Single model initial-condition large ensemble (SMILE) simulations from Global Climate Models (GCM), or Regional Climate Models (RCM), have been generated over the recent years to investigate internal variability and forced changes of the climate system—and to aid the interpretation of the observational record by providing a range of historical climate trajectories that could have been. They also offer expanded training grounds for machine learning methods and enable new and inter-disciplinary applications beyond large-scale climate dynamics (e.g. biogeochemical cycles, agriculture, air pollution, or energy generation and consumption).

We invite studies using SMILEs to look at:

• Reinterpretation of the observed record in light of internal variability
• Forced changes and impacts of natural climate variability in the past and in future projections
• New approaches to attribute observed events or trends
• Assessment of extreme and compound event occurrence
• Combining SMILEs with CMIP ensembles for robust decision making
• Large ensembles as testbeds for method development.