of environmental change by the wider community.