Seasonal-to-Multiyear Large Ensemble (SMYLE) Experiment
The SMYLE set of initialized hindcasts using CESM2 that is specifically designed to explore Earth system predictability at forecast lead times ranging from 1 month out to 2 years. In addition to extensive hindcast output from all CESM2 component models, SMYLE includes historical reconstructions for the ocean, sea ice, and land component models; an experimental setup that can be replicated and/or modified; and python code for performing post-processing and skill assessment. Details are provided below:
SMYLE at a glance
The following paper documents the SMYLE experimental design and provides a broad overview of system skill:
Yeager, S. G., N. Rosenbloom, S. Glanville, X. Wu, I. Simpson, H. Li, M. J. Molina, K. Krumhardt, S. Mogen, K. Lindsay, D. Lombardozzi, W. Wieder, W. Kim, J. Richter, M. Long, G. Danabasoglu, D. Bailey, M. Holland, N. Lovenduski, and Warren G. Strand (2022): The Seasonal-to-Multiyear Large Ensemble (SMYLE) Prediction System using the Community Earth System Model Version 2, Geosci. Mod. Dev. Discuss., [preprint], https://doi.org/10.5194/gmd-2022-60.
Acknowledging Use of SMYLE Data
We request that you cite both the data DOI (https://doi.org/10.26024/pwma-re41) as well as the dataset description paper in work that makes use of SMYLE. Please add your name to the SMYLE analysis registry to help us track who is using this resource. Finally, we welcome collaboration and information sharing, so please consider joining the CESM Earth System Prediction Working Group and presenting your work at upcoming ESPWG meetings.