We present LUCIE, a 1000- member ensemble data-driven atmospheric emulator that remains stable during autoregressive inference for thousands of years without a drifting climatology. LUCIE has been trained on 9.5 years of coarse-resolution ERA5 data with 4 prognostic variables on a single A100 GPU for 2.4 h. Owing to the cheap computational cost of inference, 1000 model ensembles are executed for 5 years to compute an uncertainty-quantified climatology for the prognostic variables that closely match the climatology obtained from ERA5. Unlike all the other state-of-the-art AI weather models, LUCIE is neither unstable nor does it produce hallucinations that result in unphysical drift of the emulated climate. Furthermore, LUCIE does not impose “true” sea-surface temperature (SST) from a coupled numerical model to enforce the annual cycle in temperature. We demonstrate the long-term climatology obtained from LUCIE as well as subseasonal-to-seasonal scale prediction skills on the prognostic variables. We also demonstrate a 20-year emulation with LUCIE here: https://drive.google. com/file/d/1mRmhx9RRGiF3uGo_ mRQK8RpwQatrCiMn/view?usp= sharing