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The Seasonal-to-Multiyear Large Ensemble (SMYLE) prediction system using the Community Earth System Model version 2

Abstract. 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 a
collection of 2-year-long hindcast simulations, with four initializations per
year from 1970 to 2019 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.
